Ifcb 38

Irving Fisher Committee
on Central Bank Statistics
IFC Bulletin
No 38
Financial inclusion
indicators
Proceedings of the Kuala Lumpur Workshop,

56 November 2012
January 2015
Contributions in this volume were prepared for the IFC Sasana Workshop held in
Kuala Lumpur on 5-6 November 2012. The views expressed are those of the authors
and do not necessarily reflect the views of the IFC, its members, the BIS and the
institutions represented at the meeting.
This publication is available on the BIS website (www.bis.org).
Bank for International Settlements 2015. All rights reserved. Brief excerpts may be
reproduced or translated provided the source is stated.
ISSN 1991-7511 (online)

ISBN 978-92-9197-036-0 (online)
Contents
Overview Key messages of the Sasana Workshop on Financial Inclusion

Indicators
Promoting financial inclusion through better data
Blaise Gadanecz and Bruno Tissot, BIS
Opening remarks
Muhammad bin Ibrahim, Deputy Governor, Central Bank of Malaysia
Keynote address
Financial inclusion issues in measurement & analysis
Kamalesh Shailesh C Chakrabarty, Deputy Governor, Reserve Bank of India
Session 1: International initiatives to promote financial inclusion and its

measurement
Overview of international and national initiatives to promote financial inclusion and
its measurement
Paul Van den Bergh, BIS
Towards a global financial inclusion data infrastructure
Djibril M Mbengue, Consultative Group to Assist the Poor, World Bank
The role and functions of the Alliance for Financial Inclusion
Sung-Ah Lee, Alliance for Financial Inclusion
Session 2: National practices to collect data on financial development,

including financial inclusion
Measuring the evolution of monetary and financial services in Portugal
Joo Cadete de Matos, Bank of Portugal
The financial inclusion data working group and the Mexican experience
Nora Garcia, Alliance for Financial Inclusion
The situation of credit to agriculture & SME
Mao Qizheng, Peoples Bank of China
Financial inclusion and its measurement in Brazil
Central Bank of Brazil
Session 3: Measuring access to and usage of financial services

The IMFs Financial Access Survey (FAS)
Andre Mialou, International Monetary Fund
Measuring financial inclusion from the demand side
Sabri nc, CAFRAL/Reserve Bank of India
The Global Financial Inclusion Index
Douglas Randall, World Bank
Collecting financial inclusion information from the supply side: the Colombian case
Ana Maria Garcia, Alliance for Financial Inclusion
IFC Bulletin No 38 iii

Session 4: Other measures of financial inclusion
What can household surveys tell us? The Bank of Italys experience
Claudia Biancotti, Bank of Italy
Households and firms access to finance in the euro area
Aurel Schubert, European Central Bank
Mapping the financial sector: microfinance and beyond
Scott Gaul, Microfinance Information Exchange
Session 5: Indicators of financial literacy, consumer protection and community

development
Measuring financial literacy: the Malaysian case
Ooi See Eim, Central Bank of Malaysia
The Banque de France experience
Jacques Fournier, Bank of France
Putting data to work: data-driven approaches to strengthening neighborhoods
Joseph Firschein, Board of Governors of the Federal Reserve System
Session 6: Development of a composite indicator/index of financial inclusion

Introduction to the OECD Handbook on composite indicators
Building a financial inclusion index for Mexico
Jos Luis Negrin, Bank of Mexico
Financial inclusion in Malaysia: tracking progress using index
Zarina Abd Rahman, Central Bank of Malaysia
Comparing financial inclusion across countries based on FINSCOPE survey data for
Africa
Aurora Bila, Bank of Mozambique
Panel Session: Promoting national and international best practices to

strengthen financial inclusion measurement and data
Sasana statement on financial inclusion indicators
iv IFC Bulletin No 38
Key messages of the Sasana Workshop on Financial
Inclusion Indicators Promoting financial inclusion
through better data 1
2
Blaise Gadanecz and Bruno Tissot
On 56 November 2012, the Central Bank of Malaysia and the Irving Fisher
Committee on Central Bank Statistics (IFC) co-sponsored an international meeting at
Sasana Kijang, Kuala Lumpur, to discuss measurement and indicators for financial
inclusion. Chaired by Deputy Governor Muhammad bin Ibrahim, who was also the
Chairman of the IFC, the meeting was attended by 61 participants from 35 central
banks, statistical offices, international organisations, NGOs and academic
institutions from Africa, Asia, Europe and North America.
The sessions covered the following aspects related to financial inclusion
indicators:
international initiatives to promote the measurement of financial inclusion;
national practices for collecting data on financial development;
measuring access to and usage of financial services;
alternative measures of financial inclusion, including SMEs access to finance;
indicators of financial literacy, consumer protection and community
development; and
the development of composite financial inclusion indicators.
The Workshop provided a welcome opportunity to promote national and
international best practices to strengthen financial inclusion measurement and data.
A key outcome of the meeting was the formal adoption of a Sasana Statement on
Financial Inclusion Indicators.
As emphasised by the IFC Chairman, this financial inclusion initiative has been
strongly supported by the UN Secretary Generals Special Advocate for Inclusive
Finance for Development, her Royal Highness Princess Maxima of the Netherlands,
who had made the following statement:
Financial inclusion is essential for employment, equitable economic growth and
development, and financial stability. To achieve these goals, policymakers need good
national data. Appropriate financial inclusion indicators will be so valuable to produce
more and comparable data on which products, delivery models, and policies have the
greatest impact on poor people and national priorities.
1
This overview benefited from valuable comments by Hock Chai Toh and Zarina Abd Rahman,
respectively Director of the Statistical Services Department and Manager at the Development
Finance and Enterprise Department at the Central Bank of Malaysia.
2
Respectively Economist and Head of Statistics and Research Support, BIS.
IFC Bulletin No 38 1
The Workshop highlighted the following main points:
Financial inclusion is a fundamental issue for governments and policymakers
around the world. It is estimated that, at the beginning of the 2000s, half of the
worlds adult population had no account at a formal financial institution, and
three quarters of poor people were unbanked.
Financial inclusion is a key policy area and the central bank community has a
particular interest in it. As emphasised in the IFC Chairmans Opening Remarks,
greater financial inclusion is essential for sustained economic welfare and for
reducing poverty. It also supports economic, monetary and financial stability, by
making saving and investment decisions more efficient, enhancing the
effectiveness of monetary policy instruments, and facilitating the functioning of
the economy. This was echoed by the presentation from the Bank of
Mozambique, which emphasised that the scope of the financial sector plays an
important role in facilitating private sector growth in developing economies.
In turn, economic stability helps to develop and strengthen a smoothly
functioning financial system that can support financial inclusion. In his keynote
address, K C Chakrabarty, Deputy Governor of the Reserve Bank of India, drew
on the various initiatives implemented in India in measuring financial inclusion to
highlight the trinity financial inclusion, financial literacy and consumer
protection that can make financial stability possible.
Data on financial inclusion raise important issues. Well founded data frameworks
are essential when developing financial services for the poor, in both formal and
informal markets. Adequate indicators are a precondition for good financial
inclusion policies, as emphasised in the presentation by the Alliance for Financial
3
Inclusion (AFI). They ensure that financial inclusion is properly assessed and that
policies aimed at developing it are adequately implemented, monitored, and
adjusted as required. Good statistics can also help to strike a fine balance
between encouraging innovation and the growth of financial services on the one
hand, and ensuring that financial stability is preserved on the other.
The IFC can be instrumental in facilitating central banks discussions on data
issues related to financial inclusion. Operating under the auspices of the Bank for
International Settlements, it is a forum of economists and statisticians from 82
central banks and monetary authorities or agencies from all regions.
This paper presents a summary of the Workshop discussions, organised around
five main themes.
First, it provides a brief overview of existing financial inclusion data collection
frameworks.
Second, it discusses how the collection, compilation, presentation and
publication of financial inclusion data could be enhanced.
Third, it reviews potential ways to fill existing data gaps, including by using
surveys, developing methodologies for qualitative indicators and measuring how
new technologies are facilitating financial inclusion.
3
The AFI is a global network of central banks and other financial regulatory institutions from
developing and emerging countries working together to increase access to appropriate financial
services for the poor (see Box A).
2 IFC Bulletin No 38
Fourth, it assesses the merits of developing composite financial inclusion indices
to enhance comparability across regions and over time.
Fifth, it underlines the importance of developing a clear analytical framework for
assessing the implementation of financial inclusion policies and standards.
1. Current data collection frameworks
Financial inclusion can be measured along several main dimensions. One dimension
refers to accessibility and corresponds to the range of financial services that are
available to, or that can be mobilised by, customers. A second dimension measures
usage, ie the extent to and ways in which customers actually make use of the
services they can access. A third dimension refers to the quality of the services, ie
how well they fit with the needs of customers. Yet another, fourth dimension
assesses how financial inclusion can actually influence the decisions of economic
agents and increase economic well-being.
Whatever the dimension of interest, data on financial inclusion are often
classified into supply- and demand-side data. Supply-side indicators serve to gauge
the provision of financial services that people can use. These statistics usually follow
a top-down approach and come from the providers of financial services. For
instance, banks will indicate the number of personal accounts opened in one
particular area. Demand-side data, on the other hand, tend to be derived from a
bottom-up approach, aimed at assessing the needs of individuals. These data are
mostly collected through surveys and can be instrumental for measuring the
qualitative aspects of financial inclusion, such as financial literacy.
A further complication, as highlighted by K C Chakrabarty in his keynote
address, is that any financial data framework has both a micro and a macro
perspective. The micro perspective arises from the need to take into consideration
information that is granular enough (eg by type of transaction, customer or
product). The macro perspective reflects, in particular, the fact that financial
inclusion has multiple economic and policy implications. All this explains why the
definition of the concept of financial inclusion is usually quite broad 4 and requires
the measurement of various indicators.
Nevertheless, a lot of data on financial inclusion already exist. The first session
of the Workshop presented the various international initiatives in this area. As
analysed in the presentations of both the BIS and the Consultative Group to Assist
the Poor (CGAP), the centrepiece relates to the work of the Global Partnership for
Financial Inclusion (GPFI). This GPFI was set up by the G20 and is supported by the
World Bank Group and the AFI. It has developed a number of financial inclusion
indicators endorsed by the G20 leaders, regrouped into the basic and secondary
5
data sets. The basic data set established in 2012 provided limited supply-side
information on financial services, in the form of statistics on the number of formally
4
The definition for India as discussed by K C Chakrabarty is as follows: Financial inclusion is the
process of ensuring access to appropriate financial products and services needed by all members of
the society in general and vulnerable groups in particular, at an affordable cost in a fair and
transparent manner by mainstream institutional players.
5
See The G20 Financial Inclusion Indicators, available on the website of the GPFI (www.gpfi.org).
banked adults and enterprises (ie who have access to financial services), of adults
and enterprises having credit granted by a regulated institution (ie who use financial
services), as well as on the points of service (ie number of branches per adult).
Shortly after this Workshop was held, the G20 Leaders endorsed in 2013 the
proposal to extend the basic set and develop a more comprehensive and holistic set
of financial inclusion indicators. This secondary data set has many additional
indicators on access to financial services, their usage, and the quality of service
delivery. It covers a much wider range of information, for instance about payments
(cashless transactions, use of mobile devices), savings, receiving of remittances,
access to insurance, and points of services. Interestingly, more emphasis is being
put on the quality-related aspects of financial inclusion, especially in terms of
financial literacy and capability, consumer protection, and usage barriers.
The G20 indicators are complemented by a number of other international data
sets. Foremost among these is the Global Financial Inclusion (Global Findex)
Database, funded by the Bill & Melinda Gates Foundation in partnership with
6
Gallup. It is based on a survey of individuals, covers 148 countries and forms a
comprehensive data set, comparable across countries and over time, making it
possible to use for tracking the effects of financial inclusion policies globally. The
2011 index includes 41 indicators, disaggregated by gender, age, education level,
income, and residence (urban or rural), with an update/extension to be released in
2015. It measures how people save, borrow, make payments and manage risk,
covering, for instance, information such as account penetration, accounts and
payments, and barriers to using financial services. It also tracks the use of bank
accounts to receive payments from various sources, eg the government, employers
and family, the frequency and mode of account access, the prevalence of informal
saving and borrowing, as well as the use of mobile money.
The IMF presentation also highlighted the usefulness of the IMF Financial
Access Survey, a global supply-side data set on financial inclusion that encompasses
internationally comparable indicators of financial access and usage by households
and non-financial corporations. This relatively low-cost exercise collects from
regulators 47 indicators that assess two dimensions of financial inclusion, ie
geographic outreach and the use of financial services (covering 189 jurisdictions
7
and a decade of data). Another source of interest is Enterprise Surveys, which
provide global and comprehensive data collected by the World Bank on the use of
financial services by small, medium and large enterprises in emerging markets and
8
developing economies.
Additional sources of information on financial inclusion include monitoring
9
efforts by international financial institutions, such as the World Bank and the IMF,
and to some extent the OECD and the BIS through its Committees, especially on
6
A Demirg-Kunt and L Klapper, Measuring Financial Inclusion: The Global Findex Database,
World Bank Policy Research Working Papers, no 6025, April 2012.
7
See http://fas.imf.org/misc/FAS_Brochure.pdf.
8
See http://www.enterprisesurveys.org/.
9
For an overview of the data and indicators available, see the Financial Inclusion and Infrastructure
page on the World Bank website, http://go.worldbank.org/8HMXYGW890.
payments issues. Box A provides a summary overview of such international data
collection efforts.
Box A Summary overview of international initiatives to measure

financial inclusion
AFI: the Alliance for Financial Inclusion aims at developing a common framework among
its members for measuring financial inclusion. It shares lessons learned on survey
methodologies, analysis, target-setting and usage of data to inform policymaking. It also
promotes the adoption of the framework in a broader international context.
BIS/Basel Committee on Banking Supervision (BCBS): organisation of regular
meetings with standard-setting bodies (SSBs) and stakeholders, in coordination with the UN
Secretary Generals Special Advocate for Inclusive Finance for Development.
BIS/Committee on Payments and Market Infrastructures (CPMI): financial inclusion

is considered in relation to various aspects of payments systems and market infrastructure.
GPFI data group: the Global Partnership for Financial Inclusion (GPFI) is a platform for
G20 and other countries and relevant stakeholders, to conduct work on financial inclusion,
identify the existing data landscape, assess data gaps and develop key performance
indicators.
OECD: the Organisation for Economic Cooperation and Development has a number of
networks and projects in the area of financial inclusion, including:
Financial Literacy Network.
Financing SMEs and Entrepreneurs.

Handbook on Constructing Composite Indicators.
OECD/INFE pilot survey 2010/11 on measuring financial literacy. This was a demand-
side survey which identified consumer vulnerabilities and education issues.
Evidence-based initiatives to enhance financial literacy and promote financial inclusion.

International Association for Research on Income and Wealth: this Association
promotes the furthering of research on national and economic and social accounting, and
has in particular encouraged related work on financial inclusion issues.
Microfinance Information Exchange (MIX): a Washington-based non-profit

international organisation that collects, validates, and analyses microfinance data. It has
various private sector partner organisations.
Finmark/Finscope
FinMark Trust is an independent trust set up in 2002 with initial funding from the UK
Department for International Development.
Finscope surveys are demand- and supply-side surveys conducted on consumers and
small businesses.
Center for Financial Inclusion: a New York-based group of key industry participants.
Various regional initiatives: such as FinScope studies in the Southern African
Development Community (SADC) region.
Various donor organisations: eg the Gates Foundation.
A large number of countries also conduct national surveys that can serve as a
gauge for measuring financial inclusion. In Session 2 of the Workshop, several
countries presented their national experiences with surveys aimed at monitoring
credit to households, SMEs and agriculture. In particular, the experience of the Bank
of Portugal was that the compilation of micro-databases can be instrumental for
monitoring the financing needs of the economy at a sufficiently granular level, and
thereby for assessing financial inclusion effectively. The presentation of the AFIs
financial inclusion data group reported on how the Mexican National Banking and
Securities Commission secured the cooperation of the various financial authorities
to ensure the design of an effective financial inclusion measurement framework. The
experience of the Peoples Bank of China was that the monitoring of credit to the
agriculture sector and to SMEs can be very effective in ensuring that the provision
of financial services can support sustainable long-term growth. Lastly, the
presentation by the Central Bank of Brazil underlined the importance of setting up
the monitoring of several indicators to support the development of financial
inclusion.
Box B provides a selected overview of national data collection efforts in the
area of financial inclusion, including those which were not specifically presented at
the Workshop.
Box B Selected country practices related to the measurement of

financial inclusion
Argentina: national survey on access to financial services.
Belgium: studies on income and wealth by the central bank.
Brazil: Incluso Financeira (yearly forum meeting); measures to monitor the

development of the network of correspondent banks.
France: national database on household credit repayment incidents.
India: research topic of the Reserve Bank of Indias Centre for Advanced Financial
Research and Learning (CAFRAL).
Italy: studies on income and wealth by the central bank.

Kenya: M-PESA mobile payment system.
Portugal: measuring the evolution of financial services, including through data from
payment systems, central credit registers and the central bank balance sheet data office.
South Africa: academic research, including by the Centre for Inclusive Banking in Africa.
US: Federal Reserve involvement in Community Development Finance.
United Kingdom:
HM Treasury initiative: Financial Inclusion Taskforce.
Private sector: Transact the National Forum for Financial Inclusion.

Non-for-profit think tank: the Financial Inclusion Centre.
2. Enhancing data collection frameworks
The Workshop reviewed a number of methodological challenges with respect to the

collection of data in the area of financial inclusion. As regards supply-side data, their
compilation is not a trivial challenge for three reasons. To begin with, supply-side
data may be susceptible to double-counting, notably because providers of financial
services tend to identify accounts rather than individuals, and because there is a lack
of financial identity in many developing countries. Furthermore, it is difficult to
segment these data to establish which parts of the population are well served (or
under-served), because they provide information on the demand for financial
services that is actually observed and not on the potential demand that could be
fulfilled. Lastly, financial suppliers and the financial products and services they offer
are diverse: that makes it difficult to aggregate the data to form a comprehensive
view on financial inclusion at a country or even at a regional level.
Turning to demand-side surveys on financial inclusion, there are also significant
challenges. This was illustrated by the presentations made during Sessions 3 and 4,
in particular by the Reserve Bank of India on measuring financial inclusion on the
demand side, and by the Bank of Italy on its research on income and wealth. First,
the sampling frame must be appropriate and, for instance, consistent with the
structure of the population census. The nature of the sample has to be sufficiently
granular to allow for the compilation of different levels of aggregation that is key to
a meaningful understanding, analysis and regulation of financial inclusion at a
country level. The survey sample should ideally focus on both households (or even
individual household members) and small businesses. Respondents must be
appropriately selected to ensure that the panel surveyed is adequately
representative of the population. And the frequency of the survey should be
relatively high (at least once every 10 years).
Regardless of the type of data, an important methodological issue pertains to
cross-country comparisons. In his opening remarks, the IFC Chairman underlined
the fact that financial inclusion data collected over the world rely on heterogeneous
concepts (eg how is an under-banked individual defined?), variables (eg access to a
bank account versus its effective use), collection practices (eg bottom-up versus
top-down approaches), methodologies (eg use of composite indicators), degree of
accuracy, and time frames (eg survey frequency).
But national data cannot be easily harmonised because financial inclusion
issues are often country-specific. Indeed, too much harmonisation of methods can
make financial inclusion data less relevant for national policymaking. To this effect,
in its presentation the AFI advocated three key steps for any financial inclusion data
strategy: (i) setting up adequate technical capacity at the country level; (ii) testing
indicators in practice at the country level; and (iii) choosing indicators that best
inform each countrys policymaking, while keeping consistency across countries. The
recommendation is, therefore, to resist the setting of global data standards ex ante,
and instead to try to achieve international consistency of those indicators that are
first deemed relevant at the country level.
From this perspective, it is worth noting that a few months after the Workshop,
the members of AFI decided to address this issue explicitly. On the occasion of the
AFI Global Policy Forum held in Kuala Lumpur in September 2013, they endorsed
the Sasana Accord 10 by which they decided to ensure, among other things, that
financial inclusion policy making and strategies can be assessed using data-based
analysis. To this end, the members agreed to set national targets on financial
10
See The Sasana Accord, http://www.bnm.gov.my/documents/2013/Sasana_Accord_AFI2013.pdf.
inclusion as well as measure progress based on common indicators (by reporting at
least the Core Set AFI Indicators, updated regularly).
3. Closing data gaps
The Workshop showed that there has been significant progress in recent years in
developing data on financial inclusion in various countries. However, much work
remains to be done to enhance the coverage of the population and also the quality
of the data collected. In addition, two important data gaps exist and would need to
be addressed as a matter of priority: the situation of small and medium-sized
enterprises (SMEs); and the quality of use of the financial services that can be
supplied to the poor.
As regards SMEs access to financing, there is a need for more information
because SMEs can make a decisive contribution towards reducing unemployment
and poverty in both developing countries where financial exclusion is high on the
policy agenda, and also in advanced economies. The various presentations by China,
Portugal and the ECB showed several interesting ways of assessing through formal
surveys on the funding needs of SMEs, as well as the availability and terms of
financing that is offered to them. But a significant challenge is the lack of clear
separation between firms and households, especially in the area of microfinance.
The reason is that households can engage in production, often on a relatively small
scale and for informal and subsistence activities. In the poorest countries in
particular, those households production units are not legal entities and are treated
as unincorporated enterprises in the statistical system. It is, therefore, difficult for
analytical purposes to differentiate between households role as consumers and
their role as producers of goods and services. One key step towards addressing this
challenge, which was emphasised in particular in the presentations of the Bank of
France and the Board of Governors of the Federal Reserve System, is to have
sufficiently granular data at hand to allow for a precise identification of households
activities and their need for financial services. Another important initiative presented
by MIX, the Microfinance Information Exchange, is the collection and publication of
data encompassing all the various institutions involved in microfinance. That helps
to enhance transparency on the services that are available (in particular, by using
geospatial analysis techniques).
The second main area where data are incomplete is the usage of financial
services, especially from a qualitative point of view. The experience of Columbia
presented by the AFI regarding the collection of supply-side information
underscored that this usage issue is crucial, if one is to correctly design financial
products and enhance financial literacy and consumer protection. On the demand
side, the presentation by the Central Bank of Malaysia on measuring financial
literacy showed how surveys can be effective in measuring consumer vulnerabilities
and in supporting the development of effective programmes to enhance financial
literacy. Moreover, the experience of India shows that data on access usually
emphasise convenience and flexibility, such as the number of bank branches or
automated teller machines (ATMs) that can be accessed by households in their
proximity. But, often, these data do not encompass quality issues, such as the
suitability of the services supplied compared to users actual needs, and the way
these services will potentially be used.
There are, in fact, several reasons why available financial services may not be
used and/or may be used without translating into good outcomes. On the demand
side, such reasons can include distance, awareness, affordability and cost, trust, lack
of documentation, religious or cultural barriers, consumer experiences, financial
illiteracy, and lack of customised products. All these factors can prevent an
individual from using a financial service that is theoretically available.
On the supply side, providers may be unable or unwilling to actually provide to
specific areas or groups the services that are part of their general offering. For
instance, banks are often unwilling to lend to poor households because of their low
income, the nature and scale of the business conducted with them, and a
perception that they are highly risky and not profitable. These factors have indeed
led to the development of microfinance as an alternative source of financial services
for entrepreneurs and small businesses, with the aim of mitigating those supply
restrictions by relying more on relationship-based services and/or the pooling of
the demand for financial services across selected groups of entrepreneurs or
households.
In turn, these demand- and supply-side dimensions interact in a way that is
difficult to measure with simple data. For instance, the mis-selling of products, and
high commissions charged by suppliers may in turn make poor households
unwilling to demand banking services. The solution for these difficulties is to survey
individual consumers and providers of financial services so as to try to capture these
qualitative aspects.
4. Developing indices of financial inclusion
Session 6 of the Workshop was devoted to developing financial inclusion indices

(FIIs). In its presentation, Mexico emphasised the usefulness of a composite financial
inclusion index, as it allows the multiple dimensions of financial inclusion to be
reduced to a single one, making it simpler for analysts and policymakers alike. In
general, such indices have no units and are constructed by making all the measured
dimensions comparable. The Malaysian presentation on developing an FII showed
that such an index can be a valuable instrument when seeking to diagnose the
financial inclusion situation for a specific geographic location, as well as to facilitate
comparisons across regions and countries. In turn, the indexes based on a set of
identified key performance indicators can be established as benchmarks and used
to identify best practices. The Indian presentation suggested that composite indices
may be easier for policymakers to target than a multitude of indicators. For instance,
measures similar to the Global Findex presented by the World Bank at the
Workshop can significantly facilitate international benchmarking exercises.
Nevertheless, FIIs cannot be considered as a universal or exclusive policy tool.
In fact, developing composite indices is not a goal in itself, and the quality of
underlying data is essential. Moreover, a less simplistic dashboard of meaningful
ratios can provide more insights on financial inclusion, since this issue is so
multidimensional. While a key criterion for computing FIIs is simplicity, financial
inclusion has different aspects specific to each country: hence it is far from
straightforward to build FIIs that are comparable internationally or even across the
regions of a single country such as India, as pointed out by K C Chakrabarty in his
keynote address. Therefore, FIIs can be very sensitive to geographical sampling, the
number of dimensions included, and when measures are taken with reference to a
benchmark to the variance of the underlying indicators (eg minimum and
maximum values). Comparisons over time can be tricky too, not least when the
composition of the index has been adjusted without backdating. Experience
suggests that such issues can be mitigated (i) if the number of dimensions included
in the FII is kept relatively limited and stable; (ii) if the index is computed for a
sample of countries that is sufficiently representative; and (iii) if it is based on a
relatively similar set of indicators, which could be easier to harmonise across
countries.
The presentation by the Bank of Italy summarised at a theoretical level the
various steps that should be followed in constructing a composite index in general,
and for FIIs in particular. To begin with, a clear theoretical framework must be
developed, so as to have a sound basis for selecting the individual indicators of
interest. As a second step, the data content, analysis, weighting and aggregation
scheme for the retained indicators must be precisely defined. Once the FII has been
constructed, sensitivity and robustness analysis are required to ensure its quality is
sufficient for instance, the indicator should not change dramatically if one of the
individual components is excluded, or if a different weighting scheme is used. An
additional criterion is the possibility of reverse engineering the information
provided by the FII, ie to clearly decompose its value into the contributions of the
various underlying indicators. Lastly, a framework must be created for representing
and communicating information provided by an FII, especially when making cross-
country comparisons on the overall performance of the index, the contribution of
the various indicators to it, and so on.
At a practical level, various countries shared their experience of FII construction
during the Workshop. The Central Bank of Malaysias index is based on several
indicators that can be grouped into four main dimensions of financial inclusion:
convenience of the access to financial services, take-up rates (ie measuring the size
of the banked population), responsible usage (measuring the banked and
underbanked population that make very little use of the financial services they can
access), and satisfaction level (ie measuring the perceived quality of the financial
services used). To compute the index, the distance from frontier approach (based
on Sarma and Pais (2011) 11) is used: first, a sub-index is calculated for each
indicator, normalised to be between 0 and 1 so as to take into consideration the
variation of the indicator between its minimum and maximum. The sub-indices are
subsequently weighted according to importance, and the FII is calculated as the
simple weighted average. If there is no good reason for thinking that one dimension
is more important than another, then the sub-indices can be weighted equally for
aggregation. The distance to the frontier is the gap between the value of the
indicator and the maximum that can be obtained across all dimensions. Another
interesting point is that the FII is computed by the Bank for different income groups
(general population, low income group etc). Obviously, such a computation is only
possible if the data compiled are granular enough.
Along similar lines, Brazil has also developed an FII based on 18 indicators that
are aggregated along three main dimensions of financial inclusion: bank
11
M Sarma and J Pais, Financial inclusion and development, Journal of International Development,
no 23, 2011, pp 61328.
penetration, availability of financial services, and use of financial services. The FIIs
are calculated for all states in Brazil and aggregated for major geographic regions. A
last example, as mentioned in the Bank of Mozambiques presentation, is the
research tool developed by a non-profit organisation that allows for the comparison
of financial inclusion across African countries.
5. The need for a clear analytical framework to assess

financial inclusion
Dealing with financial inclusion requires adequate data. This is obvious for financial
service providers: they can modify their offering of financial services and products
only if they have a good picture of where the potential customers are and what they
need. This is a key condition for ensuring that customised financial products can be
designed for specific regions and categories of consumers.
Similarly, authorities seeking to reduce financial exclusion have to rely on good
data, not only to calibrate their various policy initiatives ex ante, but also to ensure
that their outcomes can be assessed ex post, and the policy modified accordingly.
Indeed, the AFI presentation underlined the importance of ensuring that
policymaking in the area of financial inclusion is evidence-based. To this end, the
following steps should be followed: (i) diagnose the situation in terms of financial
inclusion, based on objective data; (ii) design appropriate policies; (iii) monitor
changes over time; (iv) evaluate policy impact; and (v) review and eventually refine
existing policies.
The Workshop highlighted the need to focus on the last step, ie policy
assessment. For instance, the Bank of Mozambique has instituted a regime on
minimum fees charged by commercial banks, and it is important to check whether
this has been effective in ensuring affordable and fair access to financial services by
rural poor as intended. The experience presented by India is that combining
financial inclusion data with socio-economic and demographic characteristics can
yield a number of useful insights. Both the assessment of financial inclusion and the
ensuing policy response will vary depending on the location, age, income, education
and occupation of each population segment. For instance, the way to address
financial exclusion can differ between the less populated rural zones and crowded
cities. In addition, the impact of financial exclusion on the poor, and the need for a
policy response, may vary depending on the socioeconomic characteristics of the
population. This means that the monitoring of financial inclusion policies should be
conducted at a sufficiently micro level ie at the level of individual customers or
even of specific financial transactions and/or products even if this information has
to be properly aggregated to offer a macro perspective to national policymakers.
In summary, having a clear analytical framework is a key element for ensuring
the success of financial inclusion policies. This framework can help identify specific
situations of financial exclusion, analyse the role played by various providers of
financial services, and design and assess the policy responses. The framework
should allow for the correct capturing of two key dimensions, one cross-sectional (ie
at a given point in time but across the population) and one over time.
6. Conclusion: a roadmap for enhancing financial inclusion
indicators
The Workshop showed that a significant amount of data are already available to
measure financial inclusion. The G20 basic and secondary data sets, developed by
the GPFI, form the centrepiece and they can be usefully complemented by the
Global Findex, various other indicators developed by the World Bank, the IMF and
NGOs, and national surveys.
However, data gaps still exist which limit a full assessment of financial inclusion
issues and the design of adequate policies. Gauging the availability of credit to
SMEs is, for instance, still difficult and this is of particular importance in developing
countries where there is traditionally less of a clear boundary between the
household and the SME sectors. Evaluating the quality of use and appropriateness
of financial services, looking beyond data on quantities, is also a challenge.
There is also scope for enhancing data collection methodologies. In particular,
supply- and demand-side surveys present conceptual issues which can be better
addressed, particularly by sharing more experience across countries. Besides,
technologies constantly keep changing and new ones appear (eg mobile phones)
that can alleviate financial exclusion in hitherto unforeseen ways, making previous
statistical data collection exercises obsolete. Data collection systems ought
therefore to be flexible and adjustable to allow for new set of indicators to be
included and additional data to be compiled, depending on the advancement of
technologies. As regards the harmonisation of cross-country data in the area of
financial inclusion, a right balance needs to be struck between comparability and
the need to adequately reflect country specificities bearing in mind that
characteristics of financial inclusion may vary across countries, for instance
depending on geography, state of development and culture.
Analytical frameworks have been developed to help policymakers and other
stakeholders in their assessment of financial inclusion. Even so, further progress can
also be made in this area too. Composite indices of financial inclusion can be a
useful, albeit not universal, tool to this end. Here also there is a merit in sharing
experience to ensure that these indicators have sufficiently adequate statistical
properties. The Workshop also highlighted the usefulness to policymakers in relying
on a systematic analytical framework when diagnosing financial exclusion in their
respective countries, designing appropriate initiatives to address it, monitoring
changes over time, assessing the impact of their actions, and refining their policies.
A key consideration is to analyse financial inclusion with the right amount of
(geographical and social) granularity, both across the population and over time.
Last, a number of important national and international initiatives are under way
to improve the measurement of financial inclusion. In pursuing these endeavours
further, it is essential not to duplicate existing data collection and policy efforts, 12
but, instead, to leverage on them. Moreover, as stressed during the panel session
12
For instance, and as regards the financial regulation sphere, the data collection exercises conducted
by the IMF and the World Bank, or the application of the Basel Core Principles to the regulation and
supervision of the banks and other deposit-taking institutions engaged in activities relevant to
financial inclusion.
concluding the Workshop, it is essential to promote the stocktaking of various best
practices, both across countries and at the international level, to enhance financial
inclusion measurement and data. From this perspective, and as emphasised in the
Sasana Statement published at the end of the Workshop, authorities can usefully
rely on the IFC as a platform for mobilising the central banks network and for
sharing experience so as to address the challenges related to the measurement of
financial inclusion.
Workshop on Financial Inclusion Indicators
9 am, Monday, 56 November 2012
Forum, Sasana Kijang, Kuala Lumpur
Opening remarks by Dato Muhammad bin Ibrahim, Deputy

Governor of the Central Bank of Malaysia and IFC Chairman
Good morning ladies and gentlemen,

It is my great pleasure to welcome you to the Workshop on Financial Inclusion
Indicators here at Sasana Kijang, Kuala Lumpur. The Central Bank of Malaysia is
honoured to co-host this event with the Irving Fisher Committee on Central Bank
Statistics (IFC).
Following the recent global financial crisis, financial inclusion has emerged as
an important element of the global policy agenda. Greater financial inclusion, if
properly designed, can contribute decisively towards sustainable long-term
economic growth. Many countries look at financial inclusion as a means of
achieving inclusive growth, by giving all segments of society access to financial
services. Financial inclusion can also help to reduce poverty, by providing the poor
with ways to grow and protect their incomes, smooth their consumption/investment
patterns and enhance their saving capacity through the provision of adequate
financial products. This increases the economys resilience in the face of economic
shocks, promotes financial stability, fosters the financial sector, and spurs economic
growth by mobilising savings and supporting investment.
According to the World Bank Global Findex Database, released in April 2012,
about half of the worlds adult population have an account with a formal financial
institution. But there is a large disparity between advanced and developing
economies. In high-income economies, 89% of the adult population have some
access to the financial system; this is more than twice the ratio seen in the
developing economies (41%). Among the 2.7 billion or so adults in the world who
have no formal account, most are in the developing economies and in the poorest
segments of society: the highest income quintile is more than twice as likely to have
a formal financial account, compared with those in the lowest quintile.
Now widely discussed, financial inclusion issues have attracted increasing
interest from central banks worldwide. In addition, the major multilateral agencies
involved in development issues have become very active in this area: for instance,
the International Monetary Fund (IMF) and the World Bank have sought to identify
key indicators of financial inclusion, and they have published their first set of
financial inclusion statistics in the form of the Financial Access Survey and the
Global Findex Database, respectively in 2010 and 2012.
There are also other institutions that play an active role at the international
level in developing new research, setting standards and providing a platform for
policymakers to share experiences. Among the most significant are the Global
Partnership for Financial Inclusion (GPFI), the Consultative Group to Assist the Poor
(CGAP), the Alliance for Financial Inclusion (AFI) and the International Finance
Corporation (IFC).
Recognising the significant benefit of financial inclusion, Malaysia, and in
particular the Central Bank of Malaysia, has actively supported this policy agenda at
both the domestic and international levels. Domestically, the Bank is currently
implementing the 10 high-priority financial inclusion measures outlined in the
Financial Sector Blueprint 20112020 released in December 2011. Five of them have
already been implemented, namely, agent and mobile banking, a financial literacy
outreach programme to under-served locations, training for microfinance
practitioners and financial inclusion Key Performance Indicators (KPIs). And they are
delivering real benefits to underserved communities. Capacity building is another
area where we have allocated resources. For 2012, the Bank has organised three
financial inclusion training programmes for policymakers, covering the topics
Regulation and supervision of deposit-taking microfinance institutions, Business
conduct and consumer protection and Islamic microfinance.
To reflect our continuous commitment to the global financial inclusion agenda,
the Bank will be hosting the 2013 AFI Global Policy Forum in Kuala Lumpur. This is
an important international financial inclusion forum for policymakers around the
globe. We expect to receive more than 300 participants from over 80 countries. We
wish to invite all of you here today to participate in this forum in September next
year.
Yet, promoting financial inclusion remains a significant challenge. One
frequently mentioned impediment is the lack of reliable figures to support more
effective and informed policy formulation and implementation. Reliable, accurate,
comprehensive and timely data are absolutely essential, as they help policymakers
formulate policies that address the real needs of the under-served community and
to measure their real impact.
Measuring financial inclusion is also challenging because the concept is so
variously defined. This can hinder meaningful analysis and policy discussion. For
example, some institutions cleave to a narrow and specific definition of the
underbanked population, while others include financial services for the poor and for
small enterprises.
In addition, the type of variables used to assess financial inclusion may also
differ across countries and organisations. Usually, these variables include:
access to bank accounts;
access to credit;
payment facilities;
usage and quality of financial products and services, which encompasses
consumer protection and financial literacy; and
consumer satisfaction.
But even if the variables look similar, definitions and practices vary, making
comparability difficult.
Apart from the need to develop methodologies for defining financial inclusion
and for drawing up indicators that relevantly encompass all its dimensions, one
issue for policymakers is the lack of a composite measure for financial inclusion.
Such an index, once established, would be useful way of making comparisons across
time and geography. It would also help decision-makers to gauge the effectiveness
of their policies over a period of time.
Taking into consideration the global need to address these challenges, and in
line with the IFCs desire to promote the exchange of views among central bank
economists, statisticians and policymakers, the IFC is proud to organise this
workshop jointly with the Central Bank of Malaysia. We hope it will be a useful
platform for the community of compilers, users and analysts of statistical
information in their efforts to:
share experience on the compiling of data on financial inclusion and how these
can shed light on key aspects of interest to analysts and policymakers;
review and discuss key indicators that help to define and measure financial
inclusion along with its impact; and
discuss the development of composite indicators for financial inclusion.
As regards the measurement of financial inclusion, the workshop will provide a
useful opportunity for discussion of financial inclusion measures, the new challenges
faced by policymakers, and the impact of financial inclusion initiatives on the overall
economy, particularly in terms of financial stability.
We will conclude our workshop on the second day with the issuance of the
Sasana Statement on Financial Inclusion Indicators, which will incorporate and
summarise our discussions and reflect our continuous and unwavering commitment
to supporting the global financial inclusion agenda.
On behalf of the IFC and the Bank, I would also like to take this opportunity to
put on record our heartfelt thanks to Her Royal Highness Princess Maxima of the
Netherlands, the UN Secretary Generals Special Advocate for Inclusive Finance for
Development, for her strong support of our initiative. And on this topic, I would like
to quote her words, as follows:
Financial inclusion is essential for employment, equitable economic growth
and development, and financial stability. To achieve these goals, policymakers need
good national data. Appropriate financial inclusion indicators will be so valuable to
produce more and comparable data on which products, delivery models, and
policies have the greatest impact on poor people and national priorities.
Aside from the workshop, I hope you will take the opportunity to tour this
magnificent building, the Sasana Kijang, as well as the beautiful green city of Kuala
Lumpur during your time here. Sasana Kijang exemplifies the Banks vision of
creating a centre for the development of thought leadership and for the promotion
of greater regional and international collaboration in central banking and finance.
On this note, I wish you a fruitful discussion and an enjoyable as well as
productive workshop.
Thank you and Terima Kasih!
IFC Workshop on Financial Inclusion Indicators
Co-hosted by Bank Negara Malaysia
5 6 Nov 2012, Sasana Kijang, Kuala Lumpur
Financial inclusion
issues in measurement & analysis 1
Kamalesh Shailesh C Chakrabarty,

Deputy Governor, Reserve Bank of India
1
This keynote address was prepared for the workshop. The views expressed are those of the author and do not
necessarily reflect the views of the BIS or the central banks and other institutions represented at the workshop.
Financial Inclusion Issues in Measurement and
Analysis 1
Introduction
Mr. Muhammad bin Ibrahim, Deputy Governor, Bank Negara Malaysia and
Chairman, Irving Fisher Committee, Mr. Paul Van den Bergh, Head of Statistics, Bank
for International Settlements (BIS), senior officials from Statistics departments of
Central Banks and distinguished participants at the Workshop. I am delighted to be
here for the Workshop on Financial Inclusion Indicators organized jointly by the
Bank Negara Malaysia and the BIS.
The importance of financial inclusion, based on the principle of equity and
inclusive growth, has been engaging the attention of policy makers internationally.
Achieving universal financial inclusion is, indeed, a global objective and has multiple
dimensions. While each jurisdiction will, perhaps, evolve its own delivery model, we
2
need to learn from each other and implement what is suitable in each constituency .
The Irving Fisher Committee is engaged in statistical issues that are of interest
to central banks worldwide. As we all know, Irving Fisher was not only a celebrated
economist who gave us the Fisher equation of money and the theory of real interest
rates, he was also a pioneer in the development of the theory of index numbers. He
once observed One of my chief objects has been to help make economics into a
genuine science through careful and sound analysis, usually carried out with the
3
help of mathematical methods and statistical verification. I trust this workshop will
help provide practical perspectives on the critical dimensions of measuring the
depth of financial inclusion as also help streamline data availability and related
issues. As the program structure aptly notes, financial inclusion principles and
approaches have assumed an increasingly active role at the international level in
developing new research agenda, setting standards and promoting best practices to
improve financial inclusion. The focus on financial inclusion measurements and data
gaps, that this Workshop seeks to achieve, is very much timely and important.
The agenda of this workshop is very appropriate as currently we lack reliable
and granular data on financial inclusion, which restricts our ability to fully gauge the
extent of exclusion and the ground-level impact of the initiatives being undertaken.
We need to work out appropriate data structures and associated analytical
frameworks for effective policymaking and the standardisation of various
approaches at the national, regional and global levels. In order to appreciate the
measurement and data needs, a broad understanding of the policy initiatives is
1
Keynote Address by Dr. K. C. Chakrabarty, Deputy Governor, Reserve Bank of India at the BIS-BNM
Workshop on Financial Inclusion Indicators at Kuala Lumpur on November 5, 2012. Assistance
provided by Shri A.B. Chakraborty and Shri Bipin Nair in preparation of this address is gratefully
acknowledged.
2
Financial Literacy and Consumer Protection Necessary Foundation for Financial Inclusion, RBI
Bulletin, May 2012.
3
Irving Fisher and Index Number Theory, Discussion paper by Erwin Diewert, February, 2012.
important. Against this background, I propose to briefly focus on: (i) Approaches to
financial inclusion some international / national initiatives, (ii) Conceptual
framework for measurement and analysis of financial inclusion, (iii) International
initiatives in measuring financial inclusion and (iv)Indian perspectives. I will conclude
with a few remarks.
Approaches to Financial Inclusion International Initiatives
The origins of the current approach to financial inclusion can be traced to the
4
United Nations initiatives , which broadly described the main goals of inclusive
finance as access to a range of financial services including savings, credit, insurance,
remittance and other banking / payment services to all bankable households and
enterprises at a reasonable cost. The Report of the Centre for Global Development
5
(CGD) Task Force on Access to Financial Services (October, 2009) has laid down the
broad policy principles for expanding financial access, including institutional
mechanisms, with particular emphasis on the need for ensuring data collection,
monitoring and evaluation. The G20 Toronto Summit (June, 2010) had outlined the
Principles for Innovative Financial Inclusion, which serves as a guide for policy and
regulatory approaches aimed at fostering safe and sound adoption of innovative,
adequate, low-cost financial delivery models, helping provide conditions for fair
competition and a framework of incentives for the various bank, insurance, and
non-bank actors involved in the delivery of a full range of affordable and quality
financial services.
The global financial crisis has brought the need for financial inclusion into
greater focus worldwide as it is believed that widespread incidence of financial
exclusion was one of the factors that precipitated the financial crisis. While spread of
financial inclusion is recognized through formal financial institutions such as banks,
credit unions, post offices or microfinance institutions, the approach of keeping
some/ all of these entities as a part of the core or as support players, varies from
country to country. Besides, it is important to note that the defining principles of
financial inclusion, coverage, role and responsibilities of institutions and
measurement / monitoring requirements have been evolving over the years.
Financial Inclusion National Initiatives
Several countries across the globe now look at financial inclusion as the means for a
more comprehensive growth, wherein, each citizen of the country is able to use
his/her earnings as a financial resource that they can put to work to improve their
future financial status and simultaneously contribute to the nations progress.
Initiatives for financial inclusion have come from the financial regulators, the
governments and the banking industry. While the banking sector has taken several
4
UNDP website: What is Inclusive Finance and UNDP Blue Book, 2006.
5
Report of the Taskforce set up by the Centre for Global Development (October, 2009), Co-Chairs
Patrick Honohan et al.
steps to promote financial inclusion, legislative measures have also been initiated in
some countries. For example, in the United States, the Community Reinvestment Act
(1977) requires banks to offer credit throughout their area of operation and
prohibits them from targeting only the rich neighbourhood. The German Bankers
Association introduced a voluntary code in 1996 providing for an everyman current
banking account that facilitates basic banking transactions. In South Africa, a low
cost bank account called Mzansi was launched for financially excluded people in
2004 by the South African Banking Association. In the United Kingdom, a Financial
Inclusion Task Force was constituted by the government in 2005 in order to
monitor the development of financial inclusion.
The history of financial inclusion in India is actually much older than the formal
adoption of the objective. The nationalization of banks, Lead Bank Scheme,
incorporation of Regional Rural Banks, Service Area Approach and formation of Self-
Help Groups all these were initiatives aimed at taking banking services to the
masses. The brick and mortar infrastructure expanded; the number of bank
branches multiplied ten-fold from 8,000+ in 1969, when the first set of banks were
nationalized, to 99,000+ today. Despite this wide network of bank branches spread
across the length and breadth of the country, banking has still not reached a large
section of the population. The extent of financial exclusion is staggering. Out of the
600,000 habitations in the country, only about 36,000+ had a commercial bank
branch. Just about 40 per cent of the population across the country has bank
accounts. The proportion of people having any kind of life insurance cover is as low
as 10 per cent and proportion having non-life insurance is abysmally low at 0.6 per
cent. People having debit cards comprise only 13 per cent and those having credit
cards only a marginal 2 per cent of the population.
The National Sample Survey data (200203) revealed that nearly 51 per cent of
farmer households in the country did not seek credit from either institutional or
non-institutional sources of any kind. A number of rural households are still not
covered by banks. They are deprived of basic banking services like a savings
account or minimal credit facilities. The proportion of rural residents who lack
access to bank accounts is nearly 40 per cent, and the figure rises to over three-
fifths in the eastern and north-eastern regions of India. Accordingly, our primary
objective is to take banking to all excluded sections of the society, rural and urban.
A more focused and structured approach towards financial inclusion has been
followed since the year 2005 when Reserve Bank of India decided to implement
policies to promote financial inclusion and urged the banking system to focus on
this goal. Our focus has, specifically, been on providing banking services to all the
600 thousand villages and meeting their financial needs through basic financial
products like savings, credit and remittance. The objectives of financial inclusion, in
the wider context of the agenda for inclusive growth, have been pursued through a
multi-agency approach. In 2006, the Government of India constituted a Committee
on Financial Inclusion 6, which made a wide range of recommendations on the
strategies for building an inclusive financial sector and gave a national rural financial
inclusion plan. Government of India has set up the Financial Stability and
Development Council (FSDC), which is mandated, inter alia, to focus on Financial
Inclusion and Financial Literacy issues. In order to further strengthen the ongoing
6
Chairman Dr. C Rangarajan.
financial inclusion agenda in India, a high level Financial Inclusion Advisory
Committee has been constituted by RBI. The Committee would pave the way for
developing a viable and sustainable banking services delivery model focussing on
accessible and affordable financial services, developing products and processes for
rural and urban consumers presently outside the banking network and for
suggesting appropriate regulatory framework to ensure that financial inclusion and
financial stability move in tandem. Financial sector regulators including RBI are fully
committed to the Financial Inclusion Mission. I will cover this in more detail in a
subsequent section.
Framework for Measurement and Data Analysis

Requirements Conceptual Issues
An essential pre-requisite for measurement is to understand the context and

framework of financial inclusion. Any effort to measure the various dimensions of
financial inclusion is not possible without explaining the context and framework. The
basic framework for measurement of financial inclusion should cover some
important dimensions.
First, financial inclusion, financial literacy and consumer protection are the three
major planks of financial stability. While financial inclusion acts from the supply side,
providing the financial market/services that people demand, financial literacy
stimulates the demand side, making people aware of what they can demand. The
demand side issues in financial inclusion include knowledge of financial products
and services, credit absorption capacity, etc. These issues are faced by both
developing and developed countries. The supply side issues cover financial markets,
network of banks and other financial institutions, appropriate design of products
and services, etc. These issues are mostly faced by the developing countries. A
framework for financial inclusion needs to take into account various aspects such as
the demand and supply side issues; assessment of enabling environment; issues in
penetration, barriers to financial inclusion, etc.
Second, availability of appropriate financial products, including at the very least,
savings products, emergency credit, payment products and entrepreneurial credit
are important aspects of financial inclusion environment. Further, regarding ease of
access, the various dimensions are timely access, distance, pricing and terms &
conditions. In addition to this, the fairness & appropriateness of products is also an
important dimension in the context of financial education of customers and for
consumer protection.
Third, the monitoring framework should cover transaction level, customer level
and products and services level monitoring, at the micro level. In addition,
monitoring at the macro level is also an important dimension for assessment of the
outcome of policy, viability of delivery models, etc. This calls for impact analysis and
penetration studies.
Information needs vary based on progress in implementation of financial
inclusion initiatives. Accordingly, information needs can be broadly categorized as
those needed at Proposal/Definition stage, Environmental stage, Implementation
stage, Monitoring stage and Overall Assessment stage. Some of the most basic
indicators to measure access for brick & mortar structures include number of
branches per 1000 population and number of ATMs per 1000 sq.km. On the other
hand, for alternate banking outlets such as the Information and Communication
Technology (ICT) based Business Correspondent (BC) Model, basic indicators
include ratio of branches to BC outlets, number of villages covered per BC, etc.
While monitoring products, data on number of products, types of products, return
on products and their related characteristics are important. At the implementation
& assessment stage, it is important to measure progress of initiatives through
impact analysis and penetration of financial inclusion by studying the growth /
changing pattern of customers and products, volume of transactions, returns on the
products, etc. It is important to note that for macro and micro level impact studies,
appropriately designed periodic surveys would be a useful tool. Surveys are also
needed for assessing viability of delivery models, sustenance of initiatives, gauging
the spread of financial literacy and measuring barriers to financial inclusion.
A robust financial inclusion design depends on a multiplicity of parameters
encompassing varied socio-economic backdrops and feasible financial service
delivery mechanism that would vary from region to region. This is particularly so for
a country like India, which is distinguished by its vastness of topographical,
demographic as also socio-economic diversity. Like any broad based financial
system, financial inclusion measures and performance monitoring system require a
rich body of performance data and analytics. Many a time, country comparisons
brought out by international bodies based on their dedicated database dwells on
much aggregative data comparison which, when seen granularly, bespeaks a
different story. This is very relevant in financial inclusion analytics, which requires
new kinds of identifiable indicators based on the evolving needs of financial
inclusion plan and program.
Concepts and definitional issues
The fact that financial inclusion concepts have different meanings in different
parlance has often led to difficulty in using a standard yardstick for benchmarking
its policy parameters. The associated difficulties are that the targeted variables used
for financial inclusion may differ from one country or organisation to another
because of different institutional set up. Inherent weaknesses in the linkages
between the financial inclusion database and welfare parameters of the society add
to the complexity. Moreover, there are no agreed composite measures of financial
inclusion which could facilitate comparisons across time and geography. Therefore,
in order to ensure consistency and accuracy in measurement of financial inclusion
parameters, it is essential that the parameters concerned are objectively defined in
the first stage of the measurement process. As a way forward, we need to assess
financial behaviour and understand where the challenges and opportunities lie for
the future. To do that, we need high-quality, multi-dimensional, comparable
financial inclusion data based on internationally standardized terms and concepts.
As such, the measurement needs also include analytics for correct interpretation of
data and establishment of international benchmarks.
Various dimensions of data on Financial Inclusion
There are several structural dimensions in the process of building up data on

financial inclusion. These include:
(i) Measurement of the progress in financial inclusion initiatives by way of
building up suitable indictors. Such indicators must contain data on access to
(supply of) and usage of (demand for) financial services as well as their coverage
and penetration. Measuring availability and actual use of deposit accounts, payment
services, credit for poor households (micro-credit schemes), micro-level insurance
products ought to be part of the framework.
(ii) The second aspect relates to understanding constraints or barriers for
financial inclusion and development of indicators for assessing the same.
(iii) Another important dimension is the collection of transactional data on
amount of credit extended, deposits placed, remittances made, etc. This is
important in order to gauge the effectiveness of the financial inclusion initiatives.
Merely opening of accounts without ensuring transactions undermines the
beneficial impact of the financial inclusion measures.
(iv) Finally, diverse data are required to be pooled and benchmarked at
international level. In this respect, one needs to take a stock of current status of
data relevant for building up globally applicable indicators.
Basic data covering both the quantitative and qualitative aspects can be
obtained through self-reporting templates by the formal financial intermediaries or
by means of household surveys. There is also a need for international benchmarking
of financial inclusion indicators as practices followed across the developed and
developing economies can vary considerably. It is necessary to develop standard
statistics on a comparable and consistent scale in order to set benchmarks and best
practices for structuring plans for financial inclusion.
International Databases on Financial Inclusion Indicators
Financial Inclusion is fast emerging as a candidate for being a core driver of

sustainable long-term economic growth and is, therefore, attracting the attention of
central bankers and various global developmental and financial institutions. It is,
however, emerging that a lot of ground remains to be covered in understanding the
reach of the financial sector, and particularly, the degree to which vulnerable groups
such as the poor, women, and youths are excluded from formal financial systems.
Availability of systematic indicators of the use of different financial services needs to
be improved in most economies and consequently, at the global level. It is
heartening to note that multilateral organisations such as the World Bank and the
International Monetary Fund (IMF) are paying attention to the development of
relevant database, besides focussing on the issue of financial inclusion through
policy prescriptions and guidelines.
The World Bank database, known as the Global Financial Inclusion database
(Global Findex), provides survey based data as part of the annual Gallup World Poll.
The survey conducted in 2011 covered at least 1,000 adults each in 148 economies
using randomly selected, nationally representative samples. The focus of the Global
Findex Database encompasses a set of indicators that measure how adults save,
borrow, make payments, and manage risk, stressing thereby on how a well-
functioning financial system serves the vital purpose of offering savings, credit,
payment, and risk management products to people with a wide range of needs.
Inclusive financial systems allowing broad access to financial services, without price
or non-price barriers to their use, are especially likely to benefit poor people and
other disadvantaged groups. Without inclusive financial systems, poor people must
rely on their own limited savings to invest in their education or for entrepreneurial
activities, while small enterprises would need to rely on their limited earnings to
take advantage of promising growth opportunities. This can contribute to persistent
income inequality and slower economic growth. Findex reports data in terms of the
proportion of people (of age 15+) for a number of parameters such as (a) who have
saved money with financial institutions or other sources, (b) taken loan from
financial institutions or other sources, (c) paid for health / agriculture insurance and
(d) used cheques / electronic payment / mobile payment systems for financial
transactions. The World Bank has released a research study on the database in April
2012. A snapshot of the data on some indicators for select countries is given in
Annex 1. The study reveals that:
i) 50 per cent of adult population worldwide report owning an account with a
formal financial institution, but actual operation and use of these accounts for
transactions varies widely across regions and economies 7. And when one starts
probing the numbers granularly, the actual spread of financial inclusion indicators
across countries would turn out to be wider.
ii) Financially excluded populace is predominant in developing countries, where
only 41 per cent adults have a formal account, with only 37 per cent of women
having formal account against 46 per cent of men; the gender gap widens further
because of varying degrees of income inequalities observed among the developing
countries.
iii) The cross country comparison would reveal that bank account penetration,
measured as a per cent of adult population, varies widely across the countries. In
high-income economies, account based financial inclusiveness is much higher with
89 per cent adults having accounts with formal financial entities. For India, account
8
penetration is reported to be 35 per cent (43.7 per cent for men and 26.5 per cent
for women) while China scored better at 63.8 per cent (67.6 per cent for men and
60 per cent for women). South Korea reported high account penetration at 93 per
cent, universality of education, and particularly, the spread of financial literacy.
iv) However, such aggregative nature of data masks many critical performance
related information for understanding the depth and granularity at sub-national
level. Another speciality of the database (FINDEX) used in the World Bank study is
that it is a survey based reporting system which may have small sample biases and
7
Measuring Financial Inclusion, Policy Research Working Paper, 6025, World Bank. It is based on the
first round of the Global Findex database based on indicators that measure how adults in
148 economies save, borrow, make payments, and manage risk. The indicators are constructed with
survey data from interviews with more than 150,000 nationally representative and randomly
selected adults age 15 and above in those 148 economies during the 2011 calendar year.
8
RBI Annual Report 201112 (p. 8892) contains the detailed India specific survey findings as per the
World Banks policy Research Working paper and latest status of Financial inclusion in India.
such constraints are natural for household surveys, particularly, when they involve
people in the lower rung of the financial inclusion pyramid.
Likewise, the IMF has initiated the Financial Access Survey (FAS) in 2009, in an
endeavour to put together cross country data and information relating to the issue
9
of financial inclusion and has published the data in July 2012 . According to IMF, the
FAS is the sole source of global supply-side data on financial inclusion,
encompassing internationally comparable basic indicators of financial access and
usage. It is the data source for the G-20 Basic Set of Financial Inclusion Indicators
endorsed by the G-20 Leaders at the Los Cabos Summit in June 2012. The FAS
database currently contains annual data, for the period 20042011, for
187 jurisdictions, including all G20 economies. The FAS data covers data on country-
wise availability of bank branches and ATMs per 1000 sq.km. and per
100,000 adults, number of deposit and loan accounts with banks per 1000 adults
and deposit-GDP and credit-GDP ratios. A glimpse of the data is given in Annex 2.
While such initiatives are most commendable and fill a major data gap at macro
level, it has to be reckoned that data on financial inclusion is needed at both macro
and micro levels. The latter can provide distributional characteristics of financial
inclusion and is, therefore, crucial in the context of policy initiatives and assessing
their outcome. Moreover, the IMF data reveals significant gaps at individual country
level, which needs to be bridged so as to improve its utility.
Data on Barriers for Financial Inclusion
Even within the existing set of account based financial services, lot of variations exist
in actual delivery models because of varied levels of technological absorption and
cost of operation. No less binding are the legal and bureaucratic constraints and
lack of appropriate infrastructure and financial literacy which requires to be
countered in order to bring the financially excluded segments within the formal
financial access network. For example, identifying unbanked segments for making
them bankable is a challenging task, be it habitation, land ownership title or
adopting a common authorised identification code. Benchmarking the data on
constraints hindering progress in the financial inclusion initiatives would immensely
help in identifying common concerns and replicating successful ideas across
jurisdictions. In this regard, concerted international initiatives would help build up
requisite data for good policy making.
Financial Inclusion Initiatives in India
I have already outlined some important milestones in the development of financial

inclusion policy initiatives in India. A broad definition is adopted as follows:
Financial inclusion is the process of ensuring access to appropriate financial
products and services needed by all members of the society in general and vulnerable
9
For more, one may refer http://fas.imf.org/.
groups in particular, at an affordable cost in a fair and transparent manner by
mainstream institutional players
We have adopted a bank led model in India to introduce a bouquet of products
related to savings, payments & credit together. It is recognised that only the
mainstream banking institutions have the ability to offer the suite of products
required to bring in effective/meaningful financial inclusion. Other intermediaries
and technology partners such as mobile companies have been allowed to partner
with banks in offering services collaboratively. In this context, it is necessary to point
out that MFIs/NBFCs/NGOs on their own may not be able to bring about financial
inclusion, as the range of financial products and services which are considered as
the bare minimum for financial inclusion purposes, cannot be offered by them. But
they play an extremely important role in furthering financial inclusion in the sense
that they bring people and communities into the fold of the formal financial
10
system .
Further, the initiatives are technology driven so as to make the financial services
deliverable in a cost effective manner, tailor made by the market participants to best
suit their requirements. RBI has encouraged the ICT model which would enable
banks to overcome the barriers of geography and ensure efficient financial
inclusion. The ICT based delivery model adopted should be technology-neutral to
facilitate easy up-scaling and customization, as per individual requirements. Against
this background, the major initiatives taken by RBI include the following:
i. Encouraged the SHG-Bank Linkage Model, one of the largest micro finance
models in the world, under which 4.79 million SHGs have been credit linked,
covering 97 million poor households (till March 2012).
ii. Mandated Commercial Banks including Regional Rural Banks to migrate to the
Core Banking Platform.
iii. Substantially liberalised the BC based service delivery model in phases.
iv. Permitted domestic scheduled commercial banks to freely open branches in
Tier 2 to Tier 6 centres.
v. Mandated banks to open at least 25% of all new branches in unbanked rural
centres.
vi. Substantially relaxed the Know Your Customer (KYC) documentation
requirements for opening bank accounts for small customers.
vii. Encouraged Electronic Benefit Transfer for routing social security payments
through the banking channel.
viii. Pricing for banks totally freed; Interest rates on advances totally deregulated.
ix. Separate programme for Urban Financial Inclusion initiated.
Some important features of the strategic initiatives for spreading financial
inclusion in India included:
i. A roadmap for providing banking services covering villages in a structured way.
In the first phase villages with population above 2000 was targeted. The focus
has now shifted to villages with population less than 2000.
10
Financial Inclusion and Banks: Issues and Perspectives, RBI Monthly Bulletin, November 2011.
ii. Introduction of New Products Making available a minimum of four banking
products through the ICT based BC model.
iii. Our strategy has been to create an ecosystem comprising of a combination of
branches and ICT based BC outlets for evolving an effective financial inclusion
delivery model.
iv. In order to further facilitate financial inclusion, interoperability was permitted at
the retail outlets or sub-agents of BCs (i.e. at the point of customer interface),
subject to certain conditions, provided the technology available with the bank,
which has appointed the BC, supported interoperability. However, the BC or its
retail outlet or sub-agent at the point of customer interface would continue to
represent the bank, which has appointed the BC.
v. Banks have been advised that they may set up intermediate brick and mortar
structures (in rural areas) between the present base branch and BC locations, so
as to provide support to a cluster of BCs (about 810 BCs) at a reasonable
distance of about 34 kilometers. Such branches should have minimum
infrastructure, such as a Core Banking Solution (CBS) terminal linked to a pass
book printer and a safe for cash retention for operating large customer
transactions and would have to be managed full time by banks own
employees. It is expected that such an arrangement would lead to efficiency in
cash management, documentation, resolving customer grievances and close
supervision of BC operations.
vi. The evolution of the BC model comprises of the following four stages:
Stage 1: Mobile Business Correspondents
Stage 2: Fixed Location Business Correspondent Outlets
Stage 3: Low Cost Intermediate Brick & Mortar Structures (Ultra Small
Branches)
Stage 4: Full fledged Brick & Mortar Branches
vii. Financial Inclusion Plan (FIP) for Banks All domestic commercial banks
public and private sector have drawn a Board approved three year FIP starting
April 2010.
The banking system's three Year FIPs include parameters such as :
i. No. of branches opened, of which the no. opened in unbanked villages and in
villages with population greater than or less than 2000
ii. No. of BC outlets opened
iii. No. of Basic Savings Bank Deposit Accounts opened
iv. No. of emergency credit (OD) provided
v. No. of Entrepreneurial credit (KCC/GCC) provided
vi. Transactions done in the above accounts through Brick & Mortar branches as
well as through BCs
These initiatives are being closely monitored by the Reserve Bank of India
through monthly reporting and annual comprehensive review.
Financial Inclusion Plan achievements so far
A snapshot of the progress in certain key parameters in the recent period (March
2010 June 2012) are given below (Details in Annex 3):
i. Banking connectivity to more than 1,88,028 villages upto June 2012 from
67,694 villages in March 2010.
ii. All unbanked villages with population of more than 2000 persons, numbering
around 74,000 are now connected with banks.
iii. Number of BCs increased to 120,098 from 34,532.
iv. More than 70 million basic banking accounts have been opened to take the
total number of such accounts to 147 million.
v. About 36 million people/families have been credit-linked.
In the context of this Workshop it is important to note that there has long been
a statistical system of capturing both macro and micro-level data on measurement
of financial inclusion in India, at least in respect of deposit and credit. The database
on bank branch network, led by scheduled commercial banks in India, give an idea
about the reach of the formal banking system in the form of an indicative banking
penetration measure such as average population per branch or number of deposit
bank accounts per 1000 population. The data indicates that the branch network of
scheduled commercial banks have increased during last five years, registering an
improved coverage, in terms of population per branch, from 15,700 to 12,600.
Among the newly opened bank branches during the year, the share of rural and
semi-urban branches has gone up between 2007 and 2012. During the same period,
there has been a marginal improvement in the share of deposit accounts in rural
centres and loan accounts in semi-urban (Annex 4). Such measures, however, do
not throw light on the distributional aspect of the brick-mortar based branch
network across regions or across the socio-economic spectrum of population.
There is, therefore, a need to further expand these databases in order to
improve their utility as monitoring tools and MIS. There is also a lot to learn from
the international experiences. We also need to draw out a standardised set of
yardsticks that we would be using for measuring achievements as well as evaluating
the various means to achieve the much desired goal.
Constraints to spread of Financial Inclusion
Quite clearly, the task of covering a population of 1.2 billion with banking services is
gigantic. It is clear that out of 600 thousand villages, centres that could be covered
by brick and mortar bank branch network are only around 36,000. It is well
recognized that there are supply side and demand side factors driving inclusive
growth. Banks and other financial services players are largely expected to mitigate
the supply side constraints that prevent poor and disadvantaged groups from
gaining access to the financial system. Access to financial products is constrained by
several factors which include lack of awareness about the financial products, high
transaction costs and products which are inconvenient, inflexible, not customized
and of low quality. However, we must bear in mind that apart from the supply side
factors, demand side factors such as lower income and /or asset holdings, financial
literacy/ awareness issues, etc. also have a significant bearing on inclusive growth.
Owing to difficulties in accessing formal sources of credit, poor individuals and
small and micro enterprises usually rely on their personal savings and internal
sources or take recourse to informal sources of finance to invest in health,
education, housing and entrepreneurial activities. The mainstream financial
institutions like banks have an important role to play in helping overcome this
constraint, not as a social obligation, but as a business proposition. The major
barriers cited to constrain extension of appropriate services to poor by financial
service providers are the lack of reach, higher cost of transactions and time taken in
providing those services, apart from attitudinal issues. In this regard, major barriers
to financial inclusion require to be identified.
i) Demand side barriers are (a) Low literacy levels, lack of awareness and/or
knowledge/understanding of financial products; (b) Irregular income; frequent
micro-transactions; (c) Lack of trust in formal banking institutions; cultural obstacles
(e.g., gender and cultural values).
ii) Supply side barriers are (a) Outreach (low density areas and low income
populations are not attractive for the provision of financial services and are not
financially sustainable under traditional banking business models); (b) Regulation
(frameworks are not always adapted to local contexts), (c) Business models (mostly
with high fixed costs); Service Providers (limited number and types of financial
service providers) (d) Services (non-adapted products and services for low income
populations and the informal economy); (e) Age Factor (Financial service providers
usually target the middle of the economically active population, often overlooking
the design of appropriate products for older or younger potential customers. There
are hardly any policies or schemes for the younger lot or the old people who have
retired, as the banks do not see any business from them); (f) Bank charges (In most
of the countries, transaction is free as long as the account has sufficient funds to
cover the cost of transactions made. However, there are a range of other charges
that have a disproportionate effect on people with low income).
Concluding remarks
Let me now make some concluding remarks on the challenges to financial inclusion
and, in particular, the measurement challenges. The issue of expanding the
geographical and demographic reach poses challenges from the viability
perspectives. Appropriate business models are still evolving and various delivery
mechanisms are being experimented with. Financial literacy and level of awareness
continue to remain an issue and the ICT Based BC Model is also taking time to
stabilize. It calls for coordination of all the stakeholders like sectoral regulators,
banks, governments, civil societies, NGOs, etc. to achieve the objective of financial
inclusion. Challenges of financial exclusion are faced by most countries globally and
each country has to develop its own customized solutions drawing upon its own
experiences and those of its peers across the globe.
On the measurement challenges, first, it needs to be reckoned that financial
inclusion concepts, policies, delivery models and implementation processes are still
evolving. It is, therefore, essential that the policy for achieving total financial
inclusion also keeps changing to adapt to the needs of the environment. This poses
challenges for measurement of various financial inclusion initiatives as also their
aggregation across activities, institutions, regions and so on. Statistical analysis of
performance of financial inclusion initiatives and development of benchmarking
standards can be quite complex. Second, while existing initiatives in measuring
financial inclusion are commendable, there is a need for greater focus on the micro
and distributional dimensions. Third, we should explore the need to change the
focus of present information systems of banking business from traditional
accounting model to customer centric business model. This would call for
expanding the scope of the currently used measures of financial inclusion.
I would end by once again thanking the organizers for inviting me to this forum
which, I am sure, will generate valuable debate and insight and come up with
practical solutions to the measurement challenges faced in the global movement
towards financial inclusion. I wish the Workshop all success.
Annex 1
World Bank's FINDEX Select Indicators on Financial Inclusion 2011

(Proportion of Population of Age 15+)
United United Russian

Indicator Name Germany Brazil China India
States Kingdom Federation
CREDIT:
Loan from a financial institution in
the past year 20.1 11.8 12.5 7.7 6.3 7.3 7.7
the past year, income, bottom 40% 17.6 11.1 12.3 6.3 3.5 7.7 7.9
the past year, income, top 60% 22.3 13.2 13.7 8.7 8.2 7.0 7.5
Loan in the past year 44.6 28.8 25.3 31.9 23.8 29.4 30.6
Loan in the past year, income,
bottom 40% 45.1 28.1 25.4 32.1 19.7 32.4 35.7
Loan in the past year, income, top
60% 44.2 30.2 24.6 31.7 26.6 27.3 24.9
INSURANCE:
Personally paid for health
insurance NA NA NA 6.7 7.6 47.2 6.8
Purchased agriculture insurance
(% working in agriculture, age 15+) NA NA NA 3.7 11.2 7.2 6.6
PAYMENTS:
Checks used to make payments 65.5 50.1 7.2 5.2 6.7 1.8 6.7
Electronic payments used to make
payments 64.3 65.3 64.2 7.7 16.6 6.9 2.0
Mobile phone used to pay bills NA NA NA 1.7 1.3 1.3 2.2
SAVINGS:
Saved at a financial institution in
the past year 50.4 43.8 55.9 10.9 10.3 32.1 11.6
the past year, income, bottom 40% 32.1 43.5 55.1 8.8 5.8 18.3 10.4
the past year, income, top 60% 66.5 44.3 60.0 12.4 13.3 41.7 12.9
Saved any money in the past year 66.8 56.7 67.3 22.7 21.1 38.4 22.4
Saved any money in the past year,
income, bottom 40% 51.5 56.2 67.1 18.9 12.1 23.3 19.4
Saved any money in the past year,
income, top 60% 80.2 57.7 68.1 25.4 27.1 48.9 25.8
NA: Not Available
Annex 2: IMF's FAS Database - Select Indicators on Financial
Inclusion
United United Russian

Germany Brazil China India
States Kingdom Federation
2005 2011 2005 2011 2005 2011 2005 2011 2005 2011 2005 2011 2005 2011
ATMs per 43.2 NA 240.9 NA NA NA 1.7 11.2 17.4 20.6 NA NA NA 25.4
1,000 km
ATMs per 168.6 NA 117.9 NA NA NA 22.8 152.9 108.9 119.6 NA NA NA 8.9
100,000 adults
Commercial 8.5 9.6 58.0 NA 40.9 NA 2.1 2.7 NA 7.9 NA NA 23.2 30.4
bank branches
per 1,000 km
Commercial 33.1 35.4 28.4 NA 20.2 NA 28.4 37.1 NA 46.2 NA NA 9.0 10.6
bank branches
per 100,000
adults
Deposit NA NA NA NA NA NA NA NA 705.7 1032.7 NA NA 607.3 953.1
accounts with
commercial
banks per
1,000 adults
Household NA NA NA NA NA NA NA NA NA NA NA NA 576.6 853.0
deposit
accounts with
commercial
banks per
1,000 adults
Household NA NA NA NA NA NA NA NA NA 747.4 NA NA 7.4 20.6
loan accounts
with
commercial
banks per
1,000 adults
Loan accounts NA NA NA NA NA NA NA NA NA 853.7 NA NA 100.4 142.0
with
commercial
banks per
1,000 adults
Outstanding 48.0 57.8 356.5 422.8 20.1 27.6 18.7 45.0 34.0 53.3 123.2 159.3 47.3 68.4
deposits with
commercial
banks(Percent
of GDP)
Outstanding 48.9 46.8 377.6 460.0 24.7 24.2 29.5 63.9 21.2 40.3 85.3 108.7 31.2 51.8
loans from
commercial
banks (Percent
of GDP)
NA: Not Available
Source: IMF's Financial Inclusion Survey July 2012.
Annex 3: Progress of banks 11 in Financial Inclusion Plan in
India
Year Year Year Quarter Progress

SR Particulars ended ended Mar ended ended April 11
Mar 10 11 Mar 12 June 12 March 12
1 Total No. of Branches 85457 91145 99242 99771 8097

2 No. of Rural Branches 33433 34811 37471 37635 2660
3 No. of CSPs Deployed 34532 60993 116548 120098 55555
4 Banking outlets in Villages with population >2000 37791 66447 112130 113173 45683
5 Banking outlets in Villages with population <2000 29903 49761 69623 74855 19862
6 Banking Outlets through Brick & Mortar Branches 33378 34811 37471 37635 2660
7 Banking Outlets through BCs 34174 80802 141136 147167 60334
8 Banking Outlets through Other Modes 142 595 3146 3226 2551
9 Total Banking Outlets 67694 116208 181753 188028 65545
10 Urban Locations covered through BCs 447 3771 5891 6968 2120
11 No Frill A/Cs (No. In million) 73.45 104.76 138.50 147.94 33.74
12 Amount in No Frill A/Cs (Amt In billion) 55.02 76.12 120.41 119.35 44.29
13 No Frill A/Cs with OD (No. in million) 0.18 0.61 2.71 2.97 2.10
14 No Frill A/Cs with OD (Amt In billion) 0.10 0.26 1.08 1.21 0.82
15 KCCs-Total-No. In million 24.31 27.11 30.23 30.76 3.12
16 KCCs-Total-Amt In billion 1240.07 1600.05 2068.39 2094.00 468.34
17 GCC-Total-No. in million 1.39 1.70 2.11 2.29 0.41
18 GCC-Total-Amt In bilion 35.11 35.07 41.84 43.21 6.77
19 ICT Based A/Cs-through BCs (No. in million) 13.26 31.65 57.08 62.77 25.44
20 ICT Based A/Cs-Transactions (No. In million) 26.52 84.16 141.09 45.96 141.09
11
Scheduled commercial banks (excluding RRBs)
Annex 4: Trends in banking parameters in India
31st
Items 2007 2008 2009 2010 2011 2012
March
1. No. of Commercial Banks 183 173 170 168 167 173
(a) Scheduled Commercial Banks 179 169 166 164 163 169
Of which: Regional Rural Banks 96 90 86 83 82 82
(b) Non-Scheduled Commercial Banks 4 4 4 4 4 4
2. Distribution of New Branches (%) Total 100 100 100 100 100 100
Rural 9 14 18 19 24 33
Semi-
urban 31 31 32 33 41 37
Urban 35 31 26 27 17 16
Metro 26 24 24 21 18 14
3. Distribution of Deposits Accounts

(%) Total 100 100 100 100 100 ..
Rural 29 29 30 31 31 ..
Semi-
urban 26 26 26 26 26 ..
Urban 22 22 21 21 21 ..
Metro 24 24 23 23 22 ..
4. Distribution of Loan Accounts (%) Total 100 100 100 100 100 ..
Rural 33 31 31 31 32 ..
Semi-
urban 23 22 23 23 24 ..
Urban 14 13 13 14 14 ..
Metro 30 33 33 33 30 ..
5. Average population per branch

(in '000s) Total 15.7 15.1 14.5 13.8 13.3 12.6
6. Number of Banked Centres

(Scheduled Commercial Banks) Total 34399 34426 34636 34801 35151 36391
".." : Not available.
Note: All the revenue centres (habitations) are classified in to four groups based on their population based on Census
2001 data. These groups are rural (centres having population < 10,000), semi-urban (10,000 <= population <
1,00,000), urban (1,00,000 <= population < 10,00,000) and metropolitan (population >= 10,00,000).
Source: Reserve Bank of India and Census data.
Financial inclusion issues in measurement & analysis1
Kamalesh Shailesh C Chakrabarty,

Deputy Governor, Reserve Bank of India
1
This presentation was prepared for the workshop. The views expressed are those of the author and do not necessarily reflect the views of the BIS or the central banks and
other institutions represented at the workshop.
Financial Inclusion
Issues in Measurement & Analysis
Dr. K.C. CHAKRABARTY

DEPUTY GOVERNOR
RESERVE BANK OF INDIA
1
Financial Inclusion
An essential Pre-requisite for measurement is to

understand the context and framework of Financial
Inclusion. Any effort to measure the various
dimensions of financial inclusion is not possible
without explaining the context and framework.
2
Trinity to make Financial Stability
Possible
Financial
Inclusion
Financial
Stability
Consumer Financial
Protection Literacy
3
Financial Inclusion-
Definition
Financial inclusion is the process of ensuring
access to appropriate financial products and
services needed by all the members of the society
in general and vulnerable groups in particular at an
affordable cost in a fair and transparent manner by
mainstream institutional players
4
Twin Aspects of Financial Inclusion
Financial Inclusion and Financial Literacy are twin pillars. While Financial
Inclusion acts from supply side providing the financial market/services
what people demand, Financial Literacy stimulates the demand side
making people aware of what they can demand.
Demand Side & Supply Side
Fair & Access

Appropriateness
Developing Economies face the problem of low level of literacy, poor

accessibility and low demand. Therefore it is necessary for developing an
Index for measuring both Access as well as the level of Literacy.
5
Framework of Financial Inclusion-I
Assessment of enabling environment
Penetration Issues
Barriers to Financial Inclusion
Demand for financial services
Access Issues Availability of Banking facilities
Financial Education & Literacy
Awareness of financial products and services
Availability of appropriate products
Savings Products
Emergency Credit (Overdrafts)
Remittance Products
Entrepreneurial Credit (KCC/GCC) 6
Framework of
Financial Inclusion - II
Supply of financial services

Ease of Access
Time Dimension
Distance Dimension
Pricing Dimension
Terms & Conditions
Fairness & Appropriateness of Products
Education
Consumer Protection
7
Framework of Financial Inclusion-III
Monitoring of Products
Micro Level Monitoring
Transaction level monitoring
Customer Level Monitoring
Product Level Monitoring
Macro Level Monitoring
Assessment & Outcome of Policy
Assess whether the FI model is viable
Conduct an Impact Analysis
Penetration study 8
Information Needs Stages
Proposal/Definition Stage
Environmental Stage
Implementation Stage
Monitoring Stage
Overall Assessment Stage
9
Information Needs I
Basic Indicators to Measure Access
Brick & Mortar Structures
Number of branches per 1000 population
No. of ATMs per 1000 sq.km.
Alternate banking outlets ICT based BC
Model
Product stage -
Number of Products
Types of products
10
Information Needs II
Implementation & Assessment Stage
Progress of Initiatives and Impact Analysis
Penetration of FI Growth / Changing pattern of
No. of customers
No. of products
Volume of transactions
Returns on the products
Macro and micro level impact
Use of surveys
Viability of delivery models and Sustenance of
Initiatives
Spread of Financial literacy
11
Measuring constraints in FI
International Measurement Initiatives
World Banks Financial Inclusion Database April 2012

(FINDEX)
Survey based data on 148 countries (2011) on a set of indicators
that measure, how adults save, borrow, make payments, and
manage risk.
Reported in terms of the proportion of people (of age 15+)
IMFs Financial Access Survey July 2012 (FAS)

Supply-side annual data for 187 jurisdictions, including all G20
economies for the period 2004-2011
Data on basic indicators of financial access and usage such as
spread of bank network, ATMs, Number of deposit and loan
a/cs, Deposit-GDP and Credit-GDP ratios.
12
Financial inclusion
The Indian model
Policy Developments
Inclusive Growth National Objective
RBI Initiatives since 2005
Three year Financial Inclusion Plan (2010-13)
A Structured, Planned & Integrated Effort
Rangarajan Committee on Financial Inclusion
(2006-08)
Financial Stability and Development Council
(2010)
RBI Advisory Committee on Financial Inclusion
(2012) 13
Financial inclusion
The Indian model
Adopted a Bank led model - To introduce a
bouquet of products related to Savings,
Payments & Credit together
Mainstream banking institutions only have the

ability to offer the suite of products required to
bring in effective/meaningful financial inclusion.
Other intermediaries and technology partners

such as mobile companies have been allowed to
partner with banks in offering services 14
collaboratively
Financial Inclusion - Strategy
RBI encouraged the ICT model which would
enable banks to circumvent the barriers of
geography and ensure efficient FI.
ICT based delivery model Technology-neutral
to facilitate easy up-scaling and customization,
as per individual requirements.
Strategy to create an eco-system comprising of a
combination of Branch and ICT based BC outlets
for evolving an effective Financial Inclusion
Delivery Model.
15
Financial Inclusion Initiatives- I
Encouraged the SHG Bank Linkage
Programme 4.79 million SHGs covering 97
million poor households were credit linked till
last year under the programme
Mandated Commercial Banks including
Regional Rural Banks to migrate to the Core
Banking Platform
Substantial liberalisation of the BC based
service delivery model in phases
Domestic Scheduled commercial banks
permitted to freely open branches in Tier 2 to
Tier 6 centres
16
Banks mandated to open at least 25% of all
Financial Inclusion Initiatives - II
Know Your Customer (KYC) documentation
requirements for opening bank accounts
relaxed for small accounts substantially
Encouraged Electronic Benefit Transfer for
routing social security payments through the
banking channel.
Pricing for banks totally freed. Interest rates
on advances totally deregulated
Separate programme for Urban Financial
Inclusion initiated
17
Financial Inclusion Initiatives- III
Roadmap for providing banking services A
structured way of covering villages. In the first
phase villages with population above 2000 was
targeted. The focus has now shifted to villages
with population less than 2000. BC Model - Chart
Introduction of New Products Making
available a minimum of four banking products
through the ICT based BC model.
Financial Inclusion Plan for Banks - All
domestic commercial banks - public and private
sector have drawn a Board approved 3 year
Financial Inclusion Plan (FIP) starting April 2010.
18
Financial Inclusion PLAN - Monitoring
Banks 3 Year FIPs include :
No. of branches opened, of which the no. opened
in unbanked villages and in villages with
population > than and < 2000
No. of BC outlets opened
No. of Basic Savings Bank Deposit Accounts
opened
No. of emergency credit (OD) provided
No. of Entrepreneurial credit (KCC/GCC) provided
Transactions done in the above accounts through
Brick & Mortar branches as well as through BCs
Close Monitoring by Reserve Bank of India -
19
Monthly Reporting - Annual Comprehensive
FIP Monitoring Format
SR Particulars
1 Total No. of Branches
2 , No. of Rural Branches
3 No. of branches in unbanked villages
4 Total No. of CSPs Deployed
5 Through Branches
6 No. of banking outletsOut of 1 above in villages Through BCs
7 with population > 2000 Through Other Modes
8 Sub Total : > 2000
9 Through Branches
10 No. of banking outlets in villages with population < Through BCs
11 2000 Through Other Modes
12 Sub Total : < 2000
13 Total Banking Outlets in all villages
14 No. of BC outlets in Urban Locations
15 Basic Savings Bank Deposit Accounts (BSBDAs) No. in million
16 through branches Amt. Rs. In billion
18 outstanding through BCs Amt. Rs. in billion
20 (Bank as a whole) Amt. Rs. In billion
21 No. in million
OD facility availed in BSBDAs
22 Amt. Rs. In billion 20
FIP Monitoring Format
23 No. in million
KCCs outstanding - through Branches
24 Amt. Rs. In billion
25 No. in million
KCCs outstanding - through BCs
27 No. in million
KCCs-Total (Bank as a whole)
29 No. in million
GCCs outstanding through Branches
31 No. in million
GCCs outstanding through BCs
33 No. in million
GCC-Total (Bank as a whole)
35 Savings Deposit (No. in million)
36 Savings Deposit (Amt. Rs. In billion)
37 Credit/OD (No. in million)
38 Credit/OD (Amt. Rs. In billion)
39 Term Dep./RD (No. in million)
Transactions in BC-ICT Accounts (during the quarter) *
40 Term Dep./RD (Amt. Rs. In billion)
41 EBT/Remittance (No. in million)
42 EBT/Remittance (Amt. Rs. In billion)
43 Others (No. in million)
44 Others (Amt. Rs. In billion)
45 No. in million
Total of Transactions in BC-ICT Accounts
21
FIP PROGRESS UPTO JUNE 2012
Year Year Quarter Progress
Year ended ended Mar ended Mar ended April-June
SR Particulars Mar 10 11 12 June 12 12
1 Total No. of Branches 85457 91145 99242 99771 8097
2 No. of Rural Branches 33433 34811 37471 37635 2660
3 No. of CSPs Deployed 34532 60993 116548 120098 55555
4 Banking outlets in Villages with population >2000 37791 66447 112130 113173 45683
5 Banking outlets in Villages with population <2000 29903 49761 69623 74855 19862
6 Banking Outlets through Brick & Mortar Branches 33378 34811 37471 37635 2660
7 Banking Outlets through BCs 34174 80802 141136 147167 60334
8 Banking Outlets through Other Modes 142 595 3146 3226 2551
9 Total Banking Outlets 67694 116208 181753 188028 65545
10 Urban Locations covered through BCs 447 3771 5891 6968 2120
11 No Frill A/Cs (No. In million) 73.45 104.76 138.50 147.94 33.74
12 Amount in No Frill A/Cs (Amt In billion) 55.02 76.12 120.41 119.35 44.29
13 No Frill A/Cs with OD (No. in million) 0.18 0.61 2.71 2.97 2.10
14 No Frill A/Cs with OD (Amt In billion) 0.10 0.26 1.08 1.21 0.82
15 KCCs-Total-No. In million 24.31 27.11 30.23 30.76 3.12
16 KCCs-Total-Amt In billion 1240.07 1600.05 2068.39 2094.00 468.34
17 GCC-Total-No. in million 1.39 1.70 2.11 2.29 0.41
18 GCC-Total-Amt In bilion 35.11 35.07 41.84 43.21 6.77
19 ICT Based A/Cs-through BCs (No. in million) 13.26 31.65 57.08 62.77 25.44
20 ICT Based A/Cs-Transactions (No. In million) 26.52 84.16 141.09 45.96 141.09
22
How Index can help measure penetration over and above
capturing access
Total No. of Branches 99771
No. of Rural Branches 37635
No. of villages covered 188028
No. of Business Correspondents 120098
No of people provided with Basic Savings Bank Accounts
(No. In million) 147.94
Average balance in these accounts (in Rs.) 800
No. of people credit linked (No. in million) 36.02
Average balance outstanding in these credit linked accounts (in Rs.) 60000
No. of Accounts opened by BCs (No. in million) 62.77
No. of transactions in ICT Based A/Cs opened by BCs (No. In million) 45.96
No. of transactions per BC per day 4
No. of transactions per ICT account per day .01
23
ISSUES
Demographic Spread How to provide banking
services to villages with low population
Viability?
Evolving of an Appropriate Business Model & an
Efficient Delivery Mechanism
Financial Literacy Status of awareness
National Level Coordination of all the
stakeholders like Banks, Governments, Civil
Societies, NGOs etc. required to achieve the
objective of financial inclusion.
24
Measurement Challenges
CONCLUSION
Financial inclusion concepts, policies, delivery models and
implementation processes are evolving and as such depends
on the environment. It is therefore essential that the policy for
achieving total financial inclusion has to change to adapt to the
needs of the environment.
Existing initiatives in measuring financial inclusion are
commendable, yet there is a need for greater focus on the
micro and distributional dimensions
Finally the focus of Financial Inclusion should be more on the
people aspect involved rather than the accounting aspect.
The focus of information systems in banking business have to
change from traditional accounting model to customer centric
business model. This would call for expanding the scope of
presently adopted measures of financial inclusion.
25
Thank you
kcchakrabarty@rbi.org.in
26
Overview of international and national initiatives

to promote financial inclusion and its measurement 1
Paul Van den Bergh, BIS
1
Restricted
Overview of international and national

initiatives to promote financial inclusion
and its measurement
Paul Van den Bergh
Head of Statistics and Research Support
Bank Negara Malaysia IFC workshop on Financial Inclusion Indicators,

Kuala Lumpur, 5-6 November 2012
1
The views expressed are those of the presenter and do not necessarily reflect those of the BIS
Restricted
United Nations
In September 2009 appointed Secretary General Special

Advocate (UNSGSA) for Inclusive Finance for
Development : H.R.H. Princess Maxima of the Netherlands
Presents Annual Report
Five priority themes
diversity of financial services
continuum of finance from individuals to SMEs
responsible finance and financial literacy
complementarity with work of global standard setters
data for effective policy making and financial product
development
2
Restricted
G20 initiatives
G20 set up Global Partnership for Financial Inclusion

Support provided by World Bank Group and AFI
Three GPFI subgroups
SME finance
work with standard setting bodies
data and measurement
Proposal to explore issue of consumer protection and
literacy
Data group to identify
existing FI data landscape
assessing data gaps
develop key performance indicators
3
Restricted
G20 set of FI indicators
Basic set
Formally banked adults
Adults with credit by regulated institution
Formally banked enterprises
Enterprises with credit by regulated institution
Points of service
Secondary set in development
Payments and remittances
Credit information
Financial capability
Financial consumer protection
4
Restricted
Initiatives supported by the World Bank Group
Supports G20 GPFI

Released in 2012 the Global Financial Inclusion (Global Findex)
Database, funded by Bill and Melinda Gates Foundation in
partnership with Gallup
Covers 148 countries
Measure how women, men and youth save, borrow, make
payments and manage risks
Comprehensive, comparable dataset
Can track effect of FI policies globally
Various other global surveys (entreprise, payment systems,
consumer protection, remittances) and assist national surveys
WB FI Global Practice (projects in over 60 countries)
Work with IMF on Financial Access Survey
House the Consultative Group for Assisting the Poor (CGAP)
5
Restricted
Consultative Group to Assist the Poor, CGAP (1)
Independent policy and research center dedicated to

advancing financial access for the worlds poor
Supported by 30 development agencies and private
foundations
Provides market intelligence, promotes standards,
develops innovative solutions and offers advise to
governments
financial service providers
donors
investors
Report on Financial Access in 2009, 2010 and 2011
6
Restricted
Consultative Group to Assist the Poor, CGAP (2)
2011 report reviews supply-side data landscape

Access to financial services (branches, ATMs )
Usage of financial services (number of accounts )
Quality of products and services (pricing, client value, core
tenets of consumer protection and financial capability such
as literacy)
Sources of data
Demand-side data from users (typically surveys)
Supply-side data from financial service providers
Demand and supply-side data are complementary
7
Restricted
Some other international organisations
IMF: Financial Access Survey

OECD
Financial Literacy Network
Financing SMEs and Entrepreneurs
Handbook on Constructing Composite Indicators
International Association for Research on Income&Wealth
BIS
Home of various Standard Setting Bodies which meet with
key stakeholders re FI policies
CPSS with data on payment systems
Secretariat of the Irving Fisher Committee
Own composite indicators for financial stability
8
Restricted
Princess Mxima of the Netherlands at the BIS
9
Restricted
The Irving Fisher Committee on Central Bank

Statistics
Forum for economists and statisticians to discuss data
issues related to economic, monetary and financial
stability of interest to central banks
Housed at the Bank for International Settlements
All 60 BIS shareholders are member (total 75 members)
Yearly report to BIS Governors
Secretariat at the BIS
Affiliated with the International Statistical Institute
10
Restricted
The Irving Fisher Committee on Central Bank

Statistics (2)
Organises conferences, seminars, workshops

Publishes Bulletin and Working Papers
Cooperates with other international statistical bodies or
data groups
11
Restricted
Some other international initiatives (1)
Alliance for Financial Inclusion (AFI)

Network of central banks, supervisors and other financial
regulatory authorities (95 members from 81 countries)
Membership overlaps partly with BIS/IFC
Focuses on peer learning and knowledge sharing
35 members made concrete FI commitments
Support G20 GPFI in all three sub-groups and G20 Peer
Learning Programme
Data Working Group
12
Restricted
Microfinance Information Exchange (MIX)

Washington-based non-profit international organisation that
collects, validates, and analyses microfinance data
Various private sector partner organisations
Finmark/Finscope
FinMark Trust, independent trust set up in 2002 with initial
funding of UK Dept for Int Development)
Finscope surveys: consumer survey (demand and supply
side), small business
FI indicators for various (12) African countries
13
Restricted
Center for Financial Inclusion (NY-based group of key

industry participant)
Some regional initiatives
Various donor organisations, eg Gates Foundation
14
Restricted
National data initiatives re financial inclusion

and data (1)
Many countries participate in international data collections
of World Bank Group, AFI, Finsope and others
Also national initiatives under different headings
Kenya: M-PESA mobile payment system
South Africa: academic research incl by Centre for
Inclusive Banking in Africa
Brazil: Inclusao Financeira (Forum holding yearly meeting,
last one in Porto Alegre, 29-31 October 2012), measures
to monitor development of network of bank correspondents
Belgium, Italy: studies in income & wealth by central bank
15
Restricted

and (2)
Portugal: measuring the evolution of financial services, incl
through data from payment systems, Central Credit
Register and Central Bank Balance Sheet Data Office
France: National Database on Household Credit
Repayment Incidents
India: research topic of RBIs Centre for Advanced
Financial Research and Learning (CAFRAL)
16
Restricted

and data (3)
US: Federal Reserve involvement in Community
Development Finance
United Kingdom:
HM Treasury initiative: Financial Inclusion Force
Private sector: Transact the National Forum for FI
Non-for-profit think tank: the Financial Inclusion Centre
Interest in many other countries as reflected in central bank
speeches
17
Restricted
Central banks speeches
BIS contains list of speeches by senior central bank

executives
Speeches on financial inclusion since early 2011
India (8)
Kenya, United States (5)
Fiji, Namibia, Pakistan (3)
Albania, Mexico, Solomon Islands, Uganda, Zambia,
(2)
Curacao and St Maarten, Ireland, Malaysia, Mauritius,
Philippines, Saudi Arabia, Serbia (1)
18
Restricted
General observations (1)
Various definitions of financial inclusion (confusing?)

Importance of good data for good policy
FI is multidimensional concept
Different dimensions need to be measured
Access
Usage
Qualitative information
Various international initiatives to provide methodological
guidance and coordinate international data collections
using standard indicators
19
Restricted
General observations (2)
Importance of detailed national data on various country-

specific issues (one size does not fit all)
Micro data important to improve analysis
Some efforts exist to develop composite indicators at
national and international level
Simple measures so far
But methodological issues need to be addressed
Need for cooperation at international and national level
Central bank statistical functions have expertise and are
ready to assist
20
Towards a global financial inclusion data infrastructure 1
Djibril M Mbengue, Consultative Group to Assist the Poor, World Bank
1
Towards a Global Financial Inclusion
Data Infrastructure
Djibril M. Mbengue
Microfinance Specialist
November 5, 2012
Raul Hernandez-Coss An Inspiration
Data could be a means to put the cards on

the table; data are less controversial than
policies and can get policy makers and
regulators to start talking.
2
GPFI Data and Measurement Sub-group
Context:
Global Partnership for Financial Inclusion (GPFI)
Created in 2010
Three sub-groups, and one in formation
Implementation partners (AFI, CGAP, IFC, and the World Bank)
Financial Inclusion Data and Measurement Sub-group

Co-chairs: Australia, Mexico, South Africa
Key achievements:
G-20 Basic Set of Financial Inclusion Indicators developed and adopted
by Leaders
Publication: Financial Inclusion Data: Assessing the Landscape and
Country-Level Target Approaches
3
Financial inclusion
DEFINITION THREE DIMENSIONS
Financial inclusion
refers to a state in which
all working age adults
have effective access to
credit, savings,
payments, and
insurance from formal
service providers.
4
Robust financial inclusion data architecture is
emerging
Broader coverage
Supply- Demand-
side side
Source: Adaptation from Bill & Melinda Gates

Deeper coverage Foundation (2010).
Widespread use of surveys at country level is
correlated with use national inclusion strategies
How widespread is the use of surveys to monitor
access to financial services?
80 73
70 66
62
Strategy documents and data collection
Number of countries
60 57
50
50
41
40
30 No strategy document 27 40
20
10
0
HH Survey Firm Survey Financial Institution Strategy document 45 19
Survey
Use Do Not Use
Source: CGAP/WBG Financial Access database. Based on Number of countries

responses by 120 countries.
At least one survey No survey
Note: CGAP/WBG Financial Access database. Based on

responses from 131 countries.
Source: Financial Access 2011: An Overview of Supply-Side Data Landscape, CGAP/IFC,

May 2012
The G20 Basic Set of Financial Inclusion Indicators
Supply Side: IMF Financial Access Survey
First launched at IMF/WB Annual Meetings in Istanbul, 2009

IMF FAS 2012 and beyond
Only global supply-side data source with comparable data on basic
indicators of financial access and usage (187 economies)
G20 Leaders in Cannes 2011 endorsed a recommendation for IMF
to continue and improve FAS
2012 FAS is conducted by the IMF, with IFC and CGAP support
2012 FAS questionnaire enhancements
time series for credit unions, financial cooperatives, and
microfinance institutions
separate identification of SMEs, households, life insurance, and
non-life insurance companies
http://fas.imf.org
Demand Side: Global Findex & Enterprise Surveys
Enterprise Surveys Global Financial Inclusion

Database (Global Findex)
Measures the use of financial
services by small, medium, and large Measures the use of formal and
enterprises informal financial services by
individuals through the Gallup
Conducts firm-level surveys of a World Poll Survey
representative sample of an
economys private sector Surveyed in 2011 over 150,000
individuals from 148 countries on
Surveyed over 130,000 companies payments, savings, credit and
in 135 economies insurance
http://www.enterprisesurveys.org http://www.worldbank.org/globalfinde
x
9
Formally Banked Adults
41% of adults in developing economies are bankedcompared to 89% of adults in

high-income economies.
37% of women in developing economies are bankedcompared to 46% of men
The lowest quintile of adult income earners in developing countries are less than
half as likely to have an account than those in the highest quintile.
* The IMFs FAS provides other measures of Formally Banked Adults: the number of depositors per ,1000
adults OR the number of deposit accounts per 1,000 adults
Adults with Credit by regulated institutions
Origination of New Formal Loans around the World
8% of adults in developing economies borrowed money from

a formal lender in the past year (excluding credit card debt)
compared to 14% of adults in high-income economies
* The IMFs FAS provides other measures for Adults with Formal Credit : the number of borrowers
per 1,000 adults OR number of outstanding loans per 1,000 adults.
Formally Banked Small & Medium Enterprises
The Enterprise Survey data shows that 87% of SMEs (5-99 employees) have a
checking or savings account at a formal financial institution
86% of small firms (5-19 employees) have an account, compared to 93% of medium
firms (20-99 employees)
91% of small firms in Latin America and the Caribbean have an account, compared to
77% of small firms in South Asia
* The IMFs FAS provides other measures for formally banked enterprises : the number deposit
accounts by SMEs (% of number of deposit accounts by non-financial corporations) OR number of
SME depositors (% of number of non-financial corporation depositors)
Small & Medium Enterprises with a Bank Loan or
Line of Credit (L/C)
The Enterprise Survey data
shows that 34% of SMEs (5-99
employees) have a bank loan or
L/C
21% of SMEs in Sub-Saharan

Africa have a bank loan or L/C,
compared to 36% of SMEs in
East Asia and the Pacific
35% of firms with a female senior

manager has a bank loan or L/C,
compared to 38% of firms with a
male senior manager
* The IMFs FAS provides other measures for Enterprises with Outstanding loan or line of credit by
regulated institutions: the number of loans by SMEs (% of number of loans by non-financial
corporations) OR number of SME borrowers (% of number of non-financial corporation borrowers)
Points of Service
Physical outreach of
banks is improving on
average.
ATM networks are
expanding faster than
commercial bank
branches.
ATMs & Commercial Bank Branches (medians by year)
40
Low-income countries have 3.3
commercial bank branches and 3.9 ATMs
per 100,000 adults in 2011 compared to 30
47 commercial bank branches and 18
20
ATMs per 100,000 adults for the world as

a whole
10
These numbers were 2.9 and 0.3 for

branches and ATMs, respectively, in 2004
for low-income countries.
0
2004 2005 2006 2007 2008 2009 2010 2011

ATMs/100000 adults Com. bank branches/100000 adults
The journey continues a few priorities
Country-level data
Harmonized definitions and standardized methodologies.
Data from a range of providers, beyond commercial banks.
Establishing and using financial identify.
Detailed data on customer segments.
Firm data, especially that of micro, very small, and small

enterprises.
Open data.
Source: Financial Access 2011: An Overview of Supply-Side Data Landscape, CGAP/IFC,
May 2012
15
Advancing financial access for the worlds poor
www.cgap.org
www.microfinancegateway.org
The role and functions of the Alliance for Financial Inclusion1
Sung-Ah Lee, Alliance for Financial Inclusion
1
5-6 November 2012, Sasana Kijang, Kuala Lumpur
Sung-Ah Lee, AFI

Overview
1. About AFI
2. Data in policy making
3. Data in the AFI network
4. FIDWG and its approach
5. How FIDWG fits with other
areas and initiatives
Who we are
AFI Values
AFI is a global network of financial sector Inclusivity: all partners and proven
policymakers in developing and emerging solutions are welcome
countries Diversity: unique member conditions,
unique member solutions
Founded in 2008, AFI focuses on peer Demand-Driven: action is derived from
learning and knowledge sharing among member needs and demands
policymakers and regulators
Empowerment: unlocking the knowledge
of the AFI membership
The goal of the AFI network is to
accelerate the adoption of successful Ownership: members shape and drive AFI
activities
financial inclusion policy solutions
Alignment: actions reflect national
priorities of the AFI membership
Evidence based: focus on practical

solutions with evidence
The AFI Network Today
95 member institutions from 81 countries

The importance of Data in the network
Data and measurement is key for evidence based policy making!
Diagnose
Design Monitor Evaluate
the state of
appropriate changes policy
financial
policies over time impact
inclusion
Strong demand from member institutions to discuss and

learn from each other
Financial Inclusion Data in the AFI network
It started in June 2010 at this very place
To provide access to financial services The process of ensuring access to financial
tailored to the needs of the population services and timely and appropriate product to all
Banco do Brazil members of the population and vulnerable groups

at an affordable cost in a fair and transparent
manner by the mainstream institutional players.
Reserve Bank of India
Broad access to a portfolio of financial products Financial Inclusion refers to the access and use
and services which include loans, deposit of a portfolio of financial products and services
services, insurance, pensions and payment for the majority of adult population with clear and
systems, as well as financial education and concise information attending the growing
consumer protection mechanisms demand under an appropriate regulatory
Superintendencia de Banca, Seguros y AFP framework.
(SBS) del Peru National Banking Commission in Mexico

Financial Inclusion Data Working Group
Lets agree on the elements (Access, Usage,

Quality) and a common framework
Objectives
Develop a common framework among AFI members for
measuring financial inclusion
Share lessons learned on survey methodology, analysis,
target setting and usage of data to inform policymaking
Promote the adoption of the framework in a broader
international context
The main purpose of Data in the AFI network is to inform
national policy making
AFIs approach on Financial Inclusion Data
Develop technical capacity in countries

Testing new (second generation)
indicators in practice
Informing
Policy Develop/ choose indicators that best
Making inform each countries policy making
through
data while keeping consistency
Resist global target setting
Bottom up Peer
Approach Learning Each country is able to collect
and monitor their own
comprehensive FI indicators
FIDWG in the broader network
Financial Inclusion
Strategy
Consumer Financial Mobile Financial

Empowerment and Inclusion Data Services
Market Conduct
Pacific Island
Working Group
AFI Data Working Group and GPFI
G-20s Global Partnership for Financial Inclusion (GPFI)
Recognized data as a priority

Created a subgroup on Data & Measurement
AFIs Data Working Group is represented in the
Subgroup through Mexico, Brazil, South Africa, and AFI
The Core Set of indicators have been adopted by the
GPFI and was the basis for the GPFI Basic set of
indicators
Close collaboration on quality indicators
Thank you
info@afi-global.org
www.afi-global.org
Measuring the evolution of monetary and financial services in Portugal 1
Joo Cadete de Matos, Bank of Portugal
1
Irving Fisher Committee on Central Bank Statistics / Bank Negara Malaysia
Sasana Kijang, Kuala Lumpur, 5-6 November 2012
Measuring the Evolution of Monetary

and Financial Services in Portugal
Joo Cadete de Matos

Director, Statistics Department,
Banco de Portugal
Workshop on Financial Inclusion Indicators, Kuala Lumpur, 5-6 November 2012
Measuring the Evolution of Monetary and Financial Services in Portugal
The Banco de Portugal

Provides for the stability of the national financial system, performing for
the purpose the function of lender of last resort
Carries out the prudential supervision of credit institutions and
financial companies
Is responsible for supervising the conduct of institutions in their relations
with customers the so-called banking conduct supervision to ensure
that institutions act with due diligence, neutrality, loyalty, discretion and
respect in their relations with customers, aimed at promoting a careful
assessment of their commitments and the risks they may incur
Regulates, oversees and promotes the smooth operation of payment systems,
namely within the scope of its participation in the ESCB
Provides services to financial institutions and the public at large via e.g. the
Central Credit Register (CCR) and the Central Balance Sheet Database (CBSD)
Compiles, analyses and disseminates monetary, financial, exchange and
balance of payments statistics: instrumental to decision-making;
influential to the financial services activities in the economy
The role of the central bank 2

Central Credit Register
The Central credit register (CCR) is a database managed by

Banco de Portugal that compiles information supplied by
reporting institutions (institutions that grant loans)
concerning the credit liabilities of their clients (individuals
and organizations)
The main purpose of the CCR is to support credit institutions
when evaluating the risk of granting credit to the economic
agents, allowing them to obtain information on the aggregated
indebtedness of their clients or any individual or organization
asking for a loan.
Central Credit Register (1) 3

Hamurabi code (1700 BC)
280 clauses
48. If anyone owes a debt for

a loan, and a storm prostrates
the grain, or the harvest fails,
or the grain does not grow for
lack of water; in that year he
needs not give his creditor
any grain, he washes his debt-
tablet in water and pays no
rent for that year
Countries with public credit registers or private credit bureaus

Data reported include, inter alia: loans outstanding granted to

households and corporations by type and purpose; potential loans and
securitised loans; type and value of collateral or guarantee; original
and residual maturity; credit defaults and the number of days the loan
is past due; country where the loan was granted
Some noteworthy figures concerning the CCR:
threshold: > EUR 50
5.6 million private individuals registered
over 280 thousand corporations registered
more than 2o0 participants
15 types of financial products
23.4 million monthly records (2012 average)
280 million annual movements

Public Credit Registry Coverage Indicator

Ranking Top 10
100
No. of individuals and firms listed,
90
90.7 89
as a % of adult population
80
70
60 63.8
58.9
50 56.3 56.3 56.2 56.1 53.8 53.3
40
30
20
10
0
Portugal Belgium Latvia Mongolia Bulgaria Mauritius Belarus Malaysia Gabon Spain
1 2 3 4 5 6 7 8 9 10
Source: Doing Business 2013, World Bank / International Finance Corporation

The statistical analysis of CCR data in Banco de Portugal
The use of CCR data for statistical purposes is explicitly allowed in the
Decree-law that regulates the Central credit register.
The CCR, like other micro-data repositories (individual data) has a huge
potential as a source of information for statistics and to be used in
analytical studies or research papers.
The decision to allocate the management of the CCR to the Statistics
department, in 1999, in addition to the deep reformulation of the
reporting model and the information system, concluded in 2009, were
decisive steps to enlarge the potential of this database as a source of new
and detailed statistics on the credit to the economy.
The statistical analysis of CCR data in Banco de Portugal
Several statistics based in CCR data are presently published, in the Statistical bulletin and
Bpstat on-line system, with the following frequency:
Monthly : A.11 Main indicators Loans granted by the financial sector

(loans to companies and households)
Quarterly: Statistical bulletin - Chapter B9 Several tables concerning loans

granted and indicators on overdue loans:
Non financial companies with a breakdown by branch of activity, region

of residence and by bracket of credit amount;
Households breakdown by purpose of the loan (Housing; Consumption

and other purposes) and region of residence.
These statistics are published five weeks after the end of each month
Use case: Supporting measures to prevent over-indebtedness
Complete and reliable credit records;

Mandatory consultation of indebtedness information;
Fixation of maximum interest rates in credit operations;
Action against illicit ways of credit granting;
Transparency in the description of financial instruments;
Promotion of financial literacy;
Balanced and flexible management of payment delays.
Services related to the payment systems

Monitoring the payment systems
Assessing compliance with the Core Principles for
Systemically Important Payment Systems
Issuing regulations
Exercising moral suasion
Providing settlement services for banks or payment and
clearing systems: catalyst role in developing these systems
and promoting coordination among the relevant parties
Services related to the payment systems (1) 11

Substantial increase in the use of technological innovations:

Creation of SIBS Forward Payment Solutions, a single payment
platform that meets banks needs, while developing their facilities and
technology, and extending their international scope. SIBS processes
well over 2 billion transactions per year and manages more than 14
thousand ATM, around 270 thousand POS and about 18 million cards.
SIBS handles:
Transactions originating across various channels from ATM and POS
networks, Internet and mobile phones to onboard units for vehicles,
contactless payments, etc.
All sort of payment instruments e.g., cards, direct debits, credit
transfers, cheques, ...
Interbank clearing of cheques, credit transfers and direct debits
Teleprocessing networks within the larger banks and/or through

interbank links

Multibanco
A sophisticated network shared by every bank operating in the
economy that fully integrates ATMs and EFTPOS
In addition to cash operations, it offers a wide range of more
than 60 different services (e.g., money transfers, payments for
utilities bills, payments to the State and the Social Security,
mobile phone top-ups, transport ticketing, event booking and
ticketing, )
A survey () looked at the availability and use of non-cash
functions at cash machines in other countries. Of survey
respondents, the Portuguese were the leaders in cash
machine functionality.
APACS (The UK Payments Association) Report UK Cash & Cash Machines, May 2008

Number of POS per million inhabitants Number of ATMs per million

inhabitants
30000
1800
25000 1600
20000 1400
1200
15000 1000
10000 800
600
5000 400
0 200
0
2004 2005 2006 2007 2008 2009 2010 2011
2004 2005 2006 2007 2008 2009 2010 2011
Euro area Portugal Euro area Portugal
Inhabitants per institution Inhabitants per branch

70000 2000
60000
50000 1500
40000
1000
30000
20000 500
10000
0 0
2004 2005 2006 2007 2008 2009 2010 2011 2004 2005 2006 2007 2008 2009 2010 2011
Euro area Portugal Euro area Portugal

Use of Payment Instruments in Portugal

(Quantities in millions and %)
1991 2001 2011
Quantity As a % Quantity As a % Quantity As a %
Cheques 231,4 73.0 307,9 28.7 106,7 6.0
Payment cards 1 43,9 13.8 578,7 54.0 1237,5 69.0
Credit transfers 24,0 7.6 60,2 5.6 202,0 11.3
Direct debits 17,7 5.6 119,1 11.1 243,0 13.6
Other 2 0,0 0.0 6,2 0.6 2,6 0.1
1 Not including cash withdrawals; 2 Includes bills of exchange and e-money purchase transactions.

Home banking and mobile banking
Third preferred channel to get in touch with a bank next to ATMs and
to face-to-face contact with the bank teller
The rising number of Portuguese households that have at least one
computer at home and access to a broadband Internet connection will
likely strengthen the use of home-banking
Banks get information to their customers no matter where they are and
at reduced costs (e.g., access from home, mobile device, hotspots)
Continuous expansion of Internet banking and m-banking in
Portugal: improved access to financial services
Services more affordable and more suited to the prospective
customers, particularly the marginally banked i.e., people with a
deposit account that has no electronic payment facilities and no payment
card or cheque book, including those that have a bank account but rarely
use the related electronic payment facilities and cards

Demographic branch penetration across countries

No. of branches per 100,000 people
120.00
100.00
80.00
60.00
40.00
20.00
0.00
Demographic ATMs penetration across countries

No. of ATMs per 100,000 people
150
100
50
Source: Beck, Demirguc-Kunt & Martinez Peria, Reaching out: Access to and use of banking services across countries, World Bank, 2005

Changes in households portfolio composition

(consolidated transactions, cumulative four-quarters)
Financial Accounts
data:
Composition of
assets and liabilities
by type of instrument
On the asset side,
mostly deposits
On the liability side
mostly loans (2006-
2007 when credit
expanded)
Statistics compiled by the Banco de Portugal (1) 18

Changes in NFC portfolio composition

(consolidated transactions, cumulative four-quarters)
Financial
Accounts data:
Composition of
assets and
liabilities by type
of instrument
On the asset side,
composition
changes overtime
On the liability
side mostly loans
(2007-2008)

Non-performing loans evolution

Money and
banking
statistics, CCR:
Allow focusing
on non-
performing loans
Granular data
useful for
delineating
financial policies
(Planning
Financial
Assistance to
households)

Indebtedness Ratios
(as a percentage of GDP, non-consolidated figures)
Dec-08 Dec-09 Dec-10 Dec-11 Mar-12 Jun-12
Total-non-financial sector indebtedness 366,2 391,6 402,2 420,5 422,3 432,2
Financed by:
General Government 9,8 9,8 12,9 16,7 17,9 19,4
Resident Financial sector 185,1 196,8 205,3 204,4 207,3 208,8
Corporations 65,1 67,9 68,2 68,7 67,1 68,1
Private individuals 24,8 25,6 24,2 23,1 22,8 23,2
External 81,4 91,5 91,6 107,6 107,2 112,7
The recently published chapter k of the Statistical Bulletin (non-financial

sector indebtedness) provides an integrated overview of the total financing of
the economy by institutional sector

Final remarks
Micro-databases managed by the Statistics Department of the Banco de

Portugal: Central Credit Register; Securities Statistics Integrated
System; Central Balance Sheet Database; and Balance of Payments
data
Granular micro-data are tools that allow monitoring the financing
needs of the economy (example: highly exploited for the purpose of
the EU/IMF Financial Assistance Programme )
Publishing new and integrated statistics offer a new insight
Analysing indebtedness ratios
Constructing indicators to support the financial sector
Delineating financial assistance programmes for households and
corporations
22
Obrigado pela vossa ateno
Thank you for your attention
Terima kasih atas perhatian anda
23
The financial inclusion data working group and the Mexican experience 1
Nora Garcia, Alliance for Financial Inclusion
1
Bringing Smart Policies to Life
Ms. Nora Garca

Kuala Lumpur, Malaysia
November 5, 2012
Part 1
FINANCIAL INCLUSION DATA
WORKING GROUP (FIDWG)
AFI | Kuala Lumpur | 11.05.12 | Page 2

Why measure?
Evidence-Based Policymaking
Diagnose
Design Monitor Evaluate
the state of
appropriate changes policy
financial
policies over time impact
inclusion

Framework for measuring
Ability to use Actual use of

financial services: Access Usage financial services:
- Proximity - Products
- Channels - Patterns
- Barriers Quality - Behaviors
Appropriateness of
financial services:
- Convenience
- Security
- Consumer Protection
The AFI FIDWG core set of indicators
The Core Set of indicators is

one initiative developed by the
Financial Inclusion Data Unique in the global data landscape
Working Group as a guidance equilibrium
to conduct data collection and
measurement efforts at
country-level.
Standardization
This set is a list of indicators
designed by policymakers to
be flexible for country-specific
needs, but at the same time to
allow certain amount of Flexibility
comparison and benchmarking
among countries.
Principles behind the selection of Core Set
Usefulness and
Relevance
Aspiration Pragmatism or
Practicality
Core Set of
FI indicators
Balance Consistency
Flexibility

Core Set of Financial Inclusion Indicators
Dimension Core Indicators
1) Number of access points per 10,000 adults
2.1) % of administrative units with at least one access point

Access
2.2) % of total population living in administrative units with at least
one access point
3.1) % adults with at least one type of regulated deposit account

Proxy: Number of deposits accounts per 10.000 adults
Usage
3.2) % adults with at least one type of regulated credit account
Proxy: Number of credits accounts per 10.000 adults

Further initiatives
A. Core indicators
B. Second Tier indicators
On-line availability
Barriers to access and usage
(MixMarket online platform)
Quality indicators
Other indicators
Work in
progress
C. Demand-side surveys D. Additional activities
Guidance note New knowledge material

Core questions New peer-reviews
Focus groups

On-line core indicators: catalog

On-line core indicators: country cards

On-line indicators: comparative charts

Key accomplishments
During this time:

6 peer review
G20 basic set of indicators
2 knowledge products June 2012 based on AFIs core set
Comprehensive road tested

March 2012 indicators (1st compilation)
March 2011 Core Set of FI Indicators
June 2010 Measurement Framework
March 1st FIDWG meeting

2010
Lessons learned
Focus on policy questions (strategy)
Start with available data (harmonization)
Consider partnerships (cooperation)

Part 2
THE MEXICAN EXPERIENCE

Strategy for financial inclusion
Definition of The access and usage of a portfolio of financial products and services for
Financial the population, under an appropriate regulatory framework that protects
Inclusion the interests of users and promotes its financial capabilities.
Higher number of people included
in the financial system
Financial Inclusion Curve
Quality More efficiency
Usage Better
Products
Other components
Increase in Market Conduct

Access financial
penetration
Consumer
Empowerment
Measurement of Financial Inclusion

Measurement framework
Dimension Indicators Source
Number of branches and banking agents per 10,000 CNBV financial inclusion reports
adults ABM (Bank Association of Mexico)
Access Number of ATMs/POS per 10,000 adults geo-spatial analysis
% of adult population living in a municipality with at least
one access point
Number of deposit accounts/loans per 1,000 adults CNBV: financial inclusion reports.
Number of depositors/borrowers per 1,000 adults BANXICO (Central Bank).
% of adults with an account at a formal financial ENIF 2012. National Survey.
Usage institution
% of products that correspond to banks/other financial
institutions
% of adults with at least one financial product
Financial New financial capabilities data World Bank/CNBV/Condusef

% of financial institutions with financial literacy programs Commercial banks, Condusef
Literacy and
% of complaints and claims that are solved (Commission for the Protection of
Consumer the Users of Financial Services)
Protection

Demand-side survey
In 2010 2012 CNBV developed, in
collaboration with the Ministry of
Finance and other stake holders, the
conceptual framework and design of
the first National Financial Inclusion
Survey (ENIF 2012).
The National Institute of Statistics

(INEGI) participated providing
technical assistance (sampling design
& selection) and conducting the
survey at the field in May 2012.
The results will be available by year-

end 2012.
a) Access Points
Actual situation
Up to December 2011, there are 35,702 access points, of which 11,911 are bank
branches and 21,071 banking agents. Altogether, there are 4.47 access points per each
10,000 adults.
Indicators per each 10,000 adults

(December 2011)
1.49 0.07 0.14 0.13 2.64 4.47

Demographic
Indicators
21,121* 35,702
1,155 1,014
551
Access points
11,911
Commercial Development Cooperatives Micorfinance Banking Total

Banks Banks Agents

Actual situation
Banks and Microfinance Institutions Branches per 10,000 adults
(December 2011)
55% of all municipalities still lack

access to one bank or
microfinance* institutions branch
*/ Cooperatives, popular finance entities, credit unions.
No branches (1,361 municipalities)

Low (<1.27 branches per 10,000 adults)
Medium (between 1.27 and 2.37 per 10,000 adults)
High (> 2.37 branches per 10,000 adults)

Final remarks
Financial inclusion measurement framework in Mexico was

designed considering FIDWGs discussions and conclusions
The indicators were built upon already available data and further
information was gathered to complete the or enhance them
Publication of results and establishment of a communication

strategy was considered a key issue
Cooperation with other Mexican financial authorities was crucial

in some initiatives (such as the demand-side survey)

Thank you!

The situation of credit to agriculture & SME1
Mao Qizheng, Peoples Bank of China
1
The situation of credit to
Agriculture & SME
2012-11-2 1
Agenda
The necessity of Monitoring
Credit to agricultural sector

namely three-A: Agriculture, rurAlarea, peAsantry
Credit to SME
2012-11-2 2
The necessity
Agriculture is the basis of one nations
economy
Peasants contribute a large percentage
(nearly 60%)
SME play an important role to solve the
unemployment and overcome poverty
Financial availability emerge as an critic
topic for sustainable long term growth
2012-11-2 3
Credit to Three-A
The monitoring framework
The situation
as of the end of Jun, 2012
2012-11-2 4
Monitoring framework of Credit to
Three-A
Set up in 2007, revised along the time
Mainly quarterly, some indicators
monthly
Institutional coverage: DTs
Both in national and foreign currency
2012-11-2 5
Three-A (cont.)
Indicators reflect different aspects
regional classification
Rural area and municipal institution related to three-A
borrower classification
peasants, corporations, organizations.
usage classification
farming, forestry, fishery, infrastructure, sci & tech support, product
circulation, capital goods
Credit quality classification

5 classes according to the definition of CBRC
Normal, interested, substandard, doubtful, loss
2012-11-2 6
Three-A (cont.)
Difficulties mainly lie in hard to define
what is three-A related (brewery?)
Planning improvement
Monitor the flow of money in rural area
Set up survey on financial service requirement related to
three-A
2012-11-2 7
Situation of June 2012

63.3 %
64 40
34.3
34.2
31.3 31.5
48 33.0 30
30.3 30.1
28.4
26.2
18.4 24.7 21.2
24.1 21.3 21.3 20.8 20.4 20.8
32 20
19.2 18.7 19.7 16.8 16.0
17.9 15.7 14.8 15.0 15.5 15.1 15.4 15.9
17.6
16 13.5 10
0 0
2008.12
2009.12
2010.03
2010.06
2010.09
2010.12
2011.03
2011.06
2011.09
2011.12
2012.01
2012.02
2012.03
2012.04
2012.05
2012.06
( ) ( ) ( ) ( )
2012-11-2 8

%
16 60
14.7
51.5 54
14
49.4
42.3 48
12
43.3 42
10 36
32.7 37.6
29.8 28.3
8 30
30.3 30.2 24.8
29.4 22.7
27.4 20.9 24
6
23.1 19.3 18.8 18.2
16.4 20.3 16.9 16.6 16.6 18
4 19.1
15.8 16.3 16.0 15.4 15.5 15.9 12
13.7
2 3.5 6
0 0
2008.12
2009.12
2010.03
2010.06
2010.09
2010.12
2011.03
2011.06
2011.09
2011.12
2012.01
2012.02
2012.03
2012.04
2012.05
2012.06
() () () ()
2012-11-2 9

%
63.3 36
66
33.0
55 30
25.2 25.4
44 22.0 24
24.1
17.9 19.4 19.7
19.2 17.6 16.8
33 18
18.7 18.3 17.5 16.0 15.7
14.8 15.0 15.5 15.1 15.4 15.9
15.2
22 13.8 13.7 12
10.0 11.7
9.5 10.3 9.0 9.9
9.2
11 6
2.7
0 0
08.12
09.12
10.03
10.06
10.09
10.12
11.03
11.06
11.09
11.12
12.01
12.02
12.03
12.04
12.05
12.06
( ) ( ) ( ) ( )
2012-11-2 10
Credit to SME
The monitoring framework
The situation
as of the end of Jun, 2012
2012-11-2 11
Monitoring framework of
Credit to SME
Set up in 2009, revised along the time
Monthly report
Institutional coverage: DTs
Both in national and foreign currency
2012-11-2 12
Monitoring framework of Credit
to SME (cont.)
Adopt national standards
Classification of corporation size
Classification of industries
When national standards change, the
framework revised
2012-11-2 13
Monitoring framework of Credit
to SME (cont.)
Credit classified by corporation size
big, medium, small, micro
Credit classified by quality
5 classes according to the definition of CBRC
Normal, interested, substandard, doubtful, loss
Credit classified by industries

20 industries according to national standard
Credit classified by ownership
state-owned, private, foreign controlled
2012-11-2 14

100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
10 20 30 40 5
0 6
0 7
0 8
0 9
0 0
1 1
1 2
1 1
0 2
0 3
0 4
0 5
0 6
0
.1 .1 .1 .1 .1 .1 .1 .1 .1 .1 .1 .1 .2 .2 .2 .2 .2 .2
10 10 10 10 1
0 1
0 1
0 1
0 1
0 1
0 1
0 1
0 1
0 1
0 1
0 1
0 1
0 1
0
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2012-11-2 15
43
38
33

28
23
18
13
8 50 60 70 80 90 01 1 2 1 2 3 4 5 6 7 8 9 0 11 21 10 20 30 40 50 60
.0 .0 .0 .0 .0 .0 1
.0 1
.0 0
.1 0
.1 0
.1 0
.1 0
.1 0
.1 0
.1 0
.1 0
.1 1
.1 .1 .1 .2 .2 .2 .2 .2 .2
10 10 10 10 10 10 1
0 1
0 1
0 1
0 1
0 1
0 1
0 1
0 1
0 1
0 1
0 1
0 10 10 10 10 10 10 10 10
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2012-11-2 16
100
90 21. 4 17. 2
26. 3 29. 3
80 35. 4 35. 7 35. 6
45. 8
70
24. 9
60 31. 0
50 39. 0 61. 2
34. 0 31. 6
40 45. 8
33. 9
30
53. 7
20 42. 7
31. 7 30. 3 32. 7
10 20. 3 18. 9 21. 6
0

2012-11-2 17
Thanks
2012-11-2 18
Financial Inclusion and its measurement in Brazil 1
Central Bank of Brazil
1
Financial Inclusion and its
measurement in Brazil
1
Financial Inclusion
Highlights
Macroeconomic stability contributed directly to FI,
allowing the government to advance on the social
development agenda
The government has promoted FI in many ways:
improving distribution channels, adopting targeted social
programs, increasing transparency, and adapting
regulation of financial services to low income customers
FI has become a strategic objective for BCB
We are now reaping the benefits of these policies:
All 5,565 municipalities are financially served
Individuals with active relationship with FIs grew 31% to 121

million over the last five years
2
Financial Inclusion Framework
Synergies Between Financial Inclusion and Social Programs

Financial inclusion initiatives and social programs are fully
integrated policies
Poverty Reduction innovative distribution channels enable

cash transfer programs to reach remote communities such
as Brasil sem Misria and Bolsa Famlia
Social Finance Programs innovative distribution channels

enable access to subsidized credit programs such as:
- Low income farmers program (Pronaf)
- Microcredit programs (PNMPO, Crescer)
- Social housing loan program (Minha Casa Minha Vida)
3
Complementary Distribution Channels

Financial System in Brazil is highly spread out. Three
alternative channels were fostered by BCB to
complement the traditional ones:
Correspondents - Non financial firms hired by FIs to

provide services in remote regions and more
convenient access in metropolitan areas
Credit Cooperatives - Relevant for specific activities

and social sectors
Bank Services Outposts - Bank satellite with smaller

staff and infrastructure for unassisted municipalities
4
Tools and Initiatives

Transparency regulation directed at enhanced
transparency in prices and services, standardized and
simpler contracts to support clients decision-making
Switching costs reduction regulation creating automatic
inter-bank procedures to transfer accounts, loan balances,
and personal data
Simplified accounts targeted at low income clients, they
are exempt of fees, require simpler procedures and limit
balances
Payroll-guaranteed loans legal framework that ensures
repayment favoring access to credit for workers and
pensioners, with adequate safeguards
Mandatory FI staff certification, including correspondents
5
Financial Inclusion Statistics
Savings accounts penetration has grown

Clients with savings accounts per 1,000 adults
700
675 671
650 646 641
625
Values in each
December 601
600
573
575
550
525
500
2006 2007 2008 2009 2010
Sources: BCB / IBGE 6

New clients are active users of financial services

Individuals with active relationship with FIs*
125 122.4
121.3
120
115
115
110
110
Values in each
December 105
105
millions
100 98
95 93
90 88
85
80
2005 2006 2007 2008 2009 2010 2011 2012**
* BCB General Registry of the National Financial System (CCS) Identifies FIs and their clients for
demand deposits, savings deposits, fixed-term deposits and other assets and values
** Feb 12
Source: BCB 7
Access grew most in low income classes (1)

Individuals with demand deposits account
80
70
70
63
60
52
50
39
%
40
29
30
20 16
10
0
A/B C D/E
Social Classes
2005 2010
Source: BCB Survey 8

Access grew most in low income classes (2)

Individuals with credit card
80
70
60
60
53
50
43
%
40
30
30 25
20 15
10
0
A/B C D/E
Social Classes
2005 2010
Source: BCB Survey 9

Geographic coverage increased

82% of municipalities had less than 5 points per 10,000 adults in 2000.
In 2010, 94% were above this level.
2000 2010
# of points per 10,000 adults # of points per 10,000 adults

(% of municipalities) (% of municipalities)
0 (20%) 0 (0%)
>0 to 2 (22%) >0 to 2 (0%)
>2 to 5 (40%) >2 to 5 (6%)
>5 to 10 (16%) >5 to 10 (29%)
>10 (2%) >10 (65%)
Bank branches, bank advanced outposts (PAA), credit cooperatives
(headquarters and outposts) and correspondents

Complementary channels are important

All municipalities have at least one bank branch, outpost or correspondent.
Correspondents contribute to network density.
Bank branches, outposts, credit cooperatives

Bank branches 2010
and correspondents 2010
# of points/1,000 km2 # of points/1,000 km2

(percentage of municipalities) (percentage of municipalities)
0 (38%) 0 (0%)
>0 to 1(25%) >0 to 1(3%)
>1 to 5 (26%) >1 to 5 (12%)
>5 to 10 (13%) >5 to 10 (12%)
>10 (14%) >10 (73%)

Broader access to credit (1)

Clients with loan balances over BRL 5,000
30 29.0
25
20
millions
15
10
5
Jul 03
Jul 04
Jul 05
Jul 06
Jul 07
Jul 08
Jul 09
Jul 10
Jul 11
Jan 03
Jan 04
Jan 05
Jan 06
Jan 07
Jan 08
Jan 09
Jan 10
Jan 11
up to Dec 11
Source: BCB 12
Broader access to credit (2)

Payroll-guaranteed loans
200
180
157.5
160
138.5
140 Values in each
BRL billions
December
120 107.8
100
79.3
80
64.8
60 47.8
40 32.0
20
0
2005 2006 2007 2008 2009 2010 2011
USD = 1.709 BRL (Feb 12)
Source: BCB 13
Low Income Farmers Finance Program*
14
12.0
12 11.2
10
BRL billions
8.7
8 7.2 7.1
0
2006 2007 2008 2009 2010
* Pronaf
USD = 1.709 BRL (Feb 12)
Source: BCB 14
Microcredit Program*
3.0
2.80
2.5
2.0
BRL billions
2.23
1.5
1.69
1.0
0.95
0.5 0.67
0.47
0.0
2005 2006 2007 2008 2009 2010
* PNMPO
USD = 1.709 BRL (Feb 12)
Source: MTE 15
Financial Inclusion Indicator
Financial Inclusion is multidimensional
The FII developed by BCB aggregates different dimensions
It takes as reference the inclusion index proposed by Sarma
& Pais (2010), which is based on the distance of each
variable from the benchmark (the benchmark is the
maximum score in all the dimensions considered)
Sarma & Pais uses 3 dimensions (bank penetration,
availability and use)
16
Financial Inclusion Indicator
The FII uses 18 indices, aggregated in 3 dimensions:
- 7 indices for geographical availability: the ratio between

bank branches, credit cooperatives, 4 types of bank service
outposts, and correspondents, to 1,000 km2
- 7 indices for demographic availability: the ratio between

bank branches, credit cooperatives, 4 types of bank service
outposts, and correspondents, to 10,000 adults inhabitants;
- 4 indices for use: credit to GDP ratio; credit to 1,000 adults

ratio; deposits to GDP ratio; deposits to 1,000 adults ratio
The indices for all dimensions were calculated for all states in
Brazil and aggregated for major geographic regions
17
Financial Inclusion shows relevant evolution

Financial Inclusion Indicator*
2000 2010
Indicator Ranges (pp)

8 12
12 15
15 25
> 25
* BCBs Financial Inclusion Indicator combines 18 access and usage measures
Source: BCB 18
Less well-served regions benefited most

Financial Inclusion Indicator* - Region Average
40 37.6
35 32.5
31.5
30
25 22.8 22.9
21.7 21.7
20
16.4
15.3 14.8
15 13.8 13.4 13.8
10 9.0
8.1 7.7
5.0
5 3.9
0
North Northeast Midwest Southeast South Brazil
2000 2005 2010

* BCBs Financial Inclusion Indicator combines 18 access and usage measures

Access to Banking Services
2002 2009 2011

Accounts (for the banking sector) 55,708,468 83,308,800 91,944,421
Customers 87,630,527 151,102,765 174,791,126
Branches
For the banking sector 17,049 20,046 21,278
For all financial institutions 17,756 21,287 22,628
Posts of service (for the banking sector) 32,769 53,628 60,375

ATMs 129,913 165,567 174,920
Domestic correspondents 78,539 151,351 177,925
Municipalities
Without banking services 222 - -
With banking services 5,337 5,565 5,565
Municipalities banking services coverage 96% 100% 100%
Source: BCB 20
The IMFs Financial Access Survey (FAS) 1
Andre Mialou, International Monetary Fund
1
This presentation was prepared for the workshop. The views expressed herein are those of the author and should not be attributed to the IMF, its Executive Board, or its
management.
IMF Statistics Department 11/6/2012
THE IMFs FINANCIAL ACCESS

SURVEY (FAS)
Statistics Department
The views expressed herein are those of the author and should not necessarily be attributed to the IMF,
its Executive Board, or its management 1
Outline
Overview of the FAS Project
Methodology
The 2012 FAS results
The way forward
2
Overview of the FAS Project: History
The FAS Project was unveiled jointly by HRH Princess Mxima of

the Netherlands, the U.N. Secretary Generals Special Advocate
for Inclusive Finance for Development, and the IMF Managing
Director at the World Bank-IMF Annual Meetings in Istanbul in
October 2009.
The FAS project stems from proposals in 2008 by the UN Advisors

Group on Inclusive Financial Sectors. Princess Mxima chaired
the group on which the IMF was represented.
The results from the inaugural FAS were released in the online
database in June 2010.
3
Overview of the FAS Project: Funding

The government of the Netherlands provided initial funding for
the start-up cost of the project
2012 FAS was enhanced significantly and was conducted in

close collaboration with IFC and CGAP
IMF and IFC received financial support from the Netherlands Ministry
of Foreign Affairs
CGAP received financial support from the Australian Agency for

International Development
4
Overview of the FAS Project:

Objectives
Collect and disseminate internationally-comparable financial
inclusion data for as many countries as possible, including the
newly-added items for the 2012 FAS round
Ensure returned questionnaires are accurate and completed to the

extent possible
Develop and maintain a list of country correspondents to ensure
clear channels of communication for future rounds of the survey
5
FAS Methodology: Indicators

FAS provides underlying data for 38 indicators that assess two
dimensions of financial inclusion
Access to basic consumer financial services

Examples: commercial bank branches per 1,000 km2, number of ATMs
per 1,000 km2,commercial bank branches per 100,000 adults
Use of basic consumer financial services
Examples: depositors with credit unions and financial cooperatives per
1,000 adults, number of loan accounts with microfinance institutions per
1,000 adults, outstanding deposits with commercial banks (% of GDP),
outstanding SME loans from commercial banks (% of GDP)
FAS is also the source of data covering all five categories of the
Basic Set of Financial Inclusion Indicators endorsed by the G-20
Leaders at the Los Cabos Summit in June 2012 (see next slide)
6
FAS Methodology: Indicators (cont.)

Existing Global / Dimension of
Categories Indicators Multi-country Financial Inclusion
Source Measured
1 Formally banked % of adults with an account at a formal financial Global Findex Usage
adults institution
Number of depositors per 1,000 adults OR number IMF FAS
of deposit accounts per 1,000 adults
2 Adults with credit % of adults with at least one loan outstanding from Global Findex Usage
by regulated a regulated financial institution
institutions Number of borrowers per 1,000 adults OR number IMF FAS
of outstanding loans per 1,000 adults
3 Formally banked % of SMEs with an account at a formal financial WBG Enterprise Usage
enterprises institution Surveys
Number of SMEs with deposit accounts/number of IMF FAS
deposit accounts OR number of SME
depositors/number of depositors
4 Enterprises with % of SMEs with an outstanding loan or line of credit WBG Enterprise Usage
outstanding loan Surveys
or line of credit Number of SMEs with outstanding loans/number of IMF FAS
by regulated outstanding loans OR number of outstanding loans
institutions to SMEs/number of outstanding loans
5 Points of service Number of branches per 100,000 adults IMF FAS Access
7
FAS Methodology: Institutional

coverage
FAS covers the following resident financial corporations that provide
financial services:
Other depository corporations (ODCs): all ODCs (Commercial banks,
Credit unions and financial cooperatives, Deposit-taking microfinance
institutions, Other deposit takers). The central bank is excluded.
Other financial corporations (OFCs): all OFCs except financial auxiliaries
and pension funds (Other financial intermediaries including Non-deposit
taking microfinance institutions, and Insurance corporations)
FAS covers the following resident institutional sectors that are users
of financial services:
Nonfinancial corporations (including SMEs)
Households
8
FAS Methodology: Underlying data

Underlying data for geographic outreach consist of the number of
institutions, and the number of branches and ATMs, both country
wide and in the 3 largest cities
Underlying data for the use of financial services include:

Number of depositors (customers for OFIs and insurance policy holders
for insurance corporations)
Number of deposit accounts (customer accounts for OFIs and
insurance policies for insurance corporations)
Number of borrowers
Number of loan accounts
Outstanding deposits (acquired funds for OFIs and insurance technical
reserves for insurance corporations)
Loans
9
FAS Methodology: Reference

Manuals
The FAS concepts are drawn from the methodology in the IMFs
Monetary and Financial Statistics Manual and its accompanying
Compilation Guide
Countries are provided with a set of methodological documents

where the concepts used in the questionnaire are defined
Countries are asked to document any departures from the FAS

concepts in their country notes
Country notes are published along with the data on the FAS
website
Explanatory notes with definitions are published on the IMFs website

and eLibrary
10
FAS Methodology: Data collection

and dissemination
Three-stage data collection process
National regulators (e.g. central bank) collect information from domestic
financial institutions
National regulators aggregate these data across different types of institutions,
and use the aggregated data to complete the questionnaire
FAS team collects data from the national regulators and ensures its validity
through series of checks and verifications
For 2012 FAS:

Official invitation letters were sent to central bank governors on March 20, 2012.
Follow-up emails sent to data compilers shortly thereafter.
Data collection (through the FAS questionnaire) and validation began in early
May, continuing through August
Indicator list, data reports, metadata, third-party data refined during this period
FAS data (indicators, underlying data, and metadata) released on September 19,
2012
11

and dissemination (cont.)
Each reporting country receives a FAS questionnaire which
consists of the following:
Instructions sheet
Cover Page (contact information, etc.)
Financial Access Survey (FAS questionnaire)
Notes where correspondents are invited to provide additional
explanations on data coverage and any deviations from concepts
defined for the FAS questionnaire
Validation Sheet used by correspondents and IMF staff to ensure
the accuracy of the submitted data
12

and dissemination (cont.)
During the 2012 round, the questionnaire was nearly tripled in size to
broaden the coverage of institutions that cater their services to the poor
New lines added to cover:

Number of deposit and loan accounts
Credit unions and financial cooperatives
Microfinance institutions
Life and non-life insurance
Small and medium enterprises (SMEs)
in addition to existing lines for:

Number of institutions, branches, and ATMs
Number of depositors, borrowers, and insurance policy holders
Volume of deposits, loans, and insurance technical reserves
Breakdown for households
13
The 2012 FAS results: Country count

Covers 182 countries over an 8-year period over 40,000 data points
Reporting Jurisdictions
Non-Reporting Jurisdictions
Caveat: not all countries have all data for all years
14
The 2012 FAS results: Country count

(cont.)
The biggest increase in the number of reporting countries was seen among lower
middle income and low income countries
182
FAS Reporters by Year and Income Level 1
138 135
49 High Income
43 44 Upper Middle
46
Income
40 Lower Middle
40
52 Income
34 33 Low Income
35
21 18
2010 2011 2012

1
Income groupings were determined using the World Bank's classifications
15
The 2012 FAS results: Data Availability

The easiest to collect - data on commercial banks
16

(cont.)
The most difficult data to collect - data for OFIs and
microfinance institutions on SMEs
17

(cont.)
Differences exist in the distribution of data points among
countries
Number of variables for which the data exist Number of variables for which the data exist
150
150
Bangladesh Bangladesh
Kosovo
100
100
Serbia
West Bank and Gaza West Bank and Gaza
Samoa Montenegro
Morocco Morocco Peru
Peru Georgia Armenia
Georgia Angola
Myanmar
Angola
50
Tajikistan Latvia
50
Burundi
0
0
e.
e.
e.
e.
om
m
m
om
.
pe
a
re
c
.A
ifi
ric
co
co
he
ro
c
c
c
C
in
Af
in
in
in
Pa
Eu
p
&
is
h
e
e
w
ig
dl
dl
&
em
Lo
st
H
id
id
ia
Ea
m
As
n
e
er
er
er
dl
pp
id
t
es
Lo
M
U
W
Source: IMF 2012 FAS data Source: IMF 2012 FAS data
18

(cont.)
The data coverage for more recent years is better
Collecting data on newly introduced indicators was more

difficult than collecting the previously collected data
19
The 2012 FAS results: Derived indicator

profiles
Most geographical outreach indicators tend to be correlated with
the country development levels. The chart below for Commercial
bank branches and ATMs per 1,000 km2 and 100,000 adults
illustrates this result
Commercial bank branches and ATMs

80
60
Branches per 1,000 km

Branches per 100,000 adults
40
ATMs per 1,000 km

ATMs per 100,000 adults
20
0
e
e
e
m
om
m
co
co
co
c
in
in
in
in
w
h
e
ig
dl
dl
Lo
H
id
id
m
m
er
er
w
pp
Lo
Source: IMF 2012 FAS data
20

profiles (cont.)
A similar correlation shows up between indicators of the use of
financial services and the country degree of development as
illustrated in the charts below for depositors and borrowers
from commercial banks and credit unions per 1,000 adults
Depositors and borrowers per 1,000 adults
e
m
co
in
h
ig
e
H
m
co
in
w
Lo
e
m
co
in
e
dl
e
id
m
m
co
er
in
w
e
Lo
dl
0 200 400 600 800 1,000

id
m
er
Depositors with banks per 1,000 adults

pp
U
Depositors with CU per 1,000 adults

Borrowers from banks per 1,000 adults
Borrowers from CU per 1,000 adults
21

profiles (cont.)
and outstanding deposits and loans with commercial banks
Oustanding deposits/loans with commercial banks

(% of GDP)
e
m
co
in
h
ig
e
m
H
co
in
w
e
Lo
m
co
in
e
dl
e
m
id
co
m
in
er
w
e
dl
Lo
0 20 40 60 80
id
m
er
Outstanding deposits
pp
U
Household outstanding deposits

Outstanding loans
Household outstanding loans
22
The 2012 FAS results: Data

Applications
Scatter plots with linear fitting tend to confirm the positive correlation
between access to finance indicators and GDP per capita. An
illustration is given by the chart below for the number of ATMs per
100,000 adults
ATMs per 100,000 adults and GDP per capita
80
95% CI Thailand
Lin. Fit.
ATMs per 100,000 adults
60
Malaysia
40
Fiji
Vanuatu Tonga
Samoa
20
Maldives
Vietnam Philippines
Sri Lanka
Solomon Islands
India Bhutan
Cambodia
Pakistan
Bangladesh
0
-20
6.5 7 7.5 8 8.5

GDP per capita (log)
23
The way forward

Better targeting of financial regulators (primary and secondary)
Superintendent of Banks (South America)
Microfinance institutions vs. Development Banks (South East Asia)
More accurate contact lists

Central Bank vs. Treasury vs. Other national institutions
Example of Turkey (Treasury / Central Bank), Romania (National Commission
of Financial Market), Ethiopia (CGAP)
Pursue late/non-reporters earlier
Improve data collection for those indicators that are not widely reported
(e.g., SME)
Address the coordination among national data collection agencies
24
The way forward: Funding

The estimated total cost of the FAS project for the period 2013
is provided in the chart below:
The estimated total cost of the FAS project, 2013 - 2017
Government of the Netherlands Other potential donors
$156,993
$313,985
$470,978
$525,925
$829,566
100%
$627,970
80%
$470,978
60%
$313,985
40%
33% $259,038
2013 2014 2015 2016 2017

25
Measuring financial inclusion from the demand side 1
Sabri nc, CAFRAL / Reserve Bank of India
1
CAFRAL Workshop on Measuring Financial
Inclusion from Demand Side
June 8, 2012
T. Sabri nc
Head of Research
Center for Advanced Financial Research and Learning
Reserve Bank of India
Issues Discussed at the Workshop
Why should we measure financial inclusion from the

demand side ?
What data should we collect to measure financial inclusion

from the demand side ?
What should be the sampling and survey methodologies?
What should be a measure of financial inclusion from the

demand side: an index or a dashboard?
2
Before Those
Grey Areas of Existing Definitions:

Extant definitions are fairly congruent
Access focused: do not capture usage and outcomes
Emphasise convenience and flexibility exclude issues such as
suitability, financial advice, wealth management etc.
Need focus on firms as well as households a distinction which
is very thin and unclear, currently
Conclusion: The concept of financial inclusion has to evolve beyond its

current focus on provision of access to financial products and services
with an additional focus on the quality of use.
3
Objectives of Demand Side Measurement
Financial Inclusion is measured predominantly from the supply side
top down and does not capture perspectives of small
businesses and low income households
The demand side bottom up measures would supplement the

existing supply side measures to improve the picture
Policy makers and regulators can use such measures:

to modify the policies and strategies;
to facilitate measurement of outcomes of various policy initiatives
Financial service providers can use such measures:

to design customised financial products for specific regions and
categories of consumers
4
Demand Side Financial Inclusion Indicators
Should Capture
Not just income but types of income and income sources
Savings in informal and formal institutions
Ways in which households mitigate risk
Household and small business demand for financial services
Use of bank/post office account
Payments
Savings
Credit
Insurance
Suggestion: Using measures similar to World Banks Global
Findex might help the measures to be internationally
comparable
5
Socioeconomic and Demographic Characteristics
In addition to usual socioeconomic and demographic
characteristics such as location, age, income, education and
occupation, look into finer nuances such as house or living
space, access to water, sanitation, medical facilities,
changes food consumption, migrantion status, land holding,
physical disability, social group, etc.
Purpose of collecting additional socioeconomic and

demographic parameters should be:
To correlate inclusion and exclusion;
To perform a social audit on banks progress given the results and
conclusions from tracking the impact on these parameters over time
and space;
And hence, to impact policies and strategies of regulators and
practitioners
6
Should Help Identify Barriers
There is demand for financial services. But there is need to

identify the reasons for nonusage and, more importantly,
understand why usage does not translate into good outcomes
Barriers mentioned in the World Bank Global Findex are a

good starting point: distance, awareness, affordability, trust, lack
of documentation, religious/cultural barrier, lack of money
Additional suggestions: consumer experiences, financial

literacy, lack of customised products, unwillingness of banks to
lend in certain cases, insufficient infrastructure of financial
service providers
7
Latent Demand for Financial Services: At the ATM
Kalwakurthy, Mahabunagar, Andhra Pradesh
8
Lessons from the Field
Reasons for Exclusion
Two main reasons:
low income of, and nature and scale of business with poor
households
perception that these households are highly risky and not
profitable
Another reason:
Missselling of products, high commissions and regulations to
change these
Question: Can the regulators look at these more carefully to

give us some insight on why households are making the
kind of financial decision they are making?
9
Survey Methodology
Simple but meaningful ratios would give more insight on what are
the trends rather than an index
Nature of sample should enable both national and granular level
measurement and analysis
Focus on both households as well as businesses
For households, responses should be obtained at both household
and individual member level
Should capture both financial indicators as well as key
socioeconomic and demographic indicators
Frequency: more frequently than some of the existing national
surveys such as All India Debt and Investment Survey (AIDIS)
conducted once in 10 years.
10
Noteworthy Data Collection Initiatives in India
Yale Center for Micro Finance/Institute for Financial Management

and Research longitudinal data survey: a detailed survey on how
respondents meet their financial needs and thereby helps to
identify the new financial tools which current surveys fail to
capture
The Invest India Incomes and Savings Survey (IISS): a unique unit-
record database that links the incomes, investment and savings
portfolios, insurance and credit positions, financial sector access
and the like across 321 million members of rural and urban India.
The Centre for Monitoring Indian Economy (CMIE) Household Panel

survey: quarterly for 150,000 households
11
Tentative Action Plan
Finalize methodology, questionnaire design and agreement of
content among stakeholders
Conduct a pilot survey with a sizeable portion of the sample to

ensure appropriate responses are received
Analyze the pilot results and redesign survey, if necessary
Prepare and administer the survey among the target population
Prepare survey results and cross-check against other data sources,

depending on the capacity available to mine the results
Propose additional questions to the existing Indian National Sample

Survey Office (NSSO) surveys
12
The Global Financial Inclusion Index1
Douglas Randall, World Bank
1
GLOBAL FINDEX OVERVIEW
What is the Global Findex?

The first individual-level database on financial inclusion that is
comparable across countries and time.
Based on more than 150,000 interviews with adults ages 15+,
representing more than 97 percent of the worlds adult population.
What does it measure?
Measures in detail the way in which adults in 148 economies
save, borrow, make payments, and manage risk.
Includes 41 indicators, disaggregated by gender, age, education
level, income, and residence (urban or rural).
GLOBAL FINDEX OVERVIEW
Why is the Global Findex valuable?

Provides a tool to benchmark levels of financial inclusion, measure the
impact of public policies, and identify potential clients and the demand for
new products.
Uses a consistent methodology across time and countries.
Long-term project: the first round of data was released in April 2011,
with complete updates in 2014 and 2017.
Complements country-level efforts, & supply-side data (G20 Basic Set)
Who are our partners?

The survey was carried out by Gallup, as part of its annual World Poll.
The project is funded by the Bill and Melinda Gates Foundation.
GLOBAL FINDEX ACCOUNT PENETRATION
The Global Findex covers 148 economies in 23 economies, account penetration is over 95 percent,
in 21 economies account penetration is 5 percent or less
GLOBAL FINDEX ACCOUNTS AND PAYMENTS
Regionally, account penetration varies from 89 percent in high-income economies to 24 and 18

percent in Sub-Saharan Africa, and the Middle East and North Africa, respectively
The average account penetration among all adults in the developing world is 43 percent
Among those living below $2/day, only 23 percent have a formal account
Women, youth, the poor, and rural residents are the least likely to have a formal account
A 6-9 percentage points gender gap persists across income groups in developing economies
8 percent of account holders worldwide have zero deposits and withdrawals in a typical month
50 percent of account holders in developing economies both deposit into and withdraw from their
account 1-2 times in a typical month
73 percent of account holders in developing economies typically withdraw money from a teller
Note: Respondents could choose more than one reason. The data for not enough money refer to the
percentage of adults who reported only this reason
31 percent of unbanked in Sub-Saharan Africa choose Too far away

31 percent of unbanked in Europe and Central Asia choose [I] dont trust banks
40 percent of unbanked in Latin America & the Caribbean choose They are too expensive
Significant relationship between subjective and objective measures of documentation requirements,

even after accounting for GDP per capita
38 percent of account holders in SSA use their account to receive money from family living
elsewhere
61 percent of account holders in ECA use their account to receive wagescompared to 34
percent of all account holders in developing countries and 56 percent of account holders in high-
income countries
16 percent of adults in SSA use a

mobile phone to pay bills, send or
receive money in the past 12 months
68 percent of adults in Kenya use

mobile technology, driven by the early
success of M-PESA
52 percent of adults in SSA that use

mobile technology are otherwise
unbanked
GLOBAL FINDEX SAVING
31 percent of adults in developing economies saved in the past year

56 percent of savers in developing economies saved using a formal financial institution
48 percent of savers in in Sub-Saharan Africa saved using a community-based method
GLOBAL FINDEX SAVING
40 percent of account holders in the developing world saved using a formal financial institution
7 percent of account holders in ECA saved using a formal financial institution
GLOBAL FINDEX CREDIT AND RISK MANAGEMENT
7 percent of adults in developing economies have a credit cardcompared to 50 percent of adults in high-income
economies
8 percent of adults in developing economies borrowed money from a formal lender in the past 12-months
compared to 14 percent of adults in high-income economies
17 percent of adults personally purchased health insurance; 6 percent of adults working in farming, forestry, or
fishing have crop, rainfall, or livestock insurance
GLOBAL FINDEX LOOKING AHEAD
What to look for in 2014.
1)Success of G2P reforms? Early evidence from Saudi Arabia
2)Closing the gender gap?
3)Progress below $2/day line?
4)Movement in self-reported barriers to access?
5)Growth of mobile money?
6)Growth of bank agents?

GLOBAL FINDEX WEBSITE
There is much more information on the Global Findex website:

Report and Notes (in 3 languages)
The complete questionnaire
(in 141 languages)
The complete country-level database,
including data on:
The use of accounts to receive payments
from the government, employers, family
The use bank agents
Frequency and mode of account access
Prevalence of informal saving and borrowing
The use of mobile money
Analytical tools to make customized
maps and graphs
GLOBAL FINDEX DATA RELEASE
Beginning November 27th, it will be possible to download and analyze the raw microdata users
will be able to cut the data in millions of different ways and answer very specific questions.
GLOBAL FINDEX
www.worldbank.org/globalfindex
Collecting financial inclusion information from the supply side:

the Colombian case 1
Ana Maria Garcia, Alliance for Financial Inclusion
1
Bringing Smart Policies to Life
Ms. Ana Maria GARCIA
Collecting FI information from the supply side: The Colombian case
November 5, 2012
Colombia at a glance
Region: Latin America

Adult population (18+): 30 million
GDP: US$ 328 billion (2011)
GDP per capita: US$ 7.114 (2011)
GINI: 55.9 (2010)
Source: IMF, World Bank, DANE, Wikipedia (image)

Financial system
Colombia has recovered the financial deepening after the

1998 crisis, but it still remains below the regional average
level.
Credit private sector (% GDP)

36% 35,5% 91,8
35,4%
Domestic credit (%GDP)
34%
71,2
32%
61,4
30%
28% 41,1
40,1
35,5
26% 32,4
26,1 23,7 26,4
24%
16,6
22%
20%
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2007
2008
2009
2010
2011
1995
2006
Paraguay
Chile
Uruguay
Mexico
Peru
Panama
Bolivia
Argentina
Brazil
Ecuador
Colombia
year
Source: Financial Superintendency of Colombia - SFC Source: World Bank & SFC
Country

Why measure?
To establish base line in order to measure the progress the

financial inclusion in Colombia.
To identify the underserved geographically and by

segment of population
To promote the design of policies based on

evidences.
To monitor the status of financial inclusion in the

country and measure the impact of new policies.

Policies in FI
Agent banking: The SFC authorized credit institutions to

offer their product and services trough a third party.
Microcredit: Agreements with banking sector to provide
microcredits to people without previous access to financial
system.
Basic Savings accounts: with two withdraws and one
balance inquiry cost free per month, no minimum balance
required and with simplified KYC requirements.
Simplified process for opening saving accounts: just
one per person and with some requirements on the
balance per month.
Mobile Financial Services: SFC allowed to operate agent
banking and basic savings through all electronic devices
as cell phones.
Financial Inclusion report
Main criteria
I. Use previous experiences and definitions FIDWG core
set and members knowledge
II. Use the current data

III. Coordination between governmental institutions
Banca de las Oportunidades (BdO)

+
Superfinanciera (SFC)
Institutions included on the report
Credit
Institutions
monitored by By SFC
SFC * Banks
* Finance Companies
* Financial corporations
* Credit and saving union
monitored by SFC
By BdO:
* Credit and saving union
monitored by SES
Centralized * NGOs
information
by BdO

Main indicators
Access:
1. Contact points per 10.000 adults

2. Contact points per 1.000 km2
3. Presence per municipality.
Use:
1. % adults with at least a financial product
2. Saving accounts per 10.000 adults
3. % dormant accounts
4. Accounts per balance
5. Adults with a credit account
6. Adults with credit card
7. Transactions per channel
Dimension: ACCESS
The number of contact points has increased, mostly

on those who can perform cash-in and cash-out
operations
Contact points per 10.000 adults Contact Points per 1.000 km2
Contac points
Contac points
70 240
60 210
66,1
61,7 180 209,5
50 191,4
54,7
150
40 46,5 166,5
120 138,9
30
90
20
7,6 60
10 3,9 4,1 5,5 16,8 24,2
30 11,6 12,6
0 0
2008 2009 2010 2011 2008 2009 2010 2011
Total Branches + Agents year Total Branches + Agents year
Source: BdO, SFC

CP per 10.000 adults
Branches Agent Banking

2011 5,5
Agents provide
2011 2,1
financial
2010 2,2 2010 3,3 services in
2009 2,2 2009 1,9 places with
difficult access
2008 2,2 2008 1,7
and include
0 1 2 3 4 5 0 1 2 3 4 5 self-excluded
people
ATM POS
2011 3,6 2011 54,9
2010 3,4 2010 52,8
2009 3,2 2009 47,4
2008 3 2008 39,6
0 1 2 3 4 5 0 20 40 60
Source: BdO, SFC
Per population in municipalities
Number of municipalities
2008
Branches
Without Just Just
Size of the population and
coverage branches Agents
agents
until 10.000 inhabitants 57 167 133 67
10.001 - 50.000 inhabitants 12 174 83 293
50.001 - 100.000 inhabitants 1 2 1 53
Ms de 100.000 inhabitants 0 0 2 57
Total 70 343 219 470
2011
Branches
without Just Just
Size of the population and
coverage branches Agents
agents
until 10.000 inhabitants 5 83 176 158
10.001 - 50.000 inhabitants 6 89 84 380
50.001 - 100.000 inhabitants 0 1 1 58
Ms de 100.000 inhabitants 0 0 0 61
Total 11 173 261 657
Source: BdO, SFC

Municipalities and contact point

USAGE - Deposits
% adults with at least one financial product
70% 31 The growth rate of last
number of adults (000 000)

30,3
Percentaje of adults
29,8 years is based on the

64,6% 30
65% 29,2
62,2% number of basic saving
28,7 29
28,1
accounts which are
60%
27,6 57,3%
28 mainly used to deliver
55,2% 55,5%
55% CCT programs.
27
51,1%
50% 26
Dec-2006 Dec-2007 Dec-2008 Dec-2009 Dec-2010 Dec-2011
indicator (%) adult population (millions) - right axis
Adults IDs (millions)

Product Dic-2006 Dic-2007 Dic-2008 Dic-2009 Dic-2010 Dic-2011
Saving accounts 13 15 15 16 17 19
Current accounts 1,4 1,5 1,5 1,3 1,4 1,5
Source: Colombian Banking Association Asobancaria

USAGE - Deposits
Even though the number of accounts per 10.000 adults has

increased, the number of dormant accounts are still
significant
Number of accounts per 10.000 adults
14 000
# saving accounts per 10,000 adultos

14 000
11 897 11 897
12 000 11 271 12 000 11 271
# accounts per 10 000 adults
10 001 10 096 10 001 10 096

10 000 10 000
8 000 8 000 6 858

6 155
6 000 6 000 5 343
4 905
4 000
4 000
2 000 930 937 812 808 2 000
-
2008 2009 2010 2011 0
2008 2009 2010 2011
year Year
Total savings account current account Total savings account Active savings accounts
Source: BdO, SFC

Savings accounts per 10.000 adults
Source: BdO, SFC

USAGE - Credits
Indicators on credits showed an improvement during the last

four years, microcredit presented an impressive growing
path.
Consumer 2,7 3,7 4,3 3,6 4,1 4,3
Housing 0,5 0,5 0,6 0,6 0,7 0,7
Microcredit 0,5 0,6 0,7 1,1 1,2 1,5
Credit cards 3,3 3,7 3,9 4,7 5,2 5,5

Consumer 10% 13% 15% 12% 14% 14%
Housing 2% 2% 2% 2% 2% 2%
Microcredit 2% 2% 2% 4% 4% 5%
Credit cards 12% 13% 14% 16% 17% 18%
Source: Colombian Banking Association Asobancaria

USAGE Credits cards
The growing rate on credit cards balances it is due to the

use as replacement of credits for consumer purposes.
9,5 14,5
Balances in credit cards (thousend of

9,4
9,0 13,5
Total number ot credit cards
13,4 12,5
8,5
billions)$
11,5
8,0
10,5
7,5
9,5
7,0 8,5
6,5 7,5
janv.-09
fvr.-11
juil.-11
juin-09
avr.-10
nov.-09
dc.-11
sept.-10
Meses
Number of Credit cards Balances of credit cads
Source: SFC
USAGE Transactions per channel
100%
number of operation per transactional channel (%)
90% Agent Banking

22,2%
80% 26,6%
30,1% 31,0% Automatic
70% payments
ACH
60% 9,2%
9,7%
10,0% 10,7% Mobile
50%
26,1%
40% 24,8% Internet
22,6% 22,1%
30% call center
20%
30,8% POS
28,0% 26,7% 25,3%
10%
ATM
0%
2008 2009 2010 2011
Branches
2,2 billion 2,3 billion 2,4billion 2,6billion
Ao
Source: BdO, SFC

Work in progress
Financial literacy and consumer protection

indicators (as for example number of complains).
Finding the optimums point between collection data

by municipality with this purpose.
Indicators for MFS

Take away
In terms of financial inclusion

Usage of financial products and services is
crucial design of correct products, financial
literacy and consumer protection
Small municipalities, rural areas are places with
major number of dormant accounts.
In terms of report:
Cooperation is really important
There is no necessity of invent the wheel
(FIDWG don it and it is doing it)
For policymakers, the support on this kind of
strategies is important.
Thank you!
What can household surveys tell us? The Bank of Italys experience 1
1
BANCA DITALIA
EUROSISTEMA
What can household surveys tell us?

The Bank of Italys experience
Claudia Biancotti
Bank of Italy
Economic and Financial Statistics Department

IFC and Bank Negara Malaysia, Kuala Lumpur
November 5, 2012
Why should we have household surveys?
Whats the value added of HH surveys compared to other

sources?
- distributional information;
- in multipurpose surveys, evidence on correlations (demographic

characteristics / economic behavior etc.)
This type of information is crucial whenever a policy-relevant
issue can be properly understood only by looking at specific
subpopulations. Example: if the general average of the debt-to-
income ratio is high we could be looking at a healthy economy
with good access to credit, but also at a situation where
creditworthiness of some subgroups is overestimated.
22
Household surveys and financial inclusion
Surveys on household wealth now widely recognized as policy-

relevant (ECB HFCS, OECD handbook on micro-level
measurement of wealth, LWS expanding to cover middle-income
countries etc.)
Highly detailed information from different perspectives (de
facto access to various segments of financial markets, including
informal ones; understanding of instruments; perceptions)
Examples: financial instruments held by households (from sight

accounts to complex derivatives); experienced and perceived
credit constraints; availability of informal credit from friends and
relatives; financial literacy
33
Example I: financial literacy, 2010
44
Example II: sociodemographic characteristics and risk appetite
55
The Survey on Household Income and Wealth (SHIW), 1966
1966: First Report on Household Income,

Savings and Consumption
Main topics of the Report (14 pages)
Income distribution (concentration, employees vs self-employed)
Durables (expenses for radios, grammophones, sewing machines)
How were the durables paid (consumer credit)?
Home ownership (about 50%)
The report described a nation that was still relatively poor: the rapid
pace of post-WWII economic growth implied widespread access to
new consumer goods, such as cars and television sets, but retail
financial markets were not developed. Sight accounts and government
bonds were the only instruments known to the majority of the
population. Credit to the increasing number of small businesses was
generally extended by local banks on a near-informal basis.
66
The Survey on Household Income and Wealth (SHIW), 2012
2012: most current report on Household

Income and Wealth (data on 2010)
Main topics of the Report (142 pages)
Household structure (demographic profile)
Income distribution (concentration, employees vs self-employed)
Retirement age (expectations and realizations)
Home ownership (approximately 80%)
Wealth (concentration,
concentration, prices)
prices)
Financial Assets (portfolio choices,
choices, risk aversion)
aversion)
Payment instruments
All in all, the survey has always aimed at collecting data on the
economic resources acquired, consumed or held but Italian society
has changed a lot since then, and the survey too
Participation in the financial markets is one of the relatively new

phenomena, requiring increasing attention
77
Social mobility
Use and users of the SHIW Bequest
Small area
Housing
Gender
Uncertainty
Credit-Money
Policy-relevant research projects
Consumption 1966-1986
Simulation of the impact of policy choices Retirement
Financial and wealth accounts Poverty 1987-1996
Academic research Education
BI researchers, decision-makers and Earnings 1997-2007
Inequality
academics
Savings
Wealth
The Report usually becomes an important Labour
reference for the domestic political debate Fiscal policy
on the economic conditions of households Household
also the press and the general public Methodology
Income
0 20 40 60 80 100 120 140
Bibliography of SHIW-based papers (October 2012):

Approximately 750 papers; slightly less than 30% by Bank of Italy researchers
88
Main features of the SHIW
Since 1966 (yearly up to 1986; since 1987 every two years; will
revert to being yearly in 2013!)
Sample of 8.000 households (about 20.000 individuals)
Two-stage stratified sample design (municipalities, households)
Stratification of municipalities; post-stratification of households
Panel component (about 40 per cent) since 1989
Face to face interview (use of CAPI)
Micro data freely available on the Internet (data from 1977 on)
Part of the Eurosystem HFCS
99
The questionnaire: permanent sections
A. Structure of the household at the end of the year (size; gender, age, education,
place of birth, citizenship of each member, ..)
B. Employment and incomes (job status, hours worked, wages, income from self-
employment, pensions)
C. Payment instruments and forms of saving (current accounts, credit cards,
checks, financial instruments held, .)
D. Principal residence and other property (tenure status, value, rent
paid/collected, size, location, )
E. Non-durable and durable consumer goods (annual expenses for non-durable
goods; expenses for cars, furniture)
F. Forms of insurance
G. Assessment of the interview (to be provided by the interviewer)
10
10
Example III: aggregate dynamics
Debt of Italian Households
220 80%
Financial
200 Accounts
Total value
70%
180 60%
Index (1995=100)
160 SHIW
Total value 50%
140
40%
120
SHIW
Holding (right scale) 30%
100
80 24,7% 22,6% 20%
22,6% 21,1% 22,1%
22,0%
60 10%
40 0%
1995 1998 2000 2002 2004 2006
11
11
and distributional facts (2006)
4 3
7
Not indebted
8
Fourth quartile (richest)
Third quartile
Second quartile
First quartile (poorest)

78
12
12
A sketch of the possibilities
Ad hoc sections of the questionnaire providing further information:

Capital gains, inheritances, risk aversion, housework, intergenerational mobility, use of
public services, social capital, tax evasion, income and employment expectations,
retirement expectations, financial choices, use of new technologies.
Studying financial inclusion:

- How many financially vulnerable households are there? Who and where are they?
- How does the poorest segment of the population fare with respect to access to credit,
value of debt, debt-to-income ratio, financial literacy?
- Is there any difference in attitudes to saving and risk between subpopulations with a
good understanding of financial issues and the rest? Whats the feedback on income?
- Example: recent changes in the pension system. Who is prepared and who is not? How
does the increasing relevance of private pensions affect inequality?
13
13
Example IV: financial vulnerability, 2010
14
14
Example V: credit rationing
15
15
BANCA DITALIA
EUROSISTEMA
Thank you for your attention!

Households and firms access to finance in the euro area 1
Aurel Schubert, European Central Bank
1
Households and firms access to
finance in the euro area:
On ne prte quaux riches?
Aurel Schubert
European Central Bank
IFC Kuala Lumpur
2 November 2012
Wealth as seen from above
Wealth disparities are well known

to what extent is it the same for access to finance?
2
Outline of presentation
1. Households view the Eurosystem Household

Finance and Consumption Survey (HFCS)
2. Firms view the EC/ECB Survey on the access to

finance of SMEs (SAFE)
3
What is the HFCS?
Cross-country survey collecting micro-level

data
Focus = wealth (mostly)
Country and euro area representative
Ex-ante cross-country comparability
First wave close to finish
4
Areas covered by HFCS
Real + financial assets

Household wealth Liabilities
Future pension
Pension wealth entitlements
Household saving / Income

Consumption
Wealth accumulation Inheritances + gifts
Demographics
Other covariates Employment
5
Household wealth & debt
Strong skewness wealth
distribution Lorenz curve of Income and Net Wealth
Few wealthy households

substantial effects on
aggregate statistics
Net wealth more

unevenly distributed
than income
Liabilities even more

unevenly distributed
Source: Eurosystem HFCS, data for BE, ES, FI, IT, LU, MT,
NL, AT, PT, SK
6
Household assets
Portfolio distribution: % of HH portfolios Asset participation: % of HHs holding assets
Source: Eurosystem HFCS, data for BE, ES, FI, IT, LU, MT, NL, AT, PT, SK
Main residence = largest asset

(Portfolio distribution) Increasing importance of business wealth
(Participation) highly dependent on income
Financial assets: low prevalence excl. accounts, even amongst wealthy
7
Income and liabilities
Household liabilities and income
(averages, EUR)
Focus on average
household hides
large heterogeneity
financial commitments
largely correlated with
HH income
Lower-income HHs
low access to credit
NL, AT, PT, SK
8
Household liabilities
Share of households having debts
% of households
Participation in debt
finance highly
dependent on income
More for mortgage than
for non-collateralised
debt
Mortgage debt = bulk HH

debt (~70-90%)
Source: Eurosystem HFCS, data for BE, ES, FI, IT, LU, MT, NL,
AT, PT, SK
9
Indebtness
Distribution of total liabilities by income
group
Indebted households
Liabilities
concentrated on
the high income
groups
Low income
groups have
very limited
absolute debts,
but high relative
ones 10th percentile: blue dot, Median: red lozenge, 25th to
75th: light blue rectangle, 90th percentile: blue triangle
Share of households having
debts
Has Has non-
mortgage mortgage
Has debt debt debt
BE 44.7% 30.5% 24.0%
Large cross- ES 49.9% 32.3% 30.7%
IT 24.8% 10.8% 17.3%
country
LU 58.3% 38.8% 37.0%
differences in HHs
MT 34.1% 15.6% 25.2%
access to credit NL 65.7% 44.7% 37.3%
for both mortgage PT 37.7% 26.7% 18.3%
and non- AT 35.6% 18.4% 21.4%
collateralised debt SK 26.8% 9.6% 19.9%
FI 59.8% 32.8% 51.2%
Source: Eurosystem HFCS, data for BE, ES, FI, IT, LU, MT, NL,
AT, PT, SK
11
Portfolio distribution: % of HH portfolios
Age-profile
debt participation
(as well as values)
consistent with
consumption
smoothing
NL, AT, PT, SK
12
Financial pressure
Distribution of debt-income ratio,
breakdown by income quintiles
Debt burden
particularly
large for low
income groups
How to read: in the 5th quintile of income, 80% of the indebted

households have a debt-income ratio below 2.3.
NL, AT, PT, SK
13
Financial pressure
Distribution of debt-income ratio,
breakdown by country
Large country differences

in debt-income ratios:
higher than 4 for:
20-25% HHs in NL and PT
15-20% HHs in ES and LU
~5% HHs in other countries
How to read: in Austria, 80% of the indebted households

have a debt-income ratio below 1.5.
Source: Eurosystem HFCS, data for ES, FI, IT, LU, MT, NL,
PT, SK
14
Motivation of the SME survey
(1)
Importance
SMEs economy
(2)
Monetary policy
transmission
different
(3)
Data scarcity
15
Main characteristics
ECB
Sponsors European Commission
6 months ECB waves, limited euro area

Frequency 2 years joint with European Commission, extended EU
Surveys in March and September each year

Timeliness Results published in less than a month (2 Nov)
7,500 for ECB waves

Sample size 15,000 for joint waves
Since 2010, 11 largest euro area countries

Representativeness Limited coverage EE, CY, LU, MT, SI, SK
Age, Ownership, Autonomy

Breakdowns Sector
16
Questionnaire of the SAFE
Standard formulation questions
E.g.: Over the past six months, has [X] improved, deteriorated, or
remained unchanged? where [X] is one of the topics
covered
Topics covered
Indicators of firm, external financing needs and application, terms &

conditions for loans
Financial instruments covered
Bank loans, overdrafts, credit lines, trade credit, debt securities,

equity
17
Breakdown of firms in sample
Size Countries
Category Typology
18
Overall financial situation SMEs
Change income and debt situation of euro area SME
Turnover dropping (over the preceding 6 months; in percentage of respondents)
past two semesters
Overall increase costs

50% SMEs
declaring profit
reductions throughout whole
reference period
Net percentages = balance of opinion, increased decreased (right

hand scale)
19
Most pressing problem
Access to finance
2nd most pressing problem
Access to finance is more pressing in micro firms than in

medium-sized ones (18% vs. 14%), lowest in large firms
20
The financing of SMEs
Sources of external financing of euro area SMEs
(over the preceding 6 months; % of respondents)
SMEs largely
dependent on banks
Increasing external
financing needs
Leasing/hire-
purchase/factoring
decreasing since
peak in 2nd half of
2010
Recovery of inter-
company financing
21
Availability of external financing
Change availability external financing euro area SME
(over the preceding 6 months; in percentage of respondents)
Availability overall
negative, and
decreasing
decreasing last year
over all instruments
Cited factors:
1. General economic
outlook
2. (Lack of) willingness
banks
Net percentages = balance of opinion, increased decreased
(right hand scale)
22
Success when applying for a bank loan
SMEs obtaining all of the loan they applied for
(over the preceding 6 months; in percentage of firms
SMEs have higher applying)
rejection rates & higher
fear of refusal
SMEs with increased

profits had higher rates
of successful financing
SMEs with decreased

profits are 30% more
likely to abstain from
applying for fear of
refusal
23
Needs and availability financing
Change in needs and availability of SMEs for bank
loans across euro area countries
(over the preceding 6 months; net percentages)
External financing needs Availability changes

Increase more in GR contrasted by country
Positive or neutral in most Decreasing in GR, ES, PT, NL, FI
countries, stable compared to last Negative but stable in BE, IE, IT, AT
period
24
Concluding remarks
Two (very different) surveys show European

perspective on access to finance
Household survey: structural issues
Debt more concentrated than wealth
Lower absolute liabilities for low income households,
but higher relative debt burden
SME survey: conjunctural issues
Access to finance remains second most pressing
problem
Banks = main external financing source for SMEs
Availability of external financing has decreased in
recent year
25
Way forward
Household survey
Main results to be published in February 2013
Data available for research purposes through ECB
More detailed analysis to be conducted then
SME survey
Results just published (last Friday)
Waves every six months
Big wave with all EU countries and more
questions in September 2013
26
Thank you for your time
Mapping the financial sector: microfinance and beyond1
Scott Gaul, Microfinance Information Exchange
1
Microfinance Information Exchange
Mapping the financial sector

Microfinance and beyond November 2012
The Premier Source for Microfinance

Data and Analysis
This presentation is the proprietary and/or confidential information of MIX, and all rights are reserved by MIX. Any dissemination, distribution or copying of this
presentation without MIXs prior written permission is strictly prohibited.
Who we are
Geospatial analysis for financial inclusion
Long-run trends for data on financial inclusion
2
Who are we?
MIX is the premier source for objective, qualified, and

relevant microfinance performance data and analysis.
Committed to strengthening financial inclusion and the

microfinance sector by promoting transparency, MIX provides
information on microfinance institutions (MFIs), funders,
networks and service providers dedicated to serving the
financial sector needs for low-income clients.
3
MIXs Role in the microfinance sector
MIX Basics
2002 - 2012
MFI Networks
Donors and
Investors
MFI
Regulators
Raters
4
How has data on microfinance evolved?
- Average of 250 data

points per MFI
- Steady increases in
transparency over time
5
Where does this data come from?
-Majority of data from

audits
- Ratings for some (8 15%)
- Roughly 1/3 now
unaudited (self-reported)
6
Research: recent publications using MIX data
Selected papers referencing MIX data from the last year or so (not
exhaustive):
Microfinance in evolution: An industry between crisis and advancement Cdric Ltzenkirchen,
Christian Weistroffer, Deutsche Bank Research
External Validity and Partner Selection Bias, Hunt Allcott and Sendhil Mullainathan, NYU/NBER,
Harvard
Ownership and technical efficiency of microfinance institutions: Empirical evidence from Latin
America, Roselia Servin,Robert Lensink,Marrit van den Berg, Journal of Banking & Finance
Competition, loan rates and information dispersion in microcredit markets, Guillermo Baquero,
Malika Hamadi, Andras Heinen, ESMT Research Working Papers
The Profit Orientation of Microfinance Institutions and Effective Interest Rates Peter W. Roberts,
World Development
Do Institutions Matter for Microfinance Profitability? Evidence from Africa Peter Muriu, University
of Birmingham - The Birmingham Business School
Microfinance, Financial Inclusion and Financial Development: An Empirical Investigation with an
International Perspective, Jovi C. Dacanay, Bienvenido Nito and Patricia Buensuceso. University of
Asia and the Pacific
Risky Business: An Empirical Analysis of Foreign Exchange Risk Exposure in Microfinance, Julie
Abrams, Microfinance Analytics
Over-indebtedness and Microfinance: Constructing an Early Warning Index, Vivien Kappel, Annette
Krauss, Laura Lontzek* Center for Microfinance, University of Zurich
7
Key questions for geospatial analysis
Access
o How many people are within X km of a point of
service?
o What percent of the population is served (by
product, by type of institution)?
Market trends
o What are trends in access? How has the market
grown?
o Are there hotspots or clusters of activity? Are there
areas that are relatively under-served?
8
South Africa: mapping access points
Motivating questions
o Financial Sector Charter goals for access based on physical
proximity
A sales point within 15 km of a qualifying area
A service point within 10 km of a qualifying area
A transaction point within 5 km of a qualifying area
How we did it
o Public data: Branch and PoS listings for customers + regulatory
databases
o Technology: Web scrapers to extract and consolidate data
automatically
o Results: 40K access points mapped to town level
9
South Africa: mapping access points
10
Kenya: long-run trends in channel development
11
Nigeria: testing microfinance bank locations
o Can we monitor proliferation of 900+ banks?
o Can we track or find patterns in license revocations
(200+)?
o Can we identify supply / demand gaps?
How we did it
o Location info from registry of banks posted by CBN
o Rule-based and manual scrubbing of location info
o Utilize NIGECS database of demographic data at LGA
(admin-2) level; more detailed than census data
12
Nigeria: testing microfinance bank locations
13
Sample findings from Nigeria and S. Africa
Nigeria
CGAP South Africa

Database IMF (2011)
(2010) map data
Bank branches
2.22 3.08 4.32
per 1000 km2
Bank branches
per 100,000 8.00 10.7 15.06
adults
At the district level, a clear Bottom-up aggregation of identified

inverse relationship between access points finds 50% more bank
poverty and access to branches than global surveys indicate
microfinance banks
14
Bosnia: identifying hotspots for crisis
o Could we have foreseen the microcredit crisis using spatial
data?
How we did it
o Location information a standard disclosure in audits
o Calculate market share for individual MFIs and compute HHI

(Hirschman-Herfindahl Index) to look at concentration
15
Bosnia: identifying hotspots for crises
16
Bosnia: identifying hotspots for crises
17
A growing database on microfinance

Expanding beyond microfinance to look at key
questions using geospatial analysis
o Access, supply-demand gaps
o Trends, monitoring and tracking performance
o Market concentration and early warning systems
Key principles for data and indicators
o Use public data whenever possible
o Use technology for efficiency
o Make more data publicly available
18
MIX Global and Project Partners
MIX partners with a dedicated group of industry leaders:
19
Microfinance Information Exchange
Headquarters:
1901 Pennsylvania Ave., NW, Suite 307

Washington, D.C. 20006 USA Visit us on the Web:
www.themix.org www.mixmarket.org
Regional Offices:
Baku, Azerbaijan Contact us: info@themix.org

44 J. Jabbarli st. Caspian Plaza I, 5th Floor,
Baku, Azerbaijan
Interested in learning more about MIX?

Lima, Peru
Sign up to receive our free e-mail
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newsletters!
Rabat, Morocco
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BP 408 Rabat Morocco Find us on Facebook
Follow us on Twitter: @mix_market
Hyderabad, India
Road #12, Landmark Building, 5th Floor,
Banjara Hills, Hyderabad 500034, India
20
Measuring financial literacy: the Malaysian case 1
Ooi See Eim, Central Bank of Malaysia
1
Measuring Financial Literacy
[The Malaysian Case]
Session 5
5 - 6 November 2012
Ooi See Eim

Consumer and Market Conduct Department
Bank Negara Malaysia
BANK NEGARA MALAYSIA

CENTRAL BANK OF MALAYSIA
Outline
Participation in OECD/INFE pilot survey 2010/11 on measuring

financial literacy provided a demand side measurement
Survey identified consumer vulnerabilities for financial inclusion
and education intervention
Evidence-based initiatives to enhance financial literacy level
and to promote financial inclusion
OECD/INFE pilot survey provided a demand side measurement
KNOWLEDGE & SKILLS
Survey covered financial knowledge & skills, Knowledge of financial concepts
Inflation & investment risk
behaviour and attitudes relating to various Financial numeracy
aspects of financial literacy Division & time
- Return
Survey aimed to obtain robust, - Simple & compound interest
internationally comparable data to measure BEHAVIOUR
the levels of financial literacy in each Basic Money Management
Decision maker
participating country Household budget incidence
Observations from the survey facilitated Decision making - P2Y new ownership
Sources of information - P2Y new ownership
implementation of evidence-based financial Savings Behaviour
inclusion and education initiatives Past 12 months savings method
Savings sustaining power in the event of
Participation in the survey provides income loss
opportunity to conduct repeat studies to Financial deficit incidence / response
measure and identify change over time Financial Participation

Financial products awareness
Current holdings
Past 2 years purchase
ATTITUDE
The survey findings were published in March 2012 & can be found at http://www.oecd- Attitude towards money
ilibrary.org/finance-and-investment/measuring-financial-literacy_5k9csfs90fr4-en Financial responsibility
3 3
Survey guided by parameters & sampling methodology set by OECD
Based on OECDs Core Questions Guiding principles
Translation to retain same meaning Clear survey objective, not be
distracted with other objectives
Obtained samples representative of Whole process < 30 minutes for good
attention span of respondents
Malaysian population Supplementary questions must not
a minimum of 1,000 samples overshadow or distract attention from
individuals aged 18 and above core questions
personal interviews/telephone and/or should consume < 30% of interview
face-to-face. No internet or online process
surveys must be related to main issues
being surveyed
minimum 60% success rate
weaved in within core questions to
minimise possible disruption
Allowed internal requests to collect Must exhaust other possible
extra samples on low income households sources of information before
to gauge level of literacy among low inclusion in survey (e.g. census,
income households other survey or studies)
supplementary questions on household
consumption pattern
data on awareness of consumer
education initiatives
4 4
Our experience in preparing and implementing the survey
Pilot Interviews
Questionnaire in 3 languages - Bahasa Malaysia, English & Mandarin
Focus groups - assess applicability of questionnaire in local context
Pilot interviews - assess translation, comprehension, clarity and questionnaires
duration in all 3 languages
Data collection
Fieldwork conducted nationwide within six-week period - house to house random
visits, interview individuals within each household based on last birthday
Difficulties and/or reluctance of respondents to elaborate necessitate prompting,
eg. QM3 - What did you do to make ends meet? - respondents relieved when able
to choose an answer
Conventional banking and insurance have different underlying principles to Islamic
banking and Takaful
adjustments to reflect the co-existence between conventional and Islamic
banking systems without compromising original intention
Interviewers need to clarify the differences
Quality control
Call back, visit by our staff, further verification for peculiarities, data test run
Compare with other readily available data shows similar trend (eg deposit
accounts, insurance ownership)
5
Majority of respondents have some basic knowledge of key financial concepts
Source: Atkinson, A & F.Messy (2012), Measuring Financial Literacy: Results of the OECD/International Network on Financial Education (INFE)
Pilot Study, OECD Working Papers on Finance, Insurance and Private Pensions, No.15, OECD Publishing
6
Large proportion of respondents are active savers and carefully consider
their purchases
7
About half of the respondents displayed positive attitude in planning
for the future
Source: Atkinson, A & F.Messy (2012), Measuring Financial Literacy: Results of the OECD/International Network on Financial Education (INFE)
Pilot Study, OECD Working Papers on Finance, Insurance and Private Pensions, No.15, OECD Publishing
8
With high awareness on existence of products, holding of products
can be promoted further
High basic awareness of

existence of products
90% of respondents have savings
accounts and 75% have ATM
cards to access banking services
Financial education on product
features, benefits and costs can
increase holding of new financial
products or benefit from
products currently held
9
Survey identified some consumer vulnerabilities for financial inclusion
and education intervention
Low literacy in specific components

Other observations
Knowledge
Financial literacy runs parallel
Compounding interest
to education level
Concept on diversification of risks
Lower literacy for respondents
with low household income
Behaviour
Higher literacy for respondents
Insufficient information gathering
in Central region
before acquiring financial products
High awareness on existence of
Lack of comparative shopping
products, with potential to
increase product holding
Attitude
Lack of cashflow and financial
planning
10
Examples of evidence-based initiatives to enhance financial
literacy & to promote financial inclusion
Start financial education (FE) at
Strengthen enabling
an early age - integrate FE
infrastructure for easy access
elements into school curriculum
to FE information, eg. single
(to be implemented beginning
interface platform/portal &
mobile application 1 2014)
5
Empower young adults & first
Enhance outreach to low
time borrowers to manage
literacy regions BNM 2 finances & deal with financial
MobileLINK & consumer 4 service providers with
engagement at semi-urban &
non-urban areas 3 confidence POWER!
programme
Vulnerable groups as targets for -
financial capability programme
financial literacy programme for
low income households
11 11
Financial education at an early age is key
Objective: Inculcate money management habits among students at a young age
FE in curriculum FE in co-curriculum
Collaborate with MOE to integrate School Adoption Programme (since

financial education into school 1997) Adoptive banks conduct
curriculum (work in progress) activities related to banking, insurance
Integrate FE elements in selected key and basic financial knowledge in 10,000
subjects: adopted schools
i. Money, Source of Income and School Financial Club (since 1999)
Career Choice Making available FE materials, tools &
ii. Financial Responsibility and information
Decision Making
iii. Money Management and Planning - Pocket Money Booklet since 1999
iv. Savings and Investments - Interactive website duitsaku.com
v. Credit and Debt Management since 2004
vi. Risk Management, Wealth
Protection and Insurance - Workshop for teachers equip teachers
with knowledge & skills in money
management
12
Tailored programmes for identified target groups
POWER! Programme
A targeted education programme conducted by
AKPK for young individuals and first-time borrowers
aged between 18 - 30 years
Provide skills and knowledge to effectively manage
finances
Provides useful information, tips and a financial
toolkit to encourage prudent money management
and financial discipline
Highlights consequences of financial decisions in
real life situations, focusing on common financial
products such as credit cards, hire purchase and
housing loans
13
POWER! Programme focuses on key financial disciplines
Module Key Contents

Cash flow Smart living: identify needs and wants
management Develop personal budget and cash flow/ net worth statement
Knowing financial position and net worth
Borrowing Basics Common sources of personal credit & key features of credit facilities
Key lending criteria
Cost of borrowing & setting own debt limit
Rights & responsibilities of a borrower
Using A Credit Different types of cards i.e. debit card, credit card & charge card
Card Understanding terms & conditions of credit cards
Using credit cards wisely
Responsibility to protect credit cards
Buying A Car Factors to consider when buying a car - affordability, maintenance expenses
Terms & conditions of hire purchase agreement - term charges, early
settlement & repossession
Buying A House Buying a house -- affordability, types of house ownership, location, etc
Understanding terms & conditions of housing loans/house financing
Understanding rights & responsibilities of a borrower
Consequences of default
The Importance of Contributions to over- indebtedness & consequences
Managing Your Importance of building & maintaining good credit history
Debts Advisory & redress services available to assist in better debt management 14
Reaching out to vulnerable groups of consumers
MobileLINK
Financial Capability Programme 1AZAM

A targeted financial capability programme
conducted by Credit Counselling And Debt
Management Agency (AKPK) in collaboration with
relevant ministries
Target poor households (with household income
below RM1,000)
Focuses on knowledge and skills to
Introduced in December 2011 to provide and
o effectively manage household budget; and
innovative channel for consumers in semi-urban
o cultivate savings habits and non-urban areas to interface with BNM
The programme is expected to benefit Serves consumers through the deployment of a
approximately 120,000 low income households by custom-made coach equipped with facilities and
year 2015 technologies
Provides an avenue for BNM to engage directly
with consumers and gather feedback
Financial Awareness Week 2011 was implemented in Kedah

Reaching out to the vulnerable groups at Northern region of Peninsular Malaysia
15
Enabling infrastructure to support financial education initiatives are being
strengthened for more effective implementation
Elevate the usefulness of consumer education websites through creation of a single
interface platform for easy access and with more interactive features for meaningful
interaction on issues relevant to financial consumers
Financial customer centric mobile applications are being developed for real time access
to key information and alerts both on iOS and android platforms (including leveraging on
push technology)
bankinginfo - 26 booklets published. Also insuranceinfo - 25 booklets published. Islamicfinanceinfo - info on Islamic banking
provides budget calculator, financial Also provides car premium calculator, and takaful products and services, concepts,
calculator, comparative tables, etc. consumer checklists, etc. principles and tools to manage finances
16
Thank you
The Banque de France experience 1
Jacques Fournier, Bank de France
1
IFC workshop
on Financial Inclusion Indicators
Session 5
The Banque de France Experience
Jacques Fournier, IFC Executive Member,

Banque de France
OBJECTIVES
The Banque de France experience on financial inclusion.
3 main aspects:
1. Giving everybody access to bank services

2. Promoting micro-credit
3. Reducing over-indebtedness
Jacques Fournier IFC Workshop on Financial Inclusion Indicators Session 5. The Banque de France experience 2
1. GIVING EVERYBODY ACCESS TO BANK SERVICES
Use of banks is at a record high in France as in most European countries.
Still, access can be denied to very poor people, which is problematic for many
reasons.
In Europe, 30 million people do not have access to bank services.
In France, everybody has a legal right to open a bank account. The Banque de
France designates a commercial banks if need be.
The industry in collaboration with the Central Bank has implemented an
alternative services package including a payment card with systematic
authorization (PCSA) for each operation.
The Banque de France is monitoring via specific data collection the specific
services delivery and its consistency with individual requests and local poverty.
At the end of 2011, 5,6 million PCSA had been distributed, among them 1,8
million issued in 2011.
2. PROMOTING MICRO-CREDIT
Micro-credit means in France:
Individual amounts less than 25 000 euros for professional micro-credit and
3 000 euros for personal ones.
Beneficiaries are selected and their projects are sponsored by charitable
associations, which are part to the funding, or grant guarantees.
The Banque de France has collected, since June 2011, a detailed biannual
reporting on micro-credits; respondents are charitable associations and
banks.
The goal is to measure, support, and assess, as some (still tentative)
performance indicators are embedded in the data.
Doubtful or impaired loans are rather scarce.
Micro-credits statistics (end 2011)
Outstanding amounts Number of loans
in million euros in % in units in %
Professional micro-credits 602 93 128 161 82
- regular micro-credits
186 29 39 640 26
(interest rate >0%)
- equity funds
416 64 88 521 56
(interest rate =0%)
Personal micro-credits 46 7 28 734 18
Total Source: Banque de France- DGS 648 100 156 895 100
Jacques Fournier IFC Workshop on Financial Inclusion Indicators Session 5 . The Banque de France experience 7
Professional micro-credits (million euros, end 2011)
70
60
50
40
30
20
10
0
Construction Trade and repair
Agriculture,
Source: forestry
Banque de France- DGS and fishing Business services
Accommodation and food service activities Manufacturing
Other sectors
Personal micro-credits (million euros, end 2011)
35
10
6
4
4
4
2 2
0,3 0,04 0,4
0
Employment and mobility Basic equipment
Access to housing Education and training
Energy
Source: Banque de savings
France- DGS Health
Others
Jacques Fournier IFC Executive Member Banque de France IFC Workshop on Financial Inclusion Indicators Session 5 : The Banque de France Experience 9
3. REDUCING OVER-INDEBTEDNESS
Banque de France Database on households defaults : helps banks to avoid

granting loans to unsolvent people.
Still, revolving loans are the main trigger of over-indebtedness.
2 LINES OF ACTION
1. A new regulation (as of July 2010) aims at encouraging credit institutions to
develop amortized loans, in particular via capping revolving interest rates.
Data are collected, compiled and analysed by the Banque de France.
2. The Central Bank can be asked by households to foster restructuring plans. If
the Central Bank deems it possible, and if approved by the Court, the
restructuring plan is implemented.
First results are encouraging:

revolving loans reduced by 20 % in 2 years time,
slow decrease of the over-indebtedness files in 2012 (- 2%), a modest but
significant result in crisis times.
Some tentative lessons from our experience:
Micro data are key to understand, analyse and regulate (if and when deemed
necessary) financial inclusion.
It seems possible for policy makers to add financial inclusion as a medium
term objective with the support of banks and charitable associations.
Statistical departments in Central banks have both the expertise and the
neutrality which the diverse stake holders can leverage on to proceed.
Putting data to work: data-driven approaches to strengthening neighborhoods1
Joseph Firschein, Board of Governors of the Federal Reserve System
1
Putting Data To Work:
Data-Driven Approaches to Strengthening Neighborhoods
Joseph Firschein
Deputy Associate Director, Division of Consumer and Community Affairs
Board of Governors of the Federal Reserve System
November 5-6, 2012
The views expressed in this presentation are my own and are not necessarily
the views of the Federal Reserve Board of Governors
Introduction and Organizational Context
Role of the Federal Reserve Boards Division of Consumer and Community

Affairs (DCCA)
Role of the Federal Reserve System Community Development function:
Connection to the Feds broader mission to support economic growth, financial
stability, and community reinvestment particularly in low- and moderate-income
communities
Work with 12 Federal Reserve Banks to convene external stakeholders (bankers,
nonprofit intermediaries, academic organizations, foundations), conduct applied
research, and identify emerging issues affecting low- and moderate-income
communities and consumers
1
Defining the Problem and the Opportunity
Problem:
As city and county governments seek to improve neighborhood real estate markets and
respond to the problem of vacant and abandoned properties, they often have data
scattered in different locations or a lack of a process to effectively use available data
Communities also have a mismatch between the size of their foreclosure and
neighborhood stabilization needs and available public resources
Opportunity:
Technology is permitting improvements in the development and integration of local data
systems whose costs are decreasing
Data-driven decisionmaking can improve resource allocation decisions by the public
sector, supporting economic growth and neighborhood reinvestment
2
Key Players In This Work
In addition to the technology and data aspect of this work, there is an equally
important people-based element required to assemble the right coalition of
partners who can develop and use this data
City and county governments are the primary target of these efforts, supported by
university- and nonprofit-based data intermediaries
For example, intermediary groups in 35 U.S. cities have formed a network, the
National Neighborhood Indicators Partnership (NNIP), to expand data use capacities
in other localities and advance the state of the art in the field
(www.neighborhoodindicators.org)
3
Federal Reserve Role
The Fed has played a convening role: bringing together public sector officials with
others (public sector, nonprofit, academic) who have expertise on strategic use of
data
Public sector participants see the Fed as a trusted source: we have experience
working with data, we are not trying to sell a data system, and we arent looking for
funding
The Fed has also played an information sharing role: identifying case studies on
barriers and promising practices on strategic use of data
Examples include the Putting Data to Work publication
Video case studies: Cleveland, Detroit, and Phoenix
Sharing information via regional meetings in partnership with Federal Reserve Banks
4
Example from Cleveland, Ohio
Involved a coalition of nonprofit, academic, and City staff working together to

analyze property maintenance patterns for bank-owned foreclosed properties
Was valuable for targeting limited enforcement resources: resulted in a
preliminary injunction against a major bank that owned many foreclosed properties
but was not adequately maintaining them
Provided early warnings on imminent foreclosure problems and enabled a more
strategic approach to neighborhood stabilization
Significant focus on transparency of data and involvement of nonprofit and
academic partners
See the Feds Cleveland video case study referenced on the additional
resources slide at the end of this presentation for more info on this approach
5
6
Example of Market Value Analysis (MVA) Approach
Developed by The Reinvestment Fund (TRF), a strong nonprofit policy and lending
organization in Philadelphia, Pennsylvania
Analyzed parcel-level data and developed market types and associated
interventions
Based on assumption that public subsidy is scarce and it alone cannot create a
market; subsidy must be used to leverage, or clear the path, for private investment
This approach was used in Baltimore, Maryland to implement a market-based
approach to vacant property redevelopment (Vacants to Value initiative)
See articles by Goldstein (page 49) and Janes and Davis (page 79) in the Fed
publication for more info on this approach
7
Market Cluster Characteristics
Percent of
Percent Percent Residential Percent of
Coefficient
Median Foreclosures Owner Commercial Properties Rental Housing
Market Value Analysis of Variance Vacancy
sales price as a percent Occupied or Stores with Tax Abated Units that Units per
2007/2008 of Sales factor
2006-2007 of sales 0607 2007; Dwellings; or Built are PHA Acre
price 0607
Claritas (BRT cat 3,4) 2000-2008; owned
BRT
Median $ 960,450 0.47 0.4 12.5 90.3 4.4 3.4 0.0 0.8
Dark Purple
Mean $ 928,670 0.45 0.5 37.5 74.4 5.4 4.0 0.0 4.3
Regional Median $ 550,000 0.54 0.3 4.4 29.9 6.1 4.5 0.0 18.9
Choice/ High Medium Purple
Mean $ 576,436 0.51 0.6 8.3 34.1 6.9 15.5 0.4 20.7
Value
Median $ 351,250 0.38 0.6 7.7 49.8 4.3 3.7 0.0 13.5
Light Purple
Mean $ 360,387 0.41 1.1 17.2 48.5 7.5 11.5 0.7 17.5
Median $ 220,000 0.28 0.6 14.6 64.0 3.2 0.7 0.0 8.4
Dark Blue
Mean $ 224,727 0.31 1.1 18.9 61.3 6.1 3.9 0.6 10.5
Steady
Median $ 171,000 0.28 0.6 29.1 62.5 2.9 0.0 0.0 9.5
Light Blue
Mean $ 179,421 0.32 1.2 39.2 60.4 5.3 1.3 0.5 10.9
Median $ 124,000 0.29 1.2 27.4 76.9 2.8 0.0 0.0 12.6
Light Yellow
Mean $ 125,974 0.32 1.9 36.0 71.0 4.4 1.0 0.8 12.6
Transitional
Median $ 80,000 0.41 4.3 39.2 68.5 3.4 0.0 0.0 12.7
Dark Yellow
Mean $ 82,226 0.45 5.0 46.0 63.9 5.3 1.1 2.7 12.5
Median $ 49,925 0.55 9.5 45.5 63.6 4.0 0.0 0.9 13.1
Orange
Mean $ 50,325 0.56 9.8 52.1 61.0 5.6 0.3 3.2 12.9
Distressed
Median $ 28,875 0.75 13.8 27.1 55.6 4.0 0.0 3.8 12.1
Red
Mean $ 27,153 0.81 13.7 32.7 52.9 5.6 0.4 10.8 12.5
Median $ 105,900 0.42 2.9 27.5 62.3 3.7 0.0 0.0 11.2
City Total
Mean $ 137,701 0.47 5.3 35.5 58.6 6.3 2.3 3.0 12.2
8
Using Data at Different Levels of Experience
A data-driven approach to neighborhood stabilization doesnt have be focused on

cities that have an elaborate or expensive data infrastructure
There are steps that can be taken in communities at different levels of experience:
beginning, intermediate, and advanced
The first step is to identify stakeholders (both inside and outside local government)
and acquire the relevant data
See article by Petit and Kingsley starting on page 17 of the Fed publication for
examples of steps that can be taken at these different levels of experience
9
Considerations for Other Central Banks Interested in This Work
Identify and share information on success stories: communities wont do this work
unless they see tangible example of others who are getting value from the effort
Make clear that this does not have to involve significant new technology spending
and can apply to public entities at different levels of data sophistication
The central bank is not providing the data or advising local governments on how to
use it
The central bank is a neutral convener of stakeholders and a source of information
on barriers and promising practices related to this work
10
Additional Resources
Web location of Putting Data To Work publication:

www.federalreserve.gov/communitydev/files/data-driven-publication-20111212.pdf
National Neighborhood Indicators Partners Web site:
www.neighborhoodindicators.org
Video case studies on neighborhood stabilization:
http://www.federalreserve.gov/communitydev/stablecommunities.htm
Federal Reserve Board neighborhood revitalization info:
http://www.federalreserve.gov/communitydev/neighborhoodrevitalization/neighborho
od-revitalization.htm
11
Questions?
My contact info:
Joseph Firschein
Deputy Associate Director, Division of Consumer and Community Affairs
Federal Reserve Board of Governors
Joseph.a.firschein@frb.gov
202-736-5531
12
Introduction to the OECD Handbook on composite indicators 1
1
BANCA DITALIA
EUROSISTEMA
Introduction to the OECD handbook

on composite indicators
Claudia Biancotti
Bank of Italy
Economic and Financial Statistics Department

IFC and Bank Negara Malaysia, Kuala Lumpur
November 6, 2012
What is the purpose of the handbook?
The handbook is a guide for constructing and using composite indicators

(CIs) for policy makers, academics, the media and other interested parties.
A CI is a single number summarizing many phenomena.

phenomena
Core dilemma on CIs: on the one hand they are more accessible to the public /
more suited to debate compared to a wide array of single-issue indicators; on the
other hand they entail a number of potentially arbitrary choices (components,
aggregation strategies including weighting etc.)
The handbook provides guidelines aimed at minimizing the arbitrary element by

implementing optimality principles and communicating contents transparently
CIs may be suited for some phenomena and not for others!
22
Some examples
Ease of Doing Business: computed by the World Bank, covers sub-items on

fiscal regulation, enforcement of contracts, access to credit etc.
Well-being: dashboard approach (GDP, inequality, environmental quality,

life satisfaction) vs CI: ample discussion following the Sen-Stiglitz-Fitoussi
report
Financial Development Index: computed by the World Economic Forum,

includes several dimensions ranging from risk of sovereign debt crisis to
performance of capital markets
Technology Achievement Index: computed by the UN, covers four dimensions

of innovation; used as an example throughout the handbook
Financial inclusion is a multi-dimensional phenomenon
(access/usage; households/firms; actual/perceived etc.) hence a
natural candidate for CIs
33
From the handbook: pros and cons of CIs
44
Choices involved in the creation of a CI
1) Development of a theoretical framework
2) Choice of individual indicators to draw upon
3) Data treatment/analysis for individual indicators
4) Weighting and aggregation of individual indicators
5) Sensitivity and robustness analysis for the CI(s)
6) Reverse engineering (performance of CI vs individual indicators)
7) Choice of a presentation/visualization strategy
8) Analysis of the CI(s) in relation to other relevant variables

55
Theoretical framework
Conceptual exercise: what is it that we want to measure?

measure What is the nature of the
phenomenon and what are its components? May draw on economic theory, policy
practice etc; should be theoretically sound, detailed, structured
(input/output/process components) and appropriate to the goal chosen for the CI
Examples: financial inclusion

as defined by the Indian
Economic Service is first
articulated around the where;
the G20 Action Plan is
first articulated on the what,
i.e. access, usage, quality,
formality and impact of financial
services. Likely to point out
different weaknesses at first sight!
66
Choosing individual indicators
77
Data treatment and analysis (individual indicators)
Imputation of missing values: analysis of missingness patterns (MCAR,

MAR...); single/multiple imputation; model-based/hotdeck...; strategy for
outliers; trimming of input/output.
Dimensionality-reducing multivariate analysis:

analysis principal
component/factor/ correspondence/cluster analysis to detect correlation
patterns in the data, check for relevance of variables, decomposition of
variance, influence of individual dimensions on (some) optimal linear
aggregations.
Normalization:
Normalization ranking, qualitative scores, standardization, benchmarking
etc. to avoid the apples-and-oranges problem
88
Weighting and aggregation
Trickiest part of the process! Once dimensions are selected, their relative
relevance needs to be determined. Weighting: is access to microcredit more
or less important than access to affordable health insurance? Aggregation:
should financial literacy enter a CI indicator of inclusion arithmetically or
geometrically?
Major issue at stake: marginal utility of each dimension, hence trade-off

between indicators (compensation: good or bad?)
Should be very soundly justified from a theoretical standpoint; a measure

of data-driven choices (e.g. to account for correlation) is OK but such
choices should be transparent
99
Sensitivity and robustness analysis
Standard good econometric practice (results should not be excessively

sensitive to methodological choices). Effects of...
10
10
Reverse engineering, or back to the original data
11
11
Presentation and visualization strategies
Appropriate representation depends on core message:

message e.g. tabular representation (rankings)
draws attention on the best overall performer,
performer spider diagrams emphasize the differences on
individual dimensions between a chosen country/group and others... should be clear,
consistent, transparently motivated.
motivated
12
12
Relationship to other relevant variables
13
13
BANCA DITALIA
EUROSISTEMA
Thank you for your attention!

Building a financial inclusion index for Mexico 1
Jos Luis Negrin, Bank of Mexico
1
Building a Financial Inclusion Index for Mexico
Prepared for the Workshop on Financial Inclusion Indicators.
Bank Negara, Malaysia, Kuala Lumpur, November, 2012
Jos L. Negrin. Manager of Financial Services Analysis
Disclaimer: these presentation reflects the point of view of the author and not necessarily that of
Banco de Mxico
Index
1. Motivation and Goals
2. Background on Financial Inclusion
3. Building an Index of Financial Inclusion
4. International Financial Inclusion Index
5. Regional Financial Inclusion Index
6. Final Comments
Financial Inclusion Index 2

Motivation and goals
Evidence shows that Financial Inclusion (FI) may benefit society:
Instrument to fight poverty, increase income, savings and
employment;
Allows families to better smooth their consumption.
It is not clear how to measure FI: there are many financial services
and therefore many dimensions.
An index provides an aggregation mechanism to reduce a vector of
dimensions into a number (scalar).
In this presentation we discuss the application of a FI index (FII) to
the Mexican case.
We illustrate a number of issues that arise when using a FII and
derive some (hopefully) useful lessons from this application.

Index
6. Final Comments

What is FI and how to measure it
There is no consensus definition of FI:
Mexican Banking Commission: Financial Inclusion refers to the access and use of
a financial products and services portfolio that reaches the vast majority of adult
population with clear and concise information to satisfy the growing demand,
under an appropriate legal framework.
CGAP: FI 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.
Some salient features:
Objective population (financially excluded): usually poor people and small
firms.
Relevant products: service diversity and access heterogeneity.
Three elements: access (related to infrastructure), use y quality (cost).
Service providers: formal (mainly banks but not only ) and informal.
FI services
Financial Services:
Deposit and savings (accounts) Investment Insurance (including
- Transactions : payment services Loans pension funds)
Channels to Type of
provide services: Means to access transactions:
Branches an account: Deposits
ATMs Cards (debit, Cash withdrawals
POS credit) (at branches,
Banking agents Checks ATMs, etc.)
(comisionistas) Phone/Internet Payments: with
Cell phone cards at POS,
checks or
electronic
transfers

What is FI and how to measure it
A FII:
Reduces multiple dimensions to 1.
Makes all dimensions comparable: no units.
It provides a valuable instrument to diagnose the financial inclusion
situation with respect to other countries/regions.
Theoretically, the optimal level of access would occur when the observed
usage level corresponds to a competitive environment without any
frictions, given a technology (costs) and customers preferences.
FII allows for comparisons across countries.
Building a FII provides a reference point: best practice within the sample.

Index
6. Final Comments

To build an FII
Sarma (2008) Chakravarty and Pal (2010)

Characteristics of an FII
Index Sarma Chakravarty
Characteristics (2008) (2010)
Normalization Yes Yes
Anonymity Indifferent to swapping of values across dimensions. Yes Yes
[Weighting could be appropriate in a FII not complying
with anonymity]
Monotonicity FII should be greater(lower) if one dimension Yes Yes
improves(worsens) and the rest stay unchanged.
Proximity Should be such that greater (lower) value indicates that Yes No
it is closer (farther from) the ideal (best practice).
Uniformity A greater(lower) dispersion across dimensions should Yes Yes
indicate a lower(greater) value.
Signaling Unique optimal path to reach higher value. Yes Yes
Homogeneity Dimension indicators should be independent to scaling. Yes Yes
Decreasing Lower difference in gain at higher levels of attainment Yes* Yes
benefits difference.
Dimension It should be possible to identify the contribution of each No Yes
contributions id. dimension to the FII.

FII characteristics
We use Sarma (2008) Index: it is more intuitively appealing and proximity is a
desirable characteristic.
We decided to build two complementary FII for people (not firms for now):
Infrastructure.
Usage.
We normalize dimensions by number of adults (people above 14 years old).
Concentrate on retail services since it is directed to the most vulnerable group.
In particular, in deposit and saving services, leaving credit and insurance out.
We limit our index scope to banking services due to information availability.
Need to determine:
Dimensions to be included: trade off between adding dimensions and their
importance FII concavity); differential impact of low and high values.
Countries to be considered: adding new countries affect if it has high or low
values in the dimensions included.

FII characteristics
We decided to build indices for international comparisons and indices for
states comparisons (within Mexico).
International FII: to see Mexicos relative position we choose 37 countries
(data for 2010), 5 five with lower, 31 with higher GDP per person.
The national index is more important for internal policies (targets): Improve
access of the poorest states; reduce inequalities between states, etc.
International FII National FII

Allows assessment of progress relative to Allows for public policy decisions.
other countries.
Allows identification of an empirical best Helps to focalize regulations where
practice (ideal). needed.
Strong assumptions: homogenous Data are more comparable: same
technology across countries. technology.
Dimensions depend on comparable Less restrictions for dimensions
information. selections.

Index
6. Final Comments

0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0

Spain
Portugal
France
Cyprus
Italy
Lux.
Greece
Belgium
Brazil
Switz.
Slovenia
Bulgaria
Finland
Germany
U.K.
Ireland
Malta
Denmark
Austria
Estonia
C. Rica
Sweden
Russia
Latvia
Number of POS
Lithuania
Number of ATMs
Nether.
Number of branches
Infrastructure Index
Hungary
Poland
Uruguay
Rumania
Slovakia
Czech R.
Chile
Mexico
Colom
S.Arabia
India
International FII- 2010
0.0
0.1
0.2
0.3
0.5
0.6
0.8
0.9
0.4
0.7
1.0
Finland
Sweden
Nether.
U.K.
Financial Inclusion Index

Belgium
Std deviation from Mexicos GDP per person

Std deviation from Mexicos GDP per person
Estonia
France
Slovenia
Ireland
The dimensions included are:
Portugal
Lux.
Switz.
Austria
Germany
Chile
Denmark
Latvia
Brasil
Spain
C. Rica
Lithuania
Poland
Czech R.
Hungary
Use Index
Malta
Slovakia
S. Arabia
Cyprus
Italy
Russia
Uruguay
Mexico
Number of electronic transfers
Greece
Number of transactions in POS
14
Number of transactions in ATMs
Bulgaria
37 countries sample: more dimensions, less countries with available information.
Colombia
Rumania
India
0.1
0.2
0.3
0.5
0.6
0.8
0.9
1.0
0.0
0.4
0.7

Finland
Sweden
Nether.
U.K.
Belgium
Estonia
France
Slovenia
Ireland
means.
Portugal
Lux.
Switz.
Austria
Germany
Chile
Denmark
Latvia
Brasil
Spain
C. Rica
Lithuania
Poland
Czech R.
Hungary
Malta
Slovakia
S. Arabia
Cyprus
Italy
Russia
Uruguay
Mexico
Greece
Bulgaria
Colombia
Rumania
Index of usage Without Checks
India
0.0
0.2
0.3
0.4
0.6
0.7
0.9
1.0
0.1
0.5
0.8
them: Sweden, Netherlands and Finland.

France
The dimensions selection
U.K.
Finland

Ireland
Sweden
Nether.
Belgium
Estonia
Portugal
Chile
Slovenia
Lux.
Malta
Switz.
Austria
Germany
Brasil
Changing the set of dimensions changes the index outcome.
Denmark
Latvia
Cyprus
C. Rica
Spain
Lithuania
Poland
Czech R.
Include the most efficient channels of service and the most payment
Hungary
Slovakia
S. Arabia
Italy
Mexico
Example: including checks in the index punishes countries that do not use
Uruguay
Greece
Russia
15
Bulgaria
Colombia
Index of usage Including Checks
Rumania
India

0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Spain
Portugal
France
Cyprus
Italy
Lux.
Greece
Belgium
Brazil
Switz.
Slovenia
Bulgaria
Finland
Germany
U.K.
Ireland
Malta
Denmark
Austria
Estonia
The dimensions selection
C. Rica

Sweden
Russia
Infrastructure Index
Latvia
discrete changes for a country: banking agents.
Including Banking Agents
Lithuania
Nether.
Hungary
Poland
Uruguay
Rumania
Slovakia
Czech R.
Mexico Corr
0.16
Chile
Mexico
0.12
Colombia
S.Arabia
India
16
New infrastructure dimensions: technological changes may generate
The country sample selection
Differences in development, technology, institutions and habits, makes
comparisons harder.
Restricting the sample to countries similar to Mxico (1/4 of StDev of GDP
per person): moves away from the idea of best practice.
Infrastructure IIF: Using a restricted sample
0.6
0.5
Restricted sample
0.4 Whole sample (previous)
0.3
0.2
0.1
0.0
Hungary
Uruguay
Chile
C.Rica
Latvia
Russia
Poland
Romania
Bulgaria
Estonia
Colombia
Brasil
Lithuania
Mexico
Restricting the sample does not necessarily improve the ranking position.
It seems reasonable to consider a large but balanced sample (similar
number of richer and poorer countries) and keep it stable through time.

International FII: Comparisons 2005-2010
All FII components change through time. How do we compare?
1.0
0.9 Infrastructure Index: 2005 - 2010
0.8
0.7
0.6 2010
0.5 2005
0.4
0.3
0.2
0.1
0.0
Italy
U.K.
Germany
Russia
Czech R.
Hungary
Slovenia
Chile
Estonia
Slovakia
Switz.
Greece
Finland
Lithuania
Spain
France
Latvia
Poland
Rumania
Portugal
Austria
Nether.
Bulgaria
Ireland
S.Arabia
Lux.
Malta
India
Belgium
Brazil
Denmark
Sweden
Mexico
Cyprus
Spain, the country with maximum FII, suffered because of the crisis. This
affects all countries FII (not necessarily their ranking position).
Mexicos FII improved (from 0.09 to 0.12 ) but its position in the ranking
went down (from 31 to 32).

Selecting some countries:
Spain and France IIF go down but they keep their position in the ranking (1 & 3).
Greece's IIF goes down but it improves its position in the ranking (from 9 to 7).
The Czech Republic keeps its index, but it goes down in the ranking (from 27 to 30)
Infrastructure Index Ranking of 34

(34 countries) countries
2005 2010 2005 2010
Spain 0.91 0.82 1 1
Portugal 0.60 0.71 4 2
France 0.62 0.58 3 3
Greece 0.47 0.46 9 7
Belgium 0.42 0.46 13 8
Brazil 0.34 0.42 18 9
Sweden 0.32 0.28 20 21
Russia 0.11 0.28 29 22
Czech R. 0.18 0.18 27 30
Chile 0.12 0.14 28 31
Mexico 0.09 0.12 31 32

How do we compare? Keep something fixed: compare Mexicos values for 2005
and 2010 with the rest of the countries in 2010. There is a bigger improvement.
Alternatively, identify countries whose index jump more.
1.0
Infrastructure IIF
0.9 (Mexico 2005-2010)
0.8
0.7
0.6
0.5
0.4
0.3
0.2 0.12
0.1 0.06
0.0
Chile
Germany
Hungary
Uruguay
Slovakia
Czech R.
C. Rica
Spain
Italy
Latvia
S.Arabia
Poland
U.K.
Rumania
Belgium
Austria
Slovenia
Bulgaria
Lux.
Malta
Russia
Nether.
Finland
India
Colombia
Mexico 2005
France
Brazil
Estonia
Sweden
Lithuania
Mexico 2010
Denmark
Greece
Switz.
Portugal
Ireland
Cyprus

Mexicos infrastructure FII improves in distance to the top and to the mean, but
increases in distance to the mean in SD units. The opposite occurs in usage.
Infrastructure Index Use Index

2005 2010 2005* 2010
Mean 0.348 0.344 0.308 0.319
Standard Dev. 0.203 0.169 0.187 0.236
Max 0.915 0.815 0.813 0.915
Min 0.009 0.009 0.001 0.000
Mexico's IIF 0.093 0.124 0.136 0.119
Distance to the Max 0.822 0.691 0.677 0.796
Distance to the Mean 0.254 0.219 0.172 0.200
Distance to mean in
1.253 1.300 0.917 0.847
SD units
*For the Use FFI in 2005, we only consider transactions at POS and ATM for lack of information on electronic transfers.

International FII: discussion
In most cases, there is a correspondence between infrastructure and usage IIF.
Some countries have lots of infrastructure but low usage (Spain) or low
infrastructure and high usage (Netherlands).
Infrastructure and Use Index (2010)
0.9
0.8 Finland
0.7
Swed.
0.6 Nether.
Belgium
Estonia
0.5
Use
Slov. France
0.4 Lux. Portugal
Chile
0.3 Den.
Hung. C.Rica Brazil
0.2 Malta
S.Arabia Spain
Urug. Cyprus
0.1 Mexico Russia Italy
Rom. Bulg.
0 India
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90
Infrastructure

International FII: discussion
Is Mexico at the level of access where it could be given the size of its economy?
Relate FII with GDP per person: Mexico is far from where it could be. This could
be a policy goal: to reach the trend line.
Infrastructure Index (2010) Use Index (2010)

and GDP per capita and GDP per capita
0.9 (1,000 USD) (1,000 USD)
0.9
0.8 Spain
0.8 Finland
0.7 Portugal y = 0.0037x + 0.2436 y = 0.005x + 0.1729
0.7
0.6 Sweden
France 0.6
Lux. Estonia Belgium
0.5 Brazil Greece
0.5
FII
France
0.4 Switz.
0.4 Portugal Switzerland
FII
C.Rica Denmark
0.3 Austria Chile Luxembourg
Hungary Netherlands 0.3 Brazil Germany Denmark
C.Rica
0.2 Spain
Mexico Czech R. 0.2
S.Arabia Mexico
0.1 Greece
Colombia 0.1
India Colombia
0
0
0 20 40 60 80 100 120
0 20 40 60 80 100 120
GDP per capita
GDP per capita

Index
6. Final Comments

FII for Mexican States
We include the 32 states of Mexico for 2009 and 2011.
Less problems of comparability: same technology.
Use Index
0.8 0.8
0.7 2009 0.7 2011
0.6 0.6
0.5 0.5
0.4 0.4
0.3 0.3
0.2 0.2
0.1 0.1
0 0.0
Mex.
BCS
Mex.
BCS
Col.
Gro.
Col.
Gro.
BC
Mor.
Qro.
BC
Mor.
Qro.
Q.Roo
Ver.
Q.Roo
Dgo.
DF
DF
Ver.
NL
Dgo.
Chis.
NL
Chis.
Coah.
Tab.
SLP
Jal.
Coah.
Tab.
SLP
Jal.
Ags.
Pue.
Tlax.
Ags.
Pue.
Tlax.
Hgo.
Camp.
Son.
Hgo.
Sin.
Son.
Camp.
Sin.
Gto.
Gto.
Mich.
Nay.
Oax.
Nay.
Mich.
Oax.
Yuc.
Yuc.
Chih.
Tamps.
Chih.
Tamps.
Zac.
Zac.
The dimensions included (normalized by adults) are: Number of transactions in
ATMs, credit transfers, checks, transactions in POS (Debit) and deposit accounts.

Great impact of banking correspondents in infrastructure (2009-2011).
Infrastructure FII
0.5 0.8
0.45 0.7
0.4
2009 0.6 2011
0.35
0.3 0.5
0.25 0.4
0.2 0.3
0.15
0.1 0.2
0.05 0.1
0 0.0
BC
Q.Roo
DF
Dgo.
NL
SLP
Mich
Jal.
Coah.
Ags.
Hgo.
Son.
Sin.
Gto.
Oax.
Mex.
Nay.
Gro.
BCS
Col.
Mor.
Yuc.
Qro.
Tamps.
Ver.
Chih.
Pue.
Chis.
Tlax.
Camp.
Tab.
Zac.
BC
NL
Q.Roo
DF
Dgo.
SLP
Jal.
Ags.
Mich
Son.
Hgo.
Coah.
BCS
Sin.
Oax.
Nay.
Gto.
Mex.
Yuc.
Col.
Chih.
Pue.
Gro.
Tamps.
Qro.
Mor.
Ver.
Tlax.
Chis.
Tab.
Camp.
Zac.
The dimensions included (normalized by adults) are: Number of branches,
Number of ATMs, Number of POS and Number of correspondents (2011).

Relating FII to GDP per person allows for the identification of states with
problems. Higher GDP per person, higher FII.
Infrastructure Index (2010) Use Index (2010)

and GDP per capita and GDP per capita
(1,000 Pesos) (1,000 Pesos)
0.8 0.8 DF
BCS y = 0.0024x + 0.0206
0.7 0.7
NL Mex.
Q.Roo
0.6 0.6 y = 0.002x + 0.0307
0.5 Son. Coah. DF 0.5

BC NL
Qro. BCS Coah.
FII
0.4
FII
0.4 Sin. Gto. Q.Roo

Ags.
Nay. Mor. 0.3 Son.
0.3 Chih. Sin.
Gto. Mor. Qro. Tab.
0.2 Mich. Dgo. 0.2 Chis. SLP
Tab. Dgo.
Gro.
0.1 Zac.SLP 0.1
Hgo. Oax. Zac. Jal.
0 Oax. Hgo.
Chis. 0
0 50 100 150 200 250 300 0 50 100 150 200 250 300
GDP per capita GDP per capita

Many states are low on both IIF; some need to adopt policies to foster
infrastructure deployment and others usage.
Inequality approach: reduce it by moving more up backward states.
Infrastructure and Use Index Analysis (2011)
0.8
Edo. Mex DF
0.6
NL
Coah. Q.Roo
Use
0.4 Gto.
Chih. BCS
Son.
Tab. Qro.
0.2 Chis. Dgo. Morelos
Oax. Nayarit
Gro. Jalisco
Tlax. Hgo.
0
0 0.2 0.4 0.6 0.8
Infrastructure

Index
6. Final Comments

Final Comments
FII is a useful instrument to diagnose FI situation of a country or a
region.
Sample of countries (regions) and number of dimensions included is
crucial. There are many variables; two indexes are suggested, one for
infrastructure and one for usage.
It seems better to include few dimensions.
It seems better to include a representative sample of countries and
keep it stable. Index is very sensitive to Max and Min values.
Comparisons across time are tricky: everything in the index changes.
FII is more useful when it relates to other information, like GDP.
For internal policy decisions, a regional IIF seems useful. It
complements the international FII.

Final Comments
So how is Mexico doing on FII?
At the international level:
In both usage and infrastructure FII level is low, worse in the latter.
Despite improvement in the FII value, the position in the ranking has
gone down through time.
When keeping sample fixed thorough time, improvement is clear.
Nevertheless Mexico is far from its potential level of inclusion.
This may be useful to set goals: improve FII and reach potential level.
At the state level:
States are identified according to their strength: some need fostering
infrastructure, others usage.
Goals may be set: improve FII of straggler states and reduce inequality.

Data Appendix

Appendix: Infrastructure dimensions and Index (1)
ATMs per 1,000 POS per 100 Branches per Financial Inclusion
Ranking
Country adults adults 10,000 adults Infrastructure Index
2009 2010 2009 2010 2009 2010 2009 2010 2009 2010
Spain 1.50 1.52 2.99 3.54 11.31 11.01 0.91 0.82 1 1
Portugal 1.54 1.96 1.65 3.08 6.74 7.15 0.60 0.71 4 2
France 0.93 1.06 2.13 2.69 7.86 7.36 0.62 0.58 3 3
Cyprus 0.57 0.74 1.96 2.76 11.51 10.04 0.55 0.56 5 4
Italy 0.75 0.98 2.08 2.88 6.26 6.47 0.52 0.55 6 5
Luxembourg 1.07 1.13 2.20 3.08 10.54 5.20 0.71 0.54 2 6
Greece 0.66 0.77 3.48 4.27 3.98 4.14 0.47 0.46 9 7
Belgium 0.83 1.70 1.16 1.53 5.24 4.52 0.42 0.46 13 8
Brasil 1.09 1.19 1.24 3.32 2.05 2.18 0.34 0.42 18 9
Suiza 0.89 0.96 1.77 2.29 4.36 3.98 0.46 0.42 11 10
Slovenia 0.87 1.03 1.96 2.02 4.08 3.93 0.46 0.41 10 11
Bulgaria 0.42 0.81 0.25 0.93 0.98 9.16 0.11 0.41 30 12
Finland 1.08 0.64 2.38 4.49 3.74 3.29 0.51 0.40 7 13
Germany 0.75 1.22 0.80 0.96 6.52 5.59 0.40 0.40 15 14
United
1.18 1.23 1.97 2.44 2.80 2.39 0.45 0.40 12 15
Kingdom
Ireland 0.89 0.92 1.51 2.27 3.32 3.29 0.40 0.38 16 16
Malta 0.45 0.52 2.25 3.41 3.56 3.22 0.37 0.36 17 17

Appendix: Infrastructure dimensions and Index (2)
ATMs per 1,000 POS per 100 Branches per Financial Inclusion
Ranking
Country adults adults 10,000 adults Infrastructure Index
2009 2010 2009 2010 2009 2010 2009 2010 2009 2010
Denmark 0.68 0.63 2.31 2.43 4.84 3.64 0.48 0.36 8 18
Austria 0.44 0.74 1.29 1.07 7.40 5.83 0.40 0.35 14 19
Estonia 0.74 0.88 1.11 2.28 1.99 1.78 0.28 0.32 21 20
Sweden 0.38 0.43 2.37 2.59 2.59 2.47 0.32 0.28 20 21
Rusia 0.23 1.29 0.12 0.36 2.71 3.43 0.11 0.28 29 22
Latvia 0.45 0.70 0.70 1.23 3.09 3.04 0.23 0.27 22 23
Lithuania 0.37 0.56 0.57 1.30 2.67 3.36 0.19 0.26 25 24
Netherlands 0.56 0.58 1.90 1.89 2.55 2.09 0.33 0.26 19 25
Hungary 0.41 0.57 0.48 0.92 3.67 4.09 0.22 0.25 23 26
Poland 0.28 0.52 0.52 0.77 4.06 4.15 0.20 0.24 24 27
Rumania 0.24 0.56 0.15 0.59 1.94 3.39 0.09 0.20 32 28
Slovakia 0.42 0.51 0.45 0.81 2.56 2.65 0.18 0.19 26 29
Czech Republic 0.35 0.41 0.72 1.07 2.14 2.20 0.18 0.18 27 30
Chile 0.39 0.62 0.32 0.47 1.31 1.54 0.12 0.14 28 31
Mxico 0.31 0.45 0.28 0.60 1.09 1.48 0.09 0.12 31 32
Arabia Saudita 0.29 0.57 0.28 0.42 0.77 0.83 0.08 0.11 33 33
India 0.03 0.09 0.04 0.07 1.05 1.11 0.01 0.01 34 34

Appendix: Use dimensions and Index (1)
Financial
POS transactions ATM withdrawals Credit
Inclusion Use Ranking
Country per adult per adult transfers
Index
2009 2010 2009 2010 2009 2010 2009 2010 2009 2010
Finland 155.18 232.32 48.22 38.42 181.84 0.92 0.81 1 1
Sweden 103.84 212.44 42.39 28.74 126.85 0.72 0.66 3 2
Netherlands 103.29 159.53 35.21 31.76 116.19 0.65 0.62 4 3
United Kingdom 121.57 163.87 54.61 54.19 66.06 0.85 0.59 2 4
Belgium 79.69 108.79 28.77 42.38 111.18 0.51 0.59 9 5
Estonia 69.86 137.99 43.35 38.59 85.44 0.58 0.57 6 6
France 99.51 134.96 27.86 30.24 56.45 0.56 0.46 7 7
Slovenia 42.59 62.38 38.12 33.23 94.90 0.44 0.44 10 8
Ireland 53.18 89.34 52.51 50.43 44.05 0.53 0.43 8 9
Portugal 83.13 128.24 42.79 50.30 20.62 0.64 0.42 5 10
Luxembourg 85.96 97.67 12.26 12.55 160.38 0.36 0.42 12 11
Switzerland 53.95 72.51 16.63 17.50 111.00 0.32 0.38 15 12
Austria 30.16 44.31 17.78 19.94 134.12 0.25 0.37 17 13
Germany 34.06 35.45 34.56 28.86 83.06 0.39 0.35 11 14
Chile 10.93 20.65 12.62 19.25 136.06 0.14 0.32 27 15
Denmark 138.32 195.18 3.97 3.92 63.80 0.33 0.31 14 16
Latvia 19.42 45.48 18.75 23.85 59.90 0.22 0.30 18 17

Appendix: Use dimensions and Index (2)
Financial
POS transactions ATM withdrawals Credit
Inclusion Use Ranking
Country per adult per adult transfers
Index
2009 2010 2009 2010 2009 2010 2009 2010 2009 2010
Brasil 19.60 43.00 15.92 20.21 53.10 0.20 0.27 20 18
Spain 36.59 53.57 24.79 24.18 20.62 0.33 0.24 13 19
Lithuania 17.40 32.06 17.02 22.24 40.68 0.20 0.24 19 20
Poland 8.10 25.29 15.64 21.10 45.13 0.15 0.23 23 21
Czech Republic 9.11 23.16 14.38 17.31 56.89 0.15 0.22 24 22
Hungary 10.35 23.66 13.28 13.74 70.37 0.14 0.22 26 23
Malta 13.13 26.67 26.91 30.71 17.38 0.26 0.22 16 24
Slovakia 5.84 17.09 15.71 18.53 54.72 0.15 0.22 25 25
Saudi Arabia 4.14 7.91 20.51 55.44 0.21 0.18 0.20 21 26
Cyprus 19.78 29.88 7.68 13.02 28.07 0.12 0.16 29 27
Italy 14.55 26.58 9.07 13.64 23.61 0.12 0.15 30 28
Russian
0.88 4.22 5.42 16.62 21.84 0.04 0.13 33 29
Federation
Mxico 5.62 12.55 14.60 16.86 10.40 0.14 0.12 28 30
Greece 6.93 6.42 16.50 19.59 6.56 0.16 0.12 22 31
Bulgaria 1.04 2.50 10.97 16.05 8.10 0.09 0.09 31 32
Romania 0.74 4.84 6.82 11.42 10.22 0.05 0.08 32 33
India 0.23 0.48 1.18 2.89 0.37 0.00 0.00 34 34

Appendix: Comparing indexes
Infrastructure Use Chakravarty (2010) Chakravarty (2010)
Index Sarma (2008)
(Sarma) (Sarma) (r=1) (r=1)
Year 2005 2005 2004 2003-2004 2004
(2): Transactions (3): Deposit accounts per (6): Bank branches
(3): Deposit accounts per
(3): Branches, in ATMs and in capita, deposit money (geographic and
capita, deposit money bank
ATMs and POS POS bank branches demographic
Dimensions branches (demographic
(normalized by (normalized by (demographic penetration), ATMs
penetration), Ratio of
adults). adult penetration), Ratio of (georgaphic and
deposit plus credit to GDP.
population). deposit plus credit to GDP. demographic
42 countries (ranking out 55 countries (ranking out
Sample size 34 countries 34 countries 55 countries
of 21 country sample) of 21 country sample)
Country Index Ranking Index Ranking Index Ranking Index Ranking Index Ranking
Belgium 0.419 13 0.515 9 0.637 3 0.419 2 0.703 1
Brasil 0.345 18 0.197 20 0.214 22 0.092 11 0.214 11
Bulgaria 0.107 30 0.090 31 0.246 20 0.153 9 0.256 10
Chile 0.121 28 0.139 27 0.267 19 0.192 6 0.277 9
Denmark 0.476 8 0.325 14 0.614 4 0.391 3 0.671 2
Italy 0.523 6 0.120 30 0.415 9 0.335 4 0.423 7
Saudi Arabia 0.078 33 0.176 21 0.127 39 0.048 14 0.129 13

Appendix: Adding dimensions to the index
New
dimension Change on index value from adding the nth dimension
value
2 3 4 5 6 7 8 9 10
Case 1 1 0.15 0.06 0.04 0.03 0.02 0.02 0.01 0.01 0.01
Case 2 0 -0.29 -0.08 -0.04 -0.02 -0.01 -0.01 -0.01 -0.01 0.00

10
20
30
40
50
60
0
France
Malta
Ireland
Cyprus
U.K.
Chile
Portugal
Brasil
C. Rica
Italy
Mexico
Uruguay
Spain
Greece
India
Colombia
Denmark
Belgium
Germany
Romania
Lux.

S.Arabia
Austria
Slovenia
Finland
Czech R.
Lithuania
Appendix: Checks per adult (2010)
Switz.
Slovakia
Latvia
Poland
Estonia
Russia
Bulgaria
Hungary
Nether.
39
Sweden
Dimensions to be included
Implicit in the dimensions are the goals authorities want to achieve.
Complementarities must be recognized, particularly in payments.
There is a tradeoff between adding dimensions and their importance. Due to
the concavity of the FII, additional dimensions have a decreasing effect.
Adding dimensions if the country we are getting the FII for has a low (high)
level, the impact is greater (smaller).
Adding dimensions with Max and Min values
(FII after adding new dimensions )
0.9
0.8 IIF value
0.7
0.6
Inicial New after Change
IIF = f(n)
0.5 IIF dimension introducing in IIF

0.4 value value another value
0.3
0.2
dimension
0.1 Case 1 0.5 1 0.65 0.15
0
Case 2 0.5 0 0.21 -0.29
1 2 3 4 5 6 7 8 9 10
Number of dimensions (n)

Choosing countries in sample
The country selection determines the results:
Adding new countries may affect other countries FII, specially if it the
new country has very high or low values.
It seems appropriate to keep the same country sample through time.
Adding a Country to an original 3 Country Sample
(Variation in 1 dimension)
0.7
0.6
0.5
Index Value
0.4
0.3
0.2
0.1
0
1 2 3 4 5 6 7 8 9 10
Number of branches per 10,000 inhabitants in new
country

FII for Mexican States: Inequality Approach

-0.2
-0.1
0.1
0.3
0.4
0.0
0.2
more.
Bulgaria
Russia
Portugal
Rumania
Brazil
Lithuania
Belgium
Latvia
Hungary
Poland
Estonia
Mexico
S.Arabia
Italy
Chile
Slovakia
Cyprus
Germany
India

Czech R.
Greece
Malta
Ireland
France
Switz.
Sweden
U.K.
Infrastructure IIF: Difference 2005 - 2010
Slovenia
Austria
Nether.
Spain
Finland
Denmark
Lux.
43
The difference of each countries FII between years tells us which one jumped
Financial inclusion in Malaysia:

tracking progress using index 1
Zarina Abd Rahman, Central Bank of Malaysia
1
This paper was prepared for the workshop. The views expressed are those of the author and do not necessarily reflect
the views of the BIS or the central banks and other institutions represented at the workshop.
Financial Inclusion in Malaysia:
Tracking Progress Using Index
1
Zarina Abd Rahman
Abstract
The study seeks to examine the extent of financial inclusion in Malaysia. This study
demonstrates that the measurement approach developed in the human
development literature can be usefully applied to the measurement of financial
inclusion. A conceptual framework for aggregating data on financial products and
services in different dimensions and the suggested composite index of financial
inclusion allows calculation of percentage contributions of different dimensions to
the overall achievement. This in turn enables the identification of the dimensions of
inclusion and their impact to overall financial inclusion. An index of financial
inclusion (IFI) has been developed in the study using data on four identified
dimensions of financial inclusion for Malaysia. The study also provides a tool for the
measurement of progress and to estimate the impact of appropriate policies in
order to make financial inclusion more meaningful and effective for the benefit of
society.
1
The views expressed in this study are of the strictly from the author. The author wishes
to express her deep gratitude towards Muhammad Ibrahim, Bakaruddin Ishak and
Kamari Zaman Juhari and her colleague in Development Finance Enterprise for their
useful guidance and assistance in various stages of the study. Thank you also to
Dr. Ahmad Razi for providing a thorough review and technical assistance for the paper.
She would also like to express her sincere thanks to Thomas Tan Koon Peng for his
encouragement and also giving her the opportunity to develop the financial inclusion
index for Malaysia.
Section 1: Introduction
Malaysia recognises the importance of financial inclusion as a strategy towards

sustainable long-term growth. Measures that were put in place over the last four
decades helped the country in the effort to reduce poverty and inequality levels
within society. This is reflected in the laying of many critical foundations to advance
the cause of financial inclusion; which amongst others include adding the financial
inclusion mandate in the Central Bank of Malaysias Act, creating of a diverse range
of institutions, enhancing distribution channels, providing of basic banking products
and micro financing scheme, strengthening of supporting financial infrastructure,
consumer education and protection and catalysing SME development. At the
Federal level, it was recognised that an important component of the countrys
financial sector policies is financial inclusion that ensure that all segments of society
get access to financial services irrespective of geographical location, income strata
2
or the economic activity .
The Central Bank of Malaysias Financial Sector Blueprint 20112020 charts the
development of an inclusive financial system that best serves all members of
society, particularly the underserved, in terms of access, take up, responsible usage,
quality and affordability. More intensified efforts are focused on creating innovative
channels, products and services, empowering the underserved through financial
literacy and capacity building and strengthening financial institutions and
infrastructure. The importance of a holistic monitoring and evaluation framework
has also been given emphasis to measure the state of financial inclusion more
effectively. To support this strategy, a core set of KPIs for financial inclusion have
been identified to monitor the progress with a demand-side survey conducted in
2011 and a financial inclusion index developed in 2012.
Given this background, the major objective of the study is to measure the
extent of financial inclusion in Malaysia. Section 2 provides the background of the
state of financial inclusion in Malaysia followed by the development of the financial
inclusion index in Section 3. Section 4 deals with the analysis of the results from the
index and how it could be replicated to examine the level of financial inclusion of
the different segments of the population. Section 5 concludes the study.
Section 2: Furthering the Financial Inclusion in Malaysia
The comprehensive initiatives implemented over the past decade have significantly
improved financial inclusion in Malaysia. Take-up of deposits has increased from
1,975 deposit accounts per 1,000 adults in 2000 to 3,036 deposit accounts per
1,000 adults in 2011. Meanwhile, the take-up of financing has increased from
310 financing accounts per 1,000 adults in 2000 to 895 financing accounts per
1,000 adults in 2011. These outcomes have elevated Malaysias position in various
global financial inclusion rankings, including a number one ranking since 2007 for
2
Excerpt from the Prime Ministers Speech at the ADB-BNM-EC Joint Conference themed
Beyond The Global Crisis: A New Asian Growth Model? Kuala Lumpur, 20 October 2009.
Getting Credit in the Ease of Doing Business index by the World Bank (FSBP,
2011).
Although significant progress was achieved, there are still opportunities to
further enhance financial inclusion outreach. For instance, preliminary findings
3
based on the supply-side data show that 54 percent of sub-districts (mukim) with
more than 2,000 population in the country were not served by the formal financial
system. Whilst the take-up of deposit accounts has risen, it is estimated that
8 percent of the adult population still do not have deposit accounts.
Globally, there is growing recognition of the importance of having indicators
and data to monitor the state of financial inclusion more effectively. In Malaysia,
there is a need for a comprehensive KPI framework to measure both the level of
financial inclusion from the supply and demand side perspectives. Supply-side data
are collected from financial providers while the demand-side data represent the
perspective of the consumers and allows the assessment of their needs to ensure
that they are being adequately met by the provision of services. Under this KPI
framework, financial inclusion outcomes will be measured across four dimensions
and data will be sourced reliably from various sources such as the mapping of
access points, supply side data which are adjusted to account for single users
holding multiple accounts using the National Identity Cards, and demand side
surveys of the general population and micro enterprises. The baseline measurement
is in 2011, and real improvements for the consumers are expected over time as
recommendations under the financial inclusion framework are implemented. These
identified indicators are combined to develop a financial inclusion index to facilitate
tracking of progress based on single number over time.
2.1 Literature Review on Indicator Measurement
Financial inclusion refers to the delivery of financial services of an economy to its

people (Chakravarty, 2010). The domain of financial services can be quite large and
it may vary from country to country. Several indicators have been used in the
literature to measure financial inclusion.
The AFI Core Set of Financial Inclusion Indicators (AFI Core Set) is the first step
in establishing a common measurement of financial inclusion, whereby the
indicators are meant to measure the most basic and fundamental aspects of
financial inclusion in a way that is standardised (AFI, 2011). The Core Set Indicators
address the two basic dimensions of financial inclusion; access and usage of
financial services (See Table 1).
There are various aspects to access which could be measured by the availability
of financial services, cost of access, range, type and quality of financial services
offered (Claessens 2006). The counterpart of access is exclusion. Financial exclusion
could occur due to (1) geographic limitations following under-provision of
financial services in remote and scarcely populated areas, (2) socio-economic
limitations when financial services appear inaccessible to specific income, socio-
3
Equivalent to 449 out of 837 sub-districts with more than 2,000 population based on the
results of the Mapping of Access Points Project 2011 undertaken by Development Finance
and Enterprise Department, Central Bank of Malaysia.
economic or ethnic groups, or (3) limitations of opportunity when new or small
firms with viable projects face credit constraint due to information asymmetry
and/or lack of collaterals (Beck et al, 2006). However, access is not synonymous to
usage, as defined in the AFI Core Set. Economic agents might decide not to use
accessible financial services, either for socio-economic reasons, or because the
opportunity costs are too high (Beck et al. 2006).
Table 1: Core Set of Financial Inclusion Indicators
Access 1 Number of access points per 10,000 adults at a national level

2.1 % of administrative units with at least one access point
2.2 % of total population living in administrative units with at least one
access point
Usage 3.1 % of adults with at least one type of regulated deposit account
3.1a Number of deposit accounts per 10,000 adults
3.2 % of adults with at least one type of regulated credit account
3.2b Number of loan accounts per 10,000 adults
Recently, the Global Findex database by the World Bank provides a number of
financial inclusion indicators based on surveys of more than 150,000 adult
individuals in 148 countries in 2011 (Demirguc-Kunt & Klapper, 2012).
Notwithstanding, these indicators whether at the micro or macro level if used
individually provide only partial information on the inclusiveness of the financial
system, and thus inadequately capture the extent of financial inclusion in an
economy, which may result to an inaccurate assessment.
Therefore to address this issue, an index of financial inclusion (IFI) is required,
using multidimensional approach which is able to capture information from several
dimensions in one single number. The IFI should be flexible and expandable while
being able to satisfy the following criteria:
i. It should incorporate information on as many dimensions of inclusion as
possible.
ii. It should be comparable across countries/regions/states at a particular time
period.
iii. It should be used to monitor the progress of policy initiatives for financial
inclusion in a country over a period of time.
iv. It should be easy and simple to compute.
This multidimensional approach is widely used in the construction of indices to
measure the distance from frontiers which shows how much the environment has
changed over time similar to Ease of Doing Business Index (Doing Business 2012).
The United Nations Development Programme (UNDP) has also used a similar
approach for the computation of some well-known development indices such as the
Human Development Index (HDI), the Multidimensional Poverty Index (MPI) and the
Gender Inequality Index. The approach chosen in this study is also similar to the one
used by Chakravarty (2010) and Sarma (2008) to measure financial inclusiveness.
Section 3: Development of Financial Inclusion Index
In cognisance of the diversity in the patterns of change in the different dimensions

of financial inclusion over time, it becomes necessary to get a comprehensive
picture. The study introduced an approach of measuring financial inclusion
performance by combining indicators representing the identified dimensions into a
composite index of financial inclusion, IFI. There are three steps in calculating the IFI
(Detailed mathematical representation of the method is provided in the Appendix).
IFI is computed by first calculating sub-indices for each indicator, whereby
minimum and maximum values (frontiers or targets) for each are set. The distance
to frontier approach, which defines frontier as perfect financial inclusion, is
calculated from the current position of financial inclusion.
Indexing the indicators = Ai-mi

Mi-mi
where, Ai = Actual value of indicator i.

mi = minimum value of indicators i.
Mi = maximum value indicators i.
Example:
Usage of deposit account index = (87 0) = 0.97

(90 0)
Secondly, these sub-indices are then weighted in accordance to importance

and aggregated in order to transform these sub-indices of indicators into a
dimension index, expressed as a value between 0 and 1. Since we have no evidence
to indicate that one dimension is more important than another, the dimensions are
thus weighted equally. Moreover, unlike the case of HDI, there is yet any consensus
in the literature on which set of attributes/dimensions is important to measure
financial inclusion. Finally, the IFI is the arithmetic mean of the four dimension
indices. Index ranges from 0 1, with 1 being perfect financial inclusion and 0 being
total financial exclusion.
Section 4: Analysis for Index of Financial Inclusion for

Malaysia
Leveraging on the AFI Core Set formulated by the AFI Financial Inclusion Data
Working Group (FIDWG), the Central Bank of Malaysia developed the financial
inclusion KPIs (See Figure 1) by defining four dimensions of financial inclusion for
Malaysia i.e. convenient accessibility, take-up rate, responsible usage, and
satisfaction level with each dimension having similar indicators as AFI Core Set
which has been customised to uniquely cater for the Malaysian context. The details
are as follows:
Dimension 1: Convenient Accessibility
Access refers to the ability to use available financial services and products from
formal institutions. Under an inclusive financial system, financial services should be
easily available to potential users. Availability of services can be indicated by the
number of access points providing the financial services such as bank
branches/outlets, automated teller machines (ATMs) or banking agents (BAs) or in
some countries known as banking correspondences (BCs) providing banking
services to the population. For convenient accessibility, we defined access points as
facilities that allow both cash in and cash out (AFI, 2011) and measure this
dimension using two indicators related to the availability of access points:
4
(i) percentage of mukim (sub-districts) with at least 2000 population with access
point and (ii) percentage of adult population living in mukim with at least one
access point. These indicators would capture the outreach of financial services, with
a target of having access points spread widely all over the country, with the mukim
being the smallest administrative unit with available population data.
Dimension 2: Take-up rate of financial products

Ideally, an inclusive financial system should penetrate widely amongst its users. The
size of the banked population, i.e. number of adult population with a bank account
over the total number of the adult population is a measure of the banking
penetration of the system. Thus, if every adult person in an economy has a deposit
th
4
Mukim is a sub-district in Malaysia or the 4 level administrative unit (refer to AFI Core
st
Set Indicators definition on administrative unit) where the 1 level is defined as the
nd rd
national level, followed by 2 as the state and 3 as the district.
and/or credit account, then the value of this measure would be equal to 1. The
financial inclusion demand side survey conducted by Central Bank of Malaysia in
2011 has revealed that about 92 per cent of the individuals had deposit account
with regulated financial institutions in Malaysia. The survey also provided insights
into the take-up of loans and insurance policies by the Malaysian adult population.
Based on the survey, 36% have at least a loan/financing account and 18% have life
insurance/takaful policies. These three indicators together are used to estimate the
take-up rate dimension for the main financial products offered to general
population.
Dimension 3: Responsible Usage

This dimension emerges from the concept of under-banked or marginally-
banked, as observed by Chattopadhyay, in some apparently very highly-banked
countries, a number of people with bank account are nonetheless making very little
use of the services on offer, thus having a bank account does not ensure inclusivity;
it is also imperative that the banking services are adequately utilised
(Chattopadhyay, 2011). In order to incorporate the responsible usage dimension in
the IFI, we consider two basic banking services (deposit and credit) by using the
indicators on percentage of customers with active deposits and percentage of
customers with performing financing accounts. These indicate the activities by the
customers in using the products responsibly. The number of unique active account
holders is verified by matching the national identity card numbers via the Account
and Policy Holders Survey with data submitted by financial institutions.
Dimension 4: Satisfaction Level

This particular dimension attempts to illustrate the quality dimension which is a
more complex topic both conceptually and in terms of measurement, and the
indicator of choice is a qualitative indicator obtained via responses collected in
demand side survey. In the Malaysia case, we use the percentage of customers who
are satisfied with overall financial services as the only indicator for measuring this
dimension. This dimension can be enhanced in the future provided that some
common indicators for the quality dimension are agreed upon based on the AFI
FIDWG initiatives.
4.1 Results for Malaysia
Using data from all four dimensions for Malaysia, we illustrate the IFI computation
in Table 2. In the Malaysian case, the targets are set based on the consensus view of
the Financial Inclusion Working Group at Bank Negara Malaysia, which were
benchmarked against the results of Global Findex (Demirguc-Kunt & Klapper, 2012)
for some of the more developed countries. This higher standard is set as Malaysia
aspires to reach higher income status by 2020. The weight for each indicator is set
to reflect the importance of the indicators at this point in time, but the dimensions
are weighted equally.
Depending on the values of IFI, the results are categorized into the four
following categories:
(i) 0.75 < IFI 1 high financial inclusion
(ii) 0.5 IFI <0.75 above average financial inclusion
(iii) 0.25 IFI <0.5 moderate financial inclusion
(iv) 0 IFI <0.25 low financial inclusion
Nonetheless, the importance of the indicators used could change overtime with
changes in policy emphasis and priority, while the weight for each dimension may
vary as the country progresses in terms of financial development. For example,
access and usage of financial services now go beyond the physical access points to
include virtual space such as internet and mobile banking facilities (Sarma, 2012).
As shown in Table 2, the level of financial inclusion in Malaysia as measured by
IFI is high at 0.77. Higher values indicate better performances as improvements in
the financial activity of a dimension will translate into a higher value for that
dimension. Activities contributing to lower values may require attention from the
policy point of view for improvement. We can isolate such dimensions as the
financial inclusion index enables us to calculate the percentage contributions made
by each indicator to the overall level of financial inclusion. The index can be used to
monitor performance progress and can be used to support policy recommendations
on what more is required to improve performance. This demonstrates an important
policy application of the IFI. The index can also be adjusted and expanded after a
certain period to reflect the structural changes in the financial landscape by
replacing some indicators or by including more indicators and/or dimensions as
they become more relevant for the financial inclusion agenda of the country.
In Table 3, we used the low income group data for Malaysia to illustrate how IFI
could be used to confirm whether there is a need for specific policy intervention to
cater for different level of income. In this scenario, low-income is defined as the
segment of population who earn less than RM 1,000 per month. The results showed
that low income customers have a lower score for IFI compared to the general
population in Malaysia. However, we could only go in-depth with the take-up rate
dimension due to limitations on the availability of data. The data on take-up is
based on findings from the demand survey, while the data for the others indicators
are mainly from the supply-side which we could not segregated by income group.
This shows that more granularities in data collected is required from the supply side
for better assessment. Nonetheless, using this method we have been able to
demonstrate how the index could be enhanced and expanded provided we have
adequate data for each segments of the population.
Section 5: Conclusion
The issue of financial inclusion has received widespread attention in Malaysia during
the recent years as policymakers acknowledge that one of the most important
driving forces of growth is institutionalised financial services. While overall, Malaysia
is on a sustainable growth path, the bottom 40% of the population are still
categorised as low income households. This is neither desirable nor sustainable for
the nation as the benefit of high growth will not be able to trickle down and thus a
large portion of the population will be deprived of these benefits if they are not
financially included.
We have developed an index of financial inclusion using data on four
dimensions of financial inclusion which is useful to monitor the progress of policy
initiatives for financial inclusion over a period of time. Thus far, the result has shown
that by segmenting the population for which the IFI has been estimated, there is a
gap between the low income segment and the general population (see Figure 3).
The index could help policymakers to focus on the dimensions with these gaps for
further analysis and introduce new policies and initiatives that could address related
issues or assist to narrow such gaps. It is observed from the study that the
achievement of financial inclusion in Malaysia is relatively high although some
improvements in respect of some dimensions must take place to conclude that
financial inclusion has also brought about economic and socio-political impact to
the society at large.
This result is confirmed by the financial inclusion demand survey that was
conducted in 2011. The findings from the survey and mapping of access points were
used to support the introduction of agent banking which will further enhance access
to financial services especially in the rural and remote areas. A proper agent banking
model will be able to overcome the supply and demand problems to a greater
extent. However, simply providing financial services is not sufficient. Significant
numbers of rural people are still not aware of the availability of many financial
products and due to this ignorance may not be able to take full advantage of the
available financial facilities. The survey has also revealed that the level of awareness
of the various financial services and products vary among the different segments of
the population. This pointed to the need to spread financial literacy. The need for
financial education at all levels requires intervention by the relevant ministries and
other stakeholders so that the public would be better informed on how to benefit
from the financial services and products in an effective manner.
The IFI could be used as a communication tool just as any other development
index to indicate the level or performance of a country, which would enable the
general public is made to be aware of the achievements or outcomes of reforms in
the area of financial inclusion and in a broader sense the development of the
financial sector overtime. Additionally, in order to make the IFI a more effective tool
for cross country comparison, a common set of indicators and targets with standard
reporting structure should be agreed upon among the policymakers and data
compilers.
In a nutshell, it is observed that although various initiatives have been
undertaken for financial inclusion, there is still a need to narrow the gap among the
different income groups, and this could be achieved with the help of appropriate
policies. Above all, a whole-hearted effort is called for from all the corners of the
society, in order to make financial inclusion more meaningful and effective.
Appendix
Step to Estimate the Index of Financial Inclusion
In order to compute the IFI, first a sub-index is calculated for each inclusion of
financial inclusion. Then the sub-indices are weighted and aggregated to create the
dimension index which is normalised to be between 0 and 1. The IFI is the simple
weighted average of the dimension indices.
The sub-index for the ith indicators, Xi, is computed by the following formula
Xi = Ai mi
Mi mi (1)
where
Ai = Actual value of indicator i
mi = minimum value of indicator i
Mi = maximum value of indicator i
The index for the ith dimensions, Xi, is computed by
Dimension Index, Di = wi X i (2)

i=1
where wi is the weight of the ith indicators and n is the number of indicators.
The IFI is a simple weighted average of the dimension indices, as follows:
IFI =1 wi Di (3)
n i=1
References
Alliance for Financial Inclusion (2011). Measuring Financial Inclusion: Core Set of
Financial Inclusion Indicators. Financial Inclusion Data Working Group Report, April
2011.
Alliance for Financial Inclusion (2010). Financial Inclusion Measurement for
Regulators: Survey Design and Implementation. Policy paper.
Ardic Oya Pinar, Chen G., Latortue A. (2012). Financial Access 2011: An Overview of
the Supply-Side Data Landscape. Washington DC: CGAP and IFC.
Ardi, Oya Pinar, Maximilien Heimann & Nataliya Mylenko (2011). Access to Financial
Services and the Financial Inclusion Agenda around the World, A Cross-Country
Analysis with a New Data Set, WPS 5537, World Bank/CGAP.
Bank Negara Malaysia (2011). Financial Sector Blueprint 20112020.
Beck, T. Demirguc-Kunt, A. Martinez Peria, M.S. (2006). Reaching Out: Access and Use
of Banking Services Across Countries, World Bank, Washington D.C., July 2006.
Beck, T., Demirguc-Kunt, A., & Levine, R. (2007). Finance, inequality and the Poor.
Journal of Economic Growth, 12, 2749.
Beck,T., Demirguc-Kunt, A., Peria, M., & Soledad, M. (2007). Banking services for
everyone? Barriers to bank access and use around the world. WPS 4079. World Bank
Policy Research.
Demirguc-Kunt, Asli & Klapper, Leora (2012). Measuring Financial Inclusion: The
Global Findex Database. Policy Research Working Paper No. 6025. Washington DC:
World Bank.
Chakravarty, Satya R. and Rupayan Pal (2010) Measuring Financial Inclusion: An
Axiomatic Approach Working Paper 2010003, Indira Gandhi Institute of
Development Research, Mumbai.
Chattopadhyay, Sadhan Kumar (2011). Financial Inclusion in India: A case-study of
West Bengal WPS (DEPR) 8 / 2011, RBI Working Paper Series.
Claessens, S. (2006). Access to Financial Services: A Review of the Issues and Public
Policy Objectives, The World Bank Research Observer 21 (2), 207240.
Honohan, Patrick (2008). Cross-country Variation in Household Access to Financial
Services, Journal of Banking & Finance 32, 24932500.
Sarma, Mandira (2008). Index of Financial Inclusion. Working Paper 215, ICRIER.
Sarma, Mandira (2012). Index of Financial Inclusion A measure of financial sector
inclusiveness. Working Paper No. 07/2012, Berlin Working Papers on Money,
Finance, Trade and Development.
Comparing financial inclusion across countries

based on FINSCOPE survey data for Africa 1
Aurora Bila, Bank of Mozambique
1
Comparing financial inclusion across
countries based on FINSCOPE survey data
for Africa (SADC Region)
Aurora Bila
Banco de Mocambique
Malaysia, 6 November 2012
Disclaimer
This presentation based upon and limited to the

findings from different surveys conducted by FinMark
Trust in the SADC region, based on FinScope
Studies 2009 - 2011.
The reports of the individual country surveyed are

available on www.finscope.co.za
Agenda
1. About FinMark Trust

2. About Finscope
3. Empirical evidence
4. Comparing financial inclusion indicators
5. Findings and milestones on financial
inclusion in Mozambique
About FinMark Trust
- Not-for-profit Independent trust based in
Johannesburg established in March 2002;
- Funded primarily by UKAID from the United

Kingdom's Department for International
Development (DFID) through its Southern Africa
Office;
- Main objective: is "making financial markets work

for the poor by promoting financial inclusion and
regional financial integration".
About FinMark Trust
- In pursuit of its purpose, FinMark Trust supports

institutional and organisational development which
increases access to financial services in Africa, by
conducting research to identify the systemic constraints
that prevent financial markets from reaching out to poor
consumers, and by advocating for change on the basis of
research findings.
FINSCOPE
In a nutshell, Finscope is a research tool developed by

FinMark Trust
- It is a nationally representative study of

individuals perceptions on financial services and
issues which creates insight to how people
source their income and manage their financial
lives.
- On financial inclusion, FinScope looks at the use

of, and demand for financial services, including
informal products;
-
FINSCOPE
- By means of questionaires and interviews covering

attitudes, behaviors, quality of life factors and
consumption patterns, identify factors that impede
and facilitate effective access to financial services;
- Research conducted against the same parameters

aiming to provide a basis for comparison.
Objectives:
- Measure the levels of access and usage of

financial services and describe its landscape;
- Compare access levels across countries;
- Support commercial innovation in the view that

access to finance can only improve if financial
services providers deliver products and services
to more people.
Empirical evidence
Demand -Side Surveys: 2009 - 2011 (16
countries):
Mozambique; Botswana; South Africa; Swaziland;

Lesotho; Malawi; Namibia.
Supply- Side Surveys: 2011
Mozambique; Zimbabwe; Malawi, and Zambia

Peer comparison - Financial Access
Financial inclusion in Mozambique at
a glance
Country profile
Capital: Maputo
Population: 23.4 million
Area: 799.380 square km
Literacy: 47.8% people of the age of 15+ read/write
Currency: Metical
Landline telephones in use: 95.000 (2010)
Mobile phones in use: 6 million (2011)
Financial inclusion indicators
Indicators 2009
Adults with access to financial services (%) 22,2%
Adults without acess to financial services (%) 77,8%
Adults served by formal financial services (%)
12,7%
Adults Servd by informal financial services (%)

9,6%
Adults with bank account 11,8%

- Financial inclusion is one of the top priorities on the
government agenda and is being promoted through
several initiatives. Its at core of the forthcoming
Financial Sector Development Strategy 2011-2020
(Draft recently submitted to the cabinet).
- BM recognizes that the scope and efficiency of the

financial sector plays an important role in
facilitating economic and private sector growth and
therefore, in recent years, introduced various
initiatives aimed at improving the countries framework
enabling environment for the development of
financial system.
- leveraging, in part, on the results from the surveys,
Mozambican authorities see an opportunity for great leap
forward in financial inclusion.
- In this regard, BM has instituted a regime on minimum fees

and other expenses charged by commercial banks aiming at
ensuring affordable and fair access to financial services by
rural poor; on the same perspective, some economic and
tax incentives were granted for opening branches in rural
unbanked or underserved areas.
- There are innovations happening in Mozambique. The first

mobile money issuer was launched in 2011.
- Several important new regulatory developments are taken
place;
- Joined AFI, a network of policy makers in 2011 and since

then benefits from the peer learning and knowledge
exchange among its member. Part of working groups and
recently joined the FIDWG;
- Task force to deal with financial inclusion matters.

Some general indicators After 2009
Dez-10 Dez-11 Mai-12
Bank account
1.803.534 2.115.620 2.197.598
With Mkesh 1.803.534 2.168.139 2.257.926
Mkesh accounts 52.519 60.328
Level of Bancarization
7,7% 9,0% 9,4%
With Mkesh 7,7% 9,3% 9,6%
Total population 23.405.670 23.405.670 23.405.670
17
Evolution of the banking industry since 2009
Instituies 2006 2007 2008 2009 2010 2011
Instituies de Crdito
Bancos 12 12 14 14 16 18
Soc. de Locao Finan. 2 1 1 1 1 0
Soc. de Investimento 0 1 1 1 1 1
Instit. de Moeda Electrnica 0 0 0 0 0 1
Sociedades Financeiras
Soc. de Capital de Risco 1 1 1 1 1 1
Soc. de Compras Grupo 1 1 1 1 1 1
Soc. Emitentes de Cartes 0 1 1 1 1 2
Evolution of the banking industry since 2009
Espcie 2006 2007 2008 2009 2010 2011

IMFs
Microbancos 1 1 1 3 6 8
Cooperativas de Crdito 6 6 6 6 7 7
Organizaes de Poupana e
0 0 4 9 10 10
Emprstimo
Operadores de Microcrdito 57 74 73 95 118 167
Outras Instituies
Escritrios de Representao 1 1 1 1 1 1
N. Total de instit. licenciadas pelo BM 105 121 126 153 184 238
Total de ATMs 376 425 508 614 731 850
Total de POS 3.218 3.645 4.103 4.526 4.731 6.618
Entry of 4 new commercial banks from 2009;
Licensing of Mozambican Interbank Society (National

Processing Center)
Launch of Mkesh, the 1st Mobile-Banking company

licensed by the BM
Increase in the number microfinance providers
Microcredit operators from 57 (in 2006) to

167 (in 2011);
Savings and credit organizations 10 in 2011;
7 Microbanks.
Financial inclusion Distribution of the banking
2006 network Setembro 2012
4 10
7
31 14
8
17
48
10 23
12 48
45
22
14 30
13 29
121
218
Increase in bank branches from 228 in 2006 to 470
in September 2012;
Increase in the banking network coverage in the

districts to 58 in 2012 from 28 in 2006
Increase of 41 branches in the districts in 2006 to

117 branches in September 2012
Thank You
Sasana statement on financial inclusion indicators
On 5 and 6 November 2012, Bank Negara Malaysia co-sponsored an international

meeting at Sasana Kijang, Kuala Lumpur, to discuss financial inclusion measurement
and indicators. The other sponsor was the Irving Fisher Committee on Central Bank
Statistics (Irving Fisher Committee), a forum of economists and statisticians from
80 central banks from all regions, which operates under the auspices of the Bank for
International Settlements. The meeting, which was presided by Deputy Governor
Muhammad bin Ibrahim, who is also the Chairman of the Irving Fisher Committee,
yielded the following insights:
1. Greater financial inclusion is essential for accelerating and sustaining
employment, economic growth and financial stability. There is currently no
standard definition on financial inclusion. In a narrow sense, it refers to the
delivery of accessible, affordable, appropriate and cost-effective financial
products and services to unserved or underserved households. The definition is
sometimes extended to also include products and services to unserved or
underserved enterprises. In its broadest form, financial inclusion takes into
account the qualitative aspects of inclusiveness such as financial literacy and
consumer protection.
2. As in other policy areas, good policy requires good data, at both the macro and
micro level. The meeting highlighted the crucial role of measurement and
performance indicators for the promotion of financial inclusion. Useful
initiatives have been taken at the international level to provide guidance on the
collection and dissemination of data on access to and usage of the financial
products and services, as well as qualitative aspects of financial inclusion.
International data-related initiatives include those of International Monetary
Fund, the World Bank, the G20, Consultative Group to Assist the Poor, and
International Finance Corporation, the Alliance for Financial Inclusion (AFI) and
its Financial Inclusion Data Working Group (FIDWG), and Microfinance
Information Exchange.
3. Many developing, emerging, and advanced countries are implementing current
international recommendations related to data frameworks and methodologies
for financial inclusion indicators. Nevertheless, they have yet to be fully used by
all countries due to diverse levels of financial inclusion across countries. Many
are also participating in internationally-coordinated data collection initiatives in
order to map financial inclusion in their jurisdiction and make data more
comparable across countries.
4. In addition, countries are implementing country-specific data-driven
approaches to measure financial inclusion by different dimensions, including
financial literacy, consumer protection, household indebtedness, micro credit,
the financing of small and medium-sized enterprises and community
development.
5. Central banks in different regions in the world have a clear interest in
promoting financial inclusion, in particular by improving policy-relevant data.
This includes the development of composite indicators or indices of financial
inclusion at the national and international level. While the appeal for such
interest is well-founded, a number of methodological issues need to be
carefully considered in using such indicators for policy development purposes,
both in terms of comparing financial inclusion across different countries and
over time.
6. The Irving Fisher Committee stands ready to contribute to international efforts
in the standardisation of the relevant financial inclusion measurement and
development of composite indices or dashboards. The statistical expertise of
Irving Fisher Committee members is available to national and international
organisations and groups.

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Irving Fisher Committee

on Central Bank Statistics

Proceedings of the Kuala Lumpur Workshop,

This publication is available on the BIS website (www.bis.org).

ISSN 1991-7511 (online)

Overview Key messages of the Sasana Workshop on Financial Inclusion

Session 1: International initiatives to promote financial inclusion and its

Session 2: National practices to collect data on financial development,

Session 3: Measuring access to and usage of financial services

IFC Bulletin No 38 iii

Session 5: Indicators of financial literacy, consumer protection and community

Session 6: Development of a composite indicator/index of financial inclusion

Panel Session: Promoting national and international best practices to

1. Current data collection frameworks

Box A Summary overview of international initiatives to measure

BIS/Committee on Payments and Market Infrastructures (CPMI): financial inclusion

Financial Literacy Network.

Financing SMEs and Entrepreneurs.

Evidence-based initiatives to enhance financial literacy and promote financial inclusion.

Microfinance Information Exchange (MIX): a Washington-based non-profit

Various donor organisations: eg the Gates Foundation.

Box B Selected country practices related to the measurement of

Belgium: studies on income and wealth by the central bank.

Brazil: Incluso Financeira (yearly forum meeting); measures to monitor the

France: national database on household credit repayment incidents.

Italy: studies on income and wealth by the central bank.

HM Treasury initiative: Financial Inclusion Taskforce.

Private sector: Transact the National Forum for Financial Inclusion.

2. Enhancing data collection frameworks

The Workshop reviewed a number of methodological challenges with respect to the

3. Closing data gaps

4. Developing indices of financial inclusion

Session 6 of the Workshop was devoted to developing financial inclusion indices

5. The need for a clear analytical framework to assess

Opening remarks by Dato Muhammad bin Ibrahim, Deputy

Good morning ladies and gentlemen,

Kamalesh Shailesh C Chakrabarty,

Approaches to Financial Inclusion International Initiatives

Financial Inclusion National Initiatives

Framework for Measurement and Data Analysis

An essential pre-requisite for measurement is to understand the context and

Concepts and definitional issues

There are several structural dimensions in the process of building up data on

International Databases on Financial Inclusion Indicators

Financial Inclusion is fast emerging as a candidate for being a core driver of

Data on Barriers for Financial Inclusion

Financial Inclusion Initiatives in India

I have already outlined some important milestones in the development of financial

Constraints to spread of Financial Inclusion

World Bank's FINDEX Select Indicators on Financial Inclusion 2011

United United Russian

United United Russian

NA: Not Available

Source: IMF's Financial Inclusion Survey July 2012.

Year Year Year Quarter Progress

1 Total No. of Branches 85457 91145 99242 99771 8097

3. Distribution of Deposits Accounts

5. Average population per branch