Behavior and Importance of Bank Loan Components after Monetary and Non-Monetary Shocks

Transcription

1 Behavior and Imporance of Bank oan Componens afer Moneary and Non-Moneary Shocks Wouer J. den Haan Deparmen of Economics Universiy of California a San Diego CEPR & NBER Seven Sumner Deparmen of Economics Universiy of California a San Diego Guy M. Yamashiro Deparmen of Economics California Sae Universiy a ong Beach November 2001 Absrac: In his paper we analyze he behavior of loan componens during economic downurns ha are caused by a moneary ighening and compare i o he behavior during similar non-moneary downurns. Consisen wih he lending view we find ha oal loans decrease more during moneary downurns han during non-moneary downurns. We find ha he opposie holds for commercial and indusrial loans and ha he effec is sronger for small banks. The bank lending view ypically doesn consider loan componens and inerpres he decline in oal loans as a reducion in business loans ha is believed o worsen he recession. Using a simple heoreical framework we show ha he observed subsiuion ou of (long-erm) real esae loans and ino (shor-erm) commercial and indusrial loans during periods of high ineres raes occurs in he presence of exacly he kind of fricions ha are used in he lieraure o explain he decline in oal loans such as he inabiliy of banks o arac ime deposis o replace checkable deposis. Using sae level daa we show ha hose regions where he subsiuion ino commercial and indusrial loans is he larger are also he regions where he decrease in real income is he smaller. Commercial and indusrial bank loans hus seem o dampen he effecs of high ineres raes. We would like o hank Fernando Avalos John Boyd Macro Del Negro Graham Ellio Marjorie Flavin Mark Gerler Sephan Haggard Jim Hamilon Takeo Hoshi Anil Kashyap Vincenzo Quadrini Garey Ramey Valerie Ramey Frank Smes and William Wakins for useful commens.

2 1 1. Inroducion Moneary policy is hough o be an imporan ool wih which he governmen can influence real aciviy. How moneary policy affecs he economy has been subjec o much debae and several compeing heories exis. In he absence of credible fully specified srucural models empirical work has focused mainly on he effecs of moneary policy innovaions using srucural vecor-auoregressive models or VAR s. Unforunaely i is difficul o use empirical sudies o disinguish beween compeing heories. The reason is ha alhough he compeing heories express differen views on wha causes wha hey have similar implicaions abou he general comovemen of imporan variables like money and loans. Moreover because changes in variables are affeced by agens expecaions he iming of variable movemens is also no conclusive. In his paper we esimae aggregae and regional responses o a moneary conracion for income and hree lending componens commercial and indusrial loans real esae loans and consumer loans. The goal is o use he variaion observed across regional responses and across loan componens o provide imporan addiional insighs in he moneary ransmission mechanism. Unforunaely inerpreaion of he responses of loan variables is hampered by he fac ha during an economic downurn he demand for loans changes. Moreover i is difficul o predic he direcion in which demand will push loans. Even an increase in loans can hus be consisen wih a reducion in supply if demand increased by an even greaer amoun. To deal wih his dilemma we assess which par of he loan responses is caused by changes in income by comparing he behavior of bank loans during a moneary downurn ha is

3 2 an economic slowdown caused by a moneary conracion wih a non-moneary downurn ha is a similar economic slowdown caused by real shocks. By consrucion real aciviy decreases by he same amoun during moneary and nonmoneary downurns and he main difference beween a moneary and a non-moneary downurn is ha ineres raes are higher during a moneary downurn. Consisen wih he bank lending view 1 we find ha oal loans decrease more during a moneary downurn han during a non-moneary downurn. For commercial and indusrial loans however we find ha loans are relaively sable during a moneary downurn bu decrease sharply during a non-moneary downurn. Our resuls sugges ha a fixed income levels an increase in ineres raes generae a srong subsiuion ou of real esae loans ino commercial and indusrial loans. This relaive increase in commercial and indusrial loans during moneary downurns sands in sark conras wih he bank lending view. We develop a heoreical model o explain he subsiuion ou of real esae loans ino commercial and indusrial loans during periods of high ineres raes. The fricion ha banks have difficuly replacing checkable deposis wih ime deposis during a moneary conracion plays a key role in our model. I is exacly his kind of fricion ha is used in he lieraure o moivae he bank lending channel is. Noe ha one would suspec his fricion o be more imporan for small banks. Indeed we find ha he subsiuion ino commercial and indusrial loans is more imporan for small banks. 2 Our paper is hus in spiri very similar o hose ha suppor a bank lending channel bu we show 1 See Bernanke and Gerler (1995) Kashyap Sein and Wilcox (1993) or Kashyap and Sein (1995) for a descripion of he bank lending view. 2 As in Kashyap and Sein (1995) we find ha he decrease in oal loans is bigger for small banks.

4 3 ha he fac ha loan componens differ in heir mauriies and oher characerisics cause hem o move in differen direcions during a moneary conracion in environmens ha also give rise o a lending channel. This paper is also similar in spiri o hose ha suppor a bank lending channel by showing ha bank loans have a posiive effec on real aciviy. In paricular we use he differenial responses across regions (eiher he nine census regions or he saes) o show ha he aypically low amoun of real esae loans during a moneary downurn worsens he recession and he aypically high amoun of commercial and indusrial loans dampens he recession. We are no he firs sudy o use disaggregaed empirical daa o sudy he moneary ransmission mechanism. For example Gerler and Gilchris (1994) disaggregae by firm size and show ha sales of small firms decrease more han he sales of large firms afer a moneary ighening. Kashyap and Sein (1995) disaggregae by bank size and show ha small banks reduce heir amoun of ousanding loans by more han large banks afer a moneary ighening. Alhough aracive from a heoreical viewpoin hese disaggregaion crieria have he disadvanage ha hey do no use maching measures for real and financial aciviy. For example since Kashyap and Sein (1995) do no have daa on he oupu of he cliens of small banks hey canno esablish ha he differenial response beween small and large banks is imporan for real aciviy. The advanage of disaggregaing by region is ha one can mach regional financial measures like bank loans wih regional real aciviy measures. Several sudies mos noably Peerson and Rajan (1995) have documened ha his link is uie srong for commercial and

5 4 indusrial loans. This is because commercial and indusrial loans are no sandardized producs and he relaionship beween firms and banks is clearly an imporan elemen in he marke for business loans. This paper is organized as follows. In Secion 2 we use aggregae daa o compare he behavior of loan componens during moneary and non-moneary downurns. In Secion 3 we develop a heoreical framework for he observed responses in he loan componens. In Secion 4 we sudy he differenial responses in he loan componens in differen regions and we discuss he imporance of loans for real aciviy during he moneary ransmission mechanism. In Secion 5 we evaluae differen inerpreaions for our resuls and he las secion conains concluding commens. 2. The Effec of Moneary Policy Shocks on oan Componens In his secion we analyze he behavior of aggregae loan componens during moneary downurns ha is downurns caused by a moneary ighening and compare i wih he behavior during similar downurns ha are no caused by a moneary ighening. Alhough income behavior is he same in boh ypes of recessions ineres raes are high during moneary downurns and low during non-moneary recessions. In Secion 2.1 we provide some descripive saisics o characerize he ime series behavior of loan componens and heir comovemens wih real income and ineres raes. In Secion 2.2 we discuss he empirical framework ha allows us o idenify he loan behavior during moneary and non-moneary downurns. The resuls are discussed in Secion 2.3.

6 Descripive saisics of he daa The main purpose of his secion is o documen ha he ime series behavior of commercial and indusrial bank loans is subsanially differen from ha of boh real esae and consumer bank loans. In Figure 1 we plo he HP-filered residuals of aggregae personal income 3 commercial and indusrial loans real esae loans and consumer loans. The graphs documen ha aggregae real esae and consumer loans ypically move wih aggregae income. The correlaion coefficiens beween aggregae income and real esae loans and beween aggregae income and consumer loans are eual o 0.66 and 0.72 respecively. Commercial and indusrial loans on he oher hand are less procyclical and he correlaion coefficien is only eual o To shed some ligh on he dynamic properies of he correlaion paerns we also calculae he correlaion coefficiens of he forecas errors for aggregae income and he loan componens a differen forecas horizons. 4 Figure 2 plos he resuls. We observe ha paricularly a he longer forecas horizons commercial and indusrial loans display less comovemen wih income han he oher wo loan componens. In he shor run movemens in real esae and consumer loans are also less srongly correlaed wih movemens in income and are only slighly larger han he comovemens observed for commercial and indusrial loans. Figure 3 plos he dynamic correlaion coefficiens of he hree loan componens wih he federal funds 3 Since sae oupu is no available a a uarerly freuency we use earnings by place of work. See he Appendix A for furher informaion on he daa sources. All real series are deflaed wih he consumer price index (CPI).

7 6 rae. We see ha commercial and indusrial loans are more srongly correlaed wih he federal funds rae han he oher wo loan componens. Only for consumer loans do we observe negaive correlaion coefficiens. 2.2 Idenifying assumpions The sandard sraegy o sudy he impac of moneary policy on economic variables is o esimae a srucural VAR using a limied se of aggregae variables. 5 Consider he following VAR: 6 (2.1) Z = B Z BZ + u ' ' ' where [ X f X ] 1 Z = 1 2 X 1 is a (k 1 1) vecor wih elemens whose conemporaneous values are in he informaion se of he cenral bank f is he federal funds rae X 2 is a (k 2 1) vecor wih elemens whose conemporaneous values are no in he informaion se of he cenral bank and u is a (k 1) vecor of residual erms wih k = k k 2. We assume ha all lagged values are in he informaion se of he cenral bank. To proceed one has o assume ha here is a relaionship beween he residual erms u and he fundamenal shocks o he economy ε. We assume his relaionship is given by: (2.2) u = Aε 4 This procedure is proposed in Den Haan (2000) and he sofware is available a hp://weber.ucsd.edu/~wdenhaan. The forecas errors are from a bivariae VAR when AIC is used o deermine lag lenghs and he choice of deerminisic rend erms. 5 See Chrisiano Eichenbaum and Evans (1999) for an excellen overview. 6 To simplify he expression we do no display consans and seasonal dummies ha are also included.

8 7 where A is a (k k) marixandε is a (k 1) vecor of fundamenal uncorrelaed shocks each wih a uni sandard deviaion. Thus ' (2.3) E[ u u ] = A A'. ' When we replace E[ uu ] by is sample analogue we obain n (n+1)/2 condiions on he coefficiens in A. Since A has n 2 elemens addiional resricions are needed o esimae A. We assume ha A has he following block-riangular srucure: (2.4) A = A A 0 A A A A 33 where A11 is a (k 1 k 1 )marix A21 is a (1 k 1 )marix A22 is a (1 1) marix A31 a(k 2 k 1 )marix A32 is a (k 2 1) marix and A33 is a (k 2 k 2 ) marix. Noe ha he is informaion assumpion made above implies ha A = 23 0.Assumingha A is lower riangular (and he diagonal elemens are posiive) would exacly impose he necessary number of resricions o uniuely deermine he elemens of A buheblockriangular srucure imposed in (2.4) makes fewer resricions. Neverheless Chrisiano Eichenbaum and Evans (1999) show ha he block-riangular srucure is enough o idenify he effecs of he moneary policy shock. This means ha he ordering of he variables wihin X 1 and X 2 is irrelevan for he impulse responses o he moneary policy shock. The only assumpion needed o idenify impulse response funcions of moneary policy shocks is he allocaion of variables o X 1 and X 2. We follow Bernanke and Blinder (1992) and many ohers by assuming ha he federal funds rae is he relevan moneary insrumen and ha innovaions in he federal funds

9 8 rae represen innovaions in moneary policy. Throughou his paper we assume ha X 1 is empy and ha all oher elemens are hus in X 2. Inuiively X 1 being empy means ha he Board of Governors of he Federal Reserve (FED) does no respond o conemporaneous innovaions in any of he variables of he sysem. We refer o his assumpion as he imied Conemporaneous Informaion or CI assumpion in conras wih he Full Conemporaneous Informaion or FCI assumpion for which X 2 is empy. Under he FCI assumpion he moneary auhoriy can respond o curren-period innovaions in he oher variables bu oher variables can only respond o moneary policy innovaions wih a lag. While we do believe ha over a period of a uarer he FED responds o curren informaion he daa available o economic researchers is in mos cases final revised daa. Rudebusch (1998) poins ou ha he esimaed coefficiens of he VAR will be subjec o bias and inconsisency if he FED responds o he innovaions of conemporaneous values of original daa bu he economerician assumes ha he FED responds o innovaions in he conemporaneous values of he revised daa. Therefore since he revised daa se was no available o he moneary auhoriy a he ime of heir acions we feel ha he CI assumpion is reasonable and is o be preferred o he FCI assumpion. 7 In response o a moneary policy shock he included variables like bank loans can respond direcly o he change in he federal funds rae bu hey can also reac in response o changes in variables like real income. Especially in he case of bank loans i always has been an unresolved uesion how one can deermine which par of he 7 Yamashiro (2001) esimaes an empirical VAR under boh he CI and he FCI assumpion in a similar conex and finds ha he resuls are ualiaively very similar. Croushore and Evans (2000) show ha resuls using real-ime daa are similar o hose using revised daa.

10 9 response is a direc response o he moneary ighening and which par is an endogenous response o he subseuen economic downurn. In his paper we propose o answer his uesion by comparing he behavior of bank loans afer a negaive moneary policy shock o he behavior of bank loans afer a series of income shocks. The magniudes of he income shocks are chosen such ha he resuling ime pah of real income is exacly idenical o ha observed during a moneary ighening. Expecaions abou fuure income movemens are no idenical during boh ypes of recessions since a moneary downurn is caused by a one-ime shock and a non-moneary downurn by a seuence of shocks. In Secion 4 where we discuss he resuls wih regional daa and a he end of his secion we show ha he same conclusion is reached when we compare he moneary downurn wih a similar nonmoneary downurn caused by a one-ime real shock oan componens behavior in moneary and non-moneary downurns In his secion we presen he responses of he variables o a moneary ighening and compare he behavior of loans during a downurn caused by moneary ighening wih a non-moneary downurn during which ineres raes decrease. We will refer o he difference beween he wo responses as he income-correced response. We will sar wih a descripion of he esimae VARs. Sample period and VAR specificaions In he VAR we include besides he federal funds rae he rae on 30 year morgages he rae on commercial and indusrial loans he consumer price level

11 10 deflaed aggregae commercial and indusrial bank loans deflaed aggregae real esae loans deflaed aggregae consumer loans and deflaed aggregae income. We consider wo differen VAR specificaions. In he firs specificaion he se of explanaory variables in each regression euaion is deermined by he Akaiki Informaion Crierion (AIC). 89 The maximum lag lengh considered is four. We will refer o his specificaion as he fixed-four-lag specificaion. When he lag lengh is consrained o be he same in each euaion and for each dependen variable hen AIC chooses four lags and he Schwarz or Baysian Informaion Crierion (BIC) chooses wo lags. Since he specificaion wih four lags is in many respecs similar o he firs specificaion we use as he second specificaion he one where each euaion conains wo lags of each dependen variable. We will refer o his specificaion as he fixed-wo-lag specificaion. Alhough he resuls are fairly similar for he wo specificaions here are some findings ha differ in magniude or iming. The VAR s are esimaed over he sample period from he firs uarer of 1977 o he second uarer of Daa sources and he consrucion of he bank loan variables are described in Appendix A. 8 The chosen specificaion is given in Appendix B. More precise he model selecion crierion deermines he lag lengh of each dependen variable and wheher uarerly dummies a linear rend erm and/or a uadraic rend erm should be included. 9 Since we found he resuls o es and esimae coinegraion relaionships o be no robus we chose o simply esimae he sysem in levels. Noe ha esimaion in levels leads o consisen esimaes. When i is no known wheher he variables are inegraed or coinegraed however i is no sraigh forward o deermine he appropriae penaly erms in he model selecion procedure. For example parameers corresponding o inegraed variables ha are no coinegraed converge a rae T and should for his reason obain a smaller penaly erm. We chose o assign eual penaly erms o all variables and hus assign a penaly erm ha is higher han he (asympoically) correc one o inegraed variables ha are no coinegraed. If inegraed variables ha are no coinegraed are less likely o exhibi shor-erm dependence hen our choice of penaly erms is a sensible one.

12 11 The effecs of a moneary ighening We sar by describing he effecs of a moneary ighening on variables oher han bank loans. Each figure presens he resuls for he wo VAR specificaions. In Figure 4 we plo he pah of he federal funds rae in response o a one sandard deviaion shock o afer a sudden moneary ighening. For boh VAR s a one sandard deviaion moneary policy shock leads o a approximaely a one percenage poin increase in he federal funds rae ha dies ou in roughly hree years. Figure 5 plos he response of he aggregae price level. There is slighly more difference beween he responses of he wo empirical specificaions bu in boh sysems he price level increases afer a sudden moneary ighening. 10 The lieraure refers o his posiive response as he price puzzle. Including he price index for sensiive commodiy prices doesn solve he price puzzle as i does in samples ha conain earlier daa. Also noe ha Barh and Ramey (2001) show ha including he index of sensiive commodiy prices resuls in a sicky aggregae price level only because he posiive and negaive secor responses average ou no because secor prices are sicky. Blinder (1987) argues ha prices could possibly increase afer a negaive moneary policy shock if a reducion in he supply of credi leads o a reducion in he supply of oupu. There also is he possibiliy ha he posiive price level responses are due o measuremen errors in he consrucion of he moneary policy shock. The moneary auhoriy ends o raise ineres raes during periods of rising prices. This effec is supposedly aken ou in he VAR and in his respec i is ineresing ha he price puzzle is less 10 Noe ha he increase is very small. The maximum increase in he price level is less han 0.6% for he four-lag (AIC) VAR and less han 0.2% for he wo-lag VAR.

13 12 problemaic in he VAR specificaion ha is more carefully chosen. If his effec is no aken ou compleely however i would be par of he measured moneary policy shock. If so hen he posiive prices are no he response of he moneary policy shock bu are raher jus he coninuaion of rising prices o which he federal funds rae has responded. Finally figure 6 documens ha real aggregae personal income drops in response o a moneary ighening. Bank loan behavior during moneary and non-moneary downurns Figure 7 graphs he response of ineres raes during a non-moneary downurn. Thefigureshowshainresponseoheeconomicdownurnhereisasmallreducion of he federal funds rae. Since income is idenical during he wo ypes of recessions he behavior of ineres raes is he main difference. Panels A B and C of Figure 8 compare he behavior of he hree loan componens during a moneary and a nonmoneary downurn when he fixed-wo-lag VAR is used o calculae impulse response funcions. Panel A plos he behavior of aggregae real esae loans during a moneary and a non-moneary downurn. Consisen wih ypical views of bank loans during he moneary ransmission mechanism we see ha real esae loans indeed decrease more during moneary downurns han during non-moneary downurns. In conras Panel B documens ha commercial and indusrial loans decrease sharply during nonmoneary downurns while during moneary downurns hey show a modes increase. Panel C documens ha consumer loans like real esae loans decrease more during a moneary downurn. Figure 9 shows ha he differenial behavior of real esae loans and commercial and indusrial loans across he wo ypes of economic downurns are

14 13 similar when he variable-four-lag specificaion is used o esimae he impulse response funcions. Several oher auhors have found ha shor-erm lending increases afer a moneary conracion 11. Bernanke and Gerler (1995) poin ou ha hese perverse movemens in credi aggregaes are no inconsisen wih he bank lending channel if he need o finance invenories and wage paymens during periods of falling sales caused he demand for loans o increase by an even greaer amoun. The moderae increase in commercial and indusrial loans afer a moneary conracion would hen sill be consisen wih lending consrains. The empirical finding ha commercial and indusrial loans drop subsanially during non-moneary downurns a leas uesions he view ha bank lending is more consrained during moneary downurns han during non-moneary downurns. If demand for loans is mainly affeced by income and ineres raes hen he finding ha afer correcion for he change in income commercial and indusrial loans increase during periods of high ineres raes may seem puzzling since high ineres raes are unlikely o increase he demand for loans and several papers in he lieraure argue ha he supply of bank loans increases when bank reserves and checkable deposis decrease. Considering prices only makes i more difficul o explain he income-correced increase of commercial and indusrial loans hrough demand effecs. Typically i is believed ha a moneary ighening corresponds wih sluggish or decreasing price levels which should resul in an even larger increase in he real rae. 11 Bernanke and Gerler (1995) Chrisiano Eichenbaum and Evans (1996) Gerler and Gilchris ( ) and Friedman and Kuner (1993).

15 14 Bu even for he observed price level prediced in his VAR real raes increase during a moneary downurn and decrease during a non-moneary downurn. Alhough he income process is by consrucion idenical in he wo recessions expecaions are no since he non-moneary downurn consiss of a series of shocks whereas he moneary downurn is caused by a shock in only one period. If he nonmoneary downurn is hough o be more persisen han he moneary downurn han i is no ha surprising ha firms demand for loans decrease more during a nonmoneary downurn. In his respec i is helpful ha he persisence of income in response o he idenified income shocks differs subsanially across he wo VAR specificaions. While he income response o a real shock is more persisen han he income response o a moneary shock in he fixed-wo-lag specificaion he opposie is rue for he variable-four-lag specificaion. This is documened in Figure 10. Neverheless for boh VAR specificaions we found ha commercial and indusrial loans show a subsanial decrease during he hypoheical non-moneary downurn. Commercial and indusrial loans essenially follow real income. This is documened in Figure 11 ha plos he responses of income and commercial and indusrial loans o a one-ime negaive one sandard deviaion shock. Panels A and B documen he resuls for he fixed-wo-lag and he variable-four-lag specificaion respecively. I basically shows ha in he response o real shocks i is he case ha if income revers faser o is original level hen commercial and indusrial loans do as well while if income revers slower commercial and indusrial loans rever slower.

16 15 The finding ha non-moneary shocks have less persisen effecs on real income is a much more robus finding when we consider shocks o regional income. 12 Neverheless we sill find he same differenial behavior for he loan componens across he differen recessions as is observed in his secion. We herefore don believe ha he demand for commercial and indusrial loans is higher during nonmoneary downurns because agens believe such recessions o be more emporary. If demand for commercial and indusrial loans drops during a moneary downurn and he observed level of indusrial loans is relaively sable or slighly increases he uesion arises wheher he supply of commercial and indusrial loans increases. The noion ha banks increase he supply of commercial and indusrial loans during a moneary ighening however sands in sark conras wih boh he bank lending view ha mainains ha he supply decreases and heir opponens who eiher argue ha here is no change in he supply of bank loans or ha here is a reducion in he supply bu ha i doesn affec real aciviy. In he nex secion we use a simple heoreical framework o show ha in he presence of hose fricions ha are used in he lieraure o explain ha oal loans decrease during a moneary downurn banks would subsiue ou of real esae loans and increase he supply of commercial and indusrial loans. 3. Increases in C&I oan Supply during a Moneary Tighening: An Explanaion In his secion we develop a heoreical framework o explain he observed subsiuion ou of real esae bank loans ino commercial and indusrial loans during 12 See Secion 4.

17 16 periods of high ineres raes. We sar in Secion 3.1 by considering several alernaive explanaions and providing some more empirical informaion o evaluae heir plausibiliy. A key empirical finding is ha during periods of igh money he erm premium decreases which reduces he profiabiliy of real esae loans relaive o commercial and indusrial loans. In Secion 3.2 we analyze in a heoreical framework he condiions under which a declining erm premium should lead o a subsiuion ou of real esae loans ino commercial and indusrial loans Alernaive explanaions for he subsiuion beween loans Since he subsiuion ou of real esae loans ino commercial and indusrial loans is even sronger when he loan responses are correced for he changes in income one canno argue ha he demand for commercial and indusrial loans increases during periods of economic downurn while he demand for real esae loans decreases during periods of economic downurn. I is however possible ha he demand for real esae loans is exremely sensiive o he ineres rae and he demand for commercial and indusrial loans is no. The lack of demand for real esae loans hen forces banks o find oher uses for heir funds. I is also possible ha banks shif he supply of loans owards commercial and indusrial loans because of changing risk characerisics. Figure 12 plos delinuency raes of he hree loan componens for he available sample period afer he HP-filer is used o derend he daa. The cyclical behavior of delinuency raes over his shor sample period suggess ha he risk of real esae loans increases more over he cycle bu he differences wih he risk of commercial and indusrial loans seems minor. Figure 13 plos charge-off raes for he hree loan

18 17 componens. For his series he risk of commercial and indusrial loans increases he mos during he recession of he early 1990 s and seems slighly more cyclical. The relevan uesion here hough is wheher during periods of high ineres raes (keeping real income consan) he risk of real esae loans increases relaive o he risk of commercial and indusrial loans. When we esimae a VAR for he shor sample for which he risk measures are available resuls no repored here can be summarized as follows. 13 When charge-off raes are used as he risk measure we find ha he relaive risk of commercial and indusrial loans increases during periods of high ineres raes. 14 When delinuency raes are used he relaive risk of commercial and indusrial loans again increases for several uarers afer he iniial increase in ineres raes bu is hen followed by a period during which he relaive risk of real esae loans is higher. These resuls hus do no provide suppor for he hypohesis ha he relaive risk of real esae loans increases during periods of high ineres raes bu given he shor sample we have o be careful no oo much weigh on hese findings. The resuls do no seem implausible o us because a subsanial fracion of real esae loans have fixed raes and hus should no be affeced by an increase in curren ineres raes (keeping income consan). The las candidae we consider as an explanaion for he change in he relaive supply of loan componens is a change in ineres differenials. In Figure 14 we plo he responses of he average rae on commercial and indusrial loans and he average 13 The samples are he periods from 1985:1 o 2000:2 and from 1987:1 o 2000:2 for charge-off raes and delinuency raes respecively. We ried several VARs bu o increase power we always excluded he morgage rae he rae on c&i loans consumer loans and he price level. Two lags are used in each VAR. The conclusions are robus o including hree lags and excluding he loan series.

19 18 rae on 30-year morgages ogeher wih he response of he federal funds rae. We observe ha while he rae on commercial and indusrial loans moves uie closely wih he federal funds rae he increase in morgage raes is much smaller. This is of course no surprising since morgages are a long-erm ineres rae. The graph also plos he heoreical response of he rae on a 10-year and 30-year deb insrumen when he expecaions hypohesis holds. These wo raes are even less responsive han he rae on a 30-year morgage. This suggess ha morgages are a good invesmen for banks during periods of rising federal funds raes. The finding ha real esae raes are less responsive han raes on shor-erm deb insrumens implies ha curren-period bank profis come under pressure during periods of high ineres raes since banks finance long-erm asses wih shor-erm liabiliies. Banks may wan o avoid hese downfalls in profis even when hey are emporary. One reason migh be ha low profis would affec he bank s capial posiion and he bank s capial posiion affecs he abiliy of he bank o issue loans. This is rue especially afer he implemenaion of he Basle accord in he beginning of he 1990 s bu is also hough o have been relevan before he implemenaion. We formalize hese ideas in he heoreical framework developed in he nex secion. We wan o poin ou ha even when commercial and indusrial loans do no fall during a moneary conracion i sill may be he case ha some bank dependen firms are denied credi. In fac a former loan officer 15 suggesed ha an explanaion for our findings is ha during periods of high ineres raes i becomes profiable for 14 Tha is he relaive risk increases more during a moneary downurn han during a non-moneary downurn. 15 George Gibson.

20 19 banks o issue loans o large and safe cliens while during periods of low ineres raes banks canno offer compeiive raes o hese firms wih access o he commercial paper marke. The relaively sable sock commercial and indusrial loans migh hus be an increase in lending o new (safe) cliens and a decrease in lending o less desirable cliens Declining erm premium and he supply of loan componens In his secion we develop a simple heoreical model o undersand when and why banks would wan o subsiue ou of long-erm loans (real esae loans) ino shor-erm loans (commercial and indusrial loans). The model is a wo-period model in which he ineres rae increases in he firs period and remains unchanged in he second period. The lengh of he period hus corresponds o he ime he federal funds rae remains high during a moneary ighening. Banks inves in one-period loans 1 and wo-period loans 2. Banks finance loans by checkable deposis D andime deposis T. Checkable deposis are subjec o a consan reserve reuiremen. The amoun of reserves R hus saisfies he following euaion: (3.1) R = θ D. Deposis earn no ineres bu he rae of reurn on ime deposis is eual o r >0. In he benchmark version of he model he banks have unlimied access o ime deposis a he marke rae r and he benchmark version of his model hus doesn have a bank lending channel. We assume ha banks have some monopolisic power and ha heir demand for one-period loans is deermined by he following euaion: (3.2) r 1 = λ r + β 0 - β 1 1.

21 20 We assume ha λ < 1 so ha an eual increase in boh r 1 and r decreases he demand for loans. Similarly he demand for wo-period bonds is given by (3.3) r 2 = λ(r+r )/2 + β 0 - β 1 2 where r is he ineres rae in he second period. Noe ha he demand funcion for wo-period bonds is idenical o ha of one-period bonds excep ha he benchmark ineres rae is differen. For one-period loans i is he curren rae on one-period ime deposis and on wo-period loans is i he wo-period rae according o he expecaions hypohesis. 16 The demand for checkable deposis is given by (3.4) D = α 0 - α 1 r. In he second period he bank can no longer issue wo-period loans bu can issue sill one-period loans. For he second period we herefore have he following se of euaions. (3.5) R = θ D (3.6) r 1 = λ r + β 0 - β 1 1 and (3.7) D = α 0 - α 1 r. The banks maximizaion problem is as follows: s.. max T ' T ' ' + 1 r1 ' 1 ( r1 ') + r2 2 ( r2 ) T ' r' r1 1 ( r1 ) + r2 2 ( r2 ) Tr + 1+ ρ + = (1 θ ) D + T and 2 2 = (1 θ ) D' + T'. 16 Since here is no uncerainy in his model his would be he appropriae benchmark.

22 21 The parameer ρ is he discoun rae of he bank. The hree firs-order condiions are he following: r1 (3.8) r 1 ( 1 ) + 1 = r r1 ' (3.9) r 1( 1 ') + 1 ' = r' and ' 1 1 (3.10) r r2 2) + 2( r' 1+ = r + 1+ ρ 1+ ρ Euaions (3.1) hrough (3.10) specify a sysem in he 10 unknowns r r 1 r 2 D 1 2 r r 1 D and 1 for given values of R and R. In his model we don consider he effec ha ineres raes migh have on income and we don consider he effec ha loan demand is affeced by income. The reason is ha we are ineresed in he effec of increases in he ineres rae on loan componens for a fixed level of income. Also noe ha in his model he governmen can obain a arge ineres rae level by changing he amoun of reserves. Given a arge level of he ineres rae he corresponding level of reserves deermines hen he supply of checkable deposis. The euilibrium condiion for checkable deposis hen deermines he ineres rae r. Finally for a given marke ineres rae and amoun of checkable deposis he bank s firs-order condiion deermines he loan supply. We will consider he effec of an ineres rae increase or moneary ighening on he supply of loans for several differen cases. A moneary ighening is a change in reserves such ha he ineres rae r in he firs period increases bu remains unchanged in he second period. Before we do his i migh be worhwhile o consider he case

23 22 where in response o an increase in r nex period ineres rae r andhediscoun rae ρ increase by he same amoun ha is dr = dr and dρ = dr. In his case boh shor-erm and long-erm loans decrease by he same amoun. Tha is (3.11) d 1 d2 d1 ' = =. dr dr dr Now we will consider emporary increases in he ineres rae. In he firs case banks maximize he objecive specified above and have unlimied access o ime deposis. Romer and Romer (1990) poin ou ha access o ime deposis shelers a bank s loan porfolio when reserves (and hus checkable deposis) decrease and eliminae he bank lending channel. In he oher cases we aler hese assumpions. Case I: Access o ime deposis and life-ime profi maximizaion. In he experimen considered we assume ha only r changes and ha r remains he same. 17 I is no hard o show ha in general 18 shor-erm loans decrease more han long-erm loans. Tha is d d2 (3.12) 1 < < 0. dr dr The inuiion for his resul is he following. If he ineres rae on ime deposis increases he marginal cos of loans increase. To increase he marginal revenue banks have o lower he supply of loans which will increase he ineres rae earned. They do no have o lower he supply of wo-period loans by he same amoun as he supply of 17 If he shock occurs when r = r = ρ hen a change of ρ has no effec. 18 The resuls hold as long as 1-λ-λρ /2 > 0 which will be saisfied unless an eual increase in all ineres raes have hardly any effec on loan demand.

24 23 one-period loans because a decrease in wo-period loans will increase he earned ineres in wo periods while he marginal cos increases only in he firs period. This resul is of course he opposie of wha we wan because we found ha real esae loans decreased more han commercial and indusrial loans. Moreover we found ha commercial and indusrial (or shor-erm) loans increased in response o an increase in ineres raes. Case II: Access o ime deposis and curren-period profi maximizaion. In he second case we consider he assumpion ha banks maximize only curren-period profis. Alhough no par of his model i is no unreasonable o assume ha banks care no only abou life-ime earnings. Temporary losses even if hey are followed by fuure offseing profis may be harmful for banks because i lowers heir euiy posiion emporarily which in urn can affec heir abiliy o saisfy regulaions or more generally heir abiliy o do business. The assumpion ha banks only care abou curren-period profis is of course an exreme case and only used o clarify he argumen. The firs-order condiions for he supply of his period s loan componens are now given by r1 (3.13) λ r + β 0 β = r 1 and r + r' r1 (3.14) λ + β0 β = r. 2 In response o an increase in he ineres rae wo-period loans now decrease more han one-period loans. Tha is 1

25 24 d d1 (3.15) 2 < < 0. dr dr The inuiion is again relaively simple. Each loan funcion has a benchmark rae. In he demand funcion for wo-periods loans he benchmark rae is λ imes a weighed average of his period s and nex period s ineres rae and in he demand funcion for one-period loans i is λ imes he one-period ineres rae. When ineres raes increase marginal coss increase bu because he benchmark rae increases marginal revenues increase as well. Because he benchmark rae increases by less han he ineres rae (λ < 1) marginal revenues are less han marginal coss and banks have o lower he amoun of loans o ge o he opimum loan supply. In response o an ineres rae increase he benchmark of wo-period loans increase less han he benchmark rae of one-period loans which means ha banks have o decrease he supply of wo-period loans by more o ge o he opimum loan supply. Alhough beer his is sill no he desired resul. Two-period loans now decrease more han one-period loans bu one-period loans sill go down. The reason is ha he supply of boh loan componens is compleely independen. The only relevan variable for boh loan supplies is he ineres rae on ime deposis. As long as banks have unlimied access o ime deposis boh loan componens should decrease when ineres raes increase. According o his model he observed increase in commercial and indusrial loans is inconsisen wih he premise in Romer and Romer (1990) ha banks can arac ime deposis during periods of moneary ighening.

26 25 Case III: No access o ime deposis and curren-period profi maximizaion. In his case we assume ha banks have no access o (addiional) ime deposis. In his case he balance shee consrain of he bank becomes (3.16) d 1 + d 2 =(1-θ)dD and he supply of one-period and wo-period loans are no longer independen which is made clear by he firs-order condiion: r1 r2 (3.17) r ( 1 ) + 1 = r2 ( 2 ) Now he model predics ha one-period loans increase as long as deposis don decrease oo much 19 ha is 2 (3.18) d2 d1 < 0 <. dr dr To ge he desired response in his model one hus needs wo ypes of fricions. Firs banks have o care enough abou curren-period profis o ensure ha long-erm loans decrease more han shor-erm loans. Second banks mus be limied in issuing addiional ime deposis so ha hey acually would like o decrease heir posiion of long-erm loans o finance he relaive more profiable shor-erm deposis. 4. The Imporance of Bank oan Supply for Real Aciviy In his secion we sudy he behavior of regional income and bank loan componens afer moneary and non-moneary policy shocks. The goal is wofold. Firs we wan o examine o wha exen he resuls found above for he aggregae 19 We need ha λ/2-2β 1 (1-θ)α 1 >0.

27 26 hold for individual regions. We will show ha alhough here are imporan uaniaive differences across he regional income and loan responses he ualiaive resuls are very similar. In paricular in all nine census regions we find ha during periods of high ineres raes commercial and indusrial loans increase relaive o real esae loans. Moreover if fricions are imporan for he observed subsiuion ino commercial and indusrial loans hen one would expec o find he subsiuion o be sronger in hose saes where he fracion of loans made by small banks is larger. We documen empirical suppor for his conjecure below. The second goal of his secion is o invesigae how imporan changes in bank loans are for real aciviy. The observed uaniaive differences in he subsiuion effec ino commercial and indusrial loans allows us o answer his uesion and we find ha in hose regions in which he subsiuion effec is he sronger are also he regions in which he reducion in income afer a moneary ighening is he smaller. In Secion 4.1 we discuss some key feaures of he regional daa. In Secion 4.2 we discuss he empirical framework we use o analyze our panel daa se. In Secion 4.3 we discuss he behavior of regional variables afer moneary and non-moneary shocks. Finally in Secion 4.4 we assess he imporance of commercial and indusrial loans for real aciviy Descripive saisics of he daa In his secion we use figures and simple descripive saisics o characerize he disaggregaed daa used in his paper. The main poins of his secion are as follows. Firs here is a subsanial amoun of regional variaion he regional

28 27 income series as well as he regional loan series conain considerable idiosyncraic componens. Second he ime-series paerns observed for he hree loan componens differ subsanially across regions. The disaggregaed sysems consis of eiher he nine census regions or he fify saes and he Disric of Columbia. Figure 15 indicaes he locaion of he nine census regions. 20 When larger regions are used one can expec he regional financial measures o beer correspond o he regional real aciviy measure since less bank loans will be made o cliens ouside he region. The advanage of using sae-level daa however is ha one has enough regions o es wheher observed cross-secional paerns are significan. We find he resuls of he wo disaggregaed sysems o be consisen wih one anoher. We herefore use he smaller VAR o graphically illusrae our resuls and he larger VAR o uaniaively examine cross-secional relaionships. Figure 16 plos he HP-filered real income series for he nine census regions and he naional aggregae. Alhough he regional series are clearly correlaed here also are imporan region specific movemens. The cleares example is ha of Wes Souh Cenral which during he second half of he eighies suffered from he falling agriculural and oil prices. Even sronger idiosyncraic componens can be found in he ime-series for he regional loan variables as is documened in Figure The regions include New England (Connecicu Maine Massachuses New Hampshire Rhode Island and Vermon) Middle Alanic (New Jersey New York and Pennsylvania Eas Norh Cenral (Illinois Indiana Michigan Ohio and Wisconsin) Wes Norh Cenral (Iowa Kansas Minnesoa Missouri Nebraska Norh Dakoa and Souh Dakoa) Souh Alanic (Disric of Columbia Delaware Florida Georgia Maryland Norh Carolina Souh Carolina Virginia and Wes Virginia) Eas Souh Cenral (Alabama Kenucky Mississippi and Tennessee) Wes Souh Cenral (Arkansas ouisiana

29 28 Finally we see ha he correlaion paerns of he regional income and loan series differ subsanially across he regions. We do his graphically by comparing New England and Mid Alanic. Figure 18 plos he HP-filered income and loan componens for New England and reveals a fairly srong correlaion beween income and each of he loan componens. Figure 19 displays he same series for he Mid Alanic region and illusraes ha in his region all loan componens and in paricular commercial and indusrial loans are much less correlaed wih regional income. In Table 1 we presen he correlaion coefficiens of he regional loan componens wih income. The able documens ha he correlaion paerns differ subsanially across he regions. For example alhough commercial and indusrial loans is he loan componen ha is he leas correlaed wih regional income in every region i displays srong correlaion properies in New England and Souh Alanic and hardly any correlaion in Mid Alanic and WS Cenral. Table 1: Correlaion of Real Regional HP-filered Income and oan Componens Region C&I Real Esae Consumer New England Mid Alanic EN Cenral WN Cenral S Alanic ES Cenral WS Cenral Mounain Pacific Oklahoma and Texas) Mounain (Arizona Colorado Idaho Monana New Mexico Uah and Wyoming) and Pacific (Alaska California Hawaii Nevada Oregon and Washingon).

30 Regional empirical framework In his secion we describe he empirical framework used o analyze he effec of moneary policy shocks on regional and aggregae bank loans and real aciviy. Because of he large number of variables relaive o he number of observaions we canno use sandard empirical echniues. Several sudies deal wih his dilemma by esimaing a separae VAR for each region. In conras we esimae one VAR for he complee disaggregaed sysem. This sysem includes he federal funds rae he morgage rae he rae on commercial and indusrial bank loans he consumer price level he regional deflaed values of he hree lending componens and he regional real income variables. Even for he sysem wih he nine census regions we have a oal of hiry-six regional series and four naional price series. To deal wih he large se of explanaory variables we assume ha only he aggregae values of he income and loan componen series appear in he ineres rae and price euaions. Also in he regression euaion for each regional income and loan componen series we assume ha besides he own regional series only aggregae loan and income measures are included. This model is hen a resriced VAR on he full se of variables. e ci i sand for he amoun of real commercial and indusrial loans in region i re i he amoun of real real esae loans cn i he amoun of real consumer loans and i Y he amoun of real personal income. All variables are deflaed using he naional consumer price index. A bar over a variable indicaes ha he aggregae measure is being used. The euaions for he federal funds rae f he morgage rae on commercial and indusrial loans ci r and he price level P aregivenby: re r herae

31 30 (4.1) [ ] f Q cn re ci ci re f u Y r r f B f + = = 1 ' (4.2) [ ] f Q cn re ci ci re r re u Y r r f B r re + = = 1 ' (4.3) [ ] f Q cn re ci ci re r ci u Y r r f B r ci + = = 1 ' and (4.4) [ ] P Q cn re ci ci re P u Y r r f B P + = = 1 ' ) ln(. Noe ha he assumpion is made ha he price series and he federal funds rae do no depend on he regional composiion of real income and loans. For each region (i =1oI) we esimae he following euaions: (4.5) [ ] i Y Q i i cn i re i ci cn re ci ci re i Y i u Y Y r r f B Y 1 ' ) ln( + = = (4.6) [ ] i ci Q i i cn i re i ci cn re ci ci re i ci i ci u Y Y r r f B 1 ' ) ln( + = = (4.7) [ ] i re Q i i cn i re i ci cn re ci ci re i re i re u Y Y r r f B 1 ' ) ln( + = = and (4.8) [ ] i cn Q i i cn i re i ci cn re ci ci re i cn i cn u Y Y r r f B 1 ' ) ln( + = =. Differen choices are available for he aggregae measures. The resuls repored here assume ha: (4.9) } { ) ln( 1 con re ci l I I i i l l = = and

32 31 (4.10) Y = I i =1 ln( Y I i ). Using hese aggregae measures means ha our VAR is a sandard VAR wih he resricion imposed ha all -period lagged loan componens and all -period lagged income componens have he same effec on he regressand wih he excepion of he lags of hose series ha are from he same region as he regressand. Yamashiro (2001) considers an alernaive sysem by defining: I l l i (4.11) = ln / I l { ci re con} i= 1 and I i (4.12) Y = ln Y / I. i= 1 The advanage of using hese aggregae measures is ha bigger regions have a bigger effec on he oher regions. In our specificaion a 1% increase in any of he oher regions income (or loan) series has he same effec independen of he size of he region in which he shock occurs. An advanage of using he aggregae measures specified in (4.9) and (4.10) however is ha i leads o a sysem ha is linear in he included variables which simplifies he calculaions. 21 Moreover Yamashiro (2001) finds in a similar framework ha he sysem ha uses he aggregae measures of (4.9) and (4.10) ypically has smaller sums of suared residuals and would herefore be he preferred sysem if a model selecion crierion were o be used. Which aggregae measures are used however is no very imporan since Yamashiro (2001) also 21 Alhough he sysem is linear in he included variables when (4.9) and (4.10) are used o consruc he aggregae measures he sysem is sill nonlinear in any of he aggregae variables. We discuss in he nex secion how we deal wih his complicaion.

33 32 documens ha he impulse response funcions for he wo sysems wih he differen aggregae measures are very similar. More imporanly Yamashiro (2001) also shows ha he impulses responses for he resriced sysem are similar o hose from a less resriced sysem ha is consruced by leing a model selecion procedure deermine wheher addiional regional variables should be included as separae regressors. As above we summarize he impac of he acions of he FED using impulse response funcions. These responses race ou he effecs of a one sandard deviaion shock o he federal funds rae innovaion on curren and fuure values of he endogenous variables in he sysem. The disaggregaed sysem specified above is linear in he hree ineres raes he price index he regional income series and he regional bank loan componen series. However since he sysem is specified in logarihms i is nonlinear in he aggregae income and loan variables as well as in regional oal loans. To calculae he impulse response funcions for hese variables we follow he following procedure. We assume ha he regional variables are eual o he observed sample average in he period he shock occurs. Using he impulse response funcions for he regional variables i is hen easy o calculae he acual regional values in he periods afer he shock and by simply adding up across regions we obain he aggregae values. By calculaing he log difference relaive o he aggregae value in he period before he shock one obains he impulse response funcion for he aggregae variable.