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Hal the World is Unbanked
FINANCIAL ACCESS INITIATIVE FRAMING NOTE | OCTOBER 2009
Alberto ChaiaMcKinsey & Company
Aparna Dalal
Financial Access Initiative
Tony GolandMcKinsey & Company
Maria Jose GonzalezMcKinsey & Company
Jonathan MorduchFinancial Access Initiative
Robert SchifMcKinsey & Company
The Financial Access Initiative is a consortiumo researchers at New York University, Harvard, Yaleand Innovations or Poverty Action.
www.nancialaccess.org
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Hal the World is Unbanked
www.inancialaccess.org 1
Over the past quarter century, the micro-
nance movement has propelled a global expan-sion o nancial services or the worlds poor.The Microcredit Summit Campaign, a leadingadvocacy group, counted 154 million clientsworld-wide at the end o 2008. That is impres-sive, but it is just a start relative to the unmetdemand. Experts agree that unmet demand ornance is large, but the exact number (or evena rough but credible number) has been hard topin down, with estimates ranging rom hal abillion people to three billion.
Limited inormation on the size and nature o the global population using
nancial services limits policymakers abilities to identiy whats working
and whats not, and it limits nancial services providers abilities to iden-
tiy where the opportunities lie and where they could learn rom current
successes.
This paper builds on a dataset compiled rom existing cross-country data
sources on nancial access and socioeconomic and demographic char-
acteristics to generate an improved estimate o the size and nature o the
global population that does and does not use ormal (or semi-ormal)1
nancial services.
Our key ndings are:
2.5 billion adults, just over hal o worlds adult population, do not use
ormal nancial services to save or borrow.
2.2 billion o these unserved adults live in Asia, Arica, the Middle East
and Latin America.
O the 1.2 billion adults who use ormal nancial services in Asia, A-
rica, and the Middle East, at least two-thirds, a little more than 800
million, live on less than $5 per day.2
Levels o nancial inclusion are not determined by socioeconomic or
demographic actors alone. We ound considerable variance amongst
countries when we correlated nancial services usage with national levels
o per capita income and urbanization or each country. The variation in
the data suggests that socioeconomic and demographics are not the only
drivers o nancial inclusion. Financial inclusion can be provided eective-
FINANCIAL ACCESS INITIATIVE FRAMING NOTE
2.5 billion adults, just over
hal o worlds adult popu-lation, do not use ormal
nancial services to save
or borrow.
62%o adults, nearly 2.2
billion, living in Asia, Arica
and Latin America are
unserved
A little more than 800
million served adults live
on less than $5 per day.
1. This paper considers the use o ormal and semi-ormal
nancial services. We exclude inormal nancial sources
such as moneylenders or inormal rotating savings and
credit schemes. Semi-ormal sources include micronance
institutions, which might not be subjected to the same
regulation as traditional banks.
2. This paper uses regional denitions rom the UN Human
Development Index. High-income OECD countries, as well
as Central Asia and Eastern Europe, and Latin America and
the Caribbean are excluded rom this analysis because
the methodology employed is ineective or these regions
because o their relatively high incomes in comparison to
the levels o usage. Please see the methodology section or
urther discussion.
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FINANCIAL ACCESS INITIATIVE FRAMING NOTE
3. A new version o the United Nations Human Development
Index that uses 2007 pop ulation data is avai lable at http://
hdr.undp.org/en/statistics/data/
4. The World Banks PovCal Net is an online computational
tool that provides regional and country level poverty
measures.
ly through the regulatory and policy environment and actions o individual
nancial services providers. Countries including India and Thailand have
ar wider usage o ormal nancial services than would be predicted by
their level o GDP or urbanization.
Our ndings provide empirical grounding or what many in the eld al-
ready believe to be true. It is possible to serve low income communities at
scale with nancial services, but there are still billions let to reach.
Approach
Our three core analyses address the number o adults who do and do not
use ormal nancial services, levels o usage or people living above andbelow $5/day PPP-adjusted, and correlations between levels o nancial
services use and income and urbanization.
To conduct these analyses, we built a dataset with our components o
country-level data. The country-level data or these components is includ-
ed in Table 1:
i. Percentage o adults with a credit or savings account measured
rom Honohan (2008). Honohan presents estimates, or more than
160 countries, o the raction o the adult population using ormal
nancial and semi-ormal (i.e., rom unregulated micronance insti-
tutions) services by combining data rom banks and micronance
institutions with household surveys. Honohans nancial measures
are based on population data rom 2003.
ii. 2005 population data rom the United Nations Human Develop-
ment Index online database. We dene adult population as individu-
als 15 years or older.3
iii. Percentage o population living on more than and less than $5/day,
PPP-adjusted, using most recently available data rom World Banks
PovCal Net.4
iv. 2005 per capita income and level o urbanization rom the Human
Development Index online database.
There are two key terminological distinctions in this paper that are worth
emphasizing: 1) use o nancial services, rather than access; and 2) ocus
on number o adults or households.
Use o fnancial services: In the world o nancial inclusion, experts
oten go back and orth between use o nancial services and access.
There are important conceptual debates about which o these makes or
more appropriate policy goals do we aim or people to have the oppor-
tunity to use ormal nancial services, or are there some services that
we want all people to use (e.g., savings, health insurance)? This paper
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uses data that is explicitly ocused on usage, in large part because that
is what was available. Access is more dicult to measure (though it can
be approximated by, or example, measuring proximity to ormal nancial
services outlets). Access, by denition, is always larger than usage so the
numbers here put a lower bound on access. Being undeserved does not
necessarily mean that these populations lack access. This is especially
true or low income populations who lead active nancial lives, and choose
to use inormal nancial instruments even though they have access to
ormal services. Inormal tools oer fexibility and convenience that might
be missing rom more structured nancial services. However, inormal
nancial services lack the reliability (e.g., consistent quality and availabil-
ity), security (e.g., insured savings accounts, sound insurance), aordabil-
ity and value (e.g., lower interest rates, positive real interest on savings),and potential or scale that ormal nancial services oer. The challenge in
expanding use or policymakers and nancial providers is how to provide
nancial services that match the fexibility aorded by inormal tools, and
are also reliable, secure, aordable and value-creating on a large scale.
Adults and households: Honohans data uses the concepts o adults
and households interchangeably. We realize that policymakers and nan-
cial providers might value the estimates dierently. For many policymak-
ers, especially those concerned about nancial inclusion as a tool or
poverty alleviation, household-level data may be more appropriate due tothe ocus on how many amilies can benet rom ormal nancial ser-
vices. Financial services providers may care about households or some
products (e.g., credit), while others, such as savings accounts, payments
products or health insurance, may be relevant at the individual level.
Honohans data sources provide a mix o household-level and adult-level
inormation. Some o the surveys are based on household units (such as
those rom the LSMS program); others, such as Finscope, use individual
adults as the unit.(Honohan 2008, 2496) In the uture, it could be helpul
to get usage data at both the household and adult levels in a compara-
tive way, to correct or a potential bias where data at the household level
suggests more widespread usage than is taking place (e.g., i there are twoadults in each household, and in hal o all households one adult is using
nancial services, household data could tell us that 50% o households
are using nancial services, while only 25% o adults are using services).
Key Findings
COUNTING THE UNBANKED
To obtain the total number o adults who do and do not use nancial
FINANCIAL ACCESS INITIATIVE FRAMING NOTE
The challenge in expand-
ing use or policymakersand nancial providers is
how to provide nancial
services that match the
fexibility aorded by in-
ormal tools, and are also
reliable, secure, aordable
and value-creating on a
large scale.
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services, we multiplied the percentage o adults who use nancial services
in each country rom Honohans study with the number o adults in each
country based on 2005 population data.
In 2005, out o a total world population o 6.4 billion people, 4.7 billion
were adults. As illustrated in Figure 1, only 2.2 billion o these adults used
some orm o ormal nancial services to borrow and/or save. 2.5 billion
adults, just over hal o the worlds adult population, did not use any or-
mal nancial services.
We had complete adult population and usage data or 95% o the popu-
lation. To conduct the analysis or the remaining countries or which we
had adult data but no data on usage, we used a scaling-up approach, as
shown in Table 2. We rst conducted the analyses or all countries with
complete data. We created a multiplier or each region (e.g., East Asia,
Latin America, Sub-Saharan Arica) by dividing adult population or all
countries by adult population or countries with complete data. We then
multiplied the usage data drawn rom countries with complete data withthis multiplier to get the complete usage data or all countries.
Another way to measure nancial access is to ocus on supply side data.
The Financial Access 2009 report by the Consultative Group to Assist the
Poorest (CGAP, 2009) does just that. CGAP uses new data rom a survey
o nancial regulators rom 139 countries to estimate the number o un-
banked adults in the world. They begin by counting the total number o de-
posit accounts in countries and then dividing by three (a rough estimate
o the number o deposits per banked adult world-wide). The result rom
Figure 1: 2.5 billion adults globally do not use formal or semi-formalfinancial services
4.7 2.2
2.5
SOURCE: Honohan, 2008; Human Development Index; World Bank
Adults who use and do not use formal or semi formal financial servicesgloballyBillions of adults
Do not usefinancialservices
Use financialservices
Total adultpopulation
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In Sub-Saharan Arica
80% o the adult popula-tion, 325 million people,
remains unserved, as
compared to only 8% in
high income OECD coun-
tries.
this approximation is that 2.8 billion adults are unbanked, a number which
is very similar to ours. Wherever possible, data on households or individu-
als seems most appropriate or measuring nancial usage, but it is helpul
to know that a supply-side approach yields similar results.
WHERE ARE THE UNBANKED?
Figure 2 depicts the geographical distribution o the adults who do not
use ormal nancial services. The gure plots the number and percentage
o unserved adults against the ollowing regions: East Asia, South Asia,
Sub-Saharan Arica, Latin America, Central Asia and Eastern Europe, Arab
States and High income OECD countries.
Nearly all o the 2.5 billion unserved adults live in Arica, Asia and LatinAmerica. For these regions, the total percentage o unserved adults climbs
to 60% o the adult population. The greatest number o unserved adults,
almost 1.5 billion, reside in East and South Asia. In Sub-Saharan Arica
80% o the adult population, 325 million people, remains unserved, as
compared to only 8% in high income OECD countries.
WHO ARE THE UNBANKED?
Given that nancial inclusion is a stated goal or most governments,
estimating the depth o nancial services is a useul rst step or policy-
making. We examined usage by income distribution with the help o one
strong, conservative assumption.
We divided countries population into two segments: the percentage o
Figure 2: Nearly all of the worlds financially unserved adults live in Africa,Asia and Latin America
1 Regional groupings based on UN Human Development Index
Adults who do not use formal financial services1
Millions of adults
Percent of total adult populationthat is financially unserved
193
60
136
876
612
326
2,455
250
Sub-Saharan Africa
Latin America
Central Asiaand Eastern Europe
South Asia
High income OECD
Total
East Asia
Arab States
59
58
80
65
49
67
8
SOURCE: Honohan, 2008; Human Development Index; World Bank
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population living on more than $5 a day and the percent living on less
than $5 per day. We assumed a positive 1-to-1 correlation between use o
nancial services and income level, meaning that nancial usage starts
with those above $5 per day.
This is an inherently conservative approach to estimating how ar down-
market nancial services provision goes, since it is unlikely that every
adult living on more than $5/day uses ormal nancial services. We use
India as an example in Figure 3 to illustrate our approach:
In 2005 India had 760 million adults
Using Honohan estimates, we knew that 48% o these adults, 365
million, used ormal or semi-ormal nancial services
We assumed that nancial services usage begins top-down, meaning
that the rst set o adults to use ormal services were the richest
adults
Approximately 20m adults live on more than $5/day, PPP-adjusted.
We assume that all o the adults who live on more than $5/day are
included in the ~365m adults who use nancial services
We then assume that the remainder o adults using nancial services
live on less than $5/day
This means that roughly ~345m adults live on less than $5/day and
use ormal nancial services
FINANCIAL ACCESS INITIATIVE FRAMING NOTE
>$5/day
97%
$5/day
20345
SOURCE: Honohan, 2008; Human Development Index; World Bank
Using financial services andearning < $5/day
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We applied the same approach to countries in Arica, Asia and the Middle
East. We omitted high-income OECD countries, Central Asia and Eastern
Europe, and Latin America because o the relatively small percentage o
the population living on less than $5/day, PPP-adjusted, in comparison to
the amount o nancial services usage.
Figure 4 depicts the number o adults who live on less than $5/day and
more than $5/day in East Asia, South Asia, Sub-Saharan Arica and the
Middle East who use ormal nancial services.
The news is not all bad. In these regions, 1.2 billion adults use ormal nan-
cial services. About 800 million adults, two-thirds o the served popula-
tion, actually live on less than $5/day. In South Asia alone nancial provid-
ers serve 396 million low income adults (mostly in India).
The key message rom these analyses is that hundreds o millions o
adults living on less than $5/day are already being reached with ormal
nancial services. Serving these segments at scale is not only possible,
but to a large extent, is already happening.
DRIVERS OF INCLUSION
We compared the data on nancial services usage separately with na-
tional levels o per capita income and urbanization to identiy possible
drivers o nancial inclusion through a standard correlation. Our dataset
included complete data or 102 countries in Arica, Arab states, Asia and
Latin America. We did not include the high-income OECD countries or
Central Asia and Eastern Europe because we wanted to ocus on the poor-
est countries.
FINANCIAL ACCESS INITIATIVE FRAMING NOTE
Figure 4: Hundreds of millions of adults who use financial services live onless than $5/day, PPP-adjusted
Adults who use formal financial servicesBillions of adults
$5/day, PPP-adjusted
283
25
45
45
Arab States
56
South Asia
332
81
396
73
614
26
East Asia
441
Sub-SaharanAfrica
SOURCE: Honohan, 2008; Human Development Index; World Bank
Serving adults living on
less than $5/day at scaleis not only possible, but to
a large extent, is already
happening.
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Levels o nancial inclusion are not determined by socioeconomic or
demographic actors alone. Figure 5 plots the percentage o population
who use ormal nancial services against GDP per capita (we had GDP per
capital data or 94 o the 102 countries). We ound a moderate to strong
positive correlation between usage levels and per capita income across
countries. Figure 6 plots the percentage o population who use ormal
nancial services against level o urbanization. We ound a weak positive
relationship between use o services and urbanization.5
Many countries do not t the overall pattern. For example, India and
Thailand appear to be countries with relatively low per capita income and
a large rural population, but have greater use o nancial services than
many relatively richer and more urban countries.
These ndings support the idea that countries can improve levels o nan-
cial inclusion by creating eective regulatory and policy environments and
enabling the actions o individual nancial services providers.
FINANCIAL ACCESS INITIATIVE FRAMING NOTE
Eective regulatory and
policy environments canimprove levels o nancial
inclusion.
5. The coefcient o correlation between percentage o
population using nancial services and GDP per capita is
0.64 and the coefcient o correlation between percentage o
population using nancial services and urbanization is 0.36.
Both are stat istically signicant at 1%.
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Figure 5: There is a moderate to strong relationship between GDP percapita and usage of financial services
Correlation between levels of financial inclusion and GDP per capita for Arab states, Africa, Asia andLatin America (for countries with complete data)
0
20
40
60
Percentage
ofpopulation
using
financial
services
0 5,000 10,000 15,000 20,000GDP per capita (PPP, 2005)
The red line indicates the linear prediction.
All countries
Relationship between GDP per capita and financial services
Argentina
Chile
Malaysia
Mexico
Thailand
India
CostaRica
Nicaragua
PakistanKenya
SOURCE: Honohan, 2008; Human Development Index; World Bank
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Improving Data
The quality o these analyses hinge largely on the quality o Honohans
cross-country data on nancial services usage. This data is widely cited
and is used in Finance For All, the World Banks 2008 publication on ac-
cess to nancial services.
We constructed an alternate measure or twelve countries using more
recent select nancial services country-specic data rom domestic news
sources and others analyses. The countries account or about 2 billion
people, almost one-third o the worlds population.6 In general, Honohans
data held up well against this anecdotal testing. Using these alternate
nancial measures, we estimate 2.4 billion adults who do not use ormal -
nancial services compared to our original estimate o 2.5 billion. The num-
ber o unserved adults in Asia, Arica, the Middle East and Latin America
drops rom 2.2 billion to 2.1 billion. Relatively speaking, these dierences
are small and do not change the undamental ndings.
Another consideration is that our estimates are based on population data
rom 2005. Given the rapid pace o change in nancial inclusion over the
last our years, it is likely that our analysis using Honohans data under-
reports the amount o nancial inclusion today.
Figure 6: There is a weak positive correlation between usage of financialservices and urbanization
SOURCE: Honohan, 2008; Human Development Index; World Bank
Correlation between levels of financial inclusion and urbanization for Arab states, Africa, Asia andLatin America (for countries with complete data)
0
20
40
60
Perce
ntage
ofpopulation
using
financialservices
0 20 40 60 80 100Percentage of urban population
The red line indicates the linear prediction.
All countries
Relationship between urban population and financial services
Argentina
Chile
Malaysia
ThailandSriLanka
India
Tanzania
KenyaPakistan
Nicaragua
Mexico
6. The countries were Botswana, Brazil, India, Indonesia,
Kenya, Mexico, Namibia, Nigeria, South Arica, Tanzania
Uganda, and Zambia. The alternate data sources included
Finscope Arica surveys, Reserve Bank o India report, World
Bank survey on Brazil, Business Latin America article and
Bank Rakyat o Indonesia study.
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We use his data across countries even where other data was available,
however, to ensure quality and consistency. And even at the extreme, i
nancial inclusion had increased globally by as much as 20% in the last
our years, there would still be 2 billion adults who do not use any ormal
credit or savings products today.
We undertook this analysis to create a reasonable estimate o nancial
services usage. This eort, even with its limitations, provides a quantita-
tive starting point or uture studies on the nature and amount o usage o
nancial services.
Conclusion
This study brings together available data to rame important debates on
nancial inclusion. The ndings are striking: 1) approximately 2.5 billion
adults do not currently use nancial services, about hal o the worlds
adults; 2) existing practice shows that it is possible to serve low income
populations at wide scale. Yet, billions o people, and especially those
who live on under $5/day, are not using ormal nancial services. This
can inhibit their ability to build wealth, increase their income and manage
uncertainty.
This is just a start. Updating and rening these analyses (and perhaps
even reuting them) requires more detailed household and/or adult-level
data. In the next ew years we expect that there will be better household
data that can help identiy gaps and pin down numbers more rmly. Those
eorts are crucial i policymakers are to realize their ambitions to spur the
creation o new markets and expand access to the under-served.
ReerencesHonohan, Patrick (2008). Cross-country variation in household access to nancial services.
Journal o Banking and Finance 32, May: 2493-2500.
World Bank (2008). Finance or All? Policies and Pitalls in Expanding Access. World Bank
Policy Research Report. Washington DC: World Bank.
Consultative Group to Assist the Poorest [CGAP](2009). Financial Access 2009:
Measuring Access to Financial Services around the World. September.
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Table 1: Country-level data on adult population rom United Nations Human Develop-ment Index, nancial service usage rom Honohan 2008, and income data rom WorldBanks PovCal Net.Source HDI 2007/8
"Build your
own tables"
HDI 2007/8
"Build your
own tables"
Calculated
Honohan
(2008)
Calculated HDI 2007/8
"Build your
own tables"
HDI 2007/8
"Build your
own tables"
PovCal Net
(World Bank)
PovCal Net
(World Bank)
Country Population
(m, 2005)
Popolation
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