Measuring financial inclusion: explaining variation in use of financial services across and within countries.
Demirguc-Kunt, Asli ; Klapper, Leora
IV. Mobile Money, Branchless Banking, and Beyond
As documented in section II, there is a strong correlation between
national income and financial inclusion. However, policy innovations may
still be able to bring about more inclusive financial systems even at
low levels of income. The Global Findex database allows us to observe
how public and private sector-led initiatives might change how people
engage with the formal financial system.
The success of mobile money illustrates the transformative
potential of technical progress and innovation to promote financial
inclusion. Mobile money--sometimes considered a form of branchless
banking--has allowed people who are otherwise excluded from the formal
financial system to perform financial transactions in a relatively
cheap, secure, and reliable manner (Jack and Suri 2011). Individuals
using mobile money maintain a type of account that allows them to make
deposits and withdrawals through cash transactions at a network of
retail agents. They can then transfer money or pay bills using text
messages. Many mobile money accounts--such as those provided by M-PESA
in Kenya or GCash in the Philippines--are not connected to an account at
a financial institution, but the providers are often required to store
the aggregate sums of the accounts in a bank. Customers are ordinarily
charged a fee for sending money to others or making a withdrawal from
their account.
Mobile money has achieved the broadest success in sub-Saharan
Africa, where 16 percent of adults report having used a mobile phone in
the past 12 months to pay bills or send or receive money (figure 14).
The share of adults using mobile money is less than 5 percent in all
other regions, but a few countries, including Haiti and the Philippines,
are notable exceptions to the pattern.
The degree to which mobile money is capturing the unbanked market
differs across countries. In Kenya 43 percent of adults who report
having used mobile money in the past 12 months do not have a formal
account. In Sudan the figure is 92 percent. This heterogeneity may
reflect the varied and fast-evolving regulations surrounding mobile
money. When M-PESA was launched in Kenya, it had no association with the
formal banking sector, and mobile banking customers there were exempt
from the documentation requirements imposed by banks. But governments
are increasingly favoring bank-led models in which mobile money
providers have partnerships with or are formed directly through banks
(Consultative Group to Assist the Poor 2010).
[FIGURE 14 OMITTED]
In recent years the proliferation of branchless banking has also
received growing attention as a way to increase financial access in
developing countries, particularly among underserved groups (see Mas and
Kumar 2008). One mode of branchless banking centers on bank agents, who
often operate out of retail stores, gas stations, or post offices. By
capitalizing on existing infrastructure and client relationships,
operators can expand financial access in a more cost-efficient manner.
Bank agents themselves can also be mobile, making daily or weekly rounds
among clients. Few account holders currently report relying on bank
agents (whether over the counter at a retail store or some other person
associated with their bank) as their main mode of withdrawal or deposit.
But in several Asian countries--including Bangladesh, Laos, Nepal, and
the Philippines--more than 10 percent of account holders already report
using bank agents.
There is also enormous scope for the public sector to bring about
transformative change in how adults around the globe interact with the
formal financial sector. Increasingly, governments are using formal
accounts to disburse transfer payments. In Brazil the government allows
recipients of conditional cash transfers (as part of its Bolsa Familia
program) to receive payments via no-frills bank accounts, although many
more choose to receive payments via a virtual account that does not
allow deposits or indefinite storage (Consultative Group to Assist the
Poor 2011). Still, according to Findex data, 20 percent of adults in
Brazil report receiving government transfers via a bank account, one of
the highest proportions in the developing world. In India the government
recently began depositing government pension and scholarship payments
directly into the bank accounts of almost 250,000 people in 20
districts. Officials plan to expand the program and hope it will prevent
corruption as well as expand financial access. (23) The data provide
suggestive evidence that these types of reforms may have the potential
to dramatically expand the reach of the formal financial sector to the
poorest individuals.
V. Conclusion
For most people around the world, having an account at a financial
institution serves as an entry point into the formal financial sector. A
formal account can encourage saving and open access to credit. It can
also make it easier to transfer wages, remittances, and government
payments. Broadbased access to accessible and affordable formal accounts
is a hallmark of an inclusive financial system, the absence of which can
contribute to persistent income inequality and slower economic growth.
Yet until now little was known about the global reach of the
financial sector and financial inclusion--the extent of account
ownership and the use of formal payments, saving, and credit--or about
the degree to which groups such as the poor are excluded from formal
financial systems. Systematic indicators of the use of different formal
and informal financial services were lacking for most countries.
As the first public database of indicators that consistently
measure people's use of financial products across countries and
over time, the Global Findex database fills a big gap in existing data
on financial inclusion. The data show wide gaps in account penetration
between high-income and developing countries and between the poor and
the rich within countries. Also, the data show variation in the use of
formal and informal saving and credit mechanisms. By enabling
policymakers to identify segments of the population excluded from the
formal financial sector, the data can help provide insights for the
design and prioritization of reforms.
APPENDIX A
Selected Questions from the Global Findex Survey
The text of this appendix is taken verbatim from the survey.
This next section is about banks and financial institutions. We are
trying to understand how people across the world use financial
institutions and how available they are to people. Please remember that
all information you provide is completely confidential.
--Do you, either by yourself or together with someone else,
currently have an account at any of the following places? An account can
be used to save money, to make or receive payments, or to receive wages
and remittances. Do you currently have an account at [surveyor reads A
and B]? (24)
1 Yes
2 No
A A bank or credit union (or other formal financial institution,
where applicable, like a cooperative in Latin America)
B A post office
--A debit card, sometimes called an ATM card, is a card that allows
you to make payments, get money, or buy things and the money is taken
out of your bank account right away. Do you have a debit card?
1 Yes
2 No
--A credit card is like a debit card but the money is not taken
from your account right away. You get credit to make payments or buy
things, and you can pay the balance off later. Do you have a credit
card?
1 Yes
2 No
--In a typical month, about how many times is money deposited into
your personal account(s)? This includes cash or electronic deposits, or
any time money is put into your account(s) by yourself or others.
[surveyor reads 1 through 4 and codes one response only]
1 0
2 1-2 times
3 3-5 times
4 6 times or more
--In a typical month, about how many times is money taken out of
your personal account(s)? This includes cash withdrawals, electronic
payments or purchases, checks, or any other time money is removed from
your account(s) by yourself or others. [surveyor reads 1 through 4 and
codes one response only]
1 0
2 1-2 times
3 3-5 times
4 6 times or more
--When you need to get cash (paper or coins) from your account(s),
do you usually get it ...? [surveyor reads 1 through 4 and codes one
response only; respondents can also answer that they do not withdraw
cash (coded as 5)]
1 At an ATM
2 Over the counter in a branch of your bank or financial
institution
3 Over the counter at a retail store
4 From some other person who is associated with your bank or
financial institution
--When you put cash (paper or coins) into your account(s), do you
usually do it ...? [surveyor reads 1 through 4 and codes one response
only; respondents can also answer that they do not withdraw cash (coded
as 5)]
1 At an ATM
2 Over the counter in a branch of your bank or financial
institution
3 Over the counter at a retail store
4 From some other person who is associated with your bank or
financial institution
--In the past 12 months, have you used your account(s) to ...?
[surveyor reads A through D]
1 Yes
2 No
A Receive money or payments for work or from selling goods
B Receive money or payments from the government
C Receive money from family members living elsewhere
D Send money to family members living elsewhere
--Please tell me whether each of the following is a reason why you,
personally, DO NOT have an account at a bank, credit union or other
financial institution. [surveyor reads and rotates A through G]
1 Yes
2 No
A They are too far away
B They are too expensive
C You don't have the necessary documentation (ID, wage slip)
D You don't trust them
E You don't have enough money to use them
F Because of religious reasons
G Because someone else in the family already has an account
--In the past 12 months, have you saved or set aside any money?
1 Yes [surveyor continues with next question]
2 No [surveyor skips to question 74]
--In the past 12 months, have you saved or set aside any money by
...? [surveyor reads A and B]
1 Yes
2 No
A Using an account at a bank, credit union, or microfinance
institution
B Using an informal savings club or a person outside the family
(insert local example)
--In the past 12 months, have you borrowed any money from ...?
[surveyor reads A through E]
1 Yes
2 No
A A bank, credit union, or microfinance institution
B A store by using installment credit or buying on credit
C Family or friends
D Employer
E Another private lender
--Do you currently have a loan you took out for any of the
following reasons? [surveyor reads A through E]
1 Yes
2 No
A To purchase your home or apartment
B To purchase materials or services to build, extend, or renovate
your home or apartment
C To pay school fees
D For emergency/health purposes
E For funerals or weddings
--In the past 12 months, have you used a mobile phone to ...?
[surveyor reads A through C]
1 Yes
2 No
A Pay bills
B Send money
C Receive money
APPENDIX B
Account Penetration by Country
Percent of adults with an account at a formal financial institution
All Poorest Richest
Country adults 20% 20%
Afghanistan 9 0 20
Albania 28 7 43
Algeria 33 22 50
Angola 39 31 40
Argentina 33 19 55
Armenia 17 16 24
Australia 99 97 100
Austria 97 93 99
Azerbaijan 15 13 25
Bahrain 65 64 60
Bangladesh 40 33 54
Belarus 59 37 75
Belgium 96 92 96
Benin 10 5 24
Bolivia 28 12 50
Bosnia and Herzegovina 56 35 69
Botswana 30 12 48
Brazil 56 33 71
Bulgaria 53 29 76
Burkina Faso 13 6 25
Burundi 7 3 23
Cambodia 4 0 12
Cameroon 15 14 22
Canada 96 91 98
Central African Republic 3 1 9
Chad 9 6 26
Chile 42 19 68
China 64 39 83
Colombia 30 9 62
Comoros 22 9 40
Congo, Dem. Rep. 4 0 18
Congo, Rep. 9 1 20
Costa Rica 50 30 69
Croatia 88 75 94
Cyprus 85 76 89
Czech Republic 81 70 88
Denmark 100 99 100
Djibouti 12 4 34
Dominican Republic 38 19 62
Ecuador 37 22 61
Egypt 10 5 25
El Salvador 14 1 32
Estonia 97 94 99
Finland 100 99 100
France 97 96 100
Gabon 19 4 38
Georgia 33 25 50
Germany 98 97 100
Ghana 29 17 61
Greece 78 75 85
Guatemala 22 8 52
Guinea 4 2 10
Haiti 22 4 49
Honduras 21 15 47
Hong Kong 89 78 98
Hungary 73 58 86
India 35 21 56
Indonesia 20 8 48
Iran 74 63 80
Iraq 11 5 13
Ireland 94 88 97
Israel 90 88 92
Italy 71 61 81
Jamaica 71 71 67
Japan 96 94 96
Jordan 25 16 33
Kazakhstan 42 30 55
Kenya 42 19 85
Korea, Rep. 93 86 94
Kosovo 44 24 59
Kuwait 87 86 90
Kyrgyz Republic 4 1 11
Laos 27 16 27
Latvia 90 82 95
Lebanon 37 20 54
Lesotho 18 8 29
Liberia 19 3 41
Lithuania 74 66 87
Luxembourg 95 97 94
Macedonia 74 66 85
Madagascar 6 1 19
Malawi 17 9 36
Malaysia 66 45 82
Mali 8 4 18
Malta 95 93 96
Mauritania 17 7 43
Mauritius 80 66 94
Mexico 27 12 58
Moldova 18 6 36
Mongolia 78 68 89
Montenegro 50 34 67
Morocco 39 0 0
Mozambique 40 21 56
Nepal 25 15 39
Netherlands 99 98 99
New Zealand 99 100 99
Nicaragua 14 4 31
Niger 2 0 6
Nigeria 30 12 62
Oman 74 63 92
Pakistan 10 5 19
Panama 25 18 44
Paraguay 22 4 51
Peru 20 6 47
Philippines 27 4 54
Poland 70 60 82
Portugal 81 64 87
Qatar 66 47 80
Romania 45 25 69
Russia 48 34 61
Rwanda 33 23 42
Saudi Arabia 46 32 51
Senegal 6 4 13
Serbia 62 47 70
Sierra Leone 15 4 30
Singapore 98 98 98
Slovak Republic 80 66 85
Slovenia 97 92 100
Somalia 31 12 58
South Africa 54 35 78
Spain 93 91 92
Sri Lanka 69 52 87
Sudan 7 4 15
Swaziland 29 12 44
Sweden 99 99 100
Syria 23 20 28
Taiwan 87 77 90
Tajikistan 3 1 6
Tanzania 17 3 45
Thailand 73 64 87
Togo 10 2 18
Trinidad and Tobago 76 70 85
Tunisia 32 14 63
Turkey 58 46 72
Turkmenistan 0 0 1
Uganda 20 7 37
Ukraine 41 21 59
United Arab Emirates 60 57 58
United Kingdom 97 97 97
United States 88 74 90
Uruguay 24 7 49
Uzbekistan 23 15 27
Venezuela 44 27 54
Vietnam 21 6 35
West Bank and Gaza 19 8 34
Yemen 4 0 9
Zambia 21 8 50
Zimbabwe 40 22 63
Source: Global Findex.
ACKNOWLEDGMENTS We thank Franklin Alien, Oya Pinar Ardic Alper,
Thorsten Beck, Massimo Cirasino, Robert Cull, Pascaline Dupas, Maya
Eden, Tilman Ehrbeck, Michael Fuchs, Xavi Gine, Markus Goldstein, Ruth
Goodwin-Groen, Rafil Hernandez-Coss, Richard Hinz, Jake Kendall, Aart
Kraay, Alexia Latortue, Sole Martinez Peria, Ignacio Mas-Ribo, Jonathan
Morduch, Nataliya Mylenko, Mark Napier, Douglas Pearce, Bikki Randhawa,
Liliana Rojas-Suarez, Richard Rosenberg, Armida San Jose, Kinnon M.
Scott, Peer Stein, Gaiv Tata, Jeanette Thomas, Klaus Tilmes, Asli Togan
Egrican, Augusto de la Torre, Rodger Voorhies, and Alan Winters for
their valuable and substantive comments during various stages of the
project. The team is also appreciative of the excellent survey execution
and related support provided by Gallup, Inc., under the direction of Jon
Clifton. We are especially grateful to the Bill & Melinda Gates
Foundation for providing financial support that made the collection and
dissemination of the data possible. This paper was prepared with
outstanding assistance from Atisha Kumar and Douglas Randall. This
paper's findings, interpretations, and conclusions are entirely
those of the authors and do not necessarily represent the views of the
World Bank, their executive directors, or the countries they represent.
The authors report no potential conflict of interest.
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Comments and Discussion
COMMENT BY PASCALINE DUPAS
The new Global Findex data set that Ash Demirguc-Kunt and Leora
Klapper are introducing in this paper is arguably among the most
important multicountry, repeated-cross-sectional data sets being
collected in this decade. It provides much-needed statistics on the use
of financial services around the world at a time when interest in such
services is peaking. Indeed, almost 40 years after Muhammad Yunus made
the first microloan--the first of many exciting developments in
financial services for the poor--only now are we beginning to see
concerted research efforts to map the reach and effect of these tools on
households around the world.
Country-specific micro-level studies have suggested that financial
inclusion today may be much lower than what an informed observer would
suppose from the ubiquitous media accounts. For example, recent
randomized trials suggest that at best a quarter of households take up
available loans from microfinance institutions in India, Mexico, and
Morocco (Banerjee and others 2013, Crepon and others 2011, Angelucci,
Karlan, and Zinman 2012). Dupas and coauthors (forthcoming) document
that only 20 percent of households in rural western Kenya have a bank
account, and ongoing censuses in Uganda and Malawi reveal comparable
rates (see Dupas, Karlan, and Robinson 2013). But such micro studies
tend to be clustered in a few countries or areas, and absent more
wide-reaching data, it is difficult to understand how representative and
applicable these results are. Efforts to date to provide more-exhaustive
survey evidence have remained limited: the FinScope survey sponsored by
the U.K. Department for International Development covers only 15
countries (14 of them in Africa), and the European Bank for
Reconstruction and Development's Life in Transition Survey covers
only 35 countries in Europe and Central Asia.
Given the lack of survey evidence, until the Global Findex data set
was introduced, the most extensive efforts to estimate rates of
financial inclusion worldwide had to rely on triangulation exercises
between aggregate banking data from bank regulators and microfinance
institutions (to get absolute numbers of accounts, loans, and the like)
and population counts. Thorsten Beck, Demirguc-Kunt, and Ross Levine
(2007) focus on the formal banking sector and estimate that across the
54 countries in their sample, the median number of deposit accounts per
1,000 people is 529, and across a subset of 44 of those countries, the
median number of loans per 1,000 people is 80. Patrick Honohan (2008)
builds on this effort and proposes a "composite indicator" of
access to both formal and semiformal financial services. This indicator
is constructed from estimates of the number of bank accounts and the
size of deposits relative to the total population. These estimates are
generated as functions of the number of microfinance accounts and GDP
per capita, respectively; these functions in turn are based on
correlations observed in the few countries with enough available data.
The first thing that can be done with the Global Findex database is
to check the accuracy of such extrapolation exercises. Because the
Honohan (2008) estimates are from 5 to 8 years before the Global Findex
measures, one should not expect a perfect correlation between the two,
but after downloading both sets of measures, I found the correlation to
be surprisingly high: 0.85 between Honohan's estimate of
"access to financial services" (Honohan 2008, table 2) and the
share of the population that "has an account at a formal financial
institution" in the Global Findex database (see figure 3 in the
paper). What is more, further calculations by Alberto Chaia and
coauthors (2009) based on Honohan's figures find that (as their
title states) "half the world is unbanked," which is also a
key finding from the Global Findex. I found this high rate of
consistency across the two types of sources to be very good news: it
means that estimation exercises like those of Beck and others (2007),
Honohan (2008), and Chaia and others (2009) are relatively accurate in
providing comparable cross-country measures.
But gauging and analyzing covariates of cross-country variation can
take one only so far toward a better understanding of financial
inclusion worldwide. A key advantage of the Global Findex is that it
provides information on within-country distributions, as well as on
basic individual-level covariates of financial inclusion such as income,
attitudes toward formal banks, and self-reported reasons for using or
not using a given financial tool or service. The paper highlights a few
of the many interesting patterns that the data uncover. For example, the
authors report that 13 percent of unbanked adults worldwide mention lack
of trust as a reason for not saving with a formal institution. This
suggests that reliability and quality concerns about the supply side, as
also highlighted for the specific context of western Kenya in Dupas and
others (2012), are relevant in a number of other countries, especially
in Africa and South Asia. Another stunning statistic from the Global
Findex is the extremely low (6 percent) rate of coverage with rainfall,
crop, or livestock insurance among those in developing economies whose
livelihood is farming, fishing, or forestry. Yet another interesting
finding is that mobile money services are, at least initially,
disproportionately used by those already banked. (In Kenya, the pioneer
in terms of mobile money, 57 percent of mobile money users have a formal
bank account, compared with a population mean of 42 percent.) Having
access to such basic statistics will help shape the financial inclusion
research agenda for years to come.
The other extremely appealing feature of the Global Findex data is
that they are set to be collected triennially for at least three rounds.
The first round, analyzed in this paper, took place in 2011, and two
rounds will follow in 2014 and 2017. The timing is particularly
fortuitous: the first round was collected before the mobile money
"revolution" really took hold: the survey reveals that as of
2011, only 16 percent of African adults had ever used mobile money, and
fewer than 5 percent of adults in all other regions had. By 2014 this
percentage will likely have increased considerably. The Global Findex
data set will therefore provide a series of snapshots over a
tremendously exciting decade, during which the definition of financial
inclusion itself may change as new tools such as mobile phone-based
savings accounts are further developed and adopted. Among other things,
the data will help advance research into how these new financial tools
interact with the more established tools and services.
The data set is a major advance, but there remains scope for
improvement in the next round of surveying. One such improvement would
be to add some measurement of "financial fragility." In a
recent Brookings Paper, Annamaria Lusardi, Daniel Schneider, and Peter
Tufano (2011) examined this issue for U.S. households by looking at
households' capacity to raise $2,000 in 30 days. The authors found
that nearly half of the households surveyed would probably not be able
to do so. Adding a similar question to the Global Findex survey would
enable researchers to examine how financial inclusion correlates with
financial fragility. In the existing Global Findex survey, households
are not asked whether they are credit rationed, nor are they asked
anything about the size of their current savings. Asking people directly
how much they have in savings may be too sensitive and prone to
underreporting, but asking whether and how they could access a given sum
(which would have to be adjusted to the context, for example by keeping
the ratio to the local poverty line constant) would be a great, if
indirect, way to get a sense of how deep financial inclusion is. In
ongoing work, some colleagues and I asked such a question of unbanked
rural households in Kenya, Malawi, and Uganda between 2010 and 2012. The
results, presented in table 1, show that most of the poor households in
our various samples have very limited savings, and that an
individual's financial resources are to a great extent a function
of the depth of that individual's social network.
The data reported in table 1 resonate with the present paper's
finding that 65 percent of non-account owners mention "not having
enough money to use one" as one reason for not using a bank account
(with close to half of them reporting it as their only reason).
Demirguc-Kunt and Klapper interpret this as evidence that "under
current circumstances, the costs of having an account outweigh its
benefits" (emphasis in the original). They also write that this
finding "speaks to the fact that having a formal account is not
costless in most parts of the world and that individuals with small or
irregular income streams might view an account as an unnecessary
expense, given the relatively high cost." I would like to qualify
this interpretation. Work that coauthors and I have done in Kenya and
other parts of East Africa suggests that individuals reporting "not
having enough money" to use an account would not necessarily
immediately start using accounts provided to them completely free of
charge. In Kenya, Jonathan Robinson and I have found that very few
bicycle-taxi drivers actively took up (that is, made at least two
deposits within a year in) accounts that they could open at no cost to
themselves, whereas about 40 percent of market vendors did (Dupas and
Robinson 2013a). More generally, only 18 percent of a representative
sample of unbanked households actively used accounts that were free to
open and maintain (Dupas and others 2012). Replication studies ongoing
in Uganda and Malawi suggest rates no higher than 30 percent. The fact
that these accounts have withdrawal fees may be part of the explanation
for the low take-up, but many households do not report fees as a
barrier, instead simply stating that they do not have enough money to
save. But when provided with lockboxes (a simple metal box with a
deposit slit on top and a lock and key), the same Kenyan households
mentioned above made very regular deposits and saved in just 3 months as
much as would have taken them 18 months to save in an account. Thus,
households were in fact able to save more than they themselves thought
they could. This may be due to a feature that lockboxes offer that
formal accounts may not. In essence, access to these in-house savings
tools make people pennywise: they provide a place to store amounts that
are too small to warrant a trip to the bank, thus safeguarding funds
that would otherwise be kept close at hand and so be at risk of being
frittered away on unplanned small expenditures, such as sweets for the
children or soda for visitors. (1)
What this all means is that not having enough money to warrant a
trip to the bank to deposit it is itself a function of financial
inclusion, defined more broadly to encompass use of informal financial
tools that facilitate the day-to-day management of even very small sums,
helping to grow them into bankable lump sums. The stunning findings from
the Global Findex suggest that a better understanding of what type of
tools can help unbanked households save as much as they need in order to
become "bankable" is an important avenue for future research.
Demirguc-Kunt and Klapper have provided the scientific community a much
needed database and tool, and I hope that they will make each wave of
data easily available online through a one-click download.
REFERENCES FOR THE DUPAS COMMENT
Angelucci, Manuela, Dean Karlan, and Jonathan Zinman. 2012.
"Win Some Lose Some? Evidence from a Randomized Microcredit Program
Placement Experiment by Compartamos Banco." J-PAL working paper.
Abdul Latif Jameel Poverty Action Lab, Massachusetts Institute of
Technology.
Banerjee, Abhijit, Esther Duflo, Rachel Glennerster, and Cynthia
Kinnan. 2013. "The Miracle of Microfinance? Evidence from a
Randomized Evaluation." Working paper. Massachusetts Institute of
Technology and Northwestern University.
Beck, Thorsten, Ash Demirguc-Kunt, and Ross Levine. 2007.
"Finance, Inequality and Poverty: Cross Country Evidence."
Journal of Economic Growth 12, no. 1: 211-52.
Chaia, Alberto, Aparna Dalal, Tony Goland, Maria Jose Gonzalez,
Jonathan Morduch, and Robert Schiff. 2009. "Half the World is
Unbanked." Framing Note. Financial Access Initiative.
www.microfinancegateway.org/gm/document1.9.40671/25.pdf.
Crepon, Bruno, Esther Duflo, Florencia Devoto, and William
Pariente. 2011. "Impact of Microcredit in Rural Areas of Morocco:
Evidence from a Randomized Evaluation." J-PAL working paper. Abdul
Latif Jameel Poverty Action Lab, Massachusetts Institute of Technology.
Dupas, Pascaline, and Jonathan Robinson. 2013a. "Savings
Constraint and Microenterprise Development: Evidence from a Field
Experiment in Kenya." American Economic Journal: Applied Economics
5, no. 1: 163-92.
--. 2013b. "Why Don't the Poor Save More? Evidence from
Health Savings Experiments." American Economic Review 103, no.
4:1138-71.
Dupas, Pascaline, Dean Karlan, and Jonathan Robinson. 2013.
"Expanding Access to Formal Savings Accounts in Malawi, Uganda,
Chile, and the Philippines." New Haven, Conn.: Innovations for
Poverty Action. www.poverty-action.org/ project/0477.
Dupas, Pascaline, Sarah Green, Anthony Keats, and Jonathan
Robinson. Forthcoming. "Challenges in Banking the Rural Poor:
Evidence from Kenya's Western Province." In NBER Volume on
African Economic Successes, edited by S. Johnson, S. Edwards, and D.
Weil. University of Chicago Press.
Honohan, Patrick. 2008. "Cross-Country Variation in Household
Access to Financial Services." Journal of Banking and Finance 32:
2493-2500.
Lusardi, Annamaria, Daniel Schneider, and Peter Tufano. 2011.
"Financially Fragile Households: Evidence and Implications."
BPEA (Spring): 83-134.
(1.) Dupas and Robinson (2013b) show that similar boxes enable
households to reach a given savings goal much faster.
Table 1. Survey Evidence on Financial Fragility in Kenya,
Malawi, and Uganda Percent of respondents
"If you had an emergency that
required [indicated amount]
urgently, where you would
get the money?"
Kenya Malawi
(1,000 shillings (1,000 kwacha
[approximately [approximately
Answer (a) equal to] $12) equal to] $7)
Would borrow from friends or 43 39
relatives
Would sell agricultural products 14 3
Would work more 14 21
Would sell assets 14 7
Would exclusively use savings 13 7
Would borrow from savings club 6 3
Would not be able to find the 0 18
money
"If you had an emergency that
required [indicated amount]
urgently, where you would
get the money?"
Uganda
(10,000 shillings
[approximately
Answer (a) equal to] $5)
Would borrow from friends or 50
relatives
Would sell agricultural products 9
Would work more 9
Would sell assets 14
Would exclusively use savings 15
Would borrow from savings club 2
Would not be able to find the 0
money
Source: Household survey data collected by the author and
Jonathan Robinson along with Anthony Keats (Kenya, 2010),
Dean Karlan, and Diego Ubfal (Malawi and Uganda, 2011) for
ongoing projects.
(a.) Respondents could give more than one answer.
COMMENT BY LILIANA ROJAS-SUAREZ
This paper by Ash Demirguc-Kunt and Leora Klapper makes an
important contribution to the literature by making public, and providing
the first analysis of data from, the Global Findex, a new World Bank
database comprising a variety of indicators on the use of financial
products by individuals around the world. The database was constructed
from 2011 survey data collected in interviews by Gallup, Inc., with
selected adults in 148 countries.
Before Global Findex, the available data on the characteristics of
populations excluded from formal financial institutions remained scarce
and limited to a few regional efforts. (1) Despite widespread
recognition of the welfare and efficiency benefits associated with
improved financial inclusion, and despite the large number of
initiatives, public and private, already in place around the world
aiming to increase the percentage of the population (households and
firms) with access to financial services, (2) cross-country analyses
faced severe constraints due to lack of comparable data.
In my view, one cannot overstate the importance of this new
database. Not only does it open up wide-ranging possibilities for future
research, but it also supports the efforts of policymakers, multilateral
organizations, and private donors, who now have a new tool to guide
their policies and activities for improving financial inclusion. In this
regard, the authors' plans to update the survey in a couple of
years are of particular importance.
This paper is part of a series of analytical papers by the authors
and their colleagues that utilize the Global Findex database. The
authors present and analyze some key stylized facts derived from the
survey, with a significant focus on within-country differences in
financial inclusion based on individual characteristics. Their results
are consistent with previous (scattered) evidence, and from that
perspective they validate a number of policymakers' concerns. For
example, as expected, the authors find that the percentage of
individuals in developing countries who have an account at a formal
financial institution increases by income quintile; this is not the case
in most developed economies, where a large majority of the population at
all income levels have access to such financial products. Moreover,
across the developing world, the percentage of adult women with an
account at a formal financial institution is significantly below that
for men, and this gender gap persists across income levels (quintiles)
within a given country.
The Global Findex data can be used for many other kinds of
analysis. My own research has already benefited from the availability of
this new database. Like the authors, I am interested in understanding
the determinants of the use of financial products, and I have gained
some further insights by focusing on the cross-country behavior of
variables at the macro (aggregate) level. These findings, which I will
summarize here, complement the authors' results.
The existing literature allows one to identify four categories of
obstacles to financial inclusion at the country level, which affect
either the demand for or the supply of financial services or both (see
Rojas-Suarez and Gonzales 2010 and Rojas-Suarez and Amado forthcoming):
socioeconomic constraints, macroeconomic factors, characteristics of the
operations of the formal financial system, and institutional
deficiencies. Here I will present and discuss some simple correlations
between these obstacles and one of the Global Findex indicators of
financial inclusion, namely, the percentage of the adult population with
an account at a formal institution.
With regard to the first category, it is generally expected that
countries that score high on indicators of social development, such as
access to high-quality health and education services, will also enjoy
high levels of financial inclusion. As discussed by Stijn Claessens
(2005), financial exclusion is often part of a broader social exclusion,
which is related, among other factors, to differences in education, type
of employment, and training. Income inequality has also been cited as a
socioeconomic factor influencing financial inclusion. Yet another
factor, one that affects both the demand for and the supply of financial
services, is the percentage of the population classified as middle class
(see Rojas-Suarez and Amado forthcoming for further discussion).
My figure 1 shows the correlation between financial inclusion and
an index of social development, constructed by equally weighting the
first two of the three components of the Human Development Index (HDI)
of the United Nations, which relate to health and education (the third
relates to income). The figure shows a strong positive correlation: as
expected, the developed countries display the highest values of both
financial inclusion and social development. The very high correlation
between our social development variable and GDP per capita (0.84)
supports the authors' finding that the latter explains a
significant part of the variation in account penetration across
countries.
[FIGURE 1 OMITTED]
Turning to the second category affecting financial inclusion,
namely, macroeconomic factors, I show in figure 2 the correlation
between the volatility of inflation (measured as the coefficient of
variation of inflation during 1990-2010) and financial inclusion. High
volatility of inflation captures well the adverse effects of
macroeconomic instability on the willingness of the population to hold
accounts in formal financial institutions. In economies with very high
and volatile inflation, depositors have experienced significant losses
in their real wealth. It is therefore not surprising that Argentina and
Ukraine, both of which suffered from hyperinflation in the 1990s, are
among the countries with the lowest rates of financial inclusion. By
contrast, in Thailand, which has a history of low inflation volatility,
the share of the adult population with an account at a formal financial
firm is similar to that in the developed countries.
The policy lesson is straightforward: stimulating demand for
financial services requires that individuals have trust that the real
value of their payments and savings instruments will be preserved. If
this trust is lacking, not only will the use of financial services
remain dismal, but deposits in financial institutions will tend to be
short-term as depositors stand ready to withdraw their funds at the
first sign of financial system difficulties.
[FIGURE 2 OMITTED]
A wide variety of characteristics pertaining to financial
firms' conduct of their operations are included in the third
category of obstacles to financial inclusion. Among these are
inefficiencies in collection and information processing, which may cause
prohibitively high documentation requirements; insufficient numbers of
branches, ATMs, points of sale, and other forms of financial firms'
penetration, especially in small rural communities; and high
administrative costs, which tend to increase the fixed costs of
extending loans and maintaining accounts. The authors have discussed
this type of obstacles extensively both in this paper and elsewhere
(see, for example, Allen and others 2012).
As an example at the country level, figure 3 shows the negative
correlation between financial inclusion and a commonly used measure of
banking system inefficiency: the ratio (in percent) of bank overhead
costs to total assets. As expected, developed economies display the
lowest ratios.
Another potential obstacle to financial inclusion relates to the
concentration of the banking system. High levels of bank concentration
may deter banks from lending to individuals and to small and medium-size
enterprises, since there are no competitive incentives to assess the
quality of relatively riskier potential borrowers. Since the extension
and repayment of bank loans are usually conducted through deposits in
bank accounts, this argument could find support in a negative
correlation between bank concentration and the share of adults with a
formal financial account.
[FIGURE 3 OMITTED]
However, recent studies have argued that, in a given country, the
relationship between bank concentration and financial inclusion is
strongly affected by the quality of its institutions--the fourth
category. (See, for example, Claessens 2005 and Rojas-Suarez and Amado
forthcoming.) Financial systems can develop more fully and reach a
larger segment of the population in countries with adequate observance
and enforcement of the rule of law, political stability, and respect for
creditors' and debtors' rights. In particular, when contracts
between creditors and debtors are observed and enforced, depositors have
a stronger incentive to entrust their savings to banks and other formal
financial institutions. How do bank concentration and the quality of
institutions interrelate to affect financial inclusion? In countries
with weak institutions, where the enforcement of contracts is very
difficult, the oligopolistic power arising from a highly concentrated
banking system leads to greater discrimination against riskier borrowers
(who tend to be low-income individuals and smaller firms), and financial
inclusion suffers. Such discrimination is not as commonly seen in a more
competitive banking system.
Figure 4 illustrates these relationships. The top panel shows a
negative, but low, correlation between bank concentration, measured as
the percentage of total system assets held by the country's three
largest banks, and financial inclusion (the correlation coefficient is
only -0.2). However, a very different picture arises in the bottom
panel, where the bank concentration variable is adjusted by an
additional variable measuring the quality of institutions. This
variable, called "rule of law," is taken from the World
Bank's Worldwide Governance Indicators and measures agents'
confidence in and commitment to abiding by the rules of the society; the
quality of contract enforcement, police, and the courts; and the
likelihood of crime and violence. (I have rescaled the original variable
to range from zero to 100.) The adjusted bank concentration variable is
obtained simply by multiplying it by the rule of law variable.
Taken together, the two panels of figure 4 suggest that although
bank concentration might impinge on financial inclusion directly, it
exerts its most important effect through the quality of institutions.
Two examples will clarify this point. First, it is clear from the top
panel that the developed countries are distributed across the whole
range of bank concentration, and thus little can be said about any
differences in concentration between developed and developing countries
(except that the negative relationship in the figure is driven by the
latter). However, when the bank concentration variable is adjusted by
the quality of institutions (bottom panel), most of the developed
economies migrate toward the upper right corner of the scatterplot. For
this group of countries as a whole, the relatively high level of
institutional quality seems a more relevant factor than bank
concentration for understanding the behavior of financial inclusion.
Second, consider Malaysia and Nicaragua. These two developing
countries share similar ratios of bank concentration (top panel), but
the quality of institutions is much higher in Malaysia than in
Nicaragua. Therefore, when bank concentration is adjusted for
institutional quality, Malaysia lies well to the right of Nicaragua.
This is fully consistent with a higher level of financial inclusion in
the former country than in the latter.
Although correlations such as these provide valuable insights,
fully understanding the obstacles to financial inclusion requires a
deeper analysis. The first column of my table 1 reports results of an
ordinary least squares regression in which financial inclusion (as
defined above) is the dependent variable. Consistent with the discussion
above, I include as explanatory variables the five variables that appear
on the horizontal axes in figures 1 through 4. In addition, the
regression includes two dummy variables: the first identifies developed
economies (again, those classified by the World Bank as high-income
countries), and the second emerging markets (those classified as
upper-middle-income countries). (3) The regression was estimated for a
sample of 116 countries.
[FIGURE 4 OMITTED]
Except for the coefficient on the bank overhead costs variable
(which had a p value of 0.18), all the estimated coefficients were
statistically significant. The regression's [R.sup.2] was 0.8359.
For each country category, the last three columns of table 1 show the
implied contributions of the various obstacles to financial inclusion,
calculated by multiplying each variable's estimated coefficient by
the variable's average value.
To illustrate how to interpret the table, consider the group
"other developing countries." According to the table, if all
factors affecting financial inclusion identified in the regression were
absent, this group would enjoy, on average, a financial inclusion ratio
(a share of adults holding a formal financial account) of 55 percent
(the value of the constant). With the obstacles present, however, the
predicted financial inclusion ratio for this group of countries reaches
only 25 percent. Similarly, absent the identified obstacles, 66 percent
of the adult population in emerging markets would have an account in a
formal financial institution (the sum of the constant and the
coefficient on the dummy for emerging markets). However, because of the
obstacles, the predicted ratio is only 43 percent.
The most important conclusions to be drawn from the table are as
follows. First, the degree of social development matters greatly. The
low level of social development in developing countries on average, and
to a lesser extent in emerging markets, hampers financial inclusion
through both demand and supply factors. Second, only in the developed
economies does the high level of institutional quality at least partly
offset the adverse effect of bank concentration on financial inclusion
(the sum of the implied contributions for the bank concentration
variable alone and for the bank concentration x rule of law interaction
is positive). In contrast, in emerging markets and other developing
countries on average, low institutional quality cannot counteract the
financial exclusion effects of bank concentration. Third, relative to
the other factors, inflation volatility and banking inefficiencies play
smaller roles as obstacles to financial inclusion.
My discussion here provides just a taste of the potential uses of
the Global Findex database and is intended as a complement to the
authors' analysis and their ongoing research. The authors are to be
congratulated not only for this paper but for their rich research agenda
on financial inclusion.
REFERENCES FOR THE ROJAS-SUAREZ COMMENT
Allen, F., A. Demirguc-Kunt, L. Klapper, and M. S. Martinez Peria.
2012. "The Foundations of Financial Inclusion: Understanding
Ownership and Use of Financial Accounts." Policy Research Working
Paper no. 6290. Washington: World Bank.
CAF Banco de Desarrollo de America Latina. 2011. Servicios
Financieros para el Desarrollo: Promoviendo el Acceso en America Latina.
Reporte de Economia y Desarrollo series. Caracas.
publicaciones.caf.com/publicacion?id=1502.
Claessens, Stijn. 2005. "Universal Access to Financial
Services: A Review of the Issues and Public Policy Objectives."
Presented at the OECD-World Bank Fifth Services Experts Meeting, Paris
(February).
Rojas-Suarez, Liliana, and Maria Alejandra Amado. Forthcoming.
"Improving Access to Financial Services in Latin America: Policy
Implications and Lessons from Worldwide Experiences." Washington:
Center for Global Development.
Rojas-Suarez, Liliana, and Veronica Gonzales. 2010. "Access to
Financial Services in Emerging Powers: Facts, Obstacles and Policy
Implications." Paris: OECD Development Center (March)
www.oecd.org/dev/pgd/45965165.pdf.
(1.) As the authors note, two of the best-known household surveys
that include data on the use of financial services are FinScope, a
private sector initiative funded by the U.K. Department for
International Development, which collects data for 14 African countries
and Pakistan, and the European Bank for Reconstruction and
Development's Life in Transition Survey, which covers 35 countries
in Europe and Central Asia. To these may be added the recent survey by
CAF Banco de Desarrollo de America Latina (2011) that covers 17 major
Latin American cities.
(2.) Ongoing initiatives go beyond microfinance activities and
include innovations to improve the use of payments, savings, and
insurance products. Two of the best-known initiatives are Kenya's
M-PESA money transfer service (operated by a mobile phone provider) and
the nonbank correspondent model in Brazil, which allows banks to reach
remote populations through the use of existing nonbank networks, such as
retail stores and post offices. A common characteristic of these two
initiatives is that they rely heavily on technological advances in
connectivity.
(3.) The third group of countries (omitted in the regression)
consists of all other developing countries (those classified as
lower-middle-income and low-income countries).
Table 1. Obstacles to Financial Inclusion: Implied
Contributions from Regression Analysis (a)
Regression
Independent variable coefficient
Social underdevelopment -36.29 ***
Bank concentration -0.29 ***
Bank concentration x rule
of law 0.004 ***
Volatility of inflation -3.11 **
Ratio of bank overhead costs
to assets -28.33
Dummy for developed
economy 37.94 ***
Dummy for emerging market 10.61 **
Constant 55.3 ***
Contribution (b) (percentage points)
Other
Developed Emerging developing
Independent variable economies markets countries
Social underdevelopment -4.1 -8.2 -14.5
Bank concentration -17.3 -17.1 -17.8
Bank concentration x rule
of law 19.2 10.1 7.1
Volatility of inflation -2.9 -4.9 -4.0
Ratio of bank overhead costs
to assets -0.8 -1.5 -1.3
Dummy for developed
economy 37.94 0 0
Dummy for emerging market 0 10.61 0
Constant
Source: Author's regressions.
(a.) The dependent variable is the percentage of the adult
population with a bank account in a formal institution.
Asterisks indicate statistical significance at the
*** p < 0.01 or the ** p < 0.05 level.
(b.) Measured as the regression coefficient for the
indicated obstacle multiplied by that obstacle's average
value in the indicated country group.
GENERAL DISCUSSION
While sympathetic to nonstandard approaches based on behavioral
economics, information asymmetry, and the like, Christopher Carroll
wondered whether the problem of financial inclusion might not be better
addressed using the textbook approach that assumes perfect rationality.
Looking at the list of countries with lowest participation in the
banking sector--Argentina, Greece, and Italy, for example--was enough to
suggest that many people around the world might have good reason not to
hold bank accounts. Financial inclusion, Carroll thought, might turn out
to be a good overall indicator of the quality of a country's
institutions: low inclusion could correlate with the degree to which a
society and its institutions are dysfunctional. If that was the case,
efforts to increase the penetration of bank account ownership would not
address the underlying cause of noninclusion.
Donald Kohn added that any deliberate effort to increase financial
inclusion would surely bring in people who are not well educated and who
lack familiarity with financial products. Encountering a sophisticated
modern financial system for the first time, these individuals might not
understand the risks they are taking or, worse, might suffer
exploitation. Kohn observed that this danger was not limited to
developing countries: the subprime episode in the United States could be
viewed as an attempt at financial inclusion that ended badly. The U.S.
response has been mainly to increase disclosure and transparency, but
there have also been proposals aimed, in a sense, at disinclusion by
restricting the types of financial instruments available to the general
public. Kohn asked whether the authors had investigated whether the
newly banked individuals in their samples actually understood what they
were getting, particularly on the credit side, and whether their
findings pointed to any measures that could be taken to improve their
understanding.
David Romer, also following up on Carroll's comment, cited
some specific findings in the paper that could be interpreted as
rational behavior on the part of the nonincluded. For example, the paper
reported that a large fraction of the nonincluded chose not to have a
bank account because they had too little money to make it worthwhile.
That seemed to Romer a plausibly rational response. Others said that
they had a relative with a bank account, so that in effect they did have
access to financial services even though they were counted as excluded.
Still others cited long distances to the nearest bank branch as a reason
for not having an account. Was that a market failure, or was it an
equilibrium outcome? These questions needed to be sorted out, Romer
argued, before useful policy interventions could be proposed. Romer also
asked the authors to clarify their distinction between an
individual's explicit choice of how much to save and the standard
definition of the same individual's saving as simply income minus
consumption.
Ricardo Reis suggested that the present degree of inclusion in the
banking system in some countries might actually be too high, in the
sense that many people have bank accounts but lack access to other
financial services that are arguably more important to their welfare,
such as insurance against catastrophic shocks and vehicles for
retirement saving.
Following up on Rojas-Suarez's discussion and Kohn's
comment, Reis reminded the Panel that in the United States for much of
the 19th century, a bank was an extremely risky place to keep one's
savings: the history of banks in that largely unregulated era was rife
with fraud. If bank regulation today in some developing countries is
comparably weak, that argued against pushing for greater inclusion. On a
similar note, Reis observed that, according to the paper, 78 percent of
Greeks still had bank accounts in 2011, well into that country's
financial crisis. That indicated to him a failure on the account
holders' part to appreciate the risks they were taking.
For Benjamin Friedman, one of the paper's most interesting
findings was that many people lack bank accounts because they do not
trust the banks. He suspected, however, that their distrust could arise
for different reasons. One might be the fear of bank fraud or
recklessness that Reis had mentioned, but another might be fear of
government expropriation: many Cambodians, for example, are wary of
banks because they remember when the Pol Pot regime closed all the
country's banks overnight and simply expropriated all the accounts.
An entirely different potential motivation was the fear that the bank
would not keep one's affairs secret. One needed to distinguish
among these different reasons before deciding what sort of policy
intervention was called for.
Michael Klein pointed out that people might choose to remain
unbanked for transactional reasons as well as because of lack of savings
or for other reasons. In countries where only cash is widely accepted,
the transactions-related features of a bank account--checkwriting and
debit cards, for example--would have little value.
Justin Wolfers remarked that the literature on financial access
seemed to take it as given that financial access was a good thing, and
indeed, to those who have it, it clearly is. But its benefits might be
less obvious to those who have never had it. Wolfers also identified
what he saw as a possible problem with the authors' empirical
strategy. The paper claimed to be measuring access to financial
services, meaning the ability to use those services if one wanted to,
but what it really measured was an equilibrium quantity: those who,
given the equilibrium price of holding a bank account, and taking into
account all costs, actually used one. Just as not everyone who has
access to spinach at the supermarket eats spinach, so, too, not everyone
who has access to banking services opens a bank account.
Responding to the discussion, Leora Klapper answered Romer's
question by saying that she and her coauthor were interested not in the
quantity of saving as such but in the behavioral decision to save: what
people did with the money that they were deliberately putting aside for
a specific purpose, such as for a large purchase or for retirement. What
the authors found was that these savings were typically placed in
informal ROSCAs (rotating savings and credit associations) or under the
mattress. Since the former are plagued by fraud and the latter is
extremely unsafe, it seemed to the authors natural to suppose that a
formal bank account, or at least a lockbox at a bank, was a better place
to hold that money. Klapper also noted that in some countries such as
Bangladesh, microfinance institutions are important sources of formal
savings and credit, used by many people who do not have bank accounts.
Klapper also acknowledged that the histories of some countries,
including some in Eastern Europe, provided their citizens ample reason
to be distrustful of banks, and that lack of understanding of bank terms
and conditions could result in exploitation or cause people to make poor
use of their accounts. As an example, the failure of small savers to
understand the concept of a minimum balance often leads to their savings
being eroded. Klapper therefore emphasized that greater financial
literacy was a necessary complement to sound regulation and consumer
protection: it could contribute not only to broadening the use of safer
financial services, but also to building trust in the financial system
in countries with a history of financial scandal and expropriation.
Asli Demirguc-Kunt sought to clarify that the paper was not
claiming or assuming that more people around the world should have bank
accounts. Rather, she and Klapper were interested in identifying the
perceived barriers to holding bank accounts among those who might want
to use them. They believed that if market failures are indeed preventing
broader access to bank accounts, the result was to limit people's
ability to save for education or for other worthwhile purposes, and to
limit businesses' access to capital for growth and expansion. In
presenting their findings to policymakers, they took pains to emphasize
the importance of responsible access. It was true that a majority of
their respondents chose not to hold bank accounts because they did not
have enough money to make it worthwhile, and some fraction of these
presumably would see no need for an account even if they had more money.
But a large fraction of the rest--30 percent of the total
sample--identified other barriers such as high costs, remoteness, or
instruments whose design does not conform with the potential user's
religious beliefs. These were issues that a well-crafted policy might be
able to remedy.
ASLI DEMIRGUC-KUNT
World Bank
LEORA KLAPPER
World Bank
(1.) See, for example. King and Levine (1993), Beck, Demirguc-Kunt,
and Levine (2007), Beck, Levine, and Loayza (2000), Demirguc-Kunt and
Levine (2009), Klapper, Laeven, and Rajan (2006), and World Bank
(2008a).
(2.) For a detailed literature review see World Bank (2008a) and
the references therein. Campbell (2006) also provides an overview of the
household finance field.
(3.) The Bill & Melinda Gates Foundation funded three triennial
rounds of data collection through the complete questionnaire. The next
data collection will be in 2014.
(4.) The database and the full questionnaire are available at
www.worldbank.org/global findex. Appendix A reproduces selected
questions relevant to this paper. The questionnaire is also available in
15 languages at go.worldbank.org/5XL9LXK6B0.
(5.) In a few instances, surveyors and their supervisors reported
that respondents were somewhat taken aback at the series of questions,
given the personal nature of the topic. This concern was particularly
relevant in countries with large security risks, such as Mexico and
Zimbabwe, and in countries where personal finance is widely regarded as
a private matter, such as Cameroon, Italy, and Portugal. There were also
reports from the field that the terminology and concepts used in the
survey were entirely new to some respondents. Although efforts were made
to include simple definitions of such terms as "account" and
"debit card," the unfamiliarity and complexity of the topic
were still reported to be a hurdle in several countries, including
Afghanistan, Cambodia, Chad, and rural Ukraine. Overall, however, the
rate of "don't know" or "refuse" answers was
very low. For the core questions (those not conditioned on the response
to other questions), "don't know" or "refuse"
responses made up fewer than 1 percent of the total and no more than 2
percent in any world region.
(6.) The worldwide aggregates omit countries for which Gallup
excludes more than 20 percent of the population in the sampling either
because of security risks or because the population includes non-Arab
expatriates.
These excluded countries are Algeria, Bahrain, the Central African
Republic, Madagascar, Qatar, Somalia, and the United Arab Emirates. Iran
is also excluded because the data were collected in that country using a
methodology inconsistent with that used for other countries (the survey
was carried out by phone from Turkey). The exclusion of Iran has a
nontrivial effect on regional aggregates because its population is
larger and wealthier than the populations of most other countries in the
Middle East and North Africa. For example, account penetration in the
region is estimated to be 18 percent when Iran is excluded, but 33
percent when it is included.
(7.) In some countries oversamples are collected in major cities or
areas of special interest. In addition, in some large countries, such as
China and Russia, sample sizes of at least 4,000 are collected.
(8.) For details on the data collection dates, sample sizes,
excluded populations, and margins of error, see
www.worldbank.org/globalfindex.
(9.) The exact question in the LITS survey is "Does anyone in
your household have a bank account?"
(10.) On the provider side, the International Monetary Fund
collects indicators of financial outreach such as the number of bank
branches and automated teller machines (ATMs) per capita and per square
kilometer, as well as the number of loan and deposit accounts per
capita, directly from country regulators. These data sets are important
sources of basic cross-country indicators developed at a relatively low
cost. Yet indicators based on data collected from financial service
providers have several important limitations. First, data are collected
from regulated financial institutions only and thus provide a fragmented
view of financial access. Second, aggregation can be misleading because
of multiple accounts or dormant accounts (see Beck, Demirguc-Kunt, and
Martinez Peria 2008 for a discussion). Most important, this approach
does not allow disaggregation of financial service users by income or
other characteristics. That leaves policymakers unable to identify those
segments of the population with the lowest use of financial services,
such as the poor, women, or youth.
(11.) Croatia, the Czech Republic, Estonia, Greece, Hungary,
Poland, Singapore, and the Slovak Republic are the only high-income
countries included where phone coverage is less than 80 percent.
(12.) The Kish grid is a table of numbers used to select the
interviewee in each household. First, the interviewer records the name,
sex, and age of all permanent household members aged 15 and above,
whether or not they are present, and then numbers them starting with the
oldest and ending with the youngest. Second, the interviewer finds the
column number of the Kish grid that corresponds to the last digit of the
questionnaire number, and the row number for the number of eligible
household members. The number in the cell where column and row intersect
determines the person selected for the interview. In countries where
cultural restrictions dictate matching interviewer and interviewee by
sex, respondents are randomly selected using the Kish grid from among
all eligible adults of the interviewer's sex.
(13.) In the latest-birthday method an interview is attempted with
the adult in the household who had the most recent birthday.
(14.) According to the latest available data from the World
Bank's World Development Indicators, there are 5.08 billion adults
aged 15 and above worldwide.
(15.) Exceptions include Italy (with an account penetration of 71
percent) and the United States (88 percent).
(16.) See, for example, Chawla, Betcherman, and Banerji (2007), who
provide an overview of the challenges of aging populations in Eastern
Europe and the former Soviet Union.
(17.) Because of the sensitivity of household finances and the
inhibitions brought about by face-to-face surveys, the Global Findex
survey did not probe deeply into the practices of "under the
mattress" saving in the home.
(18.) The Gallup World Poll collects information on the ownership
of credit cards but not their use.
(19.) Data on the main purpose of outstanding loans were gathered
only in developing countries, because Gallup, Inc., enforces a time
limit for phone interviews conducted in high-income countries, limiting
the number of questions that can be added to the core questionnaire.
Respondents were asked to choose from a list of reasons for borrowing,
so it is possible that reasons not listed (borrowing to start a
business, for example) are also common.
(20.) Among all respondents, 12 percent chose none of the given
reasons for not having an account.
(21.) The institutional barriers to financial inclusion are further
analyzed in Allen and others (2012).
(22.) For more on documentation requirements and safeguards against
money laundering, see Yikona and others (2011) and Financial Action Task
Force (2011).
(23.) Gardiner Harris, "India Aims to Keep Money for Poor Out
of Others' Pockets," New York Times, January 5, 2013.
(24.) For all questions the choices of "don't know"
and "refused" are also included as possible responses (results
not shown).
Table 1. Country-Level Regressions Explaining Financial Inclusion (a)
Dependent variable
Percent of adults reporting having an account at a
formal financial institution
Independent
variable 1-1 1-2 1-3 1-4
Logarithm of 0.105 *** 0.122 *** 0.157 ***
GDP per (0.014) (0.014) (0.012)
capital (d)
Low-income -0.436 ***
country (0.106)
(1) (e)
Lower-middle- -0.442 ***
income (0.079)
country
(2) (e)
Upper-middle- -0.325 ***
income (0.053)
country
(3) (e)
Domestic 0.185 ***
credit to the (0.037)
private sec
tor (percent
of GDP) (d)
Gini index (d) -0.488 **
(0.190)
Constant 0.203 -0.606 *** -0.582 ***
(0.241) (0.093) (0.126)
No. of 134 134 123 110
observations
(countries)
[R.sup.2] 0.772 0.783 0.747 0.662
p value of
F statistic
[H.sub.0]:
(1) = (2) 0.904
[H.sub.0]:
(2) = (3) 0.015
Dependent variable
Percent
Percent reporting
reporting use of
formal formal
Independent saving (b) credit (c)
variable 1-5 1-6
Logarithm of
GDP per
capital (d)
Low-income -0.316 *** -0.063 ***
country (0.027) (0.014)
(1) (e)
Lower-middle- -0.313 *** -0.052 ***
income (0.025) (0.013)
country
(2) (e)
Upper-middle- -0.276 *** -0.039 ***
income (0.024) (0.013)
country
(3) (e)
Domestic
credit to the
private sec
tor (percent
of GDP) (d)
Gini index (d)
Constant 0.407 *** 0.135 ***
(0.017) (0.009)
No. of 139 139
observations
(countries)
[R.sup.2] 0.622 0.160
p value of
F statistic
[H.sub.0]:
(1) = (2) 0.931 0.443
[H.sub.0]:
(2) = (3) 0.127 0.310
Source: Authors' regressions using Global Findex and World
Development Indicators data.
(a.) Each column reports results of a single ordinary least squares
regression. Standard errors are in parentheses. Asterisks denote
significance at the *** 1 percent, ** 5 percent, and * 10 percent
level.
(b.) Percent of adults reporting having saved or put aside money at a
formal financial institution in the past 12 months.
(c.) Percent of adults reporting having borrowed from a formal
financial institution in the past 12 months.
(d.) Data are for 2011 or the most recent year available.
(e.) Dummy variable equal to 1 when the country is a member of the
indicated country income group, and zero otherwise. High-income
countries are the omitted category.
Table 2. Individual-Level Probit Regressions Explaining Financial
Inclusion (a)
Dependent variable and country income group
Has an account at a formal financial
institution
Within-country Lower- Upper-
income quintile Low middle middle High
Bottom (1) -0.133 *** -0.185 *** -0.239 *** -0.051 ***
(0.009) (0.014) (0.019) (0.007)
Second (2) -0.113 *** -0.148 *** -0.177 *** -0.037 ***
(0.009) (0.013) (0.016) (0.007)
Third (3) -0.090 *** -0.103 *** -0.135 *** -0.019 **
(0.007) (0.011) (0.014) (0.008)
Fourth (4) -0.050 *** -0.079 *** -0.075 *** -0.004
(0.008) (0.010) (0.010) (0.008)
No. of observations 25,369 34,144 35,820 30,681
p value of F statistic
[H.sub.0]: (1) = (2) 0.02 ** 0.00 *** 0.00 ** 0.02 **
[H.sub.0]: (2) = (3) 0.02 ** 0.00 *** 0.00 *** 0.01 *
[H.sub.0]: (3) = (4) 0.00 *** 0.02 ** 0.00 *** 0.10
Dependent variable and country income group
Saved at a formal financial institution
within last 12 months
Within-country Lower- Upper-
income quintile Low middle middle High
Bottom (1) -0.084 *** -0.110 *** -0.152 *** -0.208 ***
(0.010) (0.011) (0.012) (0.018)
Second (2) -0.075 *** -0.090 *** -0.116 *** -0.137 ***
(0.006) (0.013) (0.007) (0.013)
Third (3) -0.049 *** -0.070 *** -0.085 *** -0.087 ***
(0.006) (0.008) (0.008) (0.013)
Fourth (4) -0.032 *** -0.045 *** -0.052 *** -0.032 ***
(0.006) (0.006) (0.010) (0.012)
No. of observations 25,369 34,144 35,820 30,681
p value of F statistic
[H.sub.0]: (1) = (2) 0.34 0.01 *** 0.00 *** 0.00 ***
[H.sub.0]: (2) = (3) 0.00 *** 0.10 * 0.00 *** 0.00 ***
[H.sub.0]: (3) = (4) 0.03 ** 0.00 *** 0.00 *** 0.00 ***
Dependent variable and country income group
Borrowed from a formal financial
institution within last 12 months
Within-country Lower- Upper
income quintile Low middle middle High
Bottom (1) -0.035 *** -0.036 *** -0.044 *** -0.011
(0.013) (0.008) (0.010) (0.009)
Second (2) -0.038 *** -0.034 *** -0.036 *** -0.012
(0.010) (0.010) (0.007) (0.009)
Third (3) -0.033 *** -0.018 *** -0.034 *** 0.000
(0.008) (0.007) (0.008) (0.007)
Fourth (4) -0.021 *** -0.014 *** -0.022 ** 0.002
(0.007) (0.005) (0.009) (0.007)
No. of observations 25,369 34,144 35,820 30,681
p value of F statistic
[H.sub.0]: (1) = (2) 0.72 0.75 0.27 0.90
[H.sub.0]: (2) = (3) 0.41 0.02 ** 0.83 0.12
[H.sub.0]: (3) = (4) 0.11 0.48 0.09 0.73
Source: Authors' regressions using Global Findex and World
Development Indicators data.
(a.) Each column reports results of a single probit regression of the
indicated financial inclusion measure (a dummy variable equal to 1 if
the respondent meets the indicated criterion, and zero otherwise) on
country fixed effects, the respondent's within-country income
quintile (the top quintile is the omitted category), and the
following individual characteristics: sex, age, age squared, rural
versus urban residence, education, log of household size, marital
status, and whether employed. All regressions account for
stratification and clustering in the survey design. Data are for 2011
or the most recent year available. Standard errors are in
parentheses. Asterisks denote significance at the *** 1 percent, ** 5
percent, and * 10 percent level.
Figure 1. Formal Account Penetration, by Country Income Group
Country income group (a)
Adults with an account at a formal
financial institution (percent)
Low 24%
Lower-middle 29%
Upper-middle 57%
High 89%
Source: Authors' calculations using Global Findex data.
(a.) Low-income countries are those with gross national income
per capita less than $1,025 in 2011; lower-middle-income countries,
$1,026-$4,035; upper-middle-income countries, $4,036-$12,475;
high-income countries, $12,476 or more.
Note: Table made from bar graph.
Figure 5. Formal Account Penetration in the Poorest Quintile,
Selected High-Income Countries
Percent of adults without
a formal account
United States 25.78
Gin (a) = 37.8
United Kingdom 3.05
Gini = 34.5
Australia
Gini = 33.6 2.90
Canada
Gini = 32.4 8.83
Source: Authors' calculations using Global Findex and Organization
for Economic Cooperation and Development (OECD) data.
(a.) A higher Gini index indicates greater income inequality.
Note: Table made from bar graph.
