Public Disclosure Authorized

Policy Research Working Paper 9392

Public Disclosure Authorized

Bank Lending Rates and Spreads in EMDEs

Evolution, Drivers, and Policies

Erik Feyen
Igor Zuccardi Huertas
Public Disclosure Authorized
Public Disclosure Authorized

Finance, Competitiveness and Innovation Global Practice

September 2020
Policy Research Working Paper 9392

Abstract
This paper analyzes the main trends and patterns of nomi- nonperforming loans, and non-interest income (banking
nal lending interest rates and lending-deposit interest rate characteristics); and credit bureau coverage and time to
spreads in emerging markets and developing economies. resolve insolvency (business environment). Finally, illustra-
Using data from 140 emerging markets and developing tive decompositions of the level and 10-year change between
economies, analysis shows that nominal lending rates and 2007 and 2017 of nominal lending rates find relative dif-
spreads declined between 2003 and 2017, with regional ferences across regions. On the decline of nominal interest
heterogeneity. In addition, it finds that less economically rates in that decade, rising public debt and nonperforming
and financially developed countries tend to exhibit higher loans have pushed rates up, which was counterbalanced by a
lending rates and spreads. These higher rates tend to be reduction in inflation, the policy interest rate, and overhead
driven by higher spreads, not deposit interest rates. Also, costs and a better business environment. Since the global
illustrative regressions suggest that relevant correlates of financial crisis, a common global factor has increased in
nominal lending rates include inflation, public debt, and importance and has contributed to the downward trend
policy interest rate (macro-fiscal conditions); overhead costs, in nominal lending rates.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. 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://www.worldbank.org/prwp. The authors may
be contacted at efeijen@worldbank.org and izuccardi@worldbank.org.

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 Bank Lending Rates and Spreads in EMDEs: Evolution, Drivers, and Policies

September 2020

Erik Feyen ♦
Igor Zuccardi Huertas

JEL Classification Numbers: E43, F65, G21.

Keywords: Bank lending interest rate, lending-deposit interest spread, macro-fiscal, bank
characteristics, business environment, Emerging and Developing Economies.

♦
Erik Feyen is Head of the Macro-Financial Unit in the World Bank’s Finance, Competitiveness, and Innovation (FCI)
Global Practice. Igor Zuccardi is Financial Sector Economist in the Macro-Financial Unit.
The views expressed in this paper are those of the authors and do not necessarily reflect those of the World Bank.
We are grateful to Ceyla Pazarbasioglu, Marcello Estevão, and Alfonso Garcia Mora for guidance on the topic, and
William Maloney, Franziska Ohnsorge, Pietro Calice, and other participants of Seminar “Bank lending rates and
spreads in EMDEs” on April 30, 2019 for comments. We also thank Sashana Whyte and Jiemin Ren for their help
producing Boxes 3 and 5, and Steve Gui-Diby Diego Sourrouille for their research assistance. We thank for inputs
that made the Box 6 possible, particularly to Mariano Cortes and Philip Schuler (AFR); Ana Maria Aviles and Richard
Record (EAP); Eva Gutierrez, Raquel Letelier, Alena Kantarovich, and David Knight (ECA); Oliver Masetti and Federico
Diaz Kalan (LAC); Syed Mehdi Hassan, Djibrilla Issa, Haocong Ren, and Emmanuel Pinto Moreira (MENA); Marius
Vismantas and Muhammad Waheed (SAR). For questions, please contact the authors at efeijen@worldbank.org and
izuccardi@worldbank.org.
 1. Introduction
Banks dominate credit intermediation and savings mobilization in most Emerging Markets and
Developing Economies (EMDEs). A key driver of lending interest rates is the lending-deposit
interest spread, the difference between the lending and deposit interest rates, which captures
the efficiency with which banks allocate society’s savings to its most productive uses. High
lending rates and spreads pose a challenge for policy makers: they can affect monetary policy
transmission, hinder private investment and job creation, inhibit financial development and
inclusion, and can ultimately compromise financial stability.

This paper quantitatively analyzes the main factors that are correlated with lending rates and
spreads in EMDEs. The empirical literature suggests that rates and spreads are driven by three
factors: macro-fiscal conditions, banking sector characteristics, and the business environment.
These affect four components that make up the spread: bank operational costs, risk premia,
quasi-taxes, and returns.

Using data from 140 EMDEs, we show that the country median nominal lending rate and spread
declined in EMDEs in the 15-year period between 2003 and 2017. However, country experiences
differ: in the decade 2007-2017, rates and spreads have increased in 33% and 40% of the
countries, respectively. The analysis also shows that rates and spreads have been consistently
higher in Latin America and the Caribbean (LAC) and Sub-Saharan Africa (AFR) and are also a
policy challenge in many countries in Europe and Central Asia (ECA), including Turkey and the
Russian Federation. They are typically lower in East Asia and Pacific (EAP) and Middle East and
North Africa (MENA), but exceptions exist (e.g., EAP: the Lao People’s Democratic Republic,
Mongolia, Indonesia. MENA: the Arab Republic of Egypt, Lebanon, Tunisia).

In addition, based on a quantitative analysis of lending rates and spreads, we find that:

• First, less economically and financially developed countries tend to exhibit higher lending
rates and spreads. These higher rates tend to be driven by higher spreads, not deposit
rates, suggesting that intermediation efficiency is what matters most in less developed
countries.

2
 • Second, illustrative regressions suggest that relevant correlates of nominal lending rates
include: inflation, public debt, and policy interest rate (macro-fiscal conditions), overhead
costs, non-performing loans, and non-interest income (banking characteristics), and
credit bureau coverage and time to resolve insolvency (business environment).
• Third, we conduct illustrative decompositions of the level and change of nominal lending
rates and find relative differences across regions. As regards the 2017 average lending
rate, high public debt and inflation (in South Asia-SA- and AFR) and high overhead costs
(ECA, LAC, and AFR) appear to be key components. Weak insolvency frameworks (AFR and
MENA) and high non-performing loans (NPLs) (MENA, ECA, and AFR) are also important.
As regards the decline in the decade 2007-2017, rising public debt and NPLs have pushed
rates up which was counter-balanced by a reduction in inflation, policy interest rate,
overhead costs and a better business environment. Moreover, since the global financial
crisis, a common global factor has increased in importance and has contributed to the
downward trend in nominal lending rates.

What are the policy implications to sustainably reduce rates and spreads? Policy makers in many
EMDEs have intervened through, inter alia, interest rate restrictions and directed credit and
subsidy programs -- these measures may carry unintended consequences. Policy makers are
advised to consider focusing on root causes. We elaborate on eight policy considerations which
should be evaluated holistically to avoid compromising macro-financial stability:

• Macro-fiscal conditions. First, strengthen macro-fiscal fundamentals since they exert a first-
order effect on benchmark interest rates and balance sheets. Second, avoid crowding out the
private sector particularly in shallow financial sectors.
• Banking sector characteristics. Third, support competition including from markets, non-banks,
and fintechs. Fourth, facilitate operational and scale efficiencies through enabling policy
frameworks such as for digital financial services. Fifth, strengthen bank regulation and
supervision to increase confidence in the resilience of the banking system.
• Business environment. Sixth, improve insolvency and creditor rights regimes which protects
banks against losses and promotes restructuring of viable firms. Seventh, improve

3
 information and collateral registries frameworks which reduces agency frictions. Eighth,
revisit direct policy interventions by assessing the design and impacts of, inter alia, reserve
requirements frameworks, interest rates restrictions, and directed credit and interest subsidy
programs.

This paper is organized as follows. Section 2 is the conceptual framework, which describes the
relevance of lending rates and spreads for economic development and discusses the main drivers
of lending rates and spreads according to the literature. Section 3 shows the main trends and
patterns of lending interest rates in the 15-year period 2003-2017, presents an illustrative
econometric analysis of common correlates of lending rates and spreads, and proposes
decomposition of level and change of the average lending rate in EMDEs during the decade 2007-
2017. Section 4 presents eight considerations which should be assessed holistically to formulate
a coherent policy mix that deals with the roots of high and persistent lending interest rates and
spreads in some EMDEs.

2. Why do bank interest rates and spreads matter? A conceptual

framework
A. Relevance for development

Bank interest rates are fundamental to macroeconomic and financial sector outcomes. Banks
dominate credit intermediation and savings mobilization in many EMDEs and constitute a
primary vehicle for monetary policy transmission. Bank interest rates therefore influence the
available capital in the economy (e.g., cost and volume), the pool of viable investments (e.g., size
and composition), and the health of public and private balance sheets. As a result, bank interest
rates have a significant impact on private sector-led investment, economic growth, job creation,
and the overall progress towards the twin goals.

Bank lending rates reflect nominal benchmark interest rates which are determined by macro-
economic factors. In a small, closed economy, nominal benchmark rates are determined by the
real interest rate and expected inflation. However, when the capital account is fully open and

4
neo-classical conditions hold, the domestic interest rates would depend on world interest rates
through arbitrage and expected devaluation (e.g., Edwards and Khan, 1985). Most EMDEs fall
between these extremes and various market frictions and risks exist which can keep benchmark
interest rates high. In this light, EMDEs with high public financing needs, high inflation, and
exchange rate pressures, may need to maintain high policy rates. The determinants of benchmark
interest rates are not the focus of this paper.

Bank lending rates also reflect the efficiency with which banks intermediate savings to its most
productive uses. The price of intermediation is commonly captured by the interest rate spread,
the difference between interest rates on lending and deposits, the main funding source for most
banks in EMDEs. The spread reflects four components: operational costs, risk premia, quasi-
taxes, and returns. Saving and term deposit rates in EMDEs typically closely align with nominal
benchmark interest rates with corresponding maturities such as the policy rate and government
yields. However, sight deposits -- which can be withdrawn at any time -- form a large portion of
the deposit base in many countries and typically carry a negligible nominal deposit rate.

The intermediation spread, and therefore the nominal lending rate, is influenced by various
market imperfections and risks. These include transaction costs and agency frictions along the
credit life cycle as banks originate, monitor, restructure, enforce and write-down loans -- this
drives a wedge between lending and deposit rates which is amplified by weak macro-fiscal
fundamentals, small and uncompetitive banking sectors, and challenging business environments
which all impose additional costs. Banks and depositors in EMDEs typically insulate themselves
against the associated risks by charging higher spreads, keeping lending and deposits at shorter
maturities and tightening other lending conditions (e.g., collateral, volume).

High bank interest rates and intermediation spreads pose an important challenge for policy
makers (Box 1). Box 6 provides an overview by region. Table 1 shows the countries with the
highest rates and spreads for each region. Box 3 offers a discussion on the case of Brazil.

5
 Box 1: Policy challenges of high interest rates and spreads
They may signal impediments to development and growth through the following channels:

• Monetary policy: The bank lending component of the credit channel of monetary policy
(Bernanke and Gertler, 1995) may become impaired with concomitant impacts on confidence,
investment, and aggregate demand. In particular, at high rates and spreads, few borrowers
will be in the market. Moreover, their demand for credit will likely be less responsive to
interest rate changes.

• Financial repression: Historically, governments have attempted to manage fiscal challenges by

putting downward pressure on nominal interest rates (e.g., McKinnon, 1973; and Kirkegaard
and Reinhardt, 2012). Examples of such measures include directed lending schemes and bank
interest rate restrictions. However, these measures are distortionary and may have
unintended consequences (see Box 5).

• Financial access: High lending rates amplify informational frictions which can result in credit
rationing (Stiglitz and Weiss, 1981) and distortions in credit allocation, away from otherwise
viable borrowers which are more opaque, have less pledgeable collateral, and are more
dependent on bank finance (Rajan and Zingales, 1998). As a result, the poor and small and
medium-sized enterprises are disproportionately affected (Haber et al., 2003; Rajan and
Zingales, 2003; Morck et al., 2005).

• Financial development: Chronically high and volatile lending rates may induce a bias towards
shorter-term investments as markets for longer-term finance remain underdeveloped. This
could result in underinvestment in projects with higher social returns such as housing and
infrastructure.

• Financial stability: High rates weaken balance sheets of firms and households, particularly for
those with short-term and floating-rate debts. This may affect loan portfolios of banks and
reduce their appetite to extend credit to the real economy. However, high spreads can also
reflect high bank profits which provide a buffer against shocks.

B. Drivers of bank interest rates and spreads

The literature suggests that the main demand and supply drivers that underpin the formation of
bank interest rates and spreads can be grouped into three clusters: macro-fiscal conditions,
banking sector characteristics, and the business environment (Figure 1). See Annex 1 for a
discussion of relevant examples of these determinants for EMDEs.

1. Macro-fiscal conditions: Macro-fiscal conditions determine benchmark interest rates and

the overall economic outlook which drives balance sheet health and collateral values of
existing and prospective borrowers and their demand for credit. Moreover, macro-fiscal

6
 conditions drive banks’ risk appetite and their cost of funding which in turn co-determines
their supply of credit.
2. Banking sector characteristics: Banking sector features such as the structure of assets and
liabilities, the prevalence of floating versus fixed rate and foreign currency loans, market
and pricing power, business models, economies of scale, and capacity to diversify revenue
sources are all relevant supply factors.
3. Business environment: The business environment mainly comprises implicit and explicit
taxes (e.g., (unremunerated) reserve requirements, directed lending and interest subsidy
programs, interest rate restrictions, counter-cyclical capital buffers), the information
environment (e.g., credit bureaus, collateral registries), and contracting and enforcement
costs (e.g., (unremunerated) reserve requirements, directed lending and interest subsidy
programs, interest rate restrictions, counter-cyclical capital buffers), the information
environment (e.g., credit bureaus, collateral registries), and contracting and enforcement
costs which determine the expected loss to the bank when a borrower defaults (e.g., the
insolvency and creditor rights framework).

Bank intermediation spreads can be decomposed into four basic components:

1. Operational costs: Staff and administrative costs are transferred to borrowers and
depositors and typically form the largest component in lending rates and spreads (De la
Torre et al, 2006; Poghosyan, 2012; Calice and Zhou, 2018). Operational costs are typically
lower in larger banking systems, which initially have more capacity to generate economies
of scale, and in a favorable business environment which, inter alia, allows for more
accurate borrower screening, better contract enforcement, and higher collateral
recovery.
2. Risk premia: Banks need to make allowances against expected and unexpected losses
arising from credit and other risks in their operations for economic and regulatory
reasons. 1 A key determinant is the composition of the credit portfolio. The associated

1
Many jurisdictions are moving to using expected credit loss (ECL) models for accounting provisioning purposes
which recognize losses earlier compared to incurred loss models (e.g., the adoption IFRS 9). For a regulatory
perspective, see the Basel III framework which governs minimum (capital) requirements for credit, market, liquidity
and other risks.

7
 expected losses are determined by three factors: i) the probability of default (PD), ii) the
loss the bank is exposed to when default occurs (Exposure at Default or EAD), and iii) the
ratio of the exposure that can be recovered (Loss Given Default or LGD). The PD is
influenced by macro-fiscal factors and corporate restructuring frameworks. The EAD is
determined by the outstanding amounts. And the LGD is determined by the amount of
collateral and how much of it can be recovered, which is a function of the insolvency and
credit rights framework.
3. Quasi-taxes: Monetary and macro-prudential policy instruments such as (not fully)
remunerated reserves and counter-cyclical capital buffers represent a regulatory cost for
banks (Brock and Rojas, 2000; Saunders and Schumacher, 2000; Gelos, 2006; De la Torre
et al, 2006; Calice and Zhou, 2018). Moreover, directed lending and interest subsidy
programs, interest rate restrictions, and other “financial repression”-type tools affect
rates and spreads. Moreover, directed lending and interest subsidy programs, interest
rate restrictions, and other “financial repression”-type tools affect rates and spreads. For
example, much of the costs of directed lending programs in Brazil seem to be borne by
small depositors who earn below-market rates.
4. Returns: Bank investors demand a required return on capital which determines the rate
of return on lending portfolios. This is affected by factors such as the competitive
environment and the bank’s capital structure. For example, larger bank equity buffers,
which have a higher required return than other funding sources, may translate into higher
lending spreads. 2 Indeed, there has been considerable analysis undertaken on how the
Basel III capital requirement increases would translate into higher lending rates and
spreads (BCBS, 2010).

2
Notwithstanding the irrelevance result of capital structures by Modigliani and Miller (1958).

8
 Figure 1: Drivers and macro-financial outcomes of bank lending rates and intermediation
spreads
Policy drivers and country characteristics Bank intermediation efficiency Macro-financial outcomes
Examples:
• Sovereign risk
• Monetary Macro-fiscal
conditions conditions Returns
• Crowding out
• Financial Oper-
openness Intermediation ational Macro-economic
spread costs
• Investment
Quasi • Savings
• Scale taxes
• Competition Banking Lending • Monetary
• Leverage sector Risk
rate policy
• Funding characteristics premia

Financial sector
Cost of • Depth
• Information
funding • Access
and collateral Business • Stability
environment
environment
• Insolvency and
creditor rights
• Direct policy
interventions

3. Empirical Results

A. Global trends and patterns of nominal lending interest rates and spreads

Using information from 140 EMDEs, the following patterns of lending interest rates and spreads
between 2003 and 2017 are found:

a) Nominal lending rates – In the 15-year period, nominal lending rates have consistently
declined across all regions and the EMDE median has fallen by a third -- however
significant heterogeneity within and between regions persists (Figures 2A, 2B, and 3A).
And the median lending rate has fallen by almost 5 percentage points between 2003 and
2017. The decline is consistent over time, except for the global financial crisis, when
interest rates slightly increased. However, large variation across regions persist. The
dispersion within region is also significant. In the decade 2007-2017, two-thirds of EMDEs
saw their nominal lending rates decline.

9
 b) Real lending rates – In the same period, EMDE median real lending rates have declined by
almost half. However, median real rates have increased slightly in EAP and LAC (Figure 2C
and 2D). While real rates dropped sharply during the height of the global financial crisis
in 2008 due to a spike in inflation, they rebounded quickly and were largely stable since.
Consistent with the pattern in nominal rates, real rates of poor performers (75th
percentile) are high in AFR and LAC (>10%). In the decade 2007-2017, 45% of EMDEs in
our sample experienced a decline in real lending rates.
c) Lending-deposit interest rate spreads – The median interest spread declined by one-fifth.
Only MENA experienced a slight increase in spreads (Figure 2E, 2F, and 3B). EMDE median
lending and deposit rates have fallen in tandem, consistent with a stable median spread.
The most recent spreads for 2013-17 remain the highest in LAC and AFR, respectively.
MENA is the only region where the median spread has slightly increased since 2003. In
the decade 2007-2017, 60% of EMDEs saw their spreads decline.
d) Net interest margins (NIM) – Consistent with the trend in spreads, the median NIM in
EMDEs has declined by over one percentage point. Indeed, since the global financial crisis
in 2007, 70% of countries experienced a fall in NIM in the wake of the global financial
crisis as policy rates and government yields fell, private borrower risk and defaults
increased, and banks increased their exposure to the sovereign. The rise in private sector
risk premia did not keep pace with or was properly anticipated with the rise in realized
defaults that drove the compression in NIMs. It is important to highlight that the NIM is
impacted by holdings of interest-bearing government securities and non-performing
assets which do no accrue interest income – this might distort the picture.

Box 2: Data and main variables

Data from World Bank FinStats 2019, World Development Indicators and International Financial
Statistics is used to form a panel of about 75 EMDEs for the period 2003-2017.

• Lending rates are defined as the volume-weighted average interest rate charged by banks on
short- to medium-term loans with fixed interest rates and with own funds to individuals and
corporations. This rate is normally differentiated according to creditworthiness of borrowers
and objectives of financing. For example, riskier segments such as unsecured consumer lending,

10
 SME lending and microfinance carry higher rates than loans to large enterprises. The terms and
conditions (e.g., maturity, collateral) attached to these rates differ by country, however,
limiting their comparability. We calculate real lending rates by subtracting cotemporaneous
annual inflation from the lending rate. We calculate real lending rates by subtracting
cotemporaneous annual inflation from the lending rate.

• Similarly, deposit rates are derived rates offered to resident customers and are weighted by
deposits amounts. Term deposits are not immediately redeemable and thus provide a more
stable source of funding – as a result, these carry higher interest rates, particularly in wholesale
markets. Redeemable sight deposits are most common for retail customers and are used for
payments and safekeeping – these often carry very low interest rates.

• We define our main proxy of bank intermediation efficiency, the interest spread, as the
difference between the lending and deposit interest rate.

• We use the net-interest margin (NIM) as an alternative, common efficiency measure based on
accounting data. The NIM is defined as the difference between actual interest income (e.g.,
loans and securities) and interest costs (e.g., deposits, interbank funding) divided by interest-
bearing assets. Net interest margins are not used by the private sector to make investment
decisions and they may give a distorted picture of credit intermediation efficiency through
securities holdings. Furthermore, net-interest margins are affected by interest rate changes
through duration differences of assets and liabilities.

Countries with higher nominal and real lending rates, spreads, and net interest margins tend to
be less economically developed (Figure 4). 3 This pattern also holds for measures of
intermediation efficiency. The pattern is less apparent for real interest rates. However, the
difference in GDP per capita between the lowest and highest tercile of real lending rates is still
almost $400.

High nominal and real lending rates are driven by intermediation inefficiencies, not high deposit
rates (Figure 5). Naturally, deposit and lending rates move together (Figure 5A). But which
component of lending rates is more important: funding costs or intermediation efficiencies? 4 Our

3
We separately sort countries by their average 2003-17 rates, spreads and NIMs and create terciles and then
calculate the median GDP per capita for each tercile. For example, median GDP per capita for countries with the
lowest nominal interest rates is almost $6,000. In contrast, countries with the highest nominal rates have a median
GDP per capita of just $1,700.
4
To answer this question, we sort countries into quintiles – which give us more granularity than terciles to show
marked differences between countries -- based on their 2003-17 average intermediation spread. For each quintile,
we calculate medians of the average deposit and lending rates. Across quintiles, deposit rates increase moderately
from 3.5% in the 1st quintile with the lowest spreads to a peak of 4.6% in the 4th quintile. In contrast, nominal (and
real) lending rates increase rapidly from 5.4% in the 1st quintile to 21% in the 5th quintile with the highest spreads.

11
results show that deposit rates are not higher in countries with higher intermediation spreads. In
contrast, nominal (and real) lending rates are. The wedge between nominal and real lending rates
is also higher for countries with high spreads suggesting that they suffer from weaknesses in the
macro-fiscal environment (Figure 5B, 5C, and 5D).

As expected, intermediation spreads are closely aligned with net interest margins. But the link is
weaker in countries with higher spreads, perhaps due to developmental challenges which lead
to risk mispricing and defaults (Figure 5B). The NIM is smaller in countries with higher spreads.
For example, in the 5th quintile, spreads are almost twice as large as the NIM. Given that deposit
rates are not significantly higher in the 5th quintile, this suggests that banks are not able to sustain
adequate levels of interest income, possibly due to defaults or unsuccessful restructuring
procedures.

Box 3: A Historical Perspective on High Interest Rates and Rate Spreads in Brazil

Brazil is among one of the most developed emerging economies, having achieved a sound
macroeconomic policy framework. However, its financial sector continues to face many challenges
including high interest rates and spreads. Brazil has had a long history of high and volatile inflation. To
contain inflation, Brazil implemented an inflation targeting regime in 1999 which significantly reduced
it. Nonetheless, high lending interest rates and spreads persisted which concerns Brazilian policy
makers. Notwithstanding their high levels, Brazilian interest rates have fallen from 78% on average in
1997 to 46% in 2017. Similarly, interest rate spreads have fallen from 54 percentage points to 36
percentage points in the same period.

What drives high interest rates and spreads?

The literature on the drivers of interest rates and interest rate spreads in Brazil is mixed. Typically, the
findings reflect macroeconomic and microeconomic determinants, with the importance of each varying
by study. 5 The main drivers found in that literature are:

Macroeconomic factors: Brazil’s history of high and volatile inflation and macroeconomic fundamentals
(e.g., low savings and fiscal risk) are often cited as the major contributors to high interest rates. The low
level of savings (consistently below 20%), combined with fiscal financing needs, have crowded out
private credit markets. In addition, due to the history of high inflation, there is perhaps a tendency to
overestimate medium-term inflation risk. This fosters the tendency to borrow at very high nominal
rates which have made private financial intermediation mostly short term. This has constrained
financing for longer-term capital investment and has led to high spreads.

5
Some studies such as De la Torre et al. (2006) and Afanasieff, Lhacer and Nakane (2002) find that macroeconomic
factors are the main drivers of high interest rates and interest rates spread in Brazil, while Jorgensen and Apostolou,
(2013) find that microeconomic factors are more important.

12
 Banking sector structure: The efficiency of the banking system in Brazil is relatively low due to the non-
competitive market structure. Lopes (2014) finds evidence that the structure of Brazil’s banking sector
plays an important role in keeping interest rates and interest rate spreads high. Over 72% of the
commercial banking system’s total assets belongs to the four largest banks. Due to a relative lack of
competition, banks can increase lending interest rates and spreads to make up for the decline in credit
growth.

Credit market segmentation: Segmentation also contributes to the intermediation inefficiencies. The
Brazilian development bank (BNDES) provides earmarked lending to specific sectors at below market
(subsidized) rates that are tied to the “long term interest rate” (TJLP). In Jan 2019, for household credit,
interest rates for earmarked credit were 40 percentage points lower than interest rates on non-
earmarked credit. For credit to non-financial corporations the difference was 9 percentage points. Total
earmarked lending represents approximately 50% of total credit at end-2015. After declining to one-
third of total credit in 2007, it is back to the levels in late 1990s.

During 2008-15, earmarked credit increased from 12 to close to 30% of GDP. Initially, the objective was
to counteract the retrenchment in lending by private lenders. For example, real estate lending for
households is predominantly provided through earmarked credit, and the variation in rates across
lenders is modest. These lending rates generally do not change when the central bank moves the policy
rate which means that non-subsidized credit rates need to be kept higher to achieve the same
tightening effect. As a result, nominal lending interest rates and spreads remain high.

A recent study (Pazarbasioglu et al., 2017) estimates that the fiscal cost of explicit and implicit subsidies
amounts to about 3.7% of general government revenues (1.5% of GDP) for 2015. This is mainly due to
the differential between regulated rates and market interest rates at which the government finances
its lending. Additionally, savers and employees each contribute about 0.3% of GDP to lowering interest
rates on earmarked credit. The savers and employees receive low remuneration on deposits and
contributions to FGTS, respectively.

Business environment: The business environment is cited as a factor behind high interest and interest
rate spreads as it appears to lead to higher costs that are passed on to the borrowers. High costs are
driven by implicit and explicit taxation (including under-remunerated reserve requirements and quasi
taxes associated with directed lending schemes) and high administrative costs. Administrative costs are
important especially in the smaller banks. These costs are exacerbated by the high costs of doing
banking business in Brazil such as the cost of perfecting and enforcing credit contracts. Implicit and
explicit taxation are estimated to account for 30% of interest rates spread while administrative costs
are estimated to account for approximately 25%. 6

What has been the policy response?

To reduce the cost of financial intermediation, the government implemented various policy reforms:

Reduction of informational asymmetries and improvement of business environment: The central bank
set up a database of large borrowers with the aim of reducing informational barriers to entry to the
financial system. In addition, a Bill to change the rules governing the “Cadastro Positivo” was recently
signed into law. That reform will require the mandatory contribution of negative and positive credit
events, on an opt-out basis, and will enable financial institutions to access the data on borrowers’ credit

6
De la Torre et al. (2006).

13
 scores. This will increase the scope of the credit bureaus and contribute to reducing the cost of credit
by improving financial institutions’ and other non-financial companies’ ability to determine the
creditworthiness of clients. That reform is meant to lower non-performing loans and therefore lower
interest charges.

In addition, the central bank took measures to strengthen the regulatory framework for banks.
Specifically, the country adopted reform of “duplicata eletrônica,” to improve the efficiency of
registration and trading of trade acceptances (duplicatas). The “duplicata eletrônica” reform will
eliminate paper-based documents, thereby reducing forgery and fraud and improving the legal and
transactional certainty (transparency) through the usage and registration of electronic documents.
Hence, it is expected to lead to increased efficiency and transparency and reducing transaction costs.

Reduction of credit market segmentation and long-term finance: Brazil is also reforming the lending
practices of the BNDES in order to reduce credit market segmentation. In 2017 Congress approved a
Bill replacing the Long-term Interest Rate (TJLP) with a new rate for contracts signed with BNDES, called
the Long-term Rate (TLP). The TLP, which is pegged to inflation and to the National Treasury’s cost of
funds, became effective in January 2018. 7 The aim of the newly introduced rate is to allow BNDES to
fill gaps in the market for long-term finance without imposing a cost to the government. It also mitigates
the impact on monetary transmission.

Reduction of opportunity costs and implicit taxes: In 2018 the central bank reduced reserve
requirements on savings and checking accounts. The central bank also issued a new set of rules allowing
smaller financial companies to access services provided by banks, such as automatic debit and transfers
between institutions.

B. Illustrative econometric analysis of common correlates of lending rates and

spreads

In a first exploration of a battery of commonly used variables in the empirical literature, we

calculate descriptive statistics to identify the macro-fiscal, banking, and business environment
factors which appear to be strongly associated with nominal interest rates and spreads. Table 2
shows the descriptive statistics of key drivers of spreads by tercile of the variable in column 1
(i.e., nominal interest rate, lending-deposit spread, net interest margin, and real interest rate).
The following patterns emerge: 8

7
BNDES 2017 annual report.
8
We first sorted countries by the 2003-2017 average value of the variables in column 1, and then calculated the
median value of each factor by tercile.

14
 • Macro-fiscal conditions: Countries with have higher inflation rates and volatility tend to
have higher nominal and real interest rates, and higher spreads.
• Banking sector characteristics: Countries with more efficient banking sectors (proxied by
overhead costs) and more diversified income sources (proxied by non-interest income)
have lower lending rates and spreads.
• Business environment: Countries with strong contract enforcement and rule of law tend
to have lower lending rates and spreads.

The results from econometric analysis corroborates the abovementioned associations (Box 4 and
Table 3): 9

• Macro-fiscal conditions: The inflation rate, inflation volatility, the level of public debt, and
the policy interest rate are key correlates of lending rates. However, inflation, inflation
volatility, and policy rate are not statistically associated with spreads. In addition, high
levels of public debt and government yields raise lending rates and spreads through
different channels: i) the quality of banks’ lending portfolio to the government can
deteriorate as risk of sovereign default increases (sovereign-bank nexus), ii) the cost of
funding increases as benchmark interest rates increase as sovereign risk increases, and
iii) the public sector could crowd out banks from domestic financial markets, particularly
when sovereign yields are high.
• Banking sector characteristics: Overhead costs are the most important driver of both
lending interest rates and spreads. Those banking sectors with lower efficiency ratios tend
to have higher lending rates. In addition, a more concentrated banking sector (which is
arguably less competitive), with a higher credit risk (measured by NPL-to-total gross
loans), and with less sources of income diversification tend to have larger lending interest
rates. 10

9
We would like to stress that this quantitative exercise is illustrative as many other factors are associated with
interest rates (e.g., productivity trends, demographics, money supply trends). As such, the results presented here
should be interpreted with caution: deeper country-level analysis is required given country idiosyncrasies such as
the policy environment and data issues.
10
Private credit to GDP is positively correlated to nominal interest rates, but the coefficient is not statistically
significant in general. The literature has found that the size of operations (proxied here by private credit to GDP) is

15
 • Business environment: Well-functioning insolvency regimes as well as institutions aimed
at reducing informational asymmetries (i.e., credit bureaus) are key correlates for lower
lending rates and spreads. Long periods to resolve insolvency are reflected in higher
lending rates and spreads. Finally, strong credit rights and contract enforcement are
associated with a lower cost of credit.

Regressions that account for annual global conditions produce qualitatively similar results. 11 In
the wake of the global financial crisis, the impact of global conditions on nominal lending rates in
EMDEs has increased significantly suggesting a common downward trend. We also find that
annual global conditions such as world interest rates and liquidity conditions (i.e., year-fixed
effects) produce qualitatively similar results as presented in Table 3, Panel A (see Annex 2).
Moreover, the impact of global factors on lending rates has increased significantly in magnitude
after the global financial crisis suggesting a common global driver (e.g., low or negative interest
rates in advanced economies) which has contributed to the downward trend in domestic lending
rates.

In the robustness tests in Annex 3, macro variables such as the standard deviation of inflation
(using 5-year window), and the devaluation rate of exchange rate vis-à-vis the US dollar were
included. The results showed that; (i) inflation volatility is still positive and significant for lending
rate but not for spreads (Panels A and B, equations 16 and 17); and (ii) the devaluation is not
statistically significant for either lending rate or spread (Panels A and B, equations 18 and 19).

positively associated with interest rates and spreads: the banking sector faces larger expected losses when engaged
in larger operations, for a given credit and market risk (Ho and Saunders, 1981; Maudos and Solis, 2009; Calice and
Zhou, 2018). Another hypothesis is that the coefficient is capturing the relationship between lending interest rates
and the cyclical part of lending to the private sector (as the country effects included in the regression capture
structural elements of the variable). Consequently, the positive relationship is driven by changes in demand for
credit, as excess demand for credit (i.e., increase in cyclical private credit) might drive increments in the lending
interest rates.
11
Our baseline regressions presented in the main text did not include year-fixed effects because they absorb the
variation of slow-moving variables such as for the business environment.

16
 C. Illustrative decomposition of the level and change in the nominal bank
lending rate

In the decade 2007-2017, the average 12 nominal interest rate in EMDEs fell 2.7 percentage
points, from 13.7% to 11%. We decompose the level of the nominal lending interest rate for 2017,
and the change observed of the rate during those 10 years among macro-fiscal conditions,
banking sector characteristics, and business environment (Figure 6).

The lending interest rate in 2017 (11%) can be decomposed as follows: 2.2 percentage points
explained by macro-fiscal conditions; 3.2 percentage points by banking sector characteristics, and
0.35 percentage points by the business environment. Other components are the expected mean
of lending interest rate for EMDEs (6.4 percentage points – the constant in the regression), other
country-specific unobserved conditions, and the error term.

In the decomposition analysis (Box 4), our calculations show that the main drivers are public debt
in the macro-fiscal factors, overhead costs, bank concentration, and private credit in the bank
sector factors, and long insolvency processes in the business environment factors. For example,
the average public debt is 46.8% of GDP for EMDEs in 2017, which explains 1.5 percentage points
of the interest rate. In addition, overhead costs, bank concentration, and the private credit to
GDP impact lending rates via banking characteristics, explaining 3 percentage points of the
lending rate. Finally, longer insolvency processes increase rates in 1.1 percentage points while
better coverage of credit bureaus reduces the rate in 0.75 percentage points.

The reduction of nominal interest rates between 2007 and 2017 has been led by improvements
in the business environment, followed by macroeconomic factors, while bank characteristic
factors have driven rates up. For example, increments of the credit bureau coverage (to 44.2%
of total adults in 2017 from 17% in 2007, on average) and the reduction in the number of years
to resolve insolvency processes (to 2.4 years from 3.2 years in the same period) reduced nominal
lending interest rates in 1 percentage point.

12
To ensure consistency with the regression framework which produces a conditional mean, we focus on the average
lending rate.

17
Macro factors have low impact on rates reduction due to high and rising public debt. EMDEs have
stabilized inflation rates and volatility in the period 2007-2017, driving lending rates and spreads
down. In addition, policy interest rates have reduced from 6.8 percent to 4.9 percent on average,
as result of more stable monetary and financial conditions in EMDEs. However, rising public debt
(% GDP) partially offsets the benefits of low and stable inflation, and lower policy rates. The
average public debt increased to 46.8% of GDP in 2017 from 34.5% of GDP in 2007.

Banking sector factors have deteriorated interest rates due to rising non-performing loans.
Banking sectors in EMDEs have experienced a decline in overhead costs, less concentrated
banking sectors, and more diversified sources of income which put downward pressure on rates
and spreads. However, the average NPL to total gross asset ratio increased to 6.2% from 4.3% in
the 10-year period 2007-2017.

Macro factors are important drivers for the level of the nominal lending rate in SA and AFR, bank
factors in EAP, ECA, and AFR, and business environment in AFR and MENA (Table 4). In SA and
AFR rising public debt drives lending interest rates up. Also inflation and inflation volatility have
an important impact on lending interest rates in those regions. Bank factors are led by the private
credit to GDP ratio in EAP. Overhead costs are an important driver of lending rates in LAC, ECA,
and AFR, and high non-performing loans has an important effect on rates in MENA, ECA, and AFR.
Finally, business environment factors are led by time consuming insolvency processes that
increase the lending interest rate in those regions. Between 2007 and 2017, improvements in
business environment have benefited all regions except AFR, driving down lending interest rates.
More stable macro conditions have also driven interest rates down in all regions except SA and
AFR. Bank factors have driven up rates due to rise in non-performing loans (ECA, AFR, and LAC)
and increase in bank concentration (EAP and SA).

18
 Box 4: An illustrative econometric estimation of the components in the nominal lending
interest rate

The decomposition of the nominal lending interest rate is calculated using a panel data regression
model with country fixed effects and first-order error structure to adjust for serial correlation. We use
the method of Baltagi and Wu (1999) to estimate coefficients. The Baltagi and Wu (1999) method uses
feasible generalized least squares (FGLS). We use annual data between 2005 and 2017.

Independent variables: the basic model has the following RHS variables:
• Macro-fiscal conditions: inflation rate, inflation volatility (rollover standard deviation of
inflation rate for a window of last 12 months), public debt (% GDP), savings rate (% GDP), GDP
per capita, and policy interest rate. Sources: IMF-IFS, IMF Global Debt Database, World Bank
World Development Indicators.
• Banking sector characteristics: overhead cost (% of total assets), non-performing loans (% of
total gross loans), non-interest income (% total income), bank asset concentration for three
major banks (% total banking system assets), and private credit (% GDP). Source: World Bank
Finstats 2019.
• Business environment: credit bureau coverage (% of adults), time to resolve insolvency (years).
and rule of law index. Sources: World Bank Doing Business, and Governance Indicators
(Kaufmann et al., 2010).

Decomposition of interest rate (level): Once the coefficients are estimated, we attribute each
estimated coefficient to a value of each driver. For instance, to decompose the average lending interest
rate in 2017, we calculate the average value of each driver for that year across 64 countries and multiply
them to the corresponding coefficient. Later, the results of that multiplication are added based on the
three main driving groups (i.e., macro-fiscal conditions, banking sector characteristics, and business
environment). We put together country-fixed effects and the regression estimated constant as part not
explained by the drivers. These calculations determine the components of the lending interest rate
(level).

Decomposition of interest rate (changes): We calculate the difference between the average values of
the drivers in 2017 vs. the average value of drivers in 2007. Later, we multiply that difference by the
corresponding estimated coefficient and add the results using the three driving groups. The final result
is the change in the interest rate attributed to changes in a particular driving group between 2007 and
2017.

Regional decompositions: We follow the same methodology but adjusting with country fixed effects
associated with the countries that belong to the same region.

Caveats: The decomposition is based on a panel regression using data between 2005 and 2017.
Consequently, non-linear relationships between variables, or regime structure changes are not
captured by the model. The estimations use information at country level data so, some bank-level
regressors are not included due to no existence of similar variable as a country-level information.

19
 4. Policy Implications
A. Common policy interventions

Policy makers in many EMDEs have intervened in deposit and lending markets to promote
development objectives or protect customers. While potentially useful to temporarily support
nascent markets or disadvantaged populations, these measures may carry unintended
consequences, particularly if they are not well designed to ensure and monitor additionality. This
type of policies will be less effective when borrowers and savers have alternatives such as capital
markets or overseas financial institutions which is often not the case in EMDEs. Common policy
interventions to correct or compensate for market failures include the following:

• Interest rate restrictions (see Box 5): Policy makers have imposed caps to protect
vulnerable borrowers from high lending interest rates, increase access to financial
services, and to address high market power of banks. Restrictions on deposit rates also
occur in an attempt to reduce lending rates or prevent excessive competition for deposits
during liquidity shortages. However, there are important side-effects on the composition
and maturity of bank loans and deposits (see for example Safavian and Zia, 2018 for the
case of Kenya). These include a reduction in the supply of credit to safer clients and away
from smaller and medium enterprises, a rise in non-performing loans, a fall in deposit
rates to preserve spreads, a reduction in competition and innovation, an increase in non-
interest fees, and an increase in informal lending. Promoting financial consumer literacy
and consumer protection frameworks may prove more effective policies to protect
consumers from high interest rates.
• Directed credit and interest subsidy programs: These programs are designed to provide
credit at low (or subsidized) cost to certain sectors and population segments. However,
those schemes might contribute to credit market segmentation and distortions in credit
allocation as banks tend to increase interest spreads for clients in other business lines to
compensate for the costs associated with directed credit. Further, public programs may
displace private banks altogether from certain markets. Also, monetary policy

20
 transmission may become less effective because part of the banking sector’s portfolio is
associated with direct credit which does not react to changes in monetary policy.

B. Considerations for policy makers

Policy makers should consider focusing on root causes to sustainably lowering lending rates and
intermediation spreads. Eight considerations are offered, which should be assessed holistically
to formulate a coherent policy mix. These considerations are based on World Bank operational
work, the literature, and the analysis presented in this paper.

Macro-fiscal conditions

1. Strengthen macro-fiscal fundamentals. A straightforward recommendation is for policy

makers to continue to build strong public balance sheets and sound monetary, exchange
rate, and debt management frameworks since these exert a first-order effect on
intermediation spreads and bank funding conditions. Overall, EMDEs have strengthened
these frameworks in the last few decades. However, fiscal conditions in various EMDEs
have recently deteriorated and inflation (expectations) remains vulnerable to global
demand and oil shocks (Ha et al., 2019). And for example, in AFR there has been a rise in
public debt levels and an increased reliance on domestic sources of funding.
2. Avoid crowding out the private sector. Large public sector debt levels financed through
bank lending may dissuade banks from lending to a riskier private sector and result in a
substantial increase in lending rates, particularly in countries with shallow banking
systems.

Banking sector characteristics

3. Support competition. Banking sectors in EMDEs are often dominated by large and
profitable players which reduces intermediation efficiency. While competition differs
across business segments, policy makers could consider judiciously reducing restrictions
on market entry and permitted activities; allowing competition from non-bank financial

21
 institutions including fintech and bigtech companies13; leveling the playing field in
interbank markets and moving large (quasi-) government deposits from selected
commercial banks to the central bank; deepening capital markets and strengthening
institutional investors as a viable alternative to banks’ financial services; and reducing or
eliminating switching costs such as through open banking initiatives which democratize
the use of bank customer data, and requiring effective disclosure of interest rates and
fees by bank to facilitate product comparisons. Stronger competition will not only reduce
monopoly rents, but also incentivize banks to innovate and become more efficient.
4. Facilitate operational and scale efficiencies. Banks in EMDEs are often relatively small
and may operate under legacy business models which prevent them for reaping
economies of scale and reducing operating costs which diminishes intermediation
efficiency. Indeed, high operating costs are among the most important drivers of interest
spreads and lending rates in EMDEs. Policy makers could consider encouraging bank
consolidation (with due consideration for the effects on competition and creating “too-
big-to-fail” issues), providing enabling policy frameworks for the expansion of financial
services through agent networks, and the adoption of digital financial services such as
mobile and internet banking as well as the automation of banking services and back-office
processes such as risk management and credit scoring. The adoption of financial
technologies will also reduce the need for maintaining a costly branch network and allow
a reduction in staff costs.
5. Strengthen bank regulation and supervision. A strong supervisory approach,
underpinned by prompt corrective actions, will prevent the build-up of risks in bank
balance sheets and will promote good corporate governance and risk management, as
well as compliance with supervisory standards. This increases confidence in the
soundness of the banking system which improves intermediation efficiency through
downward pressure on funding costs and risk premia.

13
For further policy considerations to reap the welfare benefits from financial technologies while mitigating the risks,
see the World Bank – IMF Bali Fintech Agenda.

22
Business environment

6. Improve insolvency and creditor rights regimes. Weak regimes contribute to contracting
and enforcement costs which boost bank operating costs and lower loan recovery rates
(LGD) and thus higher lending rates and lower intermediation efficiency. Policy makers
could consider strengthening legal and regulatory frameworks that inhibit viable
businesses from corporate restructuring; improving commercial insolvency regimes to
facilitate effective firm exit and collateral recovery; and promoting the efficiency and
independence of the judiciary and insolvency practitioners.
7. Improve information and collateral registries frameworks. Strong frameworks lower
agency and transaction costs and improve intermediation efficiency. Policy makers could
consider strengthening the legal and regulatory underpinnings of these frameworks;
updating local accounting principles and practices; promoting coverage and the quality of
information held in credit and collateral registries; and support the exchange of
information between eligible market players, such as through open Application
Programming Interfaces (APIs) 14 which may also strengthen competition.
8. Revisit direct policy interventions. While these may prove useful to compensate for
market failures and achieve fiscal or socio-economic objectives in the short term, they
may carry unintended consequences and reduce intermediation, particularly if they are
not well designed to ensure additionality. Policy makers could consider reviewing the
impacts of (unremunerated) reserve requirements frameworks, interest rates
restrictions, and directed credit and subsidy programs and carefully balance these
impacts against stated objectives to justify the merit of their distortionary effects. In doing
so, it is important not to compromise monetary and stability objectives. In this context,
the literature has shown that financial liberalization should be pursued with care and
institutional pre-conditions should be met first (e.g. Demirgüç-Kunt and Detragiache,
1998).

14
Subject to strong covenants for consumer data safeguards and cybersecurity.

23
 Box 5: Interest Rate Caps Around the World 15
Interest rate caps are a policy instrument that many EMDEs as well as developed economies use to
protect consumers from usury or to make credit more accessible. At least 76 countries recently imposed
restrictions on lending rates. Among them, there are 25 lower-middle income countries (50% of total),
suggesting a more prevalent trend of imposing ceilings on lending rates in this income group.

The use of interest rate caps around the world

Sources: EIU Global Microscope for Financial Inclusion, ADB, IMF, World Bank, National Authorities. From Ferrari et al. (2018).

Restrictions on interest rates vary substantially across countries, vis-à-vis coverage and how they work.
A taxonomy of recent trends in the use of interest rate caps classifies interest rate caps based on the
following features:

• Scope: Caps include certain types of credit instrument (e.g. payday loans, credit cards,
mortgages), cover loans by different institutions (e.g. MFIs or credit unions) or cover all types
of credit operations in the economy. For example, Canada and Australia have limits on payday
loans.
• Number of ceilings: A single blanket cap for all transactions or multiple caps have been used in
countries. South Africa has implemented ceilings for mortgages, credit facilities, unsecured
credit transactions, development credit, short‐ term transactions, other credit and incidental
credit agreements and El Salvador has established interest rate caps across all financial
institutions.
• Type: Interest rate caps are usually defined as a fixed, absolute cap or as a relative cap that
depending on the level of a benchmark interest rate. Of the 76 countries, 26 rely on absolute
caps (of which 70% are lower middle-income countries); 30 uses relative caps (predominantly

15
This box is largely based on Maimbo and Gallegos (2014) and Ferrari et al. (2018).

24
 developed countries) and 20 countries uses some form of weighted market interest rate to
determine the level of the benchmark.
• Binding: Caps can be defined as binding (below market rates) or non-binding (above market
rates).

Effectiveness and unintended consequences of interest rate caps

Effectiveness -- The effectiveness of interest rate caps in improving access to credit has not been
substantiated. The main challenge is to identify the causal effects from interest rate caps to access to
credit due to the multiplicity of types of caps used across countries. While some forms of interest rate
caps can indeed reduce the cost of borrowing for consumers and help protect borrowers from
predatory lending, caps on interest rate often have substantial side-effects. In fact, evidence regarding
the effects of interest rate caps points to significant negative effects. International experience shows
that caps have produced undesirable outcomes such as: reduction in credit supply; higher non-interest
fees and commissions and reduced transparency in the cost structure of bank lending origination;
adverse compositional changes in loan and deposit maturity; and reduce the effectiveness of money
supply (for Kenya, see Safavian and Zia, 2018).

Credit supply -- The extent of the decline in credit supply depends on the scope of the restrictions on
interest rates. While narrow caps usually target a specific market segment, broad or blanket caps affect
the overall market and further change the credit distribution from small borrowers (e.g., SMEs and
riskier sector) to less risky borrowers or the government. As lending institutions reallocate their
portfolio to larger loan sizes, borrowers may be forced to increase borrowing amounts to maintain
access to external finance, thereby increasing the risk of over-indebtedness.

Adverse selection -- Interest rate caps can distort the market and exacerbate adverse selection
problems. For instance, due to high origination costs and/or high perceived risk, financial entities can
reduce their lending supply to those who need it most and have little access to alternative sources of
credit. In extreme cases where ceilings are set at unprofitable levels, banks and microfinance
institutions may withdraw from certain locales such as rural areas or from expensive market segments
because they cannot cover their costs.

Transparency -- Cost structure transparency of lending origination is also affected by interest rate caps.
In countries where interest rate caps do not cover fees and commissions, financial institutions may
charge fees and commissions that are not considered part of the cost of the loan. This reduces price
transparency and makes it more complicated for borrowers to assess the overall cost of loans. It is also
argued that the reverse is true, that is, when caps are not set too low, interest rates will still tend to
rise toward the caps.

Monetary policy -- Interest rate caps may also reduce the effectiveness of monetary policy transmission.
For example, if ceilings are linked to the policy rate and the central bank were to lower policy rates to
stimulate credit growth, the accompanied decline in the lending rate ceiling would counter the
intended effect on credit growth and economic activity.

25
 Box 6: Regional Perspectives

Sub-Saharan Africa 16

Major Trends

Overall, median lending rates and lending-deposit spreads declined in the Sub-Saharan Africa (AFR)
region while both indicators remained above the global medians for developing countries. Median
nominal lending rate in the AFR region stood at 11.6% in 2017, down from 18.6% in 2003, a much larger
decline than in median country inflation (0.9 percentage points). The decline in median country lending-
deposit spread (-2.7 percentage points) was the highest among the World Bank regions, but
nevertheless it remains above the global median of developing countries. In 2017, the top-3 countries
in the region with the highest lending rates were: Madagascar (60%), Malawi (39%) and The Gambia
(29%). In terms of lending-deposit spreads, Madagascar, the Democratic Republic of Congo, and São-
Tomé and Príncipe have the highest spreads.

Major Challenges

The challenges facing the region are multidimensional ranging from a more challenging macro-financial
environment, acute banking system distress in some countries, and structural obstacles. There has been
a rapid build-up of public debt levels across a wide range of countries in the region since the Global
Financial Crisis and the commodity prices shocks, with an increased and material reliance on domestic
(primarily banking) sources of funding putting upward pressure on interest rate and crowding out
private sector lending. Even though the debt to GDP ratio of the median country at 35.2% of GDP in
2016 is still some 9½ percentage points of GDP lower than in 2003, it is substantially above post-HIPC
levels. Some countries are experiencing wide banking system distress with the government actively
engaged in resolution. The very challenging operating environment for many banking systems in the
region (e.g., relatively small scale, costly contract enforcement, low recovery rates, deficient
telecommunications and power infrastructure, costly security provision) is reflected in the very high
cost-to-income ratio (92.5% in 2017).

Policy Overview

The region has seen a variety of responses to the high interest rate challenge from building and
strengthening credit contracting infrastructure, to government/central bank provision of credit and
guarantees to specific sectors, and to the use of interest rate caps. Credit reporting systems have
started operations recently in various countries; collateral registries (including for moveable assets) are
being put in place. However, the effects of these initiatives will take time to manifest in lower rates as
borrower coverage and lender reporting is still limited, better infrastructure is required to support
efficient consultation of registries (e.g. digital access) and the quality of information (e.g. inclusion of
positive information) slowly improves. Government interventions aimed at lowering borrowing cost
have included the provision of public credit guarantees, subsidized credit to specific sectors which in
some cases is having a seriously distortive effect with quite limited transparency in operations.

16
This regional section is based on substantive inputs provided by Mariano Cortes (FCI-AFR), and Philip Schuler (MTI-
AFR).

26
 East Asia and Pacific 17

Major Trends

Median nominal lending rates and lending deposit spreads declined -- particularly during 2010-17 -- in
virtually all countries and are below the global median. Large countries such as China, the Philippines,
Thailand and Malaysia have single-digit average lending rates. The median nominal lending rate in the
East Asia and Pacific (EAP) region was about 8.1 in 2017 after a decline of 2.2 percentage points within
the past 15 years. Even though this median rate is below the global median of EMDEs, important
disparities exist between Lower-Middle Income Countries (LMIC) and Upper-Middle Income Countries
(UMIC). In comparison with the global median, most countries with high nominal lending rates are LMIC
(excluding Vanuatu and the Philippines). For instance, the Lao People’s Democratic Republic (20%),
Mongolia (20%), and Indonesia (11.1%) had the highest nominal lending rates in 2017. Similarly, the
lending-deposit interest rate spread in EAP has been narrowing down over since 2000. Indeed, many
countries had average spreads of 5% or less during the 2010-2017 period, including China, the Republic
of Korea, Japan, Myanmar, Thailand and Vietnam. The median lending deposit spreads have been
higher in LMIC (6.1%) than in UMIC (3%) in 2017.

Major Challenges

High lending rates and spreads are not a major issue across EAP. Most of the countries with higher
spreads are low income countries, many of which have poor financial sector data. Nominal lending rates
remain very high in: (i) Laos PDR potentially due high external debt (113.2% of GDP in 2017); and (ii)
Mongolia arguably because the country faces macroeconomic challenges including high dollarization
and external public debt (71% of GDP in 2017). In addition to those macroeconomic factors, poor
competition in the banking sector as well as weak credit infrastructure and institutional framework are
contributing to high lending rates in some countries. In Indonesia, nominal lending rates have come
down but remain high, with dispersion across credit markets (lowest for investment lending, higher for
consumption, micro credit, and working capital). In general, foreign banks have lower spreads than
state-owned banks.

Policy Overview

Some countries have attempted to overcome those challenges by capping interest rates, but results
were mixed. Indonesia introduced deposit rate caps in 2013 to overcome excessive competition in
deposit markets due to aggregate liquidity shortages. Contributing factors of high lending rates in
Indonesia include liquidity tightening, lack of economies of scale, high risk premiums, operational
inefficiencies, high market power of large banks, and weaknesses in the institutional environment.

In addition to interest rate caps, some countries introduced portfolio targets to direct credit to micro,
small, and medium enterprises (MSMEs) and launched guarantee and interest subsidy programs. To
foster credit to MSMEs, the government extended its credit guarantee and interest subsidy programs
to MSME and micro lending.

17
This regional section is based on substantive inputs provided by Ana Maria Aviles (FCI-EAP), and Richard Record
(MTI-EAP).

27
 Europe and Central Asia 18

Major Trends

In Europe and Central Asia (ECA), there are several countries with persistently high lending rates and
spreads. In particular, countries in Central Asia (Tajikistan, Kyrgyz Republic), South Caucasus (Armenia,
Azerbaijan, Georgia), Eastern Europe (Ukraine, Moldova, Belarus), Turkey and the Russian Federation
all show lending rates at or above 10%. In general, these countries also show high interest rate spreads
(in excess of 4%), with the notable exception of Georgia and Belarus. Conversely, despite having
relatively low interest rates (5-6 percent), some countries have high interest rate spreads (4-5 percent),
such as Albania, Bulgaria, Macedonia and Romania, indicating very low deposit rates.

High lending rates and interest rate spreads are generally observed across all lending markets. Riskier
segments, such as unsecured consumer and SME lending and microfinance show higher lending rates
than mortgages and corporate lending. Also, lending rates in local currency tend to be higher than in
foreign currency. Foreign-owned banks, particularly those belonging to Western European banking
groups, can offer lower lending rates as they have access to funding at a relatively lower cost. In some
countries, domestic-owned banks offer higher deposit rates to increase their market share.

Overall, nominal lending rates declined over the past 15 years in several ECA countries but there is a
dichotomy in lending rate levels in the Commonwealth of Independent States (CIS) and non-CIS
countries, while median spreads are similar in both groups. Median nominal lending rate in the ECA
region declined from 15.9% in 2003-2007 to 12.3% in 2013-2017, but it remained high in comparison
with the global median of developing countries. Median nominal lending rates are higher in CIS
countries (14.4% in 2017) than in non-CIS countries (5.6% in 2017). While median lending-deposit
spreads are similar in CIS and non-CIS countries, there are ECA countries that, despite having relatively
low interest rates, present high interest rate spreads, such as Albania, Bulgaria, Macedonia and
Romania, all of them with lending rates of 5-6 percent and spreads of 4-5 percent, indicating very low
deposit rates and a higher premium for commercial bank.

Major Challenges

High nominal lending rates in ECA are fueled by inflation and dollarization as the former generates
macroeconomic instability, and the latter worsens the inefficiency of the banking sector structure. High
nominal lending rates are driven by inflationary pressures in various countries. Several countries with
high lending rates have experienced significant currency devaluations in the last decade resulting in a
lower appetite for deposits in local currency, and an increase in dollarization. There is a general lack of
long-term funding which can contribute to high lending rates. Low savings rates and high inflation
contribute to the relatively high cost of funding. Many ECA countries have significant shortcomings in
the business environment including credit information systems, insolvency frameworks, secured
transactions framework, and payment systems. Furthermore, there is a lack of competition in several
ECA countries, and some have scale efficiency issues. Moreover, credit risk is particularly high in CIS
countries while risk aversion is prompting banks to hold more (government) securities in some
countries.

18
This regional section is based on substantive inputs provided by Eva Gutierrez, Raquel Letelier, and Alena
Kantarovich (FCI-ECA), and David Knight (MTI-ECA).

28
 Policy Overview

Some ECA countries attempted to reduce nominal lending rates by capping interest rates, and
subsidizing interest rates but they had mixed results, and structural reforms were implemented by
countries with low interest rates. Turkey introduced interest cap in March 2019 on deposit rates for
state-owned banks. In some countries, subsidized interest rates or special loan programs have been
developed to reduce nominal lending rates in specific sectors. While directed lending practices and
other type of subsidies are likely to result in lower interest rates charged to certain segments of the
market, it appears that these practices tend to be unsustainable.

Latin America and Caribbean 19

Major Trends

Both nominal lending rates and lending-deposit spreads are particularly high in Latin America and the
Caribbean (LAC) countries although both declined during the past 15 years. In the LAC region, median
nominal lending rate stood at 13% in 2013-2017 after being at 15.6% in 2003-2007. In comparison with
a global median lending rates of 8.7% during the period 2013-2017, lending rates remained particularly
high in Brazil (46.9%), Argentina (31.2%) and Uruguay (13.8%). Lending rates are lower for loans
extended in foreign currency. Despite high deposit rates (22% in Argentina, 14% in Uruguay, 9% in
Brazil, 3% in Mexico in 2017), spreads are among the highest in the world (e.g., Brazil with 38% in 2017).
There is no clear trend of a decline in median spreads, although they declined in Uruguay, Mexico and
Peru, remained stable in Brazil, and increased in Argentina and Colombia in 2013-2017.

Major Challenges

High and volatile inflation rates stemming from macroeconomic imbalances and policy uncertainties
were important drivers of high interest rates in LAC, but banking sector issues need to be addressed.
Historically, a main reason for high interest rates in LAC were high and volatile inflation rates stemming
from macroeconomic mismanagement and policy uncertainties. High nominal lending rates were
necessary to keep real rates in positive territory. The substantial decline in inflation and improvements
in macroeconomic management in most LAC countries (except the recent inflation spike in Argentina)
over the last 15 years have thus been important reasons behind the fall in lending rates. However,
inflation is not the only factor that explain LAC’s high lending rates. For instance, in 2017 real interest
rates stood at 43% in Brazil, 9.6% in Colombia and 7.6% in Uruguay. One key issue of the financial sector
in LAC is the concentration of banking assets which may reflect lower competition. The median market
share of the top 3 banks in LAC was 65.9% in 2017 and it reaches 79.6% in Peru. Competition is often
affected by the presence of large financial conglomerates that operate across countries. Another source
of inefficiency in LAC countries stems from the high presence of state-owned banks.

Policy Overview

There are institutional shortcomings regarding the strength of legal rights and depth of credit
information in LAC. In some countries, the public sector intervenes heavily in the financial sector, both
through direct lending via state-owned banks or subsidized lending by development banks, lending

19
This regional section is based on substantive inputs provided by Oliver Masetti and Federico Diaz Kalan (FCI-LAC),
and Joost Draaisma (MTI-LAC).

29
 quotas, and interest rates caps. Special rules may apply for lending for agriculture and housing projects
and applications. Those policies may have a limited impact on nominal lending rates and lending-
deposit spreads because: (i) banks may charge higher lending rates for projects that do not benefit from
directed credit schemes, (ii) bank competition is low and overhead cost is relatively high, and (iii)
sovereign risks is significant.

Middle East and North Africa 20

Major Trends

Nominal lending interest rates have been generally low and falling in the region. The median lending
rate declined to 8.1% in 2017 from 10.3% in 2003 but intra-regional heterogeneity has been observed,
with high interest rates in the Arab Republic of Egypt, Lebanon, and Tunisia.

In addition, Middle East and North Africa (MENA) overall has relatively low lending-deposits spreads.
While the spread for MENA countries was 5.7% in 2017, on average, it was 7.4% in the rest of the world
in the same period. However, spreads in the region have been steadily increasing since 2012, and in
countries like Djibouti, the spread reached 10% in 2017.

Major Challenges

Macro-fiscal conditions associated with high public financing needs and inflationary pressures are
challenges to the reduction of lending rates in some countries. Countries like Lebanon, Egypt, Tunisia
are maintaining high interest rates to stabilize their currencies, contain inflation or to finance the public
sector, which is crowding out the private sector from the credit market. As a result, banks’ net claims
on government is rising to 66% of domestic credit as of November 2018.

A lack of competition impedes innovation and financial inclusion, although its impact on lending rates
is unclear given the general lack of access to financial services. The median bank asset concentration of
the top three banks in MENA stood at 74.8% of total banking assets in 2017, while the global median
of developing countries stood at 60.5% in 2017. Moreover, some countries (e.g., Gulf Cooperation
Council -GCC- countries, Iraq, Tunisia, Egypt) have large state ownership (direct and indirect) in the
banking sector, which may be associated with an unlevel playing field and limited competition due to
significant advantages enjoyed by the state-owned banks (SOBs), including implicit guarantees, lower
cost of funding, and privileges in providing services to the public sector. Moreover, regulatory
frameworks are not always conducive to the development of non-bank financial service providers and
the entrance of foreign institutions.

Policy Overview

Credit infrastructures are being developed in various MENA countries, but more efforts are required
for the region to catch up with best practices from an overall low starting point. Improvement was
observed across the region in recent years. For example, Egypt passed the Secured Transactions Law in
November 2015, launched an electronic secured transactions registry and passed the Law of

20
This regional section is based on substantive inputs provided by Syed Mehdi Hassan, Djibrilla Issa, and Haocong
Ren (FCI-MENA), and Emmanuel Pinto Moreira (MTI-MENA).

30
 Restructuring, Preventive Reconciliation and Bankruptcy in 2018. Jordan established a credit bureau in
2015 and passed the insolvency and secured transaction laws in 2018. West Bank and Gaza passed the
Secured Transactions Law and set up a new collateral registry in 2017. Djibouti created a credit
information system by law in 2016. Other countries (e.g. above-mentioned and Tunisia) expanded the
scope of information collected and reported by their credit bureaus, and credit bureaus in Morocco,
Qatar and United Arab Emirates (UAE) recently started to provide credit scores.

Credit guarantee and subsidized programs are common but issues regarding designing, targeting, and
displacement of private credit providers distort their effects. The use of interest rate caps is less
prevalent. Credit guarantee schemes have been recently used by several MENA countries, but those
programs suffer from cumbersome process, poor targeting, and design issues related to eligibility
criteria, pricing mechanism, and sustainability, resulting in limited impacts of the programs. In addition,
subsidized programs are common in the region, run by large state-owned banks, and especially focused
on MSMEs and housing, which create market distortions and crowd out private creditors willing to
finance at market-based rates. Finally, some countries have interest rate restrictions, while others have
interest caps on loans benefiting from government incentives or subsidies, with potential distortions in
credit markets (Ferrari et al.,2018).

South Asia 21

Major Trends

In comparison with other regions, median lending rates and lending-deposit spreads marginally
declined in the South Asia Region (SAR) but both rates are above the global median of developing
countries. Median nominal lending rate in SAR declined to about 9.8% in 2017 from 12% in 2003, and
it remained above the global median of developing countries. The decline in median lending-deposit
spreads was the lowest among regions (-1%) during the reference period but median lending-deposit
spreads in SAR remains above the global median of developing countries. In 2017, countries with the
highest lending rates are as follows: Bhutan (14.2%), Sri Lanka (11.5%) and Maldives (10.1%). In terms
of lending-deposit spreads, Bhutan, Maldives, and Bangladesh have the highest spreads in descending
order.

Major Challenges

Challenges to interest rate levels in SAR differ depending on the countries’ macro-financial health and
the appetite of their public sectors for national savings. Some countries continue to have large stocks
of non-performing loans (NPLs) which by necessity add to higher lending rates for the less risky
customers. Extensive informality of many SAR economies keeps the pool of eligible borrowers much
below potential, while small capital markets concentrate most of the real sector financing risks in the
banking sector which is dependent on retail deposits – all contributing to elevated lending rates and
spreads.

Policy Overview

This regional section is based on substantive inputs provided by Marius Vismantas (FCI-SAR), and Muhammad
21

Waheed (MTI-SAR).

31
SAR is considered to be the region with the highest real (inflation-adjusted) base interest rates in the
world. Based on market sources, Sri Lanka, Pakistan, and India are among the top-5 countries in this
respect, with the benchmark policy rates adjusted for inflation all exceeding 4%. Sri Lanka’s rate is 6.2%,
the highest in the world. While monetary policy transmission mechanisms in SAR are still below policy
makers’ expectations, the high real policy rates exert significant influence on the market rates, keeping
them high and above global medians. With banks playing central roles in financial intermediation and
depending strongly on shorter-term fixed rate deposits for their funding, while extending largely
floating-rate loans, the spreads remain high to cover the interest rate risks.

32
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Gelos, M.G. (2006). “Banking Spreads in Latin America”. IMF Working paper 06/44. International
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35
 Figure 2: Interest rates and spreads trends in EMDEs
Median nominal and real lending rates have declined across all regions. However significant heterogeneity
within and between regions persists. The median interest rate spread however barely fell, but has declined
significantly in various regions.
A. Nominal lending interest rate B. Nominal lending rate by region
2003-2017
%
22
%
23
20 21
18 19
17
16
15
14 13
12 11
10 9
8 7
5
6

03-07
08-12
13-17
03-07
08-12
13-17
03-07
08-12
13-17
03-07
08-12
13-17
03-07
08-12
13-17
03-07
08-12
13-17
4
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
EAP ECA LAC MENA SA AFR
75th and 25th percentiles in dotted lines. Median value in black line. 75th and 25th percentiles box top and bottom.
C. Real lending interest rate D. Real lending rate by region
2003-2017
% %
14
15
12
10
10
8
6
5 4
2
0 0
-2
03-07
08-12
13-17
03-07
08-12
13-17
03-07
08-12
13-17
03-07
08-12
13-17
03-07
08-12
13-17
03-07
08-12
13-17
-5
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017

EAP ECA LAC MENA SA AFR

Note: real interest rate=nominal interest rate-inflation rate Median value in black line. 75th and 25th percentiles box top and bottom.
75th and 25th percentiles in dotted lines.
E. Lending-deposits spread F. Lending-deposit spread by region
2003-2017
% %
15 14

13 12
10
11
8
9
6
7 4
5 2
03-07
08-12
13-17
03-07
08-12
13-17
03-07
08-12
13-17
03-07
08-12
13-17
03-07
08-12
13-17
03-07
08-12
13-17

3
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017

EAP ECA LAC MENA SA AFR

75th and 25th percentiles in dotted lines. Median value in black line. 75th and 25th percentiles box top and bottom.
Source: World Bank Finstats 2019

36
 Figure 3: Country-level interest rates and spreads in EMDEs: 2017 vs 2007
Nominal interest rates and interest spreads have declined in most countries in the decade 2007-2017.
A. Nominal lending interest rate, 2007 vs. 2017
50
2017 (%)

BRA
45

40
MWI

35

ARG
30 TJK GMB
MOZ

25
VEN MNG
HND UGA COD
20 EGY SLE
UKR KGZ STP
NGA
FSM PRY
SUR RWA AZE
URY AGO MRT
15 KEN BTN
JAM
ARM
GTM GUY COL
SLB CRI ZMB HTI
BLR COM IDNIND GEO
10 CPV MDA
MEX BOL BLZ LKA MUS
ABW ALB BWA
PHL FJI BGR ROU
5
5 10 15 20 25 30 35 40 45 50
2007 (%)

B. Lending-deposit interest rate spread, 2007 vs. 2017

20
2017 (%)

18

16 FSM
STP

14 HND SLE
RWA GMB
JAM UGA TLS
12 GUY
MOZ BTN MRT
LSO
DJI
10 ARG SWZ NIC PNG AGO
BLZ URY COM
CRI
8 BRB NGA AZE
GTM DOM
UKR EGY MNG HTI
CPV
MMR TZA KEN
6 PAN MEX DZA
ALB
MDA TON BGR ARM
ROU BWA BOL MUS
THA BGD
4 BIH PHL PAK SUR ZMB
FJI QAT IDN
CZE
BLR VUT LKA
2 CHN ZAF
CHL VNM
MYS
GEO
HUN
LBN
0
0 2 4 6 8 10 12 14 16 18 20
2007 (%)
Source: World Bank Finstats 2019

37
 Figure 4: Economic development and rates, spreads, and margins
Countries with higher nominal and real lending rates, spreads, and net interest margins tend to be
significantly less economically developed as measured by GDP per capita.

GDP per capita by tercile of nominal lending interest rate, real lending interest rate, lending-deposit spread, or net
interest margin (US Dollars)

7000
GDP per capita (US Dollars)

6000

5000

4000

3000

2000

1000

0
T1 T2 T3 T1 T2 T3 T1 T2 T3 T1 T2 T3
Nominal lending interest Real lending interest rate Lending-deposit spread Net Interest Margin
rate

Note: Countries sorted from lowest to highest 2003-2017 average x-axis indicator. T1 and T3are terciles with the lowest and highest values, respectively. Median GDP
per capita by tercile is shown.
Source: World Bank-World Development Indicators, World Bank Finstats 2019.

38
 Figure 5: Relationship between bank interest rates and spreads
High nominal and real lending rates are driven by intermediation inefficiencies, not high deposit rates.
Intermediation spreads are closely aligned with net interest margins, but the link is weaker in countries
with higher spreads, perhaps due to developmental challenges.
A. Median interest rates and spread in EMDEs (%) B. Interest spread vs deposit rate (2003-17 averages)

20 40

Lending - deposit spread

35
15 30
10 25
20
5 15
0 10
5
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017

0
Deposit rate (median)
Lending rate (median)
0 5 10 15 20
Lending deposit spread (median)
Deposit rate

C. Interest spread vs lending rate (2003-17 averages) D. Median interest rates by quintile of the intermediation
spread (2003-17 average)
25
40
Lending - deposit spread

35 20
30 15
25
10
20
15 5
10 0
5 Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5
Lowest spread Highest spread
0
0 10 20 30 40 50 Deposit rate Nom. lending rate Real lending rate
Lending rate

E. Median intermediation spread and net interest margin by

quintile of the intermediation spread (2003-17 average)
14
12
10
8
6
4
2
0
Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5
Lowest spread Highest spread
Lending - deposit spread Net interest margin

Source: World Bank Finstats 2019.

39
Table 1: Top five of countries with high nominal lending rates and lending-deposit spreads in
2017 by region

Countries with Highest Nominal Lending Countries with Highest Lending Deposit
Regions
Rate (%) Spreads (%)

Mongolia (20.0) Micronesia, Fed. Sts. (15.6)

Micronesia, Fed. Sts. (16.1) Timor-Leste (12.5)
East Asia & Pacific Timor-Leste (13.2) Solomon Islands (10.3)
Myanmar (13) Papua New Guinea (7.87)
Indonesia (11.0) Mongolia (7.05)
Tajikistan (29.6) Tajikistan (26.0)
Kyrgyz Republic (19.8) Kyrgyz Republic (17.0)
Europe & Central Asia Azerbaijan (16.5) Azerbaijan (8.11)
Ukraine (16.3) Ukraine (7.25)
Armenia (14.4) Albania (5.82)
Brazil (46.9) Brazil (38.4)
Argentina (31.2) Paraguay (14.1)
Latin America & Caribbean Venezuela, RB (21.0) Guyana (11.9)
Honduras (19.2) Jamaica (11.1)
Paraguay (18.0) Honduras (11.0)
Egypt, Arab Rep. (18.1) Algeria (6.25)
Lebanon (8.09) Egypt, Arab Rep. (6.08)
Middle East & North Africa Algeria (8) Qatar (1.79)
Qatar (4.7) Lebanon (1.18)
-
Bhutan (14.2) Bhutan (11.4)
Sri Lanka (11.5) Maldives (6.57)
South Asia Maldives (10.1) Bangladesh (3.93)
Bangladesh (9.54) Pakistan (3.73)
India (9.50) Sri Lanka (2.56)
Madagascar (60) Madagascar (45)
Malawi (38.5) Congo, Dem. Rep. (16.4)
Sub-Saharan Africa Gambia, The (29) São Tomé and Príncipe (15.4)
Mozambique (27.8) Sierra Leone (13.8)
Uganda (21.2) Gambia, The (13.2)
Source: World Bank Finstats 2019.

40
 Table 2: Descriptive statistics of selected drivers on nominal and real lending interest rates, spreads, and net interest margin
Median values by tercile

Macro-fiscal Bank sector Business

Lending Rule of
Nominal NIM Non-
Tercile

-deposit Overhead 3 bank NPLs Credit law

Rate (%Earning St. Dev. Public GDP per Policy interest Priv.
spread Inflation costs concentrtion (%Total Bureau [-2.5
(%) Assets)
(%)
inflation Debt capita rate Gross income credit
(%)
(%) (% GDP) (US dollar) (%)
(% Total (% Bank
(%GDP)
Coverage (weak) to
Loans) (% Total
Assets) system assets) (% Adults) 2.5
Income)
(strong)]*

Nominal T1 7.1 4.2 3.7 2.3 1.2 44.0 5,792.0 3.2 3.4 69.8 7.5 18.2 49.7 0.0 -0.147
lending
T2 12.2 6.9 5.1 6.6 1.6 42.8 3,132.8 6.0 3.6 73.0 5.7 16.1 31.4 1.8 -0.521
interest
rate 19.2 10.5 7.1 8.5 2.1 42.2 1,676.0 9.6 5.5 68.7 6.1 16.6 15.1 3.1 -0.632
T3

Lending- T1 7.5 3.7 3.5 3.6 1.4 39.4 5,056.2 4.9 3.0 66.7 4.7 17.9 43.0 2.0 -0.453
deposit T2 12.1 6.9 5.4 5.2 1.5 43.2 3,625.9 6 3.9 62.4 8.8 14.6 32.3 0.8 -0.324
spread 19.5 12.0 6.4 6.6 2.0 44.3 1,959.2 7.4 5.1 78.3 6.9 18.0 15.0 0.0 -0.801
T3

Net T1 8.9 4.8 3.0 3.8 1.4 49.9 4,678.2 3.6 2.3 72.6 5.5 17.2 46.4 0.0 -0.436
Interest T2 11.4 6.5 5.0 5.0 1.4 39.7 3,862.2 5.5 4.2 62.4 6.2 18.3 31.4 3.3 -0.427
Margin 17.6 9.1 7.9 7.6 2.2 42.2 1,624.8 7.2 5.8 78.3 7.2 15.8 19.3 2.6 -0.635
T3

Real T1 7.0 3.7 4.1 4.2 1.6 42.2 3,285.5 4.5 3.8 66.3 5.5 20.3 26.3 0.7 -0.468
lending
T2 10.7 6.7 5.6 5.3 1.5 42.2 3,548.8 4.9 4.1 64.9 7.6 14.4 39.1 1.8 -0.262
interest
rate 19.2 10.7 6.6 6.6 2.0 48.1 2,886.1 6.3 4.7 75.8 6.4 15.8 21.3 2.2 -0.500
T3

Note: Terciles calculated from country average values 2003-2017 of the variable (i.e., nominal interest rate, spread, NIM, or real interest rate), and sorted from the lowest to the highest levels. T1 shows the lowest tercile,
and T3 the highest tercile. Data for drivers is median values by tercile.
*The rule of law indicator captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the
courts, as well as the likelihood of crime and violence. For more details, see Kaufmann et al. (2010)
Source: IMF-IFS, World Bank Finstats 2019, World Bank Governance Indicators, World Bank Doing Business

41
 Table 3: Main drivers of nominal lending interest rates and lending-deposit spread
Econometric Results
Panel A. Dependent variable: Nominal lending interest rate
Nominal lending interest rate
VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

Inflation (%) 0.077*** 0.074*** 0.077*** 0.081*** 0.114*** 0.111*** 0.110*** 0.140*** 0.148*** 0.142*** 0.115*** 0.116*** 0.142*** 0.066* 0.070**
(4.635) (4.231) (4.259) (4.448) (5.215) (4.937) (4.569) (5.830) (5.701) (5.456) (4.347) (4.385) (5.426) (1.800) (2.007)
St. Deviation Inflation (%) 0.050** 0.047** 0.299*** 0.377*** 0.392*** 0.403*** 0.217*** 0.213*** 0.219*** 0.201*** 0.208*** 0.216*** 0.123** 0.093
Macro-fiscal conditions

(2.131) (1.994) (6.827) (7.249) (7.504) (7.417) (4.404) (4.057) (4.153) (3.583) (3.720) (4.097) (1.993) (1.599)
Public Debt (%GDP) 0.014** 0.014** 0.017** 0.019** 0.043*** 0.013 0.040*** 0.040*** 0.040** 0.043*** 0.040*** 0.032* 0.037**
(2.157) (2.138) (2.219) (2.449) (3.738) (1.024) (2.967) (2.930) (2.520) (2.684) (2.943) (1.756) (2.325)
GDP percapita 0.000 -0.001** -0.001** -0.001** -0.001*** -0.000 -0.000 -0.000 0.000 -0.000 -0.000 -0.000**
(0.451) (-2.339) (-2.513) (-2.570) (-3.751) (-1.588) (-1.526) (-0.143) (0.162) (-1.396) (-0.331) (-2.051)
Total Savings (%GDP) 0.019 0.022 0.044* 0.016 0.032 0.036 0.031 0.032 0.036
(1.161) (1.326) (1.852) (0.729) (1.384) (1.529) (1.237) (1.271) (1.534)
Policy/money market rate (%) 0.191*** 0.205***
(4.335) (4.859)

Overhead costs (%Total Assets) 0.093 0.189** 0.214*** 0.431*** 0.432*** 0.439*** 0.416*** 0.425*** 0.173 0.244**
(1.151) (1.998) (2.698) (4.771) (4.727) (4.428) (4.174) (4.628) (1.436) (2.126)
Three bank concentration (% Total
Banking sector characteristics

0.001 0.016 0.029** 0.032** 0.024 0.018 0.031** 0.012 0.026*

bank system assets)
(0.079) (1.240) (2.163) (2.323) (1.602) (1.232) (2.267) (0.744) (1.659)
Non-performing loans (% Total
0.095** 0.087** 0.083** 0.121*** 0.120*** 0.084** 0.083 0.051
Gross Loans)
(2.584) (2.296) (2.183) (2.626) (2.595) (2.209) (1.618) (1.135)
Non-interest income (% Total
-0.033*** -0.034*** -0.033*** -0.033*** -0.033*** -0.024* -0.023*
Income)
(-3.435) (-3.433) (-2.981) (-2.961) (-3.382) (-1.883) (-1.901)

Private credit (% GDP) 0.005 0.029 0.029 0.007 0.034* 0.014

(0.295) (1.460) (1.484) (0.419) (1.706) (0.764)

Credit Bureau Coverage (% adults) -0.011 -0.011* -0.015*
Business environment

(-1.380) (-1.692) (-1.809)

Time to resolve insolvency (years) 0.443** 0.473***

(2.405) (2.623)

Rule of Law (Kaufmann et al.,2010) -0.643 -1.327

(-0.737) (-1.272)
Constant 12.403*** 12.330*** 11.623*** 11.073*** 14.001*** 13.783*** 11.796*** 12.171*** 7.432*** 7.124*** 5.410*** 3.989*** 6.771*** 6.438*** 8.995***
(178.574) (164.356) (87.859) (53.230) (24.620) (22.630) (16.838) (15.997) (9.123) (8.376) (5.914) (4.200) (7.561) (5.320) (8.685)

Observations 1,404 1,313 1,262 1,255 1,058 1,021 935 654 651 650 575 572 650 445 491
Number of country_code_ 109 103 102 101 94 92 84 64 64 64 63 62 64 50 51
Note: Annual information between 2005 and 2017 for 51 EMDE countries. Public debt includes debt data for general government. In case this data is not available, debt data for the central government
is used. Policy rate is the Central Bank or Monetary Authority’s benchmark interest rate for monetary policy. In case that policy rate is not available, money market rate is used.
Panel data regressions with first-order error structure to adjust for serial autocorrelation. Country-fixed effects included. Dummy variable capturing Global Financial Crisis period included.
t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1

42
Panel B. Dependent variable: Lending-deposit interest rate
Lending-deposit interest rate spread
VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

Inflation (%) -0.018 -0.018 -0.022 -0.017 -0.010 -0.012 -0.010 -0.005 -0.002 -0.003 -0.005 -0.005 -0.005 0.033 0.025
(-1.286) (-1.232) (-1.487) (-1.112) (-0.545) (-0.615) (-0.489) (-0.240) (-0.108) (-0.140) (-0.219) (-0.212) (-0.242) (1.097) (0.866)
St. Deviation Inflation (%) 0.014 0.010 0.174*** 0.225*** 0.223*** 0.214*** 0.037 0.026 0.025 0.029 0.034 0.013 0.040 0.023
Macro-fiscal conditions

(0.722) (0.523) (4.521) (4.800) (4.686) (4.371) (0.769) (0.497) (0.484) (0.479) (0.568) (0.248) (0.580) (0.380)
Public Debt (%GDP) 0.020*** 0.019*** 0.023*** 0.024*** 0.057*** 0.019* 0.036*** 0.039*** 0.028** 0.032** 0.038*** 0.037** 0.039***
(3.314) (3.198) (3.035) (3.094) (4.828) (1.746) (3.156) (3.329) (2.108) (2.349) (3.313) (2.447) (3.094)
GDP percapita 0.000 -0.001** -0.001** -0.000** -0.000*** -0.000* -0.000 0.000 0.000 -0.000 0.000 -0.000
(0.136) (-2.325) (-2.293) (-2.189) (-3.458) (-1.758) (-0.500) (0.267) (0.430) (-0.124) (0.488) (-0.533)
Total Savings (%GDP) 0.008 0.008 0.021 0.025 0.033* 0.027 0.028 0.028 0.027
(0.602) (0.610) (1.045) (1.392) (1.737) (1.392) (1.325) (1.337) (1.416)
Policy/money market rate (%) -0.015 -0.014
(-0.398) (-0.404)

Overhead costs (%Total Assets) 0.064 0.164** 0.155** 0.271*** 0.246*** 0.350*** 0.345*** 0.232*** 0.251** 0.227**
(0.895) (1.982) (2.391) (3.641) (3.240) (4.035) (3.951) (3.074) (2.432) (2.398)
Three bank concentration (% Total
Banking sector characteristics

0.004 0.026*** 0.036*** 0.034*** 0.028** 0.026** 0.031*** 0.020 0.031**

bank system assets)
(0.347) (2.605) (3.307) (3.108) (2.355) (2.124) (2.878) (1.528) (2.548)
Non-performing loans (% Total
0.053* 0.047 0.047 0.090** 0.086** 0.052 0.049 0.019
Gross Loans)
(1.714) (1.466) (1.473) (2.400) (2.283) (1.632) (1.202) (0.542)
Non-interest income (% Total
-0.013 -0.011 -0.021** -0.020** -0.009 -0.018* -0.010
Income)
(-1.573) (-1.364) (-2.167) (-2.116) (-1.157) (-1.690) (-0.989)

Private credit (% GDP) -0.030** -0.003 -0.001 -0.022 0.001 -0.013

(-2.096) (-0.161) (-0.063) (-1.523) (0.047) (-0.872)

Credit Bureau Coverage (% adults) -0.003 -0.003 -0.008
Business environment

(-0.424) (-0.433) (-1.230)

Time to resolve insolvency (years) 0.265* 0.270*

(1.800) (1.900)

Rule of Law (Kaufmann et al.,2010) -2.136*** -2.250***

(-2.928) (-2.685)
Constant 7.995*** 7.852*** 6.976*** 6.659*** 9.539*** 9.286*** 6.658*** 5.925*** 3.007*** 3.817*** 2.222*** 1.297 2.797*** 2.734*** 3.746***
(145.575) (132.229) (68.952) (39.759) (21.273) (19.503) (12.786) (12.308) (5.566) (6.426) (2.891) (1.583) (4.410) (2.906) (4.848)

Observations 1,318 1,227 1,175 1,148 964 931 860 628 621 617 541 538 617 415 464
Number of country_code_ 110 104 103 100 89 86 80 64 63 63 61 60 63 48 50
Note: Annual information between 2005 and 2017 for 64 EMDE countries. Public debt includes debt data for general government. In case this data is not available, debt data for the central government
is used. Policy rate is the Central Bank or Monetary Authority’s benchmark interest rate for monetary policy. In case that policy rate is not available, money market rate is used.
Panel data regressions with first-order error structure to adjust for serial autocorrelation. Country-fixed effects included. Dummy variable capturing Global Financial Crisis period included.
t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1

43
 Figure 6: Illustrative decomposition of nominal lending interest rate and changes between
2007 and 2017
%
15

13

11 Macro

9
Bank
7
Business

5
Expected
mean
3
EMDEs
(constant)
1
Error
-1
Nominal rate Macro Bank structure Business Other Nominal rate
2007 conditions 2017
Note: Average nominal lending interest rate across all EMDEs in the sample (50 countries) for 2007 and 2017. “Other” captures country specific
effects and estimation error.
Source: Authors’ calculations.

Table 4: Illustrative decomposition of nominal lending interest rate and changes between
2007 and 2017 by region
Percent
Region Observed Estimated Estimation
Macro Bank Business Other
2017 Rate Rate Error
EMDEs 11.0 12.1 2.2 3.2 0.3 6.4 -1.2
EAP 6.4 6.6 1.5 3.6 0.3 1.3 -0.2
ECA 8.8 10.5 2.2 3.1 0.3 4.8 -1.7
LAC 16.9 15.9 1.9 2.8 0.3 10.9 0.9
MENA 8.0 8.6 1.1 2.7 0.6 4.2 -0.6
SA 11.6 10.9 4.5 2.8 -0.1 3.7 0.7
AFR 11.1 12.2 3.3 3.9 1.3 3.7 -1.1
Difference 2017 vs. 2007
EMDEs -2.7 -0.8 -0.3 0.2 -1.0 0.3 -1.6
EAP -2.3 -1.9 -0.5 0.2 -1.6 0.0 -0.5
ECA -4.0 -1.5 -0.1 0.3 -1.2 -0.5 -2.5
LAC -0.6 -0.6 -0.4 0.0 -0.7 0.5 0.0
MENA -0.6 1.5 -0.3 -1.3 -0.5 3.6 -2.1
SA -0.8 0.0 0.4 0.5 -1.7 0.7 -0.8
AFR -2.2 3.6 0.3 0.2 0.1 2.9 -5.8
Note: Average nominal lending interest rate in 2017 by region. “Other” captures constant and country specific effects.
Source: Authors’ calculations.

44
 Annex 1: Determinants of bank lending rates and spreads
A. Macro-fiscal factors

Macroeconomic fundamentals: Sound monetary, fiscal, and exchange rate framework underpin
the general economic outlook which drives the overall health of public and private balance sheets
and thus the demand and supply of bank credit. Illustrative examples of macroeconomic factors
that increase benchmark interest rates include: 22

• Tighter global financial conditions determined by world interest rates and investor
appetite.
• A deterioration in sovereign creditworthiness, particularly when debt levels are high and
(external) refinancing needs are acute.
• (Expectations of) currency depreciations which are also associated with higher default
risk, particularly when currency mismatches are prevalent, and can pass-through to
inflation. Moreover, significant devaluations may also affect local currency deposit
volumes and rates.
• High and volatile inflation and weakly anchored inflation expectations. For example,
Demirgüç-Kunt et al. (2004), Claeys and Van der Vennet (2008), and Poghosyan (2012)
find a positive relationship between inflation and intermediation spreads, particularly for
EMDEs.
• Low domestic savings rates are often cited as a contributing factor to funding and liquidity
shortages resulting in high interest rates (see for example Segura-Ubiergo, 2012).
• Structural current account deficits can contribute to aggregate liquidity shortages and
increase risk in the banking system.

Further, given that government securities can be used as collateral for interbank and liquidity
transactions, sovereign risk has an impact on banks’ funding costs (De la Torre et al., 2006).

22
This is a non-exhaustive discussion. It is beyond the scope of this paper to discuss the vast macro literature on the
determinants of policy interest rates.

45
Fiscal dominance of monetary policy: In a situation where sovereign risk is high, the dynamic
between macro-fiscal factors becomes more complicated and drive up interest rates and spreads.
A rise in the policy rate to contain inflation may increase sovereign default risk and trigger a
currency depreciation which in turn will lead to higher, not lower inflation.

Crowding out effects: Large public sector financing needs relative to the size of the banking sector
(particularly when there are few institutional investors who could hold absorb the government
debt) could crowd out the private sector and drive up bank interest rates and spreads. As a result,
bank may increase their exposures to government securities or outright lending to (quasi-)
government entities.

B. Banking sector characteristics

Leverage and funding profiles: Banks with weak capital and funding structures will incur higher
costs to supply loans and may have limited capacity to intermediate lending. More liquid banks
are better able to withstand funding shocks and reduce lending rate volatility. Finally, more risk-
averse banks will prefer to build stronger capital buffers -- an expensive source of funding – and
avoid riskier investments or demand a higher premium.

Scale of bank operations: The presence of economies of scale in financial intermediation implies
lower interest rates and spreads since fixed costs (e.g., core corporate functions, branch
networks) can be distributed over a larger client base and risks can be better diversified (Angbazo,
1997; Maudos and Fernandez de Guevara, 2004; De la Torre et al., 2006).

Competition and market segment contestability: The degree of competition in lending and
deposit markets is a key determinant of intermediation spreads (Ho and Sauders, 1981). More
competition provides banks with incentives to innovate and become more efficient as high profits
driven by monopoly rents fall. The degree of competition typically differs by market segment
(e.g., wholesale and retail deposits, credit cards, large corporates) and geography (e.g., in rural
areas competition is typically much lower than in urban areas), but larger banks with higher
market shares are often able to exert more pricing power.

46
Bank ownership: The type of ownership of banks – public, private, foreign – can have an
important bearing on key drivers such as risk appetite, strategic focus, and funding costs which
affect rates and spreads. For example, foreign-owned banks could be in a more favorable position
to offer lower lending rates, as they have access to intra-group and wholesale funding at lower
cost.

Product diversification: Banks with diversified sets of products and services can offer loans with
lower intermediation spreads as lending products can be cross-subsidized with other fee-based
financial services. Moreover, banks with more diversified sources of income are better able to
withstand shocks to specific business lines and thus are better able to meet the targeted rate of
return which lowers the cost of funding.

Credit portfolio composition (e.g., floating versus fixed rate loans, currencies, non-performing
assets): The composition of banks’ lending portfolio has implications for rates and spreads. For
example, in countries where fixed-rate lending prevails, volatility of funding costs implies a risk
of compressed margins and potential losses, particularly in the absence of well-developed
derivatives markets which is the case in most EMDEs. Moreover, rates and spreads typically differ
for foreign currency lending as funding and credit risk issues differ from domestic currency
lending. Finally, banks that suffer from debt overhang problems, may respond by increasing rates
and spreads to strengthen buffers.

Macro- and micro-prudential regulation and supervision: Strong regulatory and supervisory
frameworks ensure that the banking sector is safe and resilient and that (systemic) risks and
shocks are properly identified and mitigated. They also ensure that banks adopt proper
governance and risk management frameworks. Taken together, strong regulation and
supervision can strengthen bank balance sheets at the individual and systemic level which
bolsters confidence in the banking system and help reduce intermediation costs.

47
 C. Business environment

Insolvency and creditor rights framework: Weak insolvency and creditor rights frameworks make
loan restructuring, contract enforcement, and collateral recovery more expensive and time
consuming. For example, if banks cannot efficiently recuperate collateral – because, for example
ownership cannot be ascertained or courts act too slowly -- then banks will not extent credit or
only at high interest rates. Insolvency regimes are also important for the effective management
of non-performing loans which weigh on intermediation efficiency. This has a direct impact on
the loan recovery ratio (LGD) when a borrower defaults, and therefore the expected losses which
determine lending rates and spreads. A 2012 study of Brazil’s 2005 bankruptcy law reform found
a 10-17 percent increase in debt finance at the firm level, and an 8-17 percent reduction in the
cost of credit, resulting from a reform in insolvency and creditor rights frameworks (Araujo et al,
2012). A 2012 study on Italy’s bankruptcy reform yielded similar conclusions (Rodano, et al,
2012). Reforms to creditor rights frameworks in Romania and other jurisdictions have also been
shown to yield reductions in the cost of credit and increased access (Safavian, 2006).

Information environment: Asymmetric information creates agency frictions which increases the
cost of credit intermediation. Well-functioning and regulated credit registries and private credit
bureaus can reduce information asymmetries. Similarly, strong secured transaction laws and
collateral registries allow borrowers to leverage their assets as collateral to reduce agency costs.
Open banking initiatives and Application Programming Interfaces (APIs) democratize the flow of
information and help to further alleviate these frictions and increase competition.

Direct policy interventions: These include directed lending and interest subsidy programs,
interest rate restrictions, and high (unremunerated) reserve or liquidity requirements. These
interventions are distortionary and thus affect interest rates and spreads (see also Common
Policy Interventions).

48
Annex 2: Impact of global conditions on nominal lending rates
Figure 7: Impact of global conditions (global interest rates and liquidity) on nominal lending
rates
%
0.4

0.2

0.0

-0.2

-0.4

-0.6

-0.8

-1.0

-1.2
2006 2007 2008 2009 2010* 2011* 2012 2013 2014 2015 2016 2017

Note: Year fixed effects from a panel regression using the same framework as in Table 3, Panel A, equation 14. Year fixed effects is an indicator
of the impact of global conditions (i.e., global interest rates and liquidity) on nominal lending interest rates in EMDEs. Years in asterisk are the
effect coefficients that are significantly different from zero. Panel data regression with annual information between 2005 and 2017 for 50 EMDE
countries, country and year fixed effects included, and robust errors.
Source: Authors’ calculations.

49
Annex 3: Robustness tests econometric results

Panel A. Dependent variable: Nominal lending interest rate

Nominal lending interest rate
VARIABLES (14) (15) (16) (17) (18) (19)

Inflation (%) 0.066* 0.070** 0.053 0.053 0.065* 0.066*

(1.800) (2.007) (1.504) (1.511) (1.779) (1.872)
St. Deviation Inflation (%) 0.123** 0.093 0.133** 0.107*
Macro-fiscal conditions

(1.993) (1.599) (2.096) (1.812)

5-year St. Deviation Inflation (%) 0.249*** 0.235***
(5.813) (5.474)
Public Debt (%GDP) 0.032* 0.037** 0.033** 0.024 0.035* 0.043**
(1.756) (2.325) (1.979) (1.485) (1.877) (2.469)
GDP percapita -0.000 -0.000** -0.000 -0.000* -0.000 -0.000*
(-0.331) (-2.051) (-0.728) (-1.933) (-0.395) (-1.738)
Policy/money market rate (%) 0.191*** 0.205*** 0.197*** 0.197*** 0.196*** 0.198***
(4.335) (4.859) (4.820) (4.833) (4.394) (4.593)
Devaluation rate (%) -0.007 -0.011
(-0.721) (-1.181)
Overhead costs (%Total Assets) 0.173 0.244** 0.153 0.199* 0.171 0.214*
(1.436) (2.126) (1.355) (1.769) (1.413) (1.853)
Three bank concentration (% Total
Banking sector characteristics

0.012 0.026* 0.007 0.016 0.011 0.024

bank system assets)
(0.744) (1.659) (0.448) (1.046) (0.677) (1.489)
Non-performing loans (% Total
0.083 0.051 0.043 0.056 0.085* 0.065
Gross Loans)
(1.618) (1.135) (0.915) (1.232) (1.653) (1.381)
Non-interest income (% Total
-0.024* -0.023* -0.016 -0.016 -0.022* -0.017
Income)
(-1.883) (-1.901) (-1.337) (-1.334) (-1.750) (-1.357)
Private credit (% GDP) 0.034* 0.014 0.049** 0.053*** 0.036* 0.024
(1.706) (0.764) (2.580) (2.793) (1.788) (1.238)
Credit Bureau Coverage (% adults) -0.015* -0.018** -0.015*
Business environment

(-1.809) (-2.289) (-1.803)

Time to resolve insolvency (years) 0.473*** 0.509*** 0.463**
(2.623) (3.018) (2.564)

Rule of Law (Kaufmann et al.,2010) -1.327 -2.112** -1.997*

(-1.272) (-2.087) (-1.859)

Constant 6.438*** 8.995*** 6.139*** 7.306*** 6.370*** 7.914***

(5.320) (8.685) (4.944) (6.021) (5.239) (7.395)

Observations 445 491 455 463 445 473

Number of country_code_ 50 51 51 52 50 51
Note: Annual information between 2005 and 2017 for 51 EMDE countries. Public debt includes debt data for general government. In case this
data is not available, debt data for the central government is used. St.Deviation Inflation refers to standard deviation of monthly inflation rate y-
o-y, using a one-year rolling window. 5-year St.Deviation Inflation refers to standard deviation of monthly inflation rate y-o-y, using a 5-year
rolling window. Devaluation rate is the y-o-y change of the exchange rate vs. US dollar.
Benchmark regressions in columns (14) and (15).
Panel data regressions with first-order error structure to adjust for serial autocorrelation. Country-fixed effects included. Dummy variable
capturing Global Financial Crisis period included.
t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1

50
 Panel B. Dependent variable: Lending-deposit interest rate
Lending-deposit interest rate spread
VARIABLES (14) (15) (16) (17) (18) (19)

Inflation (%) 0.033 0.025 0.031 0.029 0.033 0.022

(1.097) (0.866) (1.054) (0.999) (1.100) (0.753)
St. Deviation Inflation (%) 0.040 0.023 0.046 0.022
Macro-fiscal conditions

(0.580) (0.380) (0.651) (0.362)

5-year St. Deviation Inflation (%) 0.047 0.029
(1.125) (0.701)
Public Debt (%GDP) 0.037** 0.039*** 0.038*** 0.034** 0.038** 0.041***
(2.447) (3.094) (2.659) (2.504) (2.482) (3.057)
GDP percapita 0.000 -0.000 0.000 0.000 0.000 -0.000
(0.488) (-0.533) (0.437) (0.221) (0.441) (-0.340)
Policy/money market rate (%) -0.015 -0.014 -0.014 -0.013 -0.013 -0.004
(-0.398) (-0.404) (-0.391) (-0.375) (-0.353) (-0.124)
Devaluation rate (%) -0.004 -0.008
(-0.446) (-1.020)
Overhead costs (%Total Assets) 0.251** 0.227** 0.230** 0.244** 0.250** 0.219**
(2.432) (2.398) (2.302) (2.500) (2.414) (2.299)
Three bank concentration (% Total
Banking sector characteristics

0.020 0.031** 0.017 0.022* 0.019 0.026**

bank system assets)
(1.528) (2.548) (1.291) (1.708) (1.460) (2.062)
Non-performing loans (% Total
0.049 0.019 0.040 0.043 0.050 0.025
Gross Loans)
(1.202) (0.542) (1.012) (1.135) (1.226) (0.682)
Non-interest income (% Total
-0.018* -0.010 -0.016 -0.014 -0.017 -0.008
Income)
(-1.690) (-0.989) (-1.496) (-1.401) (-1.616) (-0.779)
Private credit (% GDP) 0.001 -0.013 0.002 0.007 0.002 -0.004
(0.047) (-0.872) (0.103) (0.431) (0.115) (-0.269)
Credit Bureau Coverage (% adults) -0.008 -0.009 -0.008
Business environment

(-1.230) (-1.348) (-1.224)

Time to resolve insolvency (years) 0.270* 0.276* 0.265*

(1.900) (1.966) (1.857)

Rule of Law (Kaufmann et al.,2010) -2.250*** -2.381*** -2.587***

(-2.685) (-2.786) (-3.007)

Constant 2.734*** 3.746*** 2.862*** 2.476*** 2.702*** 3.204***

(2.906) (4.848) (3.086) (2.717) (2.866) (3.910)

Observations 415 464 425 433 415 445

Number of country_code_ 48 50 49 50 48 50
Note: Annual information between 2005 and 2017 for 50 EMDE countries. Public debt includes debt data for general government. In case this
data is not available, debt data for the central government is used. St.Deviation Inflation refers to standard deviation of monthly inflation rate y-
o-y, using a one-year rolling window. 5-year St.Deviation Inflation refers to standard deviation of monthly inflation rate y-o-y, using a 5-year
rolling window. Devaluation rate is the y-o-y change of the exchange rate vs. US dollar.
Benchmark regressions in columns (14) and (15).
Panel data regressions with first-order error structure to adjust for serial autocorrelation. Country-fixed effects included. Dummy variable
capturing Global Financial Crisis period included.
t-statistics in parentheses *** p<0.01, ** p<0.05, * p<0.1

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