P ROBLEM S TATEMENT
Commercial banks play a crucial role in the economy as key financial intermediaries, primarily by mobilizing and lending money to households, businesses, and other economic entities A common challenge faced by banks is the emergence of credit risk when borrowers are unable to effectively utilize the funds they have borrowed This risk often arises when banks lower their credit standards to attract more borrowers, potentially leading to an increase in non-performing loans (NPLs) in the future However, if the expansion of loans is driven by a genuine increase in demand, this growth may not necessarily result in bad loans Thus, under certain circumstances, credit growth can be an indicator of credit quality.
During a recession, the financial market, particularly the banking system, faces significant challenges Commercial banks often strive to increase credit growth to meet profit objectives, while households and firms borrowing from these banks encounter difficulties in their business activities This situation can lead to a decline in credit quality, raising concerns about whether the banks' focus on boosting credit growth will be effective or if it will simply result in higher non-performing loans (NPLs), ultimately leading to losses rather than profits.
Thed e t e r m i n a t i o n o f t h e e f f e c t o f c r e d i t g r o w t h o n c r e d i t q u a l i t y isg e t t i n g moreandmoreimportantfornotonlycommercialbanksbutalsocentralbank andp o l i c y makers.Forcommercialbanks,itmayhelpthemtoconsidertheappropriate timeoflooseningortighteningcreditstandardsandthedecisiontoexpandorlimitlend
2 ingactivities.F o r c e n t r a l b a n k , i t w i l l h e l p t o c o n t r o l t h e l o a n s g r o w t h o f commercialbanks.Thisistopreventpotentialbankingcrisiswhenthecreditqualityi s t o o l o w I n somec a s e s , u n d e r s t a n d i n g t h e t r u e i n f l u e n c e o f c r e d i t g r o w t h o n c r e d i t qualitycouldhelp torecognizetherealstateofthe economyinorderto applym a c r o e c o n o m i c policiesmoreefficiently.
According to a report by the State Bank of Vietnam, Dong Nai branch (2015), the total loan balance currently stands at approximately 100 billion VND, primarily concentrated in key sectors and industries within Dong Nai province The overdue loans are maintained at a safe ratio of 2.32 percent; however, businesses continue to encounter significant challenges as commercial banks implement new debt classification standards Additionally, commercial banks have been found to violate credit regulations, including careless appraisal of credit documents, misvaluation of customer financial capacity and collateral, and inadequate supervision of capital usage Given these circumstances, commercial banks in Dong Nai may face a heightened risk of potential credit issues as their lending activities expand.
RESEARCHOBJECTIVEANDQUESTION
Thisresearchistoinvestigatetheinfluence ofbanks’creditgrowthonth eircr ed it q u a l i t y u n d e r t h e c o n t r o l o f somec h a r a c t e r i s t i c s o f b a n k s T o a c h i e v e t h e researchobjective,thisstudyattemptstoanswerthefollowingquestion:
Doesa p o s i t i v e c h a n g e i n t h e c o m m e r c i a l b a n k s ’ c r e d i t g r o w t h l e a d t o a nega ti ve changeinbanks’creditqualityinthecaseofDongNaibankingsystem
RESEARCHSCOPEANDMETHODOLOGY
Theresearchiscarriedoutinthescopeofcreditgrowthandcreditqualityof29c ommercialbanksinDongNaiprovince,Vietnam.Thedataiscollectedinthep er i o d from2009Q3to2014Q1.
Themainmethodologyofthisstudyisquantitativeanalysis.Due totheav ai lab i l i ty ofp a n e l d a t a , t h e l i m i t a t i o n s o f commone s t i m a t i o n m e t h o d s a n d t h e o bjectiv e ofcapturingthechangesofbanks’creditgrowthandcr editquality,thisresearchappliesthemethodofDifferenceGMMforthedynamicpanel datamodel.B e s id e s , thedynamicmodelcouldbeusedtogeneratelong– runcoefficientswhichr e f l e c t theequilibriumoftheeffectsofcreditgrowth.
T HESIS S TRUCTURE
Thisthesis i n c l u de s f i v e cha pte rs C h a p t e r 1 i n t r o d u c es t h e bac kg ro u nd andmotivationoftheresearch ontheeffectofcreditgrowthoncreditquality.Chapter2r e v i e w s r e l a t e d t h e o r i e s , p r e v i o u s e m p i r i c a l s t u d i e s a n d b u i l d s a c o n c e p t u a l frameworkf o r t h e r e s e a r c h C h a p t e r 3 p r e s e n t s t h e d a t a c o l l e c t i o n methoda n d q u a n t i t a t i v e techniquesforproducingnecessaryresults.Chapter4showstheresultsa n d d i s c us s i o ns C h a p t e r 5 s u m m a r i ze s m a i n r e s e a r c h f i n d i n g s , b r i n g s o u t policyimpli cations,raisessomelimitationsandsuggestsfurtherstudies.
Thisc h a p t e r i n t r o d u c e s t h e t w o maint h e o r i e s r e l a t e d t o t h e e f f e c t o f c r e d i t g ro wth o n a n d c r e d i t quality.F i r s t , t h e macroeconomicc o n t e x t f o r b a n k i n g i s toe x p l a i n howthiseffectcouldoccur.Second,theso– calledthreeshiftsaretoexplaint h e detailchannelsinwhichcreditgrowthcouldaf fectcreditqualityinthecreditmarket.T h i s c h a p t e r a l s o p r e s e n t s s o m e t h e o r i e s a b o u t t h e e f f e c t o f someb a n k c h a r a c t e r i s t i c s oncreditqualityas control factors.Besides, somerelatedpreviouse m p i r i c a l studiesandtheconcep tualframeworkforthisresearcharealsopresented.
THEMACROECONOMICCONTEXTFORBANKING
MainCharacteristicsofBanks
Banks play a crucial role as financial intermediaries, significantly impacting the economy, particularly in finance and monetary matters As a distinct industry within the economy, banks possess unique characteristics that set them apart from other sectors This research aims to analyze the banking system within the macroeconomic context by introducing three main characteristics of banks that closely relate to credit growth and credit quality.
Banksh a v e e x t r e m e l y h i g h l e v e r a g e (1).B a n k s m o s t l y u s e o t h e r p e o p l e ’ s mo neyfort h e i r p o r t f o l i o S i m i l a r l y , banksp r i m a r i l y mobiliz ec a p i ta l f o r lendingactivities.AccordingtoGavinandHausmann(1996),bankleve ragehastwoimplications.First,bankoperationsareverysensitivetothevolatilityoft hem a c r o e c o n o m i c determinantsduetoverythincapital.Theymaybecomeinsol ventaft er smallnegativechangesoftheeconomy.Second,highleveragemaybringtoa p r o b l e m relatingtothebenefitofbankshareholdersanddebt–holders.Bank managersoftengenerateriskyportfoliotobringthehighestbenefitforshareholdersw h i l e debt–holdersislimitedintheircapitalrecoveryincaseofinsolvency.
Banks often face liquidity challenges due to the mismatch between the terms of deposit liabilities and loan assets While borrowers, such as businesses and households, require long-term financing, depositors can withdraw their funds at any time, even from time deposits, albeit at lower interest rates Additionally, attempts by banks to manage loan terms are complicated by borrowers' extended repayment periods Although borrowers may temporarily refinance their loans by taking new ones to pay off old debts, this practice can be illegal and detrimental to their profitability, ultimately leading to a decline in credit quality Consequently, banks must strategically plan for additional reserves to mitigate potential illiquidity caused by adverse macroeconomic shocks.
Bankscannotevaluateexactlytheir borrowersintheexpansionarypha seoftheeconomy(3).GavinandHausmann(1996)indicatedthat“goodtimesareb adt i m e s f o r l e a r n i n g ” a b o u t t h e t r u t h o f financialc a p a c i t y o f theb o r r o w e r s T h e a d v a n t a g e s o f t h e economymaybeo n e o f t h e r e a s o n s o f l e n d i n g b o o m s T h e borrowerscouldeasilyborrowmoneyfromabanktoser vicethedebtsinanotherbank.Therefore,mostoftheborrowers appearingood statewithbanksalthough t hei r f i n a n c i a l c a p a c i t y mayb e d i f f e r e n t I n t h i s c a s e , b a n k s h a v e d i f f i c u l t y ind e t e r m i n i n g whichloansmaypotenti allybecomeNPLs.
Thesecharacteristics abovearet oimplythatt he decision ofb oos ti ng cr editg r o w t h shouldbeconsideredcarefullyespeciallyinthedisadvantageousconditio nso f macroeconomicenvironment.Bankshaveveryhighprobabilityinthedeclineofc r e d i t quality,whichisthesourceofilliquidityaswellasbankingcrisis.
ShockandVulnerabilityofBankingSystem
For banks to effectively lend mobilized money, it is essential that the growth rate of deposit liabilities exceeds the deposit interest rate If this condition is not met, banks can still address the issue by requesting borrowers to repay their matured debts However, banks face limitations in doing so, as it heavily relies on borrowers' repayment capabilities This situation can lead to a significant transfer of resources from the banking system to depositors through withdrawals and interest payments If the volume of this resource transfer is substantial, it could induce a shock to the banking system, potentially leading to its collapse and creating systemic vulnerabilities (Gavin and Hausmann, 1996).
TheEffectofMacroeconomicDeterminants
Theshockfromlargechangeinthenetresourcestransfermayoriginatefromthec hangesoftheeconomy.Whenthereisanegativeeconomicsurprisefromoneorsome macroeconomicdeterminants,therewouldbetwocases:
(ii)banksarelimitedtoinvestingactivitiesespeciallylendingandbecomeilliquid d u e tothedeclineindepositdemandorincreaseinwithdrawal.Bothcasesleadtob ankingcrisisintheformofinsolvency.Inthefirstcase,creditqualitydecreases an d b a n k s a r e n o t ablet o r e c o v e r e n o u g h p r i n c i p a l a n d i n t e r e s t tof i n a n c e t h e i r depositl i a b i l i t i e s I n t h e s e c o n d c a s e , b a n k s h a r d l y mee tt h e demando f w i t h d r a w a l s Theseconsequencesmayleadtopotentialfinancialvulner abilityinthef u t u r e However,ifbankscanboosttheirmobilizingactivities,theyw ouldhaveasourceofliquidityforwithdrawaldemandofdepositors.Besides,bankswould havemoretimetodealwiththeirNPLs.
CreditGrowthandVulnerability ofBankingSystem
Asdiscussedabove,themacroeconomicdeterminantsdonothavedirectan dcompletee f f e c t o n t h e b a n k i n g s y s t e m T h e i r a f f e c t ism a i n l y ont h e b u s i n e s s e n v i r o n m e n t a n d t h e d e p o s i t o r s ’ b e h a v i o r s T h e c o r e q u e s t i o n i s t h e r e a s o n whybankings y s t e m b e c o m e s t o o f r a g i l e t o s u f f e r f r o m t h e n e g a t i v e c h a n g e s o f t h e economy.I t is easytou n de r s t a n d t ha t b o r r o w e r s ’b usi ness activities a r e stronglyi n f l u e n c e d bythesechanges.Ifba nkshaveverycloserelationshipwiththeirb o r r o w e r s , a b s o l u t e l y , t h e y a r e a l s o i n f l u e n c e d R a p i d c r e d i t g r o w t h w o u l d b e a typicalp r o x y fo r t h i s c l o s e r e l a t i o n s h i p T h e moreb a n k s e x p a n d theirl o a n s , t h e moretheyrelyontheirborro wers.
Boostingcreditgrowthiscloselyrelatedtothethirdcharacteristicofbanks.O n c e t heyrecognizedthegoodappearancein theabilityoftheirborrowers,theyarew i l li n g tolendmore.Thiscreatesalinkbetweencredit growthandthevulnerabilityo f thebankingsystem.However,creditgrowthshouldbec onsideredasasignalofeconomicdevelopmentthanacauseofvulnerability.Then extquestionisinwhatc i r c u m s t a n c e s c r e d i t g r o w t h p e r f o r m s i t s n e g a t i v e a s p e c t s T h e a n s w e r w o u l d b e co n c er n ed aboutinformationproblems.
GavinandHausmann(1996)believedthat“… itisverydifficultforbankerstoo b t ai n informationaboutthecreditworthinessofborrowers”( p.14).First,duetotheeconomice x p a n s i o n , t h e b o r r o w e r s c a n p e r f o r m w e l l o n t h e i r c a p i t a l a n d g a i n p o s i t i v e cashflow.Thiswouldbeanadvantage opportunitytoofferloansnotonlyf o r theexistingcustomersbutalsonewborrower s.Bankswouldhaveverylimitedin format io n abouttheirnewborrowers.Thus,thepr obabilityofmisevaluatingthemm a y be q u i t e h i g h , w h i c h c a u s e s p o t e n t i a l d e c l i n e i n c r e d i t q u a l i t y i n t h e f u t u r e S e c o n d , theplentyofloanssupplyduringth eeconomicexpansionhelpstheb o r r o w e r s toapproachmorelenders.Asstatedabove,banksoffertheloansandtheb o r r o w e r s usetheseloansasasourceofpayingdebtsin otherbanks.Theseloansa c c i d e n t a l l y a n d a d v e r s e l y impactono t h e r b a n k s ’ i n f o r m a t i o n , w h i c h c r e a t e an
Macroeconomic effects informationexternalityinthecreditmarket.Thistypeofexternalityalsoleads toc r e d i t misevaluationandpotentiallylowcreditquality.
Figure2.1summarizesthemacroeconomiccontextforbanking.Thescopeofth i sr e s e a r c h c o n c e n t r a t e s o n t h e “ l e n d i n g ” a n d “ p a y i n g d e b t s ” d i r e c t i o n i n t h i s figure.Thenegativerelationship ofc red it growth andcredit quality mightreflect two situations.Firstly,theadverseshocksfrommacroeconomicde terminantswouldmakebusinessactivities becomeinefficient,whichobstructst heability ofpayingd e b t s Secondly,thegoodsignalsintheexpansionaryphase oftheeconomymightc r e a t e informationexternalitiesforbankstoevaluatetheircustomers.
CREDITGROWTHANDCREDITQUALITYTHROUGHDIFFERENTSHI FTS
Theoretically,t h e n a t u r e o f c r e d i t g r o w t h m a y n o t r e l a t e t o i t s q uality.Itmeanscreditgrowthmightnotdirectlyhaveanyinfluenceonthechangein creditquality.However,theamount ofloanswhichbanksdecideto lendwoul ddependg r e a t l y ontheperformanceofthemselvesandtheircustomers.Forinsta nce,banksu n d e r e s t i m a t e theriskoftheirborrowersandarewillingtolend more.Therefore,therelationshipofcreditgrowthandcreditqualitymightexist.
Oneoftheearlieststudiesonthetheoreticallinkbetweencreditgrowth a ndcreditqualityispresentbyClair(1992).Thislinkcomesfrombanksloweringtheirc r e d i t s t a n d a r d s t o a t t r a c t moreb o r r o w e r s T h i s a c t i o n m a y l e a d t o l o w c r e d i t q u al i t y inthefuture.Besides,whenbanksboostcreditgrowthbutthe ydonothaveany appropriatestrategiestoadministratetheirborrowers’loansusage.Creditqualityw oulddecline.
Clair(1992)alsoindicatedthatcreditgrowthmaypositivelycorrelatetocreditq u a l i t y duringt he rec ove ry orexp ans io np hases oft heec on om y orthest ru ct ur al c h a n g e s int h e f i n a n c i a l markets– f o r e x a m p le , r e d u c i n g b a r r i e r s b e t w e e n b a n k s a n d borrowerstoexpandcr editgrowthandreducecreditriskthroughdiver sif icat ion
Keeton(1999)haddevelopedatheoryaboutdifferentshiftstoinvestigatethee ff ect o f c r e d i t g r o w t h o n c r e d i t q u a l i t y T h i s s t u d y exp la ine d b o t h n e g a t i v e a n d positiverelationshipbetweencreditgrowthandcreditquality.
Thisrelationship isfirstlyexplainedbyasup pl y shiftintheloanmarket.I nt h i s research,s u p p l y s h i f t meansb a n k s h a v e d e c i s i o n o n t h e w i l l i n g n e s s t o l e n d moreand there are twowaysfor themto carryout Thefirst is to reduce the lendingr a t e ofnewloansandthesecondistolowerthecreditstandardsoftheseloans.Tomakelendingbecomeeasier,bankswouldoverestimatethevalueofcollatera lsofthel o a n s , a c c e p t t o l e n d customersw i t h l o w f i n a n c i a l c a p a c i t y o r g o t h r o u g h
S 2 D z 2 z 1 L 1 L 2 projectso f w h i c h c a s h – flowstatementisn o t a p p r a i s e d c a r e f u l l y T h e s e actionsl o w e r t h e c r e d i t s t a n d a r d s a n d p u t b a n k s i n t o a h i g h c h a n c e o f l e n d i n g t o t h e bor rowerswithlowcredit– worthiness.Theloanstotheseborrowersbecomelowq u a l i t y credit.
Undertheassumptionsthatbanksreducethelendingrateaswellaslowerthecred its t a n d a r d s o f customers,t h e o c c u r r e n c e o fs u p p l y s h i f t w h i c hb o o s t c r e d i t g r o w t h willtendtoresultinthelowcreditquality.
F IGURE 2.2 Supplyshift r eExpected rateofreturnfromloansz Measureofcreditstandards
Figure2.2presentshowthesupplyshifthaseffectontotalamountoflendingan d th elevelofcreditstandards.Intheleft–handside,the expectedrateofreturnofb an k s isafunctionofcreditstandards.Thisfigureassumesthatt hecredit– standardc o u l d bemeasuredinnumberzonthehorizontalaxis.Thehighvalueofz showst h at theborrowersareingoodstateofservicingdebts,forexample,theyhavehi ghvalueontheircollateralsortheirinvestmentprojectissafe.Banks’lendingdecision
11 e e e ismadebasingontheexpectedrateofreturnfromloanswhichismeasuredonthev e rt i cal a x i s T h i s e x p e c t e d r a t e o f r e t u r n d e p e n d s o n b o t h l e n d i n g r a t e a n d d e b t servicingcapacity.G o o d borrowerswouldbringhighexpectedr a t e ofreturnf o r b a n k s anditmightbethesameasthelending rate.If thereareanysignsof notgoodborrowers, thebanks’expectedrateofreturnwouldbelessthanthelendingrate.
According to credit standards, banks can determine a maximum expected rate of return represented by the curve r(z) As the lending rate increases, banks' expected rate of return generally rises; however, this increase has its limits Good borrowers maintain sufficient financial capacity to repay their debts despite rising rates If lending rates continue to escalate, borrowers may resort to investing in riskier projects in hopes of achieving higher returns, which could lead to inferior borrower quality and stagnate banks' expected rates of return Consequently, the upward-sloping curve r(z) illustrates the maximum expected return banks can anticipate, as they seek to earn more from borrowers with better credit profiles by offering higher lending rates.
Thecurver e ሺzሻcouldalsobeanalyzedfromthesideofexpectedratereturn.F o r anyg ivenvalueofr e ,therewouldbeaminimumcreditstandardlevelofthe borrowers.F o r e x a m p l e , a t t h e e q u i l i b r i u m p o i n t i n t h e l o a n market,b a n k s w i l l expectforr 1 Itiscertainthatbankscouldnotgiveanycredittoanyborrowerswithlow erthanz
1b e c a u s etheexpectedrateofreturnwillbelessthanr 1no matterhow highthelendingrate is.Allborrowersfromz1andhighercould receiveloanand wouldbechargedalendingratewhichishighenoughforbanktoreceiver 1 Themi nimumlevelofcreditstandardswouldbeathresholdforbankstodecidewhether theylendo r n o t T h e h i g h e r t h e e x p e c t e d r a t e o f r e t u r n b a n k s d e s i r e , t h e h i g h e r thresholdofcreditstandardstheysettotheircustomers.
The loan market, depicted on the right-hand side of Figure 2.2, influences banks' expected rates of return Banks are more inclined to lend when their anticipated returns increase, resulting in an upward-sloping supply curve Conversely, the demand curve slopes downward for two main reasons First, while banks can charge higher lending rates based on expected returns, this increases the cost of capital for borrowers, leading to decreased borrowing Second, as banks' expected rates of return rise, the threshold for credit standards also increases, reducing the number of borrowers who qualify.
Theloanmarketisintheequilibriumwhenthebankloansupplyequalstothel o a n d emand.Beforethesupplyshift,thesupplycurveisS1S1.Attheequilibrium, banks’expectedrateofreturnisr e a n dthetotalamountofloansisL1.
When banks aim to increase their total loan amounts, they often lower their credit standards to attract more borrowers, resulting in a rightward shift of the supply curve from S1 to S2 This shift leads to an increase in total lending from L1 to L2, accompanied by a decrease in the expected rate of return from r1 to r2 Consequently, banks can afford to charge lower lending rates for creditworthy borrowers while also reducing the credit standards threshold to reach a broader audience However, this decline in credit standards can lead to a higher number of borrowers with lower debt servicing capacity, resulting in non-performing loans (NPLs) and diminished credit quality.
Keeton (1999) posited that an increase in lending not driven by a supply shift could positively impact credit quality for two key reasons Firstly, the expansion in loans may stem from a positive shift in borrowers' demand, often resulting from strategic decisions to alter capital structures for improved cash flow, thereby enhancing repayment capacity and credit quality Secondly, this loan expansion can also arise from a productivity shift, where favorable conditions in borrowers' business activities lead to initial credit growth, subsequently improving credit quality.
Fordemandshift,undertheconsumptionthatincreaseintheborrowers’loansdema ndd o e s n o t r e l a t e d t o t h e i r g o o d n e s s inf i n a n c i a l c a p a c i t y – f o r e x a m p l e , r e q u i r i n g l o a n s fromb a n k s t o a v o i d h i g h i n t e r e s t r a t e i n t h e c a p i t a l marketo r r e st r u c tu r i n g capitaltoreachoptimalleveragerati o,thedemandshiftwhichboostc r e d i t growthwillleadtohighcreditquality.
2 choicefortheborrowerstoachievetheirobjectivesof– forexample,restructuringc a p i t a l Therefore,thedemandshiftalsoraisesthecreditstan dards.
Ascanbeseenin theright– handdiagram ofF i g u r e 2 3,t he increasein the loansdemandshiftsthecurve
D1D1totheright,D2D2.Thetotalamountofloans increasesf r o m L1toL2andt h e e x p e c t e d r a t e o f returnr a i s e s fromr e tor e
However,thischangedoesnothaveanyinfluenceonthecurver e ሺzሻintheleft– handdiagram.Thus,theincreaseinexpectedrateofreturnalsotightensthe minimumcreditstandards fromz1toz2.Inotherwords,whentheloansdemands increases,bankwillexpectmorereturnbyraisingbothlendingrateandthem i n i m u m cre dits t a n d a r d s T h i s a c t i o n h e l p s b a n k s t o a v o i d someb a d b o r r o w e r s an d the creditqualitywouldbeimproved.
Inademandshift,banksdonotoftenrealizedemandshift,theystillkeepthelendi ngrateandthethresholdlevelofcreditstandardsunchanged.Therewouldbemoreandmore borrowerswhomeetthecreditstandardsdesiretoborrowmoney.T h e n , theyre alizethegrowthindemandandstarttoraiselendingrateandtightencr edit standar ds.Lastly,thecreditqualityincreases.Therefore, theprocesswouldb e asfollo w:
������������↑→���������������↑→���������𝐥��𝐲↑ Forproductivityshiftundertheconsumptionthattheincreaseinloansdemand comesfromthefavorableconditionsinbusinessactivitiesoftheborrowers.A l t h o u g h thecreditstandards maydeclineinthiscase,boostingcreditgrowthcouldre su lt inhighcreditquality.
Whent h e p r o d u c t i v i t y s h i f t o c c u r s – fore x a m p l e , f i r m s h a v e s o m e i m p r o v e m e n t s intheirtechnology,theinputc ostsreduceortheeconomyisingoodcondition,theborrowerswillneedmorecredittoope ratetheirbusinessorinvestinn e w p r o j e c t s I n t h i s c a s e , b a n k s m i g h t b e l i e v e t h a t mostb o r r o w e r s w o u l d h a v e g o o d opportunitiesinbusinessand obtainmore cashinflowstoservice debts.Thus,
Changes in banks' attitudes towards credit standards can lead to a higher expected rate of return or a loosening of those standards This shift in the credit curve (z) to the left indicates that while more borrowers may be attracted due to relaxed credit standards, the increased productivity also raises the demand for loans to support business activities Consequently, this results in a significant positive shift in loan demand, leading to a dramatic increase in the total amount of loans available.
L1fr o mL2a n db a n k s ’ e x p e c t e d r a t e o f r e t u r n i n c r e a s e fromr e tor e A l t h o u g h banksloosentheircreditstandardstoacceptmorebadborrowers,theseborrowers arenotcertaintobereallybadbecausetheystillexperiencebenefitsfromprod uctivity shift.Asaresult,thecreditqualityisstillimproved.However, there wouldbeapossibilitythatbanksstillsafelykeepthecurver e ሺzሻunchangedlike thedemandshiftcase.
Asdiscussedabove,productivityshiftmakebanksloosencredit standards andmakeborrowersdemandlargeamountof loans.Thequestionofwhetherthechangeo fc r e d i t s t a n d a r d s o r c r e d i t g r o w t h h a p p e n s f i r s t w o u l d d e p e n d o n w h o c o u l d realizetheproductivity shift first Anyway,cre di t quality isimproved lastly.T he p r o c e s s wouldbeasfollow:
Productivityshift Creditgrowth↑ሺCreditstandards↓or↑ሻ Creditquality↑
Table2.1summarizestherelationshipbetweencreditgrowthandcreditquali tywiththeappearanceoflevelofcreditstandards.Besides,theorderofchangeofdifferentshift sistoconfirmthatachangeincreditgrowthinthepastmayleadtoachangeincreditqualityinthe future.Thiseffectisnotcontemporaneous.
Figure2.5showsthe positionofdifferentshiftsinthe macroeconomiccontextf o r b a n k i n g a n d theyd o n o t e x i s t s i m u l t a n e o u s l y F i r s t , b a n k s l o w e r t h e i r c r e d i t standardine v a l u a t i n g c u s t o m e r s tomakeas u p p l y s h i f t.S e c o n d , t h e d e m a n d o f c a p i t a l fromborrowersforimprovingtheirbu sinessactivitieswouldcreatead e m a n d s h i f t.F i n a l l y , t h e a d v a n t a g e s fro mm a c r o e c o n o m i c d e t e r m i n a n t s w o u l d generateaproductivityshiftforbu sinessactivities.
Principals Lending interests Withdrawals Deposit interests
CONTROLFACTORSFORCREDITQUALITY
The hypothesis developed by Berger and DeYoung (1997) suggests that measured cost efficiency can impact credit quality Low cost efficiency may indicate poor management, leading to weak credit appraisal processes within banks, which could result in offering loans to high-risk borrowers or investing in projects with inflated cash flows Additionally, inaccurate collateral valuations can cause banks to underestimate asset values, hindering their ability to recover from low-quality loans Furthermore, lax customer supervision may allow borrowers to misuse loans for high-risk investments rather than for the intended purposes assessed by banks These factors collectively contribute to a decline in credit quality.
Bergera n d D e Y o u n g ( 1 9 9 7 ) a l s o s h o w e d t h e a d v e r s e r e l a t i o n s h i p b e t w e e n measuredc o s t e f f i c i e n c y a n d c r e d i t q u a l i t y B a n k s ma yh a v e mores h o r t – termo p e r a t i n g coststomonitortheircurrentloanstoavoidNPLsinthefuture.In otherwords,skimping on somes h o r t – termcostsformonitoringloans ma yleadtolo w c r e d i t quality.Bankswouldbeles scostefficientiftheyarewillingtospendsomec o s t s w h i c h p r e v e n t themf r o m s u f f e r i n g h i g h e r l o s s e s f r o m N P L s i n t h e f u t u r e H o w e v e r , t h i s t r a d e – offreflectsa d e c r e a s e inmeasuredc o s t e f f i c i e n c y a n d a n increaseincreditqua lityafterward.
Both“BadmanagementI”and“Skimping”hypothesesrelate to themeasuredc o s t efficiency.T h e r e m a y b e a v a r i a b l e w h i c h c o u l d c a p t u r e i t H o w e v e r , i t i s n e c e s s a r y todistinguishthesetwohypotheses.Thediffere nceisinthecost,oneiswaste b e c a u s e o f c o n s e q u e n c e s o f b a d managementa n d o n e i s u s e f u l t o p r e v e n t lossesinthefuture.
Similarlyt o “Badm a n a g e m e n t I ” h y p o t h e s i s ,b a n k s ’ b a d m a n a g e m e n t includinglimitationsofcreditappraisalprocess,valuationskillandcustomersup ervisionm a y b e r e f l e c t e d byl o w p e r f o r m a n c e w h i c h c o u l d b e r e c o g n i z e d bylowp r o f i t a b i l i t y o n equityo r a s s e t s Inc o n t r a s t , b a n k s w i t h b e t t e r p e r f o r m a n c e w o u l d h a v e h i g h q u a l i t y s k i l l s o f a d m i n i s t r a t i n g l e n d i n g a c t i v i t i e s , w h i c h c o u l d r e d u c e problemloansorincreasecreditqu alityinthefuture(Louzis,VouldisandMetaxas,2011).
Banks' credit policies are shaped by the dual goals of maximizing profitability and maintaining a positive reputation According to Rajan (1994), banks often cannot showcase their efficient loan portfolios or the high performance of their customers; instead, the market primarily observes their earnings This leads banks to inflate their earnings to preserve a favorable market perception A liberal credit policy, often referred to as "pro-cyclical," allows banks to continue offering loans to high-risk borrowers, thereby masking the presence of non-performing loans (NPLs) and creating the illusion of ongoing profitability However, this approach poses future risks, as while it may temporarily boost performance, it ultimately compromises credit quality.
In 2011, it was suggested that banks could improve their credit quality by pursuing diversification opportunities For instance, banks have the potential to identify promising business projects or invest in stocks of high-growth companies This strategy could lead to a decrease in the capital allocated for lending, thereby reducing the likelihood of lending to high-risk borrowers However, while diversifying into non-lending investment fields may yield high returns, it often requires a lengthy period to recover both principal and interest Additionally, liquidity risk can arise if customers decide to withdraw funds, as banks primarily rely on liabilities for their capital structure Consequently, larger banks tend to have more diversification opportunities without significantly impacting their liquidity, whereas smaller banks face greater challenges in diversifying due to liquidity constraints.
“Too–big–to–fail”hypothesis
AccordingtoSternandFeldman(2004),largebanksbelieveingovernment’sint erventionwhent h e y areinfailureb e c a u s e t h e y h a v e e n o r m o u s i n f l u e n c e o n f i n a n c i a l market.Therefore,largerbanksarewillingtoincreasemoreleveragea ndattempttolendmore Thisactofcredit expansionincreases thechance ofa pproaching lowqualityborrowers(Louzisetal.,2011).
Underthishypothesis,theeffectofleverageoncreditqualitywouldben e g a t i v e a ndadjustedbybankssize.Whenbanksincreaseanamountofleverage,credi t quali tyof largerbankswoulddeclinemorethansmallerbanks.Itissimilartolendingaspectwhichban ksboostcreditgrowth.
Nguyen( 2 0 1 5 ) b e l i e v e d t h a t t h i s h y p o t h e s i s c o u l d b e a p p r o p r i a t e onlyf o r somel a r g e s t b a n k s I t m e a n s onlys o m e l a r g e s t b a n k s c o u l d b e a b l e t o r e c e i v e s u p p o r t f r o m g o v e r n m e n t w h i l e theya r e i n d i s t r e s s D u e t o t h i s r e a s o n , “ s o m e largestbanks”isa c h a r a c t e r i s t i c w h i c h c o u l d b e u s e d a s a n a l t e r n a t i v e f o r b a n k size–adjustmentunder“too–big–to– fail”hypothesis.
P REVIOUS E MPIRICAL S TUDIES
Clair( 1 9 9 2 ) i s o n e o f t h e e a r l i e s t a u t h o r s w h o i n v e s t i g a t e d t h e r e l a t i o n s h i p b e t w e e n creditgrowthandcreditqualityofbanksinTexasusingannualda tafrom1 9 8 0 to1990.TheauthorusedtheloanlossratioandNPLratiotomeasurecr editquality.T h e i n d e p e n d e n t v a r i a b l e s w e r e d i v i d e d i n t o t h r e e g r o u p s i n c l u d i n g : ( i ) c r e d i t growth,
(ii)financialcharacteristics(bankassets,bankequity,businessloansa n d r e a l e s t a t e l o a n s ) a n d ( i i i ) b u s i n e s s c o n d i t i o n s ( n o n – agriculturale m p l o y m e n t growth) Therewerethreetypesofcreditgrowthusedsimu ltaneouslyinthemodel:internalgrowth,growththroughbankmergerandgrowththroughb ankacquisition.
Theeffectofcreditgrowthwasconsideredin contemporaneousand 1,2and3– yearlagged v a r i a b l e s T h e methodo f o r d i n a r y l e a s t s q u a r e ( O L S ) w a s a p p l i e d int h e model.Themainresultsindicatedthatcreditg ro wt h (internalgro wthandgrowth throughbankacquisition)hadsignificantlyimprovedthecreditqualityat c o n t e m p o r a n e o u s andone– lagyearinbothcasesofusingloanlossratioandNPLratio.However,thecreditgrowth throughbankmergerhadtheinverseeffectincaseo fl o a n l o s s r a t i o b u t n o s i g n i f i c a n t i m p a c t s o n N P L r a t i o I n a d d i t i o n , m o s t o f financialcharacteristicsandb usinessconditionsarealsogoodcontrolvariables.
Ina studyof d e t e r m i n i n g f a c t o r s o f b a n k i n g c r i s e s c o n d u c t e d byK u n t a n d D e t r ag i a c h e (1998), oneof theconditionswhichcontributedto bankingcrisesisthehighratioofnon– performingassets(exceeding10percent).Thisstudyusedannuald at a ofallmarketecon omiesintheperiod1980–
In 1994, a study utilized multivariate logistic regression to analyze the impact of various independent variables on banking crises The model included three groups of variables: macroeconomic factors such as GDP growth, external terms of trade, real interest rates, inflation, and government surplus; financial factors including the ratio of money supply (M2) to foreign exchange reserves, the ratio of domestic credit to private sector GDP, bank liquidity, and credit growth; and institutional factors like GDP per capita, the quality of law enforcement, and the presence of explicit deposit insurance schemes Notably, the financial variable of credit growth, when lagged by two years, showed a significant positive influence on the likelihood of banking crises, indicating that a 10% increase in credit growth could lead to a 5.4% rise in crisis probability.
AnotherstudyontheeffectofcreditgrowthoncreditqualitywascarriedoutbyK eeton ( 1 9 9 9 ) T h i s s t u d y usesq u a r t e r l y ti me – seriesd a t a i n t h e t w o s e p a r a t e p e r i o d s : 1967–1983and1990–
1989).Thedatai s c o l l e c t e d fromS e n i o r L o a n O f f i c e r ( S L O ) c o n d u c t e d byt h e F e d e r a l R e s e r v e s i n c e 1967.Themethodofvectorauto– regression(VAR)isusedforthetwoVARsystems.Thefirstoneincludesloangrowth,c reditstandardsandGDPgapandthe secondoneincludes earnings, loansanddelinquencyrate.GDPgapandearn ingsa r e usedtocontrolforthemacroeconomicconditions.Therearetwomai nresultsd eri v ed fromthefirstVARsystem.First,loangrowthdoesnothaveanyinf luenceo n creditstandardsinthe1990–
1983.Second,creditstandardstendstolowercredit growthinbotht wo perio ds.ForthesecondVARsystem,thehigherloangrowthinthepastcouldleadtohigher delinquencyrate.
Salas and Saurina (2002) analyzed both macroeconomic and bank-specific determinants of problem loans in Spanish commercial and savings banks from 1985 to 1997 The study identified credit growth as a key bank-specific factor, categorized into two types: individual bank loan growth and the loan growth of its branch network Other significant factors included inefficiency, the rate of loans with collateral, bank size, net interest margin, solvency ratio, and market share Macroeconomic variables examined were GDP growth rate, levels of indebtedness for families and banks, and a dummy variable for Spanish bank regulation in 1988 The research employed a dynamic panel data estimator using the difference generalized method of moments (Difference GMM), taking into account the lagged effects of the determinants.
2 , 3 a n d 4 years.T h e g e n e r a l r e s u l t isthatcreditgrowth haspositive effecto nproblemloans.Indetail,branchnetwork l o a n g r o w t h i n c r e a s e s t h e r a t e o f p r o b l e m l o a ns a f t e r 3 yearsf o r commercialb a n k s F o r s a v i n g b a n k s , t h e b a n k l o a n g r o w t h a n d b r a n c h n e t w o r k g r o w t h r a i s e s p r o b l e m l o a n s i n t h r e e a n d fouryearsr e s p e c t i v e l y Ther o l e o f m a cr o e co n o mi c d e t e r m i n a n t s i s s t r o n g l y confirmeda n d someb a n k – specificv a r i a b l e s arealsosignificant.
2003toexaminetherelationshipbetweencompetitionandbank– risktakingwhich iscapturedbytheratioofN P L fromborrowers.The ap p r o a ch i n g modelincludesthreegroupsofvariables:competitionindex(calculatedbasi ngonthevalueofallloansofbank),macroeconomiccontrol variables( r e a l G D P g r o w t h r a t e ) a n d b a n k – specificv a r i a b l e s ( r e t u r n o n e q u i t y , b a n k sizeandloanratio).Theappliedr egressionmethodisdifferenceGMM.Thisstudydidnotfindanystrongevidencestoa dvocatetherelationshipofcompetitiona n d bank–risk takingexceptfortheLernerindex(Lerner,1934)of bankloans.
Thistypeo f c o m p e t i t i o n m e a s u r e m e n t h a s s t r o n g s i g n i f i c a n t a n d i n v e r t e d U – shapedef f ect o n N P L r a t i o T h i s impliedw h e n S p a n i s h b a n k s o f f e r m orec o m p e t i t i v e p o l i c i e s ontheirloanrate(reflectedbythelowvalue ofLernerindex),bankswouldfirstly experiencethe increasesinNPLsandthenthese riskswouldreduce.Besides,t h e c o n t e m p o r a n e o u s c o n t r o l o f l o a n r a t i o s eemst o b e s t r o n g l y a n d n e g a t i v e l y s i g n i f i c a n t t o NPLratio.
Thismeansbanks with highspecializationlevel oflendingw i l l facelowerriskfromthebadborrowers.
Foos,N o r d e n a n d Weber( 2 0 1 0 ) u s e d B a n k s c o p e a n n u a l d a t a o f moret h a n 1 6 , 0 0 0 b a n k s i n 16majorc o u n t r i e s ( T h e U n i t e d S t a t e s , C a n a d a , J a p a n a n d 1 3 E urop ean countries)during1997–
In a study conducted in 2007 to examine the impact of loan growth on the riskiness of individual banks, two dependent variables were identified as key indicators of credit quality: the loan losses provision ratio and the loan losses to net interest income ratio The main independent variables included abnormal loan growth (with four lags), equity to total assets ratio, and total customer loans The first model utilized a dynamic panel data approach, employing both OLS and system GMM for estimation Results indicated that abnormal loan growth positively and significantly affected loan losses provision after two, three, and four years In the second model, fixed effects were applied to account for macroeconomic conditions, revealing that average loan growth in previous years was positively and significantly associated with the loan losses to net interest income ratio Additionally, the study explored the relationship between loan growth and loan losses, factoring in the interaction effects from bank mergers and acquisitions, which were found to reduce the positive influence of loan growth, resulting in negative coefficients for lagged loan growth This suggests that loan growth in banks involved in mergers and acquisitions is negatively correlated with loan losses.
Valverde,IbanezandFernandez(2011)investigated banklendingandcre ditq ual it y inthecontextoffinancialcrisisin2 0 0 8 Theyusedasampleofban ksinS p a i n from2000Q1 to 2010Q1.
Inthisresearch,therearethreesimultaneousGMMmodelsinwhichloangrowth,NPLr atioandbankratingaredependentvariables.Othercontrolvariablesarebankco nditions(includecharacteristicscalculatedfrombank balancesheetsuchasequityo vertotalassets,costoverincomeratio,deposito v e r t o t a l l i a b i l i t i e s , size, etc.)andmarketfundamentals(GDPgrowth, real housingp r i c e growth, EURIBOR rate).Accordingto the results,the growthofbanklendinghaspositiveandsignificanteffectonNPLraioatthelaglevel oftwoyears.Somec o n t r o l variablesaresignificant,especiallyinthemodelofbankrating.
Caporale,ColliandLopez(2014)indicatedthatcreditgrowthofbanksinItalyd u r i n g t heexpansionaryphaseswouldbethecauseoftheincreaseinNPLsduringthecontra ctionaryphases.Theyusedstructuralvectorauto– regressionmodel( S V A R ) for17monthlytime– seriesfromJune1998toJune2012,includingsometypeso f l o a n s , N P L s a n d ma croeconomicp r o x i e s ( s u c h a s u n e m p l o y m e n t r a t e , consumerpriceindex,ho usingpriceindex,EURIBORrate,etc.).
Year Author Sample Period Methodology Keyfindings
1992 R.T.Clair AnnualdataofbanksinTexas 1980–1990 Ordinaryleast squared(OLS)
1999 W R.Keeton Quarterlytime–series surveyeddatafromSeniorLoanOf ficer(SLO)
1985–1997 DifferenceGMM Loangrowthincreasestherateofproble mloansafter3and 4years.
Demand shift and Productivity shift (+) Too–big–to–fail (–)
Cost efficiency Bad management I (+) or Skimping (–)
Profitability Bad management II (+) or Pro–cyclical credit policy (–) Bank size Diversification (+)
Leverage Too–big–to–fail (–)
T HE C ONCEPTUAL F RAMEWORK
Onthegroundoftheaboveliteraturereviewinrelationtoboththeoriesan demp ir i cal analyses,this researchsuggeststhe conceptualframeworkon theeffectofcreditgrowthoncreditqualityinFigure2.6.Creditgrowthisthemainvar iableofinterestintheresearchandexplanatoryvariableswouldbeusedwithlagstocapturet h e effectsfromthepast.
Thisc h a p t e r i n t r o d u c e s c r e d i t q u a l i t y m e a s u r e m e n t , d a t a a n d met hodology.F i r s t , c r e d i t q u a l i t y measurementi s b a s e d ont h e b a n k l o a n c l a s s i f i c a t i o n o f T h e S t a t e BankofVietnam.Second,thedatausedinthisresearchiscoll ectedfromthed et a i l ed balancesheetsandincomestatementsinthedatabaseofTheSta teBankofVietnam,D o n g N a i b r a n c h F i n a l l y , t h e m e t h o d o l o g y i s t o p r e s e n t t h e d y n a m i c p a n e l d a t a e s t i m a t o r , t h e d i f f e r e n c e G M M t e c h n i q u e , t h e c a l c u l a t i o n o f l o n g – runc o e f f i c i e n t s andtheeconometricspecificationofthemodel.
M EASURING C REDIT Q UALITY
Therearemanyvariableswhich couldbeusedtomeasurecreditquality.Mosto f previousstudiesusednon– performingloan(NPL)ratioasaproxy forlowcreditquality.Keeton(1999)alsousedloanlossestocapturecreditquality.
Definitiona b o u t N P L d i f f e r s amongc o u n t r i e s H o w e v e r , t h e s e d e f i n i t i o n s m o s t l y emphasizeontheoverdueloansofwhichthresholdisusuall yfrom90daysandabove.InthecontextofVietnam,thisresearchusesthedefinitionsti pulatedintheCircularNo.02/2013/TT–
NHNNdatedJanuary21,2013promulgatedbyTheS t a t e B a n k o f Vietnam.Int h i s d e f i n i t i o n , b a n k l o a n s w o u l d b e c l a s s i f i e d i n f i v e g r o u p s asfollow:
Currentd e b t s w h i c h a r e fu ll y andtimelyrecoverable i n bo th pr in ci pa ls and interestsordebtsareoverdueforlessthan10daysandtheborrowerisabletopayth eprincipalandinterestofdebtsinfullandinatimelymanner.
1 Non– performingloanratio whichiscalculatedbythetotalamounto fl oa ns c l a s s i f i ed in group3,4and 5dividedbytotalamount ofloans Thehigher thisr a ti o is,thelowerthecreditqualityis.
Eachmeasurementw o u l d playdifferent r o l e s i n l e n d i n g a c t i v i t i e s : stand ardloanswouldbepurelyhighquality;NPLsarecertainoffacingproblemsinrecover ingbothprincipalsandinterestsandbanks needtoseekforsolutions inap e r i o d oftime;loanlosseswhichhaveextremelyh i g h probabilityt o becomesomethings i m i l a r tob a n k s ’ e x p e n s e s w o u l d b e a p u r e r e f l e c t i o n o f l o w c r e d i t quality.Accordingtothedatausedinthisresearch,loanlossesarerecordedaszeroi n many banksformanyperiods.Therefore,loanlossratioisnotappropriateasavariabl einthemodel.InthecaseofbankswiththesameorequivalentNPLratio, higherstandardloanratioreflectshighercreditquality.Therefore,thisresearchusest h e ratio of
D ATA C OLLECTION M ETHOD
Allr e q u i r e d d a t a w e r e c o l l e c t e d fromt h e d a t a b a s e o f T h e S t a t e B a n k o f Vietnam includingdetailedbalancesheetsandincomestatementsof291commercialbanksfrom20 09Q3to2014Q1.Themethodofcollectingdatawouldb e basedonTheSystem ofBook– keepingAccountsofCreditInstitutionswhichispromulgated i n theD e c i s i o n
NHNNd a t e d A p r i l 2 9 , 2 0 0 4 o f S ta t e BankofVietnam.Thissystemincludesm anyaccounttypeswhichareformattedindigitsXXXX.Table3.1showsnecessaryitem scorrespondingtotheira c c o u n t types.
Customerloansinfivegroups From2XX1to2XX5
Totalassets From1XXXto3XXXand5XXX
Table3.2showsthelistofvariablescorrespondingtotheirhypothesisandtheex p e ct e d sig ns f o r themeasureo f c r e d i t quality.T h e e x p ec t e d si gn s i n t h i s t ab le tak eintoaccounttheinversemeasuresofcreditqualityandcostefficiency.Assuch,theya r e d i f f e r e n t fromt h e e x p e c t e d signsdisplayedinF i g u r e 2 6.T h e d e t a i l e d formulas ofvariablesarepresentedinTable3.3.
1 In thepresent,thereare53commercialbanksinDongNaiprovince.Duetotheavailabilityofcollecteddata andthefactthatsomecommercialbanksopennewbranchesrecently,thisresearchc a n onlycaptureenou ghdataof29bankbranchesduringthe researchperiod.
COST_EFF Too–big–to– failBadmanageme ntI
ECONOMETRICMETHODOLOGY
DynamicPanelDataEstimator
The equation presented illustrates the relationship between the credit quality of banks over time, incorporating a one-period lag for the variables involved The lag polynomial vector reflects the delayed impact of explanatory variables, while the k×1 vector represents additional explanatory factors beyond the lagged credit quality Unobserved individual characteristics of banks are captured, alongside an intercept and an error term Due to the nature of accounting data and the timing of bank decisions, the current levels of explanatory variables are excluded from the model to accurately represent their effects.
EconometricProblems
The common methods for estimating dynamic panel data models often face a high probability of endogeneity issues due to correlations among independent variables and the error term Additionally, unobserved cross-sectional characteristics, or fixed effects, are included in the error term To address these challenges, Arellano and Bond (1991) proposed transforming the data into first differences This approach helps eliminate unobserved factors and fixed effect elements, enhancing the reliability of the model.
M M w hic hi s alsog e n e r a l i z e d byArellanoandB ove r( 19 95 ) a n d Blundell andBo n d ( 2 0 0 0 ) T h i s methodw o u l d l e a d t o a moree f f i c i e n t estimation.T h u s , t h e modelwouldbere–formedas:
𝚫 � � = 𝚫� ��−� +�ሺ𝐋ሻ𝚫� �� +𝚫 �� ,ሺ2ሻ Intheequation(2),𝚫isthefirstdifferenceoperator.𝚫�� �p r o b a b l y − co rr el at e withth eerrorterm𝚫 �� ,whichcouldleadtothebiasedestimation.AccordingtoLouziseta l.(2011),thetwo– periodlagindependentvariable� ��−�which isexpectedtocorrelatewith𝚫 �� �but − notcorre latewiththeerrorterm𝚫 � � would be anappropriateinstrumentinthemodel.
Inordertoachieveanefficientestimator,theexplanatoryvariablesshouldbeex ogenous.Nevertheless,theendogeneitymaystillexistbecauseofthecorrelation betweenexplanatoryvariables𝚫���andtheerrorterm𝚫��.Inthiscase,thel a g s of
� ��(in level,nottakingfirstdifference)couldbecomevalidinstruments(Louziset al.,2011).
The Difference GMM method includes two techniques: one-step GMM and two-step GMM The one-step GMM produces consistent parameters under the assumption of independent and homoscedastic residuals, while the two-step GMM does not adhere to this assumption However, using two-step GMM can result in downward-biased standard errors Research by Arellano and Bond (1991) and Blundell and Bond (2000) indicates that the efficiency gained from two-step GMM is not significant, even in the presence of heteroscedastic errors Therefore, one-step GMM estimation may be the preferable choice.
−� and𝚫�� � − =� �� � − −� �� � − b o t h sharethesame� �� � − Therefore,firsto r d e r autocorrelationAR(1)isexpectedtoexist.Testingforsecondorderautocorrelation
� �� �(in − 𝚫 �� � − ).T h e n u l l h y p o t h e s i s o f b o t h A R ( 1 ) a n d A R ( 2 ) t e s t i s no autocorrelation.IfthetestforAR(2)rejectthenullhypothesis,testsforthevalidity ofinstrumentvariablesisnotvalid.Hence,higherp–valueofAR(2)testwouldbe better.F o r t h e v a l i d i t y ofinstrumentv a r i a b l e s , S a r g a n T e s t a n d H a n s e n Testa r e u s e d withthenullhypothesisofvalidinstrumentvariables(Roodman,2006).
Estimating TheLong–runCoefficients
Indynamicpaneldatamodel,itisnecessarytoconsiderthelong– runc o e f f i c i e n t s ofeachexplanatoryvariablebecausetheycouldreflectthecumu lativee f f e c t onindependentvariable.Inaddition,Louzisetal.
(2011)assertedthatm u l t i c o l l i n e a r i t y amongthelagsofex p l a n a t o r y variab lesma yexist,whichaffect t h e accuracyofindividualcoefficients.Therefo re,thelong–runstandarderrorsoflong–runcoefficientsmaybemorerobust.
Typicalpreviousstudiesshowedthatcreditgrowthoftenleadstothechang ei n c r e d i t q u a l i t y a f t e r o n e year.T h u s , t h i s r e s e a r c h u s e f o u r l a g s f o r e x p l a n a t o r y v a r i a b l e s becauseofquarterlydata.Themodelwouldbedemonstratedas:
Onthesuppositionthatall𝚫� �� �( − jrangesfrom1to4)increasesinoneunit,thetotalsh ort–runeffecton𝚫 from𝚫 at present(periodt)wouldbe:
Thelong–runeffectwouldcaptureallshort–runeffects.Therefore,itisequal tothesumof(3),(4)and(5)andmore.Theformulacanbewrittenas:
Sincet h e a b s o l u t e c o e f f i c i e n t o f�i so f t e n l o w e r t h a n 1 , t h e r e w o u l d b e a convergenceof� 𝐋 � 𝐋i s asumofaninfinitegeometricprogressionwithcommon ratio�.T heformulaof� 𝐋c a n beshortenedfrom(6)as:
Tosumupintheformula(7),� �is thecoefficientofoneexplanatoryvariable oflag�.�isthecoefficientof𝚫 �� � −
EconometricSpecification
Int h e e q u a t i o n ( 9 ) a b o v e ,� �𝐋 _ �𝐋 representsf o r c r e d i t q u a l i t y L e t t e r� denotest h e l a g from1 t o 4 �,�,�a n d�a r ec o e f f i c i e n t s �i sc o n t r o l fac torsincludingcostefficiency(����),profitability(���),banksize(� ��)𝐈 andleverage(𝐋 𝐕� )w i t h𝐋� 𝐕 interactingw i t h d u m m y ofthethreelargestba nks(𝐋������).Becausebanksizeiso f t e n stable, usinglagsofbanksize asexplanatoryv a r i a b l e s w o u l d l e a d t o high degreeofmulticollinearity.Thus,� ��𝐈 willbeusedascurrentlevel.
Byu s i n g t h e “ r e s t r i c t e d ” G M M p r o c e d u r e s u g g e s t e d byJ u d s o n a n d O w e n ( 1 9 9 9 ) , controlvariablesareaddedintothemodel(9)inturn.Thebase linemodel(incl ude onlyc r e d i t g r o w t h ) a n d t h e unrestrictedmodel( i n c l u d e all 4 controlv a r i a b l e s ) arealsoestimated.
Hypothesis testing
Thee f f e c t ofc r e d i t growtho n c r e d i t q u a l i t y w i l l b e d e r i v e d f r o m t h e f o u r individuallaggedcoefficients(� � ).Thesignificanceofthesecoefficientsisd e r i v e d f romthep–valueintheestimationresults.
Inaddition,the“too–big–to–fail”hypothesiscontributestomarginaleffectof creditgrowthoncreditquality.Thecoefficientofcreditgrowth���� �𝐈 with itsinteractiveterm���� �×𝐋������𝐈 ,isinterpretedasfollows:
Thelong–runcoefficientof���� �correspondingto�𝐈 �and � �is c a l c u l a t e d asformula(7),thevarianceisinformula(8).Inthecaseoflong–runcoefficientof
Thischapterpresentstheestimationresultsanddiscusses themainfindi ngs A c co r d i n g tot h e e s t i m a t i o n r e s u l t , c r e d i t g r o w t h h a s n e g a t i v e e f f e c t s o n c r e d i t q u al i t y atthelaglevelof3and4andthelong– runcoefficientofcreditgrowthisa l s o sta tis ti cal ly significant T hi s p r o v e s fo rt he ex ist ence o f s u p p l y shift an dt he i n f o r m a t i o n e x t e r n a l i t y i n t h e c r e d i t m a r k e t , a n d t h e n e g a t i v e i m p a c t f r o m t h e macroeconom icconditions.
The growth rate of deposit liabilities in the Dong Nai banking system, covering commercial banks from Q1 2010 to Q4 2014, shows a notable trend when compared to Vietnam's quarterly deposit interest rates The analysis indicates that when deposit growth rates exceed interest rates, it suggests potential vulnerabilities within the banking system During this period, credit growth was not a major concern for commercial banks However, the data implies that resources may be shifting from depositors to the banking system If credit growth fails to improve, it could create significant pressure on banks to enhance their mobilization efforts, potentially disrupting the relationship between deposit growth rates and interest rates, and reversing the flow of net resources.
Variable Observation Mean StandardDeviation Minimum Maximum
NPL_SL 551 0.0348 0.0394 0.0000 0.1977 CREDIT 551 0.0414 0.1570 –0.6192 0.5467 COST_EFF 551 0.9050 0.1627 0.4036 1.4639
Table 4.2 presents the estimation results of the one-step Difference GMM method with robust standard errors The test for second-order autocorrelation AR(2) indicates no correlation with the variable, as evidenced by the high p-value obtained from the analysis Conversely, the first-order correlation AR(1) test shows a small p-value (less than 0.05), confirming its expected outcome Consequently, the tests for instrument variables are deemed valid Additionally, both the Sargan Test and Hansen Test yield very high p-values, supporting the validity of the null hypothesis regarding instrument variables These findings suggest that the issue of endogeneity is resolved, allowing the coefficients in the model to be utilized for further analysis.
Accordingt o t h e e s t i m a t i o n r e s u l t s , t h e c o e f f i c i e n t s o f l a g g e d i n d e p en d e n t variable(∆�� �𝐋_ 𝐋 �� � − ) ispositiveandsignificant, so credit qualityint h e presentmightassociatewithitselfinthepast.Itmeanshigher(lower) creditq u a l i t y might leadt o h i g h e r ( l o w e r ) c r e d i t q u a l i t y a f t e r o n e q u a r t e r o f a particulary ear.T h e l a g g e d variablesofcreditgrowtharealsopositiveandsignificantatlag levelof3a n d 4 Itmeansthedecision ofexpandinglendingactivities ofcommercialbanksinDongNaihasnegativeinfluenceoncreditqualityafterthr eequarterstooneyear.A sstatedintheCircularNo.02/2013/TT–
NHNN,customerloanswillberecordedasNPLsiftheyareoverduefrom91daysandabo ve.Itisabouttwo– quartershorterthanthetwosignificantlaglevelsofcreditquality.Therefore,newloan sofcommercialb a n k s in D o n g Nai mighth a v e veryhighc h a n c e o f becomingN P L s a f t e r twoquartersfromtheoverduethreshold.Intheshorterperiodofoneandtwoq uarters,theeffectofcreditgrowthoncreditqualityisnotstatisticallysignificant.
Dependentvariable Baselinemodel Model(1) Model(2) Model(3) Model(4) Fullmodel
Symbol *,** and***representforsignificant levelat10%,5%and1%
Asp r e s e n t e d o nT a b l e 4 2,a l l t h e c o e f f i c i e n t s o f c r e d i t q u a l i t yv a r i a b l e s interactingwithdummy∆(���� �𝐈 ��−�× 𝐋������ �� )arenots t a t i s t i c a l l y significant.I t meanst h e e f f e c t ofc r e d i t g r o w t h o n c r e d i t qualityof t h r e e l a r g e s t commercialb a n k s i s n o t d i f f e r e n t f r o m t h e o t h e r s M o r e o v e r , almostb a n k c h a r a c t e r i s t i c s w h i c h a r e u s e d a s c o n t r o l v a r i a b l e s i n t h i s r e s e a r c h a r e n o t statisticallys i g n i f i c a n t i n e x p l a i n i n g t h e c h a n g e o f c r e d i t q u a l i t y
The analysis reveals that while the coefficient for ∆L is significant at 10%, it does not meet expectations Despite the insignificance of most coefficients, with the exception of ∆Profitability, the indicators of cost efficiency and profitability remain positive across all lag levels This outcome may partially indicate the potential presence of "skimping" and a "pro-cyclical credit policy" within the Dong Nai banking system.
Int h e l o n g – run,t h e c o e f f i c i e n t o f∆� � � � �𝐈 i sp o s i t i v e a n d significant.I t indicatesthatcreditgrowthofcommercialbanksinDongNaimaygraduallyreduce creditquality.Thetotaleffectofcreditgrowthismuchlargerthanindividualeffectf r o m l a g l e v e l s S i m i l a r t o t h e i n d i v i d u a l l a g s o f c r e d i t g r o w t h i n t e r a c t i n g with dummy,thelong– runcoefficientof∆ሺ���� �×𝐋𝐈 ������ሻisnots i g n i f i c a n t , whichprovethattheinfluenceofcreditgrowthoncreditqualityisthesameamong largestbanksandtheothers.
Theestimationresultshavepresentedthenegativerelationshipbetweencreditg r o wt h andcreditqualityofcommercialbanksinDongNai,whichisrepresentedinb o t h sho rt –runa n d l o n g – runt i m e f r a m e T h i s re lat io ns hi p r e f l e c t s tw omainimplications.First,based ontheliterature,theremaybeasupplyshiftintheloanmarket.I t a p p e a r s t h a t c o m m e r c i a l b a n k s i n D o n g N a i a r e w i l l i n g t o l o w e r t h e i r cr edit s t a n d a r d s i n o r d e r t o b o o s t l e n d i n g a c t i v i t i e s T h i s a c t i o n f r o m t h e commercialb
Lending to borrowers with poor financial capacity is a significant concern, as it increases the likelihood of default Additionally, adverse macroeconomic conditions can negatively impact business activities, particularly for households and small to medium enterprises, making it difficult for them to generate positive cash flows to meet their liabilities, which in turn leads to a rise in overdue loans Furthermore, information asymmetry in the loan market can lead commercial banks in Dong Nai to misjudge new customers, as borrowers often have access to multiple lending sources, allowing them to use funds from one loan to pay off another.
Thee f f e c t ofc r e d i t g r o w t h o n c r e d i t q u a l i t y i s n o t a d j u s t e d byt h e characteristico f l a r g e b a n k s i z e T h i s p r o v e s f o r t h e i n e x i s tenceo f “ t o o – big–to– f a i l ” hypothesisi n t h e c a s e o f c o m m e r c i a l b a n k s i n D o n g N a i A l t h o u g h s o m e b a n k s havemuchlargersizethanothers,theyseemtohavepartiallysi milarcreditg r o w t h policy.Intheaspectofthishypothesis,itmeansthatlargebanksdonoth aveo v e r – confidencepsychology(whichmakesbankbelievesintheprotectionofTheS t a t e BankofVietnam)inlendingactivities.Inmoredetails,bankswhetherlargeorsmallsizeha vethesamechangeincreditqualitycorrespondingwiththeirchangeinc r e d i t growth.
Thisis contrarytothe expectationthat largerbanksoften sufferfromg r e a t e r changeincreditqualitythanthesmallerbanks.Vietnam ba nksingenerala n d D o n g Naibanksinparticularplayaveryimportantrole inthefinancialmarket.If o n e oft h e m c o l l a p s e s , i t w o u l d i n f l u e n c e o t h e r b a n k s d u e t o t h e problemso f c r o s s – deposita m o n g b a n k s a n d n e g a t i v e p s y c h o l o g y o f p e o p l e , a n d t h e w h o l e Vietnambankingsystemwouldbeintrouble.Therefore,itmaynotr easonabletoc o n c l u d e thatcommercialbanksdonotbelieveintheinterventio nfromTheStateBanktopreventthemfromfailure.Inthescope,methodologyandavail abledataofthisresearch,thereistemporarilynoevidencetoconfirmthishypothesis.
Therelationshipbetweencreditgrowthandcreditqualitycouldbeexplaine dthroughthreeshiftsintheloanmarket.First,thesupplyshiftrepresentsforthefactt h a t b anksexpandtheirlendingactivitiesbyloweringtheircreditstandards.Inthesecircumstan ces,banks wi ll fa ce ahighprobabilityof le nd in g m o n e y tobadb o r r o w e r s
The demand for capital restructuring is driven by the need to leverage lower-cost capital from banks, prompting banks to tighten their credit standards, which in turn reduces future credit risks Favorable economic conditions enhance productivity, reflecting the efficient activities of households, firms, and other economic entities that have borrowed from banks, thereby improving their ability to finance debts Additionally, the negative relationship between credit growth and credit quality can be attributed to adverse macroeconomic shocks affecting borrowers and information problems, where banks may misjudge new customers, leading to a disregard for the negative impacts of their loans on other banks' information.
0 9 Q 3 t o 2 0 1 4 Q 1 , c r e d i t growthh a s ne g a t i v ee f f e c t o n c r e d i t q u a l i t y i nb o t h s h o r t r u n a n d l o n g r u n T h i s p a r t i a l l y provesfortheexistenceofthesupplys hiftintheloanmarketinthecontexto fDongNai.CommercialbanksinDongNaiappeartol owertheircreditqualityinthisresearchedperiodtoboostlending.Inaddition,the localeconomyofDongNaii s notagoodenvironmentforbusinessactivities.Mor eover,thecreditexternalitym a y existintheloanmarket.Thesefindingsmayder ivesomepolicyimplicationsw h i c h canbepresentedasfollows:
Now is not the right time for rapid credit growth in Dong Nai Commercial banks must exercise caution when appraising investment projects, evaluating collateral, and supervising capital usage, particularly for new customers, to avoid bad borrowers Additionally, banks should focus on advantageous sectors in Dong Nai, such as agriculture, export production, labor-intensive, and high-technology industries They need to deploy solutions to help customers overcome business challenges and classify borrowers based on their credit relationships to implement appropriate incentive policies Furthermore, it is essential for commercial banks to control new non-performing loans (NPLs) while continuing to manage existing ones effectively.
P L s a n d p r e p a r e s u f f i c i e n t p r o v i s i o n f o r c r e d i t r i s k F i n a l l y , c ommercialbanksshould keeptrackofthestate ofthe localeconomytoimplementc r e d i t policiestimelyandefficiently.
DN)needstosupervisemorecloselyoncreditgrowthofcommercial banks,esp eciallycommercialbanksw i t h highNPLratio.Mostimportantly,th e SBV– DNshouldclassify commercialbank sintogroupsbasedontheirlendingactivi tiestotargettheappropriaterateofcr edit growth,avoidinghighcreditriskofweakc ommercialbanks.Inaddition,theSBV–
DNhastocatchupwiththeconditionsoflocaleconomyaswellastherealstat eo f c o m m e r c i a l b a n k s’a c t i v i t i e s i n o r d e r t o e n h a n c e t h e e f f i c i e n c y ofadmi nistration andoperation.Finally,theyshouldco– ordinatewiththelocalg o v er n m en t s a t v a r i o u s l e v e l s t o i m p r o v e l o c a l economy,c r e a t e g o o d b u s i n e s s e n v i r o n m e n t forcommercialbanks,household sandenterprises.
Localgovernmentsshouldeasethelegalenvironmentforcommercialbank s,householdsandenterprises.First,theyneedtofacilitatehouseholdsandenterp risei n t h e p r o c e e d i n g s o f b u s i n e s s r e g i s t r a t i o n S e c o n d , theyc o u l d c o –ordinatew i t h relevant departments tosupportforenterprises’outputsandstabilizingthemarkets.L a s t b u t notleast,theyc o u l d d i r e c t
E x e c u t i v e O f f i c e t o speedupprocessofrecoveringNPLs This w o u l d hel pco m m e r c i a l banks in injecting ca pi tal intot he marketcontinuously,whichstimu latesthelocaleconomy.
Inadditiontothemainempiricalfindings,thisresearchstillhassomelimitati ons.First,theresearchscopeisonlyinDongNaiprovince.Hence, theresultw il lnotreflecttheconvincingoutcomesforthewholebankingsysteminVie tnam.S e co n d , theperiodofavailabledataisnotsufficienttocapturethelagsofverylongti me.T h e r e a r e manyformerr e s e a r c h e s w h i c h i n d i c a t e d t h a t t h e e f f e c t o f c r e d i t g r o w t h oncreditqualitymaybeeventhreeyears.Furtherstudiesmayuseda taofy ear ly f i n a n c i a l s t a t e m e n t s a n d t h e s c o p e f o r c o m m e r c i a l b a n k s i n V i e t n a m a n d other countriesintheworld.
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