WPS6592


Policy Research Working Paper                        6592




             Resolving the African Financial
                   Development Gap
 Cross-Country Comparisons and a Within-Country Study
                      of Kenya

                                 Franklin Allen
                                 Elena Carletti
                                   Robert Cull
                                    Jun Qian
                                 Lemma Senbet
                               Patricio Valenzuela




The World Bank
Development Research Group
Finance and Private Sector Development Team
September 2013
Policy Research Working Paper 6592


  Abstract
  With extensive country and firm-level data sets, this                             development in recent years. The paper finds a positive
  paper first documents that the financial sectors of most                          and significant impact of Equity Bank, a leading private
  Sub-Saharan African countries remain significantly                                commercial bank, on financial access, especially for
  underdeveloped by the standards of other developing                               underprivileged households. Equity Bank’s business
  countries. The paper also finds that population density                           model—providing financial services to population
  appears to be considerably more important for banking                             segments typically ignored by traditional commercial
  sector development in Africa than elsewhere. To better                            banks and generating sustainable profits in the process—
  understand how countries can overcome the high costs                              can be a potential solution to the financial access problem
  of developing viable banking sectors outside large                                that has hindered the development of inclusive financial
  metropolitan areas, the analysis focuses on Kenya, which                          sectors in many other African countries.
  has made significant strides in financial inclusion and




  This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of
  a larger effort by the World Bank to provide open access to its research and make a contribution to development policy
  discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.
  The authors may be contacted at rcull@worldbank.org.




         The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
         issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
         names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those
         of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and
         its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.


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                      Resolving the African Financial Development Gap:
                                                                                                    ∗
                Cross-Country Comparisons and a Within-Country Study of Kenya


     Franklin Allen, Elena Carletti, Robert Cull, Jun Qian, Lemma Senbet, and Patricio Valenzuela


                    Forthcoming in NBER Volume on African Economic Successes
                (University of Chicago Press; eds., S. Edwards, S. Johnson, and D. Weil)




 Keywords: Africa, Kenya, finance and growth, population density, Equity Bank, financial access.
 JEL Classifications: O5; K0; G0.

 ∗
   Allen is from the University of Pennsylvania, Carletti is from the European University Institute, Cull is from the
 World Bank, Qian is from Boston College, Senbet is from the University of Maryland, and Valenzuela is from the
 European University Institute. We thank the Financial Sector Deepening Trust Kenya (FSD Kenya), and in particular
 David Ferrand, for providing the household surveys, and Samuel Makome and John Staley for providing information on
 Equity Bank. We appreciate helpful comments from Patrick Alila, Meghana Ayyagari, Thorsten Beck, Shawn Cole,
 Sankar De, Simon Johnson, Marcella Lucchetta, Randall Morck, Chukwuemeka Okoye, Daniel Sarpong, Moses Sichei,
 Philip Strahan, and seminar/session participants at Harvard Business School, Wesleyan University, the ERD Workshop
 in Accra, the Financial Intermediation Research Society meetings in Prague, the NBER African Successes 2011
 conference in Zanzibar, and the Summer Research Conference at the Indian School of Business. We are grateful to
 Giulia La Mattina, Sailu Li, and Mircea Trandafir for excellent research assistance, and NBER and the authors’
 respective institutions for financial support. We are responsible for all the remaining errors.
I. Introduction

         Despite improvements in Africa’s growth performance since 2000 (see, e.g., Devarajan and

Fengler, 2013), its history of economic performance had long been disappointing and was even

described as a tragedy by some commentators (Easterly and Levine, 1997). Although less well-

documented and perhaps not surprising, the financial sectors of Sub-Saharan African countries

remain woefully under-developed, even relative to the standards of developing countries. This is

despite the fact that most of these countries have undergone extensive financial sector reforms in the

last two decades of the same proportions as other developing and emerging countries.1

         The financial development gap in Sub-Saharan Africa is stark, and the gap is visible in the

data. For example, the population-weighted trends for developing regions in terms of liquid

liabilities and private credit (both divided by GDP) show that the rest of the developing world has

seen substantial improvement relative to Africa in the last two decades, though the patterns differ

across regions. Based on the pre-crisis data, the liquid liabilities of African financial sectors

averaged below 30 percent of GDP in 2007 (see Table 1). In no other region of the developing

world did that figure stand below 40 percent. The credit side of the picture looked even worse:

private credit averaged 17 percent of GDP in Africa in 2007, compared with ratios ranging from 33

to 44 percent for other developing regions. These pre-crisis data show that the financial

development indicators of Africa improved in the period 1995-2005, but at a much slower pace than

in other developing and emerging countries in Asia, Eastern Europe, Latin America, and the Middle

East.

         The poor state of African financial development raises a number of important questions as to

what went wrong with the financial reforms in Africa and what could be improved. Is African

financial development slow in itself, or is it a reflection of broader economic and policy failures?

1
  The reform packages included price and interest rate liberalization, removal of credit ceilings, introduction of a variety
of measures for banking and capital market development, including financial regulatory schemes, and large scale
privatizations of state-owned enterprises (see, for instance, Nissanke and Aryeetey, 1998; Senbet and Otchere, 2006;
and Honohan and Beck, 2007).
                                                                                                                          2
Are the levels of financial development achieved in the developing world outside of Africa

achievable for most African countries? What factors have inhibited African financial development

to this point and how can they be overcome? Understanding these issues is of crucial importance

given the ample empirical evidence of a linkage between financial development and economic

development.2 Moreover, in the context of Africa where poverty is so widespread, the positive

finance-growth nexus is suggestive of a positive linkage between finance and poverty alleviation.3

        There is very little rigorous academic research that addresses these questions. This paper

represents a first step in addressing key issues at the heart of African financial development. We

have three goals. First, we assess whether African financial development is slower than it ought to

be, using other developing countries as a benchmark. Second, we identify factors that have more

pronounced impact on financial development in Africa than in other developing countries, using

both country- and firm-level data sets. The problem for banks, that try to increase financial

inclusion, is whether they can generate sufficient profits to sustain their business model. In this

regard, our third goal is to study Kenya, a country that has made significant strides in financial

development in recent years, and Equity Bank, a leading private commercial bank devoted to

providing banking services to lower income people. We examine the impact of Equity Bank, along

with other commercial banks, on financial access at the household level in Kenya.

        To calibrate the financial development ‘gap’ between Africa and other developing countries,

we first analyze the determinants of financial development in other developing countries (low- and

middle-income countries) via regression models based on prior research.4 We use the regression

coefficients to generate predicted levels of financial development, as measured by the ratios of

2
   See, for example, Levine (2005), Levine, Loayza, and Beck (2000), and Rajan and Zingales (1998).
3
   Recent cross-country studies also find links between financial development and (lower) incidence of poverty (Beck,
Demirguc-Kunt, and Levine, 2007; Clarke, Xu, and Zou, 2006).
4
  Our main financial development variables cover the period 1995-2007. The negative impact of the 2007-2009 global
financial crisis has been less severe in Africa than in other regions, in part because the financial sectors were more
isolated from global markets. See, for instance, Kamil and Rai (2010) for more details.

                                                                                                                         3
liquid liabilities and private credit over GDP, for Sub-Saharan African countries (excluding South

Africa). Then we compare those predicted levels with the actual levels of financial development in

the African countries. We find that most African countries tend to have lower levels of financial

development than would be predicted based on their fundamentals. The average country falls 13

percentage points short of its predicted level for liquid liabilities over GDP and 12 percentage points

for private credit over GDP.

       To identify common and unique factors associated with financial development (or lack of) in

Africa, we run regressions for other developing countries and African countries separately; we also

examine the combined sample with an African dummy, and interact the African dummy with

factors affecting financial sector development. One of our main findings is that population density

appears to be more important for banking sector development as measured by liquid liabilities and

private credit (relative to GDP) in Africa than in other developing countries. If frequent interactions

among firms, households, and investors are a necessary condition for business transactions, and

hence financial development, then our results are plausible in that many African countries are

endowed with scattered population and roads. Furthermore, we find a nonlinear relationship

between population density and banking sector development, and the largest gap between Africa

and the rest of the developing world is for those African countries ‘trapped’ in low density areas.

       We further explore the channels through which population density may affect banking sector

development by examining additional variables related to population density. We find that the

percentage of population living in the largest cities, roads per square kilometer and bank branch

penetration are all associated with a higher level of liquid liabilities relative to GDP, but the results

are weaker for the level of private (bank) credit. These results imply that the minimum viable

banking sector scale is best achieved in major cities, and that technological advances, such as

mobile telephone banking, could be one way to facilitate African financial development, especially


                                                                                                            4
on the savings side of the banking sector outside metropolitan areas.5

         Concerning other factors explaining banking sector development, we find that

macroeconomic stability and broad measures of institutional quality are less important in Africa

than in other developing countries, while the natural resource ‘curse’ (as in, e.g., Sachs and Warner,

1995, 2001) is no worse in Africa than elsewhere. Finally, regarding indicators of stock market

development, such as market capitalization and turnover, we do not find any of the factors

associated with stock market development in the rest of the world to be significant in Africa. In

large part, this is because most African stock markets remain thin, illiquid and dysfunctional; and

only very recently have Sub-Saharan African countries made a policy commitment to their

development.

         We also conduct firm-level analyses which enable us to control for both country-level

variables and firm characteristics in the same regressions. Employing the widely used World Bank

global Investment Climate Surveys (ICS), which are comprised primarily of firms that are not listed

on stock markets, we examine factors determining firms’ access to external finance in the form of

bank loans (from both domestic and foreign banks) and/or credit cards.6 The idea is that if a

country’s banking sector is more developed, firms, and in particular privately owned firms, should

have easier access to these types of formal finance. We run regression models where we include the

same country-level factors as in our cross-country studies as well as firm characteristics that could

explain substantial variations in the demand for and in the use of financial services. Once again, we

include the African dummy and interact it with both country-level and firm-level variables.

         The results of the micro-level regressions confirm that population density is an important


5
  Mbiti and Weil (2010) use the same household surveys in Kenya as in our paper, and find that increased use of M-
Pesa, one of the leading mobile phone based money transfer systems, lowers the propensity of people to use informal
savings mechanisms but raises the probability of their use of banking services.
6
  This is consistent with prior research showing that financial institutions provide the most important source of external
finance in most developing countries (e.g., Beck, Demirgüç-Kunt, and Levine 2000).

                                                                                                                         5
factor for access of firms to financial institutions. While bank branch penetration is an important

factor for access to bank finance, the coverage of roads is not as important for African firms as their

counterparts elsewhere. As with country-level studies, general infrastructural failures are not

necessarily responsible for low levels of firm access to bank finance in Africa, but rather failures are

specific to the banking sector and markets for loans. While manufacturers are more likely to access

bank finance in other countries, they show no strong tendency to rely on external finance relative to

other firms in Africa.

       Having established population density as a key factor for financial development in Africa

based on both cross-country and firm-level data, we next conduct a within-country study on Kenya.

There are several reasons for this choice. First, based on our cross-country analysis, Kenya’s level

of financial development is not too far off from the predicted level, and it has witnessed a strong

bank branch expansion in recent years. This expansion has also coincided with the emergence of

Equity Bank, a pioneering (for-profit) commercial bank that devised a banking service strategy

targeting low income clients and traditionally under-served territories. The bank is listed on the

Nairobi Stock Exchange, and has no government ownership share. Many scholars and policy

makers agree that a key obstacle to financial development is access of the disadvantaged to finance,

which would promote economic growth at the broadest scales. While the success of microfinance

institutions (MFIs), such as Grameen Bank, has captured the attention of economists and policy

makers, many MFIs currently are beset by non-repayment problems while most established

commercial banks view the sectors targeted by MFIs as ‘unbankable.’ This makes Equity Bank and

Kenya a particularly interesting ‘laboratory’ for the study of financial access. Since institutional

and macroeconomic conditions tend to be more homogeneous within a single country, the findings

from our within-country study can reinforce the results from our cross-country study.

       The absence of micro-level data at both the household and bank branch level has made the


                                                                                                       6
study of financial inclusion a challenge. Fortunately, we are able to overcome that challenge using

a new dataset on bank branch penetration at the district-level described in Allen et al. (2011),

matched with household surveys of financial usage for Kenya that were conducted in 2006 and

2009. We find that Equity Bank presence is strongly associated with increases in households’ usage

of bank accounts and bank credit between the two surveys. These effects are particularly strong for

under privileged households—those with low income and less education, who do not own a

permanent house, and those that lack any member with a salaried job.

       We also find differences in branching strategies between Equity Bank and other commercial

banks. While all bank types (including Equity Bank) open a greater number of branches in urban,

highly populated and English speaking districts, Equity Bank was more likely to expand to

underdeveloped districts than other types of banks between 2006 and 2009— less densely populated

areas and areas where the dominant language was not English or Swahili. This different branching

strategy is consistent with our earlier result that population density is a major obstacle for financial

sector development in Africa; more importantly, our results are encouraging in the sense that private

institutions can arise in such an environment to help overcome the ‘no finance, no growth trap’ in

many African countries.

       The rest of the paper is organized as follows. Section II describes the data, methodology for

benchmarking financial development across countries, and results on examining whether the actual

financial sector development in Africa is below what the fundamentals would predict using other

developing countries as benchmarks. Then Section III examines whether the variables associated

with banking development in other developing countries from our base models are related in a

similar way to African banking development. Section IV presents results on the determinants of

firm access to external finance in Africa and other developing countries. Section V presents results

on financial access in Kenya and on the effect of bank branch expansion, particularly Equity Bank


                                                                                                       7
branching strategy on the use of bank accounts and credit. Section VI offers concluding remarks.



II. Benchmarking Financial Development

         For cross-country comparison of financial development, the literature has turned

increasingly to regression analysis to examine the level and variation of financial development

across countries relying on some of the same variables that have been used to study the links

between financial development and growth (Levine, 2005).7 There is no general consensus in the

literature about the factors that should be taken into account in explaining financial development,

nor what indicators of that development are most reliable. In the choice of the latter, we ground our

analyses on the recent attempt by Beck et al. (2008) to standardize the selection of financial

development indicators. Under this approach, the potential financial development indicators are

ranked on the basis of the following criteria: (a) the directness of their linkages to welfare, (b) the

goodness of fit of regressions that explain variations in them, (c) their coverage in terms of

countries and years, and, (d) the degree to which an indicator is stable within a country from year to

year, but varies substantially across countries.8

         In most of our analysis we use the two standard indicators, namely the ratio of liquid

liabilities in the banking system to GDP and the ratio of credit to private sector to GDP. Both

measures score among the highest based on all four criteria listed above. They score especially high

relative to others on the first criterion, because they are robustly associated with long-run economic

growth (Levine 2005; Levine, Loayza, and Beck, 2000). Moreover, our analysis is rooted in

banking indicators because banks hold the vast majority of financial sector assets in Africa and
7
  As in other recent papers, we use these variables, including growth, to describe financial development (Cull and
Effron, 2008; Cull, Senbet, and Sorge, 2005). By contrast, in the finance and growth literature, the financial indicators
are among the explanatory variables used to explain growth.
8
  Beck et al. (2008) call this the ratio of within sample variance to between sample variance. They worry that high
within-country variation may reflect measurement errors or a high degree of co-movement with the business cycle.
They argue that indicators of financial development are (or at least should be) better suited to measuring longer-term
differences across countries rather than fluctuations along the cycle for a given country.

                                                                                                                            8
other developing countries.9 Despite this, we perform some robustness checks using measures of

stock market development as indicators of financial development.

        In the choice of the explanatory variables for financial development, we rely again on

previous studies, in particular those on the finance-growth nexus (e.g., Levine, 2005) and from other

studies that analyze the determinants of financial development (e.g., Beck et. al, 2008; Cull and

Effron, 2008). These studies (in particular, Beck et al. 2008) regress the indicators of financial

development on a set of variables that describe the environment in which such development takes

place, but that are exogenous to that process such as population size and density, natural resources

and offshore centers. They also include per capita income as an exogenous regressor, claiming that

its effect on financial development is contemporaneous while the effect of financial development on

income is lagged. The residuals from those regressions, therefore, provide an indication of the

extent to which the chosen government policies promote financial development.

        However, as our objective is to benchmark African financial development relative to a set of

variables that have been robustly associated with financial development in countries outside of

Africa, especially in low and middle income countries, we expand further the set of regressors by

including macroeconomic variables, such as inflation, real growth, and the current account balance,

broad measures of institutional development, and variables describing banking sector structure. We

stress from the outset that we are not necessarily estimating causal relationships for the expanded

set of regressors. For ease of exposition, however, we refer to all explanatory variables as

determinants of financial development throughout the paper.




9
  The ratio of private credit to GDP can include lending by nonbank financial institutions. As Table 2 shows, though,
the correlations between private credit/GDP and stock market capitalization, and those between liquid liabilities/GDP
and stock market capitalization/GDP are of similar magnitude (0.65 and 0.68), suggesting that the private credit measure
is not driven by the activities of nonbank financial institutions involved in stock market transactions.

                                                                                                                      9
II.1 Regression Model

        We begin the introduction of our regression model with the dependent variable:

        Financial Development indicator (FDi): This measures an indicator of financial

development in country i. We average our indicators of financial development and our explanatory

variables over multiple years (from 2001 to 2005), as is customary in the literature on financial

development and growth so as to reduce the influence of outliers. We therefore have only one

observation per country. The results are qualitatively similar when we use all yearly observations

from 1990 to 2006 in panel regressions, and when we lag the explanatory variables in either the

panel regressions or the cross-sectional regressions. Because our goal is to describe a general

picture of the factors that are robustly linked to financial development, however, we present below

only the simplest cross-country regressions in which the financial indicators and explanatory

variables are contemporaneous.10

        The regression model for the expanded set of explanatory variables is:

      (1) FDi = α + β1Populationi                         Exogenous determinants, based on Beck et al.
               + β2Population Densityi                    (2008)
               + β3Natural Resourcesi
               +β4Offshore Centeri
                 +β5Per Capita Incomei                    Plausibly exogenous, Financial development
                 +β6Population*GDP Per Capitai            affects these variables at a lag, Beck et al. (2008)
                 +β7Real GDP Growth Ratei                 Macroeconomic variables
                 +β8Inflation Ratei
                 +β9Current Account Balance/GDPi
                 +Î’10KKM Indexi                           Index of institutional development

                 +Î’11Manufacturing/GDPi                   Other variables
                 +Î’12Secondary/Primary enrolment
                 + εi


We briefly explain our variables below:

10
   For example, in the cross-sectional regression we average the explanatory variables from 1990 to 2000 and use
them to explain financial development indicators averaged from 2001 to 2005. In the panel regression, we lag the
explanatory variables by one to three years.

                                                                                                                   10
           Population: We measure population both with size and density and we expect both variables

to be positively associated with financial development. A larger population should spur financial

development due to scale and networking effects that make provision of financial services more

efficient in larger economies. Population density, as measured by the number of residents per square

kilometer, should have a positive coefficient in part because it is easier for financial institutions to

accumulate savings when a higher number of potential depositors have easy access to them.

           Natural Resources: An abundance of natural resources may have a negative effect on

financial development via the so-called “resource curse.� Consistent with this, Sachs and Warner

(1995, 2001) offer evidence that resource-rich developing countries have grown more slowly since

1960 than other developing countries.

           We measure the intensity of a country’s reliance on natural resources by using a

comprehensive approach that measures resource abundance based on trade indicators rather than

solely on oil exports:


                              Exports k - Importsk                petroleum, forest, tropical, 
Natural resources = ∑                                , where k ∈                               
                         k   Population ages 15 - 64             animal, cereal, raw materials

Natural resources =
The key advantage of this approach is that this measure of net exports is available for most

countries and, as shown by Lederman and Maloney (2008), it is more closely linked to actual

natural resource reserves than other trade-based endowment measures. If there is a resource curse

and it impacts financial development, we would expect the coefficient on this variable to be

negative.

           Offshore Centers: The financial sectors of offshore centers are typically much larger than

their economies would otherwise warrant. We measure this effect with a dummy variable for

offshore financial centers and we expect it to be positive.11

11
     One could question whether this is an exogenous variable. We follow Beck et al. (2008) in taking it as exogenous.

                                                                                                                         11
          Per Capita Income: Per capita income is expected to be positively linked to financial

development, because the volume and the sophistication of financial activities demanded is greater

in richer countries and, on the supply side, richer economies can better exploit scale economies in

the provision of financial services. Per capita income reflects an aggregation of past growth, and

hence its coefficient summarizes the long-run positive relationship between growth and financial

development. Moreover, per capita income may interact with population to produce even greater

financial development than they do individually. We test this by including the interaction between

those two variables in the regression.

          Indicators of Macroeconomic Stability: Financial development is more likely in a sound

macroeconomic environment and to measure this, we include real growth, inflation and the current

account balance (relative to GDP) in the regressions.

          (a) Real growth: The effect of real growth on financial development is ambiguous. On the

one hand, countries with rapid growth may be associated with greater financial development. On

the other hand, countries with higher levels of development, as reflected in GDP per capita, tend to

have slower growth according to ‘conditional convergence’ (Levine and Renelt, 1992; Easterly and

Levine, 1997). Given that financial development indicators tend to be highly correlated with per

capita income, it is likely that the faster growing countries will also tend to have lower levels of

financial development. We would therefore expect a negative coefficient for real growth in our

financial development regressions. The coefficient should also capture the negative short-run

relationship between financial development and growth as emphasized in Loayza and Ranciere

(2006).

          (b) Inflation: On the private credit side, inflation should slow financial development if it

makes loan contracting over extended periods more difficult. Inflation could also have a dampening

effect on liquid liabilities, making depositors more hesitant to place their savings in the formal


                                                                                                         12
financial system for fear of not being able to get them back quickly enough. Therefore, we expect

the coefficient for inflation to be negative in our regressions.

       (c) Current Account Balance: The current account balance can be seen as a rough indicator

of the health of the macroeconomic environment, and we thus expect it to be positively associated

with our financial indicators.

       Institutional Development: We include in the regression KKM, which is the measure of

broad institutional development created by Kaufmann, Kraay, and Maztruzzi (2007). Institutional

development has been found to foster financial development in developing countries (Cull and

Effron, 2008), and thus we expect a positive coefficient for KKM in our regressions.

       Manufacturing: We include the share of GDP generated by the manufacturing sector.

Industrial sectors that are relatively more in need of finance tend to grow faster in countries with

well-developed financial sectors (Rajan and Zingales, 1998). Manufacturing encompasses a broad

variety of activities that tend to rely heavily on external finance so that we expect countries with a

large manufacturing sector to have well-developed complementary financial institutions. We

therefore expect a positive coefficient for manufacturing in our regressions.

       Secondary/Primary enrollment: Finally, we want to measure the impact of risk management

on financial development. The idea is that a lack of capacity in risk management may be a deterrent

to banking sector development and broader financial sector development (e.g., stock markets). As

measuring financial capabilities across countries directly is not possible, we proxy risk management

capability with the ratio of secondary to primary school enrolment and we expect its coefficient to

be positive. Our argument is that risk management capability is fundamentally a question of human

capital development and thus of talented financial people.

       The summary statistics and the correlations between the key variables appear in Tables 1

and 2. Table 1 shows that the mean values for the financial indicators are uniformly lower in Africa


                                                                                                         13
than the rest of the world, measured in terms of liquid liabilities, private credit provision, and stock

market development. We also see marked differences in the explanatory variables between Africa

and the rest of the world, particularly with respect to population density, per capita income, current

account balance/GDP and institutional development as proxied by the KKM index.

       The correlations in Table 2 are instructive. All indicators of financial development -liquid

liabilities/GDP, private credit/GDP, stock market capitalization/GDP, and stock market value

traded/GDP- are highly correlated; and population density is positively associated with two of

them, liquid liabilities/GDP and stock market capitalization/GDP. The macro economic variables

are strongly associated with the measures of financial development in an expected way. In

particular, there is a negative relationship between the financial development indicators and both

inflation and real growth, and a positive relationship between the indicators and the current account

balance. However, the relationship with real growth is weaker than the other macro variables. The

KKM index of institutional development is strongly and positively associated with all of the

indicators of financial development. The same holds for the log of per capita income. This is

indicative of a positive long-run relationship between finance and growth. Finally, the

secondary/primary school enrollment is positively associated with the measures of financial

development.


II.2 Empirical Results at the Cross-Country Level: The African Financial Development
Gap

       To benchmark African financial development, we estimate regression Equation (1) for

countries outside Africa, which enables us predict what African financial development should be

based on the experience of these other countries. Specifically, we first run the regressions

excluding all African countries, and we derive out-of-sample predictions for African financial

development. Then we compare these predictions with the actual levels of African financial



                                                                                                      14
development to measure the development gap.12 We present models for all countries and for low

and middle income countries separately in Table 3. The latter are more reflective of the African

experience.

A. Benchmarking Results for the Determinants of Financial Development

         The results for regression Equation (1) are presented in Table 3. The signs of almost all of

the coefficients are as predicted and many of them are significant. When we use the full sample of

countries and the limited set of regressors as a benchmark, (see Columns 1 and 5), only the Per

Capita Income Variable is positively associated with both indicators of banking development, while

our proxy for Natural Resource intensity is negatively linked to those indicators. Population

Density and Offshore Financial Center are significantly positive in the liquid liabilities regression

(Column 1) but not the private credit regression (Column 5). When we benchmark to a sample of

only low and middle income countries, using the limited set of regressors (Columns 3 and 7), the

Offshore Financial Center is highly significant for both indicators, while the Per Capita Income and

Population Density variables are positive and significant, but only in the private credit regression.13

         The expanded regression results, where we include macroeconomic, institutional, and other

explanatory variables, are presented in Columns 2, 4, 6 and 8. The results show that Real Growth is

negative and significant (or very nearly so) for both private credit and liquid liabilities for both

samples of countries that we use. This indicates that the fastest growing countries tend to have the


12
   The same approach has been used, for example, to assess whether the levels of foreign direct investment received by
China are abnormally high, or whether they can be explained by fundamentals (Fan, Morck, Xu, and Yeung, 2008).
13
   These results are similar to those in Beck et al., (2008). Aside from our proxy for natural resource intensity, our
regressions differ from theirs in two ways. First, we use the more conservative cross-sectional approach, whereas they
use the full panel. We choose to do this because errors from observations from the same country are likely to be
correlated with one another. This could artificially deflate standard errors, thus increasing the significance level of
coefficients. For robustness, we ran our models also on the full panel and qualitative results are similar. Second, they
include a poverty gap variable in their regressions, which is the proportion of the population under the poverty line,
times the average distance from the poverty line (Source: Povcal Net, World Bank). Since the poverty gap is so tightly
linked to income levels, they use the residuals from a regression of the poverty gap on income in their regressions. We
did the same, but the variable provided little explanatory power and reduced our sample. Also, unlike the other variables
in our analysis, the poverty counts are based on surveys that do not occur at regular intervals, and thus the panel is
highly unbalanced. For these reasons, we present models without the poverty gap variable.

                                                                                                                      15
least-developed financial sectors, providing support for conditional convergence. As discussed

earlier, this is likely a reflection of a short-run relationship between financial development and

growth (the positive significant coefficient for per capita income summarizes the long run

relationship between accumulated growth and financial development). Inflation is negative and

highly significant across financial indicators and country samples. The coefficients are similar in

size for the full set of countries and for the low and middle income countries, indicating that

macroeconomic instability is strongly linked to financial under-development even in developing

countries. The Current Account Balance/GDP is strongly positively linked to both indicators for

both samples, though coefficients are larger for the low and middle income sample. In short, the

coefficients support our hypotheses and indicate strong links between macroeconomic outcomes

and financial development.

       Our proxy for the degree of institutional development, as represented by the KKM index, is

positive and highly significant across financial indicators and samples, providing support for the

notion that broad institutional development helps to foster financial development. The coefficients

are larger for the full-country sample than for the sample of low and middle income countries, but

they are still large even in the latter case: a one-point increase in the KKM index (5-point scale) is

associated with a 22 percentage point increase in liquid liabilities/GDP and a 21 percentage point

increase for the private credit ratio. Finally, in the expanded models, the offshore financial center

variable is significant for both indicators for low and middle income countries.

B. Predicted Versus Actual African Financial Development

       We now use the regression coefficients in Table 3 to derive a predicted level of financial

development for each country in Africa. Because they are likely to be more reflective of the African

experience, as our benchmark we rely on the models that include only low and middle income




                                                                                                         16
countries.14 Again, we are not claiming that the relationships we find in Table 3 are causal. Rather,

we are asking what the level of financial development would be if the same relationships held in

Africa as in the rest of the developing world. To the extent that predicted and actual levels of

financial development are similar, one can say that African financial development is about what it

should be.

1. African financial development gap: Liquid liabilities. Figure 1 shows that only seven of thirty-

one countries have levels of liquid liabilities/GDP which are at or above their predicted levels. Of

those, three (Angola, Sierra Leone, and Zambia) are huddled in the lower left hand corner of the

figure where actual and predicted values are very low. Because the predicted values are based on

linear regressions, they tend to be very near zero for these countries.15 That their actual levels

exceed zero by some small amount is little consolation. To a lesser extent the same is true of

Mozambique. Ethiopia has predicted and actual levels of liquid liabilities that are quite similar.

The result on Ethiopia seems surprising given the country is known to have undertaken few

financial sector reforms and has a banking system dominated by the government. While state-

owned banks collect deposits, it unlikely that they intermediate (lend) well. In fact, the dominant

bank in Ethiopia was known to have a high accumulation of liquidity and idle funds during the

sample period, and the relatively high level of liquid liabilities (relative to GDP) may be reflective

of excess liquidity. Overall, there are only two countries, namely Cape Verde and Mauritius –

among the smallest in Africa – that exceed their predicted levels by a substantial amount, but

neither of the two is particularly reflective of the African experience.

         As Figure 1 shows, twenty countries have levels of liquid liabilities that are more than ten

14
   We also tried to estimate models based only on low income countries outside of Africa, but there were too few
observations to generate meaningful results.
15
   Indeed, for Angola the predicted value is negative (and large in absolute value). This is due to its high average GDP
growth (10.6%), low KKM score (-.71), and high inflation (86.4% per year). Negative levels of financial development
are not, however, possible. For countries that have negative predicted values, we reset them to zero in both the figures.
The calculations are discussed in the text.

                                                                                                                       17
percentage points below their predicted levels. The worst ten performers, for example, range

between 24 and 47 percentage points below. The average country falls 13 percentage points short

of its predicted level, which is quite sizable, given that the average ratio of liquid liabilities to GDP

hovered between 26.5 and 30.9% from 2001 to 2005.

2. African financial development gap: Private credit. As shown in Figure 2, the level of private

credit/GDP for African countries lies far below predicted levels. Again, only seven countries exceed

their predicted levels and only Mauritius does so by more than ten percentage points. Of the

twenty-four countries with predicted levels that are higher than their actual private credit ratios,

seventeen fall short by more than ten percentage points. The ten poorest performers range from 22

to 34 percentage points below their predicted levels, and the average country falls short of its

predicted level by 12 points. The magnitude of this measure of under-performance is quite large

when one considers that the average ratio of private credit to GDP stood at 15.5% in 2005.

       The gap in the observed African financial development, relative to what is predicted by the

benchmark determinants, is stark. The levels of liquid liabilities to GDP for African countries are

about two-thirds the level predicted by statistical relationships that hold elsewhere in the developing

world. Private credit ratios are even lower, slightly better than half their predicted levels. There is

also general consistency in rankings across the two indicators. Of the bottom ten performers in

terms of liquid liabilities, seven are also among the bottom performers in terms of private credit,

and the correlation for the differences between predicted and actual levels for the two measures of

financial development is 0.85. Our results point to systematic under-performance relative to the

fundamentals that have been associated with banking sector development in the rest of the world.

To analyze this gap more in depth, in the next sections we look at whether the factors in our base

models relate to African financial development differently than to financial development in the rest

of the world.


                                                                                                       18
III. Are the Determinants of African Financial Development Different?

         So far we have defined under-development in African financial sectors in terms of the

determinants of financial sector development in other parts of the developing world. However, the

course of African financial sector development might depend on a different set of factors than those

that have been important elsewhere.16 While we are reluctant to accept that African financial

sectors have a distinct model of development, it seems plausible that some factors may be

somewhat more or less important in the African context. To see whether this is indeed the case, as a

first step, we estimate the model in Table 4 for the sample of African countries. Note that this

method essentially accepts that the level of financial development in Africa is lower than that in the

rest of the developing world, and then tries to explain variation around the African mean based on

the explanatory variables in our base models. Still, the results are instructive.17

         First, population density is much more strongly linked to both liquid liabilities/GDP and

private credit/GDP than it was elsewhere in the world. In fact, among the set of exogenous factors

in the regression Equation (1), population density is the only one that is robustly linked to our

indicators of banking development for Africa. Moreover, the interaction between population and

per capita income is positive and significant in two of the private credit regressions, although not

when South Africa is dropped from the African sample. This is reasonable, given the relatively

high level of economic development of this country. Second, among the factors that explain

financial development in the rest of the developing world, only the KKM index of institutional

development seems relevant for Africa but only for the liquid liabilities measure.

         The other additional factors in the expanded regressions in Table 4 are less important in

Africa than elsewhere. Real growth is not significant. We argued earlier that the negative,


16
    It may also be that the factors explaining financial development in Africa are the same as for the other countries but
these factors load up or contribute differently in the African context. We are open to either interpretation.
17
   Note that the offshore financial center variable does not appear in Table 4 because no African countries qualify.

                                                                                                                        19
significant coefficient for growth in the rest of the developing world was consistent with conditional

convergence, because it indicated that the least financially developed countries had higher current

growth rates. The non-result for Africa suggests that such convergence did not occur there, a point

which was also made for some African countries in Pritchett (1997). In addition, neither inflation

nor the current account balance is significant in the African regressions, whereas both were highly

significant in the rest of the world as shown in Table 3. The poor results for inflation, the current

account, and growth indicate that the macroeconomic fundamentals that are associated with

financial development in other developing countries have not been important in Africa.18


III.1. A Closer Look at Differences in Financial Development Factors

         The models in Table 4 provide a strong initial indication that the factors associated with

banking development in Africa differ from those in the rest of the world. However, those African

models are based on only a small number of observations. To address the small sample issue we re-

run our models for all low and middle income countries (including Africa) and include an

interaction between each of our explanatory variables and an Africa dummy variable. We also

include an Africa dummy variable on its own to test whether the constant for Africa in our

regressions is the same as in the rest of the developing world. This is akin to a seemingly unrelated

regression (SUR), but the coefficients on the interactions terms enable us to make more precise

statements about whether the effects for Africa are statistically different than for the rest of the

developing world and from zero (i.e., there is no effect).




18
   The results on macroeconomic fundamentals suggest that African countries were able to avoid the financial roller-
coaster ride associated with macroeconomic instability that other countries experienced during this period. However,
this apparent benefit might have come at a substantial opportunity cost associated with the same low levels of financial
development during this period. To some extent, therefore, our benchmark regressions used to predict African financial
sector development give credit to some African countries for having relatively low initial levels of development, stable
prices, and a current account surplus. These factors contribute to higher predicted financial development than has
actually occurred.

                                                                                                                     20
        The results are presented in Table 5. In the simplest regressions, we include the Africa

dummy and the limited set of regressors (Columns 1 and 4). The Africa dummy is negative for both

indicators but it is significant only in the liquid liabilities regression. Per capita income is positive

and significant for both indicators; and it remains positive when the Africa interactions are

introduced (Columns 2 and 5), although it then becomes insignificant in the liquid liabilities

regression. Moreover, per capita income is not significant when we introduce the African

interaction terms for the expanded set of regressors (Columns 3 and 6). The offshore financial

center variable is positive and significant across all specifications, though again no African country

qualifies as an offshore center and so there is no Africa interaction for the variable.

        The most relevant explanatory variable for Africa is again population density. When only

the limited set of regressors is included in the regression, population density is positive and highly

significant for both indicators of financial development (Columns 1 and 4). When the Africa

dummy is interacted with the limited set of regressors (Columns 2 and 5), the relationship is still

positive but weaker in terms of significance. When interactions are included for the expanded set of

regressors (Columns 3 and 6), the variable remains positive but is no longer significant. Despite the

insignificance of the density variable in models 3 and 6, we reject that the sum of the coefficients

for density and its interaction with the Africa dummy is zero at the p = 0.1 level for those models

(see F-statistics near the bottom of Table 5). Therefore, the relationship between population density

and our banking development indicators for African countries is significantly different from zero,

but not for the rest of the developing world in our most expansive models.

        In contrast, the relationship between natural resource intensity and banking indicators is

about the same in Africa as it is in the rest of the developing world. The coefficient for our proxy of

natural resource intensity is negative but tends not to be significant in most of the models; the

coefficient for its interaction with the Africa dummy is never significant. Thus, we reject the


                                                                                                        21
hypothesis that the relationship between natural resources and banking development is somehow

different for Africa. In other words, the relatively low levels of banking development cannot be

attributable to the natural resource curse being more severe in Africa than in other parts of the

developing world. Aside from the negative significant coefficient in model 3 (for liquid liabilities),

the results provide little support for a resource curse in terms of financial development outside

Africa as well.

       Concerning the regressors that are only included in the expanded set, the coefficient for real

growth is negative and highly significant for both indicators of financial development (Columns 3

and 6). The coefficient for the Africa*growth interaction is positive and insignificant; but, we

cannot reject the hypothesis that the sum of the two coefficients (growth and Africa*growth) is zero

(again see F-statistics near the bottom of Table 5). This indicates again that the conditional

convergence result does not hold in Africa, as was suggested by our simple comparison of the

coefficients from the base model (Table 3) with those from the African models (Table 4).

       Similar results hold for the inflation variable which remains negative and highly significant

when the interactions are included in the regressions. The interaction between inflation and the

Africa dummy is positive, marginally significant, and of a magnitude similar to that of the simple

inflation variable. We cannot, therefore, reject the hypothesis that the sum of the inflation and

Africa*inflation variables is zero, which indicates that, like real growth, inflation also has not been

associated with less financial development in Africa.

       A similar pattern also holds for the current account balance and KKM index of institutional

development variables. Both sets of coefficients are positive and significant in both specifications

(Columns 3 and 6), but their signs are negative when interacted with the Africa dummy variable.

We cannot reject the hypothesis that the sum of the two coefficients is equal to zero for both

variables; that is, neither the current account balance nor KKM index is positively related to African


                                                                                                      22
banking development. The regressions that include interaction terms therefore indicate that the high

predicted levels of financial development for Africa from our base models derive, in part, from

macroeconomic factors that matter only outside Africa.

III.2 Toward Resolving the African Finance Puzzle

       Are the determinants of financial development different in Africa? The more refined

analysis in Table 5 indicates that the determinants of banking development in Africa differ from

other developing countries in significant ways. Of particular interest is the set of factors that matter

outside Africa but not in Africa, and those that matter in Africa but not in the rest of the world.

       While macroeconomic variables and the KKM index of institutional development appear to

be important determinants of banking development in the rest of the developing world, they lose

power in the context of Africa. We also observe that the notion of a natural resource curse, which is

largely popularized in the context of resource-rich countries such as those in Africa, is insignificant

in explaining the African financial development gap. The one explanatory variable that stands out

for Africa is population density. Our results show a steeper relationship between population density

and banking indicators for Africa than the rest of the developing world.

       In general, the differences between the predicted and actual values of financial development

for Africa become smaller when the interaction terms are included in the regressions. In fact, the

average difference for the models with full interactions is very close to zero. No difference is

greater than 0.06 or less than -0.06 for either indicator of banking development. The majority of the

predicted values lie within two percentage points of their actual levels. The simple Africa dummy,

which is really a measure of our ignorance about what drives African financial development, tends

not to be significant (and never achieves significance at better than the ten percent level).

       Our findings suggest that, if mechanisms for holding down inflation, generating a current

account surplus, and conditional convergence were to work in Africa as elsewhere, the levels of


                                                                                                      23
financial development would be higher. At the same time, it remains an open question as to why

those mechanisms do not seem to work in Africa. Perhaps a period of sustained economic growth

will ignite those mechanisms, but the African puzzle continues in a different form.19

III.3 Additional Tests and Robustness Checks

         We now perform additional tests and robustness checks. In particular, we analyze in further

depth the role of population density, which shows up more strongly in Africa than elsewhere.

Moreover, we consider indicators of stock market development to test whether a gap similar to the

one found for banking development indicators arises in the context of stock markets. Although

stock markets still remain small and illiquid, African countries seem to have committed to the

equity sector in promoting financial development and several stock exchanges have emerged in

Africa in the recent past.

1. Population density: We try to better understand why sparsely populated African countries have

low levels of banking development by, first, examining the ‘shape’ of the relationship between

density and financial development based on our regression models and, second, including additional

variables related to population density in the regressions.

         Figure 3 shows the relationship between population density and liquid liabilities/GDP for

the typical African and non-African developing country based on Table 5. Specifically, the vertical

axis plots predicted values from Model 2 in Table 5. For all variables other than population density,

we use the mean value for the subsample (either Africa or other developing countries) multiplied by

the appropriate coefficient for that subsample. For population density, we run through the range of

possible values and multiply by the appropriate coefficient for each subsample to generate the

19
   Another possibility is that variables that are important for African financial development are omitted from our
models. For example, Easterly and Levine (1997) demonstrate that ethnic fractionalization explains a large share of
factors that are linked to (slow) growth within Africa. We experimented with two types of variables: fractionalization
and armed conflict. We use the fractionalization data as in Alesina et al. (2003); and we use the UCDP/PRIO dataset to
calculate the average number of armed conflicts per year for each country. Neither variable is significant in our banking
development regressions, and their inclusion does not alter the main qualitative results of our models.

                                                                                                                      24
curvature. Hence, non-population density factors (partially) account for the distance between the

curves for Africa and that for other developing countries.

       Three features of the graph for liquid liabilities in Figure 3 are worth noting: (1) the largest

differences between Africa and the rest of the developing world come at the lower end of the

density scale; (2) population density affects financial development in all countries but the

relationship is much steeper for Africa; and (3) although African countries tend to have lower

population densities, there is substantial overlap for the two samples, which suggests that

comparisons across the full range of the density spectrum are valid. The picture points to important

differences between Africa and the rest of the world, which could be due to the fact that the

minimum viable scale in banking is greater in Africa than elsewhere or to other institutional or

infrastructural failures. Regardless of the explanation, Figure 3 highlights Africa’s difficulty in

overcoming problems related to scale for the least densely populated countries.

       Similarly to Figure 3, Figure 4 shows the relationship between population density and

private credit/GDP based on the coefficients of Model 5 in Table 5. As in the figure for liquid

liabilities/GDP, the gap between Africa and the rest of the developing world is largest at the lowest

levels of population density. However, unlike for the liquid liabilities measure, that gap does not

dramatically close at intermediate density levels. This suggests that while differences in minimum

viable scale could explain differences between Africa and the rest of the developing world with

respect to savings, they cannot account for Africa’s lower levels of private credit over the full range

of population densities.

         To understand the aspects of population density that contribute to low levels of banking

development in Africa, we include additional variables in our regressions that summarize (1) spatial

population characteristics, such as large population agglomerations, (2) infrastructural and

transportation development, and (3) physical banking presence (i.e., branch penetration measures).


                                                                                                      25
The results of these new regressions can help understand what policies may be effective in fostering

African development. If population agglomeration and infrastructural development can account for

most of the strong relationship between population density and banking under-development in

Africa, provision of financial services might improve as a natural consequence of broader

economic/infrastructural improvements or policies that specifically encourage economic

participation by those that live outside urban agglomerates. On the other hand, if measures of bank

branch penetration are better able to account for the density/banking development relationship in

Africa, then it is more likely that specific banking market failures, perhaps related to relatively high

minimum viable scale, are at the heart of African under-performance.

       For spatial population characteristics, we include the percentage of the population residing

in the largest city, the percent of the population in urban agglomerates, and a measure of population

concentration with zero indicating a uniform distribution of residents across 20km by 20km cells

and one indicating that the entire population is concentrated in a single 20km by 20km cell. For

infrastructural development we include roads per square km and railroad track per square km. For

bank penetration, we include commercial bank branches per 100,000 residents (demographic

penetration) and branches per sq. km (geographic penetration). Inclusion of many of these variables

reduces sample size substantially and their coefficients are insignificant. The results for the

variables that do produce significant results (roads per sq.km, percentage of population in the

largest city, and the two branch penetration measures) are presented in Table 6.

       The population density variable is no longer significant when any of these variables are

introduced to the liquid liabilities regressions (in fact it becomes negative and significant when the

geographic branch penetration variable is included), suggesting that roads, population in major

cities, and bank branch penetration are all relevant factors in understanding the strong link between

population density and financial development in Africa. That said, only the coefficients for the


                                                                                                      26
percentage of population in the largest city and the geographic bank branch penetration measure are

consistently significant across the models in Table 6. This pattern is consistent with the notion that

physical proximity to providers of financial services accounts for much of the variation in African

banking development and thus the minimum viable scale is best achieved in major cities.

        It is also worth noting that the links to financial development for the geographic penetration

and largest city population variables are significant for liquid liabilities to GDP but not for private

credit to GDP. Again, overcoming difficulties associated with population density appears more

effective on the savings side than on the credit side.

2. Stock market development: We replace our indicators of banking development with indicators

of stock market development, namely stock market capitalization and the value of traded shares,

each divided by GDP, and re-run the models in Table 5. These are yearly observations that we

average over the sample period for each country as described above. We are unable to run our base

models from Table 6 on the sample of African countries because so few of them have indicators of

stock market development. We are able to calculate them for only Botswana, Cote d’Ivoire, Ghana,

Kenya, Malawi, Mauritius, Namibia, Nigeria, South Africa, Swaziland, Tanzania, Uganda, Zambia,

and Zimbabwe.

        The results (not reported) are notable only because none of the variables that are associated

with stock market development in the rest of the developing world are important in Africa. That is,

we cannot reject the hypothesis that the relationship between any of the variables in our models and

our indicators of African stock market development is zero. This is very likely attributable to the

early stage of development of stock markets in Africa. Once these stock markets are better

established, it might be easier to analyze the determinants of their development via cross-country

regressions.20
20
   Moreover, Bekaert, Harvey and Lundblad (2005) find that stock market liberalization, leading to free flows of
capital in and out of a home country’s equity markets, has a causal and positive impact on economic growth. But for
most African countries the stock market development (and associated institutions) has not reached the stage of
liberalization.

                                                                                                                      27
IV. Firm-level Tests on Access to External Finance

         As mentioned earlier, while cross-country studies focusing on country-level variables are

highly useful for summarizing patterns in the data and identifying relationships between variables,

they are subject to methodological issues. For example, the observed results may be driven by

measurement errors or unobservable factors. In an attempt to overcome these potential problems,

we next turn to firm-level data drawn from the Investment Climate Surveys (ICS) conducted by the

World Bank over the past decade.21 Our specific reasons for augumenting the cross-country study

with the firm-level analysis are as follows: First, while country level indicators of banking

development might suffer from measurement errors, firm-level responses about their use of

financial services should not. Second, our cross-country level regressions involve one observation

per country and thus have fewer degrees of freedom than what are available in firm-level

regressions. Third, the firm-level data sets allow us to control for a number of potentially relevant

firm characteristics (e.g., size, sector) that could explain substantial variation in the demand for, and

use of, financial services. Thus, overall the firm-level regressions can provide more precise

estimates of the relationships between financial development and our explanatory variables than the

country-level regressions. Furthermore, to the extent that we derive similar qualitative results for

the firm- and country-level regression, it provides additional, and even stronger, support for the

conclusions that we have drawn to this point based only on the country-level regressions.

         Table 7 presents summary statistics on our firm-level sample. Firm characteristics (all

dummy variables) include size (small, medium, and large based on the number of employees),

industry (manufacturing, services, and others), exporter status and foreign ownership status. It

appears that there are more small firms and non-exporting firms in the African (excluding South

Africa) sample than in other developing countries, while the fraction of firms with foreign

21
  These surveys have been used frequently in recent cross-country, firm-level studies on law, institutions, finance and
growth (e.g., Beck, Demirgüç-Kunt, and Levine, 2005; Beck, Demirgüç-Kunt, and Maksimovic, 2005).

                                                                                                                      28
ownership stakes is higher in the African sample. Firm financing sources, for both short term

(working capital) and long term (new investments) purposes, include internal (e.g., retained

earnings) and external channels. Following prior research, we focus on long term external

financing from financial institutions, the most important external financing channel in most

developing countries. Specifically, the external finance dummy equals one if a firm has access to

loans from domestic or foreign banks, or credit card debts from these institutions; and zero

otherwise. Almost 84% (74%) of firms in Africa (other developing countries) do not have access to

external finance under this definition.22

         Our basic strategy is to pattern our firm-level regressions on the country-level regressions,

but also to include variables that summarize firm characteristics. We note, however, that we have

all of the necessary variables for firms from only twelve countries in Africa. Since the standard

errors in our models allow for clustering at the country level, we are not able to include all of the

country-level regressors that were in the cross-country regressions in Table 5. However, the most

important regressors are included.23 Since the majority of firms in Africa and elsewhere do not

have access to external finance (Table 7), our dependent variable is a dummy indicating whether the

firm received any financing from a formal provider, rather than a continuous variable showing the

importance of institutional finance among all financing sources.24 Table 8 reports the marginal

effects from Probit regressions.



22
   We only include firm observations for which data is available on whether the firm has access to all three external
financing channels (domestic and foreign banks and credit cards). Summary statistics reported in Table 7 represent the
sample we use in regressions reported in Table 8.
23
   We performed a series of robustness checks in which we added each of the country-level variables from the country-
level regressions in Table 5 that do not appear in the firm-level regressions in Table 8, one at a time. Qualitative results
are very similar to those reported in Table 8.
24
   For the surveys completed through 2006, the dummy variable is equal to one if the firm reported a positive value for
finance from local banks, foreign banks or credit cards. For the surveys after 2006, those questions were broadened to
ask whether firms received finance from private or state owned commercial banks, or from non-bank financial
institutions. Since banks are the main providers of financial services throughout the developing world, this slight change
in question format does not present a major problem. Regression results are very similar when we include a post-2006
dummy variable.

                                                                                                                         29
       In the baseline model, population density is once again positive and significantly associated

with firms’ access to external finance (Column 1). The coefficient on the interaction between the

Africa dummy and population density indicates that there is no difference in the impact of this

variable for African and non-African firms. Moreover, we can reject that the sum of the

coefficients for density and its interaction with the Africa dummy is zero at the p = 0.05 level for

this model (see chi-squared statistics near the bottom of Table 8). In Model 2 we add road coverage

(per sq. km) and find it is actually negatively associated with African firms’ access to external

finance while it has a positive impact for firms from other developing nations. On the other hand,

adding this variable strengthens the (marginally positive) impact of population density for African

firms as the interaction with the African dummy is significant at 10% and the sum of the density

and Africa*density coefficients is positive and significantly different from zero at the 5% level

(Column 2).

       Another main result from Table 8 is that both the geographic and demographic bank branch

penetration variables have a positive association with access to external finance for African

countries. However, the result is significant only for geographic penetration (at 1% in Column 4).

These variables are insignificant and of smaller magnitudes in the rest of the developing world.

Population density remains positive and significant for non-African countries when the variables for

demographic and geographic bank branch penetration enter the regression (Columns 4 and 5). We

can no longer reject that the sum of the coefficients for population density and its interaction with

the Africa dummy is zero in the model with geographic branch penetration (though we continue to

do so when demographic penetration enters the regression in model 5). The results for geographic

branch penetration thus confirm those from the country-level regressions. Thus, both cross-country

and firm-level regressions point to the importance of physical proximity to providers of financial

services in Africa relative to the rest of the developing world. General infrastructural failures are


                                                                                                        30
not necessarily responsible for low levels of access to external finance for African firms, but rather

the failures seem endemic to the banking sectors and markets for debt instruments.

        In Table 8 we also introduce foreign bank ownership (at the country level) as an explanatory

variable. We did not include this variable in the country-level regressions (Tables 3-5) because we

view this as an ‘outcome’ variable for the development status of a country’s banking sector.

However, this variable is plausibly exogenous from the point of view of small, privately owned

firms in a given region. This variable is negatively associated with access to external finance for

firms from the developing world (Column 3), and for Africa (see F-statistic for foreign ownership

plus its interaction with the Africa dummy variable at the bottom of column 3). We cannot reject

that the sum of the coefficients for population density and its interaction with the Africa dummy is

zero in this model, suggesting some interplay between foreign bank presence in Africa and

population density.25

        Finally, we note that firm characteristics explain a substantial amount of variation in firms’

access to external finance, supporting the notion that firm-level regressions offer more precise

estimates than the country-level regressions. The similarities and differences between Africa and

the rest of the developing world are also instructive. Firm size affects the use of external finance

similarly in Africa and elsewhere, with smaller firms being at a substantial disadvantage relative to

larger ones. Foreign-owned firms use less external finance than local firms in Africa and elsewhere,

perhaps because they have access to non-local sources of finance such as their parent companies.

Differences also arise with respect to industry affiliation. In the rest of the developing world,

manufacturing firms rely more heavily on external finance than other firms. In Africa, those firms

show no strong tendency to rely on external finance more than others. In addition, older firms are

less likely to have access to external finance outside Africa; but more mature African firms appear

25
  The density variable remains negative and significant for the rest of the developing world when foreign bank
ownership shares are included in the regression.

                                                                                                                 31
to have an advantage in acquiring such finance.

       We conclude our cross-country and firm-level analyses with some observations. The cross-

country approach allows us to measure the financial development gap between Africa and other

developing economies and identify factors that are of particular relevance for Africa. However, we

recognize the limitations with cross-country studies, and hence attempt to address them in three

ways. First, we are cautious in drawing causation; we do, however, view variables, such as

population density and natural resources endowment, as exogenous variables for predicting

financial sector development. Second, as noted above we complement our country-level tests with

firm-level analyses, and the micro-level evidence is consistent with the country-level results. In

particular, both the cross-country and firm-level studies are consistent with the importance of

overcoming low population density to achieve financial development in Africa. Finally, we employ

new data on the outreach of financial sectors, in particular bank branch penetration measures, to

better understand the channels through which population density may affect different aspects of

financial sector development. Moreover, we explore whether transportation infrastructure, urban

agglomeration, and the share of arable land are driving the strong relationship between population

density and banking sector development in Africa.



V. Within-country Study: Equity Bank and Financial Access in Kenya

       Our cross-country and firm-level analyses both indicate that sparsely populated regions pose

a particularly severe problem for African financial development. What types of financial institutions

are best suited to providing services in such areas? A satisfactory answer to this question is well

beyond the scope of the current paper, but in this section we examine the effects of the recent

expansion of bank branch networks in Kenya to get some sense of the factors that drove a large

increase in use of bank accounts and loans within a relatively short time period.


                                                                                                      32
       As background, we begin by noting that while economic growth over the past few decades

has been disappointing in Africa, some countries began growing fast at the start of the 21st century,

at least until the negative shock of the global crisis in 2007-2009. The GDP growth for the five year

period running from 2002 to 2007 averaged over 5% in Africa, outpacing population growth and

hence implying an increase in GDP per capita (Allen, Otchere, and Senbet, 2011). However, and as

discussed earlier, despite the adoption of extensive economic and financial reforms, most Sub-

Saharan African countries still face a severe gap in the development of their financial sectors.

       Figure 5 shows the relationship between demographic bank penetration (measured by the

number of bank branches per 100,000 people) and GDP per capita across low income economies in

2003-2004. The figure suggests three stylized facts. First, there is a strong positive relationship

between bank penetration and economic development. Second, Sub-Saharan African countries are

characterized by both low income per capita and low bank branch penetration. Third, the bank

branch network in Sub-Saharan African countries is underdeveloped relative to not only the higher

income economies but other peers with similar income levels.

       The underdevelopment of the bank branch network in Sub-Saharan Africa has resulted in

low access to banking services, as illustrated by our firm-level study (on the bank credit side).

While in the early 2000s roughly half of the world’s adult population lacked any form of bank

account, in Sub-Saharan Africa over eighty percent of the adult population lacked an account (Chaia

et al. (2011) and Honohan (2008)). In line with these facts, Kendall et al. (2010) report that the

average number of bank accounts per adult is 3.7 in developed countries, 0.9 in developing

countries and less than 0.5 in Sub-Saharan African countries.

       The financial access problem in many Sub-Saharan countries and the emergence of new

business models that may contribute to solve this problem deserve particular attention. In this

respect, Kenya offers an interesting case study for financial access as in recent years it has


                                                                                                      33
witnessed a strong bank branch expansion. This expansion has coincided with the emergence of

Equity Bank, a pioneering commercial bank that devised a banking service strategy targeting low

income clients and traditionally under-served territories. The bank is listed on the Nairobi Stock

Exchange, it has no government ownership share, and it has played a key role in integrating

provision of financial services to the under privileged with formal finance. Figure 6 shows the stock

performance of Equity Bank during the period of August, 2006 to August 2011. It significantly

outperforms two of its main competitors on this measure, Barclays Bank of Kenya and the Kenya

Commercial Bank.

       The banking system in Kenya expanded greatly in terms of the number of branches in recent

years. Table 9 reports that between 2006 and 2009 the total number of bank branches in Kenya

increased from almost 600 to almost 1,000 (a 68% increase). This bank branch expansion occurred

not only in urban districts but also in rural and arid and semi-arid districts. In percentage terms, this

expansion was greater in the arid and semi-arid districts (112%), followed by rural districts (67%)

and urban districts (62%). This expansion involved all ownership categories of banks, namely

domestic private banks, foreign banks and government banks. However, as shown in Table 10, the

expansion was particularly driven by domestic private banks, among which Equity Bank played an

important role. The number of Equity Bank’s branches increased from 44 to 112 in the period 2006-

2009, representing an expansion of 155%. No other bank experienced such growth in the same

period. The number of bank branches of Barclays and Kenya Commercial Bank (i.e., the banks with

the largest branch networks after Equity Bank) increased from 62 to 119 and from 117 to 169,

representing growth of 92% and 44% respectively.

       Although most Kenyan banks have experienced branch expansion in recent years, banks

with different ownership structures have followed different expansion strategies. While foreign

banks prefer urban areas (perhaps in order to cherry-pick a set of elite customers), domestic private


                                                                                                       34
banks seem to exploit their superior knowledge of the culture and have also entered rural and arid

and semi-arid areas. For example, as part of its expansion strategy, Equity Bank focused on the use

of local languages in its branches. This strategy is quite important considering that 30-40% of the

people in central Kenya cannot speak either English or Swahili, and thus speak only a minority

language.

        As a first exploration of the bank branching strategies in Kenya, Figure 7 presents the

number of bank branches in 2006 and 2009 for three bank groups: foreign banks, government-

owned and government-influenced banks, and domestic private banks.26 The figure shows the

number of bank branches in districts that have been traditionally less attractive for major

commercial banks: rural and arid and semi-arid districts, districts in which minority languages are

more common, less populated districts and districts with low educational attainment levels.27 It

suggests two stylized facts. First, domestic private banks and government and government

influenced banks had greater presence in underdeveloped/more vulnerable districts, while foreign

banks had the least presence in those districts. Second, the bank branch expansion of local private

banks has been much stronger than the branch expansion of government banks and foreign banks.

These patterns suggest that domestic private banks, whose expansion has been led by Equity Bank

(see Table 10), played a vital role in fostering banking services in underdeveloped districts.

        The performance of Equity Bank over the last half decade has been impressive. It has

experienced an explosive growth of its assets and a significant expansion of its banking services.

Figure 8 shows that during the period 2006-2010 the assets of Equity Bank increased seven-fold;

while its customers and customer deposits increased by a factor of six. In fact, Equity Bank ended

26
   Government-owned banks are those in which the government owns a majority of shares. In (the two) government-
influenced banks the government maintains a large, but non-controlling ownership share. Throughout most of the paper,
we refer to both groups collectively as “government banks.�
27
   Rural, arid and semi-arid districts exclude Nairobi and Mombasa. High minority language districts are those where
the share of the population speaking a minority language is greater than the median. Less populated districts are those
where population density is less than the median. Districts with low educational attainment levels are those where the
share of the population with secondary or tertiary education is less than the median.

                                                                                                                   35
the year 2010 with almost six million customers. Of these, 5.4 million are in Kenya, 474,000 are in

Uganda and 28,000 are in South Sudan. As a consequence of this expansion, Equity Bank became

the fifth largest bank in Kenya in terms of the share of gross assets and deposits of the whole system

and is by far the bank with the largest number of deposit and loan accounts. The number of deposit

and loan accounts of Equity Bank represents around the 50% and 30% of the total number of

deposit and loan accounts in Kenya, respectively (see Table 11). These deposit and loan market

shares suggest that the emergence of Equity Bank has played an important role in expanding the

bank branch network to under-served territories and thus expanding the use of banking services to

population segments ignored by other large traditional commercial banks. Moreover, these stylized

facts suggest that Equity Bank’s model has not only expanded the use of financial services to

broader segments of the population but it has also created profits in the process. As shown in Figure

8, Equity Bank’s profits before taxes and shareholder funds have experienced a strong positive

trend.

         There is preliminary evidence that the bank branch expansion and emergence of Equity

Bank have paid off in terms of access to banking services in Kenya. In fact, as reported in Table 12,

the proportion of individuals having a bank account in Kenya increased considerably going from 14

percent in 2006 to 23 percent in 2009. The proportion of individuals having a loan from a bank

showed a more modest increase from 2.9 percent to 4.3 percent. This increase in access to banking

services has coincided with a stronger presence of Equity Bank. While Equity Bank had branches in

48% of the districts in Kenya in 2006, it had branches in 87% of them in 2009.

         In light of the preliminary evidence presented above, in an ongoing work Allen et al. (2011)

explore the impact of Equity Bank, as well as other banks, on the use of banking services in Kenya

by combining a new dataset on bank presence with household-level survey data in 2006 and 2009.

The dataset on bank presence is based on branch-level information that is aggregated to construct a


                                                                                                   36
district-level panel on the number of branches by bank (see Allen, et al, for details). The panel

covers 45 commercial banks that operated in 65 Kenyan districts in the period between 2006 and

2009.28 This new dataset allowed the authors to explore the relationship between bank expansion

and access to banking services over time and to exploit within-district variation in bank presence.

The household-level data is from the FinAccess surveys conducted by Financial Sector Deepening

Trust Kenya (FSD Kenya) in 2006 and 2009. The 2006 survey consisted of 4,420 completed

interviews, while the 2009 survey consisted of 6,598 completed interviews.

         Allen et al. (2011) find that the presence of domestic private banks, government banks or

foreign banks has a positive impact on access to bank accounts at the district level. Interestingly

they also find that the presence of Equity Bank in a specific district is strongly positively related to

the residents’ probability of having a bank account and/or a bank loan, which goes beyond the effect

of bank branch expansion and presence of other commercial banks in Kenya. These results are

consistent with the stylized facts that we discussed above, and they highlight the importance of

institutions, such as Equity Bank, with a business model focused on the provision of financial

services to population segments ignored by traditional commercial banks while generating

sustainable profits in the process. Such institutions can be an important solution to the financial

access problem that has hindered the development of inclusive financial sectors in many developing

countries.



VI. Conclusions

         The available evidence provides a convincing linkage between financial development and

economic development. Yet the level of financial development remains very low in Africa based

28
   Although Kenya is currently divided into 46 districts, the data set exploits a more disaggregated country division as of
the 1999 census. In the 1999 census, Kenya comprised eight provinces (Central Coast, Eastern, Nairobi, North Eastern,
Nyanza, Rift Valley, and Western) that were subdivided into 69 districts. Of these 69 districts, the survey data consider
covers 65.

                                                                                                                       37
on the standard indicators of banking and capital market development. Benchmarking based on the

correlates of financial development in other developing countries reveals a substantial gap between

predicted and actual levels of African financial development. In addition, both country-level and

firm-level tests indicate that the determinants of banking development in Africa differ from the rest

of the world in telling ways.

       For example, inflation and the current account balance explain no variation in African

financial development although they do so in other developing countries, and measures of

institutional development explain less variation in Africa than elsewhere. Why this collection of

macroeconomic fundamentals is not strongly linked to African financial development remains

unclear. However, if these macroeconomic and institutional mechanisms could be made to

function, our results suggest that the levels of financial development would rise.

       Most importantly, population density appears to be more important in Africa than elsewhere,

and our firm-level study on the access to external financing indicates that population density is

linked more to bank branch penetration in Africa than in the rest of the developing economies. We

also find a nonlinear relationship between population density and banking sector development, with

the largest gap between Africa and other developing countries observed for those African countries

‘trapped’ in the low density area. Presumably, bank branch penetration figures remain low in

Africa because of difficulties in achieving minimum viable scale in sparsely populated, low-income

areas. Therefore, technological advances, such as mobile banking, that enable users of financial

services to be located far away from their financial institutions, provide a promising way to

facilitate African financial development outside major cities, a topic that has been studied in the

context of Kenya, where the mobile payments services of M-PESA are now widely used (Mbiti and

Weill, 2010; Jack and Suri, 2010).




                                                                                                      38
       While use of mobile payments services has increased rapidly in Kenya, those services have

not proven to be effective vehicles for savings accumulation nor provision of credit (Mbiti and

Weill, 2010). We present the stylized facts showing the effects of the expansion of bank branch

networks in Kenya from 2006 to 2009 and, in particular, that of Equity Bank. While banks of all

ownership types (private, foreign, and government) expanded their networks during this period,

Equity’s expansion was most pronounced and its association with take-up of bank accounts and

loans greater than for other banks. Equity Bank was also more likely to enter previously under-

served districts and the association between Equity’s presence and increased usage of bank accounts

was larger for Kenyans with less wealth and education, that did not own a permanent home, and that

lacked a salaried job.

       The more detailed analysis of the role of Equity Bank is a subject of an ongoing work by

Allen et al (2011), and the current findings support the observations that we make in this paper.

Overall, Equity Bank’s expansion experience suggests that it is possible to serve poorer segments of

the African population while generating sustainable profits. At the same time, we recognize that the

last chapter on this topic is yet to be written and that the current configuration of banking services

provision in Kenya presents its own challenges. For example, a single bank now maintains 55% of

the deposit accounts in the banking system and, and those deposits are covered by a deposit

insurance fund, which could have implications for systemic stability. While we recognize that our

contribution is closer to the first than the last word on the determinants of financial development

and inclusion in Africa, our hope is that by combining country-, firm-, and household-level

analyses, we have been able to shed some light on the contours of the current financial development

gap and financial access issue in Africa.




                                                                                                         39
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                                                                                                43
                      Table 1: Summary Statistics on the Country Sample

                Variable                            World (minus Africa)                    Sub-Saharan Africa
                                                    Mean         Standard                 Mean          Standard
                                                                 Deviation                              Deviation
Liquid liabilities / GDP                            64.2%         47.4%                   27.5%          17.7%
Private credit / GDP                                57.7%         45.3%                   17.6%          22.7%
Stock Market Capitalization / GDP                   52.1%         60.0%                   25.6%          43.8%
Stock Market Value Traded / GDP                     34.1%         50.9%                   6.5%           20.0%
Ln(Population)                                       2.44          1.59                    2.24            1.33
Ln(Population density)                               0.44          1.94                    0.09            0.12
Natural resources                                     0.5           2.41                   0.15            0.77
Offshore center                                      4.4%         20.7%                   0.0%            0.0%
Ln(Per capita income)                                2.25           1.04                   0.38            0.94
Population * GDP per capita                          0.48           1.35                   0.03            0.07
Real GDP growth rate                                 4.1%          2.5%                    4.8%           2.7%
Inflation rate                                       5.2%          5.2%                    9.3%          15.0%
Current Account balance / GDP                        0.2%          8.1%                   -3.8%           6.7%
KKM index                                            0.33           0.9                   -0.54            0.58
Bank concentration                                   0.65           0.19                   0.81            0.14
Foreign ownership share                             27.1%         25.9%                   44.4%          24.4%
State ownership share                               15.9%         19.7%                   13.3%          16.6%
Manufacturing / GDP                                 16.8%          6.1%                   11.0%           7.3%
Secondary/Primary school enrollment                  0.81           0.24                   0.33            0.18
Roads / Area                                         1.07          1.65                    0.21            0.22
Railroads / Area                                     0.03          0.03                      0               0
Urban population                                    63.6%         20.7%                   36.2%          17.2%
Geographic branch penetration                       29.76          80.07                   7.97           22.49
Demographic branch penetration                      16.51          17.28                   2.86            3.64

       Data sources: For liquid liabilities/GDP, private credit/GDP, stock market capitalization/GDP, stock market
       value traded/GDP, and bank concentration: World Bank Database on Financial Development and Structure.
       See Thorsten Beck and Asli Demirgüç-Kunt, "Financial Institutions and Markets Across Countries and
       over Time: Data and Analysis"", World Bank Policy Research Working Paper No. 4943, May 2009.
       Natural resources is the Lederman and Maloney (2008) measure of net exports in resource intensive
       industries as described in the text. Offshore center is a dummy variable defined by IMF (2007), “Financial
       Soundness Indicators: Experience with the Coordinated Compilation Exercise and Next Steps�, STA Dept.
       The KKM index is the measure of broad institutional development created by Kaufmann, Kraay, and
       Maztruzzi (2007). Foreign and state ownership share of banking sector assets are from the World Bank
       Database of Bank Regulation and Supervision (2008). Geographic and demographic branch penetration are
       from CGAP, “Financial Access 2009: Measuring Access to Financial Services in 135 Countries.� All other
       variables are from World Development Indicators.
                                                        Table 2: Correlations Tests: World (minus Africa) sample

                                               Stock     Stock                                                      Pop *
                            Liquid      Priv                                                               Ln(Per             Real                  CA                       Forgn    State
                                                 mkt    mkt val              Ln(Pop      Nat     Offsh               GDP                 Infl.             KKM      Bank                       Manuf
N = 90                      liab. /   credit                       Ln(Pop)                                 capita             GDP                balance                      own      own
                                                capit   traded /             density)    res     center               per                rate              index    conc                       / GDP
                             GDP      / GDP                                                               income)            gr. rate             / GDP                      share    share
                                               / GDP      GDP                                                       capita
Private credit / GDP        0.64*       1
Stock market
                            0.68*     0.65*      1
capitalization / GDP
Stock market value traded
                            0.40*     0.70*    0.75*       1
/ GDP
Ln(Population)              -0.08     0.03     -0.02    0.24*         1
Ln(Population density)      0.38*      0.13    0.61*     0.10       -0.20       1
Natural resources           -0.22*     0.06    -0.06     0.03       -0.19    -0.21*       1
Offshore center              0.19     0.10     0.08      -0.08     -0.25*     0.41*     -0.07      1
Ln(Per capita income)       0.49*     0.71*    0.51*    0.51*       -0.15     0.18      0.22*    0.04       1
Population * GDP per
                             0.13     0.38*    0.18     0.47*       0.47*     -0.05     -0.12    -0.07     0.26*      1
capita
Real GDP growth rate        -0.17     -0.36*   -0.20    -0.25*      -0.18     0.31*     0.00     0.22*    -0.26*    -0.19       1
Inflation rate              -0.40*    -0.48*   -0.35*   -0.32*      0.15      -0.18     -0.08    -0.16    -0.39*    -0.10     -0.04       1
Current Account balance
                            0.37*     0.22*    0.45*    0.27*       0.02      0.58*     0.25*    0.18      0.34*    -0.02     0.09      -0.17      1
/ GDP
KKM index                   0.51*     0.79*    0.51*    0.50*       -0.19     0.13      0.16     0.01      0.88*    0.21*    -0.27*     -0.48*    0.17       1
Bank concentration          -0.11      0.01    0.12      0.04       -0.50*    0.07      0.21     -0.02     0.03     -0.35*    -0.03     -0.16     -0.06     0.18      1
Foreign ownership share      0.08     -0.17    -0.02    -0.25*     -0.43*     0.21      -0.09    0.15      0.14     -0.23*    0.11      -0.13     0.02     0.14     0.08       1
State ownership share       -0.14     -0.28*   -0.28*    -0.14      0.43*     -0.11     -0.09    -0.15    -0.41*    -0.03     0.08      0.26*     -0.05    -0.39*   -0.16    -0.32*     1
Manufacturing / GDP         -0.07      0.08    -0.15     -0.02      0.35*     -0.18     -0.26*   -0.12     0.03     0.05      -0.07      0.06     -0.15     0.10    -0.22*   -0.02     0.11      1
Secondary/Primary
                            0.25*     0.51*    0.27*    0.34*       -0.18     -0.01     0.25*    -0.12     0.76*    0.14      -0.15     -0.32*    0.08     0.76*    0.19      0.16    -0.33*   0.07
school enrollment

         Notes: (* p < 0.05).
    Table 3: Regressions on Banking Sector Development for the sample of non-African countries

This table presents OLS regressions of banking sector development, measured by liquid liabilities/GDP and credit to
the private sector extended by deposit money banks/GDP ratios, on a set of country-level variables including
endowment (population and resources), macroeconomics, institutions, banking structure and other variables. We
present models for all non-African countries and for (non-African) low and middle income countries separately.
Standard errors are presented in the brackets below coefficients.


                                                        Liquid liabilities / GDP                                     Private credit / GDP

                                                All                       Low + Middle income              All                      Low + Middle income
                                         (1)               (2)               (3)          (4)      (5)                (6)              (7)          (8)
    Ln(Population)                     -0.017            -0.003            -0.001      -0.035     0.003             0.015             0.002      -0.027
                                      (0.021)           (0.028)           (0.019)     (0.030)    (0.018)           (0.021)          (0.017)     (0.026)
    Ln(Population Density)           0.074***            0.031              0.036       0.032     0.018             0.019            0.032*      -0.020
                                      (0.023)           (0.027)           (0.022)     (0.022)    (0.019)           (0.020)          (0.019)     (0.019)
    Natural Resources               -0.038***         -0.069***            -0.034     -0.062*    -0.022*           0.0002            -0.010      -0.026
                                      (0.014)           (0.019)           (0.033)     (0.036)    (0.012)           (0.014)          (0.028)     (0.031)
    Offshore Center                   0.247*             0.133           0.369***    0.341***     0.191             0.159          0.374***    0.339***
                                      (0.145)           (0.170)           (0.112)     (0.121)    (0.123)           (0.127)          (0.096)     (0.102)
    Ln(Per Capita Income)            0.222***            0.061              0.056      -0.091   0.275***            -0.016          0.089**      -0.063
                                      (0.036)           (0.083)           (0.039)     (0.065)    (0.031)           (0.062)          (0.034)     (0.055)
    Population * GDP Per Capita         0.004            -0.013             0.006       0.061   0.071**           0.062**            -0.031       0.015
                                      (0.032)           (0.033)           (0.100)     (0.101)    (0.027)           (0.024)          (0.086)     (0.084)
    Real GDP Growth Rate                                 -2.015                      -2.619**                    -3.129***                     -1.769**
                                                        (1.514)                       (1.159)                      (1.126)                      (0.974)
    Inflation Rate                                       -1.219                       -0.946*                     -1.092**                     -0.894**
                                                        (0.751)                       (0.505)                      (0.559)                      (0.424)
    Current Account Balance / GDP                     1.519***                        1.565**                       0.392                       1.236**
                                                        (0.556)                       (0.658)                      (0.414)                      (0.553)
    KKM Index                                         0.239***                       0.215***                    0.362***                      0.210***
                                                        (0.093)                       (0.080)                      (0.069)                      (0.067)
    Manufacturing / GDP                                -1.313**                         0.436                       -0.127                        0.651
                                                        (0.652)                       (0.580)                      (0.485)                      (0.487)
    Secondary/Primary Enrollment                         -0.335                        -0.040                       -0.130                       -0.004
                                                        (0.241)                       (0.179)                      (0.179)                      (0.150)
    Constant                        0.383***          1.188***           0.467***    0.990***    -0.041          0.782***          0.248***    0.640***
                                     (0.123)            (0.223)           (0.101)     (0.167)   (0.104)            (0.166)          (0.087)     (0.141)
    Adjusted R2                        0.41               0.51              0.14         0.36     0.54               0.70             0.22         0.43
    Observations/Countries             111                 97                75           67      111                 97               75           67

   Notes: (* p< 0.1, ** p<0.05, *** p<0.01).
               Table 4: Regressions on Banking Development for the sample of African countries

This table presents OLS regressions of banking sector development, measured by liquid liabilities/GDP and credit to
the private sector from deposit money banks/GDP ratios, on a set of country-level variables including endowment
(population and resources), macroeconomics, institutions, banking structure and other variables. We present models
for all the Sub-Saharan African countries, with and without South Africa. Standard errors are presented in the
brackets below coefficients.


                                                      Liquid liabilities / GDP                                       Private credit / GDP

                                               All                       Without South Africa              All                       Without South Africa
                                        (1)              (2)               (3)          (4)         (5)             (6)               (7)           (8)
    Ln(Population)                    -0.035           -0.004           -0.029        -0.011    -0.058**          -0.021            -0.007         0.013
                                     (0.026)          (0.026)           (0.031)      (0.030)     (0.024)         (0.023)           (0.022)       (0.021)
    Ln(Population Density)          0.074***         0.080***          0.075***     0.077***     0.0314*         0.049**          0.043***      0.061***
                                     (0.019)          (0.022)           (0.020)      (0.024)     (0.018)         (0.020)           (0.014)       (0.016)
    Natural Resources                 -0.033           -0.013            -0.032       -0.010      -0.030          -0.001            -0.024        -0.015
                                     (0.036)          (0.047)           (0.037)      (0.049)     (0.033)         (0.041)           (0.027)       (0.034)
    Offshore Center                      -                -                 -            -           -               -                 -             -

    Ln(Per Capita Income)            0.038             0.022             0.043        0.018       0.009             0.054          0.048*          0.073
                                    (0.035)           (0.078)           (0.037)      (0.080)     (0.032)          (0.068)          (0.027)       (0.056)
    Population * GDP Per Capita      0.441             -0.281            0.057        0.221     2.941***         2.396***          -0.141         -0.163
                                    (0.520)           (0.508)            (1.12)      (1.197)     (0.477)          (0.442)          (0.816)       (0.834)
    Real GDP Growth Rate                               -0.447                         -0.499                       -0.607                         -0.343
                                                      (1.031)                        (1.057)                      (0.898)                        (0.736)
    Inflation Rate                                     0.060                          0.058                        -0.076                         -0.066
                                                      (0.192)                        (0.196)                      (0.167)                        (0.136)
    Current Account Balance / GDP                      -0.082                         -0.207                      -0.879*                         -0.241
                                                      (0.587)                        (0.656)                      (0.511)                        (0.457)
    KKM Index                                         0.116*                         0.119*                         0.073                          0.058
                                                      (0.062)                        (0.063)                      (0.054)                        (0.044)
    Manufacturing / GDP                                -0.376                         -0.341                       -0.085                         -0.265
                                                      (0.455)                        (0.470)                      (0.396)                        (0.327)
    Secondary/Primary Enrollment                       0.372                          0.379                         0.179                          0.144
                                                      (0.247)                        (0.252)                      (0.215)                        (0.175)
    Constant                        0.548***         0.528***         0.545***      0.518***    0.297***           0.260*         0.275***      0.309***
                                     (0.078)         (0.2613)          (0.079)      (0.3044)     (0.071)          (0.130)          (0.058)       (0.243)
    Adjusted R2                        0.36             0.53             0.34          0.52        0.64             0.78            0.24            0.45
    Observations/Countries              38               33               37            32          38               33               37             32

         Notes: (* p< 0.1, ** p<0.05, *** p<0.01).
        Table 5: Regressions for the combined sample of low and middle income and African countries

This table presents OLS regressions of banking sector development, measured by liquid liabilities/GDP and credit to the
private sector from deposit money banks/GDP ratios, on a set of country-level variables including endowment (population
and resources), macroeconomics, institutions, banking structure and other variables. We include all the low and middle
income countries (including Africa, excluding South Africa), an African dummy variable, and interactions between each of
our explanatory variables and the Africa dummy. Standard errors are presented in the brackets below coefficients, and F-
statistics are shown at the bottom of the table.

                                                         Liquid Liabilities/GDP             Private Credit/GDP
                                                         (1)        (2)           (3)     (4)        (5)           (6)
                      Africa                           -0.105*      0.078    -0.472*     -0.066     0.027        -0.331
                                                       (0.054)    (0.147)    (0.280)    (0.045)    (0.124)       (0.231)
                      Ln(Population)                    -0.009     -0.001     -0.035    0.0005      0.002        -0.027
                                                        (0.015)   (0.017)     (0.028)    (0.013)   (0.015)       (0.023)
                      Ln(Population Density)           0.050***   0.036*       0.032    0.036***   0.032*         0.020
                                                       (0.016)    (0.019)    (0.020)    (0.013)    (0.016)       (0.017)
                      Natural Resources                -0.031      -0.034    -0.062*     -0.012    -0.010        -0.026
                                                        (0.025)    (0.030)    (0.033)    (0.021)    (0.025)    (0.027)
                      Offshore Center                  0.363***   0.369***   0.341***   0.378***   0.374***   0.339***
                                                       (0.098)    (0.101)     (0.110)   (0.082)    (0.085)       (0.091)
                      Ln(Per Capita Income)            0.052**     0.056      -0.091    0.070***   0.089***      -0.063
                                                       (0.026)    (0.035)     (0.059)   (0.022)    (0.030)       (0.049)
                      Population * GDP Per Capita       0.030      0.006       0.061    -0.023     -0.031         0.015
                                                       (0.084)    (0.089)     (0.091)   (0.071)    (0.075)       (0.075)
                      Real GDP Growth Rate                                   -2.619**                         -1.769**
                                                                              (1.053)                          (0.869)
                      Inflation Rate                                         -0.946**                         -0.894**
                                                                             (0.458)                          (0.378)
                      Current Account Balance / GDP                          1.565**                          1.236**
                                                                              (0.598)                            (0.493)
                      KKM Index                                              0.215***                         0.210***
                                                                              (0.072)                            (0.060)
                      Manufacturing / GDP                                      0.436                              0.651
                                                                              (0.526)                            (0.434)
                      Secondary/Primary Enrollment                            -0.040                             -0.004
                                                                              (0.163)                            (0.134)
                      Interactions with Africa dummy
                        Ln(Population)                             -0.027     0.024                 -0.010       0.041
                                                                  (0.048)     (0.054)              (0.040)       (0.044)
                        Ln(Population Density)                     0.039       0.046                0.010         0.041
                                                                  (0.035)     (0.041)              (0.029)       (0.034)
                        Natural Resources                          0.001       0.052               -0.014         0.010
                                                                  (0.061)     (0.081)              (0.051)       (0.067)
                        Ln(Per Capita Income)                      -0.013     0.109                 -0.042       0.136
                                                                  (0.065)     (0.136)              (0.054)       (0.112)
                        Population * GDP Per Capita                0.051       0.160                -0.110       -0.178
                                                                  (1.634)     (1.833)              (1.373)       (1.513)
                        Real GDP Growth Rate                                   2.120                              1.425
                                                                              (1.930)                            (1.592)
                        Inflation Rate                                        1.004*                             0.830*
                                                                              (0.547)                            (0.452)
            Current Account Balance / GDP                             -1.773                          -1.477
                                                                      (1.168)                         (0.963)
            KKM Index                                                 -0.096                           -0.152
                                                                      (0.121)                         (0.100)
            Manufacturing / GDP                                        -0.776                          -0.916
                                                                      (0.891)                         (0.735)
            Secondary/Primary Enrollment                               0.418                           0.148
                                                                      (0.418)                          (0.345)
         Constant                               0.525***   0.467***   0.990**   0.294***   0.248***   0.640***
                                                (0.073)    (0.091)    (0.152)   (0.061)    (0.076)    (0.125)
         Adjusted R2                              0.33       0.31       0.45      0.37      0.34       0.51
         Observations                             112        112         99       112        112        99
         F(Population Density = 0 for Africa)                6.74       4.62                 3.07      4.27
           Corresponding p-value                            0.011      0.035                0.083      0.042
         F(GDP Growth = 0 for Africa)                                   0.10                            0.07
           Corresponding p-value                                       0.758                           0.798
         F(Inflation Rate = 0 for Africa)                               0.04                           0.07
           Corresponding p-value                                       0.848                           0.797
         F(KKM Index= 0 for Africa)                                     1.50                           0.52
           Corresponding p-value                                       0.224                           0.473
         F(CA Balance / GDP = 0 for Africa)                             0.04                           0.09
           Corresponding p-value                                       0.837                           0.771
         F(Natural Resources = 0 for Africa)                            0.02                           0.06
           Corresponding p-value                                       0.893                           0.802


Notes: (* p< 0.1, ** p<0.05, *** p<0.01).
                               Table 6: Factors that could explain the effect of population density on financial development
                                           (combined sample of low and middle income and African countries, excluding South Africa)

                                                                                          Dependent Variable: Liquid Liabilities/GDP
                                                     (1)           (2)            (3)           (4)         (5)         (6)                   (7)           (8)           (9)
                     Ln(Population density)       0.096***        0.050         0.013       -0.040***     -0.035
                                                   [12.083]      [1.547]       [0.2910]      [62.300]    [2.438]
                     Roads / Area                                 0.289                                              0.512***
                                                                 [1.387]                                             [12.633]
                     % Population in                                           1.141**                                                     1.222**
                     Largest City                                              [6.326]                                                     [6.455]
                     Geographic branch                                                       0.256***                                                    0.215***
                       penetration                                                           [180.65]                                                    [29.084]
                     Demographic branch                                                                     -0.013**                                                    -0.009
                       penetration                                                                           [4.264]                                                    [2.245]
                     Observations                     91            71            63             73             73            71               63           73             73
                     Adjusted R2                     0.46          0.58          0.54           0.40           0.39          0.54             0.56         0.42           0.41

                                                                                           Dependent Variable: Private Credit/GDP
                                                      (1)           (2)           (3)          (4)          (5)         (6)                   (7)           (8)           (9)
                     Ln(Population density)        0.078**        0.051         0.015        -0.050      -0.032
                                                    [4.62]        [0.40]        [0.06]       [0.23]       [0.10]
                     Roads / Area                                 0.171                                              0.399**
                                                                  [0.18]                                              [4.32]
                     % Population in                                            1.044                                                        1.090
                     Largest City                                               [0.80]                                                       [0.94]
                     Geographic branch                                                         0.248                                                      0.183
                       penetration                                                             [0.41]                                                     [0.26]
                     Demographic branch                                                                      -0.012                                                     -0.011
                       penetration                                                                           [0.17]                                                      [014]
                     Observations                     99            75            63             76            76             75               63           76             76
                     Adjusted R2                     0.45          0.49          0.55           0.42          0.42           0.45             0.57         0.42           0.41

Notes: All models estimated via OLS with White’s heteroskedasticity-consistent standard errors. We use the same model specifications as those in Table 7, Columns 3 and 7. F-statistics
are presented in square brackets. * p< 0.1, ** p<0.05, *** p<0.01. Area is defined in terms of square miles. Geographic branch penetration is the number of commercial bank branches per
sq. km as reported in CGAP (2009). Demographic branch penetration is commercial bank branches per 100,000 residents. In unreported specifications, we also included % of population in
urban agglomerates, railroad track distance per sq. km, % arable land, and a measure of the geographic concentration of population with 0 indicating a uniform distribution and 1 indicating
that the entire population is concentrated in a 20kmx20km cell (as used in Collier and Hoeffler, 2004). Inclusion of those variables substantially reduced sample size, and none produced
consistently significant results for both indicators of financial development, and when population density was excluded from the specification.
             Table 7: Summary Statistics: Firm Sample based on ICS Surveys

This table presents summary statistics on our firm-level sample (used in regressions in Table 11),
based on World Banks’ Investment Climate Surveys over the past decade. The surveys were
conducted over the period 2002-2006, with the majority of the firms surveyed once. Firm
characteristics (all dummy variables) include size (small, medium, and large based on the number of
employees), industry (manufacturing, services, and others), exporter status and foreign ownership
status. The external finance dummy equals one if a firm has access to loans from domestic or foreign
banks, or credit card debts from these institutions, and zero otherwise; we only include firm
observations for which data is available on whether the firm has access to all three external financing
channels.

                             Panel A African countries (excluding South Africa)
                                                N          Mean          Std. Dev.     Fraction of 0
            Firm Size-Small (0 or 1)           2,591        0.46           0.50              --
           Firm Size-Medium (0 or 1)           2,591        0.30           0.46              --
                Foreign (0 or 1)               2,591        0.25           0.43              --
                Exporter (0 or 1)              2,591        0.18           0.38              --
             Manufacturing (0 or 1)            2,591        0.70           0.46              --
                Services (0 or 1)              2,591        0.17           0.37              --
       Access to External Finance (0 or 1)     2,591        0.16           0.37           83.91%

                   Panel B Other Low- and Middle-income Countries (excluding Africa)

            Firm Size-Small (0 or 1)          30,525        0.34           0.47              --
           Firm Size-Medium (0 or 1)          30,525        0.33           0.47              --
                Foreign (0 or 1)              30,525        0.13           0.33              --
                Exporter (0 or 1)             30,525        0.24           0.43              --
             Manufacturing (0 or 1)           30,525        0.66           0.47              --
                Services (0 or 1)             30,525        0.27           0.44              --
       Access to External Finance (0 or 1)    30,525        0.26           0.44           73.75%
    Table 8 Regressions for the sample of low and middle income and African countries:
                                    Firm-level Results
This table presents marginal effects from Probit regressions with the dependent variable equal 1 if a firm
has access to loans from domestic or foreign banks, or credit card debts from these institutions, and
zero otherwise. The surveys were conducted over the period 2002-2006, with the majority of the firms
surveyed once. The explanatory variables include firm characteristics and a subset of country-level
variables used in Table 7. We include firms from all the low and middle income countries (including
Africa, excluding South Africa), an African dummy variable, and interactions between each of our
explanatory variables and the Africa dummy. White’s heteroskedasticity-consistent standard errors are
clustered at the country-level, and presented in the brackets below coefficients. F-statistics are shown at
the bottom of the table.

                                                    (1)            (2)           (3)           (4)          (5)
Africa Dummy                                       0.011        0.449**         0.230        -0.061        0.093
                                                 [0.104]        [0.189]       [0.169]       [0.111]      [0.147]
Small                                          -0.151***      -0.144***     -0.140***     -0.142***    -0.142***
                                                 [0.023]        [0.015]       [0.018]       [0.026]      [0.024]
Medium                                         -0.066***      -0.062***     -0.059***     -0.058***    -0.058***
                                                 [0.011]        [0.012]       [0.011]       [0.012]      [0.012]
Foreign                                        -0.046***      -0.042***     -0.048***     -0.046***    -0.046***
                                                 [0.013]        [0.014]       [0.013]       [0.013]      [0.013]
Exporter                                        0.053***       0.037***      0.059***      0.053***     0.053***
                                                 [0.011]        [0.011]       [0.011]       [0.010]      [0.011]
Manufacturing                                   0.082***        0.048**      0.068***      0.091***     0.091***
                                                 [0.030]        [0.024]       [0.025]       [0.030]      [0.029]
Services                                          -0.029         -0.033        -0.038        -0.035       -0.035
                                                 [0.023]        [0.024]       [0.023]       [0.023]      [0.023]
KKM index                                        0.067*           0.032       0.092*          0.055        0.056
                                                 [0.035]        [0.028]       [0.048]       [0.037]      [0.037]
Ln(Per Capita Income)                             -0.007         -0.029         0.006        -0.001       -0.000
                                                 [0.032]        [0.032]       [0.033]       [0.032]      [0.035]
Natural Resources                                 0.015          0.019         0.001         0.018        0.018
                                                 [0.023]        [0.018]       [0.029]       [0.022]      [0.022]
Age of the firm                                 -0.001**       -0.001**      -0.001**     -0.001***    -0.001***
                                                 [0.000]        [0.000]       [0.000]       [0.000]      [0.000]
Ln(Population Density)                          0.054***          0.021      0.045**        0.060**     0.059***
                                                 [0.019]        [0.026]       [0.018]       [0.026]      [0.020]
Roads/Area                                                       0.060*
                                                                [0.034]
Foreign Ownership Share                                                      -0.197*
                                                                             [0.119]
Number branches per 1000km2                                                                 -0.004
                                                                                           [0.034]
Number branches per 100k adults                                                                           -0.000
                                                                                                         [0.001]
Small*Africa                                      0.020        -0.014         0.008         -0.047        -0.022
                                                 [0.042]       [0.037]       [0.045]       [0.037]       [0.041]
Medium*Africa                                     0.069         0.022         0.065         -0.028         0.000
                                                 [0.043]       [0.040]       [0.043]       [0.029]       [0.033]
Foreign*Africa                                    0.013         0.017         0.049          0.005         0.000
                                                 [0.032]     [0.035]     [0.035]     [0.041]     [0.040]
Exporter*Africa                                    0.019       0.034       0.002      -0.037      -0.012
                                                 [0.031]     [0.029]     [0.031]     [0.033]     [0.028]
Manufacturing*Africa                           -0.110***     -0.076*   -0.118***    -0.097**    -0.103**
                                                 [0.034]     [0.041]     [0.033]     [0.045]     [0.042]
Services*Africa                                -0.184***   -0.148***   -0.179***   -0.157***   -0.180***
                                                 [0.021]     [0.032]     [0.032]     [0.033]     [0.027]
KKM index*Africa                                   0.035       0.108       0.072      -0.003       0.014
                                                 [0.079]     [0.080]     [0.085]     [0.089]     [0.090]
Ln(Per Capita Income)*Africa                      -0.045      -0.002      -0.049      -0.043       0.012
                                                 [0.059]     [0.073]     [0.060]     [0.084]     [0.074]
Natural Resources*Africa                         -0.035       -0.451      0.279     1.439***      0.725
                                                 [0.484]     [0.453]     [0.614]     [0.476]     [0.909]
Age of the firm*Africa                          0.002***       0.001    0.002**      0.002**    0.002***
                                                 [0.001]     [0.001]     [0.001]     [0.001]     [0.001]
Ln(Population Density)*Africa                     -0.000      0.086*      -0.014      -0.018       0.030
                                                 [0.031]     [0.051]     [0.027]     [0.045]     [0.039]
Roads/Area*Africa                                           -0.483**
                                                             [0.222]
Foreign Ownership Share*Africa                                           -0.260
                                                                        [0.170]
Num branches per 1000km2*Africa                                                    0.340***
                                                                                    [0.100]
Num branches per 100K adults*Africa                                                              0.004
                                                                                                [0.013]
Observations                                    35,912      30,054      34,622      32,276      32,276
Adjusted R square                               0.0874      0.0785      0.0904      0.0921      0.0910

test ln_pd+ln_pd*africa chi2                     4.574      5.909        2.396       1.285      7.006
p-value ln_pd                                   0.0325      0.0151       0.122       0.257     0.00812
test roads_area + roads_area_africa = 0 chi2                3.741
p-value roads                                               0.0531
test fos + fos_africa = 0 chi2                                           13.51
p-value fos                                                            0.000237
test geobrpen + geobrpen_africa = 0 chi2                                             13.54
p-value geobrpen                                                                   0.000233
test demobrpen + demobrpen_africa = 0 chi2                                                      0.0866
pvalue demobrpen                                                                                 0.769
                 Table 9 Number of Bank Branches by type of District

This table reports the number of bank branches by type of district: Urban, Rural and Arid and
Semi-Arid. This table was constructed based on alternative sources, including phone calls,
official websites, banks’ annual reports and government publications.


District                     2006                2009           Change           % Change

Urban                        290                 471              181                62
Rural                        238                 398              160                67

Arid and Semi-Arid            42                 89                47                112

Total                        570                 958              388                68


           Table 10 Bank Branches over Time: Commercial Banks in Kenya

This table reports the number of Kenyan bank branches by bank name and ownership in 2006 and
2009. The table considers only banks with 20 or more bank branches by 2009. This table was
constructed based on alternative sources, including phone calls, official websites, banks’ annual
reports and government publications.

Local private banks                       2006           2009           Change      % Change

Equity Bank                               44             112              68           155
Co-operative Bank of Kenya Ltd.           52              83              31            60
Diamond Trust Bank of Kenya Ltd.           8              32              24           300
Commercial Bank of Africa Ltd.            19              20              1             5
All local private banks                   247            450             203           82

Foreign banks

Barclays Bank of Kenya Ltd.               62             119              57            92
Standard Chartered Bank Kenya Ltd.        31              35              4             13
K-Rep Bank Ltd.                           22              30               8            36
All foreign banks                         150            258             108            72

Government and government-influenced banks

Kenya Commercial Bank Ltd.                117            169              52            44
National Bank of Kenya Ltd.               33              43              10            30
CFC Stanbic Bank Ltd.                     16              35              19           119
All gov. and gov.-influenced banks        179            262              83            46
          Table 11 Market Participation Major Commercial Banks in Kenya

This table reports market shares of gross assets, total deposits, total capital, number of deposit
accounts and number of loan accounts as at 31st January, 2011. This table was constructed based
on alternative sources, including official websites, banks’ annual reports and government
publications.

                               Gross      Total       Total      Number of        Number of
                               Assets    Deposits    Capital   Deposit Accounts Loan Accounts


 Kenya Commercial Bank Ltd     13.5%      13.2%       15.4%         10.93%             19.8%

 Barclays Bank of Kenya Ltd    10.4%       9.9%       12.2%          6.77%             14.1%

Co-operative Bank              9.0%        9.8%       7.5%          12.26%             12.0%

 Standard Chartered Bank Ltd   8.2%        8.3%       7.6%           0.92%              1.5%

 Equity Bank Ltd.              8.0%        7.8%       10.7%         47.39%             33.8%

 CfC Stanbic Bank Ltd          5.9%        6.2%       3.8%           0.47%              1.8%



                         Table 12 Use of Bank Services in Kenya

This table reports the percentage of people having a bank account and a loan from a bank in
Kenya. It also reports the presence of Equity across districts. This table was constructed based on
FinAccess 2006 and 2009 surveys and alternative sources, including phone calls, official
websites, banks’ annual reports and government publications.


Variable                                   2006                               2009

Bank account                             14.20%                              22.70%
Bank Loan                                 2.90%                              4.40%

Equity Bank presence                       48%                                 87%
      Figure 1: Liquid liabilities / GDP in African countries, actual vs. predicted values
(Notes: Based on the specification in Table 3, Column 4; negative predicted values are replaced by zero).




        Figure 2: Private credit / GDP in African countries, actual vs. predicted values
(Notes: Based on the specification in Table 3, Column 8; negative predicted values are replaced by zero.)
              Figure 3: Relationship between population density and liquid liabilities/GDP
Notes: Plots are for the typical African and non-African developing country based on Table 5.
Specifically, the vertical axis plots predicted values from Model 2 in Table 5. For all variables other than
population density, we use the mean value for the subsample (either Africa or other developing countries)
multiplied by the appropriate coefficient for that subsample. For population density, we run through the
range of possible values and multiply by the appropriate coefficient for each subsample to generate the
curvature.




              Figure 4 Relationship between population density and private credit/GDP
Notes: The vertical axis plots predicted values from Model 5 in Table 5. For all variables other than
population density, we use the mean value for the subsample (either Africa or other developing countries)
multiplied by the appropriate coefficient for that subsample. For population density, we run through the
range of possible values and multiply by the appropriate coefficient for each subsample to generate the
curvature.
                                            16


                                            14       y = 0.0023x + 1.7249

                                                         R² = 0.3899
    Number of branches per 100,000 people

                                            12


                                            10


                                             8


                                             6


                                             4


                                             2

                                                           Kenya
                                             0
                                                 0        500          1000   1500        2000     2500   3000   3500
                                                                                  GDP per capita

Figure 5. Demographic bank penetration and GDP per capita. This figure shows the
relationship between GDP per capita and the number of bank branches per 100,000 people across
low income economies in 2003/2004. The sample considers countries with a GDP per capita
below 4,000USD. The grey dots correspond to Sub-Saharan African countries. The black dots
correspond to the rest of the countries. The data source is Beck, Demirguc-Kunt, and Martinez
Peria (2007).




Figure 6. Stock market performance. This figure shows the stock market performance of
Kenya Commercial Bank (KNCB), Equity Bank (EQBNK) and Barclays Bank of Kenya (BCBL)
since August 18th, 2006. The data source is interactive chart from Bloomberg
(http://www.bloomberg.com).
                                 Rural, Arid and Semi-Arid Districts                                        High Minority Language Districts
            200                                                                           100
                       2006          2009                                                         2006          2009


            150                                                                           75



            100                                                                           50



             50                                                                           25



              0                                                                            0
                     Foreign banks          Gov.-influenced banks   Local private banks         Foreign banks           Gov.-influenced banks   Local private banks

                                       Low Populated Districts                                                     Low Educated Districts
            100                                                                           60
                      2006           2009                                                        2006           2009


             75                                                                           45



             50                                                                           30



             25                                                                           15



              0                                                                            0
                     Foreign banks          Gov.-influenced banks   Local private banks         Foreign banks          Gov.-influenced banks    Local private banks

Figure 7. Bank penetration in vulnerable districts. This figure shows the number of bank branches according to different ownership structures: Foreign
banks, Government banks, and Domestic private banks. Rural, Arid and Semi-Arid Districts exclude Nairobi and Mombasa. High minority language districts
are the ones where the share of the population speaking a minority language is larger than the median. Low populated districts are the ones where the density
population is smaller than the median. Low educated districts are the ones where the share of the population with secondary or tertiary education is smaller
than the median.
                       Number of Customer                                        Customer Deposits
                          (In Millions)                                           (In KShs Millions)
                                                            120000
   6
                                                            100000
   5
                                                             80000
   4
   3                                                         60000

   2                                                         40000

   1                                                         20000

   0                                                                0
           2006     2007        2008       2009     2010                 2006   2007      2008          2009   2010

                           Gross Loan Por tfolio                                      Total Assets
                            (In KShs Millions)                                     (In KShs Millions)
                                                            150000
  80000
                                                            120000
  60000
                                                            90000
  40000
                                                            60000

  20000                                                     30000

       0                                                        0
             2006    2007          2008      2009    2010               2006    2007      2008          2009   2010

                            Profits Before Taxes                                  Shareholders' Funds
                             (In KShs Millions)                                     (In KShs Millions)
  10000                                                     30000

   8000                                                     25000

                                                            20000
   6000
                                                            15000
   4000
                                                            10000
   2000
                                                             5000
       0                                                        0
             2006    2007          2008      2009    2010               2006    2007      2008          2009   2010

Figure 8. Equity Bank’s performance indicators. This figure shows the evolution of the number of
customers, deposits, gross loan portfolio, total assets, profits before taxes, and shareholders’ funds over the
period 2006-2010. The data source is the Equity Bank’s Annual Report and Financial Statements 2010.