Competition, Efficiency and Interest Rate Margins in Latin American Banking

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1 Competton, Effcency and Interest Rate Margns n Latn Amercan Bankng Georgos E. Chortareas a Unversty of Athens Jesús G. Garza-García b Banco de Méxco Clauda Grardone c* Unversty of Essex Abstract Hgh nterest rate spreads and low credt avalablty to the prvate sector have been persstent problems n Latn Amercan bankng n spte of the recent fnancal sector reforms. Ths paper consders the determnants of nterest rate margns focusng on ther relatonshp wth (structural and non-structural) measures of competton and non-parametrc estmates of effcency. The emprcal analyss provdes an extenson of the tradtonal Structure-Conduct-Performance framework and we estmate panel regressons usng a Generalzed Method of Moments (GMM) framework, for a sample of over 2,300 bank observatons coverng the perod The results show that the concentraton ndex and the market share have lttle or no nfluence on nterest rate margns. In contrast, we produce evdence suggestng that greater X-effcency and compettve markets result n lower spreads. Moreover, whle well captalzed banks seem to be assocated wth hgh spreads, economc growth appears to reduce them. Keywords: et Interest Margns; Bankng Competton; Effcency; Data Envelopment Analyss; Latn Amerca. JEL classfcaton: G21; D24; L11. a Department of Economcs, Unversty of Athens, 8 Pesmazoglou Street, Athens 10559, Greece. Phone: Emal: Essex Busness School, Unversty of Essex. Emal: b Drecton of the Analyss of the Fnancal System, Banco de Méxco, Ave. 5 de mayo # 1 pso 1, Del. Cuauhtémoc, Méxco, D.F. Phone: + 52 (55) , Emal: Centre for Global Fnance at Brstol Busness School (UWE). Emal: c* (Correspondng author) Essex Busness School, Unversty of Essex, Wvenhoe Park, Colchester, CO4-3SQ, UK; Tel: , Fax: +44(0) Emal: 1

2 1. Introducton Fnancal ntermedaton n Latn Amerca s relatvely low by nternatonal standards. Most countres fnancal systems are predomnantly bank-based and despte the reforms amed at lberalzng and deregulatng the fnancal sector, the regon stll experences relatvely low ratos of credt to the prvate sector. 1 These low levels of fnancal ntermedaton appear to be ted n wth hgh borrowng prces and nterest-rate spreads. Over the average nterest rate spread n Latn Amerca reached 16.19% (wth peaks of 42% n Brazl and 26% n Uruguay) compared to 5.02% n Inda, 6.21% n South East Asa, 3.26% n the Euro area and 2.9% n the US. 2 The hgh persstence of these spreads s consdered as one of the greater falures of fnancal sector reforms carred out n Latn Amerca over the last two decades. Moreover, the credt crunch and lqudty shortage generated by the global fnancal crss exacerbates ths stuaton by further decreasng the lqudty supply to the prvate sector. Whle hgh nterest rate spreads can contrbute to the strengthenng of a country s bankng system f for nstance, the profts earned are transferred by banks to ther captal bases (Barajas et al. 1998; Saunders and Schumacher, 2000a) they are also assocated wth a number of drawbacks. Hgh spreads are generally thought to reflect the neffcences of a fnancal system or, as Bernanke (1983) puts t, the costs of fnancal ntermedaton. When the spread s too hgh, t not only dscourages potental savers wth low returns but also mpedes credt expanson because of the hgher loan rates. Developng countres may be especally at rsk because ther fnancal systems and captal markets are less extended and sophstcated and ther economes are prmarly bank-based (Martnez Pera and Mody, 2004). Brock and Rojas-Suarez (2000) emphasze that hgh nterest rate spreads are assocated wth bank- 1 The average levels (over GDP) over was 28.7% n Latn Amerca, compared to 66.6% n South East Asa, 111.5% n the Euro Area and 145.7% n the UK. The only country wth a smlar rato as South East Asa s Chle, havng a rato of 66% followed by Uruguay wth 43.8% (data obtaned from the IFS/IMF (Internatonal Fnancal Statstcs provded by the Internatonal Monetary Fund). 2 Spreads are measured as lendng rate mnus depost rate as n Brock and Rojas-Suarez (2000). Spreads and nterest rate margns wll be used ndstnctvely throughout ths paper. 2

3 specfc neffcences whch mpact negatvely on credt expanson and nvestment. Hanson and Rezende Rocha (1986) and Barajas et al. (1998) on the other hand observe that hgh spreads are frequently attrbuted to hgh operatonal costs, nflaton rates, fnancal taxaton and lack of competton. The economc reasonng s straghtforward: f a handful of banks domnate the market, they can earn monopoly rents by behavng as prce setters. Ths n turn lowers competton, ncreases neffcences at the bank level and results n an overall welfare loss. Yet, f bank mergers and acqustons are drven by economes of scale, hgher market concentraton may actually result n effcency mprovements. Moreover, whle one of the ams of fnancal lberalzaton carred out n Latn Amerca was to ncrease actual and potental competton n the ndustry, banks have reacted to the new operatng envronment by strategcally ncreasng ther relatve sze through mergers and acqustons. Consequently relyng solely on concentraton measures to gauge compettve condtons may be deceptve, as t would appear that the deregulaton process n Latn Amerca has actually decreased competton, because concentraton has rsen. Typcally, competton polces are ntended to montor bankng markets and f necessary to prevent fnancal nsttutons from ganng excess market power. Although recent events demonstrate that trade-offs between the degree of competton and fnancal stablty exst, 3 n normal tmes anttrust authortes usually ntervene to prevent large banks mergers and/or acqustons actvtes f they beleve that the costs n terms of reducton n competton maybe too hgh. Ths paper consders the possble determnants of nterest rate margns n the Latn Amercan bankng markets durng the perod We specfy an emprcal model whch consttutes an extenson of the tradtonal market power (Structure-Conduct Performance SCP and Relatve Market Power RMP) and effcent-structure (X- and scale effcency) hypotheses. 4 Specfcally, we explore 3 For example, consder the recent case of the merger between Lloyds TSB and HBOS n the UK. 4 See e.g. Claeys and Vander Vennet (2008). 3

4 how (measures of) a set of varables used n the SCP and RMP frameworks (e.g., market concentraton and market share) and effcency measures explan nterest rate margns. To capture cost effcency, nstead of relyng on tradtonal fnancal ratos such as the cost-to-ncome rato, we use X- and scale effcency (as n Berger, 1995) estmated usng Data Envelopment Analyss (DEA). Then, assumng that hghly concentrated bankng markets are not necessarly uncompettve, we test the relatonshp between bank margns and a non-structural measure of bank competton (the H-statstc) ntroduced by Panzar and Rosse (1987). Fnally, we nclude a set of relevant macroeconomc and bank-specfc control varables. The correlaton between margns and market structure, rather than beng drect, may run va a hgher level of effcency. We run panel regresson models, usng a Generalzed Method of Moments (GMM) framework, smultaneously to account for ths possblty. As far as we are aware there are no smlar studes for Latn Amerca and ths s the frst to test alternatve measures of competton smultaneously n a model of determnants of bank margns. Our results ndcate that the concentraton ndex and the market share have lttle or no nfluence on nterest rate margns. In contrast, greater X- effcency and non-structural measures of competton are mportant determnants n lowerng spreads. Hgh levels of bank captalzaton are assocated wth hgh margns suggestng that banks set hgher nterest margns n response to proftablty losses caused by greater (regulatory and endogenous) captal ratos. Economc growth appears to reduce nterest rate spreads. The paper s organzed as follows: Secton 2 provdes a bref background of the bankng sector n Latn Amerca and revews the relevant lterature; Secton 3 descrbes the data sources and man methodologcal ssues; Secton 4 dscusses the results of the analyss and fnally Secton 5 concludes. 4

5 2. Background and Lterature Revew 2.1 Fnancal Intermedaton n Latn Amerca: A Brd s Eye Vew The attempts of fnancal lberalzaton experenced by Latn Amercan countres durng the 1990s affected ther bankng sectors n many ways. A notable consequence was a hgher degree of consoldaton, reflected n ncreasng degrees of market concentraton as shown n Fgure 1. Wllams et al. (2009) pont out that the consoldaton of the bankng sectors n Latn Amerca was necessary n order to restructure the fnancal systems after several fnancal crses. Fgure 1 Herfndahl-Hrschmann Index ( ) 1600 Latn Amercan HHI ndex Latn Amerca Source: Elaborated wth data from Bankscope. Notes: The Latn Amercan HHI ndex s the average of the HHI ndex of the bankng sectors for Argentna, Brazl, Chle, Colomba, Costa Rca, Paraguay, Peru, Uruguay and Venezuela for the perod under study. At the same tme the fnancal sector stll remans hghly bank-based and fnancal ntermedaton s low compared to other developed and developng natons. The level of credt ncreased durng the 1990s, to contract agan soon after the economc crses experenced n the regon (Sngh et al., 2005) and currently 5

6 remans relatvely low (see Fgure 2). 5 The persstently hgh lendng rates are among the factors explanng the relatve low ratos of credt to the prvate sector. Fgure 2 Bank Credt to the Prvate Sector n terms of GDP, Average Percentage ( : Latn Amerca and other selected countres) ARG BRA CHI COL CR PARA PER URU VEN LATAM EURO AREA SOUTH EAST ASIA UK USA Source: Data obtaned from the IFS. a The Latn Amercan average s calculated usng the followng countres: Argentna, Brazl, Chle, Colomba, Costa Rca, Paraguay, Peru, Uruguay and Venezuela. For Venezuela the average s calculated from due to data avalablty. b The South East Asa average s calculated usng the followng countres: Brune, Camboda, Malaysa, Laos, Myanmar, Sngapore, Thaland, Hong Kong, South Korea, Phlppnes and Vetnam. The average for Brune and Vetnam was computed for the years and for Myanmar from due to data avalablty. c The data for the euro area was elaborated ncludng all the countres n the European Unon. The ntal attempts of fnancal lberalzaton nourshed the antcpaton that nterest rate spreads would converge to (the lower) nternatonal levels. Foregn bank entry, the ratonale goes, could 5 The average of Latn Amerca s 28.7% compared to 66% n South East Asa, 111.5% n the Euro Area and 145.7% n the UK. The only country wth a smlar rato as South East Asa s Chle, havng a rato of 66% followed by Uruguay wth 43.8%. Nevertheless, the rest of the regon experences excessvely low ratos of credt to the prvate sector. 6

7 contrbute to lower nterest rate spreads by rasng competton, generatng effcency gans and enhancng bankng sector captalzaton through, for example, greater depost rates (Wllams et al., 2009). Nevertheless, the spreads remaned relatvely hgh compared to other ndustral and developng natons (Gelos, 2006). Whle, n general, foregn banks have operated wth lower spreads as compared to domestc banks n Latn Amerca, the man mpact of fnancal lberalzaton has been the nducement of all banks to reduce costs rather than a marked declne n spreads (Martnez Pera and Mody, 2004). Brock and Rojas-Suarez (2000) suggest that hgh spreads are typcally assocated wth bank neffcences, whch mpact negatvely on credt expanson and nvestments. The nterest rate spreads n Latn Amerca, however, persst at hgh levels compared to nternatonal standards (see Gelos, 2006) valdatng earler analyses (e.g., Brock and Rojas-Suarez, 2000). Fgure 3 presents the nterest rate spreads n Latn Amerca compared to other developed and emergng economes. Fgure 3 Interest Rate Spreads ( , yearly averages) ARG BRA CHI COL CR PARA PERU URU VEN LATAM INDIA SOUTH EAST ASIA EURO AREA USA Source: Data obtaned from the IFS. See notes to Fgure 2. 7

8 To capture the nterest rate spreads n ths paper we use the Net Interest Rate Margn (NIM), defned as the net ncome mnus the next expenses over total earnng assets of the commercal banks. Ths measure has been used extensvely n the lterature (see e.g. Ho and Saunders, 1981; Demrguc- Kunt and Huznga, 1999; among others). Table 1 reports the net nterest rate margn for commercal banks by country and by year over the perod Table 1 Commercal Banks Average et Interest Margn (%) Argentna Brazl Chle Colomba Costa Rca Paraguay Peru Uruguay Venezuela Source: Bankscope. 8

9 Whle NIMs have decreased n some countres and ncreased n others over the perod under study, 6 they reman excessvely hgh by nternatonal standards. In the aftermath of Argentna s economc crss the majorty of the countres consdered dsplayed NIM ratos averagng at 8.57% Lterature Revew Studes attemptng to capture the costs of fnancal ntermedaton typcally focus on the dfference between lendng and deposts rates, and assume that hgher spreads are assocated wth hgher fnancal neffcences. Recent emprcal studes on the determnants of bank spreads are cross-country nvestgatons coverng large samples of countres. Demrguc-Kunt and Huznga (1999) examne the determnants of nterest rate spreads usng bank-level data for 80 countres for the perod They dentfy bank-specfc, nsttutonal, regulatory and macroeconomc varables that affect bank spreads and profts. Typcally domestc bankng nsttutons n developng countres appear to have lower margns and profts than foregn banks, whle the opposte holds true n developed economes. Usng a sample of 1,400 bankng nsttutons over 72 countres n , Demrguc-Kunt et al. (2003) nvestgate the mpact on bank margns of market concentraton, regulatory and macroeconomc varables whle controllng for bank-specfc varables. They also test the mpact of ndexes of the overall nsttutonal structure of the economy, such as property rghts and economc freedom ndexes. Ther results suggest that hgher nterest margns are assocated wth ncreased regulaton amed at restrctng bank operatons and freedom to entry. Although competton s not measured drectly, ths study seems to mply that overall banks operatng n less compettve markets are able to acheve hgher 6 In partcular NIMs have decreased n Argentna (-55%), Brazl (-15%), Paraguay (-22%) and Venezuela (-62%) and ncreased n Chle (+8%), Colomba (+37%), Costa Rca (+91%) and Peru (+26%). 7 The Latn Amercan average s elaborated usng data from the database Bankscope for Argentna, Brazl, Chle, Colomba, Costa Rca, Paraguay, Peru, Uruguay and Venezuela (see for more detals Secton 3). 9

10 margns. Natonal approaches to property rghts also appear to be sgnfcant factors n explanng hgher than normal margns. Carbo-Valverde and Rodrguez-Fernandez (2007) study the determnants of nterest rate margns n seven European Unon (EU) countres for the perod They use three dfferent measures of nterest rate margns, namely, a wde accountng margn measure, the loans to deposts rato, and the Lerner ndex of monopoly power. Ther fndngs reject the hypothess that a relatonshp between the degree of concentraton and nterest rate margns exsts. They fnd, however, a strong postve relatonshp between nterest rate margns and varables such as credt rsk, lqudty rsk, nterest rate rsk and the degree of captalzaton. The degree of captalzaton s nterpreted as a mnmum premum on bank margns due to regulatory pressures relatng to solvency ssues. Moreover, ther evdence suggests a postve relatonshp between operatonal neffcency (cost-to-ncome rato) and greater margns. Hence, banks wth hgher costs tend to operate wth hgher margns (see also, Altunbas et. al., 2001). Fnally, they fnd a negatve relatonshp between GDP growth and margns, suggestng that economc growth fosters lower margns. Recently Claeys and Vander Vennet (2008) analyze the determnants of nterest rate margns n Central and Eastern European countres as compared to Western European countres over focusng on bank market structure, effcency, and changes n the regulatory framework. They test these relatonshps for a large cross-country sample n the context of the ndustral organzaton market power (SCP and RMP) and effcency hypotheses. Specfcally, scale and X-effcency measures are derved from the estmaton of a stochastc fronter approach (see e.g. Goddard et al., 2001). However the authors do not measure bank competton and smply nfer t from market structure. A man fndng s that changes n regulaton result n rsker bank actvtes and ncrease ther margns before competton effects push them down. 10

11 Several studes analyze the possble determnants of hgh lendng rates and spreads n the Latn Amercan bankng sector usng almost nvarantly panel data methodologes. Table A1 (Appendx 1) reports selected country-specfc and cross-country studes carred out snce the early 1990s. Furthermore, hgh operatng costs, macroeconomc varables and concentraton ratos are usually found to be sgnfcantly assocated wth banks margn and spreads. Brock and Rojas-Suarez (2000) apply a two-step procedure frst developed by Ho and Saunders (1981) for fve Latn Amercan countres (Argentna, Colomba, Bolva, Chle and Peru) durng The method essentally conssts n runnng two separate regresson models for the mcroeconomc and macroeconomc varables on the nterest rate spreads. Bank spreads n Bolva are explaned by mcroeconomc varables, whereas n Chle and Colomba they are explaned by both mcro and macroeconomc factors. Both the above factors, however, leave unexplaned a large porton of the spread n Argentna and Peru. Martnez Pera and Mody (2004) analyze how foregn partcpaton and market concentraton mpact bank spreads n Latn Amerca. They focus on Argentna, Chle, Peru, Colomba and Mexco over and defne the spread as the dfference between the mplct average nterest charged on loans and the mplct average nterest pad on deposts. They consder bank-specfc (such as nonperformng loans and equty) and macroeconomc varables, as well as a structural measure of market share, concentraton and merger and acquston effects. Ther evdence suggests that foregn entry lowers nterest rate margns and fosters competton. The degree of market concentraton s a strong determnant of spreads, partcularly for domestc banks. Fnally, the long-term benefts of foregn entry are materalzed through lower admnstratve costs, fosterng further cost reducton throughout the bankng system (generatng greater competton). Gelos (2006) examnes banks spreads n a wder set of fourteen Latn Amercan countres usng the net nterest rate margn as the dependent varable. The bank-specfc varables nclude bank sze, 11

12 equty, overhead costs and foregn ownershp. To measure competton the author uses the H-statstc. 8 It emerges that Latn Amercan nterest rate margns are determned by low effcency levels, hgh reserve requrements and relatvely hgh market nterest rate spreads. Moreover, a less developed legal envronment seems to contrbute to hgher spreads. On balance most studes suggest that to explan the hgh nterest spreads one should look at a multtude of factors rather than a sngle one. The determnants of spreads nclude neffcences, market structure and macroeconomc varables. Wllams et al. (2009), for example, fnd that the most common factors nfluencng spreads nclude hgh operatng costs, poor loan qualty, hgh captalzaton, reserve requrements and the macroeconomc envronment (ncludng nterest rate volatlty and GDP growth and volatlty). No analyss exsts, however, on the potental role of market power and effcent structure n Latn Amerca. As far as we are aware, only one study (Gelos, 2006) has emprcally tested the relatonshp between margns and bank competton as measured by the H-statstc. In ths paper we provde an extensve analyss of the nterest rate spreads determnants n Latn Amerca focusng explctly on the role of competton and effcency. 3. Data and Methodology 3.1 Econometrc specfcaton To examne the determnants of net nterest rate margns n Latn Amerca we employ a dynamc panel data approach. There are many advantages of usng these models. The frst advantage resdes n the ablty to acknowledge both the tme and cross-sectonal varaton n the model. The second beneft s that t allows avodng any bas between cross-country regressons. The thrd s the possblty to use nstrumental varables producng more precse and accurate estmators. In addton, these methods are useful for panels that are charactersed by a relatvely low number of years and a large number of cross- 8 The Panzar and Rosse (1987) H-Statstc s dscussed below and Appendx 2 provdes further methodologcal detals. 12

13 sectons per year. We use a GMM dynamc panel data approach as proposed by Arellano and Bond (1991) and further developed by Arellano and Bover (1995) and Blundell and Bond (1998). The emprcal model s an extenson of the orgnal market power and effcent structure hypotheses (for the conventonal models see Berger, 1995 and Goldberg and Ra, 1996) as follows: IM t 3 4 1, t 1+ β 2HHI t + β3mst + β 4ESX t + β5esst + η j X t + δ k Zt + j= 1 k= 1 = α + β IM ε (1) 1 t where NIM s the net nterest margn and s calculated as the nterest rate ncome mnus the nterest rate expenses dvded by total earnng assets. HHI s the Herfndahl-Hrschman Index measured as the squared sum of the market share n terms of total assets of the banks n the ndustry at tme t, HHI n assets t = ( MS t m= 1 ) 2. A postve coeffcent for the HHI mples a drect effect from the market structure on the nterest rate margns, ndcatve of colluson, and thus supportve of the tradtonal SCP hypothess. Greater concentraton can offset the apparent benefts of foregn bank penetraton through greater spreads (Martnez Pera and Mody, 2004). MS s the market share n terms of each bank s assets. A postve coeffcent ndcates that banks wth hgh market share are able to set prces autonomously, supportng the RMP hypothess. ESX s a measure of manageral (techncal effcency) and ESS s a measure of scale effcency. The effcent structure hypothess suggests a negatve relatonshp between NIM and both effcency varables. That s, effcency gans should be reflected n lower nterest rate margns. The vector of control varables, X ncludes a number of frm- and market- specfc characterstcs, Z s a vector of country-specfc macroeconomc varables, and fnally specfcally the controls of X t, can be wrtten as follows: ε t s the error term. More 13

14 3 j= 1 η η η + η LLR j X t = 1CAPt + 2LOAAt 3 t (2) where CAP s the degree of captalzaton measured as equty over assets, LOAA s a measure of lqudty rsk proxed by loans over assets, and LLR s the loan loss reserves whch consttutes a proxy for banks asset qualty. Saunders and Schumacher (2000a,b) ratonalze the antcpated postve relatonshp between CAP and NIM on the grounds that banks respond to the loss n proftablty due to greater captal ratos by demandng hgher nterest margns. Other reasons of ths costly relatonshp between captal and debt reflected n greater spreads nclude the excessve taxes on captal and hgh governance costs (see Brock and Rojas-Suarez, 2000). LOAA s a lqudty rsk measure estmated as the total amount of loans over total assets. As hgher LOAA ratos mply greater loans, a potental postve relatonshp wth NIM emerges. Ths s because more loans can be rsky and costly to servce and montor. These extra costs are therefore transferred as lower depost rates or hgher lendng rates (see Claeys and Vander Vennet, 2008). Fnally, the level of loan loss reserves (LLR) s a proxy for the qualty of the bank loan portfolo. The antcpated sgn on ths varable s determned by counterbalancng forces. On one hand, the hgher the loan loss reserves the lower the resources allocated to alternatve nvestment opportuntes and the LLR should be negatvely related to nterest rate margns. On the other hand, the relatonshp could also be postve because a hgh LLR may reflect a poorer loans qualty nducng banks to charge customers an extra premum for these loans and thus ncreasng the spread. We specfy the vector Z of the macroeconomc varables n equaton (1) as follows: 4 n= 1 δ δ δ δ + δ I T nz t = 1XRATEt + 2CPI t + 3GDPt 4 t (3) 14

15 where XRATE s the domestc exchange rate (home currency n terms of USD); CPI s the nflaton rate; GDP s the real growth rate of the economy and INT s the average annual market nterest rate. The exchange rate affects bank margns, although ts mpact vares dependng on the structure of the bank s assets and labltes n foregn currency (Fuentes and Basch, 1998). The nflaton rate s measured as the average percentage change of the consumer prce ndex and, n general, a postve relatonshp s expected wth NIMs. Greater nflaton ncreases the rsk of default and thus banks wll charge a hgher lendng prce that ncreases the nterest rate spreads. On the other hand, banks can often be constraned by regulatory, nsttutonal, and market factors so that are unable to keep up rasng rates when nflaton rates are hgh and varable. Moreover, nflaton affects asymmetrcally lenders and borrowers and thus ts net affect on NIMs depends on the structure of the asset sde of banks balance sheet. Economc growth has a wealth effect, rasng the net worth of depostors and reducng lendng rates (see Gelos, 2006). Fnally, snce greater market nterest rate fluctuatons ncrease nterest rate margns (Saunders and Schumacher, 2000a) the expected relatonshp between INT and NIM s postve. To observe the bank-specfc effects n relaton to the degree of competton, we consder a second model specfcaton whch allows us to measure the nfluence of the Panzar and Rosse H-statstc on bank nterest margns. 9 Falng to account for (a measure of) competton may be msleadng because n Latn Amerca banks have responded to the process of lberalzon by ncreasng sgnfcantly ther asset sze. In other words, the deregulaton process that was ntended to ncrease competton resulted n a much more concentrated ndustry. Techncally, a hgh degree of competton n the bankng sector should yeld lower spreads and t has also been documented that admnstratve expenses are postvely related to greater nterest rate 9 See Appendx 2 for detals on estmatng the H-statstc. A recent study by Schaeck et al. (2009) also provdes an extensve dscusson. 15

16 margns. The neffcences n a country s bankng sector are lkely to reflect the absence of a compettve envronment (Gelos, 2006). Thus, we wrte the modfed model as: IM t 3 4, t 1+ β2hhit + β3mst + β4h statt + η j X t + δkzt + j= 1 k= 1 = α + β IM ε (4) 1 t We call equaton (4) Model I and we estmate t for the whole Latn Amercan regon because the H- statstcs s tme-nvarant and we can only obtan one measure per country for the perod under study. To check the robustness of our fndngs, however, we ntroduce a varaton of the above model whch ncludes the nteracton varables H*ESX and H*ESS n order to capture whether the changes n the degree of competton depend on the manageral and scale effcences, respectvely. We wrte the resultng Model II as: IM t IM, t 1+ β2hhit + β3mst + β4h * ESXt + β 5H * ESSt + η j X t + δ kzt + j= 1 k= 1 = α + β ε (5) t As noted above, greater bankng competton should generate lower nterest rate margns as condtons for consumers mprove mplyng a negatve relatonshp between the H-statstc ndex and NIM. More competton lowers bank nterest rate spreads on polcy market rates and ncreases the speed of adjustment of the latter rates (Van Leuvenstejn, 2008). Carbo-Valverde (2008) explans that allocatve effcency refers to the maxmzaton of socal welfare that s acheved under perfect competton (P=MC), and thus both operatonal and allocatve effcency could affect socal welfare and market power n varous ways. He descrbes two channels through whch effcency could devate from 16

17 perfect competton: t may enable banks to depart from margnal cost prcng (allocatve neffcency) and/or t may affect the efforts of bank managers to control costs (operatonal neffcency). 10 We estmate the models descrbed n equatons (1) and (5) usng an unbalanced panel of data. Ths choce s dctated by two man reasons: frst, to account for the consoldaton process that has taken place n Latn Amerca over the perod under study; and second, to observe heterogenety between observatons and tme effects. To estmate the X- and scale effcency varables we use the non-parametrc Data Envelopment Analyss (DEA) technque (Charnes et al. 1978) whch s based on mathematcal lnear programmng. The DEA fronter s formed by the best-practce observatons yeldng a convex producton possblty set. Snce our focus s on the cost sde of bankng operatons, we employ an nput-orented, varablereturns-to-scale (VRS) model. Specfcally, we compute two varables: the X-Effcency (ESX) and the scale effcency (ESS). Ths latter s defned as the rato of Constant Returns to Scale (CRS) to VRS,.e. ESS = CRS/VRS and the value for scale effcency s bounded between 0 and 1. The VRS lnear programmng model we use s defned as follows: mn θ, λθ, st yt + Yλ 0 (6) θq Qλ 0 1λ = 1 λ 0 where θ s a scalar, λ s a N tmes 1 vector of constants, y0 s the output vector for the DMU 0 (Decson Makng Unt), Y s the matrx of outputs of the other DMUs and the number of DMUs ranges from =1 n, q 0 s a vector of nputs of DMU 0 and q s the matrx of nput of the other DMUs. The value of θ represents the effcency score for the 0-th DMU where 0 θ 1. If θ s equal to 1, then the DMU 10 Ths s the so-called quet lfe hypothess whch descrbes how market power may detrment operatonal effcency. 17

18 les on the effcent fronter and the partcular DMU (bank) s fully (.e. 100%) effcent. Removng the convexty constrant 1λ= 1 from (6) one obtans the CRS based effcency scores. In our analyss the nputs are: nterest rate expenses, personnel expenses and other operatve expenses. The output varables capture the tradtonal lendng actvtes of banks (total loans) and the growng non-lendng actvtes (other earnng assets) Data Our sample ncludes 2,305 bank observatons from nne Latn Amercan countres: Argentna, Brazl, Chle, Colomba, Costa Rca, Paraguay, Peru, Uruguay and Venezuela over the perod Balance sheet and ncome statements data are drawn from the Ftch/IBCA/Bureau Van Djk Bankscope database. We use an unbalanced panel of data to account for merger and acquston effects n the model. We obtan the macroeconomc data from the Internatonal Fnancal Statstcs (IFS) of the Internatonal Monetary Fund. Table 2 presents the descrptve statstcs of the sample. The net nterest margn, the proxy varable for nterest rate spreads, s on average 9.8%. The regon s average nflaton rate over the perod consdered s 8.6%, and GDP growth averages only 2.17%. 11 Followng e.g. Beccall et al. (2006) we use the flow of costs as one sngle nput. 18

19 Table 2 Descrptve Statstcs Observatons 2,305 Mean Standard Devaton Mnmum Maxmum NIM HHI 1, , MS LOAA CAP LLR ESX ESS XRATE , , CPI GDP INT H-stat Notes: NIM s the net nterest rate margn defned as the nterest rate ncome mnus nterest rate expenses over total earnng assets; HHI s the Herfndahl-Hrschman Index measured as the sum of squared market shares n terms of total assets; MS s the market share n terms of total assets; LOAA s a measure of lqudty rsk calculated as loans over total assets; CAP represents the degree of captalzaton and s measured as equty over total assets; LLR s a measure of default rsk calculated as loan loss reserves over gross loans; ESX s a measure of manageral effcency estmated usng DEA; ESS s a measure of scale effcency estmated usng DEA; XRATE s the average annual exchange rate (domestc currency aganst US dollars); CPI s a measure of annual nflaton; GDP s the average GDP real growth. a The varable loan loss provsons over net nterest revenue s ncluded nstead of the loan loss reserves over gross loans for Uruguay due to data avalablty. 19

20 4. Emprcal results Fgure 4 reports the average Herfndahl-Hrschman ndex (HHI) and the Panzar and Rosse H- statstcs by country, capturng the degree of concentraton and competton respectvely for the perod Fgure 4 Bank Concentraton and Competton n Latn Amerca ( ) a HHI H-statstc Argentna Brazl Chle Colomba Costa Rca Paraguay Peru Uruguay Venezuela a HHI s the Herfndahl-Hrschmann Index and s the average of the perod ( ). b The H-statstc s comprsed between 0 (monopoly) and 1 (perfect competton); 0 1 competton. Test Ho=1 accepted for Paraguay and Peru. For methodologcal detals see Appendx 2. H mples monopolstc Our results ndcate that there are wde dfferences across countres n Latn Amerca. Three countres (Peru, Costa Rca and Uruguay) show relatvely hgh HHIs. Nevertheless, the non-structural measures of competton mply that no country s experencng a monopoly and the average H-statstc for the regon s Smlarly, Levy Yeyat and Mcco (2005) fnd that the bankng systems n eght 20

21 Latn Amercan countres (durng ) are characterzed by monopolstc competton. 12 Our evdence also ndcates that the relatonshp between concentraton and competton for the bankng ndustry n Latn Amerca s not straghtforward. Fgure 4 reveals that n some countres hgh concentraton does not necessarly mply lack of competton as for example, n the case of Peru. Equally, countres that have smlar concentraton ndexes, such as Argentna and Brazl, appear to have consderably dfferent H-statstcs. These fndngs broadly suggest that hgh concentraton does not necessarly mply low competton n Latn Amercan banks, thus gvng support to our choce of ncludng both varables n the models of the determnants of nterest rate margns. 13 Specfcally, ths study frst examnes whether and to what extent market power and effcency hypotheses explan banks nterest rate spreads for each Latn Amercan country whle controllng for the effect of other bank- and market specfc varables (Table 3). Then, snce the non-structural measures of bank competton calculated usng the Panzar and Rosse (1987) s methodology are constant for each country over the perod, we pool the data for the whole Latn Amercan regon and test the mpact of the H-statstcs (and the nteracton varable between the H statstcs and X- and scale effcences) on the dependent varable NIM (results are reported n Table 4). Table 3 presents the fndngs of the panel regresson analyss (equaton 1) carred out by country. The frst market structure varable, HHI, s postve and sgnfcant, thus valdatng the SCP argument n Argentna and Peru. No sgnfcantly postve relatonshp exsts between HHI and NIM n the remanng countres. The evdence s weaker concernng the Relatve Market Power hypothess: the MS varable s postve and sgnfcant only for Colomba. 12 The countres under study are: Argentna, Brazl, Chle, Colomba, Costa Rca, El Salvador, Mexco and Peru. 13 Ths s n lne wth a recent study by Schaeck et al. (2009) that fnd that competton and concentraton capture dfferent characterstcs of bankng systems, meanng that concentraton s an napproprate proxy for competton. See also Casu and Grardone (2006). 21

22 Table 3 GMM regresson by country ( IM as dependent varable) Varables Argentna Brazl Chle Colomba Costa Paraguay Peru Uruguay Venezuela Rca NIM lagged HHI.305***.048**.23* * -.003*.474*** ** * *** MS * LOAA.202***.182*** ** *** CAP.127*** *.171** ** LLR *** ESX -.049* -.066** *** -.031** ** *.061*** ESS * XRATE ***.716** **.032*** CPI * GDP -.844*** ** INT ** * ** Constant *** * F- test (p-value) (0.00) 2.27 (0.01) 8.94 (0.00) (0.00) (0.00) (0.00) (0.00) 3.97 (0.001) (0.00) AR(1) (p-value) (0.00) (0.034) (0.062) (0.061) (0.205) (0.085) (0.066) (0.072) (0.036) AR(2) (p-value) 1.35 (0.176) 1.44 (0.151) 1.41 (0.160) (0.473) 0.64 (0.523) (0.877) 0.64 (0.521) (0.071) (0.115) Hansen-J test (p-value) (0.732) (0.267) (1.00) (1.000) (1.000) 4.59 (1.000) 2.34 (0.311) 6.10 (1.000) (1.00) Tme Dummy Observatons Yes 254 Yes 568 Yes 137 Yes 142 Yes 116 Yes 109 Yes 73 Yes 128 Yes 199 *, **, ***, denote sgnfcance from zero at 10%, 5%, 1% level respectvely Notes: NIM s the net nterest rate margn, HHI s the Herfndahl-Hrschman Index, MS s the market share n terms of assets, LOAA s a measure of lqudty rsk, CAP s a measure of the degree of captalzaton, LLR s a measure of default rsk, ESX s a measure of manageral effcency, ESS s a measure of scale effcency, XRATE s the average annual exchange rate, CPI s a measure of annual nflaton, GDP s the average GDP real growth. Arellano-Bond order 1 (2) are tests for frst (second) order correlaton, asymptotcally N(0,1). These test the frst-dfferenced resduals n the system GMM estmaton. The Hansen test s a test of overdentfcaton restrctons. Under the null hypothess, the test statstc s dstrbuted as a chsquared n the number of overdentfyng restrctons. System GMM are two-step estmates. The two-step errors are computed n accordance to the Wndmejer (2005) fntesample correcton. 22

23 Turnng to the effcency varables 14, when sgnfcant, ESX dsplays nvarantly a negatve coeffcent. Ths s the case for the majorty of countres consdered, ncludng the largest ones (.e. Argentna, Brazl and Chle). Estrada et al. (2006) and Gelos (2006) fnd smlar results, mplyng that more effcent banks tend to have lower nterest rate spreads. In contrast, the scale effcency varable, ESS, appears to be less mportant n explanng NIM. 15 Our evdence suggests that ESS s negatve and statstcally sgnfcant only for Brazl. The bank-specfc varables show mxed results, reflectng the sgnfcant dfferences across countres n Latn Amerca n terms of banks strateges and rsk profles. The varable LOAA, whch proxes for the degree of lqudty rsk, when sgnfcant, s postvely related to NIM. Ths result s n tune wth the lterature snce banks tend to pass ther lqudty rsks to consumers va ncreasng nterest rate margns. The varable CAP s statstcally sgnfcant and postvely related to NIM for four out of nne countres n the sample. Saunders and Schumacher (2000a) clarfy that hgh captal ratos can be nterpreted as a form of taxaton on bank proftablty that forces banks to mpose an addtonal premum to the banks NIM. Another possble reason s that well-captalzed banks have lower expected bankruptcy costs and lower fundng costs; hence, f loan rates do not change much wth equty, hgher levels of captal tend to mply larger NIMs (e.g., Demrguc-Kunt et al. 2003). Fnally, the results for the asset qualty varable (LLR) show a statstcally sgnfcant and postve sgn only n Uruguay. Indeed, hgher levels of loan loss reserves may sgnal both lower resources allocated to alternatve nvestment opportuntes (thus decreasng the spread) and poorer loan qualty thereby nducng banks to charge customers a premum that ncreases the spread. The results for the macroeconomc varables (XRATE, CPI, INT and GDP) are mxed. The coeffcents of the exchange rates (XRATE) are statstcally sgnfcant n four cases only and ther 14 See Table A2 n Appendx 1 for the detaled breakdown of the X- and scale effcency scores by country over the perod. 15 Fndng a sgnfcant effect for the X-effcency but not for the scale-effcency s consstent wth the emprcal evdence that X-effcences explan much more cost dfferences than scale (Berger, Hunter and Tmme, 1993; Jeon and Mller, 2005). 23

24 magntude s generally low. The exchange rate effect on NIMs s postve for Paraguay and Peru but negatve for Chle and Venezuela. We fnd that the nflaton rate (CPI) s statstcally sgnfcant and postve n Venezuela. Accordng to Fuentes and Basch (1998), ncreases n the nflaton rate ncrement both the loan and depost rates although n dfferent proportons, therefore resultng to hgher or lower nterest rate margns. The average annual nterest rate (INT) s statstcally sgnfcant only for Brazl, Peru and Venezuela, dsplayng postve coeffcents. Agan the nterpretaton of ths fndng s not straghtforward snce the structure of the banks assets and labltes dffer across countres and thus banks are nfluenced dfferently by the market nterest rate (Fuentes and Basch, 1998). Brock and Rojas- Suarez (2000) fnd that nterest rate volatlty affects spreads postvely n Bolva and Chle but not for the remanng Latn Amercan countres n study. The effect of the nflaton rate and the nterest rate on NIMs may be subject to sgnfcant nonlneartes (especally for hgh and varable nflaton and nterest rates) and dfferences n the regulatory and market envronment. Fnally, GDP growth, when sgnfcant, s negatve as s n Argentna and Chle. Ths mples that for these countres a better macroeconomc envronment decreases NIMs, possbly by reducng the lendng rate as a result of the lower credt rsk of corporate and prvate borrowers. The second step of our analyss conssts n usng a dynamc panel data methodology for the whole Latn Amerca n order to complement the analyss by ndvdual country and account for an explct measure of competton. Specfcally, ths approach allows the ncluson of the Panzar and Rosse (1987) non-structural measures of competton n the models. Model I ncorporates the country specfc H-statstc varables and Model II can be consdered a robustness check that accounts for two nteracton terms wth the effcency varables (H*ESX and H*ESS). We follow a two-step GMM panel data 24

25 procedure and the estmates are Wndmejer (2005) corrected. 16 Moreover, the models are correctly dentfed as they satsfy the second order no-autocorrelaton crteron AR(2) and the Hansen J-tests. The results are reported n Table 4 and they broadly corroborate the fndngs obtaned by estmatng the models on a country by country bass (as llustrated n Table 3). The market structure varables do not appear sgnfcant n explanng nterest rate margns. Thus, both the SCP and RMP hypotheses are rejected. Table 4 GMM regresson for Latn Amerca ( IM as dependent varable) Varables Model I Model II NIM lagged.389***.685*** HHI MS LOAA.121***.048*** CAP * LLR.029*.019** ESX -.024**.139 ESS XRATE.001*** CPI * GDP -.215*** -.274*** INT -.06* * H-stat H*ESX H*ESS AR(1) p-value *** * Constant 15.19*** F-test p-value (0.00) (0.00) (0.00) (0.00) AR(2) p-value Hansen J-test p-value 1.04 (0.30) (0.125) 0.99 (0.324) (0.475) 16 We use the correcton suggested by Wndmejer s (2005) for the possblty that the estmated asymptotc standard errors of the effcent two-step GMM estmator are downward based. See also notes to the table. 25

26 Tme dummes Country dummes No. Observatons Yes Yes 1,724 Yes Yes 1,724 *, **, ***, denote sgnfcance from zero at 10%, 5%, 1% level respectvely. Notes: NIM s the net nterest rate margn, HHI s the Herfndahl-Hrschman Index, MS s the market share n terms of assets, LOAA s a measure of lqudty rsk, CAP s a measure of the degree of captalzaton, LLR s a measure of default rsk, ESX s a measure of manageral effcency, ESS s a measure of scale effcency, XRATE s the average annual exchange rate, CPI s a measure of annual nflaton, GDP s the average GDP real growth, INT s the average annual market nterest rate and H- stat s a measure of competton. a Arellano-Bond order 1 (2) are tests for frst (second) order correlaton, asymptotcally N(0,1). These test the frstdfferenced resduals n the system GMM estmaton.the Hansen test s a test of overdentfcaton restrctons. Under the null hypothess, the test statstc s dstrbuted as a ch-squared n the number of overdentfyng restrctons. System GMM are two-step estmates. The two-step errors are computed n accordance to the Wndmejer (2005) fnte-sample correcton. The bank-specfc varables LLR and LOAA are postve and sgnfcant as expected. There s also evdence of a postve relatonshp between CAP and NIM for the regon. The role of macroeconomc varables appears less ambguous and more nformatve wth the coeffcent of the exchange rate rate beng postve and statstcally sgnfcant wth NIMs. The degree of nflaton s postvely related to greater margns. Moreover, the annual market nterest rate and GDP growth dsplay negatve relatonshps wth NIMs. Turnng to the competton and (X- and scale) effcency varables, n Model I the degree of competton exhbts a strong negatve relatonshp wth NIM. The magntude of the coeffcent s qute hgh and the results of Model II corroborate ths fndng as H*ESX shows a strong negatve relatonshp wth nterest rate margns. Nevertheless, ESS does not appear to be statstcally sgnfcant. The relatvely low mportance of the scale effcency effects as compared to X-effcences s often encountered n the lterature (see e.g. Berger et al., 1993; Jeon and Mller, 2005) and s typcally attrbuted to the fact that banks appear more effectve n reducng costs by emulatng best practce rather than growng. The negatve relatonshp between H-stat and ESX wth NIM s consstent wth the hypothess that ncreasng bankng X-effcency n a compettve market envronment can reduce nterest rate 26

27 margns. Gelos (2006) also suggests that less effcent banks are related to greater nterest margns. He alludes to the possblty that ths lnk between neffcency and greater margns can reflect weak competton. Thus, ncreased effcency n the bankng sector could be a drect effect of ncreased bankng competton. It follows that fosterng competton n the bankng sector alongsde wth ncreasng cost effcency can be strongly lnked to declnng nterest rate margns. Establshng a robust negatve relatonshp between bankng competton and margns s a key fndng that could be taken nto consderaton n the desgn of polces amng to tackle the long-lastng problem of exceptonally hgh bank spreads n many Latn Amercan countres. 27

28 5. Conclusons The fnancal lberalzaton process n Latn Amerca brought about changes n the structure of the bankng sector and trggered the elmnaton of varous restrctons ncludng foregn captal entry barrers, nterest rate celngs and floors, and credt ratonng. Despte ths wave of changes the nterest rate spreads (or NIMs), reman excessvely and persstently hgh. As a consequence credt avalablty to the prvate sector remans lmted. Moreover, the recent global fnancal crss and the subsequent lqudty shortage may further exacerbate ths stuaton. A number of studes have examned the determnants of nterest rate margns and ths paper further contrbutes to the lterature by focusng on the role of competton and non-parametrc X- and scale effcency n explanng nterest rate margns n Latn Amerca. We develop an extenson of the tradtonal Structure-Conduct-Performance framework and we produce panel regresson model estmates usng a GMM framework and focusng on a sample of over 2,300 banks over In order to obtan the effcency and the (non-structural) competton measures we use Data Envelopment Analyss (DEA) and the Panzar and Rosse (1987) H-statstcs respectvely. We also control for the bank-specfc and macroeconomc varables typcally used n the NIMs lterature. Our results show that the concentraton ndex and the market share have lttle or no nfluence on nterest rate margns. On the contrary, we uncover evdence suggestng that greater X-effcency and compettve market structures are mportant determnants n lowerng spreads. Moreover, we fnd that the degree of captalzaton ncreases spreads, whle economc growth contrbutes to ther reducton. The man mplcatons of our results are that polces amng to contan banks nterest rate margns should encouragng greater competton and ncentvze banks to enhance ther operatonal effcency. Growth enhancng macroeconomc polces can further complement the effects of such polces. 28

29 References Afanaseff, Tarsla S., Prsclla M. Vlla Lhacer and Marco I. Nakane, (2002) The Determnants of Bank Interest Spreads n Brazl. Central Bank of Brazl Workng Paper Seres, 46, Banco Central do Brasl, August Ahumada, Hldegard, Tamara Burdsso, Juan P. Ncoln and Andrew Powell. (2000) Spreads n the Argentne Fnancal System. In Why so Hgh? Understandng Interest Rate Spreads n Latn Amerca, edted by Phlp Brock and Llana Rojas-Suarez, pp Washngton, D.C.: Inter- Amercan Development Bank. Altunbas, Yener, Lynne Evans and Phlp Molyneux. (2001) Bank Ownershp and Effcency. Journal of Money, Credt and Bankng, 33(4), Arellano, Manuel and Stephen R. Bond (1991) Some Tests of Specfcaton for Panel Data: Monte Carlo Evdence and an Applcaton to Employment Equatons. Revew of Economc Studes, 58(2), Arellano, Manuel and Olympa, Bover. (1995) Another Look at the Instrumental Varables Estmaton of Error Components Models. Journal of Econometrcs, 68(1), Barajas, A., Robert Stener and Natala Salazar. (1998) Interest Spreads n Bankng: Costs, Fnancal Taxaton, Market Power, and Loan Qualty n the Colomban Case IMF Workng Paper 98/110, Internatonal Monetary Fund, August Beccall, Elena, Barbara Casu and Clauda Grardone. (2006) Effcency and Stock Performance n European Bankng. Journal of Busness, Fnance and Accountng, 33(1-2), Bernanke, Ben S. (1983) Non-monetary Effects of the Fnancal Crss n the Propagaton of the Great Depresson. Amercan Economc Revew, 73(3),

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