103009 HalfUnbanked

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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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FINANCIAL ACCESS INITIATIVE FRAMING NOTE


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-


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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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


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


53


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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


>$5/day

97%

$5/day

20345


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.


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


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.


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%.


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



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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


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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103009 HalfUnbanked