Uni e sidade do Minho
Escola de Economia e Ges ão
Vinícius Gobe i Ribei o
Adjus men in banks’ capi al a ios and i s
e ec s on Po uguese SMEs
Julho de 2020
Adjus men in banks’ capi al a ios and i s e ec s on Po uguese SMEs
Vinícius Gobe i Ribei o
UMinho | 2020
Uni e sidade do Minho
Escola de Economia e Ges ão
Julho de 2020
Vinícius Gobe i Ribei o
Adjus men in banks’ capi al a ios and i s
e ec s on Po uguese SMEs
Disse ação de M es ado
Mes ado em Economia
T abalho e e uado sob a o ien ação do
P o esso Dou o Fe nando Alexand e e
P o esso Dou o Miguel Po ela
ii
DIREITOS DE AUTOR E CONDIÇÕES DE UTILIZAÇÃO DO TRABALHO POR TERCEIROS
Es e é um abalho académico que pode se u ilizado po e cei os desde que espei adas as eg as
e boas p á icas in e nacionalmen e acei es, no que conce ne aos di ei os de au o e di ei os conexos.
Assim, o p esen e abalho pode se u ilizado nos e mos p e is os na licença abaixo indicada.
Caso o u ilizado necessi e de pe missão pa a pode aze um uso do abalho em condições não
p e is as no licenciamen o indicado, de e á con ac a o au o , a a és do Reposi ó iUM da
Uni e sidade do Minho.
Licença concedida aos u ilizado es des e abalho
h ps://c ea i ecommons.o g/licenses/by-nc-nd/4.0/
iii
Acknowledgemen s
Fi s o all, I wan o hank all he people who di ec ly and indi ec ly suppo ed and encou aged
me somehow in he comple ion o he disse a ion. In pa icula , I would like o hank all he
p o esso s who augh du ing hese yea s, especially P o esso Miguel Po ela and Fe nando
Alexand e o hei guidance, a ailabili y and ollow-up du ing he p epa a ion o his disse a ion.
I would also like o hank BPLim eam o he suppo and a ailabili y.

i
STATEMENT OF INTEGRITY
I he eby decla e ha ing conduc ed his academic wo k wi h in eg i y. I con i m ha I ha e no
used plagia ism o any o m o undue use o in o ma ion o alsi ica ion o esul s along he
p ocess leading o i s elabo a ion.
I u he decla e ha I ha e ully acknowledged he Code o E hical Conduc o he Uni e si y o
Minho.
ABSTRACT
We will s udy he c edi supply e ec s o he unexpec ed egula ion in capi al a ios (LTD a io)
imposed by he T oika unde he Economic Adjus men P og am o Po ugal, using an
exhaus i e Po uguese loan-le el da a o SMEs. The in oduc ion o LTDs a ios egula ions may
o ce banks o educe hei exposu e o c edi ma ke s and in o de o adequa e i s balance
shee , banks can educe lending o i ms causing unding p oblems o companies. In o de o
e alua e he impac o his egula ion, we will ha e o cons uc a a iable o measu e he deg ee
o exposu e o i ms’ o mo e o less a ec ed banks. The e o e, we will ha e o con ol o se e al
i m-le el balance shee a iables and sales o accoun o ma ke demand.
Using da a om Cen al Balance Shee p o ided by Banco de Po ugal, an ex ensi e da ase
con aining balance shee a iables o a ep esen a i e numbe o i ms ope a ing in Po ugal, we
conclude ha in ac he LTD a io policy had an impac in i ms’ in es men du ing he pe iod o
analysis.
Keywo ds: C edi c unch; loan o deposi a ios; egula ions; access o c edi ; c isis; liquidi y
shock.
i
Summa y
1. In oduc ion ................................................................................................................. 8
2. Loan o deposi a ios in he con ex o he Po uguese so e eign deb c isis...................... 9
3. Objec i es .................................................................................................................. 12
4. Li e a u e Re iew ........................................................................................................ 14
4.1 Loan- o-Deposi Ra ios ..................................................................................................... 14
4.2 Capi al Ra ios Regula ions ............................................................................................ 15
5. Da a Sou ces and Summa y S a is ics .......................................................................... 17
6. Va iables, Me hodology and Econome ic App oach ....................................................... 20
6.1 Va iables ...................................................................................................................... 20
6.2 Me hodology and Empi ical Model ................................................................................ 22
7. Resul s ...................................................................................................................... 25
7.1 Addi ional obus ness checks ........................................................................................ 29
8. Conclusion ................................................................................................................. 30
9. Appendix A: ................................................................................................................ 31
10. Re e ences ................................................................................................................. 32
ii
Appendix A
TABLE 5 – VARIABLES DESCRIPTION ..................................................................................... 31
Figu es Lis
FIGURE 1 – LTD RATIOS FOR PORTUGUESE FINANCIAL SECTOR AND EURO AREA ................ 10
FIGURE 2 –LOAN-TO-DEPOSIT RATIOS OF PORTUGUESE BANKS ........................................... 13
FIGURE 3 – LOANS TO NON-FINANCIAL SECTOR – BY FIRM SIZE .......................................... 14
FIGURE 4 – LTD RATIOS FOR COLLECTED BANKS IN THE SAMPLE ....................................... 20
FIGURE 5: INVESTMENT VARIATION BY DECILES OF EXPOSURE ............................................ 27
Tables Lis
TABLE 1 – CHRONOLOGY OF EVENTS.................................................................................... 11
TABLE 2 – SMES DEFINITION ................................................................................................ 18
TABLE 3 – DESCRIPTIVE STATISTICS ..................................................................................... 19
TABLE 4 – DEGREES OF EXPOSURE ...................................................................................... 21
TABLE 5 – REGRESSION ANALYSIS ........................................................................................ 26
TABLE 6 – REGRESSION ANALYSIS FOR LARGE FIRMS .......................................................... 29
14
4Figu e 3 – Loans o non- inancial sec o – by i m size
No e: Sou ce BPS a – Mone a y and inancial s a is ics - he ed ma ks indica e
he begin and he inal da e o he Economic Adjus men P og am.
A con ibu ion o he li e a u e is he iden i ica ion o a causal link be ween a nega i e liquidi y shock
(d op o LTD a ios) o a c edi supply o SMEs i ms in Po ugal, using an ex ensi e da ase linking bank
balance shee s, loans, i m balance shee s p o ided by Banco de Po ugal. We will be able o e i y he
impac o public policy and egula ions ega ding he inancial sec o and how his shock lows in o he non-
inancial sec o hough loans o Po uguese SMEs du ing he Economic Adjus men P og am.
4. Li e a u e Re iew
4.1 Loan- o-Deposi Ra ios
Acco ding o Van den End (2016), deposi - aking and loaning by banks a e closely ela ed and he wo
concep s e lec he liquidi y unc ion o banks. The Loan- o-Deposi a io is he main liquidi y indica o ha
measu es his ela ionship be ween loans and deposi s. When loans exceeds deposi s, banks’ ace a
unding p oblem and hey ha e o access inancial ma ke s, which may be mo e expensi e and uns able
(Van den End, 2016).
The mac op uden ial dimension iden i ies he link among unding imbalances a he bank le el and
sys em wide liquidi y isk, in o he wo ds, i a conside able sha e o banks ope a es wi h a unding gap,
nega i e shocks o ma ke unding can s ess he whole banking sec o , a ec ing c edi supply and
economic g ow h. Some pape s in es iga e he link be ween liquidi y a ios and s ess in economy, using

15
LTD a ios and o he a iables, o example, Le Leslé (2012) as a measu e o liquidi y p oblems a he
banking sec o .
As indica ed by Goodha e al. (2013) no single egula o y measu e is enough o add ess he se e al
sou ces o sys emic isk. Thei model in pa icula sugges s ha capi al i sel is insu icien o supp ess he
p oblems ha a ise du ing a c isis. This emphasize he impo ance o unding measu es based on he LTD
a io o alle ia e sys emic liquidi y isk, and e eals ha he a io could be used by he mac op uden ial
au ho i y o add ess long and sho e m liquidi y isks.
Acco ding o Kashyap e al.(2002) he a io will luc ua e on a end e lec ing sho - e m inancial
cycles, i ends o ise in yea s o economic boom, gi en ha he e is ma ke unding o inance he c edi
expansion, u he mo e, he a io usually le els o in u bulen economic ci cums ances when wholesale
unding is eplaced o e ail sa ings and consequen ly c edi g ow h decline.
4.2 Capi al Ra ios Regula ions
The mul iple po en ial adjus men dimensions and sca ci y o his o ical episodes o e alua e he
esponse o banks o a igh ening o liquidi y egula ion has c ea ed a wide ange o iews abou he impac
o liquidi y egula ion.
Be nanke and Lown (1991) analyse he impac o bank capi al on lending du ing he ecession in 1990-
1991 and ind ha a 1 pe cen age poin inc ease in he capi al o asse a io had a posi i e impac o 2.6
p.p in he g ow h a es o loans. On he o he hand, a s udy ca ied ou by Hancock and Wilcox (1993)
analyses bank c edi lows in 1990 and concludes ha each US$1 ha banks ell sho o egula o y capi al
educe bank c edi by US$3, hey also es ima ed he e ec o a sho all in bank capi al ela i e o a speci ic
a ge ( he hypo hesis was ha banks ha e an in e nal a ge ) and he esul s we e o e e y US$1
addi ional in capi al educe loan g ow h by US$1.5. Those pape s used c oss sec ional o panel da a
analysis ha uses egional a ia ion in bank heal h and economic condi ions o se a ela ionship be ween
capi al equi emen s and bank lending.
Some pape s ollowed a dynamic s a egy (such as VAR) placing bank capi al and economic a iables.
In his ein, Be ospide and Edge (2010) in an analysis o he US, uses Bank Holding Companies da a, o
he pe iod 1992-2009, o e i y he impac o banking capi al on lending. Those au ho s ind an impac o
0.7 o 1.2 p.p in loan g ow h o a 1 pe cen age poin inc ease in capi al a io and claim ha hei esul s
a e less conce ning han he o he s because when banks lend o big holding companies hey a e mo e
conce ned wi h he ope a ional isk han he capi al a ios.
16
O he pape s look in o banks bailou s in speci ic coun ies, Giane i and Simono (2013) analyze he
banking c isis in Japan and he e o s by he go e nmen o ecapi alize banks and each equi ed capi al
a ios. They conclude ha la ge capi al in usions made by go e nmen inc eased c edi supply and make
alloca ion mo e e ec i e.
Albe azzi and Ma che i (2010) looked in o lending in I aly a e Lehmann B o he ’s c ash, hey ind
ha acing capi al cons ain s, banks di ec loans o less iskie companies ( ligh o quali y) and he
incapaci y o bo owe s o change hei lending channel (banks), om less capi alized o s onge banks.
Khwaja and Mian (2008) de elop a new me hodology o o e come he iden i ica ion p oblem o
disen angling he dynamics o demand and c edi supply om he e ec o a speci ic policy o e en and
consequen ly he e ec s on eal economy. The s a egy is based on wi hin i m di e ences-in-di e ences
compa ing loans om dis inc banks o he same i m, hus i m ixed e ec s abso b he whole i m speci ic
change in c edi demand he e o e he di e ence es ima ed in loan changes wi hin he same i m can be
seemingly a ibu ed o di e ences in bank liquidi y shocks. The au ho s used a na u al expe ience, he
nuclea es s made in Pakis an in 1998 ha o ced banks o block dolla s wi hd awals in o de o secu e
he go e nmen agains p oblems in balance o paymen s and concluded ha a 1% decline in bank liquidi y
leads o 0,6% decline in bank’s loan o a speci ic i m. The au ho s also looked in o i s di e ences in i m’s
size, he coe icien s o 1% d op in a bank’s liquidi y leads o a educ ion in lending o small i ms o 0,87%
and o 0,3% o la ge i ms.
G opp e al. (2019), ollowing Khwaja and Mian (2008), ocus hei analysis on a speci ic e en : he
inc ease in capi al equi emen s o selec ed banks in he Eu o a ea by he Eu opean Banking Au ho i y in
2011. The au ho s build a panel linking syndica ed loans, banks and non- inancial i ms’ balance shee s
and pe o m hei analysis o bank-le el, loan-le el and i m-le el, exploi ing he exis ence o mul iple bank-
i m ela ionships. G opp e al. (2019) clus e indus ies in o de o isola e speci ic e ec s, inding ha
banks a ec ed by he e en educe hei c edi supply o syndica ed loans by 27 pe cen age poin s
compa ed o banks in he con ol g oup and o i m-le el. They also conclude ha i ms ha ob ain c edi
om a ec ed banks exhibi ed 4 pe cen age poin s less asse g ow h, 5 pe cen age poin less sales and 6
pe cen age poin s less in es men g ow h.
Finally, Iye e al. (2014), using he same app oach as Khwaja and Mian (2008), using i m mul iple
banking ela ionships in o de o con ol o demand e ec s, analysed he speci ic case ha occu ed in
Po ugal du ing he c isis in 2007, he unexpec ed eeze on Eu opean in e bank ma ke , causing a nega i e
liquidi y shock due o lack o unding o banks. The au ho s used a di e ences-in-di e ences app oach
compa ing lending be o e and a e he c isis among banks wi h di e en liquidi y shocks o di e en
17
in e bank bo owing a ios, hey ound ha he dependence o in e bank unds educes c edi supply o
i ms and a 10% inc ease in in e bank bo owing o he lending bank esul s in a 3,7% educ ion in c edi
a ailabili y o i ms, hey also ound ha he c edi supply educ ion is highe o smalle and younge
Po uguese i ms.
5. Da a Sou ces and Summa y S a is ics
We analyze he impac o he egula ion imposed by T oika unde he Economic Adjus men P og am,
du ing 2010 o 2015. In o de o e i y he causal link be ween he liquidi y shock and c edi ma ke , we
ha e o o e come he iden i ica ion p oblem ha is he e ec s o economic c isis in Eu ope and
consequen ly in Po ugal du ing he pe iod, he c isis may cause a educ ion in loans, so is di icul o see
he speci ic e ec s o he adjus men p og am (d op in LTD a ios) i sel .
We ha e access o he eco ds on all g an ed loans, which a e eco ded by he Bank o Po ugal, which
is he egula o and supe iso o he banking sys em in Po ugal. Fo hese pu poses, we employ he
in o ma ion in he c edi egis e (Cen al C edi Regis e , CRC in Po uguese) which con ains con iden ial
and e y de ailed in o ma ion a he loan le el on all comme cial and indus ial (C&I) loans g an ed o all
non- inancial publicly limi ed and limi ed liabili y companies by all banks ope a ing in Po ugal in o de o
calcula e he deg ee o exposu e o i ms o mo e o less a ec ed banks and he Cen al Balance Shee
Ha monized Panel Da a (CBHP) ha p o ides economic and inancial in o ma ion on Po uguese non-
inancial companies.
Acco ding o he CBHP manual, he da ase is cons uc ed based on Cen al Balance da abase which
con ains economic and inancial da a o companies in Po ugal annually since 2006 onwa ds, and is based
on in o ma ion epo ed h ough “In o mação Emp esa ial Simpli icada” (IES), he ha monized panel which
we will use o his esea ch, con ains only a iables ha a e consis en o e ime because hey we e no
a ec ed since he changing o accoun ing sys ems (in 2010, he eplacemen o POC o SNC).
Some delimi a ions we e de ined o he scope o he his esea ch, o example, we exclude om he
CB da abase, all la ge companies (wi h e enues g ea e han 50 mm Eu os pe yea , and employs mo e
han 250 people), so we will ollow he Eu opean Commission Recommenda ion 2003/361/CE and
selec ed only SMEs i ms, by de ini ion: SMEs a e de ined aking in o accoun he numbe o pe manen
employees, u no e and / o o al annual balance shee . Thus, a mic o en e p ise is one ha "
employs
less han 10 people and whose annual u no e o annual balance shee does no exceed 2 million eu os
";
as a small company one ha “
employs less han 50 people and whose annual u no e o annual balance
shee does no exceed 10 million eu os
” and as a medium company one ha “
employs less han 250
18
people and whose annual u no e does no exceed 50 million eu os o whose o al annual balance does
no exceed 43 million eu os
”, as we can see in Table 1 abo e,
1 Table 2 – SMEs De ini ion
Mic o
Small
Medium
Nº o wo ke s
<10
<50
<250
Tu no e
≤2M€
≤10M€
≤50M€
Annual Balance Shee
≤2M€
≤10M€
≤43M€
No es: Tu no e and Annual Balance Shee a e measu ed in Million Eu os.
We choose o no include La ge i ms, due o he acili y o hese companies o seek liquidi y h ough
o he mechanisms in he inancial ma ke , such as, issuance o deb secu i ies, IPO's and public aded
companies, which a e able o aise capi al o inance hei ope a ions h ough he sale o sha es, bo h ypes
o companies do no need exclusi ely c edi o e ed by banks o aise capi al, hus hey could bias he
ob ained esul s. Mo eo e , acco ding o CB da abase, in 2015 SMEs accoun s o app oxima ely, 99,7%
1
o
all non- inancial i ms in Po ugal, (89,1% we e Mic o companies and 10,6% we e Small & Medium i ms),
and in e ms o o al u no e , SMEs accoun s o 58,5% o o al u no e (speci ically, Mic o companies :
15,8% and Small & Medium i ms : 42,7%).
Table 2 p esen s he desc ip i e s a is ics o he a iables used in he eg ession model. Ou da ase
consis s o mo e han wo million obse a ions; he a e age i m in ou sample is hi een yea s old wi h a
median o en yea s. The mean le e age a io is 35% wi h a median o 5%, ou a e age i m is well
colla e ized ( angibili y) wi h a a io o 22%.
1
Au ho s’ own calcula ion using he Ha monized Cen al Balance Shee da abase.
19
2 Table 3 – Desc ip i e S a is ics
Va iable
Mean
Median
SD
Fi m Age
13.45
10.00
12.20
ln (To al Asse s)
11.44
11.50
2.09
Le e age
0.35
0.05
0.86
ln (Tangibili y)
0.22
0.08
0.28
ln (Ne wo h)
10.78
10.80
2.01
ln (Sales)
11.23
11.46
2.34
Expo (% o expo ing i ms)
11%
No es: Va iables a e measu e in Eu os, Fi m Age is measu ed in Yea s
Numbe o obse a ions: 2.433.912
In o de o check whe he a Bank was a ec ed o no by he policy (adjus men p og am), i is
necessa y o collec da a o hei LTD a ios, o easons o con iden iali y and una ailabili y, i was
necessa y o ca y ou a manual collec ion o LTD a ios in he documen s “Balance Shee s & Repo s” o
banks ope a ing in Po ugal h ough hei espec i e websi es. The exp ession used by banks o calcula e
LTD a ios is he same as pe BdP ins uc ion 23/2011, in o de o ha e compa able da a be ween banks,
elimina ing any conce ns ega ding he calcula ion o he a io ha could gene a e disc epan esul s in he
analysis.
Thus, acco ding o exp ession below, he a ios a e calcula ed using he ollowing a iables:
𝑇𝑜𝑡𝑎𝑙 𝐶𝑟𝑒𝑑𝑖𝑡−( 𝑃𝑟𝑜𝑣𝑖𝑠𝑖𝑜𝑛𝑠
𝐴𝑐𝑐𝑢𝑚𝑢𝑙𝑎𝑡𝑒𝑑 𝑖𝑚𝑝𝑎𝑖𝑟𝑚𝑒𝑛𝑡 𝑓𝑜𝑟 𝑐𝑟𝑒𝑑𝑖𝑡)
𝐶𝑙𝑖𝑒𝑛𝑡 𝑑𝑒𝑝𝑜𝑠𝑖𝑡
So, we ha e he ollowing se up: 18 inancial ins i u ions
2
ha ep esen s abou 80% o all c edi
g an ed in Po ugal (acco ding o he
BPLim
), wi h di e en le els o adjus men o LTD a ios, i was no
possible o ind all a ios o all banks due o he ac ha some ins i u ions did no p o ide he indica o in
hei balance shee s, as well as he abo e accoun s o calcula e he indica o .
The e o e, in he p e-adjus men pe iod (2010) we ha e an a e age a io o 140, and in subsequen
yea s, un il 2014 (deadline o banks o adjus hei a ios), he e was a nega i e adjus men o 25
pe cen age poin s o LTD a ios o he obse ed banks, acco ding o Figu e 5.
2
The inancial ins i u ions a e: BPI SA, San ande To a SA, Banco Come cial Po uguês SA, Caixa Ge al de Depósi os SA, Hai ong Bank SA ( o me Espi i o
San o In es men Bank), Finan ia SA, Banco de In es imen o Global SA, Bison Bank (Fo me BANIF Banco de In es men ), Banco Po uguês de Ges ão SA, BEST
SA, Caixa Económica Mon epio Ge al SA, Banco BIC Po uguês SA, No o Banco dos Aço es SA, Banco A lân ico Eu opa SA, Caixa Cen al de C édi o Ag ícola
Mú uo CRL, BANIF SA. Fo analysis pu poses, Banco Espí i o San o was conside ed oo, liquida ed in 2013 and ans o med in o NOVO Banco SA, hus, he
a ios om 2010 o 2013 co espond o Banco Espí i o San o and om 2014 co espond o NOVO Banco SA.

20
5Figu e 4 – LTD Ra ios o Collec ed Banks in he sample
No es: Own compu a ions. Based on he manual collec ion on “Balance Shee s & Repo s” o 18 banks ope a ing in Po ugal ( ed cu e), he blue cu e
ha e he Loan- o-Deposi a ios o he i h la ges banks in Po ugal, acco ding o BdP : Caixa Ge al de Depósi os, BPI, San ande To a, BCP, Banco
Espi i o San o (un il he bank up cy in 2013) and No o Banco ( o 2014 and 2015 alues).
6. Va iables, Me hodology and Econome ic App oach
6.1 Va iables
F om he collec ion o LTD a ios o he 18 inancial ins i u ions men ioned abo e, he
BPLim
(Mic oda a
Labo a o y o Banco de Po ugal) eam iden i ied om each i m's unique andomized code (TINA), he
a ailable c edi
3
by he inancial ins i u ion 𝑏 o i m 𝑖 in yea 𝑡, in o de o build an index ha measu es
he exposu e deg ee o i ms ( h ough loans) o banks wi h hei espec i e le els o LTD a ios.
Deno es in he exp ession (2) 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒𝑖,𝑡, exposu e deg ee o i m 𝑖 in a yea 𝑡 (Decembe alues),
𝐶𝑟𝑒𝑑𝑖𝑡 𝑖,𝑏,𝑡, amoun o c edi (in Eu os), a ailable o i m 𝑖 by bank 𝑏, a yea 𝑡, di ided by 𝐶𝑟𝑒𝑑𝑖𝑡 𝑖𝑡,
which co esponds o he o al olume o c edi made a ailable o i m 𝑖 in yea 𝑡, mul iplied by 𝐿𝑜𝑎𝑛−
𝑡𝑜− 𝐷𝑒𝑝𝑜𝑠𝑖𝑡 𝑏,𝑡 , which co esponds o Bank LTDs a io 𝑏 in he yea 𝑡.
𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒 𝑖,𝑡 =∑𝐶𝑟𝑒𝑑𝑖𝑡𝑖,𝑏,𝑡
𝐶𝑟𝑒𝑑𝑖𝑡𝑖,𝑡
𝑛
𝑏=1 𝑥 𝐿𝑜𝑎𝑛 − 𝑡𝑜−𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑏,𝑡
(1)
We ha e he ollowing si ua ion o he Deg ees o exposu e o i ms du ing he analysis pe iod,
acco ding o Table 3:
3
To calcula e his a iable, BPLim eam used a a iable ha is he sum o egula and po en ial c edi , ep esen ing he o al a ailable c edi ha a i m can
access.
80
90
100
110
120
130
140
150
160
2010 2011 2012 2013 2014 2015
All banks Fi e la ges Banks
21
3
Table 4 – Deg ees o Exposu e
Deg ees o Exposu e
Min
Max
1
0
52.63
2
52.63
73.06
3
73.06
85.00
4
85.00
97.85
5
97.85
107.69
6
107.69
113.00
7
113.00
118.99
8
118.99
127.10
9
127.1
144.00
10
144.00
379.00
No es: Own compu a ions.The deg ees o exposu e
a e he deciles o he a iable 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒𝑖,𝑡
As he abo e a iable cap u es he deg ee o exposu e o i ms o banks (mo e o less a ec ed by he
policy o inc easing LTD a ios) i is no cohe en o analyze i as a se , as we would no be able o cap u e
he di e en le els o exposu e and how hey in luence in es men . Thus, i is in e es ing o check he
a iable as an exposu e decision, in o de o g oup i ms by hei le els o exposu e and check whe he ,
gi en a highe o lowe le el, wha e ec his has on he in es men o he i ms.
I is expec ed ha gi en he exposu e le els o he i ms, he e lec ion o he dec ease in in es men will
be inc easing, ha is, i ms ha make up he las deciles, will be mo e a ec ed in he in es men a ia ion
han i ms ha make up he i s deciles.
We also include some con ol a iables (see able 2 o a de ailed desc ip ion o he a iables).
Tangibili y is used as a p oxy o colla e al, since i co esponds o a i m's capi al s uc u e. The heo y
sugges s ha companies wi h mo e angible asse s in hei capi al s uc u e a e mo e likely o aise c edi ,
gi en ha hese ypes o asse s a e easily alued by he ma ke and can be used as colla e al (Campello
and Giambona, 2013).
We will use a i m's inancial le e age as a isk measu e, gi en he le el o le e age a bank may be
mo e ap o no o gi e c edi o ha company, so we will use le e age as a isk measu e, he a iable was
calcula ed using Deb scaled by Equi y and Liabili ies.
Ne wo h is a a iable o gauge a company's heal h and i p o ides a snapsho o he i ms’ cu en
inancial posi ion, since lende s (banks) sc u inize a business's ne wo h o de e mine i i is inancially
heal hy. I o al liabili ies exceed o al asse s, a c edi o may no be oo con iden in a company's abili y o
22
epay i s loans and can a ec hei in es men s as well, hus, we ake he loga i hm o To al Asse s minus
To al Liabili ies o calcula e Ne wo h.
We will use a he loga i hm o i m’s o al asse s o con ol o he size o he i ms, acco ding o G opp
e al (2019) and i m's age as a measu e o a lende 's pe cep ion o isk and bank ela ionship, as long as
long- e m ela ionship wi h banks can acili a e access o c edi o olde i ms.
We will use he loga i hm o Sales in o de o con ol o demand e ec s and dummy a iable o
expo ing i ms.
All con ol a iables we e winso ized be o e he log ans o ma ion a he 1% le el ollowing G opp e al
(2019).
We a e in e es ed o e i y he indi ec link be ween i ms’ exposu e (in e ms o global c edi ) o banks
ha had o adequa e i s le els o LTD a ios and how his exposu e a ec s i ms’ in es men s. So, we ha e
cons uc ed a a iable o p oxy in es men using he a ia ion o ixed angible asse s, deno ed in exp ession
(2).
∆𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡𝑖𝑡 =(𝐹𝑖𝑥𝑒𝑑 𝑇𝑎𝑛𝑔𝑖𝑏𝑙𝑒 𝐴𝑠𝑠𝑒𝑡𝑠𝑡−𝐹𝑖𝑥𝑒𝑑 𝑇𝑎𝑛𝑔𝑖𝑏𝑙𝑒 𝐴𝑠𝑠𝑒𝑡𝑠𝑡−1)+𝐷𝑒𝑝𝑟𝑒𝑐𝑖𝑎𝑡𝑖𝑜𝑛𝑠
𝐹𝑖𝑥𝑒𝑑 𝑇𝑎𝑛𝑔𝑖𝑏𝑙𝑒 𝐴𝑠𝑠𝑒𝑡𝑠𝑡 (2)
6.2 Me hodology and Empi ical Model
In o de o achie e he ela ionship ha we a e seeking, his esea ch will ollow G opp e al. (2019)
and will use a Fixed E ec s model o panel da a, since i is he mos used me hod in co po a e inance.
A eg ession model wi h panel da a, wi h 𝑛 obse a ions in 𝑇 pe iods and 𝐾 a iables, can be
ep esen ed as ollows:
𝑦𝑖𝑡 =𝑥𝑖𝑡𝛽+𝜀𝑖𝑡, 𝑖 =1,2,…,𝑛;𝑡 =1,2,…,𝑇 (3)
Whe e 𝑦𝑖𝑡 is he dependen a iable, 𝑥𝑖𝑡 is a 1 𝐾 ec o con aining he explana o y a iables, 𝛽 is a
ec o o pa ame e s o be es ima ed and 𝜀𝑖𝑡 i he e o s. The sub-indices 𝑖 and 𝑡 deno e he obse a ional
uni and he pe iod o each a iable, espec i ely. Thus, in a da abase wi h balanced panel da a, he o al
numbe o obse a ions co esponds o 𝑛⋅𝑇.
I he model ollows all he classical eg ession hypo heses, one can es ima e i by O dina y Leas
Squa es (OLS), ob aining he desi ed es ima es. The main ones e e o he e o , which is assumed o be
homoscedas ic and no co ela ed in ime and space.
The p oblem o he e oscedas ici y, i de ec ed, makes i necessa y o use he me hod o Gene alized
Leas Squa es (GLS). Acco ding o Ve beek (2008), i he O dina y Leas Squa es (OLS) es ima o we e
used, no aking in o accoun he non-homoscedas ici y o he diso de s, he es ima es would s ill be une en
23
and consis en , bu would no be mo e e icien . Thus, he signi icance es s o he es ima es would be
biased i OLS was used. The same a gumen is alid in he p esence o au oco ela ion o e o s.
Ano he p oblem ha may a ise in panel da a, and ha would make he use o OLS impossible, is
endogenei y. This occu s when he co ela ion be ween some explana o y a iable 𝑥𝑗 and he e o is
di e en om ze o, ha is: 𝐶𝑜𝑣(𝑥𝑗,𝜀𝑖𝑡)≠0.
Woold idge (2016) highligh s he h ee main sou ces o endogenei y: omission o model a iables
(unobse ed he e ogenei y), measu emen e o s o he a iables and simul anei y be ween he a iables.
The mos equen p oblem wi h panel da a is he issue o unobse ed he e ogenei y. In his case, he e
would be ac o s ha de e mine he dependen a iable bu a e no being conside ed in he equa ion wi hin
he se o explana o y a iables because hey a e no di ec ly obse able o measu able. Taking in o accoun
he unobse ed he e ogenei y, he model abo e can be ew i en as ollows:
𝑦𝑖𝑡 =𝑥𝑖𝑡𝛽+𝑐𝑖+𝜀𝑖𝑡, 𝑖=1,2,…,𝑛;𝑡 =1,2,…,𝑇 (4)
Whe e 𝑐𝑖 ep esen s he unobse ed he e ogenei y in each obse a ional uni cons an o e ime.
Acco ding o Woold idge (2016), i 𝑐𝑖 is co ela ed wi h any a iable in 𝑥𝑖𝑡 and we y o apply OLS in
his case, he es ima es will be no only biased bu also inconsis en .
The same consequences occu in he model in he case whe e he classical hypo hesis ha he e is no
co ela ion be ween some explana o y a iable 𝑥𝑗 and he e o 𝐶𝑜𝑣(𝑥𝑗,𝜀𝑖𝑡)=0 is no alid. Thus, in his
case, we can only use OLS i we ha e jus i ica ion o assume ha 𝐶𝑜𝑣(𝑐𝑖,𝑥𝑗)= 0. I ha hypo hesis is
alid we can conside a new compound e m, 𝑣𝑖𝑡 ≡ 𝑐𝑖+𝜀𝑖𝑡 and es ima e he model by OLS, since we
would ha e 𝐶𝑜𝑣(𝑉𝑖𝑡,𝑥𝑗)=0.
In he case whe e 𝐶𝑜𝑣(𝑐𝑖,𝑥𝑗)=0 in o de o es ima e his equa ion consis en ly, he mos usual
app oach in he con ex o longi udinal da a is Fixed E ec s. In his es ima ion me hod, e en allowing
𝐶𝑜𝑣(𝑐𝑖,𝑥𝑗)=0 he idea is o elimina e he unobse ed e ec 𝑐𝑖, based on he ollowing assump ion
𝐸(𝜀|𝑥𝑖,𝑐𝑖)=0, whe e 𝑥𝑖≡(𝑥𝑖1,𝑥𝑖2,…𝑥𝑖𝑇) known as a s ic exogenei y condi ion. The ans o ma ion o
ixed e ec s (o ans o ma ion wi hin) is achie ed in wo s eps. Taking he mean o equa ion (4) in ime we
ob ain:
𝑦𝑖= 𝑥𝑖𝛽+𝑐𝑖+𝜀𝑖 (5)
and sub ac ing (5) om (4) o each 𝑡, we ge he ans o med equa ion o ixed e ec s,
yi −yi=(xi −xi)β+εi −εi (4)
o
𝑦󰇘𝑖𝑡 =𝑥󰇘𝑖𝑡𝛽+𝜀󰇘𝑖𝑡, 𝑖 =1,2,…,𝑛;𝑡 = 1,2,…,𝑇 (6)
30
8. Conclusion
In his hesis, ou main objec i e was o e alua e he impac o he LTDs egula ion policy imposed by
he T oika wi hin he scope o Economic Adjus men P og am o Po ugal and o es ablish a ela ionship
be ween his liquidi y shock and he i ms’ in es men capaci y. We achie e iden i ica ion by accoun ing o
he le el o exposu e o i ms o mo e o less a ec ed banks (who supplies c edi o i ms’). We p oceed by
b eaking his exposu e a iable in deciles and compa ing he magni ude e ec o less and mo e a ec ed
i ms.
Ha ing access o a e y comple e da abase, wi h de ailed in o ma ion abou all non- inancial i ms
ope a ing in Po ugal, allowed us o con ol o i m cha ac e is ics ha a e adi ionally used in co po a e
inance analysis.
We ha e ind s a is ical signi icance in he deg ee o exposu e o i ms (SMEs) a ached o he less and
mo e a ec ed bank, he magni ude o he e ec s in in es men is also c escen , in o he wo ds, gi en he
i s deciles o exposu e, he a ia ion in in es men is lowe (bu nega i e) han in he abo e deciles, in ou
p e e ed speci ica ion, i a ies om -1.5 pp (second decile) o -5.7 pp ( en h decile).
We ha e also es ima ed a obus ness check o e i y he same e ec o la ge i ms, and as expec ed
we didn’ ind any s a is ical ela ionship, a guing ha o la ge i ms, hey could swap egula loans om
comme cial banks o o he inancial solu ions and/o ely mo e in in e nal inance simply because hey’ e
la ge o ha e access o ex e nal inancial ma ke o in a case o a mul ina ional company, in acompany
loaning ( om pa en company o subsidia ies).
Ou esul s in his hesis ha e impo an policy implica ions. The egula o s should ake in o
conside a ion he composi ion o i m-bank ela ionship when imposing a egula ion, he p ocess o as
dele e age o la ge banks (in e ms o LTD a ios) a e co ela ed wi h a nega i e impac on i ms
in es men s, he educ ion o banking concen a ion should be a policy make agenda, wi h mo e op ions,
i ms could swap o banks wi hou liquidi y p oblems.

31
9. Appendix A:
1TABLE 7 – Va iables Desc ip ion
Va iable
𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒 𝑖,𝑡
∑𝐶𝑟𝑒𝑑𝑖𝑡𝑖,𝑏,𝑡
𝐶𝑟𝑒𝑑𝑖𝑡𝑖,𝑡
𝑛
𝑏=1 𝑥 𝐿𝑜𝑎𝑛− 𝑡𝑜−𝐷𝑒𝑝𝑜𝑠𝑖𝑡𝑏,𝑡
Con ol Va iables (Winso ized a 1% le el)
ln (𝑇𝑎𝑛𝑔𝑖𝑏𝑖𝑙𝑖𝑡𝑦)
ln(𝐹𝑖𝑥𝑒𝑑 𝑇𝑎𝑛𝑔𝑖𝑏𝑙𝑒 𝐴𝑠𝑠𝑒𝑡𝑠
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠 )
𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒
𝐷𝑒𝑏𝑡
𝐸𝑞𝑢𝑖𝑡𝑦 𝑎𝑛𝑑 𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠
ln (𝑁𝑒𝑡𝑤𝑜𝑟𝑡ℎ)
ln(𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠)−ln (𝑇𝑜𝑡𝑎𝑙 𝐿𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑖𝑒𝑠)
ln (𝑆𝑎𝑙𝑒𝑠)
ln(𝑆𝑎𝑙𝑒𝑠𝑡)−ln(𝑆𝑎𝑙𝑒𝑠𝑡−1)
ln (𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠)
ln (𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠)
𝐹𝑖𝑟𝑚 𝐴𝑔𝑒
𝑌𝑒𝑎𝑟𝑡− 𝑌𝑒𝑎𝑟 𝑜𝑓 𝑓𝑜𝑢𝑛𝑑𝑎𝑡𝑖𝑜𝑛 +1
𝐸𝑥𝑝𝑜𝑟𝑡
Expo =1 ; Don ’ expo =0
Explana o y Va iable
𝐼𝑛𝑣𝑒𝑠𝑡𝑚𝑒𝑛𝑡
(𝐹𝑖𝑥𝑒𝑑 𝑇𝑎𝑛𝑔𝑖𝑏𝑙𝑒 𝐴𝑠𝑠𝑒𝑡𝑠𝑡− 𝐹𝑖𝑥𝑒𝑑 𝑇𝑎𝑛𝑔𝑖𝑏𝑙𝑒 𝐴𝑠𝑠𝑒𝑡𝑠𝑡−1)+𝐷𝑒𝑝𝑟𝑒𝑐𝑖𝑎𝑡𝑖𝑜𝑛𝑠
𝐹𝑖𝑥𝑒𝑑 𝑇𝑎𝑛𝑔𝑖𝑏𝑙𝑒 𝐴𝑠𝑠𝑒𝑡𝑠𝑡
32
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