Structural and cyclical determinants of access to finance : evidence from Egypt.
Date: 2020-01-27
Institutions: European Investment Bank
Subjects: Egypt, bank, credit, economic development, financial institution, financing, public debt, small and medium-sized enterprises

ECONOMICS – WORKING PAPERS 2019/10

Structural and cyclical 
determinants of access  

to finance:

Evidence from Egypt

Structural and cyclical determinants 

of access to finance: 

Evidence from Egypt 

 
 
 
 
 
 
 
 
Structural and cyclical determinants of access to finance: Evidence from Egypt 

© European Investment Bank 

EIB Working Paper 2019/10 

This is a publication of the EIB Economics Department 

Authors: 

Frank Betz (European Investment Bank) 
Farshad R. Ravasan (University of Oxford) 
Christoph T. Weiss (European Investment Bank) 

About the EIB Economics Department  
The mission of the EIB Economics Department is to provide economic analyses and studies to support 
the Bank in its operations and in the definition of its positioning, strategy and policy. The department, 
a team of 40 economists, is headed by Director Debora Revoltella. 

economics@eib.org 
www.eib.org/economics 

Disclaimer 
The views expressed in this publication are those of the authors and do not necessarily reflect the 
position of the European Investment Bank. 

EIB working papers are designed to facilitate the timely exchange of research findings.  
They are not subject to standard EIB copyediting or proofreading. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Structural and cyclical determinants of access to
ﬁnance: Evidence from Egypt∗

Frank Betz

European Investment Bank

Farshad R. Ravasan

University of Oxford

Christoph T. Weiss

European Investment Bank

Abstract

Using panel data on Egyptian ﬁrms to explore cyclical and structural determi-

nants of access to ﬁnance, we ﬁnd that ﬁrms with more educated and more expe-

rienced managers are more likely to open a checking account, often a prerequisite

for obtaining credit. Firms that started operating in the informal sector before reg-

istering are less likely to engage with the banking system. Exploiting data on the

location of ﬁrms and bank branches, we also show that ﬁrms located in areas with

a greater presence of banks that invest more in government debt are more likely

to be credit constrained due to crowding out of the private sector.

JEL classiﬁcation: G21, O15, O17
Some keywords: Financial constraints, crowding out, managerial skills

∗Corresponding autor: Frank Betz, Economics Department, European Investment Bank, 98-100
Boulevard Konrad Adenauer, 2950 Luxembourg, Luxembourg, e-mail: f.betz@eib.org. We thank Sanne
Zwart and participants in a seminar at the European Investment Bank, the African meeting of the
Econometric Society in Rabat and the Global Development Finance Conference in Johannesburg for
helpful discussion and comments. The views expressed in this paper are those of the authors and do
not necessarily represent the views of the European Investment Bank. Farshad Ravasan gratefully ac-
knowledges support from the EIB Institute through its STAREBEI programme.

1

1

Introduction

It is well established that ﬁnancial development is connected to economic growth
(Levine, 2005). While most studies rely on macroeconomic data, a growing litera-
ture use ﬁrm-level evidence to explore different mechanisms through which ﬁnance
can inﬂuence private sector development in emerging economies (Beck et al., 2007).
This paper discusses structural and cyclical aspects of access to ﬁnance using unique
data on the location of ﬁrms and bank branches in the Middle East and North Africa
(MENA).

We ﬁrst examine the structural participation decision that looks at whether a ﬁrm
has a checking account. We then study the provision of credit over the business cycle.
In particular, we look at whether a ﬁrm is credit constrained. Credit-constrained ﬁrms
fall in one of two categories: (i) ﬁrms that applied for a loan and were rejected; and
(ii) ﬁrms discouraged from applying either because of unfavourable terms and condi-
tions or because they did not think the loan application would be approved. As only
ﬁrms that exhibit demand for credit can be constrained, this approach enables us to
separately study demand and supply of credit - by instrumenting credit demand with
a liquidity shock. The mechanisms that we analyse include entrepreneurial human
capital, institutional quality and the intermediation capacity of the ﬁnancial system.

Recent studies on access to ﬁnance in middle-income economies typically focus on
credit constraints. For example, Popov and Udell (2012) explore how, in the context of
the ﬁnancial crisis, distress events in European banks tightened credit constraints in
Eastern Europe. Gorodnichenko and Schnitzer (2013) examine how credit constraints
affect Eastern European ﬁrms’ ability to innovate. While our paper also examines
credit constraints, we argue it is important to take one step back and also look at the
prevalence of checking accounts, especially in emerging economies.

When deciding whether to open a checking account, ﬁrms trade off costs and ben-
eﬁts. Access to ﬁnance is valuable for ﬁrms with substantial growth opportunities,
while the costs can consist of taxes, licensing requirements and social insurance con-
tributions (Straub, 2005). Incurring these costs is worth less if the intermediation ca-
pacity of the banking system is weak and thus the likelihood of being credit-rationed
is high. Beck et al. (2014) provide evidence on the empirical salience of the tax evasion
channel. They ﬁnd that there is less tax evasion in countries with better intermediation
capacity.

A checking account is important for access to ﬁnance because it allows a bank to
monitor inﬂows and outﬂows and thereby reduces the information asymmetries that
plague lending to small ﬁrms.1 For instance, the SME lending methodology proposed
by Munro (2013) requires that a potential borrower opens a checking account and runs

1Beck et al. (2007) include account penetration among their indicators of banking sector outreach.

2

all transaction through this account for at least 6 months to establish reliable turnover
and cash-ﬂow ﬁgures before the bank would consider a loan application. But routing
transactions through the checking account also comes at a cost for the ﬁrm because it
can become more difﬁcult to hide these revenues from tax authorities.

We explore how ﬁrms interact with the banking sector in MENA using data on
Egypt as a laboratory. We focus on Egypt as this is the country where the issues that
we are interested in are most salient. The size of the informal economy is particu-
larly large and estimated to account for about 35% of GDP (Medina and Schneider,
2018). Informal ﬁrms are unregistered and typically excluded from the formal ﬁnan-
cial system. Moreover, as Figure 1 shows, even among registered ﬁrms, less than 70%
had a checking account in 2013 and 2016 (60% and 69%, respectively). This ﬁgure is
much lower in Egypt than in other MENA countries. This is one of the reasons why
Egyptians often refer to their economy as a “cash economy”. Last but not least, with a
population of almost 100 million that is growing by close to 2 million individuals per
year, Egypt is the pivotal country of the Southern Mediterranean.

Firm size is strongly associated with having a checking account. Figure 2 plots
the median number of employees conditional on age for ﬁrms in the MENA region.2
While old ﬁrms with a checking account tend to be larger than young ﬁrms with a
checking account, unbanked ﬁrms remain small, regardless of age. The ﬁgure is also
in line with the evidence of Eslava et al. (2019) and Hsieh and Klenow (2014) who
ﬁnd that employment growth for an average ﬁrm in the US is signiﬁcantly higher
than in Colombia, Mexico or India, where ﬁnancial systems are less developed. They
argue that the divergence in ﬁrm dynamics between the US and emerging economies
suggests higher investments by US ﬁrms in process efﬁciency and quality as well as
accessing domestic and foreign markets.

Figure 3 provides some insights as to why ﬁnancially excluded ﬁrms remain small.
First, ﬁnancially excluded ﬁrms are less likely to invest; second, they are more likely
to fund their investments exclusively from internal sources. It may well be that lack
of access to external ﬁnance prevents some proﬁtable investment opportunities from
being realised.

In addition to the structural dimension, we consider how the intermediation capac-
ity of the banking system varies over the business cycle. Whether a ﬁrm uses a check-
ing account or operates on a cash-only basis can be considered as a structural feature
of the ﬁrm and the ﬁnancial system. At the same time, some ﬁrms with checking ac-
counts are likely to be constrained in their access to credit, which may well depend on
the business cycle.

Following the uprising of 2011, Egypt went through a difﬁcult economic period.

2The ﬁgure reports predictions from a linear quantile regression net of year and industry ﬁxed

effects.

3

Output growth declined to levels barely above population growth. To assuage so-
cial pressures, the authorities increased public spending and subsidies by relying on
lending by domestic banks, which resulted in higher government debt levels. Figure
4a shows that the share of public debt in the balance sheets of banks increased sub-
stantially in the aftermath of the Arab Spring. Figure 4b suggests that the authorities
even had to monetise some of the debt by borrowing directly from the Central Bank of
Egypt: the government’s overdraft with the Central Bank increased from 6% of GDP
in 2010 to a peak of 22% in 2016. This suggests that the banking system was not able
to accommodate the reﬁnancing needs of the public sector. Heavy borrowing by the
government drove up interest rates and likely crowded out bank lending to the private
sector.

In this paper, we develop a new measure of crowding out to study the participation
of Egyptian ﬁrms in the ﬁnancial system. In addition to bank balance sheet data, the
measure exploits geographical information on the location of ﬁrms and bank branches
to proxy crowding out: a ﬁrm that is surrounded by branches of banks that invest
more in government debt will score high on the crowding out index.

The ﬁrm level data used in this paper come from the Middle East and North Africa
Enterprise Survey (MENA ES). The MENA ES provides representative samples of the
formal private sector in several MENA economies, including Egypt. It is a business
environment survey that follows the World Bank’s global methodology for enterprise
surveys. Unlike in the other MENA economies, an additional Enterprise Survey was
conducted in Egypt in 2016. This yields a panel of 600 Egyptian ﬁrms that answered
the same questionnaire in 2013 and 2016. The availability of panel data mitigates the
concern that our ﬁndings could be driven by the exceptional environment prevailing
in the aftermath of the Arab Spring. To provide some evidence for the external validity
of our results, we conduct a similar analysis using cross-sectional data from 2013 on
the other countries participating in the MENA ES.

The extent to which ﬁnancial exclusion has opportunity costs in terms of lost ﬁrm
growth likely depends on entrepreneurial skill. La Porta and Shleifer (2014) examine
the relation between entrepreneurial human capital and informality. Using data on
informal enterprises, they ﬁnd informal ﬁrms to be rather small and unproductive.
They argue that the low levels of productivity reﬂect lack of skills on the side of the
entrepreneurs. This is in line with the importance of entrepreneurial human capital
documented by Gennaioli et al. (2013). We therefore expect ﬁrms run by CEOs with
higher education or more experience to be more likely to be banked and more likely
to access credit.

Institutions and in particular, secure property rights, can also affect the trade-off
between higher costs and growth opportunities. If entrepreneurs cannot reap the re-
ward for their efforts, they will not invest in the ﬁrst place (Acemoglu et al., 2005).

4

Property rights are thus necessary to enable factor accumulation and technological
progress. Using data from ﬁve Eastern European countries, Johnson et al. (2002) show
that the perceived strength of property rights affects the willingness of entrepreneurs
to reinvest their proﬁts.

At the structural level, we ﬁnd that ﬁrms with more educated and more experi-
enced CEOs are are more likely to obtain a checking account. Firms that operated in-
formally before registering are also less likely to open an account. The results thus sup-
port the importance of the human capital channel advanced by La Porta and Shleifer
(2014). Perceptions of institutional quality, on the other hand, are not strongly asso-
ciated with ﬁnancial inclusion. The same applies to our measures of local ﬁnancial
intermediation capacity.

A similar picture emerges for credit constraints. Firms with more educated and
more experienced CEOs are less likely to be credit-constrained and discouraged to
apply for a loan. However, we also ﬁnd that crowding out reduce credit supply. Firms
located in areas with a greater presence of banks that invest more in government debt
are more likely to be credit constrained. The latter result does not generalise beyond
Egypt. This appears plausible as government reﬁnancing needs in the other MENA
countries did not increase to the same extent or they were covered by corresponding
deposit inﬂows. In other words, crowding out was particularly severe in Egypt.

The remainder of this paper is organised as follows. The next section discusses
different candidate factors that could affect the participation of Egyptian ﬁrms in the
ﬁnancial system. Section 3 describes the data of the ﬁrm-level and banking data that
we use to estimate the empirical model introduced in Section 4. Section 5 explores the
results. The last section is a conclusion.

2 Candidate factors affecting the participation of ﬁrms

in the ﬁnancial system

Firms trade-off the costs and beneﬁts of participating in the formal banking system.
Straub (2005) modiﬁes a continuous investment model with moral hazard based on
Holmstrom and Tirole (1997) to allow for a trade-off between informality and access
to the ﬁnancial system. We use this model to explore candidate factors affecting this
trade-off in one way or another.

2.1 Informality and intermediation capacity of the banking system

In the model of Straub (2005), entrepreneurs decide whether to operate formally or
informally. Operating formally entails costs in the form of registration costs, time

5

consuming processes and taxes. However, it also comes with access to bank ﬁnance,
public goods and services, such as licensing and the judicial system. The judicial sys-
tem in turn facilitates the recovery of loans in case the borrower defaults. Informal
borrowers will ﬁnd it easier to evade the judiciary and, as a result, are less attractive
borrowers. In addition, operating formally is worth less when the likelihood of being
excluded from formal credit markets is high.

The setting in Straub (2005) is not fully congruent with the data used in this paper
as all ﬁrms in the data that we use are registered. Even though there are surveys that
sample the informal sector, they suffer from their own problems - notably represen-
tativeness, which is difﬁcult to assess without a reliable sampling frame. Our sample
of ﬁrms operating in the formal sector provides us with two angles from which to
investigate. First, some of the ﬁrms that are registered report whether they started
operating in the informal sector. Second and perhaps more importantly, as Figure 1
shows, Egypt is a cash economy with 31% of formal private sector ﬁrms in 2016 oper-
ating on a cash-only basis.

The decision to become banked is similar to the registration decision. Operating on
a cash-only basis facilitates tax evasion but impedes access to the ﬁnancial system. A
checking account is important for ﬁnancial access because it reduces the information
asymmetries that plague lending to small ﬁrms. In particular, account information
helps the bank to establish reliable turnover and cash-ﬂow ﬁgures, which in turn is
crucial for creating rudimentary ﬁnancial statements. Banks are therefore likely to
withhold ﬁnancing from a borrower that operates on a cash-only basis. Whereas Jap-
pelli and Pagano (2002) and Brown et al. (2009) are concerned with information shar-
ing between creditors, we focus on the initial acquisition of information.

Everything else equal, ﬁrms should be less likely to be banked if the intermedia-
tion capacity of the banking system is weak. In this paper, we use measures of both
the cyclical and structural dimension of the banking system’s intermediation capacity.
Though of interest in its own right, controlling for the cyclical position of the Egyp-
tian economy is also necessary to isolate the effects of the other candidate explanations
outlined below. To measure cyclical variation in credit supply, we construct a crowd-
ing out index that exploits information on the location of ﬁrms and bank branches, as
in Betz and Ravasan (2016) and Popov and Udell (2012). A ﬁrm that is surrounded
by many bank branches that invest more in government debt will score high on the
crowding out index. The idea is that ﬁrms located in the vicinity of branches of banks
that rapidly increased their government debt holdings are more likely to be ﬁnancially
constrained.

6

2.2 Entrepreneurial human capital

Gennaioli et al. (2013) stress the importance of human capital for economic develop-
ment. Drawing on Lucas (1978), their theoretical framework distinguishes between
the human capital of workers and that of entrepreneurs. Whereas the human capital
of workers enters as an input into the production function, the human capital of the
entrepreneur also affects ﬁrm productivity independently. Using Enterprise Survey
data, they show that entrepreneurial human capital has a large effect on ﬁrm produc-
tivity.

La Porta and Shleifer (2014) examine the relation between entrepreneurial human
capital and informality. Using data on informal enterprises, they show that informal
ﬁrms tend to be small and unproductive. They argue that the low levels of productiv-
ity reﬂect lack of skills on the side of the entrepreneurs.

We therefore expect ﬁrms run by highly educated CEOs to be more likely to be
banked and more likely to access credit. Operating outside the ﬁnancial system carries
higher opportunity costs for better managers, in terms of lost growth, which costs
may easily outweigh the beneﬁts. CEOs with higher education are expected to exhibit
greater levels of ﬁnancial literacy that enable them to deal with the documentation
demands made by banks.

2.3 Institutions

In the post-war period, the economies of the MENA region adopted a state-centered
development model. This model has several distinct features. One of them is a public
sector that assumes the role as employer of last (and sometimes ﬁrst) resort. For exam-
ple, some countries issued employment guarantees for university graduates (World
Bank, 2004). For lack of sophisticated mechanisms for redistribution, public goods
were also provided in kind, through subsidies. In Egypt, fuel subsidies accounted for
6 percent of GDP in the ﬁscal year 2013/2014 (IMF, 2015).

More recently, policy makers in MENA have recognised that access to ﬁnance is
conducive to private sector development, which in turn is the only way to provide a
sufﬁcient number of jobs for the young and fast growing population. However, ﬁnan-
cial systems in the region are dominated by banks and bank credit is mainly directed to
large and well-connected enterprises (World Bank, 2011). In response, the authorities
and central banks have adopted ﬁnancial development objectives - sometimes implic-
itly, sometimes explicitly. The measures taken in most countries include schemes to
incentivise bank lending to SMEs, the introduction or revamping of credit bureaus,
secured transactions rules and insolvency laws.

Acemoglu et al. (2001) and Acemoglu et al. (2005) argue that institutions and, in
If

particular, secure property rights are a fundamental cause of economic growth.

7

entrepreneurs cannot reap the reward for their efforts, they will not invest in the ﬁrst
place. Property rights are thus necessary to enable factor accumulation and technolog-
ical progress. The law and ﬁnance literature also stresses the importance of property
rights for ﬁnancial development (La Porta et al., 1998, 2008). However, in this case, it is
property rights perceptions of the ﬁnanciers that matter, which we cannot investigate
as we have survey data on ﬁrms, i.e. the borrowers, but only data on the geographical
location and balance sheets of banks.

Using data from ﬁve Eastern European countries, Johnson et al. (2002) ﬁnd that
entrepreneurs are less likely to reinvest their proﬁts if they perceive property rights
not to be secure. Moreover, they are more likely to underreport output when institu-
tional quality is poor (Johnson et al., 2000). Acemoglu and Johnson (2005) distinguish
between what they refer to as property rights institutions and contracting institutions.
Whereas property rights institutions guard the individual against expropriation from
the government or elites, contracting institutions regulate transactions between pri-
vate parties, such as debtors and creditors. High quality institutions thus give incen-
tives to ﬁrms to operate formally. As ﬁrms in our data report information on per-
ceptions of property rights and the quality of the judicial system, we are also able to
explore these issues empirically.

3 Data

The data used in this paper come from the MENA Enterprise Survey (MENA ES) that
provides representative samples of the formal private sector in six MENA economies:
Egypt, Jordan, Lebanon, Morocco, Palestine and Tunisia.3 The survey covers ﬁrms
with at least ﬁve employees in the manufacturing and services sectors, where services
includes retail, wholesale, hospitality, repairs, construction, transport and informa-
tion technology (IT) ﬁrms. It does not include ﬁrms operating in agriculture, ﬁshing,
extractive industries, utilities and several service sectors - such as ﬁnancial services,
education and health care.

The MENA ES follows the World Bank’s global methodology for Enterprise Sur-
It addresses a broad range of business environment issues such as access to
veys.
ﬁnance, the quality of infrastructure, governance and the extent of corruption, the
prevalence of crime, the intensity of competition and ﬁrm performance. The samples
are stratiﬁed by ﬁrm size, sector of activity, and location within the countries. The
MENA ES covers 6,083 ﬁrms with sample size ranging from 407 ﬁrms in Morocco to
2,897 in Egypt.

3Djibouti and Yemen are also covered in MENA ES but we do not use data on these two countries
because we cannot implement our methodology to construct a measure of crowding out: data on bank
branches in Yemen are not readily available and Djibouti hardly has any spatial variation.

8

Data collection took place in the aftermath of the Arab Spring. Respondents were
interviewed in 2013 and 2014, but the reference period of the survey is the ﬁscal year
2012. The fact that data collection took place during exceptional times for the region
is reﬂected in the concerns expressed by ﬁrms. Respondents are asked to choose, from
a list of ﬁfteen questions describing the business environment, the one that currently
represents the greatest obstacle to their enterprise. In the MENA ES economies, 32
percent of ﬁrms name political instability as the top obstacle compared to only 10
percent in the rest of world (EBRD et al., 2016).

Owing to the availability of longitudinal data, we use Egypt as a laboratory. Unlike
in the other MENA ES economies, an additional Enterprise Survey was conducted in
Egypt in 2016. This yields a panel of 600 Egyptian ﬁrms that answered the same ques-
tionnaire in 2013 and 2016. The availability of panel data mitigates the concern that
our ﬁnding are driven by the exceptional environment prevailing in the aftermath of
the Arab Spring. In addition, the questions that we are interested in are most appar-
ent in the Egyptian data as it has both a high share of unbanked ﬁrms and a strong
variation in credit supply. Even though the analysis focuses on Egypt, we also use
cross-sectional data on the other MENA economies for check the robustness and dis-
cuss the external validity of our estimates.

The MENA ES measures access to ﬁnance along a range of dimensions. We rep-
resent structural participation in the ﬁnancial system with an indicator variable that
takes value 1 if the ﬁrm has a checking or savings account, and 0 otherwise. Credit con-
strained ﬁrms either had their loan application rejected or were discouraged from ap-
plying. Credit constraints are measured with a standard set of questions. The MENA
ES ﬁrst asks ﬁrms whether they have applied for a loan in the last ﬁscal year. Those
who respond afﬁrmatively are then asked whether the loan application was approved
or rejected. Firms that did not apply for a loan are asked for the main reason they did
not apply. Those ﬁrms that respond “no need for a loan” are classiﬁed as not credit
constrained. Firms that cite other reasons such as complex application procedures,
too high interest rates or collateral requirements, or simply did not believe that the
application would be approved are considered discouraged.

The share of credit constrained ﬁrms in the MENA region is low, but this reﬂects
mainly weak overall demand for loans.4 According to EBRD et al. (2016), the share
of ﬁrms that are credit constrained is lower in MENA than in any other region of the
world. But this low share of credit-constrained ﬁrms does not necessarily reﬂect suc-
cessful loan applications; instead, many ﬁrms report that they have enough capital
and do not need a loan.5 Figure 5 shows that Egypt and Palestine exhibit the low-

4For a comprehensive description of ﬁnancial sectors in MENA, see World Bank (2011).
5As only ﬁrms that need a loan can be credit constrained, the share of credit-constrained ﬁrms

cannot exceed the share of ﬁrms that need a loan.

9

est level of loan demand in MENA. At the same time, most ﬁrms in these economies
that need a loan report to be credit constrained. Within the region, the banking sys-
tems of Egypt and Palestine also display the weakest intermediation capacity. Figure
5 further shows that rejections are rare; most credit-constrained ﬁrms in the region are
discouraged from applying.

In addition to ﬁrm-level data from the MENA ES, we use data on the location of
bank branches in six countries.6 Branch addresses have been converted into coordi-
nates using the geocode utility developed by Ozimek and Miles (2011). Information
on branch location enables us to represent the banking system in the vicinity of the
ﬁrm, as in Popov and Udell (2012).

We combine information on branch location with balance sheet data on the six
largest Egyptian banks from Orbis Bank Focus to construct the crowding out index:
National Bank of Egypt, Banque Misr, Banque du Caire, Commercial International
Bank, Qatar National Bank and HSBC. The six banks account for about 60% of total
assets in Bank Focus. National Bank of Egypt, Banque Misr and Banque du Caire are
owned by the government, with Banque du Caire a subsidiary of Banque Misr. Com-
mercial International Bank is a private domestic institution. Qatar National Bank and
HSBC are foreign banks. We use banks’ security holdings as a proxy for government
bonds, which is justiﬁed by the absence of corporate bond markets in Egypt.

In Egypt, public debt is mainly held by government-owned banks. Government-
owned banks have a substantial footprint in the sector, representing about 45% of to-
tal assets in 2015. Figure 6 presents the evolution of balance sheet composition for
the three categories of ﬁnancial institutions: public domestic, private domestic, and
foreign-owned banks as share of total assets of the banking system.7 In 2010, for ex-
ample, public debt held by government-owned banks accounted for 14% of all bank-
ing system assets. Public banks held a larger share of government debt prior to the
revolution and they absorbed most of the additional debt issued in its aftermath.

Figure 7 illustrates the spatial variation in bank branches that the crowding out
index exploits. The ﬁrst plot shows the distribution of sampled ﬁrms reﬂecting the
concentration of economic activity in Egypt, i.e. along the ﬂow of the Nile. The ﬁgure
further shows that the public banks have a wider geographical footprint than private
domestic and foreign institutions, which have fewer branches and tend to be concen-
trated in major population centres.

We measure cyclical variation in credit supply with our crowding out index. This
index draws on bank balance sheet data and information on the location of ﬁrms and
bank branches to proxy crowding out. A ﬁrm that is surrounded by many branches of

6We thank Ralph de Haas for sharing with us data on bank branches in Morocco, Tunisia, Egypt,

and Jordan.

7Banking system assets are obtained as the sum of total assets of all banks with balance sheet infor-

mation in Bank Focus.

10

banks that invest more in government debt will score high on the crowding out index.
At the level of bank j the crowding out index is computed as

crowding outj =

2015
∑
t=2013

sjt − ljt
∑j ajt

−

2012
∑
t=2010

sjt − ljt
∑j ajt

(1)

where sjt and ljt denote security holdings and loans of bank j and ∑j ajt refers to total
assets in the banking system. The level of the crowding out index assigned to ﬁrm i is
then obtained as the branch-weighted average of equation 1, which takes into account
all branches within a circle of radius 10km centered on the sample ﬁrm.

We also consider two other measures of local ﬁnancial conditions that are more
structural. First, local ﬁnancial inclusion is given by the share of ﬁrms with a check-
ing account in the circle centered on ﬁrm i. Second, local ﬁnancial intermediation is
captured by the share of ﬁrms with a loan outstanding in the neighbourhood of ﬁrm i.

4 Empirical strategy

Our empirical analysis proceeds in two steps. First, we examine the ﬁrm’s structural
decision to participate in the ﬁnancial system. Second, we look at the determinants
of demand for and supply of credit. We start by estimating the following equation in
ﬁrst differences

∆ accounti = β1 ∗ ∆ human capitali + β2 ∗ ∆ institutional qualityi
+ β3 ∗ local ﬁnancial conditionsi + Xiζ + Ui

(2)

where accounti is an indicator variable taking value 1 if the ﬁrm i reports to have
a checking or savings account, and 0 otherwise. ∆ accounti refers to the change in
account status between 2013 and 2016.

To measure entrepreneurial human capital, we use an indicator variable that takes
value 1 if the ﬁrm’s CEO has a university degree, and 0 otherwise. Along with ed-
ucation, we also control for experience and gender of the CEO. Experience captures
the number of years of experience of the CEO in the sector (but not necessarily in the
ﬁrm).

We measure institutional quality along several dimensions.

In the aftermath of
the revolution, Egyptian CEOs most frequently cited political instability as the top
obstacle to the enterprise (EBRD et al., 2016). We use a question that asks respondents
to rate the extent to which political instability is an obstacle to the enterprise on a
ﬁve point scale. We collapse responses into a binary indicator equal to 1 if the CEO
considers political instability a major or very severe obstacle, and 0 otherwise.

11

Three additional indicators proxy the quality of third-party enforcement. We use
the extent to which the respondent agrees with the following statements: “The court
system is fair, impartial, and uncorrupted”, “I am conﬁdent that the judicial system
will enforce my establishment’s contractual and property rights in business disputes”,
and “The court system is quick”. Responses are coded from 1 “strongly disagree” to 4
“strongly agree”." The second measure corresponds closely to the concept of contract-
ing institutions proposed by Acemoglu and Johnson (2005).

The vector X includes various ﬁrm characteristics, including whether the ﬁrm
started in the informal sector before registering the business, ﬁrm age and ﬁrm size
- a young ﬁrm is less than 10 years old and a small ﬁrm has less than 50 employees.
We also add industry and Egyptian governorate ﬁxed effects. Ui is a disturbance term
that captures time-varying unobservables of the ﬁrm.

Whether a ﬁrm uses a checking account or operates on a cash-only basis can be
considered as a structural feature of the ﬁrm and the ﬁnancial system. At the same
time, some ﬁrms with checking accounts are likely to be constrained in their access to
credit, which may well depend on the business cycle. We deal with the supply of loans
by estimating the following equation:

∆ credit-constrainedi = γ1 ∗ ∆ human capitali + γ2 ∗ ∆ institutional qualityi

(3)

+ γ3 ∗ local ﬁnancial conditionsi + Xiθ + Ui

where credit-constrained is an indicator variable taking value 1 if the ﬁrm i reports
that either (i) the most recent loan application was rejected by the bank or (ii) the
ﬁrm was discouraged from applying, and 0 otherwise. The ﬁrm can be discour-
aged either because of unfavourable terms and conditions or because the CEO did
not think the loan application would be approved by the bank. The terms and condi-
tions that can discourage ﬁrms include complex application procedures, unfavourable
interest rates, high collateral requirements, and insufﬁcient size of loan and maturity.
∆ credit-constrainedi refers to the change in being constrained between 2013 and 2016.
The other variables are deﬁned as in equation 2. When the change in credit con-
strained status is the dependent variable in the analysis, the vector of ﬁrm character-
istics X also includes whether the ﬁrm reports that its ﬁnancial accounts were audited
by an external auditor, that it has foreign ownership, and exports more than 10% of
its sales. Table 1 reports descriptive statistics on the variables used in the regression
analysis.

In a second step, we account for endogenous credit demand. By construction, only
ﬁrms that have demand for a loan can be credit-constrained. To tease out how our
candidate explanation affect credit supply, we need exogenous variation in demand.
In a similar fashion to Gorodnichenko and Schnitzer (2013), we rely on a negative liq-

12

uidity shock as an instrumental variable for credit demand. Liquidity shock is a binary
variable equal to 1 if the ﬁrm experienced a loss due to spoilage, a power outage, or
was asked to pay a bribe in 2015, and 0 otherwise.

More speciﬁcally, the loss due to power outages is obtained as follows: “Please
estimate the losses that resulted from power outages either as a percentage of total
annual sales or as total annual losses”. The responses to this question are transformed
into an indicator equal to 1 if the ﬁrm experienced any losses due a power outage, and
0 otherwise. We proceed analogously with the measure of losses due to spoilage. The
common feature of these events is that they befall the ﬁrm. Though ﬁrms may differ
in their susceptibility to such events, these differences arguably are absorbed by the
ﬁrm-speciﬁc ﬁxed effect.

5 Results

5.1 Financial inclusion

Entrepreneurial human capital is associated with ﬁnancial inclusion. As shown in
Table 2 that reports estimates of equation 2, ﬁrms where the education of the CEO
increased between 2013 and 2016 are more likely to have opened a checking account
during this period. Typically, an increase in managerial education is due to the change
of the CEO. We therefore also control for changes in managerial experience, which
likewise is associated with ﬁnancial inclusion. At the same time, replacing a male
CEO with a woman (or replacing a female CEO with a man) cannot explain whether
the ﬁrm opened a bank account. Table 2 also shows that ﬁrms that operated informally
before registering are less likely to become banked.

We do not ﬁnd evidence that local intermediation capacity incentivises ﬁrms to
engage with the banking system. Table 3 explores the relationship between local ﬁ-
nancial conditions and the probability that a ﬁrm opens a bank account between 2013
and 2016. We consider three different measures of credit supply and one measure for
a demand shock. We do not ﬁnd that the share of ﬁrms with a bank account in the
neighbourhood of the respondent (“local ﬁnancial inclusion”) inﬂuences the decision
to open an account between 2013 and 2016. Similarly, the share of neighbouring ﬁrms
with a loan outstanding (“local ﬁnancial intermediation”) does not appear to play a
major role either. In addition, our measure of crowding out is not statistically signiﬁ-
cant.

Similarly, a negative liquidity shock does not push ﬁrms toward the banking sys-
tem. The last column of Table 3 explores whether demand for funds coming from a
negative liquidity shock can lead ﬁrms toward opening an account. However, it turns
out that a ﬁrm incurring losses due to spoilage, a power outage, or a request for a bribe

13

from a governmental ofﬁcial cannot explain ﬁnancial inclusion. It appears likely that
in times of need, unbanked ﬁrms turn to informal providers of ﬁnance.

In our context, the evidence on institutional quality is weak. The results in Table 4
exploit three different variables to proxy for the perceived quality of institutions. Po-
litical instability was most frequently mentioned as the top obstacle by Egyptian ﬁrms
in both 2013 and 2016. However, the change in political instability over this period
cannot explain the propensity of ﬁrms to open a bank account. At the same time, the
change in the perception of the enforcement of property rights - which perhaps is to a
greater extent a structural feature of the business environment than political instabil-
ity - is positively associated with the decision to open a bank account. Nevertheless,
another structural measure, namely the perception that the judiciary is fair and impar-
tial, cannot explain ﬁnancial inclusion. The same applies to perceptions of the speed
at which the court system operates.

At least two reasons come to mind why in our context the results differ from those
in Johnson et al. (2002). First, the phrasing of our questions is somewhat different. In
Johnson et al. (2002), respondents are asked for the share of ﬁrms that respondents
believe make extra-legal payments to government ofﬁcials to obtain services, licenses,
etc. Second, it may well be that perceived institutional quality affects the decision to
register and become formal rather than the decision to open a bank account.

5.2 Credit constraints

In addition to structural features of the participation of ﬁrms in the ﬁnancial system,
we consider cyclical factors, such as whether ﬁrms report to be constrained in their
access to credit. Table 5 indicates that our index of crowding out is associated with an
increase in the prevalence of credit constraints between 2013 and 2016. Commercial
banks, and in particular those owned by the government, supported the rapid rise of
government debt instead of giving credit to ﬁrms in the private sector. As a result, we
ﬁnd that ﬁrms surrounded by branches of banks that invested more in government
debt were more likely to be credit constrained.

Crowding out affects credit constraints mainly through discouragement. Credit
constrained ﬁrms can fall into two categories: (i) ﬁrms that applied for a loan and
were rejected; and (ii) ﬁrms discouraged from applying either because of unfavourable
terms and conditions or because they did not think the application would be ap-
proved. The terms and conditions that discourage ﬁrms include complex application
procedures, unfavourable interest rates, high collateral requirements, and insufﬁcient
size of loan and maturity. In Egypt, the vast majority of credit-constrained ﬁrms is
discouraged; rejected loan applications are rare (EBRD et al., 2016). Accordingly, the
second column of results in Table 5 shows that crowding out led to an increase in dis-

14

couraged ﬁrms, while the third column suggest that it did not affect the loan rejection
rate.

Table 5 shows that ﬁrms with more educated CEOs were less likely to be credit
constrained. However, while they are less likely to be discouraged, ﬁrms with more
educated CEOs are more likely to report an increase in rejection. But overall, given that
the relatively low share of ﬁrms that are rejected, this translates in a lower incidence
of credit constraints for these ﬁrms. At the same time, ﬁrms that started informally
before registering were more likely to be credit constrained. In the fourth column, we
show that the ﬁrms that opened a bank account between 2013 and 2016 were less likely
to report an increase in credit constraints.

In Table 6, we also consider the change in the demand for ﬁnance (by consider-
ing whether the ﬁrm reports to have a need for external ﬁnance) and how it affects
credit constraints. To address the endogeneity issue between the need for ﬁnance and
the credit constraints, we use an instrumental variable for the demand for ﬁnance:
whether a ﬁrm reports it was affected by liquidity shock - more speciﬁcally. whether
the ﬁrm experienced a loss due to spoilage, a power outage, or was asked to pay a
bribe. We argue that these incidents affect credit constraints only through the demand
for loan and the case of the exogeneity of the instrument is strong. The ﬁrst column of
Table 6 reports the estimates from the ﬁrst stage regression and shows that liquidity
shock is positively associated with the demand for ﬁnance. The F-test statistic (the
squared-root of the t-test statistic in a just-identiﬁed model) is above 10, suggesting
that the instrumental variable is not weak.

The second column of results in Table 6 reports 2SLS (two-stage least squares) esti-
mates and shows that the change in the need for loans (instrumented with the liquidity
shock) has a strong effect on the increase in credit constraints. As in Table 6, the index
of crowding out is associated with credit constraints. But when we consider whether
ﬁrms were discouraged or rejected, the third and fourth columns of Table 6 also indi-
cate that discouragement is driving the relationship. In other words, crowding out is
associated with an increase in discouragement but not in rejection.

The credit constraints regressions also control for the change in foreign ownership
and exporter status. While the estimates are statistically signiﬁcant in the OLS regres-
sions, Table 6 clarify that these estimates reﬂect the higher demand for loans. Condi-
tional on demand, exporters and foreign-owned ﬁrms are not more likely to be credit
constrained.

The results hold for different variants of the crowding out index. In Table 7, we
also use the instrumental variable strategy but use different measures of our crowding
out index. While our standard measure consider the bank branches that are in a 10km
radius around the ﬁrm, we also use alternative measures where we take into account
20 or 50 bank branches in the vicinity of the ﬁrm. The ﬁrst column of results in Table

15

7 is identical to the speciﬁcation in the second column of Table 6. The estimates in the
second column (20 bank branches) or third column (50 banks branches) are very close.
In the fourth column, we only consider the proximity of public banks (10km radius
around the ﬁrm) and ﬁnd that the results are qualitatively similar: crowding out is
associated with an increase in credit constraints.

In principle, entrepreneurial human capital can affect ﬁnancial inclusion via two
main channels. The ﬁrst channel operates via ﬁnancial literacy. It may well be that
more educated entrepreneurs are more comfortable dealing with banks and thus less
likely to be discouraged by the procedural aspects of a loan application. The second
channel concerns ﬁrm quality. To the extent that this channel matters, entrepreneurs
with more human capital should run ﬁrms that on average are more attractive to
banks. Table 8 compares the empirical salience of the two channels.

The results suggest that human capital does not work through ﬁnancial literacy.
The dependent variable in the ﬁrst column of results in Table 8 is an indicator variable
equal to 1 if the respondent did not apply for a loan because of complex application
procedures, and 0 otherwise. These ﬁrms are a subset of the ﬁrm that are classiﬁed
as discouraged. If entrepreneurial human capital would work via ﬁnancial literacy,
the measures of education and experience should have negative (and statistically sig-
niﬁcant) estimated coefﬁcients. But this is not the case. At the same time, ﬁrms that
started their operations informally are more likely to report increasing discouragement
resulting from procedures.

Our measures of ﬁrm quality are associated with entrepreneurial human capital.
The second column of results in Table 8 is based on an indicator equal to 1 if the ﬁrm
has a website, and 0 otherwise. The third column looks at an indicator equal to 1 if
the ﬁrm engaged either in process or product innovation during the previous ﬁnancial
year, and 0 otherwise. Finally, the dependent variable in the last column is equal to 1 if
the ﬁrm reports having expansion plans, and 0 otherwise, but this last question is ad-
ministered only to manufacturing ﬁrms. Consistent with the role of human capital in
technology diffusion advance by Nelson and Phelps (1966) and Benhabib and Spiegel
(1994), ﬁrms with a university educated CEO are more likely to have a website and to
innovate. They are also more likely to have expansion plans.8

5.3 Beyond Egypt

To provide some external validity of our results that are based on a panel on Egyptian
ﬁrms, we also use data from the Enterprise Survey on ﬁve other countries in the region:
Jordan, Lebanon, Morocco, Palestine and Tunisia. For these countries, we can only use

8The 2013 wave of the Enterprise Survey contained questions on management practices. Unfortu-
nately, these questions were not part of the 2016 edition. This is why we use the variables discussed
above instead.

16

a cross-section of ﬁrms with data from 2013. Unlike in Egypt in 2016, there was no
follow-up survey in the other countries of the region. We cannot look at changes in
ﬁnancial inclusion or credit constraints between 2013 and 2016 but consider the level
in 2013. We ﬁnd that the results of Table 9 (that do not include ﬁrms from Egypt) are
qualitatively similar to those of Table 2. Managerial education (but not manager expe-
rience) is associated with ﬁnancial inclusion, while ﬁrms that started their operations
in the informal sector are less likely to have a checking account. Using cross-sectional
data, we also ﬁnd that younger and smaller ﬁrms are less likely to interact with banks.
In Table 10, we do not use an instrumental variable approach to take into account
the need for external ﬁnance as in Table 6. Instead, we use a two-stage Heckman se-
lection model and use the liquidity shock as an exclusion restriction. The estimates in
the ﬁrst column show that the liquidity shock is associated with the need for external
ﬁnance. Our measure of crowding out is also associated with the demand for loans.
The second column indicates that ﬁrms with a more educated or experienced manager
are less likely to be credit constrained. The third column ﬁnds that this is driven by
discouragement, while the fourth column ﬁnds that managerial education and expe-
rience are not associated with being rejected. However, and unlike in our panel of
Egyptian ﬁrms, the crowding out index is not associated with credit constraints. This
is likely to be due to the fact that, following the Arab Spring, the rapid increase in gov-
ernment bonds held by banks was much more signiﬁcant in Egypt than in the other
countries of the region.

6 Conclusion

For several reasons, Egypt can be considered as a useful laboratory to answer the fol-
lowing question: why are ﬁrms disconnected from the banking system? First, the
informal economy accounts for a signiﬁcant share of overall economic activity. Many
ﬁrms in the formal sector in Egypt operate on a cash-only basis. They are excluded
from the banking system de facto, even when they are formally registered.

Second, while informality and ﬁnancial exclusion play a role in explaining the low
demand for credit, crowding out due to higher government borrowing can also dis-
courage ﬁrms to apply for bank loans. Following the uprisings of 2011, Egypt went
through a difﬁcult economic period. Output growth declined to levels barely above
population growth. To assuage social pressures, the authorities increased spending
and subsidies by relying on lending by domestic banks, which resulted in higher pub-
lic debt levels. This may have reduced bank lending to the private sector.

This paper uses a new measure of crowding out to explore if cyclical and struc-
tural issues in the business environment affect the participation of Egyptian ﬁrms in

17

the ﬁnancial system. In addition to bank balance sheet data, the measure exploits ge-
ographical information on the location of ﬁrms and bank branches to proxy crowding
out. A ﬁrm that is surrounded by bank branches that invest more in government debt
will score high on the crowding out index.

We ﬁnd that crowding out reduce credit supply for ﬁrms in the private sector in
Egypt. Firms surrounded by bank branches that invest more in government debt are
more likely to be credit constrained and discouraged to apply for a loan. We also show
that ﬁrms that started operating in the informal sector before registering are less likely
to open a bank account. Firms with more educated and more experienced managers
engage more with the ﬁnancial system and are less credit-constrained.

We also consider other countries in the region, beyond Egypt, as many ﬁrms op-
erating in the formal sector in MENA do not have a checking account. They have
adjusted production strategies to an environment where borrowing from a bank is
not an option and are disconnected from the ﬁnancial system. The results of this pa-
per suggest that while crowding out affect the cyclical demand for loans of ﬁrms that
have a bank account, it does not have a strong impact on the structural issue of the
large share of ﬁrms that are completely disconnected from the ﬁnancial sector, in good
times and bad times of the economic cycle. Instead, measures to improve the business
environment and the ﬁnancial literacy of ﬁrms’ managers may help improve ﬁnancial
inclusion in the region.

18

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Figures and Tables

Figure 1: Financial inclusion in Egypt and the MENA region

Figure 2: Median number of employees, by ﬁrm age and account status

22

Figure 3: Financial inclusion and investment

(a) Financially excluded ﬁrms are less
likely to invest

(b) Investments of unbanked ﬁrms
depend on internal ﬁnance

Figure 4: Government borrowing from local banks in Egypt in 2010-2017

(a) The government borrowed heavily
from public banks

(b) Public debt was partly monetized by
the Central Bank

23

Figure 5: Credit constraints in MENA

Figure 6: Securities and loans of banks in Egypt in 2010-2017

(a) Public banks increased their holdings
of securities

(b) Lending by public banks to the private
sector slightly decreased

24

Figure 7: Spatial distribution of bank branches in Egypt

(a) Sample ﬁrms

(b) Public banks

(c) Private banks

(d) Foreign banks

25

Table 1: Descriptive statistics

Level in 2013

∆ 2016-2013

Variable

Account
Credit constrained
Discouraged
Rejected
Need
CEO university education
CEO experience
CEO female
Local ﬁnancial inclusion
Local ﬁnancial intermediation
Crowding out index
Political instability
Property rights enforced
Courts impartial
Courts quick
Ownership
Exporter
Audit
Procedures
Website
Innovator
Expansion
Initially informal

Mean

0.66
0.24
0.23
0.01
0.30
0.81
21.26
0.05
0.71
0.11
0.95
0.79
0.55
0.57
0.27
0.08
0.11
0.77
0.07
0.50
0.29
0.77
0.08

SD

0.47
0.43
0.42
0.11
0.46
0.39
12.26
0.22
0.20
0.12
0.80
0.41
0.50
0.49
0.45
0.27
0.32
0.42
0.26
0.50
0.45
0.42
0.27

Mean

0.07
-0.01
-0.01
0.00
0.01
0.00
0.00
0.00
0.05
0.00
2.13
0.02
0.04
-0.05
-0.01
-0.01
0.03
0.05
0.03
0.04
-0.16
-0.20

SD

0.55
0.57
0.57
0.14
0.61
0.45
1.00
0.25
0.21
0.17
0.37
0.57
0.70
0.68
0.64
0.32
0.37
0.53
0.38
0.54
0.52
0.61

26

Table 2: Financial inclusion and managerial characteristics

∆ CEO university education

∆ CEO experience

∆ CEO female

Initially informal

Sample size
R2

(1)

0.127**
(0.06)

-0.153**
(0.07)
612
0.06

(2)

(3)

0.037*
(0.02)

-0.170**
(0.07)
598
0.06

-0.034
(0.08)
-0.157**
(0.07)
614
0.05

(4)

0.142**
(0.06)
0.047**
(0.02)
-0.024
(0.09)
-0.168**
(0.08)
597
0.07

Variables with a ∆ preﬁx are given by the ﬁrst difference between the 2016 and the 2013 wave of the
Enterprise Survey. The dependent variable is given by ∆ account, where account is an indicator equal
to 1 if the ﬁrm has a checking or savings account, and 0 otherwise. Robust standard errors are reported
in parenthesis. All speciﬁcations include controls for ﬁrm size, ﬁrm age, sector and governorate-level
ﬁxed effects. ***, ** and * denote statistical signiﬁcance at the 1, 5 and 10 percent levels respectively.

27

Table 3: Financial inclusion and ﬁnancial system characteristics

Local ﬁnancial inclusion

Local ﬁnancial intermediation

Crowding out index

Liquidity shock

∆ CEO university education

∆ CEO experience

∆ CEO female

Initially informal

Sample size
R2

(1)

-0.087
(0.13)

0.124**
(0.06)
0.050**
(0.02)
-0.037
(0.09)
-0.121
(0.07)
545
0.08

(2)

(3)

(4)

-0.064
(0.15)

0.129**
(0.06)
0.052**
(0.02)
-0.033
(0.09)
-0.108
(0.07)
545
0.08

-0.030
(0.03)

0.104*
(0.06)
0.042*
(0.02)
-0.035
(0.09)
-0.103
(0.08)
516
0.07

0.052
(0.05)
0.143**
(0.06)
0.045**
(0.02)
-0.024
(0.09)
-0.168**
(0.08)
597
0.08

Variables with a ∆ preﬁx are given by the ﬁrst difference between the 2016 and the 2013 wave of the
Enterprise Survey. The dependent variable is given by ∆ account, where account is an indicator equal
to 1 if the ﬁrm has a checking or savings account, and 0 otherwise. Local ﬁnancial inclusion is given
by the ﬁrst difference of the share of ﬁrms with a checking or saving account among 20 sample ﬁrms
closest to the respondent. Local ﬁnancial intermediation is constructed analoguously, but refers to
loans outstanding. The crowding out index is computed according to equation 1. Liquidity shock
is equal to 1 if the ﬁrm experienced a loss due to spoilage, a power outage, or was asked to pay a
bribe in 2015, and 0 otherwise. Robust standard errors are reported in parenthesis. All speciﬁcations
include controls for ﬁrm size, ﬁrm age, sector and governorate-level ﬁxed effects. ***, ** and * denote
statistical signiﬁcance at the 1, 5 and 10 percent levels respectively.

28

Table 4: Financial inclusion and institutional quality

∆ political instability

∆ property rights enforced

∆ courts impartial

∆ courts quick

∆ CEO university education

∆ CEO experience

∆ CEO female

Initially informal

Sample size
R2

(1)

0.064
(0.04)

0.135**
(0.06)
0.045**
(0.02)
-0.019
(0.08)
-0.174**
(0.07)
590
0.08

(2)

(3)

(4)

0.005
(0.03)

0.142**
(0.06)
0.047**
(0.02)
-0.024
(0.09)
-0.168**
(0.08)
597
0.07

-0.030
(0.03)

0.140**
(0.06)
0.048**
(0.02)
-0.022
(0.09)
-0.174**
(0.08)
597
0.08

-0.055
(0.04)
0.142**
(0.06)
0.047**
(0.02)
-0.015
(0.09)
-0.168**
(0.08)
597
0.08

Variables with a ∆ preﬁx are given by the ﬁrst difference between the 2016 and the 2013 wave of the
Enterprise Survey. The dependent variable is given by ∆ account, where account is an indicator equal
to 1 if the ﬁrm has a checking or savings account, and 0 otherwise. Robust standard errors are reported
in parenthesis. All speciﬁcations include controls for ﬁrm size, ﬁrm age, sector and governorate-level
ﬁxed effects. ***, ** and * denote statistical signiﬁcance at the 1, 5 and 10 percent levels respectively.

29

Table 5: Credit constraints: OLS estimates

(1)

(2)

(3)

(4)

∆ constrained ∆ discouraged ∆ rejected ∆ constrained

Crowding out index

∆ CEO university education

∆ CEO experience

∆ CEO female

∆ ownership

∆ exporter

∆ audit

Initially informal

∆ account

Sample size
R2

0.065**
(0.03)
-0.115*
(0.06)
-0.014
(0.02)
-0.095
(0.09)
0.187**
(0.07)
0.169***
(0.06)
-0.068
(0.04)
0.223**
(0.11)

0.062**
(0.03)
-0.154**
(0.06)
-0.035
(0.02)
-0.102
(0.09)
0.190***
(0.07)
0.147**
(0.06)
-0.059
(0.04)
0.186*
(0.11)

0.004
(0.01)
0.038**
(0.02)
0.021**
(0.01)
0.007
(0.01)
-0.002
(0.01)
0.022
(0.02)
-0.009
(0.01)
0.037
(0.03)

521
0.10

521
0.10

521
0.06

0.052*
(0.03)
-0.097
(0.06)
-0.011
(0.02)
-0.098
(0.09)
0.203***
(0.08)
0.196***
(0.06)
-0.051
(0.05)
0.214*
(0.11)
-0.104**
(0.05)
516
0.11

Variables with a ∆ preﬁx are given by the ﬁrst difference between the 2016 and the 2013 wave of the
Enterprise Survey. The crowding out index is computed according to equation 1. Robust standard
errors are reported in parenthesis. All speciﬁcations include controls for ﬁrm size, ﬁrm age, foreign
ownership, exporter status, whether the ﬁrm was audited, sector and governorate-level ﬁxed effects.
***, ** and * denote statistical signiﬁcance at the 1, 5 and 10 percent levels respectively.

30

Table 6: Credit constraints: IV estimates

(1)
∆ need

Liquidity shock

∆ CEO female

Crowding out index

∆ CEO experience

0.213***
(0.06)
0.022
(0.03)
∆ CEO university education -0.083
(0.06)
-0.002
(0.02)
-0.154
(0.09)
0.222**
(0.09)
0.232***
(0.07)
-0.067
(0.05)
0.225**
(0.10)

Initially informal

∆ ownership

∆ exporter

∆ audit

∆ need

Sample size
R2

521

(2)

(3)

(4)

∆ constrained ∆ discouraged ∆ rejected

0.043***
(0.02)
-0.052**
(0.03)
-0.018
(0.01)
0.023
(0.07)
-0.003
(0.06)
-0.006
(0.05)
-0.023
(0.02)
0.056
(0.07)
0.742***
(0.15)
521
0.74

0.040**
(0.02)
-0.092***
(0.03)
-0.038***
(0.01)
0.013
(0.07)
0.003
(0.06)
-0.025
(0.05)
-0.015
(0.03)
0.023
(0.07)
0.726***
(0.16)
521
0.71

0.003
(0.01)
0.040**
(0.02)
0.020**
(0.01)
0.010
(0.02)
-0.006
(0.02)
0.018
(0.02)
-0.008
(0.01)
0.034
(0.03)
0.016
(0.08)
521
0.07

Column (1) presents ﬁrst stage estimates where ∆ need is instrumented with liquidity shock. Liquid-
ity shock equals 1 if the ﬁrm experienced a loss due to spoilage, a power outage, or was asked to pay
a bribe in 2015, and 0 otherwise. Columns (2)-(4) show second stage IV estimates. The crowding out
index is computed according to equation 1. Variables with a ∆ preﬁx are given by the ﬁrst difference
between the 2016 and the 2013 wave of the Enterprise Survey. Robust standard errors are reported
in parenthesis. All speciﬁcations include controls for ﬁrm size, ﬁrm age, foreign ownership, exporter
status, whether the ﬁrm was audited, sector and governorate-level ﬁxed effects. ***, ** and * denote
statistical signiﬁcance at the 1, 5 and 10 percent levels respectively.

31

Table 7: Credit constraints: robustness

Crowding out index

Crowding out index 20nn

Crowding out index 50nn

Public bank

∆ CEO university education

∆ CEO experience

∆ CEO female

∆ ownership

∆ exporter

∆ audit

Initially informal

∆ need

Sample size
R2

(1)

0.043***
(0.02)

-0.052**
(0.03)
-0.018
(0.01)
0.023
(0.07)
-0.003
(0.06)
-0.006
(0.05)
-0.023
(0.02)
0.056
(0.07)
0.742***
(0.15)
521
0.74

(2)

(3)

(4)

0.045***
(0.02)

-0.051*
(0.03)
-0.015
(0.01)
0.021
(0.07)
-0.004
(0.06)
0.002
(0.05)
-0.023
(0.02)
0.057
(0.07)
0.744***
(0.15)
521
0.74

0.047***
(0.02)

-0.052**
(0.03)
-0.018
(0.01)
0.019
(0.07)
0.001
(0.06)
0.001
(0.05)
-0.023
(0.02)
0.058
(0.07)
0.750***
(0.15)
521
0.74

0.212*
(0.11)
-0.051**
(0.03)
-0.015
(0.01)
0.028
(0.07)
-0.013
(0.06)
-0.012
(0.05)
-0.025
(0.02)
0.048
(0.06)
0.778***
(0.15)
521
0.74

Variables with a ∆ preﬁx are given by the ﬁrst difference between the 2016 and the 2013 wave of the
Enterprise Survey. The dependent variable is given by ∆ constrained, where constrained is an indi-
cator equal to 1 if the ﬁrm had a loan application rejected or was discouraged from applying in the
ﬁrst place, and 0 otherwise. Columns (1)-(4) show second stage IV estimates where credit demand
is instrumented with liquidity shock. The crowding out index is computed according to equation 1.
Column (1) reproduces the results from Column (2) of Table 6. Column (2) presents results on for a
crowding out index based on the 20 bank branches closest to the respondents. Column (3) shows cor-
responding results for the closest 50 branches. Column (4) looks at the share of public bank branches
in the 10km circle centered on the ﬁrm. Robust standard errors are reported in parenthesis. All speci-
ﬁcations include controls for ﬁrm size, ﬁrm age, foreign ownership, exporter status, whether the ﬁrm
was audited, sector and governorate-level ﬁxed effects. ***, ** and * denote statistical signiﬁcance at
the 1, 5 and 10 percent levels respectively.

32

Table 8: Financial literacy and ﬁrm quality

(1)

(2)

(3)

(4)

∆ Procedures ∆ Website ∆ Innovator ∆ Expansion

∆ CEO university education

∆ CEO experience

∆ CEO female

Initially informal

Sample size
R2

0.004
(0.04)
-0.012
(0.02)
0.004
(0.06)
0.160***
(0.06)
603
0.04

0.086**
(0.04)
0.007
(0.02)
0.098
(0.07)
-0.055
(0.07)
601
0.05

0.076*
(0.04)
0.014
(0.02)
0.142
(0.09)
0.021
(0.07)
603
0.05

0.153**
(0.07)
0.025
(0.03)
-0.067
(0.17)
0.066
(0.12)
291
0.10

Prrocedures equals one if the respondent did not apply for a loan because of complex application pro-
cedures. Innovator equals one if the company engaged in either process or product innovation during
the previous ﬁnancial year. The expansion plan variable is available only for manufacturing ﬁrms,
which explains the smaller sample size. Variables with a ∆ preﬁx are given by the ﬁrst difference
between the 2016 and the 2013 wave of the Enterprise Survey. Robust standard errors are reported
in parenthesis. All speciﬁcations include controls for ﬁrm size, ﬁrm age, sector and governorate-level
ﬁxed effects. ***, ** and * denote statistical signiﬁcance at the 1, 5 and 10 percent levels respectively.

Table 9: Regional results: ﬁnancial inclusion

CEO university education

CEO experience

CEO female

Initially informal

Sample size
R2

(1)

0.063***
(0.01)

-0.094***
(0.03)
2285
0.12

(2)

(3)

0.000
(0.01)

-0.090***
(0.03)
2235
0.11

0.010
(0.02)
-0.106***
(0.03)
2285
0.11

(4)

0.063***
(0.01)
0.005
(0.01)
0.001
(0.02)
-0.079***
(0.03)
2235
0.12

The dependent variable is given by ∆ account, where account is an indicator equal to 1 if the ﬁrm has
a checking or savings account, and 0 otherwise. Robust standard errors are reported in parenthesis.
All speciﬁcations include controls for ﬁrm size, ﬁrm age, sector and country-level ﬁxed effects. ***, **
and * denote statistical signiﬁcance at the 1, 5 and 10 percent levels respectively.

33

Table 10: Regional results: credit constraints

CEO university education

CEO experience

CEO female

Crowding out index

Initially informal

Sample size

(1)
Need

-0.043
(0.06)
-0.039
(0.03)
0.204
(0.14)
0.095*
(0.05)
-0.028
(0.10)
2167

(2)
Constrained

(3)
Discouraged

(4)
Rejected

-0.126***
(0.03)
-0.051***
(0.02)
-0.007
(0.07)
-0.020
(0.04)
0.031
(0.06)
2167

-0.123***
(0.04)
-0.053***
(0.02)
-0.005
(0.08)
-0.008
(0.04)
-0.017
(0.06)
2167

-0.004
(0.01)
0.002
(0.01)
-0.001
(0.03)
-0.011
(0.02)
0.048**
(0.02)
2167

Column (1) presents ﬁrst stage estimates from a Heckman procedure, with liquidity shock excluded
from the second stage. Liquidity shock equals 1 if the ﬁrm experienced a loss due to spoilage, a
power outage, or theft, and 0 otherwise. Columns (2)-(4) show second stage Heckman estimates.
The crowding out index is computed according to equation 1. All speciﬁcations include controls for
ﬁrm age, ﬁrm size, foreign ownership, exporter status, whether the ﬁrm was audited, sector and
country-level ﬁxed effects. ***, ** and * denote statistical signiﬁcance at the 1, 5 and 10 percent levels
respectively.

34

ECONOMICS – WORKING PAPERS 2019/10

Structural and cyclical  
determinants of access  

to finance:

Evidence from Egypt

Economics Department
U   economics@eib.org

www.eib.org/economics 

European Investment Bank
98 -100, boulevard Konrad Adenauer
L-2950 Luxembourg
3 +352 4379-22000
www.eib.org – U info@eib.org

© European Investment Bank, 12/2019

eBook: QH-BK-19-010-EN-E
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