The Effect of Banks' Financial Position on Credit Growth: Evidence from OECD Countries

Finance and Economics Discussion Series Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington, D.C. The Effect of Banks’ Financial Position on Credit Growth: Evidence from OECD Countries David E. Rappoport 2016-101 Please cite this paper as: Rappoport, David E. (2016). “The Effect of Banks’ Financial Position on Credit Growth: Evidence from OECD Countries,” Finance and Economics Discussion Series 2016-101. Washington: Board of Governors of the Federal Reserve System, https://doi.org/10.17016/FEDS.2016.101. NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminary materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the research staff or the Board of Governors. References in publications to the Finance and Economics Discussion Series (other than acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers.

The Effect of Banks’ Financial Position on Credit Growth: Evidence from OECD Countries1 DavidE.Rappoport FederalReserveBoard September19,2016 Abstract Thispaperpresentsempiricalevidenceontheeffectofbanks’financialposition on credit growth using a sample of 29 OECD countries. The failure of the exogeneity assumption of explanatory variables is addressed using dynamic panel type instruments. The empirical results show that among capital, profits and liquidity at the end of the previous year, capital is the most important predictor of credit growth in the current year. The relationship between capital and credit growth is non-linear. Point estimates from the preferred econometric specification imply that at the sample mean a one standard deviation increase (decrease) incapitalisassociatedwithanincrease(decrease)of0.8(0.3)percentagepoints increditgrowthuponimpactand1.6(0.6)percentagepointsinthelong-run. JELclassification : G21,E44,G28. Keywords: Bank lending, banking, bank financial position, credit supply, OECD. 1I thank Elias Albagli, Guillermo Ordoñez and Gary Gorton for helpful comments. I also benefited from comments from seminarparticipantsatYale.TheopinionsexpresseddonotnecessarilyreflectthoseoftheFederalReserveBoardoritssta ff. 1

1 Introduction Understanding the determinants of credit growth is an important issue, as credit is considered a key transmitter of financial shocks into real activity and it is at the heart of the lending channel of monetary policy. TheseissueshavereceivedrenewedattentionaftertherecentGreatRecessionfollowingthecollapseofthe subprimehousingmarketintheUS. This paper presents empirical evidence on the effect of banks’ financial position (capital, profits and liquidity) on credit growth using a sample of 29 OECD countries. The empirical results show that among capital,profitsandliquidityattheendofthepreviousyear,capitalisthemostimportantpredictorofcredit growthinthecurrentyear. Therelationshipbetweencapitalandcreditgrowthisnon-linear. Pointestimates fromthepreferredeconometricspecificationimplythatatthesamplemeanaonestandarddeviationincrease (decrease)incapitalisassociatedwithanincrease(decrease)of0.8(0.3)percentagepointsincreditgrowth upon impact and 1.6 (0.6) percentage points in the long-run. Capital is followed in importance by profits. Liquidity only seems to affect aggregate credit growth significantly in countries where smaller banks are important. Theseresultsarerobusttothedefinitionusedtomeasurebanks’financialpositionsandeconomic conditions, and are robust to considering the organization of the bank sector in each country. The failure oftheexogeneityassumptionofexplanatoryvariablesisaddressedusingthesystemGMMestimatorfrom the dynamic panel literature. The use of this estimator for a “square” panel, instead of a “short” panel as originallydevised,presentstechnicalchallengesthatarediscussedinthepaper. The paper is related to the literature on the determinants of banks’ credit growth. This topic received considerable attention after the US recession of the early 90s, which coincided with a decline in banks’ credit. Sharpe(1995)providesaverycomprehensivesurveyofthisworkanddiscussestheextenttowhich the slowdown in credit growth was a result of weaknesses in banks’ balance sheets, increased capital requirement or more stringent regulatory practices. The author concludes that the evidence shows a robust linkbetweencreditgrowthandbothloanperformanceandbankprofitability, althoughthecausalityofthis relationship is not clear. The studies surveyed by Sharpe mostly analyze cross-sections of banks. In contrast, the results presented here use a panel of countries, adding to this literature in two dimensions. First, it investigates the generality of previous findings analyzing a single country. Second, the use of dynamic panelestimationtechniquesprovidesanicealternativefortheidentificationprobleminthisliterature. 2

The use of a panel of countries to study bank-related questions is not new, but this is the first work to analyze the effect of banks’ financial position on credit growth using this type of dataset. Ferreira (2009) usedapanelof26EUcountries,withquarterlyobservationsbetween1991and2006,tostudytheevolution of lending as a fraction of GDP and the lending channel of monetary policy. On the other hand, Levintal (2013)usedapanel28OECDcountries,withyearlyobservationsfor1980-2003,toanalyzetherealeffects of banking shocks. Levintal uses the same data source for bank information as this paper and identifies three types of bank shocks: profitability, capital, and reserves. He finds that profits, measured by ROA, is the bank shock with the most significant real e ffect. In contrast, the present paper ascribes the biggest explanatory power predicting credit growth to banks’ equity capital. Thus, to the extent that the real effect ofbankshocksoperatesthroughcredittheresultofthepresentstudyisatoddswiththeevidencepresented by Levintal (2013). Furthermore, both studies cited above use “square” panels and so are subject to the methodologicalissuesdiscussedinhere. The paper is organized as follows. Section 2 considers the specification of the economic model with a discussionaboutthevariablesthatshouldbeincludedinthemodel. Section3presentsthedatausedinthe econometric analysis. Section 4 discusses the econometric specification of the model with a discussion of howthesystemGMMestimatorisusedtoaddressthedynamicpanelbiasandthefailureoftheexogeneity assumption of the variables included in the model. It presents the main results of the paper and analyzes in detail the estimated effects of banks’ financial position on credit growth. Section 5 presents robustness checkstothemainresults,andsection6concludes. 2 Model Specification Thissectionreviewsthedeterminantsofcreditgrowthtoinformtheselectionofthevariablestobeincluded in the model. The focus of the paper is on the effect of banks’ financial position, which will be measured both from balance sheet and income statements. In particular, the effect of: (i) profits; ( ii) equity capital; and(iii)liquidity,willbeestimated. Additionalvariablesareincludedtocontrolforthetimeseriesstructure of loan growth, economic conditions and the organization of the bank sector. The definition and rationale forallthesevariablesisdiscussedbelow. 3

Time series structure: The dependent variable is the growth rate of outstanding loans, defined as the logchangeinoutstandingloans,Δ‘ = logL logL . Usingloangrowthisstandardintheliteratureand t t t 1 − − hastheadvantagesoverusingloansinlevelofbeingstationary. Itisexpectedthatloangrowthdependson past values, as outstanding loans do not fully adjust in a year, which is the frequency of the dataset. Thus, Δ‘ willdependonitsownlags. it In order to specify the other variables that will be included in the model, it is helpful to start with the followingsimplifiedversionofabankbalancesheet: Assets Liabilities L +LIB+Sec +M D +DIB+E t t t t t t t whereL standsforloans,superscriptIBforinter-banks,Sec forsecurities, M forcashormoneyholdings, t t t D fordepositsand E forequity. Allvariablesmeasuredattheendofperiodt. Moreover,let A bethesize t t t ofthebankbalancesheetortotalassetsorliabilities,andδ denotetheratioofloanstoasset,δ = Lt. Using t t At thesedefinitionswehavethat, 2 L δ A t t t Δ‘ = log = log = Δlogδ +Δa (1) t t t L δ A t 1 t 1 t 1 − ! − − ! This is, the growth rate of loans can be decomposed into changes in bank’s portfolio and the growth rate of assets. In practice any given variable can affect theses two margins, but for expositional purposes it will be helpful to consider them separately. The growth rate of assets, ceteris paribus and assuming the bank business is profitable, will depend on the availability of funds, which typically come from equity and deposits. The portfolio decision, in turn, will depend on funding costs and expected returns, which will be givenbyeconomicconditions. Banks financial position: Profits are one source of new equity. Let Y be banks (after tax) profits in t yeartandassumethattheseprofitsareusedtoincreasethebanksequitycapitalkeepingthesameleverage. Letλ bebanks’leverageattheendofyeart,equaltotheratioofassetstoequity, At. Thus,theincreasein t Et 2Iusetheconventionthatsmallcapsletterdenotethelogofcapitalletters. 4

assetsfromtheseprofits, ΔA ,isgivenby,3 ∗ A A Y ∗ t t ΔA = (E E ) − = Yλ or Δa = λ ∗ ∗ t t t ∗ t − E E A ∗− t t Thisistheincreaseinlogassetsisthereturnonassets(ROA)timesleverage,orsimplythereturnonequity (ROE). However, when equity at year-end is negative, E < 0, leverage is not defined and the previous t expressiondoesnothold. Inthiscasethebanksectorisinsolventanditwillbeassumedthatprofitsareused torebuildbanks’equity. Inotherwords,wecanthinkofthebankasanetdebtor,whowillusenewprofits topaythesedebtsfirstandthereforeweexpectnoe ffectonthesizeofthebalancesheet. Banks’equitycapitalmayplayakeyroleonbalancesheetexpansion,asemphasizedintheliterature. In general,externalfundsforbalancesheetexpansionmaycomefromtheissuanceofequity,debtordeposits. Theliteratureemphasizestheroleofbanks’sequitycapitalonfundingcostsbyalleviatingthemoralhazard problemofbankmanagers(HolmstrongandTirole,1997). Thus,itisexpectedthatbankswithhigherratios of equity to assets will be able to raise new funds at lower costs. Moreover, minimum capital requirement limitbanks’abilitytoexpandtheirbalancesheets. Therefore,wewouldexpectanonlineareffectofcapital ratiosduetoregulationthresholds(cf. PeekandRosengren1995;andThakor,1996). Finally,liquiditywillalsoplayakeyroleinthegrowthofcreditassellingsecuritiesisacheapersourceof fundsgivenadverseselectionproblems. Infact,Stein(1998)showsthatloansalesanduninsuredliabilities involve higher funding costs due to adverse selection problems as the bank has private information about their loan portfolio. It follows that banks prefer to fund lending activities by selling securities or issuing insured deposits. Therefore, the growth rate of loans may depend on availability of liquid assets and the costs of insured deposits. Kashyap and Stein (2000) measure the former as balance sheet liquidity, BSL , it defined as the ratio of securities to assets. The evidence suggests that small banks are more sensitive to thisadverseselectioncosts,thereforeliquiditymeasuresinteractedwiththefractionofsmalltototalbanks’ assetsatthecountrylevel,arealsotobeconsidered.4 Ontheotherhand,thecostofdepositswilldependon economicconditionswhicharediscussedbelow. 3Infact, A A Y Y Y t λ t =ΔA∗ ≡ A∗ − A t =A t A ∗ − 1 ⇒ Δa∗ ≡ log A ∗ =log 1+ A tλ t ≈ A tλ t t ! t! t ! t 4SeeKashyapandStein(2000)andOstergaard(2001). 5

Economic conditions: will affect the costs of deposits, expected returns on different investments and the demand for credit. The cost of deposits could be proxied as the ratio of total interest expenses to total deposits.5 Alternatively,thecostsofdepositscouldbemeasureddirectlyastheinterestrateondeposits. Expected returns on loans versus other type of assets will affect the portfolio decision. Bernanke and Blinder (1988) stress the dependence of this margin on interest rates, both on loans and on alternative investments(governmentbondsinthemodel). Anotheralternativeistoinvestinsecurities,whichexpected returnscouldbeproxiedbythereturnondomesticsecuritymarkets. Theexpectedreturnonloansdepends ontheinterestrateandontheprobabilityofborrower’sdefault,thelattercouldbecontrolledforbytheratio of loans provisions to outstanding loans. This is the mechanism emphasized by the literature on the credit risk channel. Finally, Tobin (1982) highlights the dependence of the portfolio choice on the cost of banks’ deposits,whichwerediscussedabove. Thebusinesscyclewillaffectboththedemandforcreditandlendingstandards. Creditdemandwillbe given by private and government consumption and investments decisions which are partially financed with credit. Finally, the bank literature also shows that banks change their lending standards over the business cycle.6 Organization of the bank sector: The banking literature identify other variables that may affect the growthofloansatthecountrylevel. First,theliteratureonbankefficiencyidentifyapotentialroleforbank sizeanddiversification. Attheaggregatelevelbanksizecouldbeproxybytheratioofbanks’assetstoGDP. Ontheotherhand, wecanusethefactthatlargerbankstakemorerisktousetheratiooflargebankassets to total banks assets as a measure of both diversification and economies of scale in lending activities.This ratiooflargebanksassetstototalbanksassetsmayaffectaggregatelendingjustbyacompositioneffectas theevidencefortheUShavefoundthatlargerbanksholdsmallerfractionofloanstototalassets.7 Finally, it is important to bear in mind that several variables affect loan growth through more than one channelsomethingthatneedstobeconsideredwheninterpretingtheresults. 5LoutskinaandStrahan(2009)measurecostofdepositsfromCallReportforcommercialbanksintheUSastheratiooftotal interestexpensesondepositstototaldeposits.Thedataonbanksusedhereonlyreportstotalinterestexpenses. 6AseaandBlomberg,1998;LownandMorgan,2001;SchreftandOwens,1991;Weinberg,1995. 7SeeBerger,Demsetz,andStrahan(1999). 6

3 Data Thissectiondescribesthedatausedintheeconometricanalysis,consistingofanunbalancedpanelofcountries with yearly observations. The sample of countries is determined primarily by availability of banks’ information, which is obtained from the OECD Bank Statistics database. This data set reports information for bank groups in each country. The most aggregated group is all banks, which includes: commercial banks, saving banks, cooperative banks, and other miscellaneous monetary institutions. When available information for large commercial banks and foreign commercial banks is reported separately. The subsequent analysis considers information at the country level, therefore the most comprehensive bank group is chosen for each country. Table 1 presents the list of countries present in the OECD Bank Statistics dataset and the bank group selected for the analysis.8 Table 1 considers only availability of information on credit growth, when information on all bank variables is considered the total number of observation drops from 726to705. Additionalinformationislostwhenbankvariablesaremergedwithlong-termandlendingrates leaving a total of 530 country-year observations.9 Including domestic stock market returns further reduce thenumberofobservationsto500and6moreobservationsarelostwhenrealvariables(GDP,consumption and investment) are included. It should be noted that Turkey and Luxembourg are left out of the analysis because of the information requirements. Turkey does not have information on long-term interest rates, whereastheseriesonstockmarketreturnsandlendingratesdonotoverlapinthecaseofLuxembourg. As mentioned above it will be assumed that the growth rate of credit depends on its own lagged realization, which will make additional observations to be discarded in the econometric analysis. As Table 2 shows, in the benchmark regression, the number of observations is 480. The Table also lists the sample period by countryofthedatausedintheanalysis. The OECD Bank Statistics database contains data for income statements and balance sheets of bank groupsinOECDcountries. Allfiguresareinlocalcurrencyattheendoftheperiodandaretransformedto real values using individual countries consumer price indices (CPI).10 Information on outstanding nominal 8Forfourcountries(Canada,Greece,MexicoandUS)informationonthesecondmostcomprehensivebankgroupisusedto extendthetimeseries,seenotestoTable1. 9Onereasonwhyinformationoninterestratesisnotcompleteisbecausein1999thecountriesintheEuropeanUnionchanged thewaystatisticsonlendingratesarereported.Thispresentschallengesintheconstructionoftimeseriesforlending(anddeposits) ratesasnolendingrateswiththeoldmethodologyarepublishedanymore. 10DatainmillionsofNationalcurrency,exceptforJapan(100millions)andSlovakRepublic(thousands). 7

loansforcountryiattheendofyeart,isincludedintheassetsbreakdownofthebalancesheetasitem16. UsingdomesticCPIloanseriesaredeflatedtoobtainrealoutstandingloans, L . Loangrowthisdefinedas it thelog-differenceofrealloans,Δ‘ logL logL expressedinpercents. Table3presentsmeanloan it it i,t 1 ≡ − − growthbycountryforthesampleof480observationusedintheeconometricanalysis. Thesamplemeanof creditgrowthis5.8%peryear. Irelandpresentsthelargestannualgrowthofrealcreditwithalmost22%for the period 1997-2005, whereas Mexico exhibit the largest decline in real credit with an average decline of 2.2%peryearin1995-2007. Profit measures are constructed based on income statements reported in the OECD dataset. Return on equity,(ROE)isdefinedastheratioofitem11,after-taxprofits,toitem19,capitalandreserves,expressedin percents. Capitalandreservesistheclosestmeasureofbanks’capitalreported. Table3presentsaveragesby countryofROE forthesampleusedintheestimationsbelow. Thesamplemeanis8.5%. NewZealand i,t 1 − presents the highest average ROE in the sample with almost 17%, whereas Japan presents the lowest with almost-2%. Banks’equitycapital, CAPisdefinedastheratioofitem19, capitalandreserves, toitem25, end-yearbalancesheettotal,expressedinpercents. Balancesheettotalequalsthesumofassetsorliabilities at year-end and henceforth it will be referred to as total assets. Table 3 presents means by country of CAP . Consideringallcountry-yearobservationsthemeanis6.1%,whereastakingindividualcountries i,t 1 − it ranges from 3.1% in Belgium to 10.1% in Australia. Likewise, balance sheet liquidity, BSL is defined as item 17, securities in the asset side of the balance sheet at year-end, to total assets (item 25) and it is expressed in percents. Averages for this ratio for individual countries go from 7.1% in Australia to 33.4% inGreece. WhenallcountiesareconsideredtheaverageBSL is18.9%(Table3). i,t 1 − Measures on deposits costs and loan provisions are also calculated using information from the OECD Bank Statistics dataset. DEPOSITCOSTS is defined as the ratio of item 2, interest expenses, to item 22, non-bankdeposits.11 Non-bankdepositscorrespondstodepositsheldbybankcustomersasopposedtointerbankdepositsholdbybanksamongthemselves. Table4presentsthesamplemeanforDEPOSITCOSTS considering the sample of 480 observations used in the estimations below: approximately 10%. Since informationonloanprovisions(item8.a)isnotavailableforallcountriesandyears,totalprovisions(item8) 11AccordingtothedefinitionsintheOECDBankdatasetinterestexpenses“generallyincludesinterestpaidonliabilitiesand fee expenses related to borrowing operations, and it may include in some cases the difference between the issue price on debt instrumentsandtheirparvalue”(OECD,2004). 8

is used instead in the benchmark specification. PROVISIONS is defined as the ratio of total provisions to nominal outstanding loans (item 16). The sample average of this variable is 86 basis points, as reported in Table4. Loanprovisionswillbeusedtochecktherobustnessoftheresultsinsection5. Economic conditions also include variables collected from other sources. Real effective lending rates arecalculatedasthedifferencebetweennominallendingratesfromtheIFS,line60P..ZF... andeffectiveCPI inflationobtainedfromtheOECDMainEconomicIndicators,Prices: ConsumerPrices. Reale ffectivelongterminterestratesarecalculatedasthedifferencebetweennominal10yeargovernmentbondsorsimilarand effective CPI inflation. Nominal long term rates are obtained from the OECD Main Economic Indicators and the IFS.12 Real domestic stock market returns are calculated as the log difference of real stock market priceindicesandexpressedinpercents. NominalpriceindicesareobtainedfromtheOECDMainEconomic IndicatorsandtheIFSanddeflatedusingdomesticCPItocomputerealstockmarketpriceindices. Changes in real aggregate demand are calculated as the log difference of real aggregate demand and expressed in percents. Real aggregate demand is defined as the sum of real private and government consumption and investment. AlltheseseriesareobtainedfromtheOECDMainEconomicIndicators.13 Organization of the bank sector is measured as the ratio of banks’ total assets to GDP. GDP figures corresponds to real GDP at 2000 prices published by the OECD. Real total assets at 2000 prices were computed from nominal total assets, deflated by domestic CPI. 14 Table 4 reports averages by country and for all observations of all these variables. Appendix A provides additional descriptive statistics for all the variablesinTables3and4. 4 Estimation of the Effect of Banks’ Financial Position on Credit Growth This section discusses the econometric issues that arise when estimating the effect of banks’ financial position on credit growth and specifies the benchmark econometric model for this analysis. Subsequently, it presents the main results on the effect of banks’ financial position on credit growth. First, the e ffect of profits,capitalandliquidityareestimatedindependentlywhilecontrollingforeconomicconditionsandthe 12ForChileCPI-indexedbondsyields,obtainedfromtheCentralBankofChile,areusedinstead. 13Real private consumption corresponds to households and non-profit institutions serving households. Real government consumptionisfinalconsumptionexpenditureofgeneralgovernment.Realinvestmentisgrossfixedcapitalformation. 14RealGDPfiguresforJapanarein100millionsandforSlovakRepublicinthousands. 9

organization of the banking sector. Then, alternative measures for the three dimensions of banks’ financial positionareconsidered. Additionalrobustnesschecksareprovidedinsection5. 4.1 EconometricSpecification Credit growth is defined as the log-di fference of real loans, Δ‘ logL logL . The model to be it it i,t 1 ≡ − − estimatedtakestheform: Δ‘ = α(L)Δ‘ +β X +μ +μ +v i = 1,...,N t = 1,...,T (2) it it 0 it t i it whereα(L)isalagpolinomialwithcoeficientstobeestimated, βisavectorofcoeficientstobeestimated, X is a vector of controls, μ are time effects, μ are country fixed e ffects and v is an idiosyncratic shock. it t i it Thevariablestobeincludedinthevectorofcontrols,forcountryiinyeart,followsfromthediscussionin section2. Itcomprisestwosetofvariables X = X pre Xendo ,with X pre variablesthatarepredeterminedat it it it it thebeginningofperiodtand Xendo variablesthathareendogeinoustotheidiosyncraticshockv ,givenby, it it ROE CAP CAP2 BSL pre i,t 1 i,t 1 i,t 1 i,t 1 X = − − − − it    DEPOSITCOSTS i,t − 1 PROVISIONS i,t − 1 ASSETS/GDP i,t − 1  LENDINGRATE LONGTERMRATE it it Xendo = it    STOCKRETURNS it ΔAGG.DEMAND i,t  The first set of predetermined variables measure banks’ financial position at the end of the previous year. Returnonequity,ROE measuresbanks’profits. Theratioofequitycapitaltoassets,CAP measures i,t 1 i,t 1 − − capital and the square of this variable is included to estimate potential nonlinear effects of banks’ capital aroundregulatorythresholds. Finally,balancesheetliquidity,BSL ismeasuredastheratioofsecurities i,t 1 − toassets. Othervariablescontrolforeconomicconditionsandtheorganizationofthebanksector. The identification strategy relies on two assumptions. First, it is assumed that predetermined variables andthelaggedvalueofcreditgrowthΔ‘ areweaklyexogenous;whereascontemporaneousvariablesare i,t 1 − 10

endogenous. Thisis, pre (cid:133) v Δ‘ ,X ,Δ‘ ,X ,...,Δ‘ ,X = 0 (Assumption1) it i,t 1 it i,t 2 i,t 1 i1 i1 − − − (cid:0) (cid:12) (cid:12) (cid:1) (cid:12) Second,itisassumedthattheidiosyncraticshocksareseriallyuncorrelated: (cid:133)[v v ] = 0 (Assumption2) it i,t 1 − Note that Assumption 1 does not rule out that the idiosyncratic disturbance, v could be correlated with it futurepredeterminedandcontemporaneousendogenousvariables. Nordoesitruleoutthatbankscanchange their income or balance sheet statements, according to their expectation of future credit growth, as long as theirexpectationsarenotcorrelatedwiththeerrorterm. Inotherwords,Assumption1saysthatwhenbanks formtheirexpectationsaboutfuturecreditgrowth,theydonotknowanythingaboutfutureshocks. Inmodel(2)thegrowthrateofloansdependsonitsownlaggedvalueandthecountryconsideredcausing a dynamic panel bias in the estimation. This renders OLS estimates biased. In fact, if we consider model (2)withadisturbance,ε = μ +v thenthecoefficientonlaggedcreditgrowthwillbepositivelybiased,as it i it the estimation will attribute predictive power to this variable that belongs to the country fixed e ffect in the errorterm. Onthecontrary,ifweestimatethemodelusingthewithingroup(i.e. fixede ffects)estimator,the bias will be negative due to the within group transformation. Although biased the fact that both estimates are biased in opposite directions provides a useful benchmark for theoretically superior estimators (Bond, 2002). Table 5 reports estimated coefficient for model (2) with 1 lag of the dependent variable, using OLS and FE. The coefficient on lagged credit growth is statistically significant in both specifications and these estimates imply an interval for its value between 0.19 and 0.32 (columns 1 and 2). Both ROE and BSL have positive signs and only the latter appears statistically significant in the specifications with country effects. Thesecondorderpolynomialonequitycapital,CAP isjointlystatisticallysignificantatthe5% i,t 1 − level in the FE estimation, with only CAP2 significant at the 10% level. This suggest the presence of i,t 1 − nonlinear effect for this variable on credit growth, as expected. Other coefficients have the expected signs. One exception is real LONGTERMRATE which displays a positive sign and it is statistically significant. AnotherexceptionistheestimatedcoefficientonASSETS/GDPwhichturnsnegativewhencountryeffects 11

aretakenintoaccountandthecoefficientisstatisticallysignificant. Contemporaneouschangesinaggregate demandarestatisticallysignificant,butthismightbetheresultoftheendogeneityofthisvariable. Perhaps more surprising is that contemporaneous stock returns and lending rates are not significant when country effectsareconsidered. Thedynamicpanelbiasisinverselyproportionaltothepanel’slength,T. Thisis,itislargerforshorter panels, i.e. when the temporal dimension, T is small. Table 2 shows that the average time length is 16.6 yearswithamaximumof28years, whichisnotinthe“small”rangefor T. Thus, thisbiasisnotthemain econometric concern in the estimation of this model. Nonetheless, the techniques to address this bias will serve to address the endogeneity problem or more generally the failure of the strict exogeneity assumption of the variables included in X , which is the main econometric challenge here. In general, there are two it approachestoaddressthedynamicpanelbias. Themethodsproposedintheliteraturetosolvethedynamic panelbiasrelyonconstructingsuitablesetsofinstrumentalvariablesunderassumptions1and2,usingpast informationoftheexistingvariablesforthis. Thefirstapproachdiscussedbelowconsistsoftransformingthe modelbytakingfirstdi fferences,yieldingthedifferenceGMMestimator. Next,thesystemGMMestimator is discussed which combines the former with using suitable instruments for the model in levels. The latter approachisbestsuitedincaseswheresomevariablesarehighlypersistentasthecaseathand. Arellano-Bond(1991)proposeadifferenceGMMestimatorfordynamicpanels. Theideaistotakefirst differencesofmodel(2)andtheninstrumentforendogenousvariablesinthetransformedmodel. Differencingthemodelgives, Δ2‘ = αΔ2‘ +β ΔX +Δμ +Δv (3) it i,t 1 0 it t it − where Δ2‘ = Δ‘ Δ‘ . This transformation eliminates fixed e ffects, but makes Δ2‘ endogenous, it it i,t 1 i,t 1 − − − asΔ‘ iscorrelatedwithv inthenewdisturbanceΔv . Similarly,anypredeterminedvariablebecome i,t 1 i,t 1 it − − endogenous. In fact, for predetermined variable x, the term x in Δx will be correlated with v . But i,t 1 it i,t 1 − − x (Δ‘ ) will be a suitable instrument for Δx (Δ2‘ ) as it is correlated with it and independent of i,t 1 i,t 2 it i,t 1 − − − v by Assumption 1. Deeper lags of x (Δ‘) will also be candidate instruments, as Δx (Δ2‘ ) and i,t 1 i,t 1 i,t 2 − − − 12

deeperlagsofitwillbeaswell. Thestandardwayofusing x asaninstrumentistoconsiderthevector, i,t 1 − ∙    x i x ,T i . . . ,1 − 1  where “” denote a missing value. This procedure is also referred as instrumenting in IV style. One of the ∙ shortcommings of this approach is that each additional instruments comes at the burden of reducing the sample size, as each additional lag forces to drop one time period. In contrast, in GMM framework it is possibletouse x tobuildasetofinstrumentswithoneinstrumentforeachtimeperiodandsubstituting i,t 1 − zeros for missing observations, giving rise to meaningful instrument moment conditions. This approach generatesamatrixofinstrumentsoftheform: 0 0 0 ∙∙∙   x 0 0  0 0 i . . . ,1 x 0 i . . . ,2 ∙ ∙ ∙ . ∙ ∙ ∙ . ∙ ∙ ∙ . x i, 0 T . . . − 1  Replacing missing with zeros there is no longer a trade-off between number of instruments and number of observations;thus,itiscommonpracticeintheliteratureofdynamicpanelstoincludeasmanyinstruments as possible. The number of instruments equals the number of columns of the matrix of instruments. For thelaggeddependentvariableinstrumentinginGMM-styleusingΔ‘ willgenerateT 2instruments.15 i,t 2 − − AdditionallagswillgenerateT 3, T 4,...,1additionalinstruments. Therefore,usingallavailablelags − − to construct the set of instrumental variables makes the number of instruments quadratic in T. The same is the case for any other variable that is to be instrumented. This will generate too many instruments in the case of “square” panels like the one studied here, which could be problematic. First, a large number of instruments can overfit endogenous variables (Roodman, 2006). In fact, in the extreme case where the 15ThenumberofinstrumentscouldbeT ifinformationforlaggedvaluesofvariablesareavailable. Thisisthecaseforthedata beinganalyzedhere. 13

number of instruments equal the number of observations the instrument set will span the space of the explanatoryvariables,causingtheprojectionoftheendogenousvariableintheinstrumentspacetoequalitself, violatingtheinstrumentalvariableassumption. Second,toomanyinstrumentscausenumericalproblemsin theestimationaffectingtheaccuracyoftheestimates. Third, itweakenstheHansentestofoveridentifying restrictionsleadingtoitsnon-rejection(Bowsher,2002). Therearetwowaysaroundtheproblemoftoomanyinstrumentswhichwillbeconsideredbelow. The firstoneistorestrictthenumberoflagstobeusedasinstruments. Thesecondconsistofcollapsingtheset ofinstrumentstogetoneinstrumentperinstrumentalvariable. ThelattercombineselementsoftheIVand GMMstyle,asitbuildsasingleinstrumentalvariableusing x butstillreplacesmissingwithzeros. This i,t 1 − givesasingleinstrumentusing x asinstrument: i,t 1 − 0    x i x ,T i . . . ,1 − 1  Asdiscussedabove,whenx isapredeterminedvariablelagsoneanduparesuitableinstrumentsforthe it differenced model (3). In contrast, when x is an endogenous variable suitable instruments are the second it and deeper lags of the variable. In fact, in this case the term x is correlated with v and therefore i,t 1 i,t 1 − − x willnotbeasuitableinstrumentforΔx ,but x isstillindependentofv andcouldbeusedasan i,t 1 it i,t 2 i,t 1 − − − instrument. Deeperlagsof x andΔx anddeeperlagsofitwillalsobevalidinstruments. i,t 2 i,t 2 − − EstimationsusingdifferenceGMMarereportedinTable5columns3to6. Column3presentsestimates that use 2 lags of explanatory variables as instruments in GMM style. The estimated coefficient is in the lowerrangeoftheinterval[0.19,0.32],butthenumberofinstrumentsisalmostequaltothenumberofobservations. With6lagsinGMMstyle,thenumberofinstrumentsisgreaterthanthenumberofobservation, butthealgorithmlimitsthenumberofinstrumentsbythenumberofobservations. Estimatedcoefficientsare very similar to the FE estimates, as was to be expected by the use of as many instruments as observations. Collapsingthesetofinstruments,using2and6lagsofeachexplanatoryvariableyieldsinstrumentsetswith 52and100elements,respectively(columns5and6). Thisyieldsamorereasonablenumberofinstruments, 14

but the coefficient on lagged credit growth falls outside the desired interval and there are other problems thatsuggeststhemodelispoorlyspecified. Indeed,theSargantestsrejectsthejointvalidityofthemoment restrictions.16 Moreover, the Arellano-Bond test for the independence of the idiosyncratic disturbances– Assumption 2–is rejected, suggesting serial correlation of the innovations of model (2). This assumption was key in the construction of the appropriate instrument sets. Arellano and Bond (1991) shows how to constructateststatisticsunderthenullofserialindependence,thatconvergestoanormaldistributionwhen thenumberofpanels, N islarge. Theprocedureconsistoftestingforsecondorderserialcorrelationinthe differenced residuals to test for first order serial correlation on the original disturbances. The p-values for thistestsarereportedinalltheGMMregressionsforfirstandsecondorderserialcorrelationintheoriginal disturbances, AR(2) and AR(3) for the differenced residuals, respectively. For example, using 6 collapsed lags as instruments this test indicate first order serial correlation at the 5% significance level, but cannot rejectthatthereisnoserialcorrelationofsecondorderfortheoriginaldisturbances. Therearetwowaysto take this time series pattern into account. One is to construct the instrument set starting with lag t 2 and − t 3,respectivelyforpredeterminedandendogenousvariables. However,followingthisapproachseemsto − weakentheinstrumentssignificantly. 17 Anotheristoenrichthetimeseriesspecificationofthevariablesin themodel, sotheinnovationsbecomeseriallyuncorrelated, aswedobelowincludinganadditionallagfor creditgrowthtothemodel. Table6reportstheestimatesusingOLS,FEanddifferenceGMMofmodel(2)including2lagsofcredit growthasexplanatoryvariables. NowtheArellano-Bondtestscannotrejectthenullofseriallyuncorrelated innovations. With 2 lags of the dependent variable we expect the sum of the coefficients in the α(L) polynomial to be upward and downward biased, respectively in the case of OLS and FE. Therefore, all models reportthesumoftheestimatedcoefficientsonΔ‘ andΔ‘ tofacilitatecomparison. Asitwasthecase i,t 1 i,t 2 − − beforeOLSandFEestimatesprovideausefulbenchmarktoassestheperformanceoftheoreticallysuperior estimators, [0.36,0.48] in this case. Now the model seems better specified. The sum of these coe fficients is in the desired range and the diagnostics tests do not reject the serial independence of the innovations or 16Thistestisnotconsistentinthepresenceofnon-sphericaldisturbancesasinhere;nonetheless,itisthebeststatisticformodel diagnostics.ThealternativeHansentest,whichisconsistent,hasthedisadvantageofbeingweakenedbytheuseofalargenumber of instruments (Bowsher, 2002). In fact, this test does not reject the null of valid moment restrictions in all the GMM models reportedinTable5. 17CarryingouttheestimationusingsystemGMMandrestrictingthesetofinstrumentsinthiswayyieldsstatisticalinsignificant coefficientformostvariablessuggestingthatinstrumentsareweakenedsignificantly(AppendixC). 15

the joint validity of the moment restrictions. The coefficients of the FE estimator are similar as before and the joint test on the coefficients of CAP is rejected at the 5% confidence level. Figure 1 panel (a) plots the estimated effect of CAP on credit growth based on the FE estimates. Point estimates of the FE model i,t 1 − implythatanincreaseofonestandarddeviationintheratioofbankcapitaltoassetsatthesamplemeanof 6.1%willincreasecreditgrowthby72basispointsuponimpactand1.13percentagepointinthelong-run. Despitethefact,thatthedifferenceGMMestimatespassthevalidationsofthediagnosticschecksindicatedabove,therearesomesignsofproblems,asmostcoefficientsarenotsignificant. Theproblemwiththe difference GMM estimator in this case is originated by the use of persistent individual series. Bond (2002) recommends investigating the time series properties of all the series being used in the estimation and suggests using system GMM when they are found to be highly persistent. Appendix B analyze the time series propertiesoftheindividualseries. BSLandASSETS/GDParefoundtobehighlypersistentwithestimated coefficientsfortheautoregressivetermbetween0.81and0.93,and0.98and1.04,respectively. The system GMM estimator uses both the differenced and level equations, “doubling” the number of observationsusedintheestimation. Thewayright-handsidevariablesareinstrumentedforinthedifference equations is the same as in difference GMM. For the level equations, right-hand side variables are instrumented by their differences, which are assumed independent of the individual effects. For example, for Δ‘ avalidinstrumentwillbeΔ2‘ ,asitisassumednotcorrelatedwiththefixede ffectandcorrelated i,t 1 i,t 1 − − with Δ‘ . Similarly, for a variable x which is predetermined, Δx will be a valid instrument as it is i,t 1 it it − assumed not correlated with the fixed e ffect and correlated with x . Deeper lags of them will also be valid it instruments. ForendogenousvariablesΔx anddeeperlagsmaybeusedasinstruments. i,t 1 − Column 5, system GMM with 2 collapsed lags seems the best fit for the model. LENDINGRATE and ΔAGG.DEMANDaresignificant,andLONGTERMRATEandASSETS /GDPhavethedesiredsigns. The sumofthecoefficientonlaggedcreditgrowthisontheupperpartofthedesiredrangeandthediagnostics testsdonotrejectneitherjointvalidityofmomentrestrictionnortheserialuncorrelationoftheinnovations. The estimated effects of banks’ financial position yields CAP as the only significant variable. In fact the coefficient on the linear term is significant at the 10% level and the linear and quadratic terms are jointly significant at the 5% level. This nonlinear e ffect will depend on the initial level of the ratio of equity to assets (Figure 1 panel b). For instance, starting at the sample mean of 6.1% the effect of an increase 16

(decrease) of one standard deviation in CAP is an increase (decrease) of 0.8 (0.3) percentage points in the growth rate of credit.18 The presence of lagged credit growth in the model imply that the long-run effect will be the previous effect times 1 , i.e. an associated increase (decrease) in credit growth in the long- 1 α(L) − run of 1.6 (0.6) percentage points. The coefficient on ROE displays the right sign, but it does not seem to have neither a statically or economically significant e ffect on credit growth. Balance sheet liquidity, BSL, have a negative sign in contrast to what was expected. Deposit costs at the end of the previous year and contemporaneous lending rates, stock returns and aggregate demand growth are all significant with the expected signs. The implied effects on credit growth of this point estimates from a one standard deviation increase are: DEPOSITCOSTS (ratio of interest expenses to deposits) -2.76 percentage points; LENDINGRATE 4.72 percentage points; STOCKRETURNS 5.01 percentage points; and the growth rate ofaggregatedemand,ΔAGG.DEMAND1.01percentagepoints.19 4.2 EffectofBanks’FinancialPosition Havingspecifiedthebenchmarkspecificationitisnowpossibletostudyinmoredetailthee ffectofbanks’ financial position on credit growth. Three aspects will be considered. First, the individual e ffect of each variable that measures banks’ financial positions will be considered. Then, it will be analyzed the e ffect of different measures of profits, liquidity and capital, respectively. Finally, the next section presents some robustnesschecks. Table7presentstheresultswhenbanks’variablesareincludedoneatatimetoinvestigate the significance of each one separately and potential non-linear e ffect of profits and liquidity. The first column presents the benchmark regression results to facilitate comparison. All models use two collapsed lags to construct the instrument set and the system GMM estimator. When only CAP is included in the model, estimates remain qualitatively the same. The joint significance of the linear and quadratic capital terms is affected but they are still significant at the 10% level (column 3). When only ROE is included the estimates are as before. More interesting is the estimation that considers both a linear and a quadratic profitterm(column5). BothoftheROEcoe fficientsaresignificantatthe10%level,buttheyarenotjointly 18ThestandarddeviationofCAPforthewholesampleis2.2%asreportedinAppendixA. 19StandarddeviationsarereportedinAppendixAandequal: 5.684forDEPOSITCOSTS;3.101forLENDINGRATE;20.635 forSTOCKRETURNS;and2.774forΔAGG.DEMAND. 17

significant in evidence the individual coe fficients were only marginally significant. 20 When only balance sheet liquidity, BSL is included the coefficient turns bigger in absolute value, but it is still statistically insignificant. Nosignificante ffectisfoundwhenbothalinearandaquadraticBSLtermareincluded. This analysis reinforce the result that CAP is the only significant predictor of subsequent credit growth in this sample,withaneffectthatisnonlinearaswasexpectedbythepresenceofcapitalregulations. Table8reportsestimationsfordifferentdefinitionsofbanks’profits. AsdetailedaboveROEwassetto zerowhenequitywasnegative. IfROEisdefinedastheratioofaftertaxprofitstoequity,evenwhenequity is negative results remain unchanged. This was expected as there is only one country-year observation with negative equity, corresponding to the US in 1983 (see Appendix A). Next, return on assets (ROA) at the end of the previous year is considered instead of ROE. Column 3 in Table 8 reports the results for this specification. The coe fficient on ROA is positive, but statistically insignificant. When leverage is included as an additional control the estimated coefficient on ROA do not change significantly. But the coefficientassociatedwithCAPdochange,butthejointsignificanceofthelinearandquadratictermsisnot compromised. Table9presentstheestimationresultsfordifferentdefinitionsofbanks’liquidity. Onceagain,tofacilitate comparison, the first column presents the benchmark regression results. The second column presents the results of replacing BSL with the interaction of this variable and SMALL, the fraction of small i,t 1 − banks’ assets to total assets.21 Not all countries reports information to compute this ratio so the regression includeonly18countriesand249observations. Thecoefficientonliquidityturnsstatisticallyinsignificant, inlinewithpreviousstudiesthatsuggeststhatliquidassetsareamoreimportantfundingsourceforsmaller banks. The second order polynomial on capital remains significant and now the linear term is significant by itself at the 1% level. More surprising is the fact, that the coefficient on ROE becomes significant and the coefficient on the lending rate becomes negative. The specification tests show that the joint validity of the moment conditions is rejected, whereas the independence of the original disturbances is not. Column 3 present the benchmark regression estimated with the restricted sample of 249 observations used in the 20Considering the difference GMM estimator–not reported–individual coefficients loose their significance. Only when 6 collapsedlagsareincludedusingthesystemGMMestimatorthecoefficientsonROEarejointlysignificant.AllthissuggestthatROE mighthaveanonlineareffect,butitcannotberuledoutthatthisisduetooverfitting,astheresultonlybecomesstrongwhenmany instrumentsareincluded. 21Theratioofsmallbankassetstototalassets,SMALLiscalculatedastheratioofassetsofnon-largecommercialbanks,savings banksandcooperativebankstototalbanksassets. 18

previousregression. Againthejointvalidityofthemomentrestrictionsisrejected,suggestingthatthenumberofinstrumentsistoolargerelativetothesamplesizeof249. Column4considersthesumofsecurities and reserves22 to total assets at the end of the previous year as the measure of liquidity. Comparison with thebenchmarkregressionshowsthatthecoefficientonliquidityturnsnegative,thepointestimatesofother coefficientsdonotchangesignificantly, andtheresultsofthetestofthesignificanceofcoe fficientsanddiagnosticstestarethesame. Thelastcolumnpresentstheestimationwhenliquidityismeasuredbytheratio of(non-bank)depositstototalassetsattheendofthepreviousyear. Againthecoefficientofthismeasureof liquidityturnsnegativeandtherestofthecoefficientareinlinewiththebaselineregression. Anexception isASSETS/GDPwhichchangessign. Finally, Table 10 presents the results when alternative definitions of banks’ capital are included in the model. To account for the non-linearity of the estimated effect of banks’ capital, this variable is interacted with different dummy variables. The first one is whether CAP is larger or equal to the 25th percentile i,t 1 − of the distribution of CAP in country i. The second one is whether CAP is larger or equal to 4% and i,t 1 − the third one whether is larger or equal to 6%. As could be seen from the results reported in Table 10 (columns 2-4) none of this non-linear transformations capture the nonlinear effect of capital as none of the estimated coefficients is significant. The estimated e ffect of the other variables is in line with the baseline specifications. This results correspond to countries and may not be compared in a straight forward way to the results fromindividualbanks, asstudyingaggregatebanksbalancesheetsitisnotpossibletoidentifymovements betweenindividualinstitutions. Infact,estimatespickupthemultipliereffectoffinancialtransactions. For example, a bank grants a loan to a client, who deposits part of the funds or spend them and the recipient depositstheproceedsinadomesticbank. Thenthelatterbankmaygrantaloanwiththecyclecontinuing. 5 Robustness Tests This section presents robustness tests to the benchmark regression reported above. First, real deposit rates areincludedinsteadoftheratioofinterestexpensestodepositstocontrolforthecostofdeposits. Second, 22Reservescorrespondstoitem14cashandbalanceswithCentralBankoftheOECDBankStatisticsdataset.ForJapanreserves areincludedininterbankdeposits,whichareusedinstead. 19

the ratio of large banks’ assets to total assets, LARGE, is included to control for the structure of the bank sector. Third,LOANPROVISIONS,definedastheratioofprovisionsonloanstototalloansisusedinstead of PROVISIONS to control for the riskiness of borrowers. Finally, alternative measures to control for real activityareconsidered. Table11presentsthefirstsetofrobustnesschecks. Table 12 reports further robustness checks for the way real economic activity is controlled for in the model. Once again the table starts with the benchmark estimation results (column 1). Column 2 considers changesinrealGDP,ΔGDP insteadofchangesinaggregatedemand. it Insum,theserobustnesscheckslendsupporttothemainfindingofthepaperthatbanks’equitycapitalis asignificantdeterminantofsubsequentcreditgrowthandthatneitherprofitsorliquiditydisplayasignificant roleatthecountrylevelforOECDcountries. 6 Conclusions This paper presented estimates of the effect of banks’ financial position on credit growth for a sample of 29 OECD countries. The identification relied on the assumption that country-year innovations to the growth rate of loans are independent of predetermined variables and past values of endogenous variables, and that these innovations are not serially correlated. The paper discussed how to adapt GMM estimators, designed for “short” panels, to the present context where the data is organized in a “square” panel. The main issue is on building suitable instrument sets without using too many instruments that will render the instrumentsinvalidandgenerateotherestimationproblems. ItwasarguedthatthesystemGMMestimator was to be preferred due to the presence of highly persistent series and an instrument set using two lags of independent variables collapsed to economize on the number of instruments was chosen. The empirical results shows that among capital, profits and liquidity at the end of the previous year, capital is the most importantpredictorofcreditgrowthinthecurrentyear. Therelationshipbetweencapitalandcreditgrowth isnon-linear. Pointestimatesfromthepreferredeconometricspecificationimplythatatthesamplemeana onestandarddeviationincrease(decrease)isassociatedwithanincrease(decrease)of0.8(0.3)percentage points in credit growth upon impact and 1.6 (0.6) percentage points in the long-run. These results were foundrobusttothedefinitionofthevariablesincludedinthemodelaswellaschangesinthesetofcontrols usedintheestimation. 20

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Appendix A Descriptive Statistics of Main Variables The model to be estimated is given in equation (2). Recall Δ‘ = αΔ‘ +β X +μ +μ +v . Here I present it i,t 1 0 i,t t i it − descriptivestatisticsforthevariablesSampleaccordingtoavailabilityofinformationmodel(2). TableA.1: DescriptiveStatisticsforCreditGrowthbyCountry (inpercents) Country mean min max st. dev. Australia 5.711 -20.933 32.421 11.584 Austria 5.990 5.329 6.650 0.934 Belgium 3.976 -5.812 14.950 4.482 Canada 3.628 -2.244 12.314 4.010 Chile 7.093 -2.092 14.543 5.215 CzechRepublic 2.011 -8.000 13.552 8.701 Denmark 5.244 -11.747 20.829 8.182 Finland 2.369 -16.775 33.663 11.687 France 1.255 -9.579 6.575 4.168 Germany 4.572 -3.734 9.845 2.984 Greece 13.733 -2.059 43.961 12.271 Hungary 13.757 7.276 24.391 5.195 Iceland 13.134 0.091 36.726 11.987 Ireland 21.998 3.607 48.989 15.727 Italy 4.826 -3.094 12.803 4.605 Japan -1.056 -10.214 3.597 3.497 Korea 12.331 -23.941 42.042 13.408 Mexico -2.156 -15.492 14.075 9.874 Netherlands 6.768 -10.407 25.206 9.322 NewZealand 8.270 4.884 13.115 2.422 Norway 7.509 -8.376 22.119 7.550 Poland 6.805 0.690 20.959 7.604 Portugal 10.508 -5.987 23.041 9.589 SlovakRepublic 4.234 -27.145 22.349 17.626 Spain 5.194 -9.749 11.833 5.292 Sweden 4.110 -23.421 21.980 10.602 Switzerland 3.289 -11.442 14.617 5.513 UnitedKingdom 9.240 -3.652 42.383 11.132 UnitedStates 2.575 -13.480 14.648 5.817 All 5.812 -27.145 48.989 9.092 Source:OwnelaborationbasedonOECDBankStatisticsandOECDMainEconomicIndicators. 23

TableA.2: DescriptiveStatisticsforROE1 byCountry (inpercents) Country mean min max st. dev. Australia 9.152 -0.913 35.149 8.414 Austria 8.003 7.603 8.402 0.565 Belgium 9.267 3.555 21.667 4.073 Canada 12.720 4.963 16.834 2.668 Chile 13.011 8.844 15.691 1.974 CzechRepublic 9.744 0.755 14.147 5.212 Denmark 6.774 -21.384 25.622 9.676 Finland 0.014 -49.504 24.228 19.523 France 6.150 -1.291 10.283 3.808 Germany 6.114 3.696 8.894 1.084 Greece 14.109 7.045 21.901 3.939 Hungary 15.414 10.529 19.884 3.620 Iceland 8.737 -0.883 14.852 4.750 Ireland 13.356 10.452 15.937 1.526 Italy 7.307 1.208 12.842 3.306 Japan -1.992 -22.388 15.085 12.182 Korea -0.023 -79.028 18.174 24.044 Mexico 6.920 -5.008 20.079 7.252 Netherlands 10.864 -11.195 18.023 6.267 NewZealand 16.752 6.839 23.283 4.244 Norway 5.033 -113.774 17.897 25.325 Poland 10.240 4.742 16.572 4.742 Portugal 7.084 5.770 9.528 1.227 SlovakRepublic 12.174 -29.391 26.495 18.135 Spain 8.600 1.356 11.697 2.072 Sweden 9.999 2.052 39.752 8.498 Switzerland 8.415 0.308 16.402 3.671 UnitedKingdom 13.102 1.117 21.013 5.898 UnitedStates 9.698 0.000 14.043 4.197 All 8.536 -113.774 39.752 10.692 Source:OwnelaborationbasedonOECDBankStatistics. 1ROEdefinedaszerowhenequityisnegative. 24

TableA.3: DescriptiveStatisticsforCapital(ratioofEquitytoAssets)byCountry (inpercents) Country mean min p25 max st. dev. Australia 10.096 7.063 9.918 12.344 1.196 Austria 4.621 4.504 4.504 4.737 0.164 Belgium 3.071 2.384 2.545 3.957 0.514 Canada 5.279 4.185 5.099 5.877 0.411 Chile 8.517 7.276 8.316 9.199 0.459 CzechRepublic 8.483 6.013 8.202 10.643 1.695 Denmark 7.628 5.512 6.542 9.930 1.352 Finland 6.820 5.044 6.126 10.823 1.622 France 4.260 3.124 3.996 5.064 0.539 Germany 3.793 3.271 3.557 4.242 0.310 Greece 5.732 2.443 4.552 9.886 2.343 Hungary 9.326 8.999 9.088 9.785 0.262 Iceland 7.321 6.410 6.734 7.980 0.600 Ireland 5.911 4.985 5.690 6.681 0.582 Italy 6.435 3.887 6.116 8.035 0.965 Japan 3.951 2.837 3.338 5.260 0.665 Korea 5.775 3.583 4.098 8.867 1.874 Mexico 7.349 5.298 6.389 9.713 1.256 Netherlands 3.878 2.668 3.605 4.601 0.524 NewZealand 5.700 3.676 4.805 7.686 1.218 Norway 5.457 2.904 4.544 7.295 1.245 Poland 9.492 8.348 9.151 10.204 0.694 Portugal 9.863 8.227 9.012 11.584 1.029 SlovakRepublic 7.325 3.733 4.808 13.049 2.970 Spain 7.862 6.564 7.222 9.472 0.704 Sweden 5.762 4.268 5.342 7.163 0.796 Switzerland 5.904 4.531 5.622 6.807 0.661 UnitedKingdom 4.560 3.256 4.051 5.995 0.715 UnitedStates 6.730 -11.666 5.543 10.345 4.058 All 6.087 -11.666 4.481 13.049 2.234 Source:OwnelaborationbasedonOECDBankStatistics. Note:p25=25thpercentile. 25

TableA.4: DescriptiveStatisticsforBalanceSheetLiquidity(BSL,ratioofsecuritiestoassets)byCountry (inpercents) Country mean min max st. dev. Australia 7.096 3.457 10.048 1.823 Austria 16.025 15.956 16.094 0.098 Belgium 29.528 23.251 34.169 2.485 Canada 17.304 10.224 26.325 5.343 Chile 16.060 10.811 18.998 2.686 CzechRepublic 23.766 20.422 26.887 2.389 Denmark 24.411 18.335 29.137 3.627 Finland 16.673 8.471 23.459 4.572 France 16.710 7.789 22.866 4.940 Germany 17.598 12.352 23.981 3.638 Greece 33.412 28.895 36.661 2.411 Hungary 16.430 14.107 18.731 1.692 Iceland 13.562 9.330 19.061 2.940 Ireland 23.902 19.189 29.521 3.961 Italy 14.829 9.132 22.755 4.208 Japan 19.669 14.343 27.225 4.925 Korea 17.291 12.491 24.983 3.265 Mexico 26.933 15.634 33.526 6.694 Netherlands 21.291 11.601 30.992 5.450 NewZealand 11.114 5.436 20.354 4.288 Norway 15.747 8.100 34.108 7.802 Poland 22.104 20.396 23.218 1.033 Portugal 21.373 15.000 27.348 3.973 SlovakRepublic 25.821 14.275 36.199 6.663 Spain 18.756 12.621 24.787 3.224 Sweden 21.514 11.579 29.731 5.432 Switzerland 14.995 9.636 23.524 4.822 UnitedKingdom 14.950 6.944 20.924 5.029 UnitedStates 19.119 13.943 23.386 3.373 All 18.924 3.457 36.661 6.833 Source:OwnelaborationbasedonOECDBankStatistics. 26

TableA.5: DescriptiveStatisticsforDepositCost(ratioofinterestexpensestodeposits)byCountry (inpercents) Country mean min max st. dev. Australia 8.758 5.860 16.371 3.434 Austria 8.607 8.531 8.683 0.107 Belgium 20.617 9.881 30.662 5.659 Canada 6.998 2.823 11.909 2.705 Chile 11.880 5.509 20.631 5.383 CzechRepublic 4.129 2.597 6.514 1.567 Denmark 10.115 5.795 14.292 2.913 Finland 9.517 3.611 16.466 4.041 France 21.712 11.786 31.949 6.392 Germany 9.316 7.219 12.288 1.347 Greece 10.019 4.212 14.074 3.142 Hungary 8.298 6.783 10.822 1.305 Iceland 11.769 6.526 22.087 5.259 Ireland 9.511 7.908 11.460 1.239 Italy 11.190 5.527 17.236 3.199 Japan 2.907 0.339 8.207 2.615 Korea 6.984 3.954 11.871 2.139 Mexico 20.288 9.442 47.965 11.394 Netherlands 9.225 5.599 12.257 1.865 NewZealand 6.863 4.175 11.662 2.255 Norway 9.879 4.655 18.819 3.845 Poland 7.280 4.034 12.997 4.041 Portugal 11.265 8.505 14.163 1.675 SlovakRepublic 5.707 2.947 12.361 3.216 Spain 9.180 3.973 12.763 2.462 Sweden 12.141 4.219 21.127 4.475 Switzerland 7.339 3.709 11.212 2.169 UnitedKingdom 6.881 4.053 11.084 1.883 UnitedStates 6.870 2.003 12.947 2.954 All 9.991 0.339 47.965 5.684 Source:OwnelaborationbasedonOECDBankStatistics. 27

TableA.6: DescriptiveStatisticsforRealLendingRates1 byCountry (inpercents) Country mean min max st. dev. Australia 7.060 4.282 10.869 1.931 Austria 5.284 5.069 5.499 0.304 Belgium 6.924 3.922 10.572 1.727 Canada 4.889 1.929 9.283 2.070 Chile 7.133 3.369 15.059 3.492 CzechRepublic 4.066 2.449 5.840 1.341 Denmark 7.663 4.627 11.458 1.901 Finland 5.106 2.565 9.217 1.986 France 5.796 4.465 7.589 1.055 Germany 8.101 6.757 9.192 0.658 Greece 9.644 -2.515 16.568 5.625 Hungary 4.165 1.127 6.081 1.515 Iceland 10.466 9.014 11.664 0.847 Ireland 0.997 -0.806 5.153 2.142 Italy 6.517 3.157 11.253 2.614 Japan 2.450 0.531 4.437 1.088 Korea 4.358 2.234 8.583 1.970 Mexico 5.880 1.514 24.433 6.183 Netherlands 2.511 0.671 5.490 1.660 NewZealand 7.904 6.161 9.578 1.122 Norway 6.335 0.543 11.922 2.821 Poland 7.118 4.178 12.952 3.652 Portugal 8.424 2.854 14.529 3.160 SlovakRepublic 3.463 -0.095 7.123 2.199 Spain 4.886 0.560 11.114 3.161 Sweden 6.546 2.861 12.826 2.390 Switzerland 2.759 -0.930 4.710 1.239 UnitedKingdom 4.345 1.012 8.679 1.896 UnitedStates 5.194 1.663 8.730 1.864 All 5.725 -2.515 24.433 3.101 Source:OwnelaborationbasedonOECDBankStatisticsandOECDMainEconomicIndicators. 1Realeffectivelendingratescalculatedasnominalratesminuseffectiveinflationintheyear. 28

TableA.7: DescriptiveStatisticsforratioofTotalProvisionstoLoansbyCountry (inpercents) Country mean min max st. dev. Australia 0.704 0.141 2.052 0.680 Austria 0.733 0.722 0.744 0.016 Belgium 0.638 -0.079 1.307 0.368 Canada 0.549 0.158 1.331 0.288 Chile 1.111 0.519 2.045 0.438 CzechRepublic -1.628 -2.923 0.574 1.408 Denmark 1.959 0.623 3.611 1.017 Finland 0.172 -0.105 0.813 0.278 France 0.870 0.367 1.780 0.466 Germany 0.618 0.200 0.946 0.195 Greece 1.186 0.651 1.866 0.441 Hungary 0.411 -0.084 0.662 0.227 Iceland 1.490 0.947 3.166 0.731 Ireland 0.196 0.076 0.298 0.062 Italy 1.197 0.260 1.823 0.429 Japan 0.564 0.046 1.602 0.500 Korea 1.524 0.585 3.018 0.795 Mexico 1.962 0.946 3.645 0.977 Netherlands 0.305 0.093 0.810 0.166 NewZealand 0.198 -0.141 1.042 0.317 Norway 0.924 -0.161 4.791 1.135 Poland 1.881 0.585 3.088 0.971 Portugal 2.476 1.070 4.867 1.355 SlovakRepublic -0.395 -4.010 7.255 3.278 Spain 1.406 0.452 3.151 0.585 Sweden 0.076 -6.792 2.027 1.912 Switzerland 1.001 0.372 1.797 0.399 UnitedKingdom 0.912 0.307 2.655 0.739 UnitedStates 0.761 0.305 1.545 0.371 All 0.864 -6.792 7.255 1.047 Source:OwnelaborationbasedonOECDBankStatistics. 29

TableA.8: DescriptiveStatisticsforRealLongTermRates1 byCountry (inpercents) Country mean min max st. dev. Australia 4.835 1.253 8.211 2.230 Austria 3.950 3.791 4.110 0.226 Belgium 4.449 0.566 7.331 1.838 Canada 4.466 1.233 8.405 1.967 Chile 4.571 2.550 7.330 1.720 CzechRepublic 2.465 1.568 4.006 1.045 Denmark 5.749 2.412 10.264 2.109 Finland 5.466 2.440 9.053 2.118 France 4.361 1.964 6.701 1.448 Germany 4.326 2.131 6.288 1.113 Greece 3.662 -7.233 9.825 4.351 Hungary 1.432 -1.215 3.186 1.720 Iceland 5.591 2.763 8.000 1.550 Ireland 1.567 -0.079 4.839 1.632 Italy 4.371 1.332 7.997 2.166 Japan 2.098 0.088 3.673 1.066 Korea 4.711 0.862 8.871 2.694 Mexico 4.845 -1.568 16.744 4.321 Netherlands 2.704 0.796 4.976 1.071 NewZealand 4.535 2.122 7.387 1.468 Norway 4.179 -1.344 7.436 2.115 Poland 4.382 3.034 5.451 1.022 Portugal 3.970 2.033 7.289 1.647 SlovakRepublic -0.349 -3.696 3.808 2.685 Spain 4.523 1.263 7.956 2.143 Sweden 4.684 1.248 7.788 1.942 Switzerland 1.853 -1.057 4.106 1.185 UnitedKingdom 4.206 0.977 6.707 1.433 UnitedStates 3.872 0.897 8.138 1.906 All 4.010 -7.233 16.744 2.303 Source:OwnelaborationbasedonOECDMainEconomicIndicators,IFS,andNationalSources. 1Realeffectivelongtermratescalculatedasnominalratesminuseffectiveinflationintheyear.Nominal longtermratescorrespondsto10yeargovernmentbondsorsimilar. ForChileindexedbondsyieldsare used.Yearaverages. 30

TableA.9: DescriptiveStatisticsforRealStockReturnsbyCountry (inpercents) Country mean min max st. dev. Australia 3.236 -6.930 13.666 6.803 Austria 0.214 -10.571 10.999 15.252 Belgium 7.258 -21.659 35.240 15.062 Canada 3.965 -24.217 28.142 12.499 Chile 2.663 -32.983 27.590 16.487 CzechRepublic 14.235 -33.835 40.992 30.287 Denmark 8.933 -21.735 49.235 18.504 Finland 8.229 -57.899 61.773 36.661 France 2.791 -26.417 29.879 18.528 Germany 4.919 -29.558 31.118 18.865 Greece 4.746 -43.945 67.555 30.773 Hungary 4.473 -33.609 43.942 26.845 Iceland 12.757 -38.385 46.953 23.057 Ireland 7.517 -24.900 31.969 18.579 Italy 1.165 -39.873 69.081 26.233 Japan -3.807 -35.093 24.585 18.928 Korea 1.083 -54.076 66.551 28.792 Mexico 9.665 -42.743 34.965 22.416 Netherlands 1.642 -36.478 38.252 23.711 NewZealand 1.412 -33.298 20.973 12.657 Norway 8.587 -25.134 42.439 21.201 Poland 11.236 -33.104 36.536 25.433 Portugal 3.381 -27.630 43.256 24.531 SlovakRepublic 14.402 -12.408 69.236 26.099 Spain 6.883 -22.058 62.705 22.421 Sweden 10.666 -37.691 57.308 23.887 Switzerland 5.185 -28.461 33.663 16.974 UnitedKingdom 3.317 -21.184 20.634 12.746 UnitedStates 6.194 -15.271 26.400 11.281 All 5.427 -57.899 69.236 20.635 Source:OwnelaborationbasedonOECDMainEconomicIndicatorsandIFS. Note: Computedasthelogchangesofrealstockmarketindicesfordomesticmarketsineachcountry. AllindicesdeflatedbydomesticCPIs. 31

TableA.10: DescriptiveStatisticsforRealAggregateDemand1 byCountry (inpercents) Country mean min max st. dev. Australia 3.832 0.268 6.111 1.947 Austria 2.282 2.024 2.539 0.364 Belgium 2.014 -0.880 4.503 1.353 Canada 3.038 -1.303 5.315 1.680 Chile 4.576 -4.657 10.219 4.180 CzechRepublic 3.234 1.792 4.536 1.130 Denmark 1.893 -3.230 7.291 2.492 Finland 1.692 -6.278 6.297 4.275 France 1.865 -0.560 3.818 1.236 Germany 1.722 -1.752 4.469 1.699 Greece 2.999 -1.141 5.869 2.129 Hungary 3.404 -0.915 9.159 3.437 Iceland 4.088 -2.549 12.425 4.364 Ireland 6.659 3.537 9.185 2.401 Italy 1.452 -4.536 4.557 2.096 Japan 0.923 -2.229 2.975 1.215 Korea 4.118 -15.019 10.001 5.758 Mexico 2.929 -13.258 8.283 5.671 Netherlands 2.213 -3.490 5.316 2.184 NewZealand 3.609 -1.107 7.261 2.296 Norway 2.689 -1.609 6.415 2.207 Poland 2.945 -0.387 6.857 2.584 Portugal 3.855 -0.548 6.553 2.117 SlovakRepublic 4.507 -0.598 8.413 3.213 Spain 3.146 -2.748 7.471 2.728 Sweden 1.712 -4.057 4.488 2.074 Switzerland 1.602 -1.708 3.429 1.289 UnitedKingdom 2.844 -1.676 6.905 1.827 UnitedStates 3.214 -0.542 6.202 1.606 All 2.695 -15.019 12.425 2.774 Source:OwnelaborationbasedonOECDMainEconomicIndicators. Note:Computedasthelogchangesofthesumofrealprivateconsumption(householdandnon-profits), realgovernmentfinalconsumptionandrealgrossfixedcapitalformation. 32

TableA.11: DescriptiveStatisticsforAssetstoGDPbyCountry (inpercents) Country mean min max st. dev. Australia 105.178 95.213 118.581 7.749 Austria 247.809 241.259 254.359 9.263 Belgium 291.774 219.571 365.779 35.033 Canada 140.348 110.685 180.169 21.132 Chile 100.584 82.520 136.346 15.642 CzechRepublic 109.361 97.288 124.122 11.223 Denmark 114.330 77.119 148.415 20.222 Finland 122.673 97.217 146.150 15.154 France 236.109 224.426 254.429 8.765 Germany 168.793 116.938 268.464 49.278 Greece 69.855 50.866 104.403 20.704 Hungary 73.632 59.700 94.819 13.226 Iceland 88.277 54.556 147.149 35.562 Ireland 337.898 147.228 500.307 116.351 Italy 155.749 117.336 222.295 30.046 Japan 166.883 141.734 225.223 24.361 Korea 94.198 56.608 131.700 27.326 Mexico 46.236 33.261 61.285 8.326 Netherlands 384.141 213.657 597.025 129.745 NewZealand 137.906 103.211 186.810 26.750 Norway 80.465 52.668 159.743 24.827 Poland 57.889 56.830 59.240 1.058 Portugal 148.646 103.170 197.532 36.319 SlovakRepublic 85.934 77.060 93.154 6.226 Spain 139.707 105.691 176.582 21.246 Sweden 97.837 69.222 144.522 22.638 Switzerland 409.050 258.519 660.909 119.501 UnitedKingdom 149.411 73.870 392.820 84.457 UnitedStates 93.180 79.512 113.440 12.628 All 160.274 33.261 660.909 108.564 Source:OwnelaborationbasedonOECDBankStatisticsandOECDMainEconomicIndicators. 33

B Time Series Properties of Individual Variables Here I present an analysis of the time series properties of the individual series used in the benchmark model. To facilitate comparison dependent variables are the explanatory variables used in the benchmark regressions with the sametimingconventionandrestrictingthesampletothesampleofmodel(2). 34

) ‘Δ(htworGtiderCrofssecorPseireSemiTfonoitamitsE :1.BelbaT ti )8( )7( )6( )5( )4( )3( )2( )1( :elbairaVtnednepeD MMGmetsys MMGecnereffid EF SLO ‘Δ ti despalloc6 despalloc4 despalloc2 despalloc6 despalloc4 despalloc2 ***213.0 ***423.0 ***963.0 ***582.0 ***003.0 ***053.0 *451.0 ***372.0 ‘Δ 1 t,i − )001.0( )7890.0( )501.0( )6790.0( )0790.0( )0001.0( )2380.0( )1180.0( ***662.0 ***772.0 ***423.0 ***952.0 ***772.0 ***723.0 **231.0 ***032.0 ‘Δ 2 t,i − )5370.0( )7270.0( )6680.0( )6660.0( )9560.0( )6180.0( )4350.0( )6670.0( seY seY seY seY seY seY seY seY stcefferaeY seY seY seY seY seY seY seY oN 1stceffeyrtnuoC 875.0 106.0 396.0 445.0 775.0 876.0 582.0 405.0 ‘Δ+ ‘Δ 2 t,i 1 t,i 912.0 − 2σ − /2σ tiv iμ snoitcirtsertnemomfoytidilavtnioj : H 0 011.0 881.0 292.0 9960.0 371.0 924.0 tsetnagraS 000.1 000.1 000.1 000.1 000.1 000.1 tsetnesnaH detalerrocnuyllaireseraslaudiser : H 0 564.0 904.0 532.0 534.0 543.0 102.0 )2(RArofdnoB-onallerA 782.0 672.0 922.0 582.0 262.0 212.0 )3(RArofdnoB-onallerA 63 43 23 43 23 03 stnemurtsniforebmuN 742.0 513.0 2R 464 464 464 844 844 844 464 464 snoitavresborebmuN 92 92 92 92 92 92 92 92 seirtnuocrebmuN nihtiwehtotdnopserrocEFrof2R.secnereffidgnikatybstceffeyrtnuocetanimilesnoissergerMMGecnereffiddna)EF(stceffEdexiF1:setoN .ylevitcepser,%01dna%5,%1tatnacfiingisetoned*,**,***.sesehtnerapnisrorredradnatstsuboryticitsadeksoreteH.2R 35

EORrofssecorPseireSemiTfonoitamitsE :2.BelbaT )8( )7( )6( )5( )4( )3( )2( )1( :elbairaVtnednepeD MMGmetsys MMGecnereffid EF SLO EOR 1 t,i − despalloc6 despalloc4 despalloc2 despalloc6 despalloc4 despalloc2 ***505.0 ***694.0 ***674.0 ***335.0 ***915.0 ***394.0 ***724.0 ***815.0 EOR 2 t,i − )6190.0( )8090.0( )901.0( )5280.0( )1380.0( )501.0( )8370.0( )541.0( seY seY seY seY seY seY seY seY stcefferaeY seY seY seY seY seY seY seY oN 1stceffeyrtnuoC 7960.0 2σ/2σ tiv iμ snoitcirtsertnemomfoytidilavtnioj : H 0 589.0 659.0 349.0 329.0 858.0 948.0 tsetnagraS 000.1 000.1 000.1 000.1 000.1 000.1 tsetnesnaH detalerrocnuyllaireseraslaudiser : H 0 088.0 278.0 558.0 609.0 498.0 378.0 )2(RArofdnoB-onallerA 876.0 386.0 886.0 576.0 186.0 786.0 )3(RArofdnoB-onallerA 63 43 23 43 23 03 stnemurtsniforebmuN 372.0 053.0 2R 974 974 974 364 364 364 974 974 snoitavresborebmuN 92 92 92 92 92 92 92 92 seirtnuocrebmuN nihtiwehtotdnopserrocEFrof2R.secnereffidgnikatybstceffeyrtnuocetanimilesnoissergerMMGecnereffiddna)EF(stceffEdexiF1:setoN .ylevitcepser,%01dna%5,%1tatnacfiingisetoned*,**,***.sesehtnerapnisrorredradnatstsuboryticitsadeksoreteH.2R 36

LATIPACrofssecorPseireSemiTfonoitamitsE :3.BelbaT )8( )7( )6( )5( )4( )3( )2( )1( :elbairaVtnednepeD MMGmetsys MMGecnereffid EF SLO PAC 1 t,i − despalloc6 despalloc4 despalloc2 despalloc6 despalloc4 despalloc2 *173.0 *373.0 *953.0 843.0 743.0 143.0 ***535.0 ***828.0 PAC 2 t,i − )591.0( )791.0( )391.0( )342.0( )242.0( )042.0( )021.0( )2290.0( seY seY seY seY seY seY seY seY stcefferaeY seY seY seY seY seY seY seY oN 1stceffeyrtnuoC 155.0 2σ/2σ tiv iμ snoitcirtsertnemomfoytidilavtnioj : H 0 813.0 571.0 741.0 802.0 680.0 240.0 tsetnagraS 000.1 000.1 000.1 000.1 000.1 000.1 tsetnesnaH detalerrocnuyllaireseraslaudiser : H 0 500.0 500.0 500.0 100.0 100.0 100.0 )2(RArofdnoB-onallerA 600.0 500.0 600.0 500.0 500.0 500.0 )3(RArofdnoB-onallerA 63 43 23 43 23 03 stnemurtsniforebmuN 243.0 696.0 2R 084 084 084 464 464 464 084 084 snoitavresborebmuN 92 92 92 92 92 92 92 92 seirtnuocrebmuN nihtiwehtotdnopserrocEFrof2R.secnereffidgnikatybstceffeyrtnuocetanimilesnoissergerMMGecnereffiddna)EF(stceffEdexiF1:setoN .ylevitcepser,%01dna%5,%1tatnacfiingisetoned*,**,***.sesehtnerapnisrorredradnatstsuboryticitsadeksoreteH.2R 37

LSBrofssecorPseireSemiTfonoitamitsE :4.BelbaT )8( )7( )6( )5( )4( )3( )2( )1( :elbairaVtnednepeD MMGmetsys MMGecnereffid EF SLO LSB 1 t,i − despalloc6 despalloc4 despalloc2 despalloc6 despalloc4 despalloc2 ***750.1 ***250.1 ***441.1 ***150.1 ***450.1 ***281.1 ***638.0 ***349.0 LSB 2 t,i − )6060.0( )4750.0( )3470.0( )431.0( )331.0( )561.0( )2430.0( )9510.0( seY seY seY seY seY seY seY seY stcefferaeY seY seY seY seY seY seY seY oN 1stceffeyrtnuoC 962.0 2σ/2σ tiv iμ snoitcirtsertnemomfoytidilavtnioj : H 0 12700.0 21300.0 6120.0 22300.0 200.0 400.0 tsetnagraS 000.1 000.1 000.1 000.1 000.1 000.1 tsetnesnaH detalerrocnuyllaireseraslaudiser : H 0 014.0 904.0 244.0 183.0 383.0 434.0 )2(RArofdnoB-onallerA 595.0 495.0 826.0 406.0 406.0 646.0 )3(RArofdnoB-onallerA 63 43 23 43 23 03 stnemurtsniforebmuN 877.0 698.0 2R 084 084 084 464 464 464 084 084 snoitavresborebmuN 92 92 92 92 92 92 92 92 seirtnuocrebmuN nihtiwehtotdnopserrocEFrof2R.secnereffidgnikatybstceffeyrtnuocetanimilesnoissergerMMGecnereffiddna)EF(stceffEdexiF1:setoN .ylevitcepser,%01dna%5,%1tatnacfiingisetoned*,**,***.sesehtnerapnisrorredradnatstsuboryticitsadeksoreteH.2R 38

STSOCTISOPEDrofssecorPseireSemiTfonoitamitsE :5.BelbaT )8( )7( )6( )5( )4( )3( )2( )1( :elbairaVtnednepeD MMGmetsys MMGecnereffid EF SLO STSOCTISOPED 1 t,i − despalloc6 despalloc4 despalloc2 despalloc6 despalloc4 despalloc2 ***357.0 ***067.0 ***667.0 ***299.0 ***680.1 ***931.1 ***756.0 ***188.0 STSOCTISOPED 2 t,i − )221.0( )841.0( )961.0( )631.0( )551.0( )102.0( )101.0( )7540.0( seY seY seY seY seY seY seY seY stcefferaeY seY seY seY seY seY seY seY oN 1stceffeyrtnuoC 074.0 2σ/2σ tiv iμ snoitcirtsertnemomfoytidilavtnioj : H 0 300.0 022.0 280.0 400.0 063.0 322.0 tsetnagraS 000.1 000.1 000.1 000.1 000.1 000.1 tsetnesnaH detalerrocnuyllaireseraslaudiser : H 0 372.0 952.0 552.0 683.0 293.0 593.0 )2(RArofdnoB-onallerA 608.0 008.0 297.0 267.0 247.0 137.0 )3(RArofdnoB-onallerA 63 43 23 43 23 03 stnemurtsniforebmuN 896.0 558.0 2R 774 774 774 954 954 954 774 774 snoitavresborebmuN 92 92 92 92 92 92 92 92 seirtnuocrebmuN nihtiwehtotdnopserrocEFrof2R.secnereffidgnikatybstceffeyrtnuocetanimilesnoissergerMMGecnereffiddna)EF(stceffEdexiF1:setoN .ylevitcepser,%01dna%5,%1tatnacfiingisetoned*,**,***.sesehtnerapnisrorredradnatstsuboryticitsadeksoreteH.2R 39

ETARGNIDNELrofssecorPseireSemiTfonoitamitsE :6.BelbaT )8( )7( )6( )5( )4( )3( )2( )1( :elbairaVtnednepeD MMGmetsys MMGecnereffid EF SLO ETARGNIDNEL 1 t,i − despalloc6 despalloc4 despalloc2 despalloc6 despalloc4 despalloc2 ***585.0 ***395.0 ***785.0 ***535.0 ***755.0 ***955.0 ***715.0 ***547.0 ETARGNIDNEL 2 t,i − )521.0( )421.0( )521.0( )4990.0( )7590.0( )8780.0( )511.0( )3090.0( seY seY seY seY seY seY seY seY stcefferaeY seY seY seY seY seY seY seY oN 1stceffeyrtnuoC 513.0 2σ/2σ tiv iμ snoitcirtsertnemomfoytidilavtnioj : H 0 463.0 833.0 501.0 175.0 519.0 765.0 tsetnagraS 000.1 000.1 000.1 000.1 000.1 000.1 tsetnesnaH detalerrocnuyllaireseraslaudiser : H 0 547.0 447.0 347.0 227.0 917.0 127.0 )2(RArofdnoB-onallerA 742.0 742.0 552.0 232.0 632.0 452.0 )3(RArofdnoB-onallerA 63 43 23 43 23 03 stnemurtsniforebmuN 905.0 966.0 2R 674 674 674 374 374 374 674 674 snoitavresborebmuN 92 92 92 82 82 82 92 92 seirtnuocrebmuN nihtiwehtotdnopserrocEFrof2R.secnereffidgnikatybstceffeyrtnuocetanimilesnoissergerMMGecnereffiddna)EF(stceffEdexiF1:setoN .ylevitcepser,%01dna%5,%1tatnacfiingisetoned*,**,***.sesehtnerapnisrorredradnatstsuboryticitsadeksoreteH.2R 40

SNOISIVORProfssecorPseireSemiTfonoitamitsE :7.BelbaT )8( )7( )6( )5( )4( )3( )2( )1( :elbairaVtnednepeD MMGmetsys MMGecnereffid EF SLO SNOISIVORP 1 t,i − despalloc6 despalloc4 despalloc2 despalloc6 despalloc4 despalloc2 ***666.0 ***076.0 ***706.0 ***336.0 ***936.0 ***985.0 ***474.0 ***666.0 SNOISIVORP 2 t,i − )8750.0( )1750.0( )4660.0( )4750.0( )4650.0( )2070.0( )5560.0( )8490.0( seY seY seY seY seY seY seY seY stcefferaeY seY seY seY seY seY seY seY oN 1stceffeyrtnuoC 504.0 2σ/2σ tiv iμ snoitcirtsertnemomfoytidilavtnioj : H 0 282.0 321.0 189.0 491.0 270.0 329.0 tsetnagraS 000.1 000.1 000.1 000.1 000.1 999.0 tsetnesnaH detalerrocnuyllaireseraslaudiser : H 0 426.0 426.0 606.0 706.0 806.0 195.0 )2(RArofdnoB-onallerA 102.0 202.0 002.0 212.0 312.0 112.0 )3(RArofdnoB-onallerA 63 43 23 43 23 03 stnemurtsniforebmuN 943.0 905.0 2R 974 974 974 364 364 364 974 974 snoitavresborebmuN 92 92 92 92 92 92 92 92 seirtnuocrebmuN nihtiwehtotdnopserrocEFrof2R.secnereffidgnikatybstceffeyrtnuocetanimilesnoissergerMMGecnereffiddna)EF(stceffEdexiF1:setoN .ylevitcepser,%01dna%5,%1tatnacfiingisetoned*,**,***.sesehtnerapnisrorredradnatstsuboryticitsadeksoreteH.2R 41

SETARMRETGNOLrofssecorPseireSemiTfonoitamitsE :8.BelbaT )8( )7( )6( )5( )4( )3( )2( )1( :elbairaVtnednepeD MMGmetsys MMGecnereffid EF SLO ETARMRETGNOL 1 t,i − despalloc6 despalloc4 despalloc2 despalloc6 despalloc4 despalloc2 ***846.0 ***586.0 ***537.0 663.0 974.0 195.0 ***354.0 ***906.0 ETARMRETGNOL 2 t,i − )481.0( )281.0( )612.0( )103.0( )573.0( )375.0( )241.0( )401.0( seY seY seY seY seY seY seY seY stcefferaeY seY seY seY seY seY seY seY oN 1stceffeyrtnuoC 791.0 2σ/2σ tiv iμ snoitcirtsertnemomfoytidilavtnioj : H 0 880.0 531.0 080.0 820.0 250.0 910.0 tsetnagraS 000.1 000.1 000.1 000.1 000.1 000.1 tsetnesnaH detalerrocnuyllaireseraslaudiser : H 0 448.0 948.0 268.0 339.0 198.0 888.0 )2(RArofdnoB-onallerA 072.0 062.0 452.0 543.0 192.0 162.0 )3(RArofdnoB-onallerA 63 43 23 43 23 03 stnemurtsniforebmuN 865.0 246.0 2R 074 074 074 164 164 164 074 074 snoitavresborebmuN 92 92 92 92 92 92 92 92 seirtnuocrebmuN nihtiwehtotdnopserrocEFrof2R.secnereffidgnikatybstceffeyrtnuocetanimilesnoissergerMMGecnereffiddna)EF(stceffEdexiF1:setoN .ylevitcepser,%01dna%5,%1tatnacfiingisetoned*,**,***.sesehtnerapnisrorredradnatstsuboryticitsadeksoreteH.2R 42

SNRUTERKCOTSrofssecorPseireSemiTfonoitamitsE :9.BelbaT )8( )7( )6( )5( )4( )3( )2( )1( :elbairaVtnednepeD MMGmetsys MMGecnereffid EF SLO SNRUTERKCOTS 1 t,i − despalloc6 despalloc4 despalloc2 despalloc6 despalloc4 despalloc2 *534.0 **244.0 **774.0 153.0 053.0 *204.0 ***681.0 ***422.0 SNRUTERKCOTS 2 t,i − )422.0( )222.0( )212.0( )712.0( )812.0( )902.0( )9340.0( )2470.0( seY seY seY seY seY seY seY seY stcefferaeY seY seY seY seY seY seY seY oN 1stceffeyrtnuoC 6690.0 2σ/2σ tiv iμ snoitcirtsertnemomfoytidilavtnioj : H 0 289.0 639.0 829.0 019.0 907.0 508.0 tsetnagraS 000.1 000.1 000.1 000.1 000.1 000.1 tsetnesnaH detalerrocnuyllaireseraslaudiser : H 0 238.0 028.0 267.0 949.0 359.0 958.0 )2(RArofdnoB-onallerA 455.0 455.0 255.0 195.0 095.0 575.0 )3(RArofdnoB-onallerA 63 43 23 43 23 03 stnemurtsniforebmuN 205.0 594.0 2R 774 774 774 574 574 574 774 774 snoitavresborebmuN 92 92 92 92 92 92 92 92 seirtnuocrebmuN nihtiwehtotdnopserrocEFrof2R.secnereffidgnikatybstceffeyrtnuocetanimilesnoissergerMMGecnereffiddna)EF(stceffEdexiF1:setoN .ylevitcepser,%01dna%5,%1tatnacfiingisetoned*,**,***.sesehtnerapnisrorredradnatstsuboryticitsadeksoreteH.2R 43

DNAMED.GGAΔrofssecorPseireSemiTfonoitamitsE :01.BelbaT )8( )7( )6( )5( )4( )3( )2( )1( :elbairaVtnednepeD MMGmetsys MMGecnereffid EF SLO DNAMED.GGAΔ 1 t,i − despalloc6 despalloc4 despalloc2 despalloc6 despalloc4 despalloc2 503.0 403.0 *443.0 **573.0 **973.0 **324.0 ***403.0 ***204.0 DNAMED.GGAΔ 2 t,i − )091.0( )781.0( )402.0( )761.0( )571.0( )212.0( )3070.0( )9490.0( seY seY seY seY seY seY seY seY stcefferaeY seY seY seY seY seY seY seY oN 1stceffeyrtnuoC 821.0 2σ/2σ tiv iμ snoitcirtsertnemomfoytidilavtnioj : H 0 145.0 423.0 533.0 627.0 354.0 482.0 tsetnagraS 000.1 000.1 000.1 000.1 000.1 000.1 tsetnesnaH detalerrocnuyllaireseraslaudiser : H 0 037.0 627.0 228.0 198.0 309.0 789.0 )2(RArofdnoB-onallerA 825.0 035.0 485.0 406.0 406.0 556.0 )3(RArofdnoB-onallerA 63 43 23 43 23 03 stnemurtsniforebmuN 372.0 323.0 2R 974 974 974 874 874 874 974 974 snoitavresborebmuN 92 92 92 92 92 92 92 92 seirtnuocrebmuN nihtiwehtotdnopserrocEFrof2R.secnereffidgnikatybstceffeyrtnuocetanimilesnoissergerMMGecnereffiddna)EF(stceffEdexiF1:setoN .ylevitcepser,%01dna%5,%1tatnacfiingisetoned*,**,***.sesehtnerapnisrorredradnatstsuboryticitsadeksoreteH.2R 44

PDG/STESSArofssecorPseireSemiTfonoitamitsE :11.BelbaT )8( )7( )6( )5( )4( )3( )2( )1( :elbairaVtnednepeD MMGmetsys MMGecnereffid EF SLO PDG/STESSA 1 t,i despalloc6 despalloc4 despalloc2 despalloc6 despalloc4 despalloc2 − ***401.1 ***601.1 ***601.1 ***338.0 ***538.0 ***738.0 ***689.0 ***830.1 PDG/STESSA 2 t,i )9220.0( )0320.0( )9120.0( )011.0( )111.0( )011.0( )2810.0( )5110.0( − seY seY seY seY seY seY seY seY stcefferaeY seY seY seY seY seY seY seY oN 1stceffeyrtnuoC 475.0 2σ/2σ tiv iμ snoitcirtsertnemomfoytidilavtnioj : H 0 180.0 1040.0 430.0 803.0 911.0 680.0 tsetnagraS 000.1 000.1 000.1 000.1 000.1 000.1 tsetnesnaH detalerrocnuyllaireseraslaudiser : H 0 252.0 252.0 252.0 722.0 922.0 922.0 )2(RArofdnoB-onallerA 841.0 641.0 641.0 961.0 661.0 761.0 )3(RArofdnoB-onallerA 63 43 23 43 23 03 stnemurtsniforebmuN 149.0 589.0 2R 974 974 974 264 264 264 974 974 snoitavresborebmuN 92 92 92 92 92 92 92 92 seirtnuocrebmuN nihtiwehtotdnopserrocEFrof2R.secnereffidgnikatybstceffeyrtnuocetanimilesnoissergerMMGecnereffiddna)EF(stceffEdexiF1:setoN .ylevitcepser,%01dna%5,%1tatnacfiingisetoned*,**,***.sesehtnerapnisrorredradnatstsuboryticitsadeksoreteH.2R 45

C Additional Regressions TableC.1: SystemGMMEstimatesoftheEffectofBankFinancialPositiononCreditGrowth residualsseriallycorrelated(order1) DependentVariable: Δ‘ it 4lags 4collapsed 12collapsed allcollapsed (1) (2) (3) (4) Δ‘ 0.318*** 0.308** 0.222*** 0.214*** i,t 1 − (0.054) (0.126) (0.072) (0.071) ROE 0.050 0.117 0.117** 0.054 i,t 1 − (0.066) (0.101) (0.058) (0.061) CAP -0.074 -1.072 1.722 0.384 i,t 1 − (0.151) (2.228) (2.122) (0.718) CAP2 0.008 0.032 -0.143 -0.031 i,t 1 − (0.016) (0.127) (0.142) (0.056) BSL 0.076 -0.019 0.006 -0.009 i,t 1 − (0.050) (0.177) (0.144) (0.081) DEPOSITCOSTS -0.243** -0.367* -0.311* -0.288** i,t 1 − (0.102) (0.217) (0.175) (0.132) PROVISIONS -0.068 0.084 0.450 -0.212 i,t 1 − (0.315) (1.263) (0.678) (0.450) LENDINGRATE 0.424** 0.213 0.324 0.666*** it (0.177) (0.731) (0.457) (0.243) LONGTERMRATE 0.491** 0.465 0.794** 0.396 it (0.237) (0.737) (0.399) (0.345) STOCKRETURNS 0.044** 0.112 0.074* 0.052*** it (0.017) (0.082) (0.040) (0.017) ΔAGG.DEMAND 1.250*** 0.372 0.780*** 1.161*** it (0.217) (0.452) (0.248) (0.230) ASSETS/GDP 0.005 0.004 0.011 0.014* i,t 1 − (0.004) (0.015) (0.010) (0.008) H : CAP =0 0 i,t 1 CAP2− =0[p-value] [0.852] [0.777] [0.509] [0.854] i,t 1 Yeareffect−s Yes Yes Yes Yes Countryeffects1 Yes Yes Yes Yes Numberobservations 480 480 480 480 Numbercountries 29 29 29 29 Numberofinstruments 480 73 169 383 H : jointvalidity 0 ofmomentrestrictions Sargan[p-value] [0.655] [0.363] [0.739] [0.137] Hansen[p-value] [1.000] [1.000] [1.000] [1.000] H : residualsare 0 seriallyuncorrelated Arellano-BondforAR(2)[p-value] [0.003] [0.017] [0.004] [0.004] Arellano-BondforAR(3)[p-value] [0.028] [0.095] [0.037] [0.043] Notes:1FixedEffects(FE)andArellano-Bondregressionseliminatecountryeffectsbytakingfirstdi fferences. Heteroskedasticityrobuststandarderrorsinparentheses. ***,**,*denotesignificantat1%,5% and10%,respectively. 46

Tables and Figures Table1: BankGroupsandsamplewithinformationforloangrowthbyCountry Numberof # Country BankGroup Sample observations 1 Australia Allbanks 1987-2003 17 2 Austria Allbanks 1988-2008 21 3 Belgium Allbanks 1982-2009 28 4 Canada† Allbanks 1983-2009 27 5 Chile Allbanks 1991-2009 19 6 CzechRepublic Allbanks 1994-2005 12 7 Denmark Allbanks 1980-2008 29 8 Finland Allbanks 1980-2009 30 9 France Allbanks 1989-2008 20 10 Germany Allbanks 1980-2008 29 11 Greece† Commercialbanks 1980-2009 30 12 Hungary Commercialbanks 1995-2008 14 13 Iceland Allbanks 1980-2003 24 14 Ireland Allbanks 1996-2008 13 15 Italy Allbanks 1985-2009 25 16 Japan Allbanks 1990-2008 19 17 Korea Allbanks 1991-2008 18 18 Luxembourg Allbanks 1980-2008 29 19 Mexico† Allbanks 1991-2009 19 20 Netherlands Allbanks 1980-2009 30 21 NewZealand Allbanks 1991-2009 19 22 Norway Allbanks 1980-2009 30 23 Poland Allbanks 1994-2008 15 24 Portugal Commercialbanks 1980-2008 29 25 SlovakRepublic Allbanks 1997-2009 13 26 Spain Allbanks 1980-2008 29 27 Sweden Allbanks 1980-2008 29 28 Switzerland Allbanks 1980-2008 29 29 Turkey Commercialbanks 1982-2009 28 30 UnitedKingdom Largecommercialbanks 1985-2008 24 31 UnitedStates† Allbanks 1980-2007 28 All 726 Average 23.42 Source:OwnelaborationbasedonOECDBankStatistics. Notes:†Canadaallbankschainedwithcommercialbanksfor1982-1987.Greeceallbankschainedwith largecommercialbanksfor1979-1988.Mexicoallbankschainedwithcommercialbanksfor1990-1999. USallbankschainedwiththesumofcommercial,savingandcooperativebanksfor1979. 47

Table2: NumberofObservationsandSamplePeriodforBenchmarkRegressionbyCountry Country Observations SamplePeriod 1 Australia 13 1991–2003 2 Austria 2 1998–1999 3 Belgium 25 1983–2007 4 Canada 25 1984–2008 5 Chile 14 1996–2009 6 CzechRepublic 5 2001–2005 7 Denmark 22 1981–2002 8 Finland 16 1988–2004 9 France 15 1990–2004 10 Germany 22 1981–2002 11 Greece 13 1986–2003 12 Hungary 8 2001–2008 13 Iceland 10 1994–2003 14 Ireland 9 1997–2005 15 Italy 24 1986–2009 16 Japan 18 1991–2008 17 Korea 17 1992–2008 18 Mexico 12 1995–2007 19 Netherlands 16 1994–2009 20 NewZealand 17 1992–2008 21 Norway 27 1981–2008 22 Poland 6 2001–2006 23 Portugal 11 1989–1999 24 SlovakRepublic 8 2000–2007 25 Spain 22 1981–2002 26 Sweden 25 1981–2005 27 Switzerland 28 1981–2008 28 UnitedKingdom 23 1986–2008 29 UnitedStates 27 1981–2007 All 480 1981–2009 Average 16.55 1989.72–2005.55 Min 2 1981–1999 Max 28 2001–2009 Source: OwnelaborationbasedonOECDBankStatistics,OECDMainEconomicIndicators,IFS,and NationalSources. 48

Table3: BankVariablesMeansbyCountry (inpercents) Country Δ‘ ROE CAP BSL it i,t 1 i,t 1 i,t 1 − − − Australia 5.711 9.152 10.096 7.096 Austria 5.990 8.003 4.621 16.025 Belgium 3.976 9.267 3.071 29.528 Canada 3.628 12.720 5.279 17.304 Chile 7.093 13.011 8.517 16.060 CzechRepublic 2.011 9.744 8.483 23.766 Denmark 5.244 6.774 7.628 24.411 Finland 2.369 0.014 6.820 16.673 France 1.255 6.150 4.260 16.710 Germany 4.572 6.114 3.793 17.598 Greece 13.733 14.109 5.732 33.412 Hungary 13.757 15.414 9.326 16.430 Iceland 13.134 8.737 7.321 13.562 Ireland 21.998 13.356 5.911 23.902 Italy 4.826 7.307 6.435 14.829 Japan -1.056 -1.992 3.951 19.669 Korea 12.331 -0.023 5.775 17.291 Mexico -2.156 6.920 7.349 26.933 Netherlands 6.768 10.864 3.878 21.291 NewZealand 8.270 16.752 5.700 11.114 Norway 7.509 5.033 5.457 15.747 Poland 6.805 10.240 9.492 22.104 Portugal 10.508 7.084 9.863 21.373 SlovakRepublic 4.234 12.174 7.325 25.821 Spain 5.194 8.600 7.862 18.756 Sweden 4.110 9.999 5.762 21.514 Switzerland 3.289 8.415 5.904 14.995 UnitedKingdom 9.240 13.102 4.560 14.950 UnitedStates 2.575 9.698 6.730 19.119 All 5.812 8.536 6.087 18.924 Source:OwnelaborationbasedonOECDBankStatisticsandOECDMainEconomicIndicators. 49

Table4: EconomicConditionsMeansbyCountry (inpercents) DEPOSIT PROVI- LENDING LONG-TERM STOCK ΔAGG. ASSETS Country COSTS SIONS RATE RATE RETURNS DEMAND TOGDP i,t 1 i,t 1 it it it it it − − Australia 8.758 0.704 7.060 4.835 3.236 3.832 105.178 Austria 8.607 0.733 5.284 3.950 0.214 2.282 247.809 Belgium 20.617 0.638 6.924 4.449 7.258 2.014 291.774 Canada 6.998 0.549 4.889 4.466 3.965 3.038 140.348 Chile 11.880 1.111 7.133 4.571 2.663 4.576 100.584 CzechRepublic 4.129 -1.628 4.066 2.465 14.235 3.234 109.361 Denmark 10.115 1.959 7.663 5.749 8.933 1.893 114.330 Finland 9.517 0.172 5.106 5.466 8.229 1.692 122.673 France 21.712 0.870 5.796 4.361 2.791 1.865 236.109 Germany 9.316 0.618 8.101 4.326 4.919 1.722 168.793 Greece 10.019 1.186 9.644 3.662 4.746 2.999 69.855 Hungary 8.298 0.411 4.165 1.432 4.473 3.404 73.632 Iceland 11.769 1.490 10.466 5.591 12.757 4.088 88.277 Ireland 9.511 0.196 0.997 1.567 7.517 6.659 337.898 Italy 11.190 1.197 6.517 4.371 1.165 1.452 155.749 Japan 2.907 0.564 2.450 2.098 -3.807 0.923 166.883 Korea 6.984 1.524 4.358 4.711 1.083 4.118 94.198 Mexico 20.288 1.962 5.880 4.845 9.665 2.929 46.236 Netherlands 9.225 0.305 2.511 2.704 1.642 2.213 384.141 NewZealand 6.863 0.198 7.904 4.535 1.412 3.609 137.906 Norway 9.879 0.924 6.335 4.179 8.587 2.689 80.465 Poland 7.280 1.881 7.118 4.382 11.236 2.945 57.889 Portugal 11.265 2.476 8.424 3.970 3.381 3.855 148.646 SlovakRepublic 5.707 -0.395 3.463 -0.349 14.402 4.507 85.934 Spain 9.180 1.406 4.886 4.523 6.883 3.146 139.707 Sweden 12.141 0.076 6.546 4.684 10.666 1.712 97.837 Switzerland 7.339 1.001 2.759 1.853 5.185 1.602 409.050 UnitedKingdom 6.881 0.912 4.345 4.206 3.317 2.844 149.411 UnitedStates 6.870 0.761 5.194 3.872 6.194 3.214 93.180 All 9.991 0.864 5.725 4.010 5.427 2.695 160.274 Source:OwnelaborationbasedonOECDBankStatistics,OECDMainEconomicIndicators,IFS,andNationalSources. 50

Table5: EstimationsbyOLS,FixedEffectsandDifferenceGMM (1lagofΔ‘ ) it DependentVariable: Δ‘ (1) (2) (3) (4) (5) (6) it OLS FE 2lags 6lags 2collapsed 6collapsed Δ‘ 0.318*** 0.188** 0.182** 0.182** 0.106 0.148* i,t 1 − (0.0590) (0.0799) (0.0745) (0.0745) (0.0799) (0.0802) ROE 0.0505 0.0446 0.0446 0.0451 -0.00231 -0.00934 i,t 1 − (0.0746) (0.0623) (0.0582) (0.0580) (0.0714) (0.0566) CAP -0.0741 -0.178 -0.203 -0.198 -0.434 -0.508 i,t 1 − (0.156) (0.145) (0.126) (0.128) (0.541) (0.392) CAP2 0.00751 0.0384* 0.0373* 0.0382* 0.0312 0.0341 i,t 1 − (0.0191) (0.0214) (0.0206) (0.0199) (0.0709) (0.0405) BSL 0.0759 0.177* 0.191* 0.194** 0.0149 0.0564 i,t 1 − (0.0537) (0.0991) (0.0980) (0.0961) (0.398) (0.320) DEPOSITCOSTS -0.243*** -0.0519 -0.0710 -0.0674 -0.222 -0.314 i,t 1 − (0.0695) (0.152) (0.147) (0.148) (0.236) (0.289) PROVISIONS -0.0681 -0.428 -0.484 -0.479 -0.727 -0.632 i,t 1 − (0.488) (0.533) (0.499) (0.498) (1.154) (0.738) LENDINGRATE 0.424** 0.106 0.0875 0.0939 1.500* 0.913 it (0.188) (0.271) (0.263) (0.261) (0.856) (0.731) LONGTERMRATE 0.491* 1.068** 1.150*** 1.148*** -0.0499 0.573 it (0.254) (0.390) (0.393) (0.393) (1.014) (0.837) STOCKRETURNS 0.0436* 0.0269 0.0298 0.0303 0.205*** 0.150*** it (0.0245) (0.0201) (0.0186) (0.0187) (0.0600) (0.0484) ΔAGG.DEMAND 1.250*** 1.079*** 1.101*** 1.097*** 0.694 0.515 it (0.188) (0.211) (0.196) (0.194) (0.489) (0.345) ASSETS/GDP 0.00548 -0.0309*** -0.0325*** -0.0324*** -0.103** -0.0761*** i,t 1 − (0.00389) (0.0111) (0.0113) (0.0110) (0.0463) (0.0291) Yeareffects Yes Yes Yes Yes Yes Yes Countryeffects1 No Yes Yes Yes Yes Yes H : CAP =CAP2 =0 [0.882] [0.021] [0.004] [0.004] [0.059] [0.018] 0 i,t 1 i,t 1 − − H : jointvalidityofmomentrestrictions 0 Sargantest [0.134] [0.146] [0.053] [0.252] Hansentest [1.000] [1.000] [1.000] [1.000] H : residualsareseriallyuncorrelated 0 Arellano-BondforAR(2) [0.004] [0.004] [0.013] [0.014] Arellano-BondforAR(3) [0.054] [0.054] [0.235] [0.170] Numberofinstruments 444 446 52 100 R2 0.462 0.435 Numberobservations 480 480 446 446 446 446 Numbercountries 29 29 29 29 29 29 Notes:1FixedEffects(FE)andDifferenceGMMregressionseliminatecountryeffectsbytakingdifferences.R2forFE correspondstothewithinR2.klagsmeansklagsareusedtoinstrumenteachexplanatoryvariable,i.e. x ,...,x i,t 1 i,t k areusedasinstrumentsforΔx whenx isapredeterminedvariableandx ,...,x areusedasinst−rumentsfo−r it it i,t 2 i,t 1 k Δx whenx isanendogenousvariable.Heteroskedasticityrobuststandarde−rrorsinpa−re−ntheses.P-valuesinbrackets. it it ***,**,*denotesignificantat1%,5%and10%,respectively. 51

Table6: EstimationsbyOLS,FixedEffectsandGMM (2lagsofΔ‘ ) it DependentVariable: Δ‘ (1) (2) (3) (4) (5) (6) it DifferenceGMM SystemGMM OLS FE 2collapsed 6collapsed 2collapsed 6collapsed Δ‘ 0.215*** 0.151** 0.153** 0.170** 0.243*** 0.232*** i,t 1 − (0.0632) (0.0630) (0.0742) (0.0711) (0.0603) (0.0623) Δ‘ 0.267*** 0.208*** 0.204*** 0.213*** 0.241*** 0.241*** i,t 2 − (0.0629) (0.0515) (0.0634) (0.0565) (0.0618) (0.0506) ROE 0.0519 0.0465 0.0296 0.0181 0.0598 0.0617 i,t 1 − (0.0614) (0.0578) (0.0815) (0.0564) (0.0651) (0.0549) CAP -0.173 -0.223* -0.747 -0.569** -0.389* -0.253 i,t 1 − (0.156) (0.124) (0.466) (0.278) (0.221) (0.225) CAP2 0.0127 0.0389** 0.00241 0.0221 0.0521 0.0603** i,t 1 − (0.0191) (0.0188) (0.0642) (0.0416) (0.0355) (0.0294) BSL 0.107* 0.211** 0.0661 0.168 0.0886 0.0703 i,t 1 − (0.0553) (0.0950) (0.421) (0.274) (0.185) (0.153) DEPOSITCOSTS -0.297*** -0.113 -0.367 -0.467 -0.486*** -0.398*** i,t 1 − (0.0765) (0.142) (0.266) (0.301) (0.158) (0.147) PROVISIONS -0.0787 -0.334 -0.0118 -0.00792 0.288 0.335 i,t 1 − (0.453) (0.495) (1.025) (0.672) (0.678) (0.463) LENDINGRATE 0.410** 0.0466 1.744 0.580 1.521* 0.454 it (0.187) (0.248) (1.078) (0.700) (0.840) (0.535) LONGTERMRATE 0.548** 1.163*** -0.0436 0.755 -0.405 0.826 it (0.269) (0.370) (1.191) (0.754) (0.895) (0.614) STOCKRETURNS 0.0493** 0.0347 0.225*** 0.152*** 0.243*** 0.162*** it (0.0230) (0.0208) (0.0677) (0.0460) (0.0602) (0.0416) ΔAGG.DEMAND 1.193*** 1.093*** 0.560 0.462 0.723* 0.551** it (0.164) (0.215) (0.434) (0.304) (0.380) (0.229) ASSETS/GDP 0.00477 -0.0291*** -0.0702 -0.0369 0.00939 0.00749 i,t 1 − (0.00399) (0.0104) (0.0549) (0.0353) (0.0138) (0.0118) Yeareffects Yes Yes Yes Yes Yes Yes Countryeffects1 No Yes Yes Yes Yes Yes Δ‘ +Δ‘ 0.482 0.359 0.356 0.383 0.483 0.473 i,t 1 i,t 2 − − H : CAP =CAP2 =0 [0.538] [0.028] [0.033] [0.048] [0.029] [0.067] 0 i,t 1 i,t 1 H : jointv − alidityofm−omentrestrictions 0 Sargantest [0.717] [0.742] [0.771] [0.886] Hansentest [1.000] [1.000] [1.000] [1.000] H : residualsareseriallyuncorrelated 0 Arellano-BondforAR(2) [0.691] [0.754] [0.983] [0.985] Arellano-BondforAR(3) [0.611] [0.642] [0.701] [0.700] Numberofinstruments 52 100 65 113 R2 0.510 0.467 Numberobservations 464 464 430 430 464 464 Numbercountries 29 29 29 29 29 29 Notes: 1FixedEffects(FE)regressionseliminatecountryeffectsbytakingfirstdi fferences. R2forFEcorrespondsto thewithinR2. klagsmeansklagsareusedtoinstrumenteachexplanatoryvariable,i.e. x ,...,x areusedas i,t 1 i,t k instrumentsforΔx when x isapredeterminedvariableand x ,...,x areusedasins−truments−forΔx when it it i,t 2 i,t 1 k it x isanendogenousvariable. Heteroskedasticityrobuststandard−errorsinp−a−rentheses. P-valuesinbrackets. ***,**, it *denotesignificantat1%,5%and10%,respectively. 52

Table7: EstimatesoftheEffectofBankFinancialPositiononCreditGrowth DependentVariable: Δ‘ (1) (2) (3) (4) (5) (6) it ROE CAP BSL ROE2 BSL2 Δ‘ 0.243*** 0.233*** 0.266*** 0.247*** 0.227*** 0.235*** i,t 1 − (0.0603) (0.0604) (0.0609) (0.0614) (0.0583) (0.0638) Δ‘ 0.241*** 0.241*** 0.249*** 0.235*** 0.230*** 0.235*** i,t 2 − (0.0618) (0.0581) (0.0607) (0.0661) (0.0607) (0.0590) ROE 0.0598 0.0554 0.165* 0.0568 i,t 1 − (0.0651) (0.0649) (0.0997) (0.0631) ROE2 0.00174* i,t 1 − (0.00103) CAP -0.389* -0.295 -0.465** -0.414* i,t 1 − (0.221) (0.264) (0.230) (0.244) CAP2 0.0521 0.0515 0.0558* 0.0453 i,t 1 − (0.0355) (0.0398) (0.0331) (0.0349) BSL 0.0886 0.0553 0.0957 0.116 i,t 1 − (0.185) (0.211) (0.184) (0.744) BSL2 -0.00152 i,t 1 − (0.0183) DEPOSITCOSTS -0.486*** -0.562*** -0.479*** -0.568*** -0.486*** -0.529*** i,t 1 − (0.158) (0.178) (0.184) (0.184) (0.150) (0.153) PROVISIONS 0.288 0.416 -0.0590 0.0967 0.550 0.375 i,t 1 − (0.678) (0.658) (0.611) (0.638) (0.608) (0.638) LENDINGRATE 1.521* 1.713* 1.770** 1.661** 1.339 1.802** it (0.840) (0.890) (0.865) (0.846) (0.857) (0.789) LONGTERMRATE -0.405 -0.737 -0.611 -0.921 -0.319 -0.652 it (0.895) (0.951) (0.879) (0.946) (0.867) (0.876) STOCKRETURNS 0.243*** 0.224*** 0.242*** 0.239*** 0.237*** 0.258*** it (0.0602) (0.0716) (0.0686) (0.0646) (0.0582) (0.0587) ΔAGG.DEMAND 0.723* 0.833** 0.893** 0.858** 0.620 0.865** it (0.380) (0.407) (0.402) (0.404) (0.384) (0.350) ASSETS/GDP 0.00939 0.00901 0.0106 0.00921 0.00892 0.0111 i,t 1 − (0.0138) (0.0126) (0.0140) (0.0121) (0.0129) (0.0133) Δ‘ +Δ‘ 0.483 0.473 0.515 0.482 0.457 0.470 i,t 1 i,t 2 − − H : CAP =CAP2 =0 [0.029] [0.076] [0.019] [0.023] 0 i,t 1 i,t 1 − − H : ROE =ROE2 =0 [0.189] 0 i,t 1 i,t 1 − − H : BSL =BSL2 =0 [0.950] 0 i,t 1 i,t 1 − − H : jointvalidityofmomentrestrictions 0 Sargantest [0.771] [0.693] [0.638] [0.689] [0.869] [0.871] Hansentest [1.000] [1.000] [1.000] [1.000] [1.000] [1.000] H : residualsareseriallyuncorrelated 0 Arellano-BondforAR(2) [0.983] [0.972] [0.922] [0.967] [0.859] [0.985] Numberofinstruments 65 56 59 56 68 71 Numberobservations 464 464 464 464 464 464 Numbercountries 29 29 29 29 29 29 Notes: SystemGMMestimatesusing,2collapsedlagsofexplanatoryvariablesasinstruments. Allmodelsconsider countryandyeareffects. Heteroskedasticityrobuststandarderrorsinparentheses. P-valuesinbrackets. ***,**,* denotesignificantat1%,5%and10%,respectively. 53

Table8: EstimatesUsingAlternativeMeasuresofBanks’Profits DependentVariable: Δ‘ (1) (2) (3) (4) it ROEeven ROAand ifE<0 ROA LEVERAGE Δ‘ 0.243*** 0.243*** 0.223*** 0.233*** i,t 1 − (0.0603) (0.0603) (0.0542) (0.0591) Δ‘ 0.241*** 0.241*** 0.233*** 0.236*** i,t 2 − (0.0618) (0.0618) (0.0599) (0.0602) ROE 0.0598 0.0594 i,t 1 − (0.0651) (0.0652) ROA 1.917 1.882 i,t 1 − (1.451) (1.845) LEVERAGE -1.570 i,t 1 − (1.849) CAP -0.389* -0.399* -0.433* -15.40 i,t 1 − (0.221) (0.223) (0.245) (21.27) CAP2 0.0521 0.0528 0.0505 0.799 i,t 1 − (0.0355) (0.0357) (0.0307) (1.142) BSL 0.0886 0.0884 0.0655 0.0630 i,t 1 − (0.185) (0.185) (0.180) (0.148) DEPOSITCOSTS -0.486*** -0.486*** -0.506*** -0.415* i,t 1 − (0.158) (0.158) (0.149) (0.217) PROVISIONS 0.288 0.285 0.561 0.0378 i,t 1 − (0.678) (0.677) (0.829) (1.095) LENDINGRATE 1.521* 1.522* 1.484* 1.475* it (0.840) (0.840) (0.841) (0.864) LONGTERMRATE -0.405 -0.407 -0.313 -0.379 it (0.895) (0.895) (0.841) (0.826) STOCKRETURNS 0.243*** 0.243*** 0.236*** 0.264*** it (0.0602) (0.0602) (0.0595) (0.0642) ΔAGG.DEMAND 0.723* 0.724* 0.603 0.730* it (0.380) (0.380) (0.425) (0.415) ASSETS/GDP 0.00939 0.00939 0.0112 0.00900 i,t 1 − (0.0138) (0.0138) (0.0144) (0.0149) Δ‘ +Δ‘ 0.483 0.483 0.457 0.469 i,t 1 i,t 2 − − H : CAP =CAP2 =0 [0.029] [0.029] [0.025] [0.692] 0 i,t 1 i,t 1 − − H : ROA =LEVERAGE =0 [0.490] 0 i,t 1 i,t 1 − − H : jointvalidityofmomentrestrictions 0 Sargantest [0.771] [0.771] [0.629] [0.810] Hansentest [1.000] [1.000] [1.000] [1.000] H : residualsareseriallyuncorrelated 0 Arellano-BondforAR(2) [0.983] [0.983] [0.984] [0.947] Numberofinstruments 65 65 65 68 Numberobservations 464 464 464 463 Numbercountries 29 29 29 29 Notes: SystemGMMestimatesusing,2collapsedlagsofexplanatoryvariablesasinstruments. Allmodelsconsider countryandyeareffects. Heteroskedasticityrobuststandarderrorsinparentheses. P-valuesinbrackets. ***,**,* denotesignificantat1%,5%and10%,respectively. 54

Table9: EstimatesUsingAlternativeMeasuresofBanks’Liquidity DependentVariable: Δ‘ (1) (2) (3) (4) (5) it restricted SEC+RES DEPOSITS BSL*SMALL sample ASSETS ASSETS Δ‘ 0.243*** 0.358*** 0.405*** 0.228*** 0.243*** i,t 1 − (0.0603) (0.0713) (0.0925) (0.0628) (0.0599) Δ‘ 0.241*** 0.0558 0.227*** 0.232*** 0.265*** i,t 2 − (0.0618) (0.0858) (0.0819) (0.0577) (0.0477) ROE 0.0598 0.414** -0.291*** 0.0681 0.0874 i,t 1 − (0.0651) (0.175) (0.0987) (0.0650) (0.0661) CAP -0.389* -0.963*** -0.674* -0.392* -0.358 i,t 1 − (0.221) (0.348) (0.394) (0.235) (0.250) CAP2 0.0521 0.0145 0.0292 0.0491 0.0472 i,t 1 − (0.0355) (0.0363) (0.0357) (0.0338) (0.0332) BSL 0.0886 0.191 i,t 1 − (0.185) (0.183) BSL SMALL 0.00874* i,t 1 i,t 1 − ∗ − (0.00526) (SEC+RES)/ASSETS -0.0588 i,t 1 − (0.131) DEPOSITS/ASSETS -0.174* i,t 1 − (0.0908) DEPOSITCOSTS -0.486*** -0.241 -0.644** -0.489*** -0.563*** i,t 1 − (0.158) (0.251) (0.313) (0.152) (0.171) PROVISIONS 0.288 -0.00191 -1.859* 0.289 0.593 i,t 1 − (0.678) (1.138) (1.037) (0.659) (0.582) LENDINGRATE 1.521* -0.531 0.0682 1.833** 1.824* it (0.840) (0.541) (0.531) (0.911) (0.952) LONGTERMRATE -0.405 0.556 1.279 -0.462 -0.405 it (0.895) (1.107) (0.819) (0.893) (0.818) STOCKRETURNS 0.243*** -0.0357 0.122 0.233*** 0.207*** it (0.0602) (0.0887) (0.0814) (0.0606) (0.0682) ΔAGG.DEMAND 0.723* -0.271 -0.771 0.868** 0.802** it (0.380) (0.785) (0.863) (0.379) (0.395) ASSETS/GDP 0.00939 -0.0233** -0.0205 0.0113 -0.00333 i,t 1 − (0.0138) (0.0116) (0.0166) (0.0150) (0.0143) Δ‘ +Δ‘ 0.483 0.414 0.632 0.460 0.508 i,t 1 i,t 2 − − H : CAP =CAP2 =0 [0.029] [0.00008] [0.046] [0.022] [0.068] 0 i,t 1 i,t 1 − − H : jointvalidityofmomentrestrictions 0 Sargantest [0.771] [0.002] [0.002] [0.779] [0.423] Hansentest [1.000] [1.000] [1.000] [1.000] [1.000] H : residualsareseriallyuncorrelated 0 Arellano-BondforAR(2) [0.983] [0.940] [0.171] [0.970] [0.992] Numberofinstruments 65 65 65 65 65 Numberobservations 464 249 249 464 464 Numbercountries 29 18 18 29 29 Notes: SystemGMMestimatesusing,2collapsedlagsofexplanatoryvariablesasinstruments. Allmodelsconsider countryandyeareffects. Heteroskedasticityrobuststandarderrorsinparentheses. P-valuesinbrackets. ***,**,* denotesignificantat1%,5%and10%,respectively. 55

Table10: EstimatesUsingAlternativeMeasuresofBanks’Capital DependentVariable: Δ‘ (1) (2) (3) (4) it Δ‘ 0.243*** 0.240*** 0.237*** 0.232*** i,t 1 − (0.0603) (0.0586) (0.0635) (0.0625) Δ‘ 0.241*** 0.240*** 0.244*** 0.236*** i,t 2 − (0.0618) (0.0626) (0.0619) (0.0605) ROE 0.0598 0.0474 0.0555 0.0591 i,t 1 − (0.0651) (0.0660) (0.0628) (0.0644) CAP -0.389* i,t 1 − (0.221) CAP2 0.0521 i,t 1 − (0.0355) CAP (CAP P25) 0.0754 i,t 1 i,t 1 − ∗ − ≥ (0.807) CAP (CAP 4%) 0.292 i,t 1 i,t 1 − ∗ − ≥ (0.363) CAP (CAP 6%) 0.258 i,t 1 i,t 1 − ∗ − ≥ (0.218) BSL 0.0886 0.105 0.0919 0.0753 i,t 1 − (0.185) (0.212) (0.185) (0.177) DEPOSITCOSTS -0.486*** -0.478*** -0.509*** -0.551*** i,t 1 − (0.158) (0.174) (0.168) (0.171) PROVISIONS 0.288 0.198 0.427 0.504 i,t 1 − (0.678) (0.671) (0.708) (0.715) LENDINGRATE 1.521* 1.404* 1.561* 1.707** it (0.840) (0.798) (0.869) (0.861) LONGTERMRATE -0.405 -0.476 -0.464 -0.544 it (0.895) (0.889) (0.931) (0.933) STOCKRETURNS 0.243*** 0.210*** 0.233*** 0.241*** it (0.0602) (0.0551) (0.0640) (0.0650) ΔAGG.DEMAND 0.723* 0.669* 0.740* 0.883** it (0.380) (0.368) (0.400) (0.375) ASSETS/GDP 0.00939 0.00550 0.00917 0.0128 i,t 1 − (0.0138) (0.0112) (0.0133) (0.0146) Δ‘ +Δ‘ 0.483 0.479 0.482 0.467 i,t 1 i,t 2 − − H : CAP =CAP2 =0 [0.029] 0 i,t 1 i,t 1 − − H : jointvalidityofmomentrestrictions 0 Sargantest [0.771] [0.542] [0.767] [0.700] Hansentest [1.000] [1.000] [1.000] [1.000] H : residualsareseriallyuncorrelated 0 Arellano-BondforAR(2) [0.983] [0.985] [0.947] [0.998] Numberofinstruments 65 62 62 62 Numberobservations 464 464 464 464 Numbercountries 29 29 29 29 Notes: System GMM estimates using, 2 collapsed lags of explanatory variables as instruments. All modelsconsidercountryandyeareffects. Heteroskedasticityrobuststandarderrorsinparentheses. Pvaluesinbrackets.***,**,*denotesignificantat1%,5%and10%,respectively. 56

Table11: RobustnessChecksI:DefinitionofDepositCosts,ProvisionsandOrganizationofBankSector DependentVariable: Δ‘ (1) (2) (3) (4) (5) (6) it DEPOSIT restricted LOAN restricted RATE LARGE sample PROVISIONS sample Δ‘ 0.243*** 0.300*** 0.350*** 0.350*** 0.247*** 0.225*** i,t 1 − (0.0603) (0.0708) (0.0709) (0.106) (0.0757) (0.0732) Δ‘ 0.241*** 0.265*** 0.0783 0.212** 0.302*** 0.306*** i,t 2 − (0.0618) (0.0477) (0.0841) (0.0825) (0.0533) (0.0551) ROE 0.0598 0.0607 0.407** -0.301*** -0.0339 0.0414 i,t 1 − (0.0651) (0.0633) (0.183) (0.0730) (0.0594) (0.0614) CAP -0.389* -0.110 -0.876*** -0.648* -0.135 -0.256 i,t 1 − (0.221) (0.186) (0.310) (0.358) (0.234) (0.287) CAP2 0.0521 0.0790** 0.0243 0.0142 0.107*** 0.102*** i,t 1 − (0.0355) (0.0317) (0.0367) (0.0409) (0.0347) (0.0377) BSL 0.0886 0.395** 0.163 0.204 0.538*** 0.453** i,t 1 − (0.185) (0.186) (0.190) (0.178) (0.179) (0.201) DEPOSITCOSTS -0.486*** -0.370 -0.297 -0.388*** -0.506*** i,t 1 − (0.158) (0.228) (0.208) (0.140) (0.116) DEPOSITRATE 1.280 it (0.823) PROVISIONS 0.288 -0.0816 0.155 -1.972** 0.324 i,t 1 − (0.678) (0.859) (1.105) (0.854) (0.754) LOANPROVISIONS -1.476* i,t 1 − (0.819) LENDINGRATE 1.521* 0.471 -0.330 0.0128 1.366** 1.711** it (0.840) (0.794) (0.470) (0.471) (0.652) (0.840) LONGTERMRATE -0.405 -0.284 0.634 1.184 -0.252 -0.0590 it (0.895) (1.250) (0.714) (0.876) (0.676) (0.773) STOCKRETURNS 0.243*** 0.225*** -0.00534 0.0745 0.228*** 0.231*** it (0.0602) (0.0463) (0.0797) (0.0960) (0.0686) (0.0695) ΔAGG.DEMAND 0.723* 0.928*** -0.178 -0.599 0.746*** 0.721** it (0.380) (0.356) (0.735) (0.797) (0.242) (0.315) ASSETS/GDP 0.00939 0.00618 -0.0196** -0.0221* 0.0151 0.0189 i,t 1 − (0.0138) (0.0134) (0.00992) (0.0132) (0.0104) (0.0145) LARGE -0.0645 i,t 1 − (0.0585) Δ‘ +Δ‘ 0.483 0.565 0.429 0.562 0.549 0.530 i,t 1 i,t 2 − − H : CAP =CAP2 =0 [0.029] [0.031] [0.000] [0.079] [0.00002] [0.000001] 0 i,t 1 i,t 1 − − H : jointvalidityofmomentrestrictions 0 Sargantest [0.771] [0.648] [0.002] [0.0005] [0.317] [0.206] Hansentest [1.000] [1.000] [1.000] [1.000] [1.000] [1.000] H : residualsareseriallyuncorrelated 0 Arellano-BondforAR(2) [0.983] [0.433] [0.894] [0.223] [0.669] [0.664] Numberofinstruments 65 65 68 65 65 65 Numberobservations 464 443 254 254 354 354 Numbercountries 29 29 29 29 29 29 Notes:SystemGMMestimatesusing,2collapsedlagsofexplanatoryvariablesasinstruments.Allmodelsconsidercountry andyeareffects.Heteroskedasticityrobuststandarderrorsinparentheses.P-valuesinbrackets.***,**,*denotesignificant at1%,5%and10%,respectively. 57

Table12: RobustnessChecksII:ControlsforRealActivity DependentVariable: Δ‘ (1) (2) (3) (4) it Δ‘ 0.243*** 0.266*** 0.254*** 0.216*** i,t 1 − (0.0603) (0.0629) (0.0636) (0.0605) Δ‘ 0.241*** 0.243*** 0.219*** 0.253*** i,t 2 − (0.0618) (0.0635) (0.0724) (0.0614) ROE 0.0598 0.0804 0.0333 0.0694 i,t 1 − (0.0651) (0.0658) (0.0710) (0.0684) CAP -0.389* -0.490** -0.547* -0.343 i,t 1 − (0.221) (0.200) (0.314) (0.217) CAP2 0.0521 0.0608 0.0354 0.0598** i,t 1 − (0.0355) (0.0388) (0.0338) (0.0296) BSL 0.0886 0.140 0.256 0.299* i,t 1 − (0.185) (0.219) (0.235) (0.182) DEPOSITCOSTS -0.486*** -0.481*** -0.402 -0.266 i,t 1 − (0.158) (0.166) (0.248) (0.225) PROVISIONS 0.288 0.362 -0.0184 0.498 i,t 1 − (0.678) (0.702) (0.624) (0.666) LENDINGRATE 1.521* 1.081 1.629* 1.317* it (0.840) (0.861) (0.890) (0.766) LONGTERMRATE -0.405 -0.187 -0.892 -0.626 it (0.895) (0.947) (0.916) (0.865) STOCKRETURNS 0.243*** 0.280*** 0.251*** 0.242*** it (0.0602) (0.0733) (0.0702) (0.0482) ASSETS/GDP 0.00939 0.00526 0.00311 0.00264 i,t 1 − (0.0138) (0.0145) (0.0114) (0.0120) ΔAGG.DEMAND 0.723* 0.771* it (0.380) (0.418) ΔGDP 0.340 it (0.600) ΔCONSUMPTION -0.0997 it (1.062) ΔINVESTMENT 0.236 it (0.402) ΔGOVERNMENT 1.395* it (0.785) INFLATION -0.404 i,t 1 − (0.287) UNEMPLOYMENT -0.398 it (0.344) Δ‘ +Δ‘ 0.483 0.509 0.474 0.469 i,t 1 i,t 2 − − H : CAP =CAP2 =0 [0.029] [0.003] [0.093] [0.011] 0 i,t 1 i,t 1 − − H : ΔCONSUMPTION =ΔINVESTMENT =ΔGOVERNMENT =0 [0.028] 0 it it it H : jointvalidityofmomentrestrictions 0 Sargantest [0.771] [0.889] [0.942] [0.676] Hansentest [1.000] [1.000] [1.000] [1.000] H : residualsareseriallyuncorrelated 0 Arellano-BondforAR(2) [0.983] [0.986] [0.728] [0.898] Numberofinstruments 65 65 71 71 Numberobservations 464 464 464 462 Numbercountries 29 29 29 29 Notes: SystemGMMestimatesusing,2collapsedlagsofexplanatoryvariablesasinstruments. Allmodelsconsidercountryand yeareffects.Heteroskedasticityrobuststandarderrorsinparentheses.P-valuesinbrackets.***,**,*denotesignificantat1%,5% and10%,respectively. 58

Figure1: PredictedCreditGrowthbyCAP. (a)FixedEffects (b)SystemGMM 2 2 ∆ l FE system GMM it 1.5 1.5 1 1 0.5 0.5 0 0 −0.5 +1 s.d. mean −1 s.d. −0.5 −1 0 2 4 6 8 10 0 2 4 6 8 10 CAP CAP i,t−1 BasedonpointestimatesfromFEestimation,Table Based on point estimates from system GMM es- 6column2: 0.223CAP+0.0389CAP2. timation, Table 6 column 5: 0.389 CAP + − − 0.0521CAP2. 59