A New Data Set On Competition In National Banking Markets...A New Data Set On Competition In National Banking Markets 271 market power. It has already been used in empirical applications

A New Data Set On Competition In National BankingMarkets

BY SOFRONIS CLERIDES, MANTHOS D. DELIS, AND SOTIRIOS KOKAS

We estimate the degree of competition in the banking sectors of 148 countries over theperiod 1997–2010 using three methods: the Lerner index, the adjusted Lerner index, andthe profit elasticity. Marginal cost estimates required for all methods are obtained using aflexible semi-parametric methodology. All three indices show that competitive conditionsin banking deteriorated during the period 1997–2006, improved until 2008, and deterioratedagain thereafter. Levels of competition differ across regions and income groups, but there isgradual convergence over time. Banking system is less competitive in sub-Saharan Africaand low income countries and more competitive in Europe and Central and South Asia andOECD countries.

Keywords: Bank market power, competition, world sample.JEL Classification G2, D40.

I. INTRODUCTION

The issue of banking sector competition has attracted much interest in recentyears, not least because of the financial crisis. Alongside the standard objectiveof achieving effective market competition, the issue has additional significance inbanking because of the sector’s crucial role in the allocation of credit. Economicdevelopment relies upon financial intermediation services that link users withproviders of capital and allow consumption decisions to be smoothed over time.There are at least three key questions of interest in the theoretical and empiricalliterature. First, does the degree of bank competition impact the efficiency of creditallocation (Cetorelli and Strahan, 2006; Bonnacorsi and Dell’Ariccia, 2004)?Second, how does the intensity of competition affect the financial system’s stability(Keeley, 1990; Boyd and De Nicolo, 2005)? Third, what are the macroeconomicoutcomes of banking-sector competition (Cetorelli, 2004)?

To address these important questions, one first needs to come up with mean-ingful measures of the intensity of bank competition. Some studies (e.g., Cetorelliand Strahan, 2006) followed the early industrial organization tradition and usedconcentration measures as a proxy for bank competition. There is however agrowing consensus that concentration measures – useful as they may be in re-vealing important structural characteristics of the industry – are not good proxiesfor bank competition (Beck, Demirguc-Kunt and Levine, 2006; Claessens andLaeven, 2004). Instead, recent literature favors measures like the price-cost mar-gin or Lerner index (Lerner, 1934), the adjusted-Lerner index (Koetter, Kolari andSpierdijk, 2012), the H statistic (Panzar and Rosse, 1987), and the profit elasticity(Boone, Griffith and Harrison, 2005). Computing any of these measures (with the

Corresponding author: Sotirios Kokas, Essex Business School. Email: [email protected].

C© 2015 New York University Salomon Center and Wiley Periodicals, Inc.

268 Sofronis Clerides et al.

exception of the H statistic) requires knowledge of marginal cost. Given that dataon marginal cost are not usually available, an important first step in the construc-tion of a competition index is the estimation of marginal cost using econometricmethods.

The objective of this paper is to present a new set of estimates for bank competi-tion in 148 countries for the period 1997–2010 and to analyze trends and patternsin the evolution of competition during that period. Competition is measured interms of the Lerner index, the adjusted-Lerner index and the profit elasticity. Thepaper contributes to the literature on bank competition in four important ways.First, we use a novel technique to obtain accurate estimates of marginal cost andmarket power for each observation in the sample. The smooth coefficient modelwe employ is a semiparametric approach that allows for a flexible cost structure.Second, we have the broadest coverage compared to existing studies, with 148countries from 1997 to 2010. While most studies focus on a specific region orincome group, our data set allows studying the degree of within-country bankcompetition in a large number of countries. With coverage up to 2010, we are ableto analyze the evolution of bank competition during the financial crisis. Third, wecompute three different indicators favored by the recent banking literature, namelythe Lerner index, the adjusted-Lerner index and the profit elasticity. Fourth, weclean the raw data required to estimate these indices from double-counting stem-ming from mergers and acquisitions, ownership issues, inflexible features of theBankscope database, and other problems.

All indices indicate substantive changes in bank competition over time andacross income and regional classification. The Lerner index, the adjusted-Lernerindex and the profit elasticity produce similar overall patterns of competition overtime. We observe a gradual decline in the intensity of competition from 1997 to2006. The trend reverses for a brief period (2007–2008) but then market powerincreases again in 2009 and 2010. This pattern raises interesting questions aboutthe relationship between competitive conditions and financial stability. Notably,bank competition seems to weaken during the upward phase of the businesscycle and to become more intense when economic conditions worsen. We do notformally explore this link in the current work but leave it open as an importantquestion for future research.

The empirical results also highlight important differences across regions andincome groups. On average, the banking systems of Sub-Saharan Africa are theleast competitive, followed by East Asia and Pacific. Europe and Central andSouth Asia have the most competitive banking sectors. Non-OECD countries witheither high or low income levels have less competitive banking sectors than middle-income countries. OECD countries have lower bank market power overall. Despitethe differences, competition levels across income and regional groups seem to beconverging over time.

The three competition measures largely coincide when it comes to the mainoverall trends but do differ when compared at a finer level. We do not take astand on which index is best. Each measure has its own theoretical interpretation

A New Data Set On Competition In National Banking Markets 269

and its calculation presents different empirical challenges. Rather than propose aspecific measure, we provide (section 2) an extensive discussion of the theoreticaljustification for each index that we hope will be a useful guide to potential usersseeking to identify their preferred measure.

The structure of the paper is as follows. Section II presents an overview of bankcompetition measures. Section III describes our data and the empirical method-ology. Section IV presents the empirical results on the various indices of bankcompetition. Section V concludes.

II. EMPIRICAL METHODS FOR THE ESTIMATION OFCOMPETITION

Estimation of competition has a long history in industrial organization, datingback to Lerner (1934). Lerner defined his “index of the degree of monopoly power”(or “degree of monopoly” for short) as

Lerneri = Pi − mciPi

, (1)

where Pi and mci are firm i’s price and marginal cost respectively. The indexranges between 0 and 1, with zero corresponding to perfect competition and largervalues reflecting a greater degree of market power. The Lerner index remains tothis day the most widely and popular measure of market power and the intensityof competition.1 The main reasons for its popularity are simplicity, an intuitiveinterpretation, and relatively mild data requirements. The Lerner index measuresthe ability of an individual bank to charge a price above marginal cost. Whendata on prices and marginal cost are available, the Lerner index can be computedas a simple ratio. Because marginal cost is rarely available, researchers have toeither estimate a cost function or impose equilibrium conditions derived fromtheoretical models in order to obtain marginal cost estimates. The former methodis usually used in the banking literature, while the latter is preferred by industrialorganization economists.2 In this paper we will follow the banking literatureapproach of estimating a cost function.

Koetter, Kolari and Spierdijk (2012) have argued that the conventional approachof computing the Lerner index assumes both profit efficiency (optimal choice ofprices) and cost efficiency (optimal choice of inputs by firms). As a result, theestimated price-cost margins do not correctly measure the true extent of marketpower. The argument reflects a distinction pointed out by Lerner (1934) himself,who states that “for practical purposes we must read monopoly power not aspotential monopoly, but as monopoly in force” (p. 170; italics as in the original).

1Throughout the paper we will use market power as a measure of competition intensity. This is usuallyan innocuous assumption, although research has shown that the Lerner index does not always point inthe expected direction when competitive conditions change (Stiglitz, 1989; Boone, 2008).2The use of equilibrium conditions is the hallmark of the New Empirical Industrial Organization(NEIO) literature; see Bresnahan (1989) for an overview.


In other words, the Lerner index measures actual (exercised) market power, whileKoetter, Kolari, and Spierdijk (2012) are interested in measuring potential marketpower. To that end, the authors propose an adjustment that results in the efficiency-adjusted Lerner index:

adjusted Lerneri = πi + tci − mci · qiπi + tci , (2)

where π i is the profit of bank i, tci is total cost, mci is marginal cost and qi is totaloutput. Like the standard Lerner index, the adjusted Lerner ranges from 0 to 1,with larger values indicating greater market power.

Boone (2008) proposes a new competition measure called relative profit differ-ences (RPD). He shows that in most theoretical models of oligopolistic competi-tion, the difference in profit between more and less efficient firms is increasing inthe intensity of competition. Consider an increase in competition – due perhapsto a decrease in entry costs or to goods becoming closer substitutes. It will causethe profits of all firms to decline but inefficient firms will suffer a greater declinethan efficient firms. In other words, the profits of an efficient firm will increaserelative to the profits of a less efficient firm as increased competition will penalizethe least efficient firm more severely.

RPD is a theoretical construct that is difficult to compute in practice. Boone,Griffith and Harrison (2005) have proposed the profit elasticity (PE) as an empiricalanalogue of RPD. The PE is the percentage decrease in profits resulting from aone percent increase in the marginal cost:

profit elasticityi =∂ ln πi∂ ln mci

. (3)

The PE (sometimes called the Boone indicator) should be negative becauseprofits and marginal cost are inversely related. An efficient firm will have a lowerPE (in absolute value) as its profits will be less affected by a change in marginalcost. For example, if PE = −0.2, a 1 percent increase in the marginal cost (dueto a decrease in the efficiency level) will decrease its profits by 0.2 percent. IfPE = −0.5, a 1 percent increase in the marginal cost will decrease its profits by0.5 percent. A large absolute value of the PE can be interpreted as a reduction inthe ability of the bank to contain its losses due to an increase in competition. Theprofit elasticity therefore links bank performance to differences in efficiency (interms of marginal cost).

Boone, Griffith and Harrison (2005) suggest that the PE can be estimated fromthe equation:

ln πi = α + βi ln mci . (4)The coefficient β i is the desired profit elasticity index of market power. Intu-

itively, equation (4) examines the relationship between profits and marginal cost;or, differently phrased, the indirect relationship between profits and efficiency.The profit elasticity can take any value and is therefore a continuous indicator of


market power. It has already been used in empirical applications in various indus-tries (e.g., Delis, 2012; Van Leuvensteijn, Sorensen, Bikker and van Rixtel, 2013;Boone and Van Leuvensteijn, 2010). The index has also received some criticism;for example, Schiersch and Schmidt-Ehmcke (2010) show that the profit elasticitymakes critical assumptions relative to firm size (the biggest firms are assumed tobe the most efficient) and relative to the definition of the extent of the market.

It is worth pointing out that a measure of the profit elasticity can be derivedfrom the adjusted-Lerner index by solving for π in equation (2) and differentiatingwith respect to marginal cost. The result is

profit elasticityi =qi · mci

qi · mci − tci (1 − ad justed Lerneri ) . (5)

Hence, the adjusted Lerner and profit elasticity are two closely related concepts.Many older banking studies measured competition with the so-called H statistic.

The statistic is based on the test for “monopoly” equilibrium proposed by Panzarand Rosse (1987; quotation marks included in the paper’s title). Panzar and Rosse(henceforth PR) show that under very general conditions, the sum of the factorprice elasticities of a monopolist’s reduced form revenue equation must be non-positive. They further show that in commonly used oligopolistic models this sumis strictly positive; they note however that this last result is based on relativelysimple oligopoly models and is therefore less general than the monopoly result.They also point out that a monopoly facing a perfectly elastic demand curve canhave a negative H statistic under some conditions, even though it has no marketpower. They conclude that “a rejection of the hypothesis that H < 0 must meanthat the revenue functions of the observed firms are influenced by the actions ofothers” (p. 447).

PR presented the H statistic to be a test for monopoly behavior and avoidedinterpreting it as a measure of competition.3 Nonetheless, their findings gave riseto a large literature – mostly in banking – that used that H statistic as a measureof competition. This literature has been recently criticized by Bikker, Shaffer,and Spierdijk (2012), who show that much of the analysis using scaled revenueequations leads to misleading results, while even “the unscaled P-R test is aone-tail test of conduct.” They conclude that “the P-R revenue test results in anonordinal statistic for firm conduct that is less informative than prior literaturehas suggested.” Based on these findings and on our own reservations due to thelack of a clear theoretical justification, we decided not to compute the H statisticin this paper.

The Lerner, adjusted-Lerner and profit elasticity indices each have their ownmerits and drawbacks. Simplicity and direct applicability to empirical studies arestrong arguments in favor of the Lerner and adjusted-Lerner indices, while theexistence of a solid theoretical foundation justifies the use of the profit elasticity

3They did show that with constant elasticity demand and a CRS Cobb-Douglas technology, the Hstatistic is equivalent to the Lerner index: L = H/(H-1).


index. We do not take a stand on which measure better reflects competition. We usethe three indices described above to measure banking-sector competition in a broadsample of 148 countries. We make all estimates available to the wider researchcommunity and let researchers decide which indicator is preferable, given theobjectives of their research agenda. In fact, we will claim in the next section thatprobably the most important issue underlying all indices is the robust estimationof marginal cost at the bank-year level, which is required with all approaches.

III. DATA AND METHODOLOGY

DATA

Like many other banking studies, we rely on Bankscope as our primary sourceof bank-level data. Our sample covers the period 1997–2010. We exclude earlieryears because of concerns associated with coverage and accounting issues. Ourfocus is on commercial banks, savings banks and cooperative banks. We excludereal-estate and mortgage banks, investment banks, other non-banking credit institu-tions (mainly operating in Germany), specialized governmental credit institutions,bank-holding and other holding companies.4 Besides bank-holding companies, theexcluded institutions are less dependent on the traditional intermediation functionand have a different financing structure compared to our focus group. In short,our focus in this study is on banks carrying out traditional banking activities.Inclusion of bank-holding companies could lead to double counting, as these arecorporations controlling one or more banks. We always check that we have thesubsidiaries of these companies in the sample to avoid false exclusion of somebanks. Further, we have included in our sample only countries that had at leastthree banks in each year of our panel.

We apply three further selection rules to avoid including duplicates in oursample. This is an essential part of the sample-selection process that is absentfrom most empirical studies using the Bankscope database (for a similar strategywith ours, see Claessens and van Horen, 2014). First, even though we do notinclude bank-holding companies, we still need to exclude double entries betweenparent banks and subsidiaries.5 Bankscope’s consolidation code system allowsdownloading either consolidated or unconsolidated statements, but in some casesinformation on either unconsolidated or consolidated statements of certain banksis not available.6 We use either the consolidated or the unconsolidated statementdepending on which one is available. This is a non-trivial process that requiresthe re-examination of all banks on an individual basis to avoid double-counting.

4The main activities of excluded financial institutions relate to the following: provide mortgages; assistcorporations and governments in a range of services (e.g., M&As, raising capital, etc.); provide creditto public sectors; provide funding for public or municipal projects.5For the determination of the subsidiary we follow the definition generally applied in the literature,i.e. 50% or more of the shares of the subsidiary owned by the parent bank.6A consolidated statement is the statement of a bank integrating the statements of its subsidiaries orbranches. An unconsolidated statement does not integrate subsidiaries.


Notably, there are cases of banks with subsidiaries in domestic or in foreigncountries and one should be very careful to avoid double-counting of subsidiariesthat are established, for example, in a foreign country.7

Second, we account for mergers and acquisitions (M&As). We went throughall the M&As one-by-one and made sure that both banks appear separately inthe sample before the M&A and only the merged entity or the acquiring bank isincluded in the sample after the event. For example, if bank A and bank B mergedin 2005, we create a new entity AB after 2005 and exclude the separate financialaccounts of A and B that might still be reported for some time after the merger. Weidentify M&As and their timing using Bankscope and the websites of the mergingparties.

Third, in the US there are many distinct banks that have the same name but areactive in a different state. To solve this issue, we relate the value of total assetsof, say, bank i in the last year this bank appears in our sample with Bankscope’sidentification number for bank i. This also allows avoiding problems with ourprocedure concerning M&As described above. This data cleaning process yieldsan unbalanced panel with 89,778 observations, corresponding to 12,206 banksoperating in 148 countries between 1997 and 2010.

In order to compute the desired competition indices we first need to calculatethe variables π , p, q and tc and then to estimate a cost function. The latter has thegeneral form:

tcit = f (qit, wl,i t , wk,i t , wd,i t ), (6)

where wl,i t , wk,i t and wd,i t are factor prices for labor, capital and deposits respec-tively. To identify bank inputs and outputs for the cost function we use the inter-mediation approach, which assumes that deposits are inputs used in the productionprocess to produce bank outputs, and has been shown to be the preferred approachby a number of studies (e.g., Berger and Humphrey, 1997; Hughes and Mester,1993). In particular, we measure bank total costs (tc) by real total expenses, andbank output (q) by real total earning assets. This measure for bank output relatesto traditional banking activities, therefore our main indices reflect competitionin these activities. We construct real variables were appropriate, using the GDP

7Let us provide some examples to clarify this point. Assume that bank A1 is the parent bank witha consolidated (C) statement and banks A11, A12 and A13 are subsidiaries and unconsolidated (U)statement. If we include all banks in our sample we will have 3 duplicates. Hence, we need to subtracteither the percentage of the subsidiaries or to exclude the subsidiaries from the sample. The formersolution is not feasible because we do not have enough information for the percentage and the timeduration of the ownership of the subsidiaries. Thus, we resort to the later solution. Two other examplesfor the case of banks with foreign subsidiaries that we account for using the same strategy are (i) B1is a parent bank with a C statement, B11 is a subsidiary bank operating in the domestic market with aC or a U statement and B111 is a sub-subsidiary bank operating in the domestic market and (ii) B1 isa parent bank with C statement, B12 is a subsidiary bank operating abroad with a C or a U statementand B121 is a sub-subsidiary bank operating in the domestic market with a U statement.


deflator (obtained from the World Bank).8 Real total earning assets include loans,securities, and other earning assets (such as investments and insurance assets).

We also specify a cost function with the same three inputs but two outputs,namely real total earning asset (q1) and off-balance sheet items (q2). We employthis alternative specification because many banks have a significant volume ofoff-balance sheet items and naturally use their inputs to produce these outputs.Our competition index with this approach reflects competition in the traditionalbanking activities after controlling for the fact that many banks also use theirinputs toward the production of off-balance sheet items.

The three input prices are proxied as follows: the price of labor wl by the ratioof personnel expenses to total assets;9 the price of physical capital wk by the ratioof capital expenditures to fixed assets; and the price of deposits wd as total interestexpenses over total customer deposits. We measure bank profits π using totalprofits before taxes. For the Lerner index and the adjusted-Lerner index we needthe aggregate output price p. This is calculated as the ratio of total income overtotal earning assets (Beck, Jonghe and Schepens, 2013).

As a final step, we clean our sample from outliers in the sense of unreasonablynegative values for total assets and total expenses. Additionally, for the three inputprices we drop 1% of our sample from each end of their distribution. This excludesunreasonably high or low input prices (Delis, 2012; Claessens and Laeven, 2004).Notably, the initial dataset before all the steps of the cleaning process describedabove includes 300,180 observations for 21,445 banks operating in 149 countriesbetween 1997 and 2010. Our final dataset consists of 89,778 observations for12,206 banks operating in 148 countries between 1997 and 2010. Most of theobservations are dropped due to some form of double-counting stemming fromBankscope’s consolidation system and M&As. This shows that the data-cleaningprocess is extremely important in generating sensible indices of bank competition.Table 1 reports the number of banks for all years, Table 2 provides detailedinformation on the construction of the variables, and Table 3 reports summarystatistics.

ECONOMETRIC METHODOLOGY FOR THE ESTIMATION OF MARKET POWER

An important concern for the empirical estimation of market power is the choiceof a proper functional form to obtain observation-specific estimates of marginalcost. The selection of a functional form is crucial because an inappropriate choicewill invalidate any inference. At a minimum, the production technology should beallowed to vary (i) across banks operating in the same industry and (ii) over time.

8As is standard in the macroeconomics literature, for Taiwan we use the GDP deflator of China andfor Netherlands Antilles we use the GDP deflator of Aruba.9We divide by total assets instead of the number of employees because Bankscope has limited in-formation on the number of employees. The related literature follows a similar approach (e.g., Delis,2012; Claessens and Laeven, 2004).


Tabl

e1:

Num

ber

ofba

nks

inth

esa

mpl

e

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

All

coun

trie

s3,

895

4,05

16,

349

6,26

26,

184

6,04

75,

937

6,06

27,

362

7,48

67,

829

7,83

87,

531

6,94

5

Inco

me

grou

psL

I54

6674

8790

9811

112

013

814

016

119

120

718

0L

MI

223

250

282

278

273

283

299

337

391

397

427

440

446

391

OE

CD

3,15

43,

241

5,44

35,

281

5,19

45,

013

4,81

54,

848

5,76

55,

693

5,77

75,

652

5,52

45,

219

HI

110

112

107

116

116

116

114

128

141

142

140

133

113

94U

MI

354

382

443

500

511

537

598

629

927

1,11

41,

324

1,42

21,

241

1,06

1R

egio

nalg

roup

sE

AP

119

118

119

100

9810

311

213

717

218

319

922

022

220

8E

CA

8383

145

164

183

236

290

324

625

791

955

1,00

481

164

9L

AC

222

271

291

333

318

282

284

291

280

293

322

345

357

326

ME

NA

4944

4848

4246

4951

4957

7489

9789

SA76

8386

8993

100

111

114

121

117

125

127

135

123

SSA

8299

110

130

138

145

151

154

181

181

210

237

242

205

Not

es:L

Ire

fers

toth

elo

w-i

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LM

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s,O

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high

-inc

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sot

her

than

OE

CD

coun

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s,an

dU

MI

refe

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uppe

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EA

Pre

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s,L

AC

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Lat

inA

mer

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and

Car

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untr

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ME

NA

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tern

and

Nor

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fric

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SAre

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rnA

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san

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b-Sa

hara

nA

fric

anco

untr

ies.


Table 2: Data description

Variable Measure Notation

For aggregate output (1 output)

Earning assets Natural log of deflated total earning assets(measure of a bank’s output)

q

Price of output Total income/ total earning assets P

For disaggregate outputs (2 outputs)

Earning assets Natural log of deflated total earning assets(measure of bank’s output)

q1

Off-balance sheet items Natural log of deflated off-balance sheetitems (measure of bank’s output)

q2

Price of earning asset Total income/ total earning assets P1Price of off-balance sheet Total income/ off-balance sheet P2Expenses Natural log of deflated total interest

expenses and total noninterest expenses(measure of a bank’s total cost)

tc

Price of deposits Total interest expenses/ total customerdeposits

wd

Price of labor Personnel expenses/ total assets wlPrice of physical capital (Overheads-personnel expenses)/ fixed

assetswk

Profits A bank’s deflated total profits before tax π

NPI Natural log of

{1 if π ∈ �+

|π | if π ∈ �− NPI

Positive profits Natural log of

{π if π ∈ �+1 if π ∈ �− π

+

We rely on a semiparametric approach, which has some appealing features thatwe analyze below.

We start from the cost function implied by a standard log-linear productionfunction and impose the usual linear homogeneity restriction in inputs prices (thatis, we normalize total cost and the input prices by the price of deposits beforetaking logs). We end up with the following cost function:

ln tcitc = b1 + b2 ln wl,i tc + b3 ln wk,i tc + a1 ln qitc. (7)

We estimate this equation with a semi-parametric partial linear smooth coeffi-cient (PLSC) model, which uses the local polynomial fitting regression and theGaussian kernel function to obtain estimates of a1 for each bank i at time t incountry c. A thorough discussion of the PLSC model can be found in Fan andZhang (1999) and Mamuneas, Savvides and Stengos (2006). Delis (2012) and


Table 3: Summary statistics

Variable Observations Mean Std. dev. Min. Max.

For aggregate output (1 output)

Earning assets 89,778 11.71 2.02 6.83 21.38Price of output 89,778 0.09 0.07 0.02 0.71

For disaggregate output (2 output)

Earning assets 89,778 11.71 2.02 6.83 21.38Off-balance sheet items 79,875 9.16 2.47 −1.58 21.26Price of earning asset 89,778 0.09 0.07 0.02 0.71Price of off-balance sheet 79875 39.30 811 0.00 110,787Expenses 89,778 12.25 2.10 5.29 22.42Price of deposits 89,778 0.06 0.09 0.00 1.03Price of labor 89,778 0.02 0.01 0.00 0.09Price of physical capital 89,778 1.70 3.71 0.13 56.96Profits 81,939 7.00 2.13 −2.24 16.76NPI 89,778 0.58 2.09 −2.27 16.69Positive profits 89,778 6.39 2.83 −2.24 16.76Lerner index 88,202 0.22 0.12 −0.20 0.95Adjusted-Lerner index 85,832 0.17 0.12 −0.20 0.95Profit elasticity 89,778 −0.42 0.08 −0.96 −0.32Notes: The table reports summary statistics (number of observations, mean, standard de-viation, minimum and maximum) for the variables used in the empirical analysis. Thebank level variables are defined in Table 2. The measures of competition are defined inEqs. (1), (2) and (4) for the Lerner index, the adjusted-Lerner index and the Profit elasticity,respectively, and are estimated using the methodology in Section 3.2.

Delis, Iosifidi and Tsionas (2012) use this model to estimate marginal cost; thesummary that follows is based on their discussion.

We can write the econometric form of the total cost equation as

Yi = E(Yi |Wi ) + ei = Xiβ1 + Viβ2(Zi ) + ei . (8)In this equation, β2 is a function of one or more variables of dimension k

added to the vector Z, which is an important element of the analysis and will bediscussed below. The presence of a linear part in Eq. (8) is in line with the idea ofa semiparametric model as opposed to a fully nonparametric one. The coefficientsof this part are estimated in a first step as averages of the polynomial fitting byusing an initial bandwidth chosen by cross-validation. In the second step we usethese average estimates to re-define the dependent variable as

Y ∗i ≡ Yi − Xi β̂i = Viβ2 (z) + e ∗i . (9)


The coefficient β2(z) is a smooth but unknown function of z. We estimate β2(z)using a local least squares approach.10 Using the PLSC technique, we allow themodel to be linear in the regressors but their coefficients are allowed to change“smoothly” with the value of other variables z.

We can now re-write Eq. (7) in econometric form as

ln tcitc = b1 + a1(zitc) ln qitc + b2,i tc ln wl,i tc + b3,i tc ln wk,i tc + eitc, (10)where e is a stochastic disturbance and z is the smoothing variable required by thePLSC approach. The model is semiparametric in the sense that it treats only thecoefficient a1 as a function of z. From Eq. (10) we can obtain the marginal costfor each bank i at time t in country c as ∂tcitc/∂qitc = a1 (zitc) (tcitc/qitc).11

A critical issue in the estimation process is the choice of the variable(s) thatcomprise Z. The best candidates are variables that shift a1 and exhibit variationacross banks, countries and time. We need variables that shift marginal cost andthe natural candidates to use are the input prices; they are observation-specific byconstruction and they clearly affect marginal cost. Any cost function derived froma cost minimization problem will be a function of, among other thing, input prices.Numerous studies that have estimated cost functions using a translog specificationfind that input prices are indeed important determinants of marginal cost. Delis,Iosifidi and Tsionas (2012) propose using the linear combination of input pricesas the z variable and show that it works well in this context. We follow theirsuggestion and define the smoothing variable as zitc = ln wl,i tc + ln wk,i tc. As arobustness check we experimented with various other combinations (such as theproduct of the input prices or linear combinations with different weights) andfound that our results are not sensitive to the choice of z function.

Once we have our estimates of marginal cost we use them to calculate theLerner index from Eq. (1) and the adjusted-Lerner index from Eq. (2), as well asto estimate the profit elasticity from Eq. (4). A problem that arises with the latteris the fact that we have to take the logarithm of profits, and profits can be negative.Bos and Koetter (2011) propose a solution using the following transformation forbank profits (π )12:

π+ ={

π if π ∈ �+1 if π ∈ �− and NPI =

{1 if π ∈ �+

|π | if π ∈ �− . (11)

10Mamuneas, Savvides and Stengos (2006) discuss in detail how this function can take specificparametric formulations (such as linear) that can be tested against the general unknown specification.They also provide formulae for the local least squares criterion.11We use ln tcitc = b1 + a1(zitc) ln q1,i tc + a2 ln q2,i tc + b2,i tc ln wl,i tc + b3,i tc ln wk,i tc + eitc for themodel with the off-balance sheet items. Given that we focus on the market power stemming fromtraditional banking activities, marginal cost is still derived from ∂tc/∂q1,i tc = a1(zitc)(tcitc/q1,i tc).12There are two alternative approaches. The first is to truncate profits and exclude banks that incurlosses and the second is to rescale profits by adding the maximum loss observed in the sample plus asmall number to each observation on bank profits. The obvious drawback of the first approach is thatit introduces selection bias. The rescaling approach is a good approximation when the shift is smallrelative to the magnitude of the shifted variable but will bias the estimated coefficient if the shift islarge, which may be the case here. We experimented with both of these alternative approaches but theyproduced coefficients which were either insignificant or unreasonable.


Table 4: Correlation between indices of weighted market power by marketshare

Lerner index Adjusted -Lerner index Profit elasticity

Lerner index 1.00Adjusted-Lerner index 0.86* 1.00Profit elasticity 0.33* 0.31* 1.00

Notes: The * mark denotes statistical significance at the 1% level.

This is an indicator that captures both profits and losses since π+ can be usedas a left-hand side variable in the regression equation and the negative profitindicator (NPI) as a right-hand side variable. Hence, the actual estimated equationequivalent to that of Eq. (4) is

ln π+i tc = a + β(zitc) ln mcitc + γ ln NPIitc + uitc. (12)Estimation of equation (10) and subsequently of equation (12) via the PLSC

technique presents some considerable interrelated advantages. Most importantly,the semiparametric nature of the method implies that no assumption regarding thefunctional form of the underlying cost function is made globally. This is importantas it usually quite difficult for the researcher to be certain about the validity of thechosen functional form. The PLSC method allows for considerable flexibility inthe estimation of the cost function because of variation in the structural variablesemployed. In their survey paper, Reiss and Wolak (2007) are very skeptical aboutusing a specific functional to estimate cost functions without a prior analysis ofthe data, since an “incorrect” cost function can bias the estimation and inferenceof marginal cost in an unknown direction and magnitude. The flexibility of thesemiparametric technique takes into account the heterogeneity in the productiontechnology across banks and countries and hence allows the use of large inter-national samples of banks employing different production technologies. Finally,Mammen, Rothe and Schienle (2012) establish theoretically that the two-step esti-mation procedure reduces measurement errors stemming from unobserved countryfactors. Delis, Iosifidi and Tsionas (2012) and Wheelock and Wilson (2012) pro-vide empirical support to these arguments by showing that estimation of marginalcost using semiparametric and nonparametric methods produces significantly bet-ter results than parametric techniques and commonly used functional forms likethe translog.

IV. ESTIMATES OF BANK COMPETITION

In Table 4 we report the pairwise correlation coefficients between the Lerner,the adjusted-Lerner and the profit-elasticity measures (the calculation weighs ob-servations by bank size, measured as total earning assets). The Lerner and theadjusted-Lerner indices are highly correlated, while the correlation coefficients


Tabl

e5:

Ave

rage

esti

mat

esof

mar

ket

pow

er(w

eigh

ted

bym

arke

tsh

ares

)us

ing

the

Ler

ner

inde

x

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Mea

n

Afg

hani

stan

0.38

10.

237

0.08

40.

362

0.29

90.

147

0.25

2A

lban

ia0.

120

0.32

10.

210

0.20

10.

184

0.21

50.

293

0.27

40.

317

0.30

30.

318

0.35

90.

223

Alg

eria

0.15

30.

165

0.06

50.

153

0.22

90.

387

0.24

40.

459

0.59

00.

648

0.53

30.

624

0.52

80.

513

0.37

8A

ndor

ra0.

255

0.29

60.

354

0.35

90.

305

0.37

30.

459

0.50

50.

505

0.50

70.

439

0.28

10.

386

Ang

ola

0.27

50.

313

0.28

10.

397

0.49

80.

427

0.41

20.

267

0.45

90.

492

0.42

70.

467

0.39

3A

ntig

uaan

dB

arbu

da0.

051

0.09

00.

123

0.13

30.

266

0.33

40.

344

0.19

2A

rgen

tina

0.21

70.

170

0.18

90.

218

0.13

60.

121

0.01

90.

167

0.25

70.

285

0.24

50.

209

0.32

50.

318

0.20

5A

rmen

ia0.

182

0.23

50.

215

0.18

80.

280

0.34

80.

375

0.38

90.

374

0.36

40.

354

0.32

90.

226

0.28

40.

296

Aus

tral

ia0.

253

0.24

80.

211

0.28

5−0

.085

0.22

50.

250

0.23

30.

218

0.16

50.

250

0.25

10.

209

Aus

tria

0.14

70.

122

0.13

20.

146

0.14

50.

154

0.18

90.

185

0.18

20.

174

0.16

60.

151

0.20

60.

260

0.16

8A

zerb

aija

n0.

533

0.37

00.

377

0.53

50.

436

0.38

20.

375

0.43

50.

441

0.38

80.

388

0.41

10.

380

0.27

50.

409

Bah

amas

,The

0.15

90.

173

0.21

00.

272

0.29

40.

214

0.32

10.

356

0.39

30.

388

0.42

10.

333

0.39

10.

390

0.30

8B

ahra

in0.

205

0.17

70.

175

0.16

10.

177

0.23

90.

223

0.28

40.

265

0.20

10.

189

0.23

20.

211

Ban

glad

esh

0.03

0−0

.033

0.07

00.

114

0.13

40.

142

0.13

80.

164

0.21

40.

189

0.21

10.

256

0.27

50.

339

0.16

0B

elar

us0.

092

0.20

90.

112

0.17

80.

120

0.18

30.

168

0.15

00.

182

0.21

10.

186

0.17

40.

241

0.24

60.

175

Bel

gium

0.10

30.

138

0.14

50.

162

0.16

60.

150

0.16

10.

158

0.12

20.

143

0.07

1−0

.016

0.07

90.

155

0.12

4B

erm

uda

0.09

70.

114

0.11

80.

156

0.12

00.

194

0.21

00.

131

0.26

90.

266

0.27

40.

128

0.21

10.

229

0.18

0B

oliv

ia0.

138

0.18

60.

206

0.17

90.

194

0.23

90.

203

0.14

50.

177

0.22

10.

238

0.30

00.

261

0.27

40.

211

(Con

tinu

ed)


Tabl

e5:

(Con

tinue

d)

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Mea

n

Bos

nia

and

Her

zego

vina

0.21

50.

237

0.23

30.

256

0.18

30.

230

0.25

10.

229

Bot

swan

a0.

246

0.30

70.

248

0.32

40.

326

0.33

80.

353

0.33

70.

357

0.32

80.

269

0.29

40.

309

0.33

60.

312

Bra

zil

0.13

70.

160

0.15

50.

132

0.14

40.

161

0.22

80.

219

0.24

40.

274

0.27

80.

195

0.29

40.

259

0.20

6B

ulga

ria

0.30

90.

283

0.33

90.

360

0.37

20.

378

0.38

50.

338

0.32

30.

343

0.34

3B

urki

naFa

so0.

277

0.38

60.

337

0.27

00.

236

0.35

00.

348

0.31

70.

342

0.30

60.

308

0.24

60.

266

0.34

60.

310

Cam

bodi

a0.

478

0.46

90.

337

0.38

60.

436

0.43

60.

450

0.48

40.

517

0.37

90.

363

0.43

0C

amer

oon

0.58

00.

499

0.45

10.

420

0.38

50.

479

0.43

20.

426

0.43

50.

390

0.31

40.

345

0.43

0C

anad

a0.

135

0.10

80.

179

0.16

80.

166

0.19

40.

202

0.22

90.

187

0.21

50.

190

0.15

20.

258

0.30

40.

192

Cay

man

Isla

nds

0.17

60.

176

Chi

le0.

161

0.16

00.

204

0.20

60.

238

0.28

30.

194

0.15

00.

160

0.22

80.

308

0.21

70.

411

0.38

30.

236

Chi

na0.

405

0.38

30.

254

0.27

50.

259

0.34

60.

379

0.39

90.

385

0.39

00.

429

0.40

70.

417

0.44

90.

370

Col

ombi

a0.

146

0.08

10.

030

0.08

50.

146

0.15

20.

244

0.28

30.

322

0.27

90.

312

0.31

80.

341

0.37

90.

223

Cos

taR

ica

0.07

30.

084

0.07

60.

182

0.18

50.

183

0.23

50.

220

0.21

40.

226

0.21

30.

175

0.14

50.

222

0.17

4C

ote

d’Iv

oire

0.37

90.

386

0.32

20.

300

0.26

30.

241

0.23

00.

273

0.26

60.

276

0.30

30.

286

0.27

70.

263

0.29

0C

roat

ia0.

209

0.16

70.

169

0.22

60.

202

0.21

50.

251

0.27

10.

282

0.25

70.

268

0.25

30.

274

0.30

10.

239

Cub

a0.

824

0.76

10.

731

0.68

90.

569

0.70

30.

785

0.78

70.

701

0.61

10.

470

0.55

70.

536

0.65

10.

670

Cyp

rus

0.15

50.

151

0.28

40.

107

0.11

10.

143

0.17

60.

208

0.18

80.

253

0.28

40.

202

0.23

30.

249

0.19

6C

zech

Rep

ublic

0.18

00.

158

0.16

70.

166

0.16

20.

239

0.26

70.

298

0.34

30.

328

0.32

80.

277

0.44

00.

444

0.27

1D

enm

ark

0.16

50.

175

0.14

10.

147

0.25

10.

265

0.39

00.

180

0.18

40.

161

0.13

50.

104

0.21

80.

213

0.19

5D

omin

ican

Rep

ublic

0.18

90.

180

0.16

60.

190

0.19

00.

198

0.17

50.

115

0.18

40.

202

0.22

00.

226

0.22

00.

266

0.19

4E

cuad

or0.

050

−0.1

240.

297

0.12

70.

113

0.18

50.

197

0.22

70.

268

0.27

60.

268

0.24

10.

234

0.26

50.

187

Egy

pt,A

rab

Rep

.0.

065

0.06

50.

314

0.23

80.

171

ElS

alva

dor

0.11

90.

169

0.16

60.

178

0.24

40.

288

0.28

20.

304

0.32

60.

365

0.35

90.

365

0.38

00.

447

0.28

5

(Con

tinu

ed)


Tabl

e5:

(Con

tinue

d)

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Mea

n

Est

onia

0.26

20.

029

0.01

40.

052

0.20

40.

271

0.32

80.

347

0.34

10.

364

0.32

30.

313

0.28

60.

373

0.25

1E

thio

pia

0.27

00.

257

0.34

40.

285

0.40

60.

331

0.57

40.

573

0.57

40.

612

0.53

80.

616

0.65

00.

595

0.47

3Fi

nlan

d0.

055

0.00

00.

338

0.35

40.

266

0.20

70.

174

0.18

80.

194

0.11

80.

267

0.28

00.

203

Fran

ce0.

100

0.10

70.

128

0.11

20.

132

0.15

20.

168

0.20

50.

220

0.22

10.

197

0.17

20.

229

0.24

80.

171

Gam

bia,

The

0.49

50.

569

0.55

10.

552

0.52

90.

530

0.43

70.

401

0.41

70.

272

0.33

00.

253

0.31

70.

435

Geo

rgia

0.33

50.

362

0.31

80.

339

0.34

10.

341

0.31

60.

351

0.33

30.

282

0.26

20.

230

0.23

50.

311

Ger

man

y0.

171

0.15

10.

164

0.13

90.

132

0.15

70.

175

0.18

90.

185

0.20

40.

166

0.15

30.

193

0.23

40.

172

Gha

na0.

160

0.44

20.

419

0.13

70.

412

0.41

40.

435

0.48

30.

442

0.29

30.

274

0.24

10.

324

0.34

4G

reec

e0.

169

0.20

10.

404

0.21

50.

000

0.04

40.

112

0.13

60.

183

0.21

60.

173

0.10

40.

184

0.15

10.

164

Gua

tem

ala

0.08

80.

124

0.12

60.

186

0.22

80.

246

0.25

10.

242

0.25

30.

248

0.25

70.

204

Hai

ti0.

123

0.11

90.

116

0.17

20.

156

0.10

80.

224

0.09

90.

145

0.17

10.

178

0.19

70.

183

0.18

30.

155

Hon

dura

s0.

338

0.26

20.

186

0.12

90.

165

0.19

70.

256

0.18

00.

205

0.24

00.

250

0.27

20.

233

0.20

80.

223

Hon

gK

ong

SAR

,Chi

na0.

238

0.18

70.

243

0.27

30.

165

0.35

10.

389

0.42

90.

300

0.27

60.

260

0.17

60.

299

0.34

30.

281

Hun

gary

0.15

30.

144

0.08

70.

122

0.16

30.

181

0.22

60.

219

0.24

50.

243

0.25

00.

192

0.22

30.

313

0.19

7Ic

elan

d0.

167

0.17

50.

200

0.06

80.

145

0.21

00.

231

0.26

90.

336

0.36

30.

331

0.42

60.

337

0.48

90.

268

Indi

a0.

121

0.14

60.

120

0.15

80.

158

0.20

90.

244

0.30

30.

282

0.26

60.

241

0.18

60.

194

0.21

10.

203

Indo

nesi

a0.

134

0.04

30.

030

0.10

70.

129

0.16

00.

228

0.32

50.

248

0.25

60.

295

0.31

10.

315

0.35

60.

210

(Con

tinu

ed)


Tabl

e5:

(Con

tinue

d)

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Mea

n

Iraq

0.46

30.

316

0.38

9Ir

elan

d0.

177

0.17

50.

253

0.21

50.

148

0.13

50.

228

0.21

70.

144

0.13

20.

146

0.14

60.

196

0.20

50.

180

Isra

el0.

153

0.06

40.

092

0.12

40.

084

0.10

20.

116

0.17

70.

150

0.19

80.

206

0.14

10.

197

0.10

80.

136

Ital

y0.

157

0.20

00.

143

0.20

30.

183

0.21

80.

218

0.17

90.

241

0.25

80.

240

0.19

80.

238

0.23

60.

208

Jam

aica

0.12

80.

158

0.20

10.

289

0.21

60.

271

0.23

30.

267

0.27

80.

271

0.30

10.

293

0.33

40.

249

Japa

n0.

246

0.24

60.

259

0.25

90.

250

0.23

00.

266

0.26

10.

282

0.28

50.

286

0.24

20.

191

0.23

30.

253

Jord

an0.

152

0.18

20.

173

0.14

70.

239

0.23

70.

325

0.36

20.

490

0.40

00.

363

0.34

90.

370

0.41

90.

301

Kaz

akhs

tan

0.24

50.

310

0.30

60.

246

0.34

70.

366

0.35

90.

393

0.35

60.

329

0.34

00.

243

0.23

00.

077

0.29

6K

enya

0.15

30.

262

0.27

00.

311

0.32

10.

318

0.38

00.

371

0.36

10.

391

0.36

90.

344

0.32

60.

384

0.32

6K

orea

,Rep

.0.

071

0.11

50.

219

0.17

90.

266

0.31

10.

316

0.33

10.

310

0.29

10.

271

0.19

10.

221

0.25

80.

239

Kuw

ait

0.09

20.

239

0.28

70.

299

0.36

70.

444

0.51

70.

555

0.56

50.

470

0.39

30.

384

Kyr

gyz

Rep

ublic

0.17

60.

323

0.11

60.

371

0.37

50.

460

0.36

50.

397

0.45

40.

319

0.35

90.

327

0.33

7L

aoPD

R0.

232

0.01

90.

000

0.25

20.

478

0.55

50.

669

0.29

20.

285

0.35

30.

314

Lat

via

0.28

00.

214

0.25

70.

280

0.27

10.

303

0.33

70.

356

0.36

20.

327

0.30

50.

241

0.24

70.

227

0.28

6L

eban

on0.

168

0.14

90.

141

0.14

40.

127

0.14

10.

163

0.14

20.

151

0.14

90.

144

0.17

90.

190

0.22

60.

158

Lib

ya0.

535

0.57

60.

535

0.05

00.

401

0.52

30.

597

0.69

10.

248

0.46

2L

ithua

nia

0.26

90.

154

0.24

20.

151

0.18

30.

217

0.18

40.

252

0.28

90.

306

0.31

10.

245

0.17

80.

205

0.22

8L

uxem

bour

g0.

103

0.09

50.

115

0.13

40.

118

0.13

40.

151

0.18

90.

207

0.19

80.

184

0.13

70.

242

0.28

50.

164

Mac

aoSA

R,C

hina

0.12

70.

132

0.16

60.

184

0.19

00.

290

0.35

40.

396

0.36

60.

296

0.28

00.

325

0.39

50.

423

0.28

0M

aced

onia

,FY

R0.

498

0.35

30.

346

0.29

70.

303

0.26

50.

317

0.31

70.

359

0.35

90.

365

0.31

40.

261

0.24

20.

328

Mad

agas

car

0.55

50.

565

0.50

70.

377

0.32

10.

356

0.45

10.

458

0.47

10.

492

0.44

10.

337

0.27

10.

260

0.41

9

(Con

tinu

ed)


Tabl

e5:

(Con

tinue

d) 1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Mea

n

Mal

awi

0.42

00.

460

0.44

30.

390

0.26

30.

357

0.36

00.

371

0.39

00.

491

0.52

50.

438

0.42

20.

360

0.40

6M

alay

sia

0.27

70.

246

0.27

10.

362

0.34

40.

355

0.35

10.

352

0.35

50.

353

0.36

00.

366

0.36

20.

409

0.34

0M

ali

0.25

20.

266

0.29

80.

253

0.32

40.

307

0.33

50.

304

0.31

10.

367

0.32

50.

304

0.32

10.

286

0.30

4M

alta

0.21

40.

217

0.24

90.

226

0.22

50.

239

0.27

30.

307

0.34

50.

339

0.33

60.

292

0.31

00.

362

0.28

1M

auri

tani

a0.

574

0.50

50.

313

0.34

00.

186

0.46

30.

466

0.27

50.

277

0.43

10.

333

0.37

8M

auri

tius

0.17

40.

198

0.18

00.

183

0.20

40.

326

0.27

90.

324

0.33

00.

279

0.26

20.

284

0.30

40.

399

0.26

6M

exic

o0.

011

0.00

20.

063

0.01

70.

280

−0.0

25−0

.023

0.04

6M

oldo

va0.

353

0.38

80.

401

0.41

30.

380

0.38

40.

408

0.35

10.

289

0.34

10.

340

0.28

40.

222

0.30

90.

347

Mon

golia

0.31

60.

220

0.27

20.

255

0.22

60.

263

0.21

40.

167

0.20

00.

219

0.20

70.

190

0.22

9M

onte

negr

o0.

000

0.27

50.

238

0.16

10.

204

0.25

60.

205

0.19

70.

231

0.19

6M

oroc

co0.

217

0.23

70.

217

0.29

40.

310

0.32

90.

329

0.37

50.

305

0.33

70.

336

0.35

90.

354

0.36

40.

312

Moz

ambi

que

0.26

30.

236

0.31

90.

259

0.27

90.

272

0.19

40.

238

0.25

90.

340

0.36

80.

375

0.38

50.

356

0.29

6N

amib

ia0.

183

0.02

30.

490

0.42

50.

270

0.25

50.

256

0.24

90.

241

0.27

00.

266

Nep

al0.

355

0.24

70.

319

0.36

20.

357

0.34

80.

231

0.25

80.

273

0.31

10.

292

0.33

30.

326

0.28

30.

307

Net

herl

ands

Ant

illes

0.11

40.

142

0.21

00.

130

0.12

90.

145

Net

herl

ands

0.12

60.

127

0.14

30.

204

0.21

30.

109

0.09

40.

160

0.15

40.

135

0.17

70.

183

0.14

90.

256

0.15

9N

ewZ

eala

nd0.

121

0.08

50.

230

0.20

70.

226

0.27

20.

249

0.20

00.

211

0.19

60.

173

0.20

40.

198

Nic

arag

ua0.

201

0.22

00.

237

0.29

50.

327

0.34

20.

370

0.37

90.

367

0.30

4N

iger

0.26

10.

399

0.06

60.

206

0.14

50.

206

0.14

30.

233

0.30

40.

322

0.26

50.

352

0.33

60.

328

0.25

5N

iger

ia0.

228

0.29

00.

304

0.27

60.

296

0.26

80.

275

0.26

40.

313

0.31

70.

309

0.32

50.

195

0.22

40.

277

Nor

way

0.16

90.

061

0.14

60.

157

0.15

50.

128

0.15

90.

219

0.26

50.

230

0.17

60.

146

0.26

60.

263

0.18

1

(Con

tinu

ed)


Tabl

e5:

(Con

tinue

d)

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Mea

n

Om

an0.

309

0.27

40.

283

0.25

80.

301

0.39

20.

398

0.42

80.

423

0.42

00.

378

0.42

90.

464

0.36

6Pa

kist

an0.

040

0.02

3−0

.014

0.04

50.

119

0.18

50.

259

0.27

00.

395

0.36

80.

321

0.27

70.

288

0.27

60.

204

Pana

ma

0.19

60.

134

0.31

70.

259

0.25

50.

300

0.36

30.

322

0.30

60.

275

0.32

00.

311

0.30

50.

313

0.28

4Pa

pua

New

Gui

nea

0.25

00.

259

0.08

80.

401

0.64

10.

520

0.50

40.

611

0.61

40.

530

0.49

00.

446

Para

guay

0.27

80.

181

0.10

40.

041

0.09

20.

015

−0.1

140.

052

0.14

00.

131

0.13

30.

208

0.16

80.

216

0.11

8Pe

ru0.

219

0.20

30.

184

0.16

00.

174

0.25

90.

295

0.31

50.

357

0.36

40.

351

0.38

70.

438

0.39

00.

293

Phili

ppin

es0.

264

0.27

20.

177

0.00

10.

065

0.21

40.

298

0.23

70.

239

0.24

80.

239

0.19

30.

278

0.32

50.

218

Pola

nd0.

170

0.17

50.

162

0.16

50.

166

0.16

90.

137

0.17

40.

190

0.23

90.

246

0.21

50.

232

0.24

10.

192

Port

ugal

0.11

90.

131

0.10

40.

168

0.30

50.

202

0.23

00.

294

0.19

80.

162

0.13

80.

082

0.08

70.

065

0.16

3Q

atar

0.24

20.

318

0.47

10.

522

0.51

40.

551

0.43

50.

398

0.37

00.

375

0.42

0R

oman

ia0.

233

0.21

50.

214

0.19

90.

247

0.19

00.

202

0.26

20.

236

0.22

10.

209

0.22

40.

234

0.27

80.

226

Rus

sian

Fede

ratio

n0.

207

0.06

10.

410

0.37

70.

454

0.34

40.

310

0.33

90.

307

0.29

70.

282

0.27

20.

239

0.20

20.

293

Rw

anda

0.18

70.

205

0.25

70.

109

0.00

40.

320

0.35

20.

343

0.24

90.

343

0.23

7Sa

nM

arin

o0.

185

0.26

20.

400

0.39

70.

328

0.33

50.

435

0.50

60.

504

0.46

00.

382

0.19

50.

366

Saud

iAra

bia

0.26

30.

261

0.25

40.

247

0.31

10.

405

0.49

00.

501

0.49

00.

488

0.34

00.

225

0.36

20.

288

0.35

2Se

nega

l0.

356

0.42

80.

351

0.34

40.

364

0.35

20.

345

0.34

20.

330

0.34

00.

307

0.32

70.

297

0.28

10.

340

Serb

ia0.

374

0.47

20.

362

0.33

60.

217

0.24

90.

228

0.23

40.

176

0.29

4Se

yche

lles

0.19

80.

508

0.55

90.

567

0.59

50.

594

0.37

70.

528

0.49

1Si

erra

Leo

ne0.

190

0.40

00.

646

0.53

50.

481

0.47

40.

519

0.47

20.

386

0.28

70.

188

0.24

70.

328

0.39

6Si

ngap

ore

0.24

80.

232

0.36

20.

353

0.29

70.

230

0.41

40.

361

0.30

90.

331

0.37

60.

489

0.43

80.

342

Slov

akR

epub

lic0.

092

0.03

20.

029

0.14

20.

158

0.18

30.

216

0.24

60.

267

0.29

10.

284

0.30

40.

322

0.39

00.

211

Slov

enia

0.21

40.

213

0.22

40.

238

0.18

80.

210

0.21

40.

252

0.26

60.

252

0.24

90.

184

0.23

70.

269

0.22

9So

uth

Afr

ica

0.10

50.

163

0.16

70.

179

0.20

40.

300

0.21

10.

177

0.15

50.

233

0.22

20.

199

0.21

70.

229

0.19

7Sp

ain

0.13

00.

161

0.22

80.

181

0.17

90.

196

0.23

80.

275

0.24

20.

246

0.22

90.

207

0.29

20.

305

0.22

2Sr

iLan

ka0.

149

0.17

70.

114

0.10

20.

094

0.15

00.

224

0.21

00.

210

0.19

60.

171

0.14

60.

171

0.23

20.

168

(Con

tinu

ed)


Tabl

e5:

(Con

tinue

d) 199

719

9819

9920

0020

0120

0220

0320

0420

0520

0620

0720

0820

0920

10M

ean

Suda

n0.

395

0.26

60.

246

0.25

80.

145

0.31

70.

180

0.29

10.

257

0.27

70.

171

0.22

30.

193

0.21

40.

245

Swed

en0.

186

0.16

80.

156

0.18

20.

183

0.16

90.

206

0.27

70.

234

0.22

40.

178

0.16

00.

223

0.24

40.

199

Switz

erla

nd0.

168

0.13

20.

126

0.15

60.

124

0.16

50.

179

0.18

00.

122

0.12

50.

039

0.03

60.

129

0.17

90.

133

Syri

anA

rab

Rep

ublic

0.00

00.

064

0.30

90.

568

0.56

90.

567

0.34

6Ta

iwan

0.15

90.

165

0.22

70.

349

0.28

30.

307

0.27

80.

248

0.21

80.

294

0.34

20.

261

Tanz

ania

0.47

10.

439

0.39

00.

423

0.39

50.

392

0.35

70.

343

0.40

1T

haila

nd0.

171

0.01

10.

045

0.10

60.

148

0.23

30.

290

0.37

50.

375

0.28

80.

289

0.33

40.

369

0.38

90.

245

Togo

0.11

10.

191

0.21

60.

446

0.22

50.

129

0.27

60.

315

0.25

90.

307

0.25

90.

282

0.24

40.

344

0.25

8T

rini

dad

and

Toba

go0.

195

0.19

30.

231

0.26

60.

284

0.30

20.

360

0.34

70.

309

0.32

10.

338

0.34

50.

313

0.44

20.

303

Tun

isia

0.56

20.

557

0.45

80.

302

0.29

20.

267

0.18

90.

208

0.22

10.

285

0.29

50.

323

0.33

10.

346

0.33

1T

urke

y0.

022

0.03

40.

143

0.04

6−0

.017

0.11

20.

190

0.24

00.

286

0.22

60.

227

0.20

90.

335

0.32

00.

169

Uga

nda

0.40

10.

360

0.36

80.

341

0.36

7U

krai

ne0.

229

0.26

90.

316

0.21

10.

229

0.18

20.

245

0.23

30.

221

0.24

30.

220

0.31

40.

250

0.21

40.

241

Uni

ted

Ara

bE

mir

ates

0.30

70.

298

0.31

40.

295

0.34

00.

462

0.50

70.

516

0.51

60.

359

0.34

60.

372

0.45

30.

468

0.39

7U

nite

dK

ingd

om0.

182

0.18

40.

177

0.24

30.

110

0.16

90.

282

0.29

20.

254

0.24

10.

236

0.10

30.

294

0.30

80.

220

Uni

ted

Stat

es0.

239

0.22

90.

252

0.22

40.

266

0.33

20.

355

0.32

10.

304

0.26

80.

227

0.23

90.

344

0.35

20.

282

Uru

guay

0.07

20.

076

0.07

30.

097

0.03

70.

248

0.01

30.

241

0.09

00.

190

0.26

90.

363

0.18

10.

250

0.15

7U

zbek

ista

n0.

378

0.30

70.

301

0.37

10.

364

0.32

10.

223

0.18

10.

239

0.27

50.

283

0.22

90.

212

0.24

80.

281

Ven

ezue

la,R

B0.

291

0.28

30.

217

0.18

20.

226

0.30

10.

327

0.34

30.

276

0.29

30.

281

0.26

30.

265

0.30

60.

275

Vie

tnam

0.37

90.

346

0.31

40.

345

0.26

40.

292

0.27

30.

349

0.33

60.

282

0.27

70.

208

0.19

80.

205

0.29

1Y

emen

,Rep

.0.

055

0.22

60.

231

0.20

00.

272

0.24

20.

204

Zam

bia

0.04

70.

172

0.11

70.

194

0.23

40.

224

0.10

10.

233

0.29

60.

340

0.34

00.

299

0.33

70.

288

0.23

0Z

imba

bwe

0.29

70.

299

0.29

8M

ean

0.21

20.

207

0.22

60.

230

0.22

80.

255

0.28

00.

298

0.29

90.

301

0.29

40.

277

0.29

00.

305

0.26

6

Not

es:T

heta

ble

repo

rts

aver

age

estim

ates

ofm

arke

tpow

er(w

eigh

ted

bym

arke

tsha

res)

byco

untr

yan

dye

ar.A

vera

ges

are

obta

ined

from

the

bank

-lev

eles

timat

esof

mar

ketp

ower

usin

gth

eL

erne

rin

dex,

asth

isis

defi

ned

inE

q.(1

).H

ighe

rva

lues

refl

ecth

ighe

rm

arke

tpow

er(l

ower

com

petit

ion)

.


Tabl

e6:

Ave

rage

esti

mat

esof

mar

ket

pow

er(w

eigh

ted

bym

arke

tsh

ares

)us

ing

the

adju

sted

-Ler

ner

inde

x

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

Mea

n

Afg

hani

stan

0.41

20.

129

0.01

70.

400

0.25

90.

027

0.20

7A

lban

ia0.

515

0.25

80.

133

0.12

50.

187

0.15

20.

295

0.24

70.

293

0.24

80.

227

0.27

30.

211

Alg

eria

0.00

90.

028

0.04

20.

041

0.05

80.

230

0.14

50.

098

0.20

70.

460

0.42

00.

512

0.49

60.

468

0.23

0A

ndor

ra0.

220

0.25

40.

315

0.32

40.

299

0.36

60.

443

0.47

90.

438

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288 Sofronis Clerides et al.Ta

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2

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