Ariss. THIS IS THE PAPER THE REFREREE REPORT IS ON.pdfJour.docx

Ariss. THIS IS THE PAPER THE REFREREE REPORT IS
ON.pdf
Journal of Banking & Finance 34 (2010) 765–775
Contents lists available at ScienceDirect
Journal of Banking & Finance
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c
a t e / j b f
On the implications of market power in banking: Evidence
from developing countries
Rima Turk Ariss *
Department of Economics and Finance, Lebanese American
University, 13-5053, Chouran Beirut 1102 2801, Lebanon
a r t i c l e i n f o a b s t r a c t
Article history:
Received 5 February 2009
Accepted 3 September 2009
Available online 6 September 2009
JEL classification:
D4
G15
G21
L11
N20
Keywords:

Bank efficiency
Financial stability
Lerner
Market power
0378-4266/$ - see front matter � 2009 Elsevier B.V. A
doi:10.1016/j.jbankfin.2009.09.004
* Tel.: +961 1 786456x1644; fax: +961 1 867098.
E-mail address: [email protected]
1 While encouraging competition is important for c
and Zingales, 2003), theory offers opposing arguments
beneficial for economic activity (Gorton and Winton, 2
2 We thank an anonymous referee for pointing
competition” hypothesis as an alternative to the ‘‘quest
in driving banks into foreign markets.
This paper investigates how different degrees of market power
affect bank efficiency and stability in the
context of developing economies. It sheds light on the
competition-stability nexus by documenting and
analyzing the complex interactions between a tripod of
variables that are central for regulators: the
degree of market power, bank cost and profit efficiency, and
overall firm stability. The results show that
an increase in the degree of market power leads to greater bank
stability and enhanced profit efficiency,
despite significant cost efficiency losses. The findings lend
empirical justification to the traditional view
that increased competition may undermine bank stability, and
may bear significant implications for
stressed banking systems in developing economies.
� 2009 Elsevier B.V. All rights reserved.
1. Introduction
Over the past two decades, policymakers in various parts of the

world have taken steps to liberalize financial markets,
promoting
foreign competition and deregulating interest rates.1 Heightened
competitive pressures in banking encourage financial
institutions
to enter new markets where competition is low, or where
efficiency
gains may be materialized.2 Evidence on efficiency gains from
enter-
ing new markets is, however, mixed. Sengupta (2007) explores
the
impact of competition on firms’ access to credit by viewing
bank
competition as competition between different asymmetrically
in-
formed principals. He develops a model to explain the perceived
bias
(which is stronger in developing countries) of foreign and large
domestic banks in lending to large businesses and neglecting
small
firms, while better informed local banks continue to find a
market
among small enterprises. Lensink et al. (2008) report that
foreign
ll rights reserved.
redit market efficiency (Rajan
as to whether competition is
003).
attention to the ‘‘dodging
for market power” conjecture
ownership negatively impacts bank efficiency. They also
provide evi-
dence to suggest that the relative efficiency of domestic vs.
foreign

banks is dependent on host and home country conditions.
The recent global dimension of banking is modifying the pre-
vailing structures, and may bear significant implications on the
efficiency levels of the industry. While the literature on banking
efficiency is vast, few studies have investigated the impact of
the
prevailing market structure on the efficiency in the delivery of
financial services but only in the context of developed countries
(Maudos and De Guevara, 2007; Koetter et al., 2008; Schaeck
and
Cihak, 2008; Delis and Tsionas, 2009). Other research has taken
the opposite stand to consider bank efficiency as a determinant
of the degree of market power (Casu and Girardone, 2006; De
Guevara and Maudos, 2007).
In parallel, as banks expand the scope of their activities and
identify new growth opportunities across national borders, they
tend to gain market power, raising concerns among regulators
about issues of moral hazard and excessive risk-taking.3 An
intense merger activity is continuously taking place globally,
3 Berger et al. (2003) identify two dimensions of globalization,
bank national and
bank reach, to find that multinational firms rely on host banks
with limited reach to
deliver ‘‘concierge” service and expand further from their home
nation. Their results
suggest that the extent of globalization may remain limited.
http://dx.doi.org/10.1016/j.jbankfin.2009.09.004
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766 R. Turk Ariss / Journal of Banking & Finance 34 (2010)

765–775
notwithstanding little empirical evidence on economies of scale
and scope, and the social costs associated with monopoly rents
(De Nicoló, 2000).4 A current debate prevails on the
implications
of a higher degree of market power on bank stability. A
voluminous
body of literature has emerged to address the competition-
stability
nexus in banking, though no consensus has been reached yet.
The
seminal article by Keeley (1990) has shown that increased
compe-
tition and deregulation erode franchise value and increase the
probability of bank failure. Recent research, however,
demonstrates
that less competitive markets are less stable (Boyd and De
Nicoló,
2005).
This paper investigates the implications of market power on is-
sues of bank efficiency and stability in developing countries
where
capital markets are relatively underdeveloped, and banks repre-
sent the main providers of credit to the economy. Developing
countries provide a fertile laboratory to examine issues of
compe-
tition because they are engaged in a process of deregulation,
bank
privatization and financial liberalization, while the industry is
wit-
nessing more consolidation. Changing banking structures, in
turn,
raise concerns about competitive conditions, the efficiency in
the
delivery of financial services, and overall bank stability.5 These

is-
sues are of particular importance in light of the adverse implica-
tions of the recent financial crisis for developing countries
(International Monetary Fund, 2009).
Related research which examines simultaneously the inter-relat-
edness between bank competition, efficiency, and stability is by
Schaeck and Cihak (2008) on European and US banking. The
authors
use a traditional Lerner index to establish that competition
increases
bank efficiency. They also estimate the Boone indicator (a
country-
level measure of the intensity of competition based on the idea
that
more efficient firms will gain market share in a competitive
environ-
ment) to show that competition increases bank soundness.
This study differs from previous work in terms of sample cover-
age and methodology. First, no prior research has to our
knowledge
addressed the complex interaction between competition,
efficiency
and stability in the context of developing countries. Second, we
investigate the inter-relatedness between key variables of
interest
– market power, efficiency and stability – at the bank level to
lend
more support to the analysis. Specifically, the Lerner index is a
bank-level measure of the degree of competition, which is pre-
ferred over nation-wide proxies such as traditional
concentration
ratios or the Panzar and Rosse (PR, 1987) H-statistic. Third,
since
no consensus prevails in the literature regarding how best to

assess
the degree of market power in banking (Carbó et al., 2009), we
con-
sider three different specifications of the Lerner index. In
addition
to the traditional price mark-up over marginal cost estimation
(Berger et al., 2009), we employ a structural model to derive
two
other adjusted Lerner indices: an efficiency-adjusted Lerner and
a
funding-adjusted Lerner (Maudos and De Guevara, 2007;
Koetter
et al., 2008). The intuition is that both bank stability and
efficiency
may affect the degree of market power, resulting in an
endogeneity
bias in the traditional Lerner estimation. Thus, the three
different
Lerner specifications are likely to provide more robustness to
the
analysis. In addition, we run a series of sensitivity checks using
other proxies of bank stability and by implementing alternative
estimation procedures (cross-section vs. panel data, Tobit vs.
logis-
tic regressions).
4 Regulators play an important role in approving or obstructing
mergers, taking
prompt corrective action, chartering de novo bank entry
(DeYoung, 2003), and maybe
ensuring stability in developing countries.
5 As suggested by an anonymous referee, we acknowledge that
banking sectors in
developing countries are subject to random macro shocks (such
as exchange rate
fluctuations) and to systematic shocks (such as state-wide

regulation on foreign
ownership). It is however, very hard to totally disentangle these
effects from the
tripod analysis undertaken herein.
The findings indicate that banks with more market power en-
dure significant cost efficiency losses, but they manage to
improve
their profit efficiency levels. As banks implement growth
strategies
(hoping to increase market power or to dodge competition),
ensu-
ing gains from revenue diversification outweigh their
deteriorating
cost efficiency. In parallel, banks with more market power
achieve
higher records of overall stability. The results lend empirical
justi-
fication across developing countries to the traditional view that
in-
creased competition may undermine bank stability. They also
bear
significant implications for stressed banking systems.
The rest of the paper is organized as follows: Section 2 reviews
the relevant literature. It first presents the main arguments in
favor
of more competition in banking and the expected efficiency
gains,
followed by a discussion on the opposing views for the
implications
of market power on bank stability. Section 3 introduces the
estima-
tion procedure of each of market power, bank efficiency, and
bank
stability. Section 4 describes the data and methodology. Section
5

presents the results of the tripod estimation (market power,
bank
efficiency, and bank stability) and analyzes the empirical
findings
on the implications of market power in banking. Section 6
performs
a series of sensitivity checks for robustness and Section 7
concludes.
2. Literature review
2.1. Market power and bank efficiency
The arguments in favor of greater competition, in principle, ap-
ply to all industries and derive from applying classical
industrial
organization economics. Berger and Hannan (1998) argue that
banks not exposed to competition tend to be less efficient than
banks subject to more competition. When market power
prevails,
managers may pursue objectives other than profit maximization,
and they do not have incentives to work hard to keep costs
under
control, thereby reducing cost efficiency. The authors find
evidence
that a ‘‘quiet life” effect prevails in US banking.
Compared to the voluminous body of literature on bank effi-
ciency, research on the relationship between market structure
and bank efficiency is limited for developed markets and practi-
cally non-existent for developing countries.6 Casu and
Girardone
(2006) derive bank efficiency estimates using the non-
parametric
Data Envelopment Analysis methodology and include it as an
exog-
enous variable in estimating the PR H-statistic. Using a sample

of
banks in the European Union, they do not find a clear
relationship
between efficiency and competition. However, the PR H-
statistic is
contested as a continuous and long-run measure of competition
(Shaffer, 2004). It is also calculated at the national level and
cannot
be used to assess firm-level decisions of banks.
Three studies use the Lerner index or a bank-level measure of
competition to investigate the implications of market power on
bank efficiency in the context of developed countries. Maudos
and De Guevara (2007) find a positive relationship between
market
power and cost efficiency in European banking, thus rejecting
the
‘‘quiet life” hypothesis. However, a comprehensive analysis of
the
relationship between market power and efficiency should
consider
both cost and profit efficiency. While cost minimization is a
neces-
sary condition to maximize profits, banks may also achieve
higher
profits by diversifying their revenue sources. Using US and
Euro-
pean samples, Schaeck and Cihak (2008) report that competition
improves profit efficiency. A less innocuous problem arises
from
reverse causality between bank efficiency and the degree of
market
power. Under the Efficient Structure Hypothesis, most efficient
banks survive competitive pressures and gain market share at
6 Berger and Humphrey (1997) provide a detailed review of the
literature on

banking efficiency, which is also updated by Berger and Mester
(2003).
8
R. Turk Ariss / Journal of Banking & Finance 34 (2010) 765–
775 767
the expense of less efficient banks (Demsetz, 1973). Koetter et
al.
(2008) acknowledge that competition and efficiency are inter-
twined. They use a structural model to find support for the
predic-
tions of the Efficient Structure Hypothesis rather than those of
the
‘‘quiet life” hypothesis for US banks.
More recently, Delis and Tsionas (2009) provide an empirical
framework for the joint estimation of efficiency and market
power
for a sample of European and US banks. The authors use a novel
maximum localization technique to derive bank-specific
estimates
of market power, and report a negative relationship between
mar-
ket power and efficiency, in line with the predictions of the
‘‘quiet
life” hypothesis.
2.2. Market power and bank stability
The traditional ‘‘competition-fragility” view contends that mar-
ket power in banking may be desirable, despite possible ensuing
efficiency losses. A bank with market power is likely to reduce
the information asymmetry problem and develop on-going rela-

tionships with individual firms (Petersen and Rajan, 1995).
Incum-
bent banks are prone to screen borrowers and differentiate
between low- and high-quality debtors (Cetorelli and Peretto,
2000). This may improve loan portfolio quality and enhance
bank
stability. Besanko and Thakor (1993) find that banks which
appro-
priate informational rents from developing relationships with
bor-
rowers may have more incentives to limit their risk exposure.
Keeley (1990) finds that increased competition has eroded the
franchise value of US banks, leading to more risk-taking and a
surge of bank failures in the 1980s.7 Carletti and Vives (2008)
re-
view the literature on competition and stability and show that,
while
banking is no longer an exception in the enforcement of
competition
policy rules in the European Union, market power may have a
mod-
erating effect on bank risk-taking incentives.
Recently, a counter trend has emerged both at the theoretical
and
empirical levels to support the ‘‘competition-stability” view and
re-
fute the traditional trade-off between market power and bank
sta-
bility. Caminal and Matutes (2002) show that monopoly banks
incur monitoring costs and are inclined to originate risky loan
port-
folios; Beck et al. (2004) report that bank stability is enhanced
in
both more concentrated and competitive markets; and Allen and
Gale (2004) argue that this relationship is complex and case

depen-
dent. Boyd and De Nicoló (2005) argue that the implications of
mar-
ket power have to be examined separately for the deposit and
loan
markets. Banks with more loan market power are in a position
to
charge higher rates for loan customers. This makes it harder for
bor-
rowers to repay loans, thereby exacerbating moral hazard
incentives
to shift into riskier projects and possibly resulting in a riskier
set of
bank clients due to adverse selection considerations.
A large body of empirical evidence employs concentration
ratios
to support the ‘‘competition-stability” view (see for example De
Nicoló, 2000; De Nicoló et al., 2004; Boyd et al., 2006; Uhde
and
Heimeshoff, 2009). Alternatively, Schaeck et al. (2009) use the
PR
H-statistic to show that competitive banking markets are more
sta-
ble than monopolistic systems. Schaeck and Cihak (2008)
employ
another country-level measure of the intensity of competition
(the Boone indicator) to establish that competition increases
bank
soundness through the efficiency channel.
Berger et al. (2009) show that two strands of the literature (the
‘‘competition-fragility” and the ‘‘competition-stability” views)
need not necessarily yield opposing predictions regarding the
ef-
fects of competition on bank stability in the context of

developed
countries. While loan market power can result in riskier loan
port-
folios, banks may protect their overall franchise value using
other
7 For a survey of the literature on financial stability and
competition, see Carletti
and Hartmann (2003).
means, such as increasing their equity capital or engaging in
other
risk-mitigating techniques.
3. Tripod estimation methodology: market power, bank
efficiency and bank stability
3.1. Market power
This paper employs three different specifications of Lerner to
investigate the implications of market power: a conventional
Lern-
er (Berger et al., 2009), an efficiency-adjusted Lerner (Koetter
et al.,
2008), and a funding-adjusted Lerner (Maudos and De Guevara,
2007).
The conventional Lerner indicator of market power is defined
as:
ðPTA � MCTAÞ=PTA: ð1Þ
The Lerner index captures the essence of pricing power because
it measures the disparity between price and marginal costs ex-
pressed as a percentage of price. Ideally, output price or PTA
should
take into consideration the price of loans and deposits
separately.
However, the statistical data does not provide sufficient grounds

to estimate separate prices or rates for loans and deposits. Loan
rev-
enues cannot be disentangled from those earned on other fixed-
in-
come investments, and deposit interest expenses cannot be
isolated
from interest which is paid on other liabilities. Consequently,
the
construction of the Lerner index rests on the estimation of price
and marginal costs of a single indicator of total banking
activity.
Following the literature, total assets account for the aggregate
prod-
uct of the bank.8 Under the assumption that the heterogeneous
flow
of services produced by a bank is proportional to its total assets,
PTA is
calculated as the ratio of total revenues to total assets.
In order to derive MC, we estimate the following translog cost
function for each country separately to reflect different
technolo-
gies, while capturing bank specificities using bank fixed effects:
ln Cost ¼ b0 þ b1 ln Q þ
b2
2
ln Q 2 þ
X2
k¼1
ck ln W k þ
X2
k¼1

ak ln Zk
þ
1
2
X2
k¼1
X2
j¼1
hkj ln W k ln W j þ
1
2
X2
k¼1
X2
j¼1
jkj ln Zk ln Zj
þ
1
2
X2
k¼1
/k ln Q ln W k þ
1
2
X2

k¼1
gk ln Q ln Zk
þ
1
2
X2
k¼1
X2
j¼1
xkj ln W k ln Zj þ d1Trend þ
1
2
d2 Trend
2
þ d3 Trend � ln Q þ
X2
k¼1
kk Trend � ln W k
þ
X2
k¼1
qkTrend � ln Zk þ e; ð2Þ
where bank costs (Cost) are a function of output (Q for total
assets),
three input prices (W1 for the price of funds, W2 for the price

of
physical capital, and W3 for the price of labor), a vector of
fixed net-
puts (Z1 for fixed assets, Z2 for total nominal value of off-
balance
sheet items, and Z3 for equity capital), and technical change
(Trend
to capture movements in the cost function over time).9 Standard
symmetry restrictions and input price homogeneity of degree
one
are required to estimate (2). We also scale cost and input prices
by
See, for example, Angelini and Ceterolli (2003).
9 Data for developing countries is expected to be noisy. In
estimating Eq. (2), we
exclude outliers on input prices at the 99th percentile. Appendix
A provides
information on descriptive statistics for variables entering Eq.
(2).
12
765–775
W3, and netputs by Z3 to correct for heteroskedasticity and
scale
biases. Marginal costs MCTA are then computed as
10:
MC ¼
Cost

Q
b1 þ b2 ln Q þ
X3
k¼1
/k ln W k þ d3 Trend
" #
: ð3Þ
There are two potential problems associated with the estima-
tion of the conventional Lerner index. First, the Efficient
Structure
Hypothesis postulates that efficiency (and stability) may be
driving
market structure, and reverse causality is likely to prevail
between
the variables of interest. Conventional Lerner indices implicitly
as-
sume full bank efficiency and fail to consider the possibility
that
banks may not exploit pricing opportunities resulting from
market
power. Following Koetter et al. (2008), we account for the
endoge-
neity bias by deriving efficiency-adjusted Lerner indices from a
sin-
gle structural model:
ðARTA � MCTAÞ=ARTA: ð4Þ
AR denotes average revenues, or TR
^
=TA, and TR

^
¼ TP
^
þTC
^
. The
key to obtaining an efficiency-adjusted Lerner index is to
estimate
expected profits TP
^
from an alternative profit function (defined be-
low), and to combine them with expected total costs TC
^
derived
from Eq. (2). Unlike the conventional Lerner of Eq. (1), such a
struc-
tural model allows for the simultaneous estimation of both bank
efficiency and the degree of market power, thereby addressing
endogeneity concerns.
The second issue associated with traditional Lerner calculation
is that MC estimation following Eq. (2) is likely to reflect some
form
of monopoly power that has arisen in the deposit market, based
on
the bank’s ability to raise funds at a cheap cost. Typically, in
pricing
loans, bank managers cover their funding costs, factor in a risk
pre-

mium to reflect the uncertainty surrounding the loan contracting
problem, and charge on top of that another premium to reflect
the exercise of their market power. So, effectively, some form
of
deposit market power is already reflected in the pricing of
loans.
Maudos and De Guevara (2007) argue that including financial
costs
and consequently the price of deposits in the cost function cap-
tures the effect of market power in banking and may bias the
find-
ings. By excluding funding costs, one is likely to obtain a
‘‘raw” or
‘‘clean” proxy of pricing power that is not distorted by market
power which had previously originated in the deposit market
while raising funds. More specifically, we estimate a variety of
Eq. (2) by including only operating costs (the price of labor and
the price of physical capital) in the translog cost function, and
omitting financing costs (the cost of funds). After calculating an
operating MC for each bank at each time period following Eq.
(3)
but including only two factor prices, we derive a funding-
adjusted
Lerner index from the structural model specified by Eq. (4).
The differences among the three Lerner specifications can be
briefly summarized as follows. Contrary to the conventional
Lerner
index, the efficiency-adjusted Lerner accounts for the inter-
relat-
edness of competition and efficiency. Thus, it may provide a
better
basis to examining the implications of the degree of market
power
on issues of efficiency and stability. The funding-adjusted
Lerner

further accounts for market power that may have previously
been
exercised in the deposit market, and which is otherwise likely to
bias the findings.
3.2. Bank efficiency
A voluminous body of literature has extensively investigated
the
concept of bank efficiency using theoretical and applied
models.11
10 More details on the estimation of the Lerner index can be
found in Berger et al.
(2009).
11 See Berger and Mester (2003).
Cost and profit efficiency levels measure how well a bank is
pre-
dicted to perform relative to other banks in a particular sample
or a peer group for producing the same output bundle under the
same exogenous conditions. Following the intermediation ap-
proach, banks are modeled as financial intermediaries that
collect
deposits and other liabilities and transfer them into interest-
earn-
ing assets such as loans and investments (Sealey and Lindley,
1977). Using parametric stochastic frontier analysis, cost and
profit
efficiency scores are estimated from the following equation:
ln A ¼ fðln Q; ln WÞþ ln e; ð5Þ
where A is either total operating costs or total profits, and Q
and W
denote bank output and input prices defined above. The
underlying

functional form used is the translog specification of Eq. (1)
where
the dependent variable is either bank profits of operating
costs.12
The error term e is decomposed into v � u (v + u) for the profit
(cost)
model, where v and u are two components that are assumed to
be
multiplicatively separable from the rest of the function. While v
is
a two-sided disturbance that accounts for uncontrollable
(random)
factors, u is a one-sided non-negative inefficiency term. Using
the
maximum likelihood technique, Eq. (5) is estimated separately
for
each country with bank fixed effects to derive individual bank
effi-
ciency scores (Battese and Coelli, 1992). Following Berger and
Mes-
ter (2003), alternative profit efficiency is preferred over the
standard
profit function because of the international dimension of the
sample.13
3.3. Bank stability
The Z-index assess overall stability at the bank level (Boyd et
al.,
2006; Berger et al., 2009). This proxy of bank stability
combines
indicators of profitability, leverage, and return volatility into a
sin-
gle measure. It provides information on the number of standard
deviation units by which profitability would have to decline
before

bank capitalization is depleted. It is given by the ratio:
Z ¼
ROA þ E=TA
rROA
; ð6Þ
where ROA and E=TA are the average return on assets and
equi-
ty to total assets, respectively, over the sample period, and
rROA
is the standard deviation of return on assets. The bank stability
indicator increases with higher profitability and capitalization
levels, and decreases with unstable earnings reflected by a
higher standard deviation of return on assets. Stated differently,
an increase (decrease) in the Z-index indicates a decrease (in-
crease) in overall bank risk exposure and more (less) bank
stability.
Since it is difficult to assess and capture bank stability using a
single measure, the sensitivity of the results is also checked
using
risk-adjusted measures of return for each bank following
Mercieca
et al. (2007) as:
RORROA ¼
ROA
rROA
and RORROE ¼
ROE
rROE
; ð7Þ

where RORROA and RORROE denote risk-adjusted ROA and
ROE,
respectively. Here again, higher values of risk-adjusted rates of
re-
turn indicate more bank stability.
Following the literature and in order to avoid taking the
logarithm of a negative
value, we add a constant to profits for all the banks in the
sample (Berger and Mester,
2003).
13 Also, information on output prices that is necessary for
estimating standard profit
efficiency is not available.
Table 1
Country and bank representation in the sample. Source:
BankScope.
Region Africa East/South Asia and Pacific Eastern Europe and
Central Asia Latin America and Caribbean Middle East
No. of countries 14 8 20 14 4
No. of banks 98 156 292 233 42
Average total assets (USD mn) 212.99 1494.44 486.97 619.39
4385.28
15 The results of specification tests indicate that a cross-section
analysis of banks is
preferred over a panel specification.
16 In such a two-stage approach, efficiency scores are derived
from a first-stage

regression and then their determinants are identified. This
methodology is preferred
over a one-stage model where the exogenous variables of Eq.
(8) enter as additional
controls in the cost/ profit functions.
17 We also implement a non-linear logistic specification by
transforming cost and
profit efficiency scores into ln[Eff/(1 � Eff)]. However, the
specification tests indicate
that Tobit models are preferred over the logistic transformation.
18 In order to reduce scale bias, we consider the natural
logarithm of the Z-index.
19 In the cost/profit functions from which efficiency scores are
estimated, equity
capital enters as a fixed netput, and the Z-index also includes
the ratio of equity to
assets, implying that equity is considered twice. Using
alternative measures of bank
risk in Eq. (8), RORROA and RORROE, lends more support to
the analysis.
20 Unlike the Lerner index, the estimation of concentration
ratios and the PR H-
statistic occurs at the country level.
21 We also test for the presence of endogeneity using an
instrumental Tobit model
775 769
4. Data and methodology
4.1. Data

The data consists of bank-level financial statements for the
years 1999–2005, retrieved from the BankScope database
provided
by Fitch-IBCA (International Bank Credit Analysis Ltd.) and
which
has comprehensive coverage in most countries. The sample in-
cludes commercial banks in developing countries from five
differ-
ent world regions, including Africa, East/South Asia and
Pacific,
Eastern Europe and Central Asia, Latin America and Caribbean,
and the Middle East.14 The original sample is filtered by
excluding
banks with less than three consecutive yearly observations, and
banks for which data on the main variables are not available
(such
as loans, personnel expenses or net income). We also exclude
obser-
vations with negative values for loans, interest revenues and
interest
expenses. This reduces the sample to an unbalanced panel of
821
banks in 60 countries or a total of 4670 observations. Tables 1
and
2 provide descriptive statistics on the sample.
In terms of the number of banks, the Eastern Europe and Central
Asia region dominates the sample, although the average size of
banks over the period under study is largest in the Middle East,
fol-
lowed by the East/ South Asia and Pacific region.
In contrast to loans to assets ratios usually observed for banks
in
developed countries (generally above 60%), the highest average

loans to assets is 51% for banks operating in Latin American
and
Caribbean and the lowest credit risk exposure in other emerging
markets stands at less than 39% for Middle East. This might
indicate
that banks in developing countries have fewer incentives to
engage
in lending activities compared to developed nations. It could be
that
the legal setup and investor protection in developing countries
do
not encourage banks to increase portfolio risk. Credit bureaus,
if
existent, may not play an active role in developing countries,
and
the presence of investor protection laws does not guarantee effi-
cient judicial enforcement. In this light, bank managers may
prefer
to generate income from other less risky uses of funds compared
to
loans. Figures in Table 2 indicate that banks in developing
countries
generally rely equally (if not more so) on other earning assets as
alternative uses of funds. To illustrate, in the case of African,
Middle
Eastern and Eastern Europe and Central Asia banks, the share of
other earning assets on average exceeds the proportion of loans
on banks’ balance sheets, while the two ratios are almost at par
with each other for East and South-East Asia banks.
Figures appearing in Table 2 also show that deposits and short-
term funding represent the main sources of funds, and that
banks
in developing countries are on average well capitalized. Banks
operating in the Middle East region have the lowest net interest
margin and return on assets, while the highest records of profit-

ability as measured by ROA are for banks in Africa.
4.2. Methodology
In order to investigate the implications of the degree of market
power on bank efficiency and stability, we run several cross-
sec-
tion regressions following the baseline model:
14 The grouping of countries into different regions follows the
World Bank
classification.
Y ¼ fðMarket Power; Portfolio Characteristics;
Regulatory EnvironmentÞ; ð8Þ
where the dependent variable Y measures each of bank cost effi-
ciency, alternative profit efficiency and stability, all of which
are
calculated at the bank level to run cross-section regressions.15
Since
bank cost and alternative profit efficiency scores are bound
between
zero and one, Tobit models are more appropriate because they
bet-
ter fit models where the dependent variable is derived from a
first-
stage regression (Greene, 2005).16 The results of specification
tests
also confirm that a Tobit specification is preferred to a
conventional
treatment of efficiency scores.17
In turn, the Z-index proxies bank stability, with larger values
indicating more bank stability and less bank risk potential.18 As
sensitivity checks, the two other risk-adjusted rates of returns
indi-
cators used are RORROA and RORROE.

19
The main independent variable in (8) is the degree market
power measured by the Lerner index, or the mark-up of price
over
marginal costs, with higher values implying higher pricing
power
and less competitive market conditions. The Lerner index is the
preferred measure of the degree of market power compared to
other traditional indicators of market structure because it is ob-
served at the bank level, similar to the unit of analysis of bank
effi-
ciency and stability to which they are related.20 The three
different
specifications of the Lerner index used include a conventional
Lerner,
an efficiency-adjusted Lerner, and a funding-adjusted Lerner
that
further accounts for market power arising in the deposit market.
The last two adjusted Lerner measures derive from a structural
model which, as explained in the previous section, better
account
for the inter-relatedness between market power and bank
efficiency,
thereby addressing endogeneity concerns.21
Bank portfolio characteristics include portfolio size (or bank
size measured by the natural logarithm of total assets) and mix
(or the bank’s credit exposure measured by the ratio of loans to
as-
sets). Previous studies have established that bank regulation,
supervision, and the institutional framework affect banking
system
soundness (see for example Beck et al., 2004 and Barth et al.,
2007).

Eq. (8) controls for the Regulatory Environment with two
indica-
tors, foreign ownership and legal rights. Foreign bank
ownership
for cost and profit efficiency, and a two-stage least-squares
model for the Z-index. The
specification tests indicate that exogenous models are more
appropriate than
instrumental models, probably because the above-mentioned
Lerner indices already
account for some of the endogeneity that is likely to be present
in the models.
Table 2
Descriptive statistics by region (% of total assets). Source:
BankScope.
Region Africa East/South Asia and Pacific Eastern Europe and
Central Asia Latin America and Caribbean Middle East
Net loans 42.38 49.84 46.90 51.06 38.52
Other earning assets 53.27 48.70 48.27 44.81 59.34
Total deposits 75.69 81.46 72.73 71.68 83.15
Total equity 13.78 10.55 17.49 18.83 10.88
Net interest margin 5.96 2.99 4.57 7.92 2.55
Net income 1.87 1.18 1.48 1.43 1.12
765–775
is a dummy variable that is set to one when foreign
shareholding
exceeds 50% of total bank ownership. Legal rights represents an
in-
dex measuring the degree to which collateral and bankruptcy

laws
facilitate lending. It ranges from 0 to 10 with higher scores
indicat-
ing that collateral and bankruptcy laws are better designed to
ex-
pand access to credit (Djankov et al., 2007). All regressions
include
the natural logarithm of per capita Gross Domestic Product to
con-
trol for different levels of economic development across
developing
countries. Finally, bootstrapping the standard errors of all
variants
of Eq. (8) allows a better comparison of results across
specifications.
5. Empirical results
5.1. Tripod estimation results
Table 3 presents the results of the country averages of the con-
ventional Lerner index, the efficiency-adjusted Lerner index,
the
funding-adjusted Lerner index, cost efficiency, profit
efficiency,
the Z-index, and risk-adjusted rates of return over the period of
1999–2005.
The conventional Lerner figures show varying degrees of
market
power across countries, but the figures are generally closely
aligned across all regions (around 30% price mark-up over
marginal
costs) except for Latin America and the Caribbean where the
con-
ventional Lerner is as low as 17%. The estimated efficiency and
funding-adjusted Lerner indices also vary across countries and

re-
gions.22 In line of the findings of Koetter et al. (2008) for US
banks,
the magnitude of the efficiency-adjusted Lerner generally
exceeds
that of the conventional index. Similarly, the funding-adjusted
Lern-
er is also, on average, larger than the conventional Lerner,
suggesting
that the latter generally overestimates the degree of market
power,
and thus justifying the use of alternative Lerner specifications.
The measure of bank cost (profit) efficiency is the actual level
of
costs (profits) relative to an efficient cost (profit) frontier. The
effi-
ciency estimation results appearing in Table 3 are in line with
those reported in the literature, with higher scores indicating
bet-
ter efficiency levels (see Berger and Humphrey, 1997). While
cost
efficiency levels are closely aligned across world regions, profit
efficiency levels exhibit greater disparity. It should be noted,
how-
ever, that cross-country efficiency comparisons are to be treated
with caution. It would be wrong to conclude that banks in the
Mid-
dle East are more profit efficient than banks in Latin America
and
the Caribbean. The reported efficiency averages per country or
per region can only serve as reference, since a different frontier
is
estimated for each country.23
Measures of bank stability indicate that, on average, banks

operating in Middle East developing countries appear to be ex-
posed to the lowest risk potential compared to banks in other re-
22 As might be expected, correlation analyses show that various
Lerner indices are
highly correlated.
23 In the robustness section, we estimate cost and profit
efficiency scores from a
common global frontier while controlling for individual country
effects. The main
results are maintained.
gions. This finding is corroborated using the Z-index and risk-
adjusted measures of returns. According to figures reported in
Ta-
ble 2, banks in the Middle East lend the lowest proportion of
assets
compared to banks in other regions, and they rely mostly on
other
earning assets as uses of funds. It could be the case that their
earn-
ings are likely to be more stable, resulting in higher Z-scores
and
risk-adjusted rates of return on average.24
The next section analyzes the implications of market power on
bank efficiency and stability in a multivariate setting which
con-
trols for bank and country differences.
5.2. Implications of market power
Table 4 reports the results of the different estimations of Eq. (8)
using bank cost and alternative profit efficiency as dependent
vari-
ables, and Table 5 considers the Z-index and RORROA
measures of

bank stability as exogenous variables. Each table includes the
three
different specifications of the Lerner index: a conventional
Lerner
(Model 1), an efficiency-adjusted Lerner (Model 2), and a fund-
ing-adjusted Lerner (Model 3).
Following Berger et al. (2009), we include a quadratic term for
the Lerner index to allow for a non-linear relationship between
competition and each of bank efficiency and stability. In order
to
establish the sign of the relationship between the independent
var-
iable (Lerner index) and each of the dependent variables, the
inflection point is calculated for every specification by setting
the
first-order derivative to zero and comparing its value to the
empir-
ical distribution of the Lerner index data. To illustrate, the
inflec-
tion point of Model 1 in Table 4 is �4.62, while the 1st
percentile
of the Lerner index data occurs at �0.43, implying that more
than
99% of the degree of market power data lies above the
inflection
point. Given that the sign of the quadratic coefficient in Model
1
is negative, the resulting estimated function is a downward ori-
ented or reverse parabola that decreases above the inflection
point.
Therefore, the empirical estimation supports a negative
association
between a bank’s degree of market power and its level of cost
effi-
ciency. A similar analysis for each estimated model reports the

sign
of the relationship between variables of interest (+/�).
The significant negative relationship between a bank’s degree
of
market power and cost efficiency holds across all three Lerner
specifications. This suggests that banks with more market power
operating in developing countries are not able to reduce costs
and achieve lower cost efficiency levels compared to their
peers.
Hughes et al. (2003) argue that management may signal market
power by maintaining large offices and other excessive
spending,
possibly driving significant cost efficiency losses. However,
except
for Delis and Tsionas (2009) who report similar negative
associa-
tion between market power and efficiency for a sample of EU
and US banks, the results do not agree with those reported for
developed countries. In the context of the EU, Maudos and De
Guevara (2007) find that banks with more market power are
able
to achieve higher cost efficiency levels, and Casu and Girardone
24 Interestingly, none of the four developing countries in the
Middle East has had to
bail out banks following the recent global financial crisis.
Table 3
Tripod estimation results for the degree of market power, bank
efficiency and stability. Source: Author’s calculations.
Country Conventional
Lerner

Efficiency-adjusted
Lerner
Funding-adjusted
Lerner
Cost
efficiency
Profit
efficiency
Z-index Risk-adjusted
ROA
Risk-adjusted
ROE
East/South Asia and Pacific
Bangladesh 21.79 70.91 67.51 82.56 33.62 45.48 2.45 2.55
Cambodia 46.75 57.40 56.53 82.54 44.51 40.64 3.61 4.67
India 24.96 62.37 56.64 84.21 34.79 24.90 3.28 3.26
Indonesia 23.47 64.56 60.06 82.15 48.10 23.78 2.55 2.95
Malaysia 44.28 78.48 77.20 76.91 51.31 40.55 1.68 1.44
Nepal 30.68 72.25 70.36 82.09 41.97 31.88 4.41 5.15
Pakistan 11.59 61.61 57.90 84.77 26.04 22.71 1.70 1.40
Vietnam 20.58 75.71 73.46 83.59 33.77 37.65 2.92 3.97
Average 28.01 67.91 64.96 82.35 39.26 33.45 2.83 3.17
Eastern Europe and Central Asia
Albania 27.67 57.43 55.13 83.46 29.31 24.10 1.62 1.86
Armenia 34.46 32.77 31.74 84.95 53.56 15.65 2.40 2.57
Azerbaijan 30.72 33.92 32.02 84.88 52.84 20.27 1.90 1.23
Bulgaria 29.31 40.05 37.81 84.00 31.87 50.75 3.59 4.57
Croatia 29.04 45.53 41.66 83.34 29.85 36.06 2.68 3.06
Czech Republic 25.30 62.18 60.11 82.22 24.64 29.66 1.56 2.21

Georgia Rep. 41.30 28.78 26.57 83.97 59.90 26.63 3.13 2.78
Hungary 24.60 46.01 40.84 84.28 36.95 19.69 1.77 2.35
Kazakhstan 32.17 35.44 31.65 79.68 43.20 20.39 2.40 2.30
Latvia 30.35 57.76 56.99 81.89 33.01 21.38 1.95 2.17
Macedonia 47.57 44.79 42.29 82.72 52.57 51.36 3.56 4.05
Moldova Rep. of 33.03 22.17 20.10 84.80 57.11 20.25 2.45 3.09
Poland 23.34 50.95 45.93 82.96 29.50 20.81 1.63 2.00
Romania 15.26 28.55 22.26 84.78 42.59 16.12 0.47 0.73
Russian Fed. 3.49 36.62 33.93 83.55 39.87 27.37 2.00 2.18
Serbia 41.42 10.66 8.84 88.37 46.97 103.93 4.79 4.78
Slovakia 19.72 30.77 28.67 85.95 29.88 18.31 1.72 2.17
Slovenia 26.72 60.68 56.57 82.75 30.74 36.87 2.35 2.96
Ukraine 25.76 33.23 27.29 84.99 36.54 20.62 1.38 1.42
Uzbekistan 38.98 26.41 20.83 85.45 50.12 22.74 4.54 3.83
Average 29.01 39.24 36.06 83.95 40.55 30.15 2.39 2.62
Latin America and Caribbean
Argentina 8.82 10.96 5.80 84.14 38.15 18.50 0.17 0.36
Bolivia 21.91 25.67 20.39 85.29 21.99 17.33 0.63 0.67
Brazil 23.00 46.17 35.11 78.21 52.99 20.88 2.46 2.33
Chile 20.71 46.22 45.05 81.18 33.69 25.69 1.12 0.97
Colombia 20.78 1.08 -11.15 86.88 37.51 11.71 1.13 1.12
Costa Rica 19.53 54.46 50.49 82.07 29.28 36.19 3.95 4.47
Dominican
Republic
11.41 37.19 30.28 84.29 44.89 20.27 2.09 2.45
Ecuador 19.53 15.39 14.65 86.33 28.12 27.45 2.49 2.10
El Salvador 26.98 42.25 38.62 82.64 31.82 34.65 1.61 1.75
Honduras 19.98 34.96 28.77 84.66 35.46 25.87 2.56 2.91
Paraguay 0.08 6.09 �18.05 88.99 27.78 16.14 1.92 1.89
Peru 22.35 18.02 13.74 84.82 23.32 27.00 1.72 1.51
Uruguay 6.07 53.06 37.83 89.57 25.75 12.52 0.49 0.70
Venezuela 28.95 18.16 13.03 84.56 47.72 18.78 2.66 2.32
Average 17.86 29.26 21.76 84.55 34.18 22.36 1.79 1.82

Middle East
Iran 30.53 79.35 76.71 88.01 76.96 21.45 3.06 2.50
Jordan 32.27 58.50 54.84 82.68 40.28 34.84 3.15 2.45
Lebanon 10.31 71.23 66.95 84.78 23.99 41.71 2.98 3.40
Saudi Arabia 43.12 61.86 58.42 83.20 64.49 75.80 6.20 5.49
Average 29.06 67.74 64.23 84.67 51.43 43.45 3.85 3.46
Africa
Angola 48.38 35.16 32.50 86.39 56.93 9.05 1.65 1.95
Burkina Faso 32.78 35.54 33.51 85.11 43.65 14.03 2.09 1.71
Cameroon 46.21 57.02 54.63 82.67 44.04 14.29 2.24 1.84
Congo 25.96 -33.17 -23.49 91.76 31.80 13.40 1.68 2.06
Ghana 26.85 29.72 25.25 83.95 51.93 27.78 5.63 4.88
Ivory Coast 32.90 27.25 25.06 85.27 25.89 12.50 0.67 0.60
Kenya 24.39 43.72 41.04 82.58 36.78 62.76 4.18 4.49
Mauritius 19.56 80.41 78.65 82.82 36.35 21.91 1.57 1.95
Mozambique 16.98 1.36 2.17 86.26 55.66 17.37 1.99 1.84
Nigeria 32.96 11.64 3.18 85.75 67.74 23.34 4.29 2.40
Senegal 30.07 38.47 37.11 85.35 42.02 26.36 3.83 3.06
Sudan 24.97 30.10 28.59 85.19 30.45 14.35 1.48 1.64
(continued on next page)
775 771
Table 3 (continued)
Country Conventional
Lerner
Efficiency-adjusted
Lerner

Funding-adjusted
Lerner
Cost
efficiency
Profit
efficiency
Z-index Risk-adjusted
ROA
Risk-adjusted
ROE
Tunisia 25.80 57.71 55.09 80.66 32.40 36.19 2.83 2.29
Zambia 35.78 11.48 10.31 84.28 61.04 12.01 2.67 2.82
Average 30.26 30.46 28.83 84.86 44.05 21.81 2.63 2.39
Lerner index is the price mark-up over marginal cost expressed
as a percentage of price (PTA � MCTA)/PTA. A higher Lerner
index indicates a higher degree of monopoly power.
Three varieties of the Lerner are reported, a conventional
Lerner, an efficiency-adjusted Lerner, and a funding-adjusted
Lerner. Cost and profit efficiency indices are estimated
employing stochastic frontier analysis. Higher scores indicate
better efficiency levels. Z-index = ((ROA + E/TA)/rROA) where
ROA is return on assets, E/TA is equity to assets, rROA
is the standard deviation of ROA. A larger Z-index indicates
more stability and less bank risk. Risk-Adjusted ROA and ROE
– calculated as ROA/rROA and ROE/rROE, respectively,
are other indicators of bank stability. All figures except for the
Z-index are in %.
Table 4

Market power and bank efficiency.
Dependent variable: cost efficiency Dependent variable:
alternative profit efficiency
Model 1:
conventional
Lerner
Model 2: efficiency-
adjusted Lerner
Model 3: funding-
adjusted Lerner
Model 1:
conventional
Lerner
adjusted Lerner
Model 3: funding-
adjusted Lerner
Lerner index �0.0351
(0.0122)***
�0.0365
(0.0061)***
�0.071
(0.0056)***
0.4241
(0.0714)***

0.0479
(0.0258)*
0.043
(0.0275)
(Lerner index)2 �0.0038
(0.0186)
�0.0125
(0.0153)
�0.0092
(0.0082)
0.0393
(0.1597)
�0.0208
(0.0093)**
�0.0156
(0.0157)
Loans to assets 0.0116
(0.0158)
0.0184
(0.0117)
0.0085
(0.0117)
�0.0562
(0.0335)*

�0.0833
(0.0416)**
�0.0808
(0.0448)*
Ln (total assets) 0.0012
(0.0014)
0.002
(0.0011)*
0.0032
(0.0010)***
�0.01
(0.0038)***
�0.0015
(0.0056)
�0.0012
(0.0047)
Foreign
ownership
�0.01
(0.0064)
�0.0093
(0.0047)**
�0.0054
(0.0052)

0.0244
(0.0219)
0.0282
(0.0244)
0.0278
(0.0229)
Legal rights �0.0005
(0.0010)
�0.0004
(0.0011)
0.0018
(0.0011)
�0.0055
(0.0042)
�0.003
(0.0045)
�0.0033
(0.0052)
Ln (GDP pc) �0.0056
(0.0013)***
�0.0051
(0.0013)***
�0.0054
(0.0011)***

�0.0049
(0.0056)
�0.0189
(0.0075)**
�0.0186
(0.0078)**
Inflection point
Sign of
relationship
�4.62
–
�1.46
–
�3.86
–
�5.40
+
1.15
+
1.38
+
Marginal effects �0.0104 �0.0226 �0.0347 0.0273 0.0364
0.0311
Results from Tobit regression models to explain the

implications of market power on bank cost efficiency and
alternative profit efficiency. Bank cost efficiency is derived
from
a cost function, and profit efficiency is derived from an
alternative profit function. Higher values of cost and profit
efficiency scores indicate better cost and profit efficiency
levels. The degree of market power is proxied by the Lerner
Index or the price mark-up over marginal cost, with higher
values indicating a higher degree of pricing power.
Three different specifications of Lerner are included, a
conventional Lerner, a funding-adjusted Lerner, and an
efficiency-adjusted Lerner. The natural logarithm of total assets
accounts for bank size, and the loans to assets ratio accounts for
the portfolio mix of banks. Foreign bank ownership implies that
total foreign shareholding exceeds 50% of
total bank ownership (BankScope). Legal rights measures the
degree to which collateral and bankruptcy laws facilitate
lending (Djankov et al., 2007). The natural logarithm of
per capita GDP accounts for differences in economic
developments across countries.
All models are run with bootstrapped standard errors (reported
in parentheses) clustered by country. Data are for a cross-
section of 821 banks from 60 developing countries.
* p < 0.1.
** p < 0.05.
*** p < 0.01.
765–775
(2006) conclude that the effect of competition on cost efficiency
is
not clear-cut. The findings are also not in line with those for the
US
reported by Koetter et al. (2008), namely that banks with more

market power are also the most cost efficient. One should be
cau-
tious, however, before concluding that the results support the
‘‘quiet life” hypothesis. It is likely that the higher costs that are
associated with more market power are eventually channeled to
bank clients which, in turn, may feed into higher prices and
possi-
bly boost bank profit efficiency.
The results indicate that bank portfolio composition and size
are not significant determinants of cost efficiency using the
con-
ventional Lerner, but the coefficient on total assets turns
signifi-
cantly positive when using the efficiency and funding-adjusted
Lerner indices. This suggests that, when accounting for
endogene-
ity bias, larger banks are able to achieve higher cost efficiency
lev-
els. Foreign banks presence is associated with lower cost
efficiency
levels, but this finding is significant only when considering the
effi-
ciency-adjusted Lerner. The regulatory environment in terms of
le-
gal rights does not significantly affect cost efficiency, while a
higher level of economic development is significantly
negatively
associated with bank cost efficiency. It could be that growth
strat-
egies in developing economies outweigh cost efficiency
consider-
ations over the short-term.
While it seems that banks do not respond favorably to a higher
degree of market power in terms of controlling costs more

effec-
tively, it is important to assess whether they are able to generate
more revenue and/ or improve the performance of their lending
activities. Table 4 also shows the implications of the degree of
mar-
ket power (using the three different Lerner specifications) on
bank
alternative profit efficiency. The corresponding inflection point
for
Model 1 is estimated at �5.40, and the sign of the quadratic
term is
positive, pointing to a direct association between market power
and alternative profit efficiency. This significant positive
relation-
ship is persistent across all models of alternative specifications
of
the Lerner index. The findings provide evidence against the
‘‘quiet
life” hypothesis. They are opposite to those reported by Schaeck
and Cihak (2008) who establish a positive effect of competition
on
alternative profit efficiency for EU and US banking. In
developing
Table 5
Market power and bank stability.
Dependent variable: Z-index Dependent variable: risk-adjusted
ROA
Model 1:
conventional
Lerner

adjusted Lerner
Model 3: funding-
adjusted Lerner
Model 1:
conventional
Lerner
adjusted Lerner
Model 3: funding-
adjusted Lerner
Lerner index 0.5162
(0.1408)***
0.2948
(0.1176)**
0.3184
(0.1017)***
3.6224
(0.6644)***
1.4498
(0.3263)***
1.4766
(0.3795)***
(Lerner index)2 0.0412
(0.0673)

0.0718
(0.0966)
0.0983
(0.0896)
0.2979
(0.6073)
0.1068
(0.3259)
0.1887
(0.2760)
Loans to assets 0.4534
(0.1782)**
0.4475
(0.1537)***
0.4693
(0.1595)***
1.2634
(0.7101)*
1.1474
(0.6826)*
1.2382
(0.9213)
Ln (total assets) 0.0132
(0.0241)

0.0096
(0.0186)
0.012
(0.0229)
0.2514
(0.0743)***
0.2649
(0.0723)***
0.2782
(0.0778)***
Foreign
ownership
�0.1123
(0.0825)
�0.12
(0.0977)
�0.1269
(0.0863)
�0.1625
(0.4025)
�0.1753
(0.4032)
�0.2008
(0.3955)

Legal rights 0.0585
(0.0169)***
0.0536
(0.0162)***
0.052
(0.0176)***
0.0523
(0.0925)
0.0418
(0.0765)
0.0354
(0.0708)
Ln (GDP pc) 0.0243
(0.0299)
0.0092
(0.0292)
0.0115
(0.0370)
�0.2148
(0.1202)*
�0.3285
(0.1086)***
�0.3186
(0.1046)**

Inflection point
Sign of
relationship
�6.26
+
�2.05
+
�1.62
+
�6.08
+
�6.79
+
�3.91
+
Marginal effects 0.1238 0.1449 0.1467 0.3443 0.2516 0.2386
Results from regression models to explain the implications of
market power on the Z-index and risk-adjusted returns. The
natural logarithm of the Z-index, a proxy for bank
stability is calculated as the ratio (ROA + ETA)/rROA, where
ROA is return on assets, ETA is equity to assets and rROA is
the standard deviation of ROA. Higher Z-index values
indicate more bank stability. The other dependent variable, risk-
adjusted ROA, is another proxy for bank stability. It is
calculated as ROAi=rROAi ; where ROAi is the bank’s
average return on assets. Higher risk-adjusted ROA values
indicate more bank stability. The degree of market power is

proxied by the Lerner index or the price mark-up over
marginal cost, with higher values indicating a higher degree of
pricing power. Three different specifications of Lerner are
included, a conventional Lerner, a funding-adjusted
Lerner, and an efficiency-adjusted Lerner. The natural
logarithm of total assets accounts for bank size, and the loans to
assets ratio accounts for the portfolio mix of banks.
Foreign bank ownership implies that total foreign shareholding
exceeds 50% of total bank ownership (BankScope). Legal rights
measures the degree to which collateral and
bankruptcy laws facilitate lending (Djankov et al., 2007). The
natural logarithm of per capita GDP accounts for differences in
economic developments across countries.
All models are run with bootstrapped standard errors (reported
in parentheses) clustered by country. Data are for a cross-
section of 821 banks from 60 developing countries.
* p < 0.1.
** p < 0.05.
*** p < 0.01.
25 Similar results (not reported) obtain when risk-adjusted ROE
is used.
775 773
countries, banks with more market power are able to derive
signif-
icant revenue gains from diversified portfolios. Also, the results
using the conventional Lerner show that banks that lend a
higher
portion of their assets are significantly less profit efficient, and
that
larger banks are marginally less profit efficient compared to
their

peers. This finding is in line with the literature that reports
little
or no benefit from consolidation and conglomeration (De
Nicoló,
2000).
Table 5 shows the results of the implication of market power on
bank stability, using the Z-index and risk-adjusted return on
assets
as proxies of overall bank stability.
The inflection point for Model 1 of Table 5 using the Z-index
and
the conventional Lerner specification is �6.26, which is far
below
the 1st percentile of the conventional Lerner index data. Since
the sign of the quadratic term is positive, the estimated function
is an upward parabola that rises after the inflection point,
implying
a direct relationship between the degree of market power and
the
Z-index. The results point to a significant and positive
relationship
between a bank’s degree of market power and its level of
stability
across all Lerner specifications. This suggests that banks with a
lar-
ger degree of market power also enjoy a higher level of overall
bank stability and reduced risk potential. The findings for
develop-
ing countries thus support the traditional view on the trade-off
be-
tween bank competition and stability, and do not agree with
those
reported by De Nicoló et al. (2004), Boyd et al. (2006) ,
Schaeck

et al. (2009).
Banks which lend a greater portion of their assets exhibit signif-
icantly higher Z-indices, suggesting that firms that have a
higher
credit risk exposure (higher loans to assets ratios) are in fact
ex-
posed to a lower level of overall bank risk. It could be the case
that
banks in developing countries which are active in extending
credit
to the economy hedge their portfolios or hold more equity
capital
in order to reduce their risk potential and ensure that their
overall
stability is safeguarded. This is crucial given the fact that
capital
markets are practically non-existent in developing economies,
and banks represent the main source of credit for firms. Further,
better protection of legal rights fosters bank stability, while
foreign
banks presence does not seem to have a significant effect on
overall
bank risk exposure.
In order to check the sensitivity of the Z-index results to other
indicators of bank stability, Table 5 also shows the implications
of market power on risk-adjusted ROA.25 Here again, a positive
sign
is consistently reported between different measures of market
power and risk-adjusted ROA. This indicates that a higher
degree
of market power is significantly positively associated with
larger
risk-adjusted rates of returns. Thus, the sensitivity checks using
proxies other than the Z-index support the positive association

be-
tween market power and bank stability. As markets become
more
concentrated and banks gain market power, financial
conglomerates
in developing countries are likely to benefit from greater overall
financial stability and lower variability of returns. The
coefficients
on loan portfolio composition and bank size are positive and
gener-
ally significant, implying that banks that engage in more
lending
activities are able to achieve higher risk-adjusted rates of
return,
and the effect is more pronounced at larger banks.
To sum, the empirical analysis for developing countries shows
that a higher degree of market power results in profit efficiency
gains and enhanced bank stability, despite significant cost effi-
ciency losses. In order to analyze the marginal effects of a
higher
degree of market power on key variables of interest, the bottom
row of Tables 4 and 5 reports the computed market power
Table A.1
Descriptive statistics on variables entering the cost and profit
functions. (Variables are in logarithmic format.) Source:
BankScope.
Region Cost Profit Q Wl Wk Wf Z1 Z2 Z3
East/South Asia and Pacific 10.28 8.74 13.02 �4.67 �0.22
�2.97 8.25 10.84 10.43 Mean
1.65 1.69 1.55 0.65 0.60 0.57 1.62 2.10 1.28 Std. dev.

4.29 1.61 8.50 �8.46 �2.46 �6.55 3.26 3.74 7.31 Min.
13.48 13.33 16.81 �2.34 0.88 �1.02 11.55 14.64 13.44 Max.
Eastern Europe and Central Asia 9.46 7.95 11.99 �4.08 �0.41
�3.16 8.23 9.38 10.01 Mean
1.40 1.60 1.50 0.73 0.74 0.74 1.38 2.37 1.20 Std. dev.
4.20 2.71 8.03 �7.32 �3.56 �6.93 3.22 1.39 6.85 Min.
13.37 13.32 16.08 �2.29 0.88 �0.69 11.49 14.65 13.38 Max.
Latin America and Caribbean 10.24 8.31 12.20 �3.75 �0.20
�2.59 8.31 10.55 10.23 Mean
1.55 1.66 1.53 0.80 0.75 0.86 1.74 2.36 1.18 Std. dev.
5.30 0.69 7.86 �9.93 �3.23 �5.67 2.08 2.94 6.91 Min.
13.57 13.52 17.01 �2.27 0.88 �0.64 11.54 14.66 13.42 Max.
Middle East 11.12 9.44 14.06 �4.70 �0.81 �3.22 9.47 11.43
11.06 Mean
1.52 2.11 1.66 0.49 0.65 0.63 1.36 1.80 1.15 Std. dev.
5.31 1.95 10.25 �6.89 �2.93 �4.68 6.37 2.56 7.39 Min.
13.54 13.89 17.48 �3.25 0.83 �1.62 11.55 14.56 13.42 Max.
Africa 9.18 7.88 11.65 �3.95 �0.33 �3.34 8.17 9.74 9.48 Mean
1.12 1.57 1.18 0.77 0.78 0.88 1.40 1.82 1.08 Std. dev.
5.63 2.30 8.15 �8.18 �3.56 �10.3 4.03 3.99 6.83 Min.
12.10 11.40 14.73 �2.29 0.86 �1.17 11.18 13.31 12.58 Max.
Total 9.94 8.31 12.36 �4.13 �0.34 �2.98 8.32 10.21 10.16
Mean
1.57 1.73 1.62 0.81 0.73 0.80 1.57 2.32 1.24 Std. dev.
4.20 0.69 7.86 �9.93 �3.56 �10.3 2.08 1.39 6.83 Min.

13.57 13.89 17.48 �2.27 0.88 �0.64 11.55 14.66 13.44 Max.
765–775
elasticities across all models. It is interesting to note that the
abso-
lute value of the cost efficiency elasticity of market power is
well
below the profit efficiency elasticity of market power for two
Lern-
er specifications, and that the two marginal effects are almost
sim-
ilar in absolute terms using the funding-adjusted Lerner. For
example, using Model 2, a one percent increase in the degree of
market power, on average, reduces bank cost efficiency by
2.26%
and improves profit efficiency by 3.64% across developing
coun-
tries. Further, the positive relationship between all Lerner
indices
and bank stability is also translated into positive elasticities or
marginal effects.
6. Sensitivity analysis
We run a series of sensitivity checks on the baseline model of
Eq. (8).26 First, the logistic transformation is implemented
instead
of a Tobit model when bank efficiency scores are considered.
We also
estimate cost and profit efficiency scores from a global frontier
while
accounting for country differences instead of using the country-
spe-
cific estimates of efficiency. Using these different estimates,
the pre-

vious findings on the implications of market power on bank cost
and
profit efficiency levels are maintained for developing countries.
Other robustness checks consist of removing the quadratic term
of the Lerner index from all specifications, and including
control
variables (other than legal rights) which are retrieved from the
World Bank’s Doing Business for the business environment.
The
main previous results obtain.
Finally, a time dimension is introduced for the Z-index and for
all other variables (except for Regulatory Environment), and
panel
regressions are run as sensitivity checks. The Z-index is
allowed to
vary across time for each bank following De Nicoló et al.
(2004),
and using the following equation:
Zit ¼
ROAit þ E=TAit
jROAit � ROAij
; ð9Þ
where ROAit and E/TAit are the bank’s return on assets and
equity to
total assets at time t, respectively, and ROAi is the period
average re-
26 The results are not reported in order to conserve space, but
they can be obtained
from the author.
turn on assets for bank i. Similarly, time-varying efficiency
scores

and Lerner indices are considered to estimate the baseline
model
of Eq. (8). The advantage of the panel dimension of the data is
that
it allows investigating the impact of lagged values of market
power
on bank efficiency and stability, thereby addressing resilient
endo-
geneity concerns, notwithstanding the questionable variability
of
the Z-index across time. We run the estimations using bank
fixed
effects and with robust standard errors clustered by country.
The
results (not reported) support the previous findings using the
cross-section models. When banks in developing countries enjoy
a
higher degree of market power, they do not manage their costs
effectively. Instead, they are able to achieve higher profit
efficiency
levels while at the same time deriving greater firm stability.27
7. Summary and conclusions
Most emerging countries have recently embraced financial lib-
eralization as a means to achieve higher rates of economic
growth.
With the globalization of financial services worldwide, the bank
model is shifting toward a universal banking system to provide
a
wide array of financial services (including commercial
activities,
investment banking and insurance underwriting) under the um-
brella of the same financial conglomerate. As competitive
condi-
tions tighten and banks seek to increase their degree of market
power, policymakers are concerned with the overall

implications
of changing banking structures, especially in light of the
adverse
implications of the late global financial turmoil onto developing
countries.
The relationship between competition policies and financial
stability is poorly documented for developing countries and no
consensus prevails in the literature on the implications of
market
power on bank stability. This paper examines the impact of a
high-
er degree of market power on each of bank efficiency and
stability.
Using data from 821 banks in 60 developing countries over the
per-
iod 1999–2005, we compute proxies for the degree of market
power, bank efficiency and bank stability, all of which are
27 We also account for endogeneity using instruments to
explain the degree of
market power, including activity restrictions, banking freedom
indicators and the
percentage of state-owned assets (Berger et al., 2009). The main
findings obtain.
775 775
estimated at the bank level. This tripod empirical approach may
represent a more holistic way of analyzing the competition-
stabil-
ity nexus in banking. We find a significant negative association
be-
tween bank market power and cost efficiency, and a significant
positive relationship between market power and each of bank

profit efficiency and overall stability.
In developing countries, banks that command a high price
mark-up over marginal costs may be reasonably adept at
improv-
ing their profit efficiency, but they do not do so well in terms of
cost efficiency levels. As geographical and regulatory borders
re-
cede and the use of information technology intensifies, the
global
dimension of banking may evolve to create new opportunities
for
bank managers who ensure a wider spectrum of returns, while
possibly passing on the resulting excessive costs to their clients.
Further, as banks gain market power, they also benefit from
greater firm stability and reduced risk potential. This result sup-
ports the traditional view that increased competition may under-
mine bank stability. It can also provide a rationale for the
intense
merger activity that has taken place over the last two decades in
the context of developing countries. More importantly, the
finding
may be relevant for policymakers in developing countries where
the banking system is strained. The global dimension of the
recent
financial crisis has demonstrated that no country is immune to
the
turmoil hitting financial markets in developed countries. While
antitrust laws in the US ensure that banking markets remain
com-
petitive (Berger et al., 2009) and competition policy is taken
seri-
ously in the EU (Carletti and Vives, 2008), the results suggest
that increased market power in stressed banking systems of
devel-

oping countries may in fact be welcome due to the likely
increase
in bank soundness.
Acknowledgements
I am grateful to Philip Molyneux, Claudia Girardone, Iftekhar
Hasan, Gianni De Nicoló, Ike Mathur (editor), and an
anonymous
referee for their helpful comments and suggestions, in addition
to the participants at the Tor Vergata 2008 and the FMA 2008
con-
ferences. Any remaining errors are my responsibility.
Appendix A
See Table A.1.
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On the implications of market power in banking: Evidence from
developing countriesIntroductionLiterature reviewMarket power
and bank efficiencyMarket power and bank stabilityTripod
estimation methodology: market power, bank efficiency and
bank stabilityMarket powerBank efficiencyBank stabilityData
and methodologyDataMethodologyEmpirical resultsTripod
estimation resultsImplications of market powerSensitivity

analysisSummary and conclusionsAcknowledgementsAppendix
AReferences
gifford-how-to-referee.pdf
How to Referee a Research Paper
Divc GitTord
(with help from Roy Lcvin, Jim Horning, and Bob Ritchie)
Februaiy 11. 19S2 10:52AM
To start. let’s imagine that an author has sent his new paper to a
journal to be considered for
publication. The journal’s editor must decide if the paper should
be published and, if it is to be
published, how it can be improved. IThe editor is responsible
for ensuring that published papers are
significant. accurate, and clear, as well as for the timely
publication of important material. To
accomplish this the editor sends the paper to three to five
experts, or referees for analysis. Based
on the referee’s reports the the editor will decide to do one of
three things with the paper:
1. Reject.
-
2. Unconditionally accept.
3. Accept on the condition that the author makes certain minor
revisions. These revisions
are usually based on suggestions that the referees have made.

4. Return for major revision to be followed by another round of
refereeing.
There is an important difference between a review and refereer
report. The purpose of a review is
to evaluate the final form of a paper for a general audience.
Book reviews, movie reviews, and
other sorts o reviews arc analogs in other fields. The purpose of
a referee’s report is to provide
construcove criticism on an intermediate form of a paper for an
audience of two: the editor and the
author.
To this end, a referee’s report should include:
1. Simple things like the name of the paper, its author, the
referee’s name, and the date of
the report
2. A brief discussion of the manuscript’s content, its
importance, and its relation to other
works in the field.
3. A recommendation as to whether the editor should publish
the paper or not. The
recommendation should be made on the grounds of the paper’s
importance, originality,
and clarity. Different referees sometimes will make different
recommendations, and the
editor will typically make his or her own decsion.
A referee can also suggest that the paper is more appropriate for
another journal. For
example, the paper might be too theoretical for a general
audience.
4.’ Jnformaüon relevant to making the publish/don’t ubIish
decisidn. Basically, th

reasoN- for the recommendation should be documented,
together with any factors that
mighr’r.end to shift the balance the other way. For example.
say: “this. is a rehash of
mateiL,priginal1y published in...” instead of “this isn’t very
original”; “it will be
important for...” not just “this is novel”; include statements like
“the strong pointS
are.. but it suffers from the following defects...”.
5. Constructive criticism for the author. How could the
manuscript be improved? Why
isn’t it publishable? What specified errors should be correctcd?
What relevant work
should be compared, or at least referenced? How could the
organization of the paper
be improved?. How could the English be improved?
The finished report should be concise, clear, and convincing.
Although editors sometimes read
papers themselves, they usually prefer reports that do not
assume that they have read the paper or
that they arc experts in its specialty.
I likc to structure my reports in two sections: general comments
and specific comments. General
comments are organized along thematic lincs. Specific
comments follow the order of the text in the
paper. pcintng out things that I didn’t understand. inaccuracies,
misspc1!c words, clumsy wording.
etc. If you have caustic comments to make, put them in a cover
lcctcr c in a separate section so
thc editor can easily excise them before he scnds the report to

the author. The cover letter is also
useful to transmit other information, e.g. “I also refereed this
papcr for journal X -- why is the
author submitting it multiple places?”.
Here are some things to keep in mind when you write a report:
1. Take quick tuimaround seriously. Nothing is worse than
agreeing to do it and delaying
months. A timely return with ocher suggested potential referees
is infinitely better.
Delays in refereeing tend to make journals seem like old news,
publishing “new work”
that is in fact several years old. For many journals, the delays in
refereeing are the
primary contributor to the long interval between submission of a
manuscript and its
publication.
2. If you agree to referee something, recall you are the quality
control for the correctness
of the asserted results, so take that part seriously.
3. Read the paper, draft or sketch a report immediately, ignoring
second thoughts. Then
set the report aside for a few days. If on rereading your draft it
still seems right, polish
it a bit and send it oiL If it needs modification or you have
second thoughts, now is
the time to change it.
4. Remember the editor is the final arbiter, and he or she wants
your honest and best
judgement, even if you are not completely sure it is correct.
You are not the only

referee, and if you are wrong, the others will catch it, or the
author can correct you on
rebuttal.
5. Beginning referees often feel that they never get a paper that
really falls within their
realm of expertise. This is probably because beginning referees
are usually Ph.D.
students who have been concentrating on a very specific topic.
Don’t refuse to referee
a paper just because it isn’t directly related to your thesis topic.
Read the papers
referenced by the manuscript to learn about the subject area.
You will learn a lot from
the process.
6. Refereeing is part of the tax on competent professionals for
publishing their own work.
Failure to pay the tax will diminish your standing in the
professional community.
However, there are many good reasons for not agreeing to
referee a specific paper. If
you know you can’t referee the paper in a reasonable amount of
time (leaving
tomorrow for a two month vacation), if you have a complete
lack of interest in the
subject area (why did they send me a paper on ardvarks?), or if
the area is truly above
your head (Unified field theory?), then decline.
7. Remember, your primarj responsibility is to the readership of
the journal. Fairness to
the author is important (you will be one too!), but definitely
secondary. Don’t
recommend acceptance of a substandard paper just because the

author has worked hard.
An editor can more easily soften a coo-critical report than
toughen a lax one.
8. You should be objective. If you disagree with the approach of
the author, it may be
that neither yours nor the author’s approach has been
definitively proved superior.
Thus, you should set aside this disagreement to evaluate the
paper objectively.
9. You should not take unfair advantage of the unpublished
results you read in
manuscripts. - - -
10. Try not to be too authoritarian in your report. -
Have fun!
guidelines.rtf
A referee report on an academic paper.
The paper has been provided it’s the Turk Ariss, R. (2010) On
the implications of market power in banking: Evidence from
developing countries. Journal of Banking and Finance 34: 765-
775
Rima Turk ArissNo plagiarism4000 words
20+ academic journals referencing Harvard style.
Abstract
Introduction
The view of the paperLiterature reviewSupporting views
Opposing viewsYour Criticism Suggestions on how to improve
the paperYour viewEvaluation
Analysis
Akademik please do your best for this paper do all the required

research about how to write a referee report on an academic
journal. Improvise to get an outstanding paper at the end.
guidereferee.pdf
Page 1 of 2 8/28/2013
The University of British Columbia
Department of Economics
Economics 560: Economics of Labour
Professor Nicole M. Fortin
Fall 2013
Tuesday, Thursday: 14:00-15:30
Buchanan B218
Guidelines for your Referee’s Report
In the course of this class, you will be asked to write two (2)
referee reports on assigned
papers that are at the “working paper” stage. As you pursue a
career as a professional economist,
writing referee reports will become part of your usual duties. As
a student, it will give you the
opportunity to study a paper in great detail, develop critical
thinking skills, and learn about the
fine craft of economic writing. Your referee reports should be

3-4 pages long and should include
at the end a recommendation to the editor (that normally goes in
the cover letter) as to whether
the paper should be (1) accepted for publication as it stands, (2)
accepted, subject to minor
revisions, (3) returned to the author for major revisions, a
judgement on publication to be made
after resubmission, or (4) rejected. Let’s assume that the paper
has been directed at a high level
field journal such as the Journal of Labor Economics or the
Journal of Human Resources of
which I am a co-editor.
A referee report normally begins with a short summary of the
objectives of the paper, and
of what the authors have accomplished in the paper. The key
questions that you want to answer in
this part of the report are: What did the authors view themselves
as doing and what did they
accomplish? This part should generally be no more than one
half-page to one page long, given
the editor has also read the paper. It should be quite neutral in
tone as if you were recording the
information for yourself. Your summary of the paper is a way of
establishing your credibility to
the authors and the editor, who want to know that you have
carefully studied their paper and that
you have understood the key points made by the paper and the
nature of the contribution to
knowledge. You are also providing the authors, and the editors,
with an alternative introduction to
and a summary of their work.
At this stage, you may also want to place the paper in the
literature and may wish to

indicate which parts (theoretical development, empirical results,
methodology, policy
implications) make a (i) very important contribution, or (ii)
fairly important contribution or (iii) a
not very important contribution to the literature. Note that it is
the authors’ responsibility to
establish the fact that an important contribution has been made.
You may want to peruse a key
paper that the authors cite as a source of debate or that provides
the motivation for their paper.
You may also wish to make a brief comment on the expositional
qualities of the paper; that is, is
the paper straightforward to read and self-contained, or is the
exposition convoluted. You
shouldn’t hesitate to make positive comments, if warranted,
even if your judgement is ultimately
going to be harsh.
The second part of the report, the critical analysis, is the most
important one and should
be 2 -3 pages long. In this part you discuss the merits of the
paper itself and whether the paper
does make a contribution to knowledge that is worthy of
publication in this prestigious journal.
You may want to organize your discussion by going from the
big picture to the smaller details.
Page 2 of 2 8/28/2013
1) Overall view: The most important question to answer here is:
Did the paper

accomplish what it set out to do? Did the authors take the best
approach, and were
they successful in their approach? You may also want to
evaluate the importance and
originality of the question. Many papers are correct in their
internal logic and fill
some gap in the literature, but do not make an important
advance in knowledge.
2) Main concerns: If there are some critical problems with the
manuscript in that the
authors’ analysis is incorrect in some manner then it is
important to state these
problems clearly in your evaluation. Here is a list of potential
problems, luckily only
a few might be present in the paper that you are evaluating. It
could be that a) the
description of the related literature is inappropriate to the actual
material in the paper;
b) the logical argument is not tight, including incorrect
application of economic
concepts or erroneous mathematical derivations; c) the
econometric tools are being
used inappropriately; d) there is only a loose link between the
economic model and
the empirical analysis; e) the coefficients of interest are not
properly identified or not
shown to be robust enough; f) the interpretation of empirical
results is inappropriate
given the available data and econometric strategy used or goes
beyond what has
actually been proven, g) the conclusions are incorrectly made or
expressed; and h)
the contribution to the literature is inaccurately described. You
can also comment on
the structure and organization of the paper and make

suggestions as to whether the
reader might be better served by alternative ways of presenting
and discussing the
theory and/or empirical results. Avoid making suggestions that
the authors have not
hope to being able to follow. If you are suggesting a “revise and
resubmit” (options 2
and 3 in the opening paragraph above), you should point out
some possible solutions
to the problems that you identify.
3) Smaller points These are concerns that are usually easily
corrected, but that are
important nevertheless. You may want to mention a) areas
where the author's line of
thought is hard to follow or confusing; b) important
mathematical derivations that
are obscure; c) spelling and grammatical errors; d) missing data
sources and poorly
constructed Tables or Figures; e) references to the literature
that are missing or
incorrect.
how_to_referee.pdf
How
to
Write
a
Referee
Report
Mar/n

Farnham
Refereeing
is
essen/al
to
academic
research
• Journal
editors
are
the
ul/mate
gatekeepers
when
it
comes
to
publica/on
– Their
job
is
to
decide
what
papers
are
1)

credible;
2)
important;
and
3)
of
interest
to
their
readership
– With
hundreds
of
papers
submiDed
each
year,
editors
need
help
making
judgments
on
which
get
published
– Referees
are
unpaid,

specialized
assistants
to
editors
in
this
process
Refereeing
is
essen/al
to
academic
research
• Credibility
is
obviously
very
important
– If
studies
are
published
that
use
flawed
methods

or
falsified
results,
the
credibility
of
the
en/re
field
suffers
– Good
gatekeeping
by
an
editor
and
his/her
referees
benefits
all
of
us,
even
if
we
don’t
publish
in
that
journal

– Bad
gatekeeping
reflects
badly
on
all
of
us
Refereeing
is
essen/al
to
academic
research
• Since
publica/on
is
a
signal
of
quality,
refereeing
allows
us
to
beDer

select
our
reading
material
(i.e.,
it
saves
us
/me)
– Good
editors
(and
referees)
pick
up
good
new
work
by
young
unknown
scholars
that
we
might
not
otherwise
know
was
there

– Without
expert
quality
control,
we’d
just
sit
around
reading
the
same
old
people
with
good
reputa/ons
• Because
that’s
the
only
signal
of
quality
we’d
receive
How
does
it
work?

• Your
first
referee
report
may
be
given
to
you
by
your
advisor
– It’s
good
training
for
you
– It’s
a
good
way
for
your
advisor
to
see
how
you
think
and

what
you
know
– It’s
also
a
good
way
for
your
advisor
to
make
it
home
in
/me
for
dinner
• As
you
get
known
in
the
field,
editors
will
begin
to

send
you
requests
to
review
manuscripts
Handling
requests
to
review
• If
asked
to
review
a
manuscript,
your
obliga/on
is
to
– Referee
the
work
if
you
are
qualified

– Tell
the
editor
if
you
are
not
qualified
to
referee
the
work
– Suggest
appropriate
alterna/ve
reviewers
if
you
can’t
do
the
review
– Agree
to
a
/me
by
which
you

will
complete
the
review
– Complete
a
substan/ve
and
helpful
review
on
/me
Is
it
ever
OK
to
say
“No!”?
• At
this
point
in
your
career,
only

if
you’re
not
qualified
to
comment
or
if
there’s
a
conflict
of
interest
• Later
on,
you
may
find
yourself
becoming
popular
with
editors
– Kees,
Daniel,
others
get
swamped
with
requests

– If
demand
for
your
services
gets
too
high,
you
can
start
refusing;
but
that’s
years
off
at
this
point
– Then
you’d
start
refusing
to
referee
for
journals
that
are
of
less

interest
to
you
(and
that
you
intend
never
to
publish
in)
Is
it
ever
OK
to
say
“No!”?
• Keep
in
mind
that
if
you
refuse
to
referee
for
a

journal
(more
than
once
or
twice)
you
may
begin
to
reduce
your
probability
of
gebng
published
at
that
journal.
• Some
journals
require
you
to
referee
if
you
submit
to
them

– Berkeley
Electronic
Press
requires
that
you
do
two
referee
reports
for
every
paper
you
submit
– Or
else
pay
them
~$250
per
submission.
What
does
a
referee
do?

• Your
job
is
twofold.
You
1)
advise
the
editor;
and
2)
advise
the
author(s)
– Ul/mately,
your
job
is
to
tell
the
editor
whether
or
not
they
should
publish
it
and
why

or
why
not
– But
along
the
way
you
do
a
valuable
assessment
of
the
paper
which
you
are
expected
to
share
with
the
authors
– Generally,
a
referee
writes
two
documents

• Comments
for
the
authors
• A
review
and
recommenda/on
for
the
editor
• There
can
be
substan/al
overlap
between
these
docs
Organiza/on
of
the
report
to
the
editor
• There’s

no
one
way
to
do
it
but
the
following
is
how
I
tend
to
do
it
1) Summary
of
the
work
2) Recommenda/on
to
the
editor
(publish,
revise,
or
reject)
3) Discussion

of
the
importance
of
the
work
and
how
it
contributes
to
knowledge
4) Methodological
issues
(if
any)
5) Sugges/ons
for
revision
(if
any)
Summary
of
the
work

• The
editor
has
hopefully
read
the
paper,
but
it
may
have
been
awhile
• The
summary
reminds
them
what
the
main
findings
and
methods
of
the
paper
are
– Summarizing
the
work
is

also
useful
for
authors,
because
if
your
summary
doesn’t
match
their
idea
of
what
the
paper
is
about,
they’ll
know
they
need
to
communicate
beDer!
Recommenda/on
to
the
editor

• Here
you
just
tell
the
editor
what
you
think
they
should
do
with
the
paper
• This
is
hard!
• You
need
to
make
an
objec/ve
assessment
based
on

– The
quality
and
content
of
the
paper
– The
quality
and
typical
content
of
the
journal
(i.e.
does
the
paper
match?)
– I’ve
rejected
a
paper
at
the
AER
that
I
then
accepted

at
the
JPubE
(with
virtually
no
changes)
Discussion
of
importance
and
contribu/on
• This
can
be
easy
if
the
authors
have
done
a
good
lit
review
and
mo/va/on
of
the

paper
– However,
if
you
don’t
agree
with
their
assessment
of
how
the
paper
fills
a
gap
in
the
literature,
you
need
to
explain
why
you
don’t
agree
• Don’t
take
what
the
authors

say
at
face
value.
Think
cri/cally
about
whether
their
paper
is
really
as
big
a
contribu/on
as
they
say
it
is
– If
they
lack
good
mo/va/on
but
you
see
the
paper

as
filling
an
important
gap,
you
should
tell
the
editor—you
should
also
tell
the
author
how
to
beDer
mo/vate
their
paper
Methodological
issues
• This
is
usually
the

meat
of
the
report
• We
all
come
at
research
from
different
angles
– There
are
many
ways
to
skin
a
cat
– You
will
(hopefully)
have
ques/ons,
concerns,
sugges/ons,
etc.
about
the
methods
used

by
the
authors
• Have
they
dealt
with
a
key
endogeneity
problem?
• Have
they
made
an
unrealis/c
and
unnecessary
assump/on?
• Would
the
model
benefit
from
certain
changes
• Is
the
dataset

up
to
the
task?
Methodological
issues
• I
olen
ask
ques/ons
of
authors
– Did
you
try
this?
– Might
this
alterna/ve
hypothesis
explain
your
findings?
– Can
I

see
evidence
in
support
of
this
assump/on?
– Can
you
do
the
following
test?
• Some/mes
I
ask
that
certain
things
be
added
to
the
paper
(or
dropped)
Sugges/on
for

revision
• Referees
will
typically
suggest
things
that
ought
to
be
done
in
order
to
make
the
paper
acceptable
for
publica/on
– You
can
do
this
even
if
you’re
rejec/ng
the
paper

– Consider
the
author’s
next
submission.
This
could
be
useful
informa/on
– If
you
plan
to
reject
the
paper
don’t
tell
the
author,
“I’d
recommend
this
for
publica/on
by
this
journal
if
you

did
the
following...”
Notes
on
Wri/ng
• I
think
most
people
don’t
comment
much
on
wri/ng,
but
I
tend
to
– If
the
paper’s
badly
wriDen
I
tell
them
and

tell
them
why
and
how
it
could
be
improved
– It’s
kind
of
embarrassing
to
do
this,
but
some/mes
it
needs
to
be
done
– The
trick
here
is
to
try
to

be
nice.
It’s
hard
when
you’ve
spent
hours
trying
to
figure
out
what
the
hell
the
authors
are
trying
to
communicate
to
you.
How
blind
is
the
process?

• It
used
to
be
double--‐blind
and
some
journals
s/ll
try
to
do
this
– The
internet
makes
one
side
of
the
double-‐blind
approach
virtually
impossible
– If
you
get
a
manuscript
with

no
names
on
it,
but
it’s
posted
somewhere
online,
you’ll
easily
iden/fy
the
authors
• Most
journals
now
do
single-‐blind
refereeing
– You
know
the
authors
but
they
(in
principle)
don’t
know

you
Don’t
Google
authors
before
wri/ng
the
report
• Try
to
write
an
objec/ve
review
without
considering
who
the
authors
are
– If
you
find
they’re
grad
students
you

might
treat
them
differently
from
a
senior
prof
at
Harvard.
– The
point
is
for
every
manuscript
to
get
a
fair
review
– If
you
must,
Google
the
authors
once

you’re
done
Things
to
avoid
• Being
nasty.
– Small,
insecure
people
some/mes
write
nasty
reports.
They
can
be
devasta/ng
to
authors.
– Pretend
the
authors
are
friends
of

yours.
Be
honest,
but
think
about
their
feelings.
• Telling
the
authors
to
write
a
new
paper
– Some
referees
love
to
tackle
the
paper
as
their
own
project
and
totally
revise

it
(or,
tell
the
authors
to
totally
revise
it)
– Don’t
write
this
paper!
Write
that
paper
instead!
– Render
a
verdict
on
the
paper,
sugges/ng
modest
revisions
if
necessary.

Things
to
Avoid
• Gratuitous
self-‐cita/on
– If
you
really
want
the
authors
to
know
who
you
are,
this
will
give
you
away.
– The
editor
is
unlikely
to
be
impressed.

– Certainly
cite
yourself
if
the
paper
would
benefit
from
the
authors
reading
your
work
• Going
into
too
much
detail
for
the
editor
– The
editor
is
busy.
Make
the
editor’s
report
one

page
or
less;
highlight
key
issues.
Make
a
strong
case
for
the
posi/on
you
take
Things
to
Avoid
• Demanding
perfec/on
– The
authors
are
limited
to

about
20
pages
to
make
whatever
case
they’re
trying
to
make
– Their
case
won’t
be
100%
water/ght
• Models
could
be
tweaked
• Alterna/ve
empirical
specifica/ons
could
be
tried
• Other
datasets
could

be
explored
– Set
a
reasonable
standard.
You
don’t
have
to
be
100%
convinced
by
what
they’ve
done.
You
should
find
their
argument
compelling,
but
it
doesn’t
need
to
be

water/ght.
Things
to
Avoid
• Doing
it
at
the
last
minute
– The
best
report
will
be
one
that
involved
stewing
over
the
paper
for
some
/me.
– Read

the
paper
soon
aler
you
get
it
from
the
editor.
Then
sit
on
it
• Open
a
file
where
you
keep
notes
• Write
down
thoughts
as
they
come
up
– Reread
the

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