Problemstatement
Theshortageo f capitalanddifficultiesi n accessingbankl o a n s w e r e t h e m o s t challengingi ssuesf o r S M E s Accordingt o a s u r v e y ofS M E s DevelopmentDepartment-
M i n i s t r y ofPlanningandInvestment,onlyone-thirdofSMEscanaccesstobankfunds;one- thirdhasobstaclestoreachtheloans;andone- thirdcannotaccess.AmongbusinessesinVNwhichcouldnotaccesstobankloans,the80%doesno tmeetloanconditions.Forexample,i n Quang Binh,onlyabout30%ofSMEsaccessto loansbanksandinterestratesup to25%.
Inthecrisis,bankcreditforsmallfirmsisreducedmorethanbankcredit fort h e largeones( GertlerandG i l c h r i s t , 1 9 9 4 ; GilchristandZakrajsek,1995).T h e m a i n reasoni s t h a t s m a l l firmsa r e m o r e dependento n b a n k creditastheyh a r d l y h a v e accesst o alternativefinancing sources,suchasfinancialmarketsandmoneymarkets.CaoSyKiem,chairmanoft h e VietNamS m a l l a n d M e d i u m -
S i z e d EnterprisesAssociation,saidl a c k o f f u n d s anddifficultiesi n accesst o capitali s t h e centrald i f f i c u l t y ofS M E s Becauseo f s m a l l o w n capital,90%ofSMEsloansforbusi ness,ofwhich70%isbankloans.However,SMEsfindd i f f i c u l t y toaccessloans,duetosmal lscaleproduction,weakbusinessmanagement,lackofcollateral,etc.
SurveyofVietnamC h a m b e r o fCommerceandIndustry(VCCI)indicatedt h a t lacko f capita lisoneofthebiggestreasonsthatforcedbusinessestostopoperatingin2013.Itisthecauseof3 8 1
%ofb u s i n e s s ’ s narrow.OneoftheS M E s ’ m a j o r f i n a n c i n g sourcesforinvestmenti sbankloans(41.9%)andmorethan50%SMEshaveinterestrateshigher thant h e y canafford. Onlyabout2 0 % o fbusinesseswereablet o accessloansinspite ofth ei r s m a l l productionsc alea nd lackoffinancialtransparency.63.1% SMEs doesnotapply forbankl o a n becauseofi nadequatecollateral,highinterestrate,complexitiesi n applicationprocess,etc
Paradoxically,the bankingsystemis fallinginto a"capitalinventory".Concernof banks,i n l e n d i n g processi s t h e r i s k o f b a d debte s p e c i a l l y i n theperiodi n whicht h e b addebtreachesanalarmingratein thewholeof bankingsystem.
Sothedifficultiesthoseenterprisesfacewhenborrowingformbankarewhat.Thereisa lotofresearchestrytofindouttheanswertothatquestion.Accordingtotheoryofasymmetricinfor mationbetweenb o r r o w e r s andbanks,t h e factorsd e t e r m i n i n g accesst o creditofenterpri sescanbeclassifiedinto threemaingroups:
(i) agroupedfactorr e p r e s e n t i n g f o r Owner’scharacteristics(Biggsetal,2001), (Gartneretal,2011),(Nguyen& Luu,2013),c o m p r i s e s education,ethnicity.
(ii) agroupedfactorrepresentingf o r Firm’scharacteristics(Biggsetal,2001),
(Le,2013)consistso f firmage,firmsize,typeoffirm,asset,liabilities…
(iii) agroupedfactorrepresentingforRelationshipbetweenbanksandborrowers(Biggsetal, 2001),(Bebczuk,2004),Voetal,2011)includespreviouslyborrowed,overduedebt.
However,t h e previouss t u d i e s wereh e t e r o g e n e o u s definitionso f t h e variables.F orexample,revenue(Gartneretal,2011)andnumberofemployee(Voetal,2011),
(Saidetal,2 0 1 3 ) wereusedasrepresenting firmsize.Therefore,theimpactoffactorsoncreditaccessisdifferentbetweenstudies.
Accordingt o a b o v e p r o b l e m s , t h i s paperaimst o indicateDeterminantso f accesst o fo rmalcreditbysmallandmediumenterprises(SME)inVietnam.Basedonthedataseto f 1427e nterprisefrom“CharacteristicsoftheVietnamesebusinessenvironment:evidencefromaSMEsurveyin2009”,theresearchhasappliedprobitmodeltoidentifydeterminantso f accesstofor malcreditbysmallandmedium-sizedenterprises(SMEs) inVietnam.
Researchobjectives
Generalresearcho b j e c t i v e i s t o e x a m i n e d e t e r m i n a n t s o f accesst o formalcreditb yS M E s i n Vietnam.
Specificobjectivesare: a ToinvestigatefactorsthateffectofprobabilitiesofaccesstoformalcreditbySMEsi n Vi etnam. b Torecommendp o l i c y i m p l i c a t i o n s i n ordert o i m p r o v e S M E s ’ s accesst o f ormalcredit
Researchquestions
Organizationofthe study
Theresto f t h e p a p e r isorganizedi n t o f o u r chapters.C h a p t e r 2 presentsLiteraturer eviewofSMEs,theoreticalreview,andempiricalstudieswhichwerecarriedoutinsideando u t s i d e o f Vietnam.Chapter3 describesS M E s creditmarketi n Vietnam,data,researchm e t h o d o l o g y andanalyticalframework.Chapter4 analysesthe empiricalresults,identifiesdeterminant sofSMEsaccessandgivessomequantitativeanalysisofthosefactors.Chapter
5concludes,suggestss o m e practicalp o l i c y implicat ions; l i m i t a t i o n anddirectionf o r furt herstudiesarealsodiscussedin thischapter.
SMEdefinition
Theterm" S M E " hasa w i d e rangeofdefinitions.M o s t o f organizationsandcountriesdeterminesmall businessesbasedon thenumber ofemployees,revenueandassets.
$15m i l l i o n inannualrevenue,and$15millioninassets.EuropeanUniondefinesSMEsasthose enterpriseswithbetween10and250employees,andmorethan10millioneuroturnoverorannu albalances h e e t total.American,m e a n w h i l e , describedS M E s i s a m a x i m u m o f 1 0 0 em ployeesandl e s s t h a n $ 3 millionrevenue.Egyptdefiness m a l l businessesasfirmshavem o r e th an5andless than50employees.
Inperiods2001-2009, based onGovernmentDecree90/2001ND-CP, SMEs inVietnamwasidentifiedasfollows:
CP,SMEsisdifferentlycategorizedbasedo n t h e totalcapital(mustequalt h e totalassetsi n bala ncesheeto f enterprises)andT h e averageyearlynumberofworkers.
Source:Government‘sDecreeNo.r56/2009/NĐ-CPdate30,June2009
Theoreticalliterature
Theoryof monopoly
(Gilchrist& Zakrajsek,1995).However,s m a l l firmsarem o r e dependentonbankcreditandthe yhardlyhaveaccesstoalternativefinancingsources,suchasfinancialmarkets.
Inthisview,thebankscharacterizedasamonopolist.Thebankswithmonopolypowerm a n i p u l a t e theinterestrateandcontractstogainmaximi ze profits.Therefore,theyusuallyc hargeSMEs higherinterestrateandcollateralrequirements(Beck,2008).
MonopolylendersreducewelfareofSMEsbecausecreditcostsmoreandtheirlivingstandardsfluc tuatem or e andm o r e (becausec o s t l y creditreducest h e i r de ma nd f o r cr ed it ) However,they mustgetloansfromthemonopolistfortheiroperation.Themonopolistraisesinterestratesu n t i l t h e marginalrevenuefromhigherratese q u a l s t h e marginalcostf r o m lowerloandemand.
Theoryofasymmetricinformation
Informationa s y m m e t r y i s unevend i s t r i b u t i o n betweens e l l e r s andb u y e r Itc a n h a v e effecto n d e c i s i o n making.Int h e financialmarket,asymmetricinformationbetweenbor rowersandlendersincreaseobstacleoftrade(Ray(1998).Borrowersalwayshavebetterinformati onabouttheirprojectsthanlenders.Accordingtothebanklendingview,financialmarketsarecha racterizedbyimperfectionsandbankassets(loans,securities)areimperfects u b s t i t u t e s (BernankeandGertler,1995).StiglitzandWeiss(1981)showthatinterestrateisdeterminednot onlythedemandforcapitalbut also theriskiness oftheborrowers.
Thereforetheoriesofcreditmarketfocusonasymmetricinformationwhichimpliesadverseselectio n(beforet h e a g r e e m e n t i s m a d e ) andm o r a l hazard(aftert h e agreementi s m a d e )
Adverseselectionexistswhentheprobabilityo f repayingloanofborrowersisnotestimatedcor rectly.Inthiscase,lowerriskborrowersmayincurhigherinterestrate(Bester,
1987).Therefore,theystopborrowingbecausethehighratesdecreasetheircreditprofileandprofit.O nt h e o t h e r h a n d , higherr i s k e n t e r p r i s e s cangainl o a n s w i t h lowerinterestr a t e Finally ,thelendershavealoanportfolioofalmosthigherriskenterprises.
Indevelopingcountries,besideadverseselection,moralhazardisacontroversialfactoro n creditmarkets.Moralhazardappearswhentheloansarenotusedforinitialpurpose.Thelendersf i n d i t difficultcontrollingborrowers’l o a n u t i l i z a t i o n Inordert o reducehigherinterestpay ments,theyarepressedtoseek highprofitableprojectsdespite ofrisk increase (Bester,1987)
Informationalasymmetry,hight r a n s a c t i o n costsandu n c e r t a i n t y ares p e c i f i c chara cteristicsofcreditmarkets.Thesecharacteristicstypicallyleadtoproblemsofadverseselectionand moralhazard.
Thisisinlinewiththeliteraturesince,inordertoreducetheanticipatedriskandmoralhazarda ssociatedwith lending,banksuse collateralasoneoftheir instruments.Therefore,t h e largerth ecapital,themoreafirmisabletoobtainaloansinceithasenoughcollateral.Fort h i s reason,Ber gerandUdell(1994)f o u n d t h a t s m a l l e r andyoungerfirmsa r e m o r e l i k e l y tofacehighe rcostoffinancing sincetheyarerequiredto offermorecollateralt h a n largerfirms.
BARRIERSTOFINANCEFORSMEs
Access to credit is essential for creating an economic environment that allows firms to grow and thrive, enhancing performance, facilitating market entry, and promoting innovation (Thorsten, 2011; Beck, 2008; Klapper, 2006) Firms with better access to credit can more effectively capitalize on growth and investment opportunities, which is crucial for the efficient growth of the SME sector Credit is often necessary for SMEs to advance their production technologies, such as transitioning from manual to automated processes (Abhijit, 2011) However, SMEs frequently struggle to secure external financing, which limits their operational capabilities and growth potential (Berger & Udell, 1998).
(Galindo&Schantiarelli,2003).SMEsfacedisproportionatebarrierstofinance,especiallyin developingcountries.
Financingf o r S M E s i s l i m i t e d , particularlyw h e n comparedt o c o m m e r c i a l debtf o r l a r g e firmsandmicrofinance.Basedo n W o r l d Bank,2 0 1 0 , o n e o f t h e most- severeo b s t a c l e t o growthofSMEsisfinancingconstrains.Theyareresultofhighcostsuchasad ministration,collateralandlackofexperience.Ontheotherhand,commercialfinanceistoodiffic ulttos u p p o r t SMEsduetohighcostandrisks.SMEscapitalneedsarenotsatisfiedbymicrolo ans(Karlan,2011).
In developing countries, banks face significant challenges in lending to small and medium-sized enterprises (SMEs) due to a shortage of information and regulatory constraints Key reasons for the insufficient debt provision include lower returns, higher administrative costs, and greater risk perceptions Additionally, an uninspiring regulatory environment and a lack of intermediary skills, information, experience, and capacity further hinder banks' ability to support SMEs Consequently, banks struggle to provide the long-term capital that SMEs require, resulting in a lending market that fails to meet their capital needs.
Becauseofthehighercosts,lackofskillsandhigher(perceived)risksofinvestmentinS M E s translate,Bankschargem o r e t h a n i n t e r e s t ratesandcollateralr e q u i r e m e n t s (Bech,2008).However,p o s t i n g collaterali s complicatedbyt h e factt h a t m o s t S M E s operatei n enviro nmentswithweakpropertyrightsandpoorcontractenforcement,inwhichborrowersd o nothavel egaltitlestohouseorland,andthereforecannotusetheseascollateral(Hernando,2000)
EMPIRICALSTUDIES
Internationalempirical studies
Bebczuk (2004) conducted a logit regression analysis on data from 140 Argentine companies in 1998 to identify the determinants of SME access to credit loans The study revealed three key findings: first, firm size, tangibility, and the length of the lending relationship do not significantly influence the likelihood of obtaining a loan Second, profitability, debt ratio, and the utilization of overdraft credit positively correlate with the probability of securing a loan Lastly, the likelihood of obtaining a loan decreases as liquidity increases.
AninterestingpaperofBiggsatel(2002),theyidentifiedthecharacteristicsthatinfluenceacc esstocreditinKenyan.Theyuseddataof182Kenyabusinesseswhichaccount for72%outputin4industries(metalworking,foodprocesses,textileandwood).Theyfoundt h a t th emainfactorswhichaffectonaccesstobankoverdraftsincludededucationofowner,c o m p a n y s i z e , a v a i l a b i l i t y ofcollateralandl e n g t h o f relationshipw i t h banks.Borrower’se t h n i c i t y hasl i t t l e effecto n s u p p l i e r credit.Meanwhile,i t doesn o t influencea c c e s s t o overdraft.
Anotherv i e w ofG a r t n e r atel(2011),theyu s e datafromt h e P a n e l S t u d y ofEntrepreneu rialDynamicsI I ( P S E D II)whichwascollectedb e t w e e n October2 0 0 5 andJ a n u a r y 2
0 0 6 t o i d e n t i f y t h e financingb e h a v i o r s o f companiesd u r i n g i n t h e U S A Theyf o u n d thatFirmcharacteristics,suchaspotentia l salesrevenue,legalformof thebusiness,andwhet heritisregistered,affecttheacquisitionofexternalsourcesoffinancing.Onotherhand,owneredu cationa n d t h e company’sn e t w o r t h alsoimpactt h e a c q u i s i t i o n o f certaintypesoffinancing.
T he y perceiveinnascentventures,relationshipbetweenexpectedrevenuesandfinancingamount ispositive; thefirmsize is notsignificantfortheselectiondecisionoffundingsource.
In a study conducted by Said (2013), the researchers investigated the factors influencing the accessibility of banking facilities for 36,492 firms in Egypt using the Heckman two-stage selection model They first identified the determinants of having banking facilities and then analyzed the factors contributing to banking problems The findings revealed that smaller companies are more likely to encounter banking issues Additionally, the study indicated that the age of a firm does not significantly affect its access to banking facilities, while factors such as sales turnover, economic activity, labor, capital, and legal form play a significant role.
Similarly,Le(2013)attemptedtoidentifydeterminantsofcreditaccessbyChinesefirms.S h e u sedt h e logitm o d e l t o analyzed a t a w h i c h werecollectedfrom1 2 , 4 0 0 enterprisessurvey edaroundChinain2005.Shefoundthatfirmage,typeofownership,loanquota,sale,profitandregio naredeterminationso f a c c e s s t o credit.A l l v a r i a b l e s havep o s i t i v e relationshipw i t h p r o b a b i l i t y ofaccesst o credit.T h e highestsignificantvariablei s l o a n q u o t a
Vietnamese empiricalstudies
According to Le (2012), a study utilizing cross-sectional enterprise survey data and a logit model examined the participation of Vietnamese SMEs in the credit market Data were gathered from a survey of 1,024 enterprises across five representative regions of Vietnam: the Red River Delta, North Central Coast, Mekong River Delta, South Central Coast, and Southeast The findings revealed that the value of machinery, the proportion of loans from banks, and the percentage of national sales positively influence the probability of accessing credit Conversely, the availability of overdraft facilities negatively impacts this probability Additionally, different industries exhibit varying probabilities of credit access, with the service sector having the highest likelihood Notably, businesses in the Red River Delta and Central North regions have a greater probability of securing bank loans compared to other areas.
Int h e o t h e r s t u d y , NguyenandLuu( 2 0 1 3 ) collecteda paneldataseta n d appliedt h e Un ordered-
A study conducted in 2007 and 2009 categorized independent variables into four groups: owner’s characteristics, firm’s characteristics, network, and regions The findings revealed that owner’s characteristics, such as age, experience, and ethnicity, significantly influence the ability to secure formal loans In contrast, among the firm’s characteristics, only firm size was found to affect the likelihood of accessing formal finance, while factors like ownership type, firm age, and profitability did not show a significant impact Additionally, companies with diverse networking opportunities demonstrated a higher probability of obtaining bank financing Interestingly, rural-based firms were found to have better access to bank loans compared to firms located in larger cities like Hanoi, Ho Chi Minh City, or Haiphong.
InthestudyofLe(2013),sheidentifiedthecharacteristicsthatinfluenceaccesstocrediti n Viet nam.Thedatasetwasconductedin fiveregionscontaining14provincesandhad1,150observationsin
2005 Sheappliedlogitmodelandfoundfourfactorimpacts on probabilityofaccesstocredit.Fourvariablesaretypeofownership,export,profit,newfixedass et.Theyhavepositivesignwith abilityaccesstocredit.
Anotherv i e w o f V o atel(2011),theyusedd a t a o f 1 6 9 firmsw e r e collectedi n s i x provi ncesVietnam.T h e y r u n t h e logisticregressiont o f i n d t h e relationshipbetweent h e chances ofgettingloanwithfirmage,sizefirm,owner’sexperienceandproductionnetwork.T h e y f o u n d t h a t t h e a b i l i t y o f gettingl o a n increasedf o r o l d e r f i r m s , largerfirms,m o r e experi enceandp a r t i c i p a t i o n i n productionn e t w o r k s T h e l e n d e r s s e e m prefere n t e r p r i s e s whichhavecollateral,and quantitybusinessplans.
No Author Data Methodology Finding
Logitmodel Therewere threeexcitingfindingsintheirstudy.Fi rstly,thefirmsize,tangibilityandt h e lengtho f t h e l e n d i n g relationshiph avenotsignificancesontheprobabilit yofobtaining al oa n Secondly,t h e profit,t h e debtratioandt h e u s e o f ov erdraftcredithavepositiverelationshi pw i t h t h e p r o b a b i l i t y ofobta iningaloan.Finally,theprobabilit yo f o b t a i n i n g a l o a n decreasewh en liquidityishigher.
182Kenyanfir msi n f o u r sec tors:textile, w o o d , food, metalin1993
Probitmodel Firmsize,lengthofrelationshipwitht h e lender,educationofowner/ manageranda v a i l a b i l i t y ofcollater alareimportantdeterminantso f access tobankoverdrafts.Thee t h n i c i t y ofb orrowerhasnotimpactonaccesstoov erdraftsbutithasl i t t l e impacto n acc esst o creditsupplier.
214 USA nascententrepr eneurswascoll ectedbetween October2 0 0 5 and
Firmcharacteristics,suchasp o t e n t i a l salerevenue,legalformofth e busines saffecttheacquisitionofpersonaland externalsourcesoffinancing.Owner’se ducation,andt h e entrepreneur’snetw orth,alsoaffecttheacquisitionofcertai ntypeso f financing
Thesmallert h e c o m p a n i e s are,t h e highertheprobabilityofhavingbank ingproblemsis.Theageofthefirmh asnotsignificant effectono b t a i n i n g bankl o a n s w h i l e salesturnove r,economicactivity,labor,capital,a ndlegalformaresignificant.
Logitmodel Firmage,typeofownership,loanquot a,sale,profitandregiona r e determinat ionsofaccesstocredit.A l l variablesh avep o s i t i v e relationshipwithproba bilityofaccesst o credit.T h e highestsi gnificantvariableis loan quota.
1,024 Vietnameseent erprisesi n f i v e representativ eregion ofVietnam:Re dR i v e r Delta, theNorthCentr eCoast,Mekon gRiverDelta,S outhCentreC oastandSouth East
The value of machinery, the proportion of bank loans, and the percentage of national sales positively influence the probability of accessing credit, while the availability of overdraft facilities has a negative impact Different industries exhibit varying probabilities of obtaining credit, with the service sector having the highest likelihood Additionally, businesses located in the Red River Delta and Central North regions have a greater probability of securing bank loans compared to other areas.
7 Nhung Panel data Owner’s characteristics including
Nguyen, 7900 age, experience, ethnic do
(2013) Vietnamese Firms i z e impacto n p r o b a b i l i t y firmsin2005, o faccesstoformalfinance.
2009 networkingtend to have higher probabilitytoaccessto bank.
Rural-basethefirmsseemaccess moret h e bankdebtst h a n firms locatedi n b i g citiesl i k e Hanoi,H o Chi Minh orHaiphong
1,150 Vietnamesefir ms infiveregionsc ontaining1 4 pr ovincesi n 200 5.
Logitmodel Typeofownership,export,profit,new f i x e d assetimpacto n p r o b a b i l i t y o faccesstocredit.Theyarepositively relatedw it h ability ofaccesstocredit.
169firmswere collectedi n six Vietnamprovin ces
Theability ofgetting loanincrease sf o r o l d e r firms,largerfirms,moree xperienceandactivei n productionnet works.Thelendersseempreferenterpri seswhichh a v i n g collateral,goodcre ditprofilesa n d q u a n t i t y businessp lans.
Conceptualframework
As aresult ofasymmetricinformation,banksareunabletograntloansforSMEs.Inordert o m i n i m i z e negativ eimpactso f asymmetricinformation,t h e b a n k s r e l y o n p r i v a t e informationo n b o r r o w e r s collectedthroughrepeatedinteraction.Inaddition,p u b l i c informationisoneofthemostimporta ntchannelsforthebankstoapproveofcreditapplication.Therefore,thebanksalwaysprefersuchold erandlargerenterprises.Moreover,t h e businesseswhichhavelongerrelationshipswiththeb anksarealsomorelikelytobeinggrantedloans.
Thefactorsdeterminingaccesstocreditofenterprisescanbecategorizedintothreemaingroups :(i)Group1concernsforOwner’scharacteristicscompriseseducation,ethnicity,
(ii)Group2concernsforFirm’scharacteristicsconsistsoffirmage,firmsize,typeoffirm,
(iii)Group3concernsforRelationshipbetweenbanksandborrowersincludes previouslyborro wed,overduedebt.
Relationship between lenders and borrowers Borrow
1 Owner’sethnicity(Biggsetal,2001),(Nguyen&Luu,2013)
Thisfactorcouldbe apositive coefficient.However,somestudiesfoundthatthis factoris n o t statisticallysignificantonprobabilityofaccesstocredit.Inthemodel,ethnicityofowneris a dummyvariable.Itequals 1 ifowner’sethnicityis Kinh.
Educationofownermayalsoassistinmanagingthebusiness.Ownerwithgoodeducationalbackgr oundc a n givet h e goodidea,rightd e c i s i o n t o i m p r o v e productivity.Therefore,t h e banksm a y p r e f e r t o l e n d t o enterprisesw i t h educatedo w n e r Int h e m o d e l , educationo f owneris adummyvariable.Itequals1 ifownercompletedCollege/University/post-graduate.
Theo l d e r enterprisesm a y h a v e m o r e experienceso f accesst o credit.T h e y i m p r o v e t h e i r reputationandrelationshipwithbanks.Therefore,thenegativeeffectsofasymmetricinformationo n t h e p r o b a b i l i t y o f accesst o creditarem i n i m i z e d S o w e expectp o s i t i v e relatio nshipbetweennumberofyearsof operationandprobabilityofaccess tocredit.
Thisfactorcouldbeastatisticallysignificantcoefficient.Inthemodel,thisvariableequals1ifenterpris ei s Private( s o l e proprietorship)/
Thehigher- revenueenterprisesmayh a v e m o r e profitandfastert u r n o v e r sale.Therefore,t h e y mayhav ehigherfinancialcapacityandprobabilityrepayment.Therefore,bankscanfeelsecuref o r t h e i r l o a n s Inaddition,revenuei s o n e o f t h e m o s t importantfactorswhent h e banksissuedcreditq u o t a t o enterprises.P o s i t i v e relationshipbetweenr e v e n u e andp r o b a b i l i t y ofaccess tocreditwasexpected.ItismeasuredbyVNĐbillion.
Numbero f employeei s o n e o f t h e determiningfactorsofc o m p a n y s i z e T h e s m a l l e r t h e companiesare,thehigherprobability ofhavingbankingproblemsi s Therefore,thebigge rfirmmaybeeasiertoaccessbankcredit.Therefore,weexpectthiscoefficienthaspositivesign.
7 Valueo f l a n d a s s e t , b u i l d i n g asset,e q u i p m e n t asset,i n v e n t o r y ( B e b c z u k , 2004),(Biggsetal,2001),(Voet al, 2011)
Thesevariablesrepresenta v a i l a b i l i t y ofcollateralo r liquidity.T h e h i g h e r a v a i l a b i l i t y ofcollateralorlowerliquidityenterpriseswillhavemoreprobabilityofaccesstocredit.Thes ecoefficients areexpectedtohavepositivesign.TheyaremeasuredbyVNĐbillion.
Totalliabilitiesincludetheformalandinformaldebts.Theformaldebtsmaybeincludebankl o a n s andaccountpayable.T h e enterpriseshavebankl o a n s t h a t m e a n t h e bankshaveinformationofenterprises.Thebankswilldecreasethenegativeimpa ctofasymmetricinformationandadverseselection.Therefore,w e expectt h i s coefficienthasp o s i t i v e sign.Totalliabilities,accountpayablearemeasuredbyVNĐbillion.
Thisvariablerepresentsliquidity.Thehigherliquidityenterpriseswillhavelowerprobabilityo f ob tainingaloan.Thesecoefficientsareexpectedtohavenegativesign.They aremeasuredbyVNĐ billion.
10 Borrow,overdue debt(Bebczuk,2004),(Biggsetal,2001),(Voetal,2011)
The“borrow”variablerepresentst h e c o m p a n i e s whichhadbankl o a n s i n t h e past.T h a t m eanstheenterpriseshavetherelationshipwithbanks.The“overduedebt”representscreditprofileo fcompanies.Thisisthedummyvariable.Itequals1ifcompanyfailstoserviceitsdebtontimein 2008.Theexpectedsignof“Borrow”ispositiveandnegativefor“overduedebt”.
Inorder totesthypothesesbasedupon therelationshipbetweenanexplanatoryvariableandindependentvariables, theexplanatoryvariableisconsideredasprobabilityof SMEsaccesstoformalcredit.
Theexplanatoryvariableofobtainstwovalues:Access i=1, t he ith SME isselectedto lend
Accessi=0,if SME still inneedof aloan but beingreject
99.Hasyourfirma p p l i e d f orbankl o a n s o r o t h e r for malcredits i n c e August20 07
106.Whyhast h e firmn o t ap pliedforformalloanssinceAug ust2007(lastsurvey)?
Don’twantto incurdebt Processtoodiff icult
TheXivectorincludeseducation,ethnicity,firmage,firmsize,typeoffirm,previouslyborro wed,overduedebt…
Ethnicity Owner’sEthnicity =1 ifKinh,0 otherwise N/A
=1 if College/University/post- Owner’sE d u c a t i o n graduate,
RevenueE mployeeL andBuildin gEquipme ntI n v e n t o r y Liabili tiesAP
=1 ifPrivate(soleproprietors hip) /Limited Typeoffirm: liabilitycompany,
=1 ifyes,0 otherwise in 2008, Hadborrowedfrombanks in
Backgroundof SMEFinancinginVietnam
0 0 7 Totalregisteredenterpriseincreasedf r o m 1 4 4 5 3 enterprisesi n 2 0 0 0 t o 499.519enterpr isesin2010.Ontheotherhand,Vietnamesefinancialmarkethasopenedandliberalizedsince200 0.Itwitnessedtheaccessionofthedomesticandforeign.Theyprovidecapitalforbusinessesinclu dingSMEs.
Thea v a i l a b i l i t y o f creditresourcesf o r S M E s i s o n e o f t h e m a i n factorsf o r ent repreneurialactivity.AccordingBeck(2008)thefirmswithgreateraccesstocapitalarem o r e a blet o e x p l o i t growthandinvestmentopportunities.M o s t o f enterprisesh a v e newinvestment sduring2005-2009,howeverithastenddecline.The proportionoftheinvestmentdecreasesfrom62.42%in2005to60.7%in2009.Oneofthemainreaso nsofdecreaseisthechangeofthesource capital.Before2009,internalfundswerethemain cap italforinvestment,however,in2009;banksorcreditinstitutionsbecomethemaincapitalforinvestm ent.
Source:A u t h o r c a l c u l a t e d fromCharacteristicso f t h e Vietnameseb u s i n e s s environ ment:evidencefroma SME surveyin 2005, 2007, 2009
A d d to c a p a c it y R e p la c e o ld e q u ip m e n t Im p ro v e p ro d u c ti v it y Im p ro v e q u a lit y o fo u tp u t P ro d u c e a n e w o u tp u t S a fe ty E n v ir o n m e n ta lr e q u ir e m e n ts O th e rp u rp o s e
THE MAIN PURPOSE OF THE INVESTMENT
Enhancing access to credit is essential for fostering efficient growth in the SME sector, as it creates an economic environment conducive to firm development and prosperity (Thorsten, 2011) Improved access to credit not only boosts firm performance but also facilitates market entry, supports company growth, reduces risks (Beck, 2008), and encourages innovation and entrepreneurial activities (Klapper, 2006) Enterprises utilize credit to expand capacity, replace outdated equipment, enhance productivity, improve output quality, and meet safety and environmental standards Notably, the drive to "add to capacity" accounts for over 50% of these investments.
Source:A u t h o r c a l c u l a t e d fromCharacteristicso f t h e Vietnameseb u s i n e s s environ ment:evidencefroma SME surveyin 2005, 2007, 2009
Accesst o crediti s o n e o f t h e importantf a c t o r s f o r S M E s g r o w t h anddevelopmen t.However,SMEsoftenfacethecreditconstrainedsituation.Thenumberofenterprisesapplying andobtainingbank loans orotherformsofformalcreditin 2005,2007and2009ares h o w n i n t a b l e 3.1.
Enterprise Yes No Yes No Yes No appliedfor (1108) (1713) (970) (1665) (998) (1660) formalloan 39.28 60.72 36.81 63.19 37.55 62.45
Problemsgetting Yes No Yes No Yes No loans (214) (894) (200) (770) (221) (777)
Source:A u t h o r c a l c u l a t e d fromCharacteristicso f t h e Vietnameseb u s i n e s s environ ment:evidencefroma SME surveyin 2005, 2007, 2009
Thenumberso f enterpriseshaveappliedf o r b a n k l o a n s whichdecreasei n t h e period2005- 2009.Theenterprisesappliedf o r formall o a n whichm a y behaveproblemsgettingl o a n s , s o t h e i r l o a n applicationwered e n i e d T h e proportiono f e n t e r p r i s e s hasp r o b l e m s g ettingloansincreasefrom19.31%in2005to22.1%in2009.Themainreasonsaredifficultiesin obtainingclearancefrombankauthoritiesandlackofcollateral.
The lack of collateral has hindered a proper assessment of the enterprise's potential, while complicated government regulations and administrative challenges have made it difficult to obtain necessary approvals from banking authorities.
Source:A u t h o r c a l c u l a t e d fromCharacteristicso f t h e Vietnameseb u s i n e s s environ ment:evidencefroma SME surveyin 2005, 2007, 2009
Whethero r n o t creditapplicantshaveproblemsi n o b t a i n i n g l o a n s , t h e y s t i l l consider themselvesinneedofaloan.Theycanbeclassifiedascreditconstrained.Creditconstrainedgroupm aybeincludesomeoftheenterpriseswhichdidnotapplyforformalloans.Around7 0 percentofth eseenterprisesdonotwantinneed ofloan orincurdebt.Theresto f non- applicantgroupdidnotapplybecausetheydidnothaveadequatecollateral,difficultinlendprocesse sorhighinterestrates.Theseenterprisescanbeconsideredashavinglimitedaccesst o credit.Addin gtheseenterprisesintothecreditconstrainedgroup,theproportionofgroupi s 4 4 7 % in
Why Don’t Enterprises Apply for Loans ?
Inadequate Don’t want Process too difficult Didn’t need one Interest rate too high Already heavily indebted Other collateral to incur debt
Source:A u t h o r c a l c u l a t e d fromCharacteristicso f t h e Vietnameseb u s i n e s s environ ment:evidencefroma SME surveyin 2005, 2007, 2009
StateOwnedCommercialBank(SOCB) is the mostimportantformalsourceforSMEs.
Itsshareisalways60%higheroftotalformalsource.Thenextpositionisprivate/ jointstockbanks(above10%).Inaddition,foreignbankshaveincreasinglystrongerroleinthefinanc ials u p p o r t fortheSMEs.
Source:AuthorcalculatedfromCharacteristicsoftheVietnamesebusinessenvironment:evide ncefroma SME surveyin 2005,2007, 2009
ThebanksappreciateCertificateo f LandU s e Righto r h o u s i n g whichcanb e usedascollater alf o r t h e m o s t importantformall o a n Certificateo f LandU s e Rightwereusedascollateralf o r m o r e t h a n 5 0 percento f t h e m o s t importantformall o a n andt h e proportionincreaseduring2005-2009.
Land Housing Capital equipment Personal belongings Other
Source:A u t h o r c a l c u l a t e d fromCharacteristicso f t h e Vietnameseb u s i n e s s environ ment:evidencefroma SME surveyin 2005, 2007, 2009
Enterpriseswhichareincreditconstrainedgrouphavethe option ofaccessingtotheinformalcreditmarket,which is fairlywell-developed inVietnam.The proportionappliedf o r informalcreditof creditconstrainedgroupisalwayshigherthannon- creditconstrained.These proportionshavetendedtoincreasefor bothgroups.
Source:A u t h o r c a l c u l a t e d fromCharacteristicso f t h e Vietnameseb u s i n e s s environ ment:evidencefroma SME surveyin 2005, 2007, 2009.
Data
The data for this study was obtained from the Survey of Small and Medium Scale Enterprises (SMEs) in Vietnam, conducted in 2009 by the Institute of Labour Studies and Social Affairs (ILSSA) under the Ministry of Labour, Invalids and Social Affairs (MOLISA) and the Department of Economics at the University of Copenhagen The survey encompasses 2,659 enterprises, representing a cross-section of the SME sector Data was collected through questionnaires focusing on SMEs' access to credit, covering essential owner characteristics such as education and ethnicity, as well as firm characteristics including type of firm, age, revenue, number of employees, and total liabilities.
Researchmethodology
Descriptive analysis
Inthissection,theanalysisconsistsofcalculationandcomparisonofSMEcreditmarkets,borro wer’scharacteristics,l o a n s andtermso f l o a n byusingd e s c r i p t i v e statistics.T h e descriptiv estatisticsanalysisalsoprovidessomeinsightofvariousfactorsrelevantto theu s e ofloans,whichwillbebeneficialtoanalysesandestimationsoftheeconometricmodeldi scussedin thefollowingsection.
Econometricmodel
SMEcharacteristicsofcreditaccessa r e analyzedi n t h i s s e c t i o n D u e t o u s i n g non- r a n d o m l y selecteds a m p l e s i n t h e S M E s 2 0 0 9 datasett o e x a m i n e p r o b a b i l i t y o f a ccesst o creditofSMEs,theresultcouldbebiasedestimatesbyusingtheOLSanalysisbecauseofa d i c h o t o m o u s dependentvariable.W h e r e t h e r e s p o n s e variablei s d i c h o t o m o u s , w e u s e t h e cumulativedistributionfunction(CDF)forregressionsmodel.Theprobitmodelisthe estimatingonethatemergesfromnormalCDF.Theprobitmodelisoneofthemostsuitablem o d e l ofbinaryresponsevariable,isusedtotesthypothesesabouttherelationshipbetweent h e depe ndentandindependentvariables.
Weassumet h e p r o b a b i l i t y o f a c c e s s tocreditoft h eithenterprisesdependsonanun observableu t i l i t y i n d e xI i(latent variable),t h a t i s determinesbye x p l a n a t o r y variables(Xi).WeshowtheindexI ias: I i 0 i
is thecriticaland ifI exceeds theenterprisewillaccesstocredit.
Wheref 0 1 1i n ni ist h e s t a n d a r d normalp r o b a b i l i t y d e n s i t y f u n c t i o n evaluatedat 0 1 1i n ni .Thi sevaluationw i l l dependontheparticularvalueoftheXvariables.
DescriptiveStatistics
Basedon thedataset,only998enterprises(37.55%)haveappliedforformalloans.Thatmeansthese enterprisesstillneedbankloansandwerecategorizedasloandemandgroup.Figure4.1:
Source:CalculatedfromCharacteristicsoftheVietnamese businessenvironment: evidence froma SME surveyin 2009
Inaddition,someofenterpriseswhichdidnotapplyfortheformalloans(1.660enterprises)w ereclassifiedasloandemandgroup.Around74.16%of1.660enterprisessaidt h a t theydonotwa nttoincurdebtornotneedaloan.Itmeans25.84%(428enterprises)stilli n needofaloan.Thereason theydidnotapplyforaformalloanbecausetheydidnothaveadequatecollateral,interestratesaret o o higho r difficultiesi n borrowingprocess.A d d i n g t h e s e enterprises,thetotalenterprisesofl oandemandgroupare 1.427ones(53.69%).Therefore,the sampleofstudyis 1.427 enterprises.
Source:CalculatedfromCharacteristicsoftheVietnamese businessenvironment: evidence froma SME surveyin 2009
Basedo n t h e sample,t h e r e i s m u c h likelihoodo f enterprisescouldt o u c h t h e formalfi nancialmarket.T h e shareofenterprisesa c c e s s crediti s 68.61%.T h a t means9 8 % o f enter priseswhichappliedforformalloancanhavetheloanamounts.
Source:CalculatedfromCharacteristicsoftheVietnamese businessenvironment: evidence froma SME surveyin 2009
Table4.3providesbasicsummarystatisticsof explanatoryvariablesusedin thestudy.Aq u i c k glanceatthefiguresrevealsthattherearesubstantialvariationsinRevenue,Empl oyee,Land,LiabilitiesandArwith theirstandarddeviation which is almost5timesof themean. Min andmaxvaluesofRevenue,Employee, Aralsoindicatethatthese variablesexhibit aw i d e rangeoffigures.
Variable Observations Mean StdDeviation Min Max
Source:CalculatedfromCharacteristicsoftheVietnamese businessenvironment: evidence froma SME surveyin 2009
Thestudyhas5qualitative variablesincluding ethnicity,Education,From,BorrowandOv erduedebt.Mostowners’enterprisesareKinhethnic(95.66%).Themajority ofownershavegra duatedf r o m college,u n i v e r s i t y orp o s t - g r a d u a t e (64.82%).O n l y 3 6 1 6 % ofenterprisesaresole proprietorshiporLimitedLiabilityC ompany.Around 44.78%ofenterprisesd o n o t h a v e r e l a t i o n s h i p s w i t h t h e b a n k and n o previousl o a n s C o n s e q u e n t l y , thereareafewenterpriseshaveoverduedebts
Source:CalculatedfromCharacteristicsoftheVietnamese businessenvironment: evidence froma SME surveyin 2009
Empiricalresults
Table4.4: Correlation matrix ethnicity education yearf ro m revenu eemploye elandbuil dings equipmenti nventoriesl i a b i l i t i e s ap arborro w overduede bt ethnicity
1.00 education year from revenue employee land buildings equipment inventories Liabilitities ap ar borrow overdu~t access
Accordingt o t a b l e 4 4 , therei s a highcorrelation(moret h a n 0 3 ) betweenvariableswhichrepresentassets(land,b u i l d i n g s , equip ment,inventories)andliabilities(liabilities,Ap).Highcorrelationcoefficientoftwovariablesmayimplythereis amulticollinearityin theregression
Inaddition,regressionm o d e l whichu s e s c r o s s sectiond a t a alwaysfacesw i t h heteroscedasticity.However,i n p r o b i t m o d e l , hete roscedasticitydoesnotresultinbiasedparameterestimates.Therefore,theresearchdoesnotapplyBreusch-Pagantesttoremovea n y linear form ofheteroscedasticity.
The studyrunstheregressionmodelwith15explanatoryvariableswhichareclassifiedinto3groups:Own er’scharacteristics,Firm’scharacteristics,Relationshipwithlenders.
Notes:*, **, ***denote10%,5%,and1%levelofsignificance,respectively.
Source:CalculatedfromCharacteristicsoftheVietnamese businessenvironment: evidence froma SME surveyin 2009
The study presents the first regression model incorporating 15 explanatory variables categorized into three groups: Owner's characteristics, Firm's characteristics, and Relationship with lenders Initial regression results indicate that variables such as Education, Liabilities, and Borrowing are significant at the 1% level, while Equipment is significant at the 10% level Conversely, variables including Ethnicity, Year, From, Employee, Revenue, Land, Building Inventories, AP, AR, and Overdue debt do not show significance at the 10% level.
Accordingt o t h e h y p o t h e s i s i s Land,BuildingInventoriesrepresenta v a i l a b i l i t y o fcollateral.TheresearchuseWaldtest totestthecontributionofthe3variablesabove.The n u l l hypothesisofWaldtestisallvariablessimultaneouslyby0.Thetestresultsshowedthat thePvalueequal0.34.WhenthePvalueishigherthanthealpha(10%),wedonotrejectthen u l l hypot hesis.Thatmeansallthecoefficientsareequaltozerosimultaneously.Therefore,t h e researchre movesall3 variablesandr u n againregressionm o d e l w i t h 1 2 explanatoryvariables.
Theresearchu s e W a l d t e s t t o t e s t t h e contributiono f LiabilitiesandApwhichrepresenta v a i l a b i l i t y o f formald e b t T h e n u l l hypothesiso f W a l d t e s t i s t w o variabless i m u l t a n e o u s l y by0.Thetestresultss howedthatthePvalueequal0.010.WhenthePvaluei s lowerthanthealpha(10%),werejectthen ullhypothesis.Thatmeansallthecoefficientsarenotequaltozerosimultaneously.Therefore,theres earchrunagainregressionmodelwith1 2 e x p l a n a t o r y variables.
ThesecondregressionresultshowsthatvariablesincludingEducation,LiabilitiesandBorro wwhicharesignificantat1%level.Equipmentissignificantat5%level andEmployeei s significantat 10%level.
Thesecondmodelisnestedwithinfirstmodel.Therefore,theresearchapplieslikelihoodrat io(LR)testtofindthebettermodel.TheLRtestisatestofthesufficiencyofas m a l l e r modelversus amorecomplexmodel Thenullhypothesisoftheteststatesthatthes m a l l e r model(second model) providesasgoodafitforthedataasthelargermodel(firstmodel).Thetestassumesseco ndmodelisnestedwithinfirstmodel.Thetestresultsshowedt h a t thechi- squaredvalueforthetest(3.64)aswellasthep-valueforachi- squaredof3.64w i t h threedegreesoffreedom.ThePvalueequal0.3036higherthanthealpha(10
%),wedon o t rejectthenullhypothesis.Inotherwords,secondmodelofferssignificantlyb e t t e r goodness-of- fitt h a n f i r s t m o d e l Therefore,t h e researchk e e p regressionm o d e l w i t h 1 2 e x p l a n a t o r y variables(secondmodel).
Regressionresultsh ows t h e relationshipsbetweenprobabilitieso f access tocreditwith dete rminants.
Unexpectedly,Educationhasanegativesign.Thatmeansownerswhohighereducationw i l l havelowerprobabilityofaccesstocreditthanothers.Thisvariableissignificantat1%level.Simila rt o ethnicity,educationi s n o t informationwhichm u s t bedeclaredi n l o a n applications.Theref ore,whenbanksconsiderloanapplications,theymaybedo notappreciatet h e Educationofborrowers.
Employeeisoneofthedeterminantsofcompanysizewhichhashighlysignificantonaccesst ocredit.T h e regressionresultshowsthisvariableissignificantat10%level.Thisvariable,asexpec tedly,i s p o s i t i v e relationshipw i t h p r o b a b i l i t y o f a c c e s s t o credit.Theresultis in linewithpreviousstudiesbyVoetal (2011),Saidetal(2013).
Accordingtotheregressionresult,Equipmentwhichrepresentsavailabilityofcollaterali s p o s i t i v e ands t a t i s t i c a l l y significantat1 0 % l e v e l implyingani n c r e a s i n g r e l a t i o n s h i p o n p r o b a b i l i t y ofaccesstocredit.ThisfindingiscorrespondingwithpreviousstudiesbyBebczu k(2004),Biggsetal(2009),(Voetal (2011).
Asexpected,Liabilitiesimpactspositivelyandstronglyonprobabilityofaccesstocredit.T h i s variableisveryhighlysignificantat1%level.TheresultisinlinewithpreviousstudiesbyBebczuk( 2
0 0 4 ) , Le(2012).Th e enterpriseshavebankl o a n s t h a t m e a n t h e banksh a v e informationofente rprises.Thebankswilldecreasethenegativeimpactofasymmetricinformationandadverseselection So,enterprises’probabilitieshavetendedhigher.
Borrowrepresentst h e c o m p a n i e s whichhadb a n k l o a n s i n t h e past.Thatmeanst h e enterpriseshavetherelationship withbanks.Thisvariable,asexpectedly,ispositive relations hipwithprobabilityofaccesstocredit.Thisvariableisveryhighlysignificantat1%level.T h i s f i n d i n g i s c o r r e s p o n d i n g w i t h p r e v i o u s studiesbyBebczuk( 2 0 0 4 ) , Biggsetal(2009).
Unexpectedly,E t h n i c i t y , Year,From,Revenue,A p , A r andOverdued e b t aren o t si gnificantat10%level.
% l e v e l Thatresulti s n o t appropriatew i t h t h e hypothesisandpreviousstudiesbyBiggsetal( 2009)andNguyen&Luu(2013).InVietnam,theloanapplicationsarenotrequiredaboutborrow er’sethnicity.Therefore,theresultreflectstherealsituationinVietnam.
Surprisingly,Yeari s not significantat1 0 % level This variablewasregardedasfactorwhich reducesnegativeimpacto f asymmetricinformation( A d v e r s e s el ec ti on ) Int h e l o a n applicati on,theborrowerusually presentscompany'shistoryasanevidence oftheirstatus,p o s i t i o n andprestigein businessenvironment.
Similarly,Formdoesn o t impacto n probabilityo f accesst o credit.Thatmeanst h e decisionsofbanksarenotinfluencedbyenterprises’form.Becausethisvariablemaybedosen o t pr ovideinformationabouttheabilityofrepayofborrowers.Thisfindingisnotcorrespondingw ithastudyofGartneretal(2011),Le(2013).However,theresultreflectst h e realsituation inVietnam.
Unexpectedly,R e v e n u e i s n o t significantat10%level.Revenuei s oneo f t h e m o s t im portantfactorswhenthebanksissuedcreditquotatoenterprises.Banksareusuallybasedo n estim atedrevenuet o calculatel o a n d e m a n d o f t h e enterprises.Dependingo n t h e l o a n demand,cre ditrating,businessp l a n , profitandcollateral;banksissuedcreditq u o t a f o r business.Onceente rpriseshavecreditquotas,enterprisescanborrowtoaddworkingcapitalf o r thebusiness.Thetota lloansarealwaysless orequalcreditquotas.
Land,Building,Inventories,A p andA r aren o t significantat1 0 % level.Inrealsituation,t hosefactorsareusedastool tocontrolorcheckmanage-loan(moralhazard).
Overdue debt significantly impacts a company's credit profile and its relationship with banks Surprisingly, this variable is not statistically significant at the 10% level In practice, banks can assess borrowers' credit profiles, including overdue debt and credit rankings, through The Credit Information Centre (CIC) Companies with a history of bad debt will have a lower credit ranking, which may still allow them to borrow, but banks will demand more collateral and impose stricter controls.
Accordingto theregressionresult,theresearchcalculatesthe probabilityofaccesstocreditforeachobservation.Theresearchfinds that50%ofenterpriseshavehigherp r o b a b i l i t y than 0.754.(table4.6)
Source:CalculatedfromCharacteristicsoftheVietnamese businessenvironment: evidence froma SME surveyin 2009
Weseethat themeanofPr(access)is68.5%andthat thestandarddeviationis 0.181
ThemedianPr(access)(the 50thpercentile)is 75.4% The25thpercentileis 50.7%,andthe
The 75th percentile of the dataset is 82.2%, with a minimum value of 31.5% and a maximum value of 100% The four smallest values in the dataset are 31.5%, 33.3%, 35.3%, and 35.6%, while the largest value is 1 Skewness, which measures the asymmetry of a distribution, is -0.131, indicating that the distribution is skewed left since the median (75.4%) is greater than the mean (68.5%) Additionally, kurtosis, which assesses the peakedness of a distribution, has a value of 1.629, suggesting a flatter distribution compared to the normal distribution benchmark of 3.
Inprobitmodel,coefficientsarethepartialeffectsofeachexplanatoryvariableon– thelatentvariable.Togettheeffectofaunitincreaseinxkon the probabilitythaty=1,theresearchusemarginaleffect.
Inp r o b i t m o d e l , marginaleffecti s n o t a constant.Iti s t h e standardnormalp r o b a b i l i t y d e n s i t y f u n c t i o n anddependsontheparticularvalueoftheexplanatoryvariables.Theresearchc alculatesmarginal effectsat means(MEMs)ofexplanatory variables.MEMs arecomputed bysettingthevaluesofXvariablesattheirmeans,andthenseeinghowachangei n o n e o f theXkvariableschangesP(Y =1) (detailtable4.7)
(*)dy/dxis fordiscretechangeof dummyvariablefrom0 to 1
Source:CalculatedfromCharacteristicsoftheVietnamese businesse n v i r o n m e n t : evidencef roma SME surveyin 2009
Thefirstl i n e o f t h e outputindicatest h a t t h e marginaleffectswerecalculatedaftera p r o b i t estimation.T h e secondl i n e o f t h e o u t p u t describest h e f o r m o f y andt h e p r e d i c t c ommandthatwewouldtypetocalculateyseparately.Thethirdlineoftheoutputgivesthevalueo f l a t e n t variable(y=0 7 8 5 ) givent h e valueso f e x p l a n a t o r y variable,w h i c h a r e dis playedin thelastcolumnof thetable(meanvalue).
Theresultshowsthattheprobabilityofaccesstocreditofgroupgraduatedfromcollegew i l l lower6 3 7 % t h a n groupn o n - graduatedf r o m college( h o l d i n g allo t h e r variablesatmean).
TheMEMsofEmployeeis0.0007implyingthatifanenterprisehasonemoreemployee,i t ’ p r o b a b i l i t y will increase0.07%,(assumeallothervariablesequaltheirmeans).
The Marginal Effects of Equipment (MEMs) is 0.0243, indicating that if an enterprise increases its equipment by one billion, the probability of access to credit will rise by 2.43%, assuming all other variables remain constant Similarly, the MEMs of Liabilities is 0.0330, suggesting that a one billion increase in liabilities will boost the probability of access to credit by 3.30%, again holding other variables at their mean Overall, the findings reveal that the probability of credit access for the borrowed group is 25.55% higher compared to the non-borrowed group, with all other variables controlled.
MEMsareeasyto explainand popular.However,MEMsarecriticizedbecause(a) norealenterprisesmayactuallyhavemeanvaluesonalltheXs(b)norealenterpriseshasavaluel i k e 0 6 5 o n a categoricalvariablel i k e Education,or0.05Borrow(c)effectsareo n l y calculat edatonesetof values, themeans.
Therefore,thestudycomputedAverageMarginalEffects(AMEs)whichamarginaleffecti s com putedforeachcase,andtheeffectsarethenaveraged.AMEsprovideabetterrepresentationofhowch angesin XkaffectP(Y =1).
Source:CalculatedfromCharacteristicsoftheVietnamese businessenvironment: evidence froma SME surveyin 2009
Theresultshowsthatonaverage,theprobabilityaccesstocreditofgroupgraduatedfromcollege willlower6.77%thangroupnon-graduatedfromcollege(holdingallothervariablesequal).
TheAMEsofEmployeetis0.0007implyingthatifanenterprisehasonemoreemployee,i t ’ p r o b a b i l i t y will increaseaveragely0.07%(assumeallothervariablesareleftunchanged). TheAMEsofEquipmentis0.0252implyingthatifanenterprisehasonemore billionsofequipment,it’probabilitywillincreaseaveragely2.52%
TheAMEsofLiabilitiesis0.0342implyingthatifanenterprisehasonemorebillionsofLiabiliti es,it’ probabilitywillincrease3.42%(assumeallothervariablesare leftunchanged). Theresultshowsthatonaveragetheprobabilityaccesstocreditofgroupborrowedwillhighe r25.92%thangroupnon-borrowed(holdingallothervariables equal).
MarginalEffectsattheMeans(MEMs)arecomputedbysettingthevaluesofXvariablesattheir means,andthenseeinghowachangeinoneoftheXkvariableschangesP(Y=1).W i t h Averag eMarginalEffects(AMEs)amarginaleffectiscomputedforeachcase,andtheeffectsa r e t h e n ave raged.M a n y preferA M E s becausetheyt h i n k theyp r o v i d e a b e t t e r representationof howchangesin XkaffectP(Y =1).
Conclusion
State-Owned Commercial Banks (SOCBs) serve as the primary formal source of financing for SMEs, particularly valuing the Certificate of Land Use Rights or housing as collateral for essential loans However, enterprises seeking formal loans often face challenges, primarily due to difficulties in obtaining clearance from bank authorities and a lack of collateral As a result, credit-constrained enterprises frequently turn to the informal credit market, with a higher proportion of these groups applying for informal credit compared to their non-credit-constrained counterparts This trend has been on the rise for both groups.
Asymmetricinformationisthemaintheoryoftheresearchtoclassifythefactorsdeterminin gaccesstocreditofSMEsintothreemaingroups:Group1concernsforOwner’scharacteristicscom priseseducation,ethnicity,G r o u p 2 c o n c e r n s f o r f i r m ’ s characteristicsconsistsoffirmage ,firmsize,typeoffirm;Group3concernsfor relationshipbetweenbanksandborrowersincludespreviouslyborrowed,overduedebt.
Basedon thedataset of1427enterprisefrom“Characteristicsof theVietnamesebusinessenvironment:evidencefrom a SME surveyin2009”, theresearchhas appliedprobitmodel toi d e n t i f y thedeterminantso f t he accesst o formalcreditbysmalla n d m e d i u m - s i z e d enterprises(SMEs) in Vietnam.
TheresultshowsthatEducation(negative),Employee,Equipment,LiabilitiesandBorrow(pos itive)whicharesignificanto n p r o b a b i l i t i e s o f accesst o c r e d i t T h e researchf i n d s t h a t 5 0 % o f enterpriseshavep r o b a b i l i t y o f accesst o credith i g h e r t h a n 7 5 4 % T h e paperf indsthatEthnicity,Year,From,Revenue,Ap,Ar,Overduedebtdonotcontributetocreditaccessof SMEs andarenotsignificantat 10%level.
Inconclusion,theformalcreditmarketplaysaveryimportantroleforcapitalofSMEs.Howev er,accesst o t h i s sourcei s s t i l l a challengef o r S M E s T h e b a r r i e r s , d i f f i c u l t i e s i n a ccessingcreditfromformalsourceshaveforcedtheSMEstoinvolveintheinformalcreditmarket.
PolicyImplication
The regression analysis indicates that the characteristics of business owners, specifically ethnicity and education, have minimal impact on the likelihood of accessing credit for SMEs in Vietnam While ethnicity shows a positive correlation, it is not statistically significant at the 10% level, and loan applications do not require disclosure of the borrower's ethnicity Additionally, the analysis reveals a negative relationship between education and credit access, suggesting that owners with higher education levels may face lower probabilities of obtaining credit Similar to ethnicity, education is not a mandatory disclosure in loan applications, which may lead banks to overlook the educational background of borrowers when evaluating loan requests.
Employee size, equipment availability, and liabilities significantly influence access to credit markets Larger firms typically have an easier time securing bank credit due to their higher financial capacity and repayment probability, which makes banks feel more secure in granting loans Equipment serves as collateral, reducing moral hazard and allowing banks to monitor loan utilization effectively; thus, greater collateral availability increases the likelihood of credit access for enterprises Additionally, when banks have information on a company's existing loans, they can mitigate the risks associated with asymmetric information and adverse selection Consequently, liabilities positively and strongly impact the probability of accessing credit.
Thirdly,theprobabilityofaccesstocreditofgroupborrowedwillhigherthangroupnon- borrowed.Thatmeansthebankseempreferenterpriseshavetherelationshipwithbanks.Thebanksus ethe historyoftransactiontovaluefinancialcapacityofSMEs.Therefore,the banksd e n y adverse selection.
Limitationsanddirectionsforfurtherstudies
Theresearchobjectivei s t o i d e n t i f y d e t e r m i n a n t s o f accesst o formalcreditbyS M E s i n Vietnam.Thedatasetofresearchwascollectedin2009withsampleof1427enterprise.Thes t u d y findsfourfactorssignificantlyimpactonSMEscreditaccessibilitybutnotfoundinthed a t a setsuch asfirmage,typeoffirm,Revenueandlengthofrelationship withthe lender.
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Kunt.2 0 0 7 “SmallandM e d i u m EnterprisesacrosstheGlobe,”SmallBusinessEcono mics,29,415–434.
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APPENDIX1:MAINQUESTIONNAIRE:SURVEYOFSMALLANDMEDIUMSCALEMAN UFACTURINGENTERPRISES(SMES)INVIETNAM2009
93.Totalassetsin 2008(end-year)(1,000VND)( i n marketvalue) a) Totalphysicalassetsa a)Land ab)Buildings ac)
(Aq93a) (Aq93aa) (Aq93ab) (Aq93ac) (Aq93ad) (Aq93ae) (Aq93af)
99 Hasyourfirma p p l i e d f o r b a n k l o a n s oro t h e r formalcredits i n c e August2 0 0 7 ( l a s t s urvey)?
Code:Yes(1),No(2) a)Ifyes,why? (Aq100a)
Code:Lackofcollateral(1);Didnot deliveraproperdescriptionofthe potential ofthe enterp rise(2);C o m p l i c a t e d governmentr e g u l a t i o n s (3)Administrativedifficultiesino b t a i n i n g clearancefrombankauthorities(4);Other(5).
101 Howmanyformalloans(short/ longterm)haveyourfirmobtainedsinceAugust2007(lastsurvey)? a) Numberofformalshorttermloans b) Numberofformallongtermloans
_(Aq102a)Code:S t a t e OwnedCommercialB a n k (SOCB)(1),Private/joints t o c k b a n k (2),For eignb a n k (3),SocialP o l i c y Ba nk (4),DAF(Developmenta s s i s t a n c e fu nd ) (5),Targeted programs(6),Other(7)
103 wmanyformalloanapplications(shortandlong term)havebeendeniedsinceAugust2007? a) Numberofformalshorttermloans b) Numberofformallongtermloans
104 Specificationofthe mostimportant(invalueterms)currentformalloan. a) Source (Aq104a)
Codes:StateOwnedCommercialBank(SOCB)(1),Private/ jointstockbank(2),Foreignb a n k (3),SocialP o l i c y B an k (4),DAF(Developmenta s s i s t a n c e fu nd ) (5),Targetedp r o g r a m s (6), Othersources(7) b) Amount originallyborrowed(1,000VND). c) Whichyearandmonth didyouborrow?
(Aq104b) (Aq104c) c1)Whatis thedurationoftheloan(months) (Aq104c1) d) Currentliability(1,000VND). e) Interestrate, %month (Aq104d)
(Aq104e) f) Didyourfirmhaveto offerassetsascollateralforthe loan? (Aq104f)
Code:Yes(1),No(2) fa)IfYes,what kind ofcollateral? (Aq104fa)
(1);Housing(2);Capital equipment(3);Personalbelongings(4);Other(5). g) Isthereaguarantorforthisloan?
(Aq104g) ga)Ifyes,whichrelations doguarantorandthefirmhave? (Aq104ga)
Code:Family(1);F r i e n d s (2);T r a d e partnero r businessrelationship(3),Creditguaranteefun d(4),Member of thisguaranteeorganization(5);Other(6)
105 Doyoustill thinkthatyouarein need of aloan? (Aq105)
Code:Yes(1),No(2) a) Ifyes(1),why? (Aq105a)
Code:T o p a y debt/ tocompensatef o r losses(1);f o r recurrentspendings(2);investment(3);Other(4) b) Ifno (2),why? (Aq105b)
Code:Haveenoughownfunds(1),don’twant/needtoinvest(2),other(3)
106 Whyhast h e f i r m n o t appliedf o r formall o a n s s i n c e August2 0 0 7 (lastsurvey)? (Aq106)
Code:I n a d e q u a t e collateral(1),D o n ’ t w a n t t o i n c u r debt(2),Processt o o difficult(3),Di dn’tneedone(4),Interestrate too high(5),Alreadyheavilyindebted(6),Other(7).
APPENDIX 2: CORRELATION OF DEPENDENT VARIABLE
The analysis reveals significant correlations among various factors, including ethnicity, education, and financial metrics Notably, education shows a positive correlation with employee numbers (0.1569) and liabilities (0.1199), while year and revenue exhibit weaker relationships Equipment and inventories demonstrate stronger associations with employee metrics, highlighting their importance in operational efficiency Additionally, the relationship between borrowing and overdue debts indicates potential financial risks, with a notable correlation to access and probability metrics Understanding these interconnections can provide valuable insights for strategic decision-making in business operations.
Loglikelihood=-763.29079 PseudoR2 = 0.1404 access Coef Std.Err z P>|z| [95%Conf.Interval] ethnicity 210607 1747206 1.21 0.228 -.131839 55305 education -.2229666 0849048 -2.63 0.009 -.3893769 -.056556 year 003127 003736 0.84 0.403 -.0041955 010449 from 0368943 0963353 0.38 0.702 -.1519196 225708 revenue 0013585 0019704 0.69 0.491 -.0025035 005220 employee 0023848 0015184 1.57 0.116 -.0005911 005360 land -.0072493 0080765 -0.90 0.369 -.023079 008580 buildings 0715107 0457734 1.56 0.118 -.0182036 161224 equipment 072408 0412491 1.76 0.079 -.0084386 153254 inventories -.0389642 0463487 -0.84 0.401 -.1298061 051877 liabilities 1095085 0386417 2.83 0.005 0337722 185244 ap -.1168573 0925894 -1.26 0.207 -.2983292 064614 ar 138291 1112542 1.24 0.214 -.0797632 356345 borrow 8563727 0753907 11.36 0.000 7086096 1.00413 overduedebt -.1632543 1494691 -1.09 0.275 -.4562083 129699
probit accessethnici tye duc ati on ye ar fr om re venue empl oyee e q u i p m e n t li abi lities ap arb orr owoverduedebt
LRc h i 2 ( 1 2 ) = 245.60 Prob>chi2 = 0.0000 Logl i k e l i h o od = - 7 6 5 1 0 8 7 7 PseudoR 2 = 0.1383 access Coef Std.Er r z P>|z| [95%C o n f I n t e r v a l ] ethnicity 2123034 1752826 1.21 0.226 -.1312442 555850 education -.221982 0847723 -2.62 0.009 -.3881327 -.055831 year 0031889 0037162 0.86 0.391 -.0040947 010472 from 0436802 0954529 0.46 0.647 -.143404 230764 revenue 0012499 0015498 0.81 0.420 -.0017875 004287 employee 002408 0014471 1.66 0.096 -.0004283 005244 equipment 0825895 0409012 2.02 0.043 0024247 162754 liabilities 1121144 0364867 3.07 0.002 0406017 183627 ap -.1235 0921527 -1.34 0.180 -.3041159 057115 ar 1296437 1103902 1.17 0.240 -.0867172 346004 borrow 8492163 0751358 11.30 0.000 7019527 996479 overduedebt -.1576927 149053 -1.06 0.290 -.4498312 134445
3 Note:0 failur esan d 1 0 su c ce s se s c omp l et e l yde t er mi n e d.