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					                                              Reaching out:
            Access to and use of banking services across countries




              Thorsten Beck, Asli Demirguc-Kunt and Maria Soledad Martinez Peria*


                                            First draft: April 2005
                                             This draft: July 2006

                                            Abstract:
This paper is a first attempt at measuring financial sector outreach and investigating its
determinants. First, we present new indicators of banking sector outreach across 99 countries,
constructed from aggregate data provided by bank regulators. Second, we show that our
indicators closely predict harder to collect micro-level statistics of household and firm use of
banking services and are also associated with measures of firm financing obstacles in the
expected way. Finally, we explore the association between our outreach indicators and standard
determinants of financial sector depth. We find many similarities but also some differences in the
determinants of outreach and depth.



JEL Classification: G2, G21, O16
Keywords: financial development, banking sector outreach, financing obstacles




*
  The authors are with the World Bank’s research department. Corresponding author: Thorsten Beck, World Bank,
1818 H Street NW, Washington DC 20433, Ph: 202-473-3215, Fax: 202-522-1155, E-mail: TBeck@worldbank.org.
We would like to thank Ross Levine for insightful discussions and Jerry Caprio, Stijn Claessens, Augusto de la
Torre, Xavier Giné, Patrick Honohan, Leora Klapper, Anjali Kumar, Inessa Love, Susana Sánchez and seminar
participants at Bangladesh Bank, the Central Bank of Argentina, the Inter-American Development Bank, the
International Monetary Fund, and the World Bank for useful suggestions. Andrew Claster, Subika Farazi, and
Hamid Rashid provided excellent research assistance. This paper’s findings, interpretations, and conclusions are
entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors,
or the countries they represent.
                                        Reaching out:
               Access to and use of banking services across countries




                                            Abstract:
This paper is a first attempt at measuring financial sector outreach and investigating its
determinants. First, we present new indicators of banking sector outreach across 99 countries,
constructed from aggregate data provided by bank regulators. Second, we show that our
indicators closely predict harder to collect micro-level statistics of household and firm use of
banking services and are also associated with measures of firm financing obstacles in the
expected way. Finally, we explore the association between our outreach indicators and standard
determinants of financial sector depth. We find many similarities but also some differences in the
determinants of outreach and depth.



JEL Classification: G2, G21, O16
Keywords: financial development, banking sector outreach, financing constraints




                                                1
1. Introduction

        Banking sector outreach varies significantly across countries. In Ethiopia there is less

than one branch per 100,000 people, while in Spain there are 96. In Albania, there are four loans

per 1,000 people and the average loan size is 15 times GDP per capita, while in Poland there are

774 loans per 1,000 people and the average size of loans is only one third of GDP per capita.

This paper is a first attempt at measuring outreach and at investigating its determinants. First, we

present new indicators of banking sector outreach across 99 countries, constructed on the basis of

aggregate banking data provided by bank regulators. Second, we show that our indicators closely

predict harder to collect micro-level statistics of household and firm use of banking services and

are associated with measures of firm financing obstacles in the expected way. Finally, we

explore the association between our outreach indicators and standard determinants of financial

sector depth.

        To date, the literature on financial sector development has focused on measuring,

assessing the determinants, and evaluating the economic impact of financial sector depth,

commonly represented by the ratio of private sector credit to GDP.1 In contrast, perhaps due to a

dearth of adequate data (see Honohan 2004b), little is known about the breadth or outreach of

financial systems across countries, their determinants, and their impact on desirable development

outcomes.2

        The importance of broad financial services outreach can be justified in several ways. The

first argument builds on the theoretical and empirical finance and growth literature, as surveyed

by Levine (2005) and the importance of a well-developed financial system for economic

development and poverty alleviation (Beck, Demirguc-Kunt and Levine 2004 and Honohan

1
  See Levine (2005) for a review of this literature.
2
  While there are numerous country case studies on measuring access to financial services (and in some cases its
consequences) at the household and/or firm level, there are no systematic cross country comparisons of access to
finance. See Claessens (2006) for an overview.

                                                       2
2004a). Financial market imperfections such as informational asymmetries, transactions costs

and contract enforcement costs are particularly binding on poor or small entrepreneurs who lack

collateral, credit histories, and connections. Without broad access, such credit constraints make

it difficult for poor households or small entrepreneurs to finance high-return investment projects,

reducing the efficiency of resource allocation and having adverse implications for growth and

poverty alleviation (Galor and Zeira, 1993).3                Second, one of the channels through which

financial development fosters economic growth is through the entry of new firms (Klapper,

Laeven and Rajan, 2006) and the Schumpeterian process of “creative destruction.” This implies

that talented newcomers have access to the necessary financial services, including external

finance. Access to finance for large parts of the population is thus seen as important to expand

opportunities beyond the rich and connected and also as crucial for a thriving democracy and

market economy (Rajan and Zingales, 2003). Third, access to finance can also have important

effects on technological progress and the generation of ideas, since without the perspective that

ideas will be financed, individuals in the economy will have little incentive to think creatively

(King and Levine, 1993). Finally, access to finance can be seen on a similar level as access to

basic needs such as safe water, health services, and education (Peachey and Roe, 2004).

         Access to financial services, however, is not synonymous to the use of financial services.

Economic agents might have access to financial services, but might decide not to use them,

either for socio-cultural reasons, or because opportunity costs are too high. Therefore, it is

necessary to carefully distinguish between two different concepts when discussing financial




3
  Capital market imperfections are at the core of theoretical models that show redistributing wealth from the rich to
the poor would enhance aggregate productivity and therefore growth. In the absence of well-functioning capital
markets and broad access to financial system, it is this wealth redistribution that creates investment opportunities.
See Banerjee and Newman (1993) and Aghion and Bolton (1997).

                                                         3
sector outreach, namely: (i) access and the possibility to use financial services and (ii) actual use

of financial services.4

           In order to characterize banking sector outreach across countries, this paper introduces

two classes of indicators that correspond to the different concepts of access to and use of

financial services. Specifically, we present data across developed and developing countries on

the number of branches and ATMs relative to population and area, to capture the geographic and

demographic penetration of the banking system. Data on the number of branches are available

for 98 countries and statistics on the number of ATMs were obtained for 89 countries. We

interpret higher branch and ATM intensity in demographic and geographic terms as indicative of

higher possibilities of access and the opportunity to use financial services by households and

enterprises.

           To measure the actual use of deposit and credit services, we present indicators on the

number of loan and deposit accounts relative to population and on the average loan and deposit

sizes relative to GDP per capita. Loan data are available for 44 countries, while data on deposits

were obtained for 54 countries. We interpret higher number of loan and deposit accounts per

capita and lower average loan and deposit amounts relative to GDP per capita as indicating use

of deposit and credit services by a greater share of the population and by “smaller” clients.

           We conduct a number of “reality checks” to establish the validity of our indicators as

measures of outreach. In particular, we show that our aggregate and admittedly crude indicators

of outreach are good predictors of measures of outreach based on hard to collect micro data, such

as the share of households with bank accounts and the share of small firms with bank loans.

Thus, in the absence of survey measures on the use of deposit and loan services for a broad

cross-section of countries, our aggregate indicators provide an adequate approximation of the


4
    Also see the discussion in Beck and de la Torre (2006).

                                                              4
extent to which household and firms use deposit and loan services, respectively. Also,

reassuringly, we are able to show that our outreach indicators are associated with perception-

based measures of firm financing obstacles in the expected way – in countries with greater

outreach firms report facing lower financing obstacles.

       The final part of our empirical analysis explores cross-country variations in outreach. In

particular, we examine whether outreach is associated with the same factors that are found to

drive financial sector depth. Our findings reveal that both outreach and depth indicators are

positively associated with the overall level of economic development, the quality of the

institutional environment, the degree of credit information sharing, the level of initial

endowments, and the development of the physical infrastructure. At the same time, outreach and

depth indicators are negatively correlated with the cost of enforcing contracts and the degree of

government ownership of banks. However, only financial sector depth is positively associated

with the level of creditor rights protection. Finally, historical variables, such as legal origin and

religion have a less consistent impact on financial outreach relative to depth. In particular,

economies with a French legal origin seem to have lower levels of depth, but not consistently

lower levels of outreach. Similarly, while predominantly Protestant societies appear to have

deeper financial sectors than Catholic societies, the same cannot be said consistently about

banking sector outreach.

       Notwithstanding the novelty of these indicators of financial access and use, it is important

to be cognizant of the limitations of this data collection effort. First, unlike indicators used in the

finance and growth literature, to date, our data are only available at one point in time. This

prevents us at this stage from exploring the relationship between financial outreach and

economic development over time and from exploiting within-country variation in banking

system outreach. Second, our data and analysis focus exclusively on two banking services,


                                                  5
deposit-taking and lending, and thus abstract from other important financial services, such as

payment and insurance, for which data have proven more difficult to obtain. In addition, we

concentrate on banks and, therefore, do not take into account other financial service providers,

such as microfinance institutions or cooperatives, due to the scarcity of data on these institutions.

Third, our indicators are crude quantity-based indicators of outreach that ignore new delivery

channels of financial services, fail to consider the costs of accessing and using banking services,

and are unable to capture differences in the size and geographical (urban versus rural)

distribution of loans and deposits.             Also, the proposed indicators might be subject to

measurement error. In particular, failure by poor or dysfunctional economies to measure outreach

adequately might be problematic, since it might lead to a disproportionate downward bias in

measured access and an overstating of the economic consequences of improved outreach.5

Finally, our indicators measure equilibrium outcomes, affected by both demand and supply

factors.

           Despite these shortcomings, we see this data compilation effort and the associated

analysis as a useful and important first step towards developing more accurate indicators of

access to and use of financial services. The remainder of the paper is organized as follows.

Section 2 describes the data collection process and introduces our indicators of outreach. Section

3 discusses the cross-country variation in outreach. Section 4 presents a number of reality

checks to show the validity of our data. In particular, we demonstrate the predictive power of our

indicators relating them to household- and firm-survey based statistics on financial services use

and also show that our indicators are associated with perception-based measures of firm

financing obstacles as expected. Section 5 examines the association between the outreach



5
 This measurement error seems to be more of a problem in the case of loan and deposit accounts than in the case of
physical branch and ATM outlet, as the latter are easier to verify by supervisory entities than the former.

                                                        6
indicators and country characteristics that have been found to affect financial sector depth.

Section 6 concludes and offers directions for future research.



2. Data: indicator sources and definitions

        This paper presents a new data set that seeks to measure access to and use of banking

services across 99 countries in 2003-2004. To gather these data, we developed a questionnaire

that we circulated among bank regulatory agencies across countries. The main questions from

this survey focus on obtaining information on the number of bank branches, number of ATMs,

and the aggregate number and value of bank loans and deposits.6 For countries that did not

provide responses to our questionnaire, we gathered data from alternative sources, including

government publications and official websites. A detailed list of all the sources used for each

country can be found in appendix Table A.1.

        Our survey refers exclusively to deposit money banks – all financial institutions that have

“liabilities in the form of deposits transferable by check or otherwise usable in making

payments” (IMF 1984, p. 29) - for two main reasons. First, in a majority of countries, the

banking sector intermediates most of the funds in the economy. Second, the banking sector is

regulated and statistical information for this sector is easier to obtain and better in quality than

data for other non-bank financial service providers (such as credit unions, cooperative, finance

companies, and microfinance institutions), which are often not regulated.

        Using data gathered through our survey of bank regulatory bodies and from other

sources, we put together the following indicators of banking sector outreach:

             1- Geographic branch penetration: number of bank branches per 1,000 km2


6
 We also included questions on payment transactions (value and number) and on the size distribution and
rural/urban split of bank loans and deposits. However, most countries were unable to provide answers to these
questions; hence it is not possible to conduct a systematic analysis of these data.

                                                        7
             2- Demographic branch penetration: number of bank branches per 100,000 people

             3- Geographic ATM penetration: number of bank ATMs per 1,000 km2

             4- Demographic ATM penetration: number of bank ATMs per 100,000 people

             5- Loan accounts per capita: number of loans per 1,000 people

             6- Loan-income ratio: average size of loans to GDP per capita

             7- Deposit accounts per capita: number of deposits per 1,000 people

             8- Deposit-income ratio: average size of deposits to GDP per capita

          Indicators (1) through (4) measure outreach of the financial sector in terms of access to

banks’ physical outlets. The data for each of these indicators, across 98 countries in the case of

branches and 89 countries in the case of ATMs, are shown in Table 1.7 The indicators of

branches and ATMs per square kilometer help characterize the geographic penetration of the

banking sector. They can also be interpreted as proxies for the average distance of a potential

customer from the nearest physical bank outlet. Higher geographic penetration would thus

indicate smaller distance and easier geographic access. Per capita measures of branches and

ATMs are used to capture the demographic penetration of the banking sector. They proxy for

the average number of people served by each physical bank outlet.                            Higher demographic

penetration would indicate fewer potential clients per branch or ATM and, therefore, easier

access.

          Indicators (5) through (8) measure the use of banking services. We focus exclusively on

bank deposits and loans because these are the main services offered by banks for which we were

able to gather information across countries. In particular, we collected information on the

number and value of loans for 44 countries, and information on the number and value of deposits

for 54 countries. This information is shown in Table 2. We interpret higher figures of indicators

7
  For reference, this table also shows the ratio of private credit to GDP to measure financial sector depth and the
level of GDP per capita to proxy for economic development in each country.

                                                           8
based on the number of loans and deposits to signal greater use of services. On the other hand,

we interpret higher values for the average size of loans or deposits to GDP per capita to indicate

that banking services are more limited in use, since they are likely only to be affordable to

wealthier individuals or larger enterprises.

       Despite the fact that our outreach indicators are easy to understand and interpret, they are

not without specific shortcomings, aside from the general limitations discussed above. In

particular, area- and population-based ratios of the number of branches and ATMs assume a

uniform distribution of bank outlets within a country’s area and across its population, while in

most countries bank branches and ATMs are concentrated in urban areas of the country and are

accessible only to some individuals. At the same time, because one individual or firm may

receive more than one loan or have more than one deposit account, the number of loans and

deposit accounts is far from being a perfect proxy of the number of people that use these services

in a country. Also, the average size of loans and deposits to GDP per capita might not be

representative of the value of services that a typical individual might receive. Nevertheless, in

the next section we show how these indicators are correlated with the underlying statistics we

care about – the actual percentage of households and firms that use banking services in a

country.



3. Characterizing access to and use of banking services across countries

       Indicators of banking outreach vary sharply across countries. Table 3 Panel A presents

descriptive statistics for all outreach indicators. The number of branches per area varies from less

than 0.18 branches per 1,000 square kilometers (the lowest 5th percentile of the distribution) for

Bolivia, Botswana, Guyana, Kazakhstan, and Namibia to more than 119.65 branches per 1,000

square kilometers (the top 5th percentile of the distribution) for Bahrain, Belgium, Malta,


                                                 9
Netherlands, and Singapore. The median number of branches per 1,000 square kilometers is

4.80, which is representative of the values for Estonia and Sweden.

       Ethiopia, Honduras, Madagascar, Tanzania, and Uganda have less than 1.24 branches per

100,000 people (bottom 5th percentile), while Austria, Belgium, Portugal, Italy, and Spain have

more than 49.74 branches per 100,000 people (top 5th percentile). The median figure for the

number of branches per 100,000 people is 8.42. Indonesia, Turkey, Iran, Colombia, Kuwait, and

Poland have indicators close to this value.

       In terms of number of ATMs per area, Tanzania, Zambia, Nepal, Madagascar, and

Guyana are at the bottom of the distribution with less than 0.26 ATMs per 1,000 square

kilometers, while the top 5th percentile includes Korea, Malta, Bahrain, Japan, and Singapore

with more than 253.12 ATMs per 1,000 square kilometers. The median for the number of ATMs

per 1,000 square kilometers is 10.07 and Sri Lanka and Costa Rica are close to this value.

       The number of ATMs per 100,000 people is lowest for Bangladesh, Nepal, Madagascar,

Pakistan, and Tanzania, with less than 0.58 ATMs per 100,000. On the other hand, Canada,

Japan, Portugal, Spain, and the United States have more than 101.46 ATMs per 100,000 people.

The median value for this indicator is 16.63, representative for Mexico, Malaysia, Lebanon,

Thailand, and Venezuela.

       The median value of the number of loans per capita is 80.57 loans per 1,000 people, with

the values for Peru, Ecuador, Jordan and Namibia close to this figure. The lowest 5th percentile

of the distribution is 6.35 loans per 1,000 people, including Albania, Uganda, and Madagascar.

The top 5th percentile of this distribution includes countries with more than 700.56 loans per

1,000 people, such as Greece, Israel, and Poland.

       The median value across countries of the loan-income ratio is 3.75, representative for

Lithuania and Singapore. The top 5th percentile for this indicator is 17.91 and includes Belgium,


                                               10
Madagascar, and Bolivia. On the other hand, the bottom 5th percentile is 0.68 and includes El

Salvador, Turkey, and Poland.

        In terms of the number of deposits per capita, the median value of this indicator is 529

deposit accounts per 1,000 people, representative for Guyana and Venezuela. The top 5 th

percentile of the distribution for this indicator is 2,569, (that is, more than 2.5 deposit accounts

per capita) which includes Austria, Belgium, and Denmark. The bottom 5th percentile has fewer

than 62 deposit accounts per 1,000 people, among them Bolivia, Madagascar, and Uganda.

        For 50% of countries in our sample, the deposit-income ratio is below 0.66; Argentina,

Turkey, and Ecuador are close to this value. The top 5th percentile for the distribution of the

average size of deposits to GDP per capita is 6.40, including Zimbabwe, Madagascar, and

Lebanon. On the other hand, with a deposit-income ratio below 0.11, values for Russia, Iran,

and the Dominican Republic fall in the lowest 5th percentile of the distribution of this ratio.

        Panel B of Table 3 and Figures 1a, 1b, and 1c show a positive association between GDP

per capita and indicators of the number of branches, ATMs, loans, and deposits. This table along

with Figure 1d also show that both loan-income and deposit-income ratios are negatively

correlated with GDP per capita, although not significantly at the 5% level in the case of loans.8

At the same time, indicators of the number of banking outlets and loan and deposit accounts tend

to be positively correlated with each other and with the standard measure of financial sector

depth, the share of private credit to GDP.




8
  The insignificant correlation between GDP per capita and loan-income ratio might be due to better access to
mortgage loans with economic development, which might offset the negative impact of GDP per capita on average
loan-income ratio.

                                                      11
4. Conducting “reality checks” on the proposed outreach indicators

        To a large degree the validity and usefulness of the aggregate banking sector outreach

indicators we propose will depend on whether they track measures of outreach based on

household or firm level surveys which are much harder to obtain and update. Thus, we conduct

some simple “reality checks” to justify the use of our indicators in the absence of micro-level

measures.

        First, we examine the correlation between user-based data from household and firm

surveys and our indicators of deposit and loan use and we run regressions to test whether our

aggregate outreach indicators are useful in predicting these observable micro-level data on

outreach.9 Second, we regress perception-based measures of firm financing constraints on our

outreach indicators to determine whether our indicators relate to the severity of financing

obstacles in the expected way, namely, whether in countries with greater outreach firms report

facing lower financing obstacles.10

        The country-level data we use on the percentage of households that have a bank account

were constructed and compiled from different household surveys by Claessens (2006) and

Gasparini et al. (2005). Data on the share of small firms with bank loans and on perceived firm

financing constraints come from the World Business Environment Survey (WBES). The WBES

is a unique database of firm-level surveys conducted in 1999 and 2000 for over 10,000 firms in

81 countries.11 The survey includes a broad variety of firms of different ownership structures,

sectors, legal forms, and – most importantly – different sizes; 80% of the surveyed firms are

small or medium-sized, with fewer than 500 employees. Aside from questions on the actual use

of bank loans, firm managers were asked to rate whether access to finance was an obstacle to the

9
  We are grateful to Patrick Hohonan for this suggestion.
10
   In the case of firm financing obstacles we place less emphasis on prediction since the obstacles measure used is
based on firm perceptions and is intrinsically unobservable.
11
   For a detailed discussion of the survey see Batra, Kaufmann, and Stone (2002).

                                                        12
operation and growth of their firm. Responses vary between a rating of one (no obstacle), two

(minor obstacle), three (moderate obstacle) and four (major obstacle).

           Table 3B shows the country-level correlation between our outreach indicators and the

survey-based measures of the percentage of households with bank accounts and the share of

small firms with bank loans. The share of households with bank accounts is positively and

significantly correlated at the 1% significance level with the geographic and demographic branch

and ATM indicators and with the loan and deposit per capita ratios. Also, as expected the share

of households with bank accounts is negatively correlated with the loan and deposit income

ratios, but in the case of the former the pairwise correlation is not statistically significant. The

share of small firms with bank loans is significantly correlated with branches, ATMs and loans

per capita and with ATMs per square kilometer. However, the share of deposits per capita and

the deposit income ratio are not significantly correlated with the share of small firms with bank

loans.

           To assess the power of our outreach indicators in predicting the use of financial services

by households, we regress the share of households with bank accounts (Household share) on the

log of number of deposit accounts per capita (Deposits per capita) and the log of number of

branches per square kilometer (Branches per km2), with robust standard errors in parentheses:12

Household share = 0.493+ 0.186 log(Deposits per capita) + 0.055 log(Branches per km2) (1)
                 (0.309) (0.04)                           (0.018)

Estimating equation (1) with 19 observations yields an R2 of 74%.                               Both variables enter

significantly at the 1% level. The regression results suggest that a larger number of accounts

relative to the population and a higher geographic branch penetration are both positively

associated with more households having bank accounts. Table 4, columns 1 and 2, present both



12
     We include our outreach indicators in log transformation as they are distributed with fat tails.

                                                             13
the actual share of households with bank accounts and the predicted share from regression (1). 13

The correlation between the predicted share of household and the actual share of households with

bank accounts is 87%.

         The results in equation (1) remain largely unchanged if we focus separately on the rural

and the urban share of households with bank accounts as dependent variables. Also, if we focus

on the share of households in the lowest income quintile with bank accounts and re-run the above

regression, both deposits per capita and branches per km2 enter positively and significantly.14

         A regression of small firm share on the log of the number of loan accounts per 1,000

people (Loans per capita) and the log of branches per km2 people yields the following result

(robust standard errors in parentheses).

Small firm share = -0.122 + 0.093 log(Loans per capita) + -0.016 log(Branches per km2)                             (2)
                   (0.226) (0.033)                        (0.020)

This regression has 26 observations and an R2 of 30%. While the log of loans per 100,000

people is significant at the 1% level, geographic branch penetration does not enter significantly.

Table 4, columns 3 and 4, present both the actual share of small firms with bank loans and the

predicted share from regression (2).15 The correlation between the predicted share of small firms

with bank loans and the actual share is 54%. Given the limited sample of firms surveyed by the

WBES in each country and the lack of census data on firm financing patterns, the predictive

power of aggregate loan use indicators is more limited than in the case of deposit services.16

         While the results on the share of households with accounts and the percentage of small

firms with bank loans are preliminary and have to be interpreted with caution, they show the


13
   To avoid that the predicted value falls below zero or above one, we use a tobit regression to predict the share of
households with bank accounts. The coefficients and significance levels are almost the same as in the OLS
regression.
14
   These additional estimations are available upon request.
15
   As in the case of regression (1), we use a tobit regression to predict the share of small firms with bank loans.
16
   Unfortunately, we do not have information for enough countries on the location of enterprises to run separate
regressions for rural and urban firms.

                                                          14
potential usefulness of our aggregate outreach indicators.                 In the absence of consistent

household- and firm-survey based measures, the analysis conducted so far suggests that proposed

outreach indicators can serve to estimate and update micro-based measures of access which are

very costly to obtain.

        To assess the relationship between outreach across countries’ and firms’ financing

obstacles, we conduct the following estimations using firm-level data:

        Fi,k =0 + 1 Outreachi + 2 Depthi + 3 X i,k + i,k                                         (3)

where Fi,k is the rating of financing obstacles reported by firm k in country i. X is a set of

country- and firm-level control variables, including regional dummies17 and dummy variables for

government-owned and foreign-owned firms, exporters, firms in manufacturing and services

(with firms in other sectors captured in the constant) and small and medium-sized firms (with

large firms being the omitted category).

        Given that financing obstacle is a polychotomous dependent variable with a natural order

(where higher values indicate larger financing constraints), we estimate equation (3) as an

ordered probit model. Because omitted country characteristics might cause error terms to be

correlated for firms within the same country, we allow for clustered error terms by country.

Finally, given that we are regressing firm-level variables on country-level outreach indicators,

we believe that concerns about reverse causality should be of secondary importance.

Nevertheless, we err on the side of caution and interpret our results as associations, without

making inferences regarding causation.

        Firm-level results for financing obstacles are shown in Table 5. The findings confirm the

expectation that in countries with greater outreach firms report facing lower financing obstacles.

In particular, we find that the severity of financing obstacles is negatively correlated with the
17
  We include dummy variables for Sub-Saharan Africa, Asia and Pacific, the Americas and Europe (including
Former Soviet Union), with the Middle East and North Africa being the omitted category.

                                                      15
presence of physical bank outlets (in particular the ratio of branches per capita, ATMs per capita,

and ATMs per square kilometer) and the number of loans per capita. On the other hand, higher

values of the loan and deposit income ratios are positively correlated with financing obstacles.

These results hold even though we control for the depth of the financial system. We also find

evidence that firms in deeper financial systems as measured by private credit to GDP report

lower financing constraints; however, this result is not robust and varies across different samples

and depends on which outreach indicator we include.

            As the WBES provides survey responses to more detailed questions on financing

obstacles, we also estimated regressions using survey responses on: (i) the extent to which firms

report needing special connections to access finance; and (ii) the degree to which access to long-

term loans are obstacles to firms’ operation and growth. Our main finding that greater outreach

is associated with lower financing obstacles is confirmed in those estimations.18



5. Which country characteristics are correlated with outreach?

            What factors are correlated with the large variations in outreach indicators across

countries? Do the same variables that explain financial sector depth matter for outreach? In

particular, what role do factors such as the level of economic development, the quality of

institutions, the informational and regulatory environment, the availability of physical

infrastructure, the ownership structure of the banking sector, and historical determinants of

financial sector depth play? This section explores the empirical relation between our outreach

indicators and an array of country-level variables previously found to affect financial sector

depth. In every case, we compare our findings for the outreach indicators with results from




18
     In the interest of space, these results are not reported here but are available upon request.

                                                              16
regressions for financial sector depth, measured by the ratio of credit to the private sector

expressed to GDP.

         Table 6 provides correlations among the country-level variables we consider.19 Since

many of these variables are highly correlated with each other, it is impossible to include all of

them together without running into serious multicollinearity problems. Thus, in Table 7, we

perform OLS regressions for each outreach indicator including regional dummy variables and a

single country-level variable capturing, respectively, the overall level of economic development,

the quality of legal institutions, the informational and regulatory environment, the availability of

physical infrastructure, structural features of the banking sector, and historical determinants of

financial sector depth.20,21 As in Table 5, we use logs of the outreach indicators to take account

of the fat tails in their distribution. Due to the parsimonious nature of our estimations, we

interpret results as partial correlations and we stay away from making inferences regarding

causality.

         Our estimations reported in Table 7 yield a number of interesting results. First, a

country’s level of economic development – as measured by the log of GDP per capita - is

positively associated with all of our outreach indicators as well as with the measure of financial

sector depth. Second, financial sector outreach and depth indicators are positively associated

with the quality of the overall institutional environment. We measure the latter by using the

Kaufman, Kraay and Mastruzzi (2003) Governance Index, which averages six sub-indices

measuring rule of law, control of corruption, voice and accountability, political stability,

19
   Appendix Table A.3 presents descriptive statistics for the different country characteristics.
20
   Similar to previous studies on the determinants of financial development (La Porta, Lopez-de-Silanes, Shleifer,
and Vishny, 1997; Beck, Demirguc-Kunt, Levine, 2003; and Djankov, McLiesh, and Schleifer, 2006) we do not
include GDP per capita in all estimations because this variable tends to be highly correlated with most of the
regressors that capture specific aspects of development, giving rise to multicollinearity problems that complicate the
interpretation of results.
21
   We also ran regressions of the outreach indicators on log of area and log of population. While log of area only
enters significantly and negatively in the regressions of geographic branch and ATM penetration, the log of
population does not enter significantly in any regression.

                                                         17
government effectiveness and regulatory quality. All of the outreach indicators and the ratio of

private sector credit to GDP are significantly correlated with the measure of governance.22

        Third, when it comes to the impact of the contractual and informational environment of

credit markets, we observe some differences in the results for depth and outreach. While both

outreach and depth indicators are correlated with the credit information environment and to a

lesser extent (in the case of outreach) with the cost of contract enforcement, the specific rights of

creditors appear only to affect financial sector depth but not outreach. We capture the

informational environment through a Credit Information Index from the World Bank Doing

Business database that measures rules affecting the scope, accessibility and quality of credit

information available through either public or private bureaus. Higher values of the index

(ranging from 1 to 6) indicate a stronger informational environment. The Cost of Contract

Enforcement (also from the Doing Business Database) measures the official cost of going

through court procedures, including court costs and attorney fees where the use of attorneys is

mandatory or common, or the costs of an administrative debt recovery procedure, expressed as a

percentage of the debt value. Finally, Creditor Rights is an index developed by Djankov,

McLiesh, and Shleifer (2006), following La Porta, Lopez-de-Silanes, Shleifer, and Vishny (1997,

1998), that ranges from 0 to 4, with higher numbers indicating stronger creditor rights. The

credit information index is associated with the standard measure of financial sector depth as well

as with all of the outreach indicators except for the deposit-income ratio. The cost of contract

enforcement is negatively associated with the private credit to GDP ratio, the branch and ATM

ratios, as well as the ratio of deposit accounts per capita but, surprisingly, has no impact on the

loan outreach ratios. Finally, the creditor rights variable has a significant impact on the financial



22
  Using alternative indicators such as Rule of Law, compiled by ICRG, or Property Right protection, collected by
the Heritage Foundation, yields similar results.

                                                       18
sector depth measure, but fails to enter significantly in any of the regressions for the outreach

indicators with the exception of geographic ATM penetration.

       Fourth, the ownership structure of the banking sector appears to have a consistent effect

on the extent of financial sector depth but less so on the degree of outreach. Government and

foreign bank ownership are measured by the share of banking assets held by government-owned

and foreign-owned banks, respectively. These data come from Barth, Caprio, and Levine (2001).

Both the presence of government-owned banks and the participation of foreign banks in the

system are negatively associated with depth. The share of government-owned banks in the

system appears to have a negative impact on the access indicators – the branch and ATM ratios –

but is not significantly correlated with the loan and deposit indicators. On the other hand, the

presence of foreign banks is only negatively and significantly associated with the loan and

deposits per capita indicators. Thus, the outreach regressions do not robustly support the

frequently upheld view that government-owned banks help improve outreach, or the assertion

that foreign-dominated banking sectors are characterized by having a narrower reach, due to

foreign banks’ tendency to cherry-pick the best and often wealthiest customers.

       Fifth, we uncover some differences in the association between certain historical factors

and outreach vis-à-vis depth. In particular, we examine the role of initial endowments, legal

origin and religion, which previous studies have found to affect financial sector depth. We find

that consistent with previous studies, endowments (captured here by countries’ absolute latitude)

are positively correlated with depth but less so with outreach, since regressions yield significant

associations for only three of the eight outreach indicators. In particular, the demographic branch

and ATM indicators are positively associated with absolute latitude, while we find a negative

and significant coefficient on the deposit-income ratio.




                                                19
            We also find some differences in the correlation of legal origin and religion with depth

and outreach. Regarding legal origin, the literature has found that economies with legal

institutions based on the French Civil Code tend to be less financially developed than those that

originated from the British Common Law. Our regressions including dummies distinguishing

between countries of French, German, Socialist, Scandinavian, and British legal origin (the

omitted category) support this result when it comes to depth (although in our sample of countries

French legal origin is not significant), but not strongly in the case of outreach. In fact, French

legal origin dummy is never significant in any of the regressions and has the wrong sign in some.

Further, the legal origin dummies enter jointly significantly in some, but not in all outreach

indicator regressions.23 Similarly, while previous studies have found that countries that are

predominantly Protestant tend to be more financially developed than Catholic societies (Stulz

and Williamson, 2003), the evidence is less robust when it comes to the outreach indicators. Yet,

the religion dummy variables enter jointly significant in many regressions and countries that are

predominantly Muslim or follow other religions, appear to have less outreach than

predominantly Protestant countries.

            Finally, indicators of physical, in particular, communications infrastructure are positively

associated with banking sector outreach and breadth. Better infrastructure reduces the cost of

banking service delivery and makes the extension of bank outlets more cost-effective, thus

increasing the access to and use of banking services.                        We include the ratio of Telephone

mainlines per capita to proxy for the communication infrastructure. Telephone mainlines per

capita enters significantly in all regressions with the expected sign.24

            While our results on the determinants of banking sector outreach vis-à-vis depth are

interesting, they have to be interpreted with caution. In the absence of a more structural model,

23
     These F-tests area available upon request.
24
     We also tried an indicator of transportation infrastructure, rail km per area, with the same results.

                                                              20
we are silent on whether our results reflect the effects of demand or supply factors and on the

causality chain between banking system outreach and other country characteristics.



6. Conclusions

       This paper introduces a new set of banking sector outreach indicators – measures of the

access to and use of deposit and lending services. While admittedly crude, they are the first such

indicators for a broad cross-section of developed and developing countries.           They are an

important complement to indicators of the depth and efficiency of financial systems commonly

used in the finance literature. This paper conducts a number of “reality checks” to test the

validity of the proposed indicators. First, we show the predictive power of our indicators in

tracking more costly to obtain micro-level indicators of outreach such as the share of households

with bank accounts and the percentage of small firms with bank loans. Second, we verify that

that our indicators relate to measures of financing constraints as expected: firms report facing

less severe financing obstacles in countries with better outreach. Finally, the paper looks into the

determinants of financial outreach vis-à-vis depth.

       The indicators introduced in this paper should be seen as a first attempt at developing

consistent and comparable cross-country indicators of banking system outreach. Ultimately,

though, estimating the proportion of the population that has access to and uses financial services,

identifying obstacles to access, and designing policies to overcome these obstacles and expand

access, will require a combination of different data compilation efforts and methodological

approaches. Some of these efforts are already underway. Specifically, Beck, Demirguc-Kunt and

Martinez-Peria (2006) use bank-level data to assess the degree to which there are barriers to

banking across countries associated with fees and other costs and requirements to use deposit,

loan, and payment services. Furthermore, Claessens and Demirguc-Kunt (2006) propose to


                                                21
undertake detailed household level surveys across a large number of countries to develop more

detailed measures of access at the household level. This of course is a timely and costly exercise.

In the mean-time, as discussed above, the aggregate outreach indicators described in this study

might be useful to measure access to and use of financial services across countries..




                                                22
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                                                24
                               TABLE 1: Branch and ATM Penetration Across Countries
Geographic branch (ATM) penetration refers to the number of branches (ATMs) per 1,000 square kilometers. Demographic branch (ATM) penetration
refers to the number of branches (ATMs) per 100,000 people. Reported indicators are based on data collected via a survey of bank regulators. The
questions asked were as follows: number of Branches – “How many bank branches do deposit money banks have (combined for all banks) in your
country?” Number of ATMs – “How many ATMs (automated cash withdrawal machines) are there in your country” Data sources are in Appendix
A.1. and A.2. Country ordering for each indicator is included in parentheses; higher numbers in parentheses reflect lower values of the indicators.

Country             Geographic branch    Demographic           Geographic ATM      Demographic ATM      Private Credit to   GDP per capita
                    penetration          branch penetration    penetration         penetration          GDP
Albania             2.45 (63)            2.11 (85)            2.74 (62)           2.37 (76)            .                    1,933
Argentina           1.40 (76)            10.01 (39)           2.09 (65)           14.91 (50)           0.205                3,381
Armenia             8.23 (43)            7.59 (55)            1.49 (68)           1.37 (78)            0.076                915
Australia           0.77 (83)            29.86 (15)           1.66 (66)           64.18 (14)           0.879                26,062
Austria             52.47 (14)           53.87 (2)            84.95 (15)          87.21 (7)            1.025                31,202
Azerbaijan          3.90 (54)            4.11 (71)            .                   .                    .                    865
Bahrain             135.21 (5)           13.48 (31)           269.01 (5)          26.83 (31)           0.576                10,791
Bangladesh          47.46 (17)           4.47 (67)            0.61 (77)           0.06 (89)            0.245                376
Belarus             2.28 (67)            4.79 (64)            2.41 (63)           5.06 (67)            0.070                1,770
Belgium             181.65 (3)           53.15 (3)            229.28 (6)          67.09 (12)           0.773                29,205
Belize              1.67 (73)            14.67 (27)           .                   .                    0.543                3,583
Bolivia             0.13 (95)            1.53 (90)            0.40 (81)           4.80 (69)            0.558                894
Bosnia              3.15 (59)            3.86 (72)            4.38 (58)           5.36 (65)            .                    1,682
Botswana            0.11 (97)            3.77 (73)            0.27 (84)           9.00 (59)            0.163                4,290
Brazil              3.05 (60)            14.59 (28)           3.72 (60)           17.82 (40)           0.346                2,788
Bulgaria            9.81 (39)            13.87 (29)           21.09 (34)          29.79 (26)           0.149                2,538
Canada              1.56 (74)            45.60 (7)            4.64 (57)           135.23 (1)           0.967                26,380
Chile               1.98 (70)            9.39 (43)            5.06 (55)           24.03 (32)           0.694                4,591
China               1.83 (71)            1.33 (93)            5.25 (54)           3.80 (70)            1.236                1,094
Colombia            3.74 (55)            8.74 (47)            4.10 (59)           9.60 (57)            0.262                1,747
Costa Rica          7.52 (45)            9.59 (42)            10.07 (45)          12.83 (52)           0.240                4,365
Croatia             18.62 (27)           23.36 (19)           31.96 (27)          40.10 (23)           0.416                6,356
Czech Republic      14.73 (29)           11.15 (35)           25.84 (31)          19.57 (37)           0.424                8,375
Denmark             47.77 (16)           37.63 (10)           66.51 (18)          52.39 (17)           1.100                39,429
Dominican Rep.      10.83 (36)           6.00 (60)            27.24 (29)          15.08 (49)           0.335                1,821
Ecuador             4.38 (51)            9.30 (44)            2.97 (61)           6.32 (62)            0.353                2,066
Egypt               2.45 (63)            3.62 (74)            1.21 (70)           1.78 (77)            0.579                1,220
El Salvador         14.58 (30)           4.62 (66)            34.89 (24)          11.07 (56)           0.047                2,204
Estonia             4.85 (49)            15.19 (25)           18.43 (36)          57.7 (16)            0.248                6,210
Ethiopia            0.28 (88)            0.41 (98)            .                   .                    0.294                97
Fiji                2.52 (62)            5.51 (62)            5.69 (52)           12.46 (54)           0.322                2,696
Finland             3.26 (58)            19.06 (22)           13.55 (41)          79.21 (8)            0.558                31,007
France              46.94 (18)           43.23 (8)            76.33 (16)          70.30 (10)           0.857                29,267
Georgia             2.32 (66)            3.14 (78)            0.86 (75)           1.17 (80)            .                    768
Germany             116.90 (6)           49.41 (6)            144.68 (8)          61.16 (15)           1.178                29,081
Ghana               1.43 (75)            1.60 (89)            .                   .                    .                    375
Greece              25.53 (22)           30.81 (13)           39.39 (22)          47.55 (20)           0.546                16,203
Guatemala           11.49 (33)           10.12 (37)           22.93 (32)          20.20 (35)           0.189                2,009
Guyana              0.12 (96)            3.12 (79)            0.25 (85)           6.50 (61)            .                    965
Honduras            0.46 (87)            0.73 (94)            2.22 (64)           3.56 (72)            0.388                1,001
Hungary             31.04 (21)           28.25 (16)           32.30 (25)          29.40 (28)           0.309                8,182
India               22.57 (24)           6.30 (59)            .                   .                    0.277                563
Indonesia           10.00 (38)           8.44 (49)            5.73 (51)           4.84 (68)            0.236                971
Iran                3.40 (57)            8.39 (50)            0.51 (80)           1.25 (79)            0.281                2,061
Ireland             13.41 (31)           23.41 (18)           27.78 (28)          48.49 (19)           1.020                37,637
Israel              47.82 (15)           14.74 (26)           61.01 (20)          18.81 (38)           0.859                16,686
Italy               102.05 (7)           52.07 (4)            131.71 (10)         67.20 (11)           0.750                25,429
Japan               34.82 (20)           9.98 (40)            396.98 (4)          113.75 (4)           1.115                34,010
Jordan              5.98 (47)            10.02 (38)           5.60 (53)           9.38 (58)            0.721                1,858
Kazakhstan          0.14 (94)            2.47 (82)            0.39 (82)           7.01 (60)            0.125                1,995
Kenya               0.77 (83)            1.38 (92)            0.56 (78)           0.99 (81)            0.258                434
Korea               65.02 (12)           13.40 (32)           436.88 (3)          90.03 (6)            1.197                12,634




                                                                      25
                        TABLE 1: Branch and ATM Penetration Across Countries (Continued)
Geographic branch (ATM) penetration refers to the number of branches (ATMs) per 1,000 square kilometers. Demographic branch (ATM) penetration
refers to the number of branches (ATMs) per 100,000 people. Reported indicators are based on data collected via a survey of bank regulators. The
questions asked were as follows: Number of Branches – “How many bank branches do deposit money banks have (combined for all banks) in your
country?” Number of ATMs – “How many ATMs (automated cash withdrawal machines) are there in your country?” Data sources are in Appendix
Tables A.1 and A.2. Country ordering for each indicator is included in parentheses; higher numbers in parentheses reflect lower values of the indicators.

Country          Geographic branch         Demographic          Geographic ATM        Demographic ATM      Private Credit to     GDP per capita
                 penetration               branch penetration   penetration           penetration          GDP
Kuwait           11.05 (35)                8.27 (51)            26.32 (30)            19.69 (36)           0.644                 14,848
Kyrgizstan       0.82 (82)                 3.11 (80)            .                     .                    0.041                 344
Lebanon          79.18 (8)                 18.01 (24)           73.90 (17)            16.81 (44)           .                     4,224
Lithuania        1.81 (72)                 3.39 (75)            15.34 (39)            28.78 (30)           0.128                 5,273
Madagascar       0.19 (92)                 0.66 (95)            0.07 (88)             0.22 (86)            0.081                 323
Malaysia         7.39 (46)                 9.80 (41)            12.40 (42)            16.44 (47)           1.352                 4,164
Malta            375.00 (2)                30.08 (14)           462.50 (2)            37.09 (25)           1.083                 9,699
Mauritius        71.92 (10)                11.92 (34)           133.00 (9)            22.04 (33)           0.559                 4,265
Mexico           4.09 (53)                 7.63 (54)            8.91 (46)             16.63 (45)           0.181                 6,121
Namibia          0.11 (97)                 4.47 (67)            0.30 (83)             12.11 (55)           0.438                 2,312
Nepal            2.96 (61)                 1.72 (86)            0.15 (86)             0.09 (88)            0.272                 237
Netherlands      163.81 (4)                34.23 (11)           223.02 (7)            46.60 (21)           1.407                 31,548
New Zealand      4.19 (52)                 28.04 (17)           7.53 (47)             50.36 (18)           1.101                 19,021
Nicaragua        1.29 (77)                 2.85 (81)            1.18 (71)             2.61 (75)            0.424                 748
Nigeria          2.41 (65)                 1.62 (88)            .                     .                    0.136                 370
Norway           3.41 (56)                 22.92 (20)           .                     .                    0.870                 48,592
Pakistan         9.10 (41)                 4.73 (65)            1.02 (73)             0.53 (85)            0.260                 464
Panama           5.16 (48)                 12.87 (33)           6.49 (48)             16.19 (48)           0.922                 4,328
Papua New Guinea 0.20 (91)                 1.64 (87)            .                     .                    0.147                 617
Peru             0.89 (81)                 4.17 (70)            1.24 (69)             5.85 (64)            0.248                 2,247
Philippines      21.40 (25)                7.83 (53)            14.52 (40)            5.31 (66)            0.405                 989
Poland           10.25 (37)                8.17 (52)            21.72 (33)            17.31 (42)           0.265                 5,487
Portugal         57.45 (13)                51.58 (5)            121.50 (12)           109.09 (5)           1.318                 14,665
Romania          13.26 (32)                13.76 (30)           12.02 (43)            12.47 (53)           0.073                 2,719
Russia           0.19 (92)                 2.24 (83)            0.53 (79)             6.28 (63)            .                     3,022
Saudi Arabia     0.56 (86)                 5.36 (63)            1.54 (67)             14.70 (51)           0.554                 8,366
Singapore        636.07 (1)                9.13 (46)            2,642.62 (1)          37.93 (24)           1.159                 21,492
Slovakia         11.33 (34)                10.28 (36)           32.21 (26)            29.21 (29)           0.441                 5,922
Slovenia         2.14 (69)                 2.19 (84)            64.56 (19)            66.14 (13)           0.352                 13,383
South Africa     2.22 (68)                 5.99 (61)            6.49 (48)             17.50 (41)           0.689                 3,530
Spain            78.90 (9)                 95.87 (1)            104.18 (14)           126.60 (2)           0.992                 20,343
Sri Lanka        20.41 (26)                6.87 (57)            10.91 (44)            3.67 (71)            0.274                 965
Sweden           4.74 (50)                 21.80 (21)           6.43 (50)             29.56 (27)           0.830                 33,586
Switzerland      70.54 (11)                37.99 (9)            131.10 (11)           70.60 (9)            1.589                 42,138
Tanzania         0.23 (89)                 0.57 (96)            0.07 (88)             0.17 (87)            .                     275
Thailand         8.71 (42)                 7.18 (56)            20.69 (35)            17.05 (43)           1.044                 2,309
Trinidad and
Tobago           23.59 (23)                9.22 (45)            52.44 (21)            20.49 (34)           0.404                 7,769
Turkey           7.81 (44)                 8.50 (48)            16.54 (38)            18.00 (39)           0.171                 3,365
Uganda           0.67 (85)                 0.53 (97)            0.90 (74)             0.70 (83)            0.051                 245
Ukraine          .                         .                    0.78 (76)             0.93 (82)            .                     1,024
United Kingdom   45.16 (19)                18.35 (23)           104.46 (13)           42.45 (22)           1.301                 30,278
United States    9.81 (39)                 30.86 (12)           38.43 (23)            120.94 (3)           1.628                 37,388
Uruguay          1.23 (79)                 6.39 (58)            .                     .                    0.517                 3,308
Venezuela        1.28 (78)                 4.41 (69)            4.81 (56)             16.60 (46)           0.110                 3,319
West Bank-Gaza   18.33 (28)                3.27 (76)            18.17 (37)            3.24 (74)            .                     1,026
Zambia           0.21 (90)                 1.52 (91)            0.09 (87)             0.65 (84)            .                     413
Zimbabwe         1.11 (80)                 3.27 (76)            1.15 (72)             3.38 (73)            0.235                 634




                                                                         26
                              TABLE 2: Use of Loan and Deposit Services Across Countries
Loan (deposit) accounts per capita refer to the number of loans (deposits) per 1,000 people. Loan (deposit) – income ratio refers to the average size of
loans (deposits) per GDP per capita. Reported indicators are based on data collected via a survey of bank regulators. The questions asked were as
follows: Number of Loans – “How many loans are there in your country right now that have been issued by deposit money banks? (Please include
loans from deposit money banks to individuals, businesses and others, including home mortgages, consumer loans, business loans, trade loans, student
loans, emergency loans, agricultural loans, etc.)” Value of Loans – “What is the total value of these loans? (Please specify currency and units.)
Number of Deposits – “How many deposit accounts are there at deposit money banks in your country right now? (Please include all current (checking)
accounts, savings accounts and time deposits for businesses, individuals and others.)” Value of Deposits – “What is the total value of these deposits?
(Please specify currency and units.)” Data sources are in Appendix Tables A.1 and A.2. Country ordering for each indicator is included in parentheses;
higher numbers in parentheses reflect lower values of the indicators.
   Country               Loan accounts per Loan-income ratio        Deposit accounts Deposit-income ratio Private Credit to GDP per capita
                         capita                                     per capita                               GDP
   Albania               4.42 (43)            15.41 (4)             161.25 (47)        2.75 (9)                                  1,933
   Argentina             154.19 (16)          1.77 (37)             368.73 (37)        0.58 (29)             0.205               3,381
   Armenia               41.23 (39)           1.93 (34)             111.38 (49)        1.00 (22)             0.076               915
   Austria               647.64 (4)           1.84 (36)             3,119.95 (1)       0.26 (45)             1.025               31,202
   Bangladesh            54.73 (31)           5.22 (16)             228.75 (43)        1.60 (16)             0.245               376
   Belgium               59.47 (29)           21.09 (2)             3,080.31 (2)       0.38 (41)             0.773               29,205
   Bolivia               9.53 (41)            27.89 (1)             40.63 (53)         5.81 (5)              0.558               894
   Bosnia                114.09 (18)          3.19 (24)             429.40 (32)        1.87 (13)             .                   1,682
   Brazil                49.59 (35)           6.18 (13)             630.86 (25)        0.40 (39)             0.346               2,788
   Bulgaria              73.85 (26)           4.24 (20)             1,351.37 (16)      0.26 (45)             0.149               2,538
   Chile                 417.74 (8)           1.60 (38)             1,044.82 (22)      0.46 (34)             0.694               4,591
   Colombia              .                    .                     612.21 (26)        0.42 (37)             0.262               1,747
   Czech Republic        .                    .                     1,922.83 (9)       0.42 (37)             0.424               8,375
   Denmark               450.99 (7)           2.09 (33)             2,706.07 (3)       0.22 (49)             1.100               39,429
   Dominican Rep.        50.10 (34)           6.71 (11)             719.52 (24)        0.10 (52)                                 1,821
   Ecuador               77.09 (25)           2.63 (29)             419.54 (34)        0.63 (28)             0.335
                                                                                                             0.353               2,066
   El Salvador           126.89 (17)          0.39 (43)             456.69 (30)        0.12 (51)             0.047               2,204
   Fiji                  67.09 (28)           4.75 (18)             444.42 (31)        1.13 (21)             0.322               2,696
   France                .                    .                     1,800.84 (11)      0.40 (39)             0.857               29,267
   Greece                776.48 (1)           0.83 (41)             2,417.64 (5)       0.29 (43)             0.546               16,203
   Guatemala             45.79 (38)           3.19 (24)             403.54 (35)        0.55 (30)             0.189               2,009
   Guyana                .                    .                     571.03 (27)        1.37 (18)             .                   965
   Honduras              67.27 (27)           6.13 (14)             287.27 (41)        0.74 (25)             0.388               1,001
   Iran                  48.19 (36)           2.91 (27)             2,249.28 (6)       0.04 (54)             0.281               2,061
   Israel                709.90 (3)           1.58 (39)             .                  .                     0.859               16,686
   Italy                 328.15 (11)          2.35 (32)             975.64 (23)        0.47 (33)             0.750               25,429
   Jordan                80.39 (23)           8.20 (9)              465.48 (29)        1.41 (17)             0.721               1,858
   Kenya                 .                    .                     69.98 (51)         6.26 (4)              0.258               434
   Lebanon               93.42 (20)           9.13 (7)              382.53 (36)        6.65 (3)              .                   4,224
   Lithuania             58.86 (30)           3.65 (23)             1,166.45 (19)      0.21 (50)             0.128               5,273
   Madagascar            4.38 (44)            18.35 (3)             14.46 (54)         9.31 (1)              0.081               323
   Malaysia              328.97 (10)          2.95 (26)             1,250.10 (17)      0.92 (23)             1.352               4,164
   Malta                 407.21 (9)           6.24 (12)             2,495.81 (4)       1.22 (20)             1.083               9,699
   Mauritius             207.13 (15)          2.75 (28)             1,585.99 (14)      0.53 (31)             0.559               4,265
   Mexico                .                    .                     309.57 (39)        0.46 (34)             0.181               6,121
   Namibia               80.74 (22)           5.16 (17)             422.96 (33)        1.27 (19)             0.438               2,312
   Nicaragua             95.61 (19)           2.49 (30)             96.12 (50)         4.70 (7)              0.424               748
   Norway                .                    .                     1,610.78 (13)      0.23 (48)             0.870               48,592
   Pakistan              21.93 (40)           14.26 (5)             191.84 (45)        2.63 (10)             0.260               464
   Panama                297.84 (12)          5.32 (15)             .                  .                     0.922               4,328
   Papua New Guinea .                         .                     119.77 (48)        2.48 (11)             0.147               617
   Peru                  77.92 (24)           2.45 (31)             316.19 (38)        0.74 (25)             0.248               2,247
   Philippines           .                    .                     302.05 (40)        1.77 (14)             0.405               989
   Poland                773.87 (2)           0.33 (44)             .                  .                     0.265               5,487
   Romania               .                    .                     1,207.88 (18)      0.25 (47)             0.073               2,719
   Russia                54.11 (32)           4.23 (21)             1,892.28 (10)      0.07 (53)             .                   3,022
   Saudi Arabia          47.45 (37)           7.73 (10)             214.13 (44)        2.28 (12)             0.554               8,366
   Singapore             513.23 (6)           3.84 (22)             1,670.88 (12)      1.62 (15)             1.159               21,492
   Spain                 556.48 (5)           1.91 (35)             2,075.96 (7)       0.44 (36)             0.992               20,343
   Switzerland           .                    .                     1,985.84 (8)       0.29 (43)             1.589               42,138
   Thailand              247.87 (14)          4.56 (19)             1,423.12 (15)      0.83 (24)             1.044               2,309
   Trinidad and Tobago .                      .                     1,073.48 (21)      0.35 (42)             0.404               7,769
   Turkey                264.51 (13)          0.65 (42)             1,114.23 (20)      0.68 (27)             0.171               3,365
   Uganda                5.79 (42)            10.74 (6)             46.64 (52)         3.93 (8)              0.051               245
   Venezuela             93.04 (21)           1.02 (40)             486.74 (28)        0.48 (32)             0.110               3,319
   West Bank-Gaza        50.15 (33)           8.25 (8)              253.99 (42)        4.91 (6)              .                   1,026
   Zimbabwe              .                    .                     173.56 (46)        7.98 (2)              0.235               634

                                                                         27
                                                   TABLE 3: Outreach Indicators: Descriptive Statistics and Correlations

Panel A: Descriptive Statistics
Table provides number of countries for which there are data for each indicator, along with the mean, standard deviation, minimum, maximum, median, 5th, and 95th percentile for each
indicator. Data sources for each indicator are in Appendix Table A.1.

                        Geographic branch   Demographic branch   Geographic ATM     Demographic ATM     Loan accounts per     Loan-income ratio    Deposit accounts per   Deposit-income ratio
                        penetration         penetration          penetration        penetration         capita                                     capita
Number of Responses     98                  98                   89                 89                  44                    44                   54                     54
Mean                    29.89               13.80                74.94              28.11               198.53                5.64                 943.94                 1.61
Standard deviation      79.41               15.98                289.57             32.21               222.83                5.79                 858.27                 2.14
Minimum                 0.11                0.41                 0.07               0.06                4.38                  0.33                 14.46                  0.04
5th percentile          0.18                1.24                 0.26               0.58                6.35                  0.68                 61.81                  0.11
Median                  4.80                8.42                 10.07              16.63               80.57                 3.75                 528.89                 0.66
95th percentile         119.65              49.74                253.12             101.46              700.56                17.91                2,569.40               6.40
Maximum                 636.07              95.87                2,642.62           135.23              776.48                27.89                3,119.95               9.31


Panel B: Correlation between Outreach Indicators, Micro-Level Measures of Outreach, Financial Sector Depth, and Economic Development
Pairwise correlation coefficients between Table 1 and Table 2 outreach indicators (expressed in logs), two micro level indicators of outreach (the share of households with bank
accounts, and the share of small firms with bank loans), a measure of financial sector depth (private credit to GDP) and GDP per capita. Data sources are in Appendix Tables A.1.
*, **, *** denote significance at 10, 5, and 1 percent levels, respectively.
                         Geographic    Demographic Geographic     Demographic Loan         Loan-income Deposit      Deposit-     Private credit GDP per        Household
                         branch        branch      ATM            ATM         accounts per ratio       accounts per income ratio to GDP         capita         share
                         penetration   penetration penetration    penetration capita                   capita
Demographic branch
penetration              0.695***
Geographic ATM
penetration              0.876***      0.685***
Demographic ATM
penetration              0.437***      0.764***     0.754***
Loans per capita
                         0.530***      0.672***     0.619***      0.635***
Loan-income ratio
                         -0.145        -0.264*      -0.211        -0.291*     -0.657***
Deposits per capita
                         0.570***      0.754***     0.656***      0.709***    0.735***     -0.356**
Deposit-income ratio
                         -0.207        -0.443***    -0.268*       -0.430***   -0.327**     0.489***     -0.722***
Private credit to GDP
                         0.435***      0.559***     0.506***      0.532***    0.606***     0.221        0.511***     -0.018
GDP per capita
                         0.535***      0.829***     0.733***      0.893***    0.736***     -0.296*      0.786***     -0.505***        0.715***
Household share          0.718***      0.870***     0.793***      0.792***    0.748***     -0.169       0.826***     -0.621***        0.857***    0.924***
Small firm share         0.138         0.294**      0.342**       0.422***    0.525***     -0.169       0.230        -0.075           0.495***    0.449***     0.667**




                                                                                             28
                         TABLE 4: Predicting Use of Financial Services with Outreach Indicators
Column (1) presents the share of households with bank accounts, using data from Claessens (2006) and Gasparini et al. (2005). Column (2)
presents the predicted share of households with bank accounts calculated using the coefficients from the regression of column 1 on the log
number of deposit accounts per 1,000 people and the log number of branches per 1000 square kilometer. Column (3) presents the share of
surveyed small firms (firms with 5 to 50 employees) with bank loans, using data from WBES. Column (4) shows the predicted value of the
share of small firms with bank loans based on the log of loan accounts per 1,000 people and the log number of branches per 1000 square
kilometer.
   Country                Household    Predicted   Small       Predicted                          Household    Predicted   Small       Predicted
                          share with   household   firm        small                              share with   household   firm        small
                          bank         share       share       firm                               bank         share       share       firm
                          account      (2)         with        share                              account      (2)         with        share
                          (1)                      bank        (4)                                (1)                      bank        (4)
                                                   loans                                                                   loans
                                                   (3)                                                                     (3)
   Albania                                 0.146       0.038      0.132     Lithuania                              0.470       0.198      0.361
   Argentina                               0.249       0.536      0.457     Madagascar
   Armenia                     0.089       0.144       0.000      0.302     Malaysia                               0.560      0.520       0.501
   Austria                     0.814       0.821                  0.532     Malta                                  0.872                  0.454
   Bangladesh                              0.349      0.111       0.299     Mauritius                              0.725                  0.418
   Belgium                     0.927       0.871                  0.285     Mexico                     0.250       0.274      0.071
   Bolivia                                 0.003      0.500       0.237     Namibia                    0.284       0.151                  0.439
   Bosnia                                  0.318                  0.414     Nicaragua                  0.047       0.065      0.357       0.413
   Brazil                      0.427       0.385      0.280       0.336     Norway                                 0.564
   Bulgaria                    0.002       0.590      0.156       0.354     Pakistan                   0.122       0.233      0.222       0.243
   Chile                                   0.455      0.690       0.547     Panama                                            0.538       0.498
   Colombia                    0.412       0.391      0.400                 Papua New Guinea                       0.040
   Czech Republic                          0.676      0.125                 Peru                                   0.201      0.600       0.400
   Denmark                     0.991       0.794                  0.499     Philippines                            0.357      0.429
   Dominican Republic                      0.479      0.619       0.316     Poland                                            0.280       0.577
   Ecuador                     0.218       0.331      0.412       0.372     Romania                                0.586      0.151
   El Salvador                             0.411      0.469       0.398     Russia                                 0.436      0.195       0.391
   Fiji                                    0.312                  0.368     Saudi Arabia                           0.125                  0.361
   France                      0.963       0.725      0.529                 Singapore                              0.838      0.600       0.467
   Greece                      0.789       0.745                  0.562     Spain                      0.916       0.775      0.565       0.511
   Guatemala                   0.165       0.376      0.524       0.306     Switzerland                            0.763
   Guyana                      0.137       0.202                            Thailand                               0.593                  0.471
   Honduras                                0.157      0.441       0.397     Trinidad and Tobago                    0.595      0.894
   Iran                                    0.626                  0.332     Tunisia
   Israel                                                         0.543     Turkey                                 0.542      0.456       0.479
   Italy                       0.704       0.657      0.545       0.456     Uganda                                 0.014                  0.170
   Jordan                                  0.367                  0.370     Venezuela                              0.292      0.323       0.410
   Kenya                       0.100       0.032                            West Bank-Gaza                         0.317                  0.307
   Lebanon                                 0.471                  0.341     Zimbabwe                               0.124




                                                                       29
                                                                      Table 5:
                                       Banking System Outreach and Firm Financing Obstacles – Firm Level Results
Ordered probit estimation with robust standard errors performed. Dependent variable, General Financing Obstacle, is based on the World Business Environment Survey
question: “Please judge on a four point scale how problematic is financing for the operation and growth of your business: 1) No obstacle 2) Minor obstacle 3) Moderate obstacle
4) Major obstacle” Additional firm-level binary control variables (foreign ownership, government ownership, exporter, manufacturing, services, SME) and regional dummies
are included in regressions, but their coefficients not shown below. Definitions and data sources are in appendix tables A.1 and A.2. Robust standard errors are in brackets.
The symbols *, **, *** indicate significance at 10, 5, and 1 percent levels, respectively.
 Variables                                                         Firms’ perception of the severity of financing obstacles (higher number imply greater severity)
 Log private credit to GDP                        -0.134         -0.139         -0.176          -0.105          -0.161          -0.057       -0.224        -0.106       -0.156
                                                  [0.089]        [0.071]**      [0.060]***      [0.065]         [0.064]**       [0.052]      [0.057]*** [0.072]         [0.081]*
 Log demographic bank penetration                                -0.124
                                                                 [0.071]*
 Log geographic bank penetration                                                -0.057
                                                                                [0.036]
 Log demographic ATM penetration                                                                -0.134
                                                                                                [0.051]***
 Log geographic ATM penetration                                                                                 -0.079
                                                                                                                [0.034]**
 Log of loan accounts per capita                                                                                                -0.19
                                                                                                                                [0.039]***
 Log loans-income ratio                                                                                                                      0.153
                                                                                                                                             [0.035]***
 Log deposits account per capita                                                                                                                           -0.071
                                                                                                                                                           [0.071]
 Log deposits-income ratio                                                                                                                                              0.1
                                                                                                                                                                        [0.061]*
 Observations                                     6894           6001           6001            5660            5660            2695         2695          3231         3231
 Pseudo R-squared                                 0.02           0.03           0.03            0.04            0.03            0.04         0.04          0.03         0.03




                                                                                       30
                                                    TABLE 6: Pairwise Correlations Between Country Characteristics
Definitions and data sources are in Appendix A.2. Summary statistics are in Appendix A.3. The symbols *, **, *** indicate significance at 10, 5, and 1 percent levels, respectively.
                                           Log of GDP    Log of             Log of area    Governance     Creditor         Cost of         Credit           Share of          Share of
                                           per capita    population                        index          rights           enforcement     information      assets in         assets in
                                                                                                                                           index            govt.-owned foreign-
                                                                                                                                                            banks             owned banks
 Log of population                         -0.16
 Log of area                               -0.18*        0.73***
 Governance index                          0.87***       -0.21**            -0.20**
 Creditor rights                           -0.03         -0.21**            -0.26**        -0.03
 Cost of enforcement                       -0.44***      0.09               0.1            -0.43***       -0.02
 Credit information                        0.58***       -0.03              -0.05          0.56***        -0.06            -0.29***
 Share of assets in government-owned banks -0.27**       0.31***            0.16           -0.32***       -0.10            0.23**          -0.30***
 Share of assets in foreign-owned banks -0.14            -0.41***           -0.21*         0.04           0.06             0.04            0.02             -0.29***
 Telephone lines per capita                0.91***       -0.07              -0.14          0.86***        -0.04            -0.47***        0.46***          -0.16             -0.19
 Catholic                                  0.15          0.01               0.04           0.09           -0.35***         -0.13           0.21**           0.05              0.00
 Muslim                                    -0.29***      0.18*              0.11           -0.41***       0.21**           0.15            -0.28***         0.21*             -0.2*
 Other religions                           0.07          0.19*              -0.10          0.05           0.11             -0.05           0.02             0.18              -0.13
 Latitude                                  0.58***       -0.07              -0.03          0.51***        -0.02            -0.45***        0.11             0.03              0.05
 French legal origin                       -0.03         0.08               0.00           -0.10          -0.21**          0.08            0.28***          -0.03             -0.2*
 Socialist legal origin                    -0.10         -0.11              -0.07          -0.15          0.05             -0.10           -0.38***         0.18              0.35***
 German legal origin                       0.32***       0.12               -0.04          0.29***        0.07             -0.18*          0.22**           0.06              -0.18
 Scandinavian legal origin                 0.32***       -0.11              0.00           0.37***        -0.07            -0.18*          0.07             -0.17             -0.18
                                           Telephones
                                           lines per                                       Other                           French legal    Socialist        German
                                           capita        Catholic           Muslim         religions      Latitude         origin          legal origin     legal origin
 Catholic                                  0.12
 Muslim                                    -0.31***      -0.42***
 Other religions                           0.09          -0.28***           -0.16
 Absolute latitude                         0.69***       0.12               -0.08          -0.05
 French legal origin                       -0.12         0.41***            0.05           -0.25***       -0.27***
 Socialist legal origin                    -0.01         0.17*              0.07           0.01           0.48***          -0.39***
 German legal origin                       0.37***       -0.02              -0.12          0.25***        0.18*            -0.18*          -0.12
 Scandinavian legal origin                 0.41***       -0.18*             -0.1           -0.07          0.36***          -0.16           -0.11            -0.05




                                                                                            31
                                                     TABLE 7: Which Country Characteristics Are Correlated with Outreach?
This table summarizes the results of regressions of outreach indicators on other country characteristics. Each cell represents the result of one regression: Outreach Indicator = 0 + 1X + 2Regional
dummies + u. X refers to different country characteristics (e.g., governance, legal origin, etc.) included one at a time in the regressions. Coefficients on the regional dummies are not shown in the interest
of space. All regressions are run with OLS estimation with robust standard errors performed. Definitions and data sources are in appendix tables A.1 and A.2. Robust standard errors are in brackets. *, **,
***, denote significance at 10, 5 and 1 percent levels, respectively.
                                                  Log                Log              Log                Log                Log of loan       Log loan-         Log deposit     Log deposit       Log private
                                                  geographic         demographic      geographic         demographic        accounts per      income ratio      accounts per    income ratio      credit to GDP
                                                  branch             branch           ATM                ATM                capita                              capita
                                                  penetration        penetration      penetration        penetration
Log GDP per capita                                0.585              0.577            1.088              1.073              0.846             -0.265            0.645           -0.348            0.5
                                                  [0.140]***         [0.047]***       [0.150]***         [0.073]***         [0.137]***        [0.120]**         [0.106]***      [0.116]***        [0.048]***
Governance Index                                  0.826              0.815            1.44               1.41               1.241             -0.295            0.797           -0.369            0.807
                                                  [0.234]***         [0.077]***       [0.258]***         [0.146]***         [0.206]***        [0.166]*          [0.173]***      [0.202]*          [0.073]***
Creditor rights                                   0.1                -0.061           0.322              0.162              0.128             0.051             -0.034          0.23              0.147
                                                  [0.146]            [0.088]          [0.168]*           [0.137]            [0.180]           [0.157]           [0.126]         [0.149]           [0.077]*
Cost of enforcement                               -0.023             -0.016           -0.026             -0.028             -0.033            0.02              -0.024          0.01              -0.017
                                                  [0.013]*           [0.007]**        [0.016]*           [0.016]*           [0.022]           [0.017]           [0.006]***      [0.007]           [0.004]***
Credit information                                0.416              0.36             0.597              0.555              0.513             -0.181            0.229           -0.063            0.332
                                                  [0.103]***         [0.046]***       [0.103]***         [0.078]***         [0.128]***        [0.101]*          [0.087]**       [0.109]           [0.043]***
Share of assets in govt. owned banks              -0.935             -1.27            -2.357             -2.351             -1.788            -0.037            -0.852          -0.15             -1.55
                                                  [0.905]            [0.464]***       [1.235]*           [0.836]***         [1.879]           [1.271]           [1.153]         [1.226]           [0.420]***
Share of assets in foreign owned banks            -0.931             -0.278           -0.984             -0.35              -1.274            0.765             -1.049          0.683             -0.525
                                                  [0.581]            [0.340]          [0.647]            [0.435]            [0.643]*          [0.511]           [0.465]**       [0.458]           [0.286]*
Absolute latitude                                 -1.361             2.128            -0.21              3.564              -0.377            -0.231            1.054           -2.607            2.279
                                                  [1.504]            [0.819]**        [1.903]            [1.251]***         [2.275]           [1.332]           [1.403]         [1.096]**         [0.735]***
French legal origin                               0.511              -0.202           0.41               -0.293             -0.211            -0.027            -0.104          -0.38             -0.287
                                                  [0.535]            [0.272]          [0.710]            [0.439]            [1.042]           [0.606]           [0.622]         [0.467]           [0.203]
Socialist legal origin                            -2.322             -1.937           -1.942             -1.566             -1.847            -0.177            -1.173          -0.411            -1.631
                                                  [0.547]***         [0.301]***       [0.717]***         [0.523]***         [1.283]           [0.942]           [0.761]         [0.676]           [0.385]***
German legal origin                               1.06               0.229            2.054              1.273              0.489             -0.655            0.105           -0.931            0.462
                                                  [0.508]**          [0.239]          [0.918]**          [0.744]*           [1.134]           [0.824]           [0.667]         [0.509]*          [0.222]**
Scandinavian legal origin                         -1.715             -0.562           -1.014             -0.057             0.127             -0.529            -0.07           -1.125            -0.153
                                                  [0.746]**          [0.289]*         [0.859]            [0.493]            [1.134]           [0.824]           [0.674]         [0.508]**         [0.294]
Catholic                                          0.481              -0.292           -0.139             -0.682             -0.342            -0.399            -0.135          -0.534            -0.591
                                                  [0.517]            [0.263]          [0.562]            [0.334]**          [0.743]           [0.469]           [0.353]         [0.360]           [0.249]**
Muslim                                            -0.776             -1.038           -1.901             -1.773             -1.067            0.213             -0.356          0.068             -0.647
                                                  [0.567]            [0.292]***       [0.603]***         [0.573]***         [0.934]           [0.463]           [0.591]         [0.580]           [0.317]**
Other religions                                   1.247              -0.624           1.139              -0.42              1.095             -0.536            0.819           -0.472            -0.181
                                                  [0.734]*           [0.360]*         [1.052]            [0.778]            [0.888]           [0.476]           [0.392]**       [0.410]           [0.341]
Telephones lines per capita                       4.357              3.938            7.149              6.673              7.166             -2.475            4.611           -2.386            3.889
                                                  [1.122]***         [0.368]***       [1.331]***         [0.706]***         [1.021]***        [0.856]***        [0.892]***      [0.902]**         [0.346]***




                                                                                                     32
                                                 TABLE A.1: Indicator Data Appendix
Country          Source                          Data Current as of:   Comments
Albania          Regulator Survey                December 2003
Argentina        Regulator Survey                December 2003         Housing loans, information provided separately, not included
Armenia          Regulator Survey                December 2003
Australia        Regulator Survey                June 2003
Austria          Regulator Survey                December 2003         Number of Loans and Value of Deposits reflect domestic loans and deposits only, Value of
                                                                       Loans and Number of Deposits reflects both domestic and foreign loans and deposits
                 European Card Review            December 2002         Number of ATMs: European Payment Cards
Azerbaijan       National Bank of Azerbaijan     October 2004          Number of Branches: Bulletin of Banking Statistics - Table 4.1 Number of branches of
                 Republic                                              operating credit organizations
Bahrain          Regulator Survey                December 2002         Number of Branches current as of December 2003. Loan and deposit information for full
                                                                       commercial banks only
Bangladesh       Regulator Survey                December 2003
Belarus          Regulator Survey                December 2003
Belgium          Regulator Survey                December 2002
Belize           Central Bank of Belize          December 2003         Number of Branches: Quarterly Financial Information of Commercial Banks
Bolivia          Regulator Survey                December 2002         Number of Loans actually reflects number of borrowers
                 Centro de Estudios Monetarios   December 2001         Number of ATMs: Payment System Statistics in Countries of Latin America and the
                 Latinoamericanos                                      Caribbean 1997-2001 - Table 6: Cash Dispensers, ATMs and EFTPOS Terminals
Bosnia           Regulator Survey                December 2004
Botswana         Regulator Survey                December 2003
Brazil           Regulator Survey                June 2003             Number of Loans actually reflects number of borrowers
Bulgaria         Regulator Survey                December 2002
Canada           Bank for International          December 2003         Number of Branches: Statistics on Payment and Settlement Systems in Selected Countries
                 Settlements                                           Figures for 2003 – Table 5: Institutional Framework
                 Canadian Bankers Association                          Number of ATMs: ABM Market in Canada, May 2004
Chile            Regulator Survey                December 2003
China            Regulator Survey                December 2003
                 OTC Reporter                    July 2001             Number of ATMs: “High Growth Special Situation” March 24, 2005
Colombia         Regulator Survey                December 2003
Costa Rica       Centro de Estudios Monetarios   December 2001         Number of Branches and Number of ATMs: Payment System Statistics in Countries of
                 Latinoamericanos                                      Latin America and the Caribbean 1997-2001 Table 4 – Institutional Framework and Table 6
                                                                       – Cash Dispensers, ATMs and EFTPOS Terminals
Croatia          Regulator Survey                September 2004
Czech Republic   Regulator Survey                December 2002
Denmark          Regulator Survey                December 2002
Dom. Rep.        Regulator Survey                December 2004         Number of Loans actually reflects number of borrowers
Ecuador          Regulator Survey                December 2004
Egypt            Central Bank of Egypt           July 2003             Number of Branches: “Egyptian Banking Sector Reform Policy: Areas of Future Actions”
                 Egypt Ministry of                                     Number of ATMs: “E-Business – A New Way of Doing Business”
                 Communications and
                 Information Technology
El Salvador      Regulator Survey                March 2004
Estonia          Regulator Survey                December 2004
Ethiopia         Ethiopian Consulate General     December 2001         Number of Branches: Country Facts 3.8 Financial Institutions
                 California
Fiji             Regulator Survey                December 2003
Finland          Regulator Survey                December 2003         Number of Branches and Number of ATMs current as of December 2002
France           Regulator Survey                December 2004         Number of ATMs current as of December 2003, Value of Loans, Number of Deposits,
                                                                       Value of Deposits current as of June 2004
Georgia          National Bank of Georgia        February 2005         Number of Branches: Bulletin of Monetary and Banking Statistics January-February 2005,
                                                                       Table 3.1. Financial Institutions
                 Penki Koninentai                September 2003        Number of ATMs: Julija Mosina “Lithuanian Representatives Visited Caucasian
                                                                       Countries”, September 22, 2003
Germany          Regulator Survey                December 2002
Ghana            Bank of Ghana                   December 2001         Number of Branches: Major Banks Branches Network Nationwide
Greece           Regulator Survey                December 2003         Number of ATMs current as of December 2002, Number of Loans, Value of Loans,
                                                                       Number of Deposits and Value of Deposits current as of January 2003 and reflect loans and
                                                                       deposits to domestic enterprises and households
Guatemala        Regulator Survey                December 2003
                 Centro de Estudios Monetarios                         Number of Branches: Sistemas de Compensación y Liquidación de Pagos y Valores en
                 Latinoamericanos                                      Guatemala Junio 2004 – Table A4: Marco Institucional
Guyana           Regulator Survey                December 2003         Number of Deposit Accounts: Payment System Statistics in Countries of Latin America and
                 Centro de Estudios Monetarios   December 1999         the Caribbean 1997-2001 – Table 4: Institutional Framework
                 Latinoamericanos
Honduras         Regulator Survey                December 2003
Hungary          Regulator Survey                December 2003
                 National Bank of Hungary                              Number of ATMs: Eva Keszy-Harmath “The Payment Card Business in Hungary 2003”
India            Reserve Bank of India           June 2004             Number of Branches: Trend and Progress of Banking in India 2003-2004 November 29,
                                                                       2004
Indonesia        Bank Indonesia                  December 2001         Number of Branches: Annual Report 2003, Table 8.1
                                                 January 2005          Number of ATMs: Offices of Financial Institutions and Cash Services – ATMs
Iran             Regulator Survey                December 2004
Ireland          Regulator Survey                December 2004
Israel           Regulator Survey
Italy            Regulator Survey                December 2002

                                                                         33
                                          TABLE A.1: Indicator Data Appendix (continued)

Country           Source                     Data Current as of:   Comments

Japan             Regulator Survey           March 2003
                  ATM Marketplace            April 2002            Number of ATMs: Ulric Rindebro “Spain: Ahead of the ATM Curve” April 5, 2002
Jordan            Regulator Survey           December 2002
Kazakhstan        Bank for International     December 2002         Number of Branches: Payment Systems in Kazakhstan, Table 5: Institutional Framework
                  Settlements                                      Number of ATMs: Payment Cards, Table 2
                  Bank of Kazakhstan         October 2004
Kenya             Regulator Survey           December 2004
Korea             Regulator Survey           December 2002
Kuwait            Regulator Survey           December 2004
Kyrgizstan        Kyrgizstan                 November 2004         Number of Branches: List of Commercial Banks in the Kyrgyz Republic and their Branches
                  Development Gateway
Lebanon           Regulator Survey           December 2003
Lithuania         Regulator Survey           December 2003
Madagascar        Regulator Survey           December 2004
Malaysia          Regulator Survey           December 2003
Malta             Regulator Survey           December 2003
Mauritius         Regulator Survey           December 2003
Mexico            Regulator Survey           December 2002
Namibia           Regulator Survey           December 2003
Nepal             Nepal Rastra Bank          October 2001          Number of Branches: Banking and Financial Statistics No. 43, Commercial Banks B9
                  Nepal News                 August 2003           Number of ATMs: Binam Raj Ghimire “ATMs vs. Tellers: ATMs in Nepali Banks”
Netherlands       Regulator Survey           December 2002
New Zealand       Regulator Survey           March 2003            Number of Branches and Number of ATMs: Comparison of Payment Methods (Non-Cash) 2000-
                  Bankers’ Association       December 2003         2004
Nicaragua         Regulator Survey           December 2004
Nigeria           Central Bank of Nigeria    December 2003         Number of Branches: Major Economic, Financial and Banking Indicators, Table 2 – Financial
                                                                   and Banking Indicators
Norway            Regulator Survey           December 2003
Pakistan          Regulator Survey           December 2004
Panama            Regulator Survey           December 2004
Papua NewGuinea   Regulator Survey           December 2004
Peru              Regulator Survey           December 2003
Philippines       Regulator Survey           December 2002
Poland            Regulator Survey           December 2003
Portugal          Regulator Survey           December 2003
Romania           Regulator Survey           December 2004
Russia            Regulator Survey           December 2003
                  Central Bank of Russia     December 2002         Number of ATMs: Russian Payment System
Saudi Arabia      Regulator Survey           December 2003
Singapore         Regulator Survey           January 2005          Number of loans actually reflects number of borrowers
Slovak Republic   Regulator Survey           December 2003
Slovenia          Regulator Survey           December 2003
South Africa      Regulator Survey           December 2002
Spain             Regulator Survey           December 2003
Sri Lanka         Central Bank of Sri        December 2003         Number of Branches and Number of ATMs: Annual Report 2003 Section 10.8 and Table 10.12
                  Lanka
Sweden            Regulator Survey           December 2003         Number of Branches, Number of ATMs, Number of Deposits and Value of Deposits current as of
                                                                   December 2002
Switzerland       Regulator Survey           December 2002
Tanzania          Regulator Survey           December 2003
                  Bank of Tanzania           November 2004         Number of Branches: Registered Commercial Banks
Thailand          Regulator Survey           December 2004
Trinidad Tobago   Regulator Survey           December 2003
Turkey            Regulator Survey           December 2003
Uganda            Regulator Survey           September 2004
Ukraine           US & Foreign               February 2001         Number of ATMs: Olena Stephanska, David Hunter and Bela Babus “Card Payment Systems in
                  Commercial Service                               Ukraine”
United Kingdom    Regulator Survey           December 2002         Number of Branches and Number of ATMs current as of December 2001
United States     Federal Deposit            June 2004             Number of Branches (FDIC-insured only): “Branching Continues to Thrive as the US Banking
                  Insurance Corporation                            System Consolidates” October 20, 2004
                  American Bankers           December 2002         Number of ATMs: ATM Fact Sheet
                  Association
Uruguay           Banco Central de           September 2004        Number of Branches: Superintendencia de Instituciones de Intermediación Financiera Red Física
                  Uruguay                                          de las Empresas de Intermediación Financiera Número de Sucursales
Venezuela         Regulator Survey           December 2004
                  Centro de Estudios         December 2001         Number of ATMs: Payment System Statistics in Countries of Latin America and the Caribbean
                  Monetarios                                       1997-2001 – Table 6: Cash Dispensers, ATMs and EFTPOS Terminals
                  Latinoamericanos
West Bank and     Regulator Survey           April 2005
Gaza
Zambia            Regulator Survey           December 2003
Zimbabwe          Regulator Survey           December 2004         Number of ATMs current as of April 2005


                                                                          34
                              TABLE A.2: Country Characteristics - Definition and Sources

Variable              Definition                                                                           Source                                Date
Private credit to GDP Private credit by deposit money banks and other financial institutions as a          World Bank Financial Structure   5 Year Average
                      share of GDP                                                                         and Economic Development           1999-2003
                                                                                                           Database
Log of population      Natural log of population in millions of people                                     World Bank World Development         2003
                                                                                                           Indicators
Log of area            Natural log of area in 1000s squared kilometers                                     World Bank World Development         2003
                                                                                                           Indicators
GDP per capita         GDP in US dollars at market exchange rates / Total population                       World Bank World Development         2003
                                                                                                           Indicators
Governance index       Average Score on Six Governance Indicators (Voice and Accountability,               World Bank Aggregate                 2004
                       Political Stability, Government Effectiveness, Regulatory Quality, Rule of          Governance Indicators
                       Law, Control of Corruption) – Data from Surveys of Enterprises, Citizens
                       and Experts. High score corresponds to better governance.
Credit information     Scored on 0-6 Scale, Score Increasing with Availability of Credit                   World Bank Doing Business            2004
index                  Information,                                                                        Indicators
Creditor rights        Index of creditor rights following La Porta et al. (1998). A score of one is        Djankov et al. (2006)                2003
                       assigned when each of the following rights of secured lenders are defined in
                       laws and regulations: (1) there are restrictions such as creditor consent or
                       minimum dividends, for a debtor to file for reorganization, (2) secured
                       creditors are able to seize their collateral after the reorganization petition is
                       approved, (3) secured creditors are paid first out of the proceeds of
                       liquidating a bankrupt firm, as opposed of other creditor such as government
                       or workers, and (4) management does not retain administration of its property
                       pending the resolution of the reorganization. The index ranges from 0 (weak)
                       to 4 (strong creditor rights).
Cost to enforce        Total enforcement cost, including legal fees, assessment, court fees                World Bank Doing Business            2004
contract (Percent of                                                                                       Indicators
Debt)
Share of assets in     Percentage of banking system assets in banks 50%+ owned by government               World Bank Bank Regulation and Published 2004,
government-owned                                                                                           Supervision Database           Data from 2001
banks
Share of assets in     Percentage of banking system assets in banks 50%+ owned by foreign                  World Bank Bank Regulation and Published 2004,
foreign-owned banks    entities                                                                            Supervision Database           Data from 2001
Telephone mainlines    Total telephone mainlines / Total population                                        World Bank World Development        2002
per capita                                                                                                 Indicators
French legal origin    Dummy equal to 1 if a country legal system is of French Civil Law origin.           La Porta et al. (1998)
British legal origin   Dummy equal to 1 if a country legal system is of British Common Law                 La Porta et al. (1998)
                       origin.
German legal origin    Dummy equal to 1 if a country legal system is of German Civil Law origin.           La Porta et al. (1998)
Latitude               Absolute value of the latitude of a country, scaled between 0 and 1.                La Porta et al. (1998)
Catholic               Dummy equal to 1 if the largest proportion of the population is Catholic or         Stulz and Williamson (2003)
                       Orthodox Christian.
Muslim                 Dummy equal to 1 if the largest proportion of the population is Muslim.             Stulz and Williamson (2003)
Other                  Dummy equal to 1 if the largest proportion of the population is Atheist,            Stulz and Williamson (2003)
                       Buddhist, Hindu, Indigenous, or Jewish.




                                                                          35
                                                   TABLE A.3: Country Characteristics– Summary Statistics
                                                              Definitions and data sources are in Table A.2.

Variable                                           Mean             Median               Standard Deviation    Minimum   Maximum
Population (in millions)                           49.48            10.35                158.25                0.26      1288.4
Area (in thousand squared kilometers)              936.17           197.1                2355.67               0.32      16888.5
GDP per capita                                     8,268.61         2,453.97             11,736.27             96.74     48,591.84
Governance index                                   0.06             -0.17                0.92                  -1.59     1.92
Cost to enforce contract (Percent of Debt)         23.76            17.60                22.82                 4.20      136.50
Credit information index                           3.32             4                    2.02                  0.00      6
Creditor rights                                    1.98             2                    1.08                  0.00      4
Share of assets in government-owned banks          0.15             0.07                 0.21                  0.00      0.96
Share of assets in foreign-owned banks             0.37             0.24                 0.31                  0.00      1
Telephone mainlines per capita                     0.22             0.14                 0.21                  0.00      0.74
French legal origin                                0.39             0.00                 0.49                  0.00      1
Socialist legal origin                             0.22             0.00                 0.42                  0.00      1
German legal origin                                0.04             0.00                 0.21                  0.00      1
Scandinavian legal origin                          0.04             0.00                 0.18                  0.00      1
Absolute latitude                                  0.33             0.33                 0.19                  0.01      0.71
Catholic                                           0.39             0.00                 0.49                  0.00      1
Muslim                                             0.23             0.00                 0.42                  0.00      1
Other (Atheist/Buddhist/Hindu/Indigenous/Jewish)   0.09             0.00                 0.28                  0.00      1




                                                                                   36
                                             Figure 1a: Median Branch Penetration by Income Quintile                                                                                                                   Figure 1b: Median ATM Penetration by Income Quintile
                              50                                                                                                                                                                   100


                              45                                                                                                                                                                    90


                              40                                                                                                                                                                    80


                              35                                                                                                                                                                    70
  Median Branch Penetration




                                                                                                                                                                  Median ATM penetration
                              30                                                                                                                                                                    60


                              25                                                                                                                                                                    50


                              20                                                                                                                                                                    40


                              15                                                                                                                                                                    30


                              10                                                                                                                                                                    20


                               5                                                                                                                                                                    10


                               0                                                                                                                                                                       0
                                         1                         2                           3                                      4                  5                                                         1                       2                       3                                4                5
                                                                                     GDP per Capita Quintiles                                                                                                                                            GDP per capita quintiles


                                                 Median Branches per 1,000 sq. km.                            Median Branches per 1,000,000 people                                                                                      Median ATM per 1,000 sq. km.                MedianATM per 1,000,000 people




                                       Figure 1c: Median Loans/Deposits Per Capita by Income Quintile                                                                                                      Figure 1d: Median Loans/Deposits - Income Ratios by Income Quintile
                                                                                                                                                                                                   9
                              2500


                                                                                                                                                                                                   8



                              2000                                                                                                                                                                 7



                                                                                                                                                                                                   6




                                                                                                                                                                   Loan/Deposits - Income Ratios
Loans/Deposits per Capita




                              1500
                                                                                                                                                                                                   5



                                                                                                                                                                                                   4

                              1000

                                                                                                                                                                                                   3



                                                                                                                                                                                                   2
                               500


                                                                                                                                                                                                   1



                                   0                                                                                                                                                               0
                                             1                     2                             3                                4                  5                                                         1                       2                          3                             4                    5
                                                                                     GDP per capita quintiles                                                                                                                                           GDP per Capita Quintiles

                                                                       Median Loans per Capita       Median Deposits per capita                                                                                              Median Loan-Income Ratio         Median Deposit-Income Ratio



                                                                                                                                                             37

				
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