THE HOME BIAS AND THE CREDIT CRUNCH:A REGIONAL PERSPECTIVE

Andrea F. Presbitero Gregory F. UdellAlberto Zazzaro

Working paper no. 60

November 2012

The Home Bias and the Credit Crunch:

A Regional Perspective∗

Andrea F. Presbitero Gregory F. Udell Alberto Zazzaro†

November 3, 2012

Abstract

A major policy issue is whether troubles in the banking system reflected in the bankruptcyof Lehman Brothers in September 2008 have spurred a credit crunch and, if so, how andwhy its severity has been different across markets and firms. In this paper, we tackle thisissue by looking at the Italian case. We take advantage of a dataset on a large sample ofmanufacturing firms, observed quarterly between January 2008 and September 2009. Usingdetailed information about loan applications and lending decisions, we are able to identifythe occurrence of a credit crunch in Italy that has been harsher in provinces with a largeshare of branches owned by distantly-managed banks. Inconsistent with the flight to qualityhypothesis, however, we do not find evidence that economically weaker and smaller firmssuffered more during the crisis period than during tranquil periods. By contrast, we findthat financially healthier firms were more intensely hit by the credit tightening in function-ally distant credit markets than in the ones populated by less distant banks. This result isconsistent with the hypothesis of a home bias on the part of nationwide banks.

JEL Classification: F33, F34, F35, O11

Key words: Banking; Credit crunch; Distance; Home bias; Flight to quality.

∗We thank an anonymous referee, Riccardo De Bonis, Raoul Minetti, Steven Ongena and participants atthe MoFiR workshop on banking (Ancona, 2012), at the Post-Crisis Banking Conference (Amsterdam, 2012),and at seminars held at the Universita di Milano Bicocca and Universita Politecnica delle Marche for valuablesuggestions.†Andrea F. Presbitero (corresponding author), Department of Economics – Universita Politecnica delle Marche

(Italy), Money and Finance Research group (MoFiR) and Centre for Macroeconomic and Finance Research (Ce-MaFiR). E-mail: a.presbitero@univpm.it; personal web page: https://sites.google.com/site/presbitero/. GregoryF. Udell, Indiana University, Kelley School of Business and Money and Finance Research group (MoFiR). E-mail: gudell@indiana.edu. Alberto Zazzaro, Department of Economics – Universita Politecnica delle Marche(Italy) and Money and Finance Research group (MoFiR). E-mail: a.zazzaro@univpm.it; personal web page:http://utenti.dea.univpm.it/zazzaro/.

1

1 Introduction

The financial crisis that began in the third quarter of 2007 originated with the bursting of areal estate bubble in the US and hit its peak in the quarters immediately after the collapse ofLehman Brothers in September 2008. This led to massive capital shocks to the US bankingsystem that quickly propagated to Europe as global interbank loan markets seized up. Thecontagion from the US shock was subsequently exacerbated by Europe’s own problems in thereal estate sector in countries like Ireland and Spain compounded by sovereign debt problemsparticularly in the southern Euro zone.

One of the most feared and debated consequences of the crisis in both Europe and the UShas been the possible credit crunch caused by the contraction of banks’ capital and the adverseliquidity shocks in interbank markets. However, identifying the existence of a credit crunchduring a global crisis, disentangling the shrinking of credit supply from the parallel reduction incredit demand, and distinguishing the factors that may have driven differences in the severity ofthe crunch across firms and markets are major concerns to policymakers and one of the biggestchallenges facing empirical work. In the absence of unusual natural experiments that createan easily identifiable supply shock (e.g. Khwaja and Mian; 2008; Peek and Rosengren; 1997)several identification strategies have been employed in the literature. One strategy is to exploitcredit registry data on firms that have multiple lenders in order to control for demand effects(e.g. Albertazzi and Marchetti; 2010; Iyer et al.; 2010; Jimenez et al.; 2012; Gobbi and Sette;2012). Another approach is to apply a disequilibrium model to identify credit constrained firms(e.g. Carbo-Valverde, Rodriguez-Fernandez and Udell; 2011; Kremp and Sevestre; 2011). Analternative approach to identify constrained firms, that we will follow in this paper, is to usesurvey data that contain information on loan applications and bank decisions (e.g. Popov andUdell; 2012; Winston Smith and Robb; 2011; Ferrando and Mulier; 2011; Puri et al.; 2011).

A number of studies have analyzed the effects on the credit markets in the country wherethe crisis began (i.e., the US). These studies have found evidence of significant shocks to thesupply of credit by large and small banks (e.g. Contessi and Francis; 2010; Gozzi and Goetz;2010; Ivashina and Scharfstein; 2010; Santos; 2010). However, missing from the research on theimpact of the credit crunch in the US is an analysis of the impact across different categories ofborrowers and regions. Virtually all of this research on credit in the US during the crisis eitherfocuses on large firms (e.g. Ivashina and Scharfstein; 2010) – the least likely to be affected by thecrunch – , or bank balance sheet information (e.g. DeYoung et al.; 2012), or on indirect evidencesuch as the Federal Reserve’s Senior Loan Officer Survey (e.g. Udell; 2009)1. As a consequenceof data limitations in the US2, firm level analysis of the effect of the current crisis on small andmedium enterprises (SMEs) has been substantially limited to Europe. In general these studieshave confirmed a credit crunch in the European credit markets (e.g. Albertazzi and Marchetti;2010; Carbo-Valverde, Degryse and Rodriguez-Fernandez; 2011; Carbo-Valverde, Rodriguez-Fernandez and Udell; 2011; Ferrando and Mulier; 2011; Iyer et al.; 2010; Jimenez et al.; 2012;Puri et al.; 2011). The evidence also indicates that younger, smaller and informationally more

1One exception is a study of how start-up firms faired during the crisis (Winston Smith and Robb; 2011).This study used the Kaufman Firm Survey and was confined to very young and very small firms. Examples ofindirect evidence include a study of how large firms supplied trade credit during the crisis, some of which likelywent to small firms (Garcia-Appendini and Montoriol-Garriga; 2011), and a study by Gozzi and Goetz (2010)who show that metropolitan areas where banks relied less on retail deposits experienced a more severe economicdownturn during the crisis.

2Unlike many European countries the US does not have a public credit registry. In addition, the best availablefirm level data on SME finance in the US, the Federal Reserve’s Survey of Small Business Finance (SSBF), wasdiscontinued just before the crisis began. While the SSBF data were not panel data, they did contain extensivedata on firm characteristics, financial statements and loan terms. Moreover, the next survey would have beenconducted in the middle of the crisis, had it not been discontinued.

2

opaque firms may have been more severely affected, suggesting the existence of a flight to qualityon the part of banks during the crunch (e.g. Artola and Genre; 2011; Canton et al.; 2010; Holtonet al.; 2011; Popov and Udell; 2012).

Our paper adds to this growing empirical literature on the determinants of the credit crunchin two ways. First, we explore whether and how the hierarchical structure of banks in the localmarket affects the severity of the credit crunch in that market. Second, we look deeper intothe question of which type of firms are more exposed to credit tightening by investigating thecommon conjecture that small and risky firms suffer most if operating in credit markets largelypopulated by nationwide, distantly-managed banks rather than by local banks.

Our specific focus on the hierarchical structure of the local banking market centers on theissue of whether borrowers whose banks are “less local” are more vulnerable. The theoreticaland empirical literature on commercial lending suggests that hierarchical banks are less ableto provide relationship lending to SMEs because of difficulties associated with producing andtransmitting soft information (Stein; 2002; Berger et al.; 2005; Liberti and Mian; 2009). Thisimplies that as the “functional distance” between the loan officer and the headquarters wherefinal lending decisions are made increases, banks are less able to make relationship-based loansand access to credit to local firms becomes tighter (Alessandrini et al.; 2009).

In this paper, we explore this issue by conjecturing that, in times of crisis, banks retractdisproportionally from markets which are distant from their headquarters. If this actuallyoccurrs, then the adverse effect of functional distance on firms’ access to credit should beobserved to be more pronounced in the months following the collapse of Lehman Brothers. Inaddition, we investigate whether the withdrawal of banks from local markets is the result of aflight to quality or a home bias effect. To establish which of the two effects prevails, we testwhether more large and safe enterprises in more functionally distant banking systems are less(flight to quality) or more (home bias) likely to suffer from a contraction of credit after Lehman’scollapse.

Our study is closely related to studies that have examined the foreign ownership of banks andwhether shocks to parent banks are propagated across borders affecting the lending activitiesof their foreign operations (e.g. Cetorelli and Goldberg; 2011; Popov and Udell; 2012). A fewrecent contributions have considered the existence of a home bias in banks’ lending reactionsto adverse shocks to their own financial conditions at times of global crisis, by looking at thebehavior of international banks in syndicated loan market (Galindo et al.; 2010; de Haas andvan Horen; 2012; Giannetti and Laeven; 2011).

In this paper, we take a national perspective by studying the credit crunch in Italianprovinces during the present financial crisis. To this end, we exploit detailed survey infor-mation on loan applications and their outcome using a large sample of manufacturing firms.This allows us to separate demand and supply effects, and to identify the existence and sever-ity of credit crunch across firms and markets. In particular, we merge firm-level data withinformation on the spatial distribution of bank branches in order to assess the effect of theorganizational structure of the local banking systems on access to credit to local firms. Dataavailability apart, Italy, like many other countries in Europe and elsewhere, is characterizedby a large number of small firms which are strongly dependent on loans from local banks tofinance their investments and business activity, and by a number of nationwide banks whichspread their subsidiaries and branches across provinces often located at a great distance fromthe home province where they are headquartered. This makes Italy a representative case study,having broader implications for the analysis of the current credit crunch and the relevance ofthe home bias effect in shaping the supply of loans.

By way of preview we find that the shock to global liquidity surrounding the Lehman collapsewas transmitted to the real sector in Italy in terms of a significant contraction in both demand

3

for and supply of credit. Hence, we find evidence that a credit crunch occurred in Italy. However,inconsistent with the flight to quality hypothesis that the credit crunch has been significantlymore severe for small and economically weaker firms than for large and “good-quality” ones,we show that the likelihood of the former being credit-rationed during the crisis period wasnot significantly higher than in tranquil periods. By contrast, we find robust evidence that thepenetration of functionally distant banks in local credit markets exacerbated the credit crunch.However, our results reject the hypothesis of a flight to quality in functionally distant creditmarkets, while they are consistent with the hypothesis of a home bias on the part of nationwidebanks. In fact, we find that the contraction of credit supply has targeted large and healthyfirms — the ones that, according to theory, are likely to borrow from distantly-headquarteredbanks — relatively more in functionally distant credit markets than in the ones populated bybanks functionally close to the local economy.

The remainder of the paper is structured as follows. In the next section we offer a briefreview of the related literature. Section 3 discusses the extent of the 2008-2009 banking crisisin Italy. In sections 4 and 5 we present our data and variables, and a descriptive analysis of thecredit crunch. In section 6 we discuss our identification strategy, while sections 7 and 8 presentthe results of our model estimations and the robustness exercises. Section 9 offers a discussionof our findings and a conclusion.

2 Functionally distant banks, home bias and access to credit

Our paper builds on the literature that has analyzed the link between banks operating infunctionally distant local credit markets and firms’ access to credit. There are several reasonswhy the presence of subsidiaries and branches of (foreign or domestic) banks headquartered at ageographical distance may adversely influence the availability of credit to local firms, resultingin a home bias. These reasons have to do with: (i) asymmetric information and agency costs;(ii) internal capital markets and corporate politics.

2.1 Asymmetric information and agency costs

The existence of information asymmetries between the bank and the firm makes lending a verylocal activity.3 A crucial part of information about the firm’s creditworthiness is soft and so-cially embedded. As a result, it can be conveniently recovered and processed only by loanofficers working and living in the same neighborhoods where the borrowers operate, who cannoteasily and perfectly transmit this information to senior managers at the upper layers of theparent bank. Accordingly, loan officers benefit from informational rents and banks bear agencycosts in order to align the interests of the former with those of bank shareholders and to miti-gate moral hazard in communication (Agarwal and Wang; 2009; Agarwal and Hauswald; 2010;Hertzberg et al.; 2010; Uchida et al.; 2012). The more hierarchically organized and (physicallyand culturally) distant from the local economy is the parent bank, the greater the shortfalls incommunication channels (Stein; 2002). For example, costs and uncertainty of loan reviews in-crease with physical distance from the bank’s headquarters where loan reviewers report (Udell;1989), as well as trust between bank’s managers and local loan officers tends to be lower whenthe cultural distance between the geographical areas where the staff of the bank’s decisionalcentres and local offices work and live is great (Cremer et al.; 2007). For such reasons, distant

3With regard to lines of credit, in the US, the median distance between the firm and the lending bank branchwas 4 miles in 1993 and 3 miles in 2003 (Brevoort and Wolken; 2009), while it was still lower in Europe: 1.58miles in Italy (Bellucci et al.; 2010), 1.4 in Belgium (Degryse and Ongena; 2005) and 0.62 in Sweden (Carlingand Lundberg; 2005).

4

banks have an incentive to constrain local branches from lending to soft-information-intensiveborrowers, such as small and innovative enterprises (Dell’Ariccia and Marquez; 2004). Similarly,career-concerned loan officers can be induced to assume a conservative attitude towards loans tosmall and new borrowers based on soft, uncommunicable information and a too liberal approachtowards large and well-established borrowers who are evaluated with hard, easily transferableinformation (Hirshleifer and Thakor; 1992; Berger and Udell; 2002).

To the extent that the cost of funds is lower for branches of large and functionally dis-tant banks, local credit markets tend to segment, with distant banks ’cream-skimming’ high-return, informationally transparent borrowers, and local banks specializing in lending to soft-information borrowers. Market segmentation may result in higher overall lending to the economyor in extensive credit rationing to small firms, according to whether local banks are more or lessefficient at screening small, opaque firms and the average quality of such firms is high or low(Dell’Ariccia and Marquez; 2004; Sengupta; 2007; Detragiache et al.; 2008; Gormley; 2011).

Consistent with theoretical predictions, empirical evidence shows that branches and sub-sidiaries of functionally distant banks tend to be less engaged in loans to small businesses andother soft-information-based investment projects, have a comparative disadvantage in relation-ship lending, ask for lower collateral, have a lower share of bad loans, are less prone to assistfirms facing financial distress and are less efficient (Berger et al.; 2001; Berger and DeYoung;2001; Mian; 2006; Alessandrini et al.; 2008; DeYoung et al.; 2008; Jimenez et al.; 2009; Micucciand Rossi; 2010).

In addition, a number of studies provide evidence in support of the hypothesis that firmsin markets populated by functionally distant banks have, on average, lower access to credit.Detragiache et al. (2008) look at poor countries and find that the total amount of loans to theprivate sector (normalized to GDP) and the rate of credit growth are negatively correlated withthe foreign bank penetration (measured by the share of bank assets owned by foreign banks).The negative impact of foreign banks is confirmed by Gormley (2010), who documents that inIndia firms in districts with a foreign bank, especially if they are small sized and endowed withlow tangible assets, have a significantly lower probability of obtaining long-term loans. UsingItalian data, Alessandrini et al. (2009, 2010) show that firms are more likely to be financiallyconstrained and less inclined to introduce innovations if they are located in provinces wherea large share of branches belong to banks headquartered in physically distant provinces andin provinces with different social and economic environments. Similar findings with regardto France are documented by Djedidi (2010), while Presbitero and Zazzaro (2011), again withItalian data, find that in highly competitive markets the presence of functionally distant banks isdetrimental to relationship lending. Finally, Ozyildirim and Onder (2008) show that in Turkishprovinces whose bank branches are distant from their headquarter the credit-to-GDP ratio hasa low or even negative impact on local growth, suggesting that local branches of distant bankstend on average to fund less profitable projects.

2.2 Internal capital markets and corporate politics

The existence of an internal capital market has contrasting effects on lending to local firms bybranches of banks headquartered at a distance (Morgan et al.; 2004). On the one hand, capitalinflows from parent banks allow branches and affiliate banks to promptly increase lending inresponse to a boom in the local economy (de Haas and van Lelyveld; 2010) and to be partlyinsulated from idiosyncratic liquidity shocks (Houston et al.; 1997; Dahl et al.; 2002). Onthe other hand, by having the opportunity to move funds across regions, multi-market banks(whether foreign or nationwide) may transmit financial shocks from one economy to another(Peek and Rosengren; 1997, 2000; Berrospide et al.; 2011; Imai and Takarabe; 2011; Schnabl;

5

2012; Cetorelli and Goldberg; 2011) and may be more inclined to reduce local lending when thelocal economy and deposit growth slow down (Campello; 2002; Cremers et al.; 2010).

However, capital allocation and the internal liquidity flows across bank branches and affiliatesare only partially driven by lending opportunities, while they are affected by corporate politics,by the power and influence of local managers inside the organization and by the economic, socialand political importance of the local economy for the CEOs and other top managers at the bank’sheadquarter (Meyer et al.; 1992; Scharfstein and Stein; 2000). Once again, the headquarter-to-branch distance tends to affect both the local managers’ ability to attract internal resources andthe bank’s favoritism attitude towards local economy needs (the home bias). For example, it isreasonable to assume that the managers of branches located at a great distance from the centerhave little power to attract internal resources and influence capital budgeting decisions (Carlinet al.; 2006). In the same vein, the more physically and culturally close the bank’s headquartersand top management are to a region, the greater is the incentive to favor the local economy andlocal firms (Landier et al.; 2009).

The weak links of functionally distant banks with the local economic community and theweight of the home bias and corporate politics for internal capital allocation may be morepronounced in times of global crisis when the amount of loanable funds is lower. Consistentwith this, Giannetti and Laeven (2011) find that the portfolio share of syndicated loans issuedby a bank in the home country is larger than that issued in foreign countries, and that thehome bias tends to significantly increase in periods when the home country is in a bankingcrisis. At the same time, when a host banking system experiences a crisis, foreign bankscontract loans to local borrowers less than domestic banks, and to a much smaller extent thanwhen they face negative shocks at home. Similarly, de Haas and van Horen (2012) find thatduring the 2007-2009 financial crisis international banks participating in cross-border syndicatedloans reduced their lending exposure to countries further away from their headquarters morethan their exposure to geographically close countries. Galindo et al. (2010) find some evidenceto suggest that cultural distance also matters. They show that, while foreign banks in LatinAmerica during the crisis generally amplified external financial shocks, Spanish banks operatingin Latin America did not. In the same vein, and with reference to the same period, Aiyar (2011)shows that foreign subsidiaries and branches in the United Kingdom decreased their lendingto local businesses by a larger amount than domestically owned banks. Likewise, using dataon emerging eastern European countries, Popov and Udell (2012) find that small firms locatedin cities where the majority of lending banks are headquartered abroad are more likely to becredit-rationed, especially if banks in the area are financially distressed, and de Haas et al.(2011) find that foreign banks reduced their loan supply to local firms earlier and faster thandomestic banks. Finally, Gambacorta and Mistrulli (2011) show that during the global crisisItalian firms borrowing from distant banks experienced a larger increase in interest rates anddecrease in loan supply, and Barboni and Rossi (2012) find that firms borrowing primarily fromlocal banks were less credit rationed.

3 Bank credit in Italy during the 2007-2009 financial crisis

The bursting of the U.S. housing bubble in 2007 had only a limited impact on banks’ lendingactivities in Italy compared with those in other European countries, and it was only in the thirdquarter of 2008, soon after the failure of Lehman Brothers that the crisis started to show itscontractionary effects on local credit markets (Aisen and Franken; 2010).

A more traditional intermediation model4, coupled with strict prudential supervision by the

4A banking system that ’does not speak English’, according to the colorful metaphor used by Giulio Tremonti,

6

national regulatory authority, had restrained Italian banks from holding excessive quantitiesof securitized U.S. subprime loans and other toxic asset-backed securities in their portfolios(Quaglia; 2009; Bonaccorsi Di Patti and Sette; 2012). At the end of 2007, the total exposureof Italian banks to structured financial products amounted to only 4.9 billion Euros (2% ofsupervisory capital of Italian banks), and the value of securitization transactions in Italy wasonly 7% of total transactions in Europe (OECD; 2009). Consequently, the capital and liquiditypositions of Italian banks, as well as their profitability, experienced little deterioration duringthe first phase of the crisis between July 2007 and September 2008, such that only a smallfraction of them perceived these factors as constraining their lending to the corporate sector.5

Throughout the 2007 bank loans grew steadily at high rates – at around 10% quarter-on-quarter annual growth rate (see Figure 1) –, with banks broadly maintaining unchanged creditapproval standards (see Figure 2, panel (a)). There was a slight tightening of credit conditions,particularly for large banks, occurred in the last quarter of 2007, when the expansion of loansto households and small firms decelerated, although it continued to be at largely positive rates.

Figure 1: Loan growth by bank size: 2005-2009

Lehm

an's

colla

pse

-.05

0

.05

.1

.15

Perc

enta

ge c

hang

e in

ban

k lo

ans

31/12

/2004

30/06

/2005

31/12

/2005

30/06

/2006

31/12

/2006

30/06

/2007

31/12

/2007

30/06

/2008

31/12

/2008

30/06

/2009

31/12

/2009

All banks Large banks Small banks

Notes: This figure is constructed from Bank of Italy data (see the on-line statistical database). The percentage change inloans is calculated from quarterly data on a year-on-year basis.

Things drastically changed after Lehman’s default, when the problems of accessing interbankfunds exacerbated and deterioration of the quality of loan portfolios weakened the profitabil-ity and the capital position of Italian banks (Angelini et al.; 2011; Bonaccorsi Di Patti andSette; 2012). From September to December 2008, the flow of non-performing loans (borrowers)increased by 93% (89%), while the ratio between utilized and available credit lines and the over-drawn increased by 10%6. According to the Bank of Italy statistics, the profitability of banks,as measured by return on equities, more than halved, falling from 9.23% in 2007 to 3.92% in2008.

the Italian Minister for the Economy and Finance of the time, in a TV interview in October 2008.5See the Euro Area Lending Survey for the Euro area and Italian banks available at

www.ecb.int/stats/money/surveys/lend/html/index.en.html.6See Bollettino Statistico, Bank of Italy, various issues (www.bancaditalia.it/statistiche/stat mon cred fin/)

and Panetta and Signoretti (2010).

7

Figure 2: Credit standards on loan approval by Italian banks

Lehm

an's

colla

pse

-0.20

0.00

0.20

0.40

0.60Di

ffusio

n in

dex

Jan-0

3Ja

n-04

Jan-0

5Ja

n-06

Jan-0

7Ja

n-08

Jan-0

9Ja

n-10

Jan-1

1Ja

n-12

Over the past three months Over the next three months

(a) Diffusion index of credit tightening

Lehm

an's

colla

pse

-50

0

50

100

Net p

erce

ntag

e

Jan-0

3Ja

n-04

Jan-0

5Ja

n-06

Jan-0

7Ja

n-08

Jan-0

9Ja

n-10

Jan-1

1Ja

n-12

Over the past three months Over the next three months

(b) Net percentage of banks that tightened creditstandards

Notes: These figures are constructed from answers to questions 1 and 6 of the ECB Bank Lending Survey on ItalianBanks. Question 1: ”Over the past three months, how have your bank’s credit standards as applied to the approval ofloans or credit lines to enterprises changed?”. Question 6: ”Please indicate how you expect your bank’s credit standardsas applied to the approval of loans or credit lines to enterprises to change over the next three months”. The five possibleanswers to questions 1 and 6 are: (i) tighten considerably; (ii) tighten somewhat; (iii) remain basically unchanged; (iv)ease somewhat; (v)ease considerably. The diffusion index varies between −1 and 1; it is computed as the weighted meanof answers (i)-(v), where the values attributed to each answer are 1, 0.5, 0, −0.5 and −1 and the weights are the observedfrequencies. The net percentage is given by the difference between the percentage of banks that experienced (expected)a tightening of credit standards – answers (i) and (ii) – the percentage of banks that experienced (expected) an easing ofcredit standards – answers (iv) and (v). See: www.ecb.int/stats/money/surveys/lend/html/index.en.html.

By the end of 2008, the growth rate of loans by large banks became negative (-2.9%), whilethat of small banks decelerated from 14 to 10 percent between December 2007 and December2008 (Figure 1), to further decrease to 2.4% at the end of 2009. The percentage of Italianbanks participating in the ECB Bank Lending Survey which stated they had tightened creditstandards was 87.5% and 100% over the last two quarters of 2008. But what is more interestingis that from April 2008 to April 2009 these banks systematically underestimated the evolutionof the credit supply over the next quarter (Figure 2, panel (b)), suggesting that the bankruptcyof Lehman Brothers coincided with and, at least partially, contributed to an unexpected shockto Italian banks.

4 Data and variables

4.1 Data sources and the construction of the dataset

We draw on data concerning firms’ financial conditions and the geographical distribution ofbank branches from two sources: 1) a monthly survey of about 3,800 Italian manufacturingfirms, interviewed from March 2008 to February 2010 by the ISAE (Institute of Studies andEconomic Analysis), recently becoming part of the ISTAT (Italian Institute of Statistics); and2) the monthly data on bank branch openings and closures compiled by the Bank of Italy.

The ISAE-ISTAT survey data cover Italian firms with at least five employees (the averagesize is 74 employees, while the median is 18) and was recently updated and re-engineered withthe main aim of increasing the comparability of the Italian data with those released by the otherEuropean institutions, such as the Ifo Business Climate Survey, while still maintaining a focuson the traditional sectors of Italian specialization (Malgarini et al.; 2005). The representativesample is stratified by geographical areas, economic activities and number of employees. A

8

specific section of the survey was added in March 2008 on firm access to credit that providesinformation on firms’ demand for credit and its rationing decisions. These data do not providethe identity of the bank and information about the strength of the banking relationship. Thedata, however, do contain information that allow us to control for some specific firm character-istics which we discuss below. Further, the data include information on the firm’s location atthe administrative province level7.

Our bank data include information on the openings and closures of branches are at thebank-province level, allowing us to calculate the stock of branches per bank per province inany quarter. These data are complemented with information on bank governance (i.e. mutual,cooperatives, listed) and asset size (large, medium, small), on the location of their headquartersand holding company structure (when applicable).

Because we only have the ISAE survey data for the months of March, June, September andDecember and because of observations with missing values and outliers, we end up with anunbalanced panel consisting of 3,623 firms and 23,140 observations, observed quarterly between2008:1 to 2009:3.8 Within this dataset, we distinguish two main periods: the pre-Lehman period(PRE−LEHMAN), from 2008:1 to 2008:3, and the post-Lehman (POST−LEHMAN), from2008:4 to 2009:3.

In the following subsections we will describe in detail the data and the construction of thevariables (their summary statistics are reported in Table 1 in Appendix A).

4.2 Firm-specific variables

4.2.1 Access to credit

The two main dependent variables regarding firms’ access to bank credit distinguish the demandand supply of credit. DEMAND is an indicator variable which assumes value one for firmswhich report direct contacts with one or more banks in the previous quarter in order to seekcredit (i.e., we exclude firms stating that they just went to the bank to ask for information).RATIONED, a variable observed only for firms which applied for credit in the given quarter,is a dummy variable which is equal to one for firms which stated they did not obtain the desiredamount of bank credit. Lacking loan-level data, our variables about the loan demand and supplydo not refer to a specific bank-firm relationship, but they indicate firms which demanded creditto and which have been credit rationed by the banking system as a whole.

Using information on the demand for and supply of credit we build a variable measuringa firm’s access to credit in the pre-Lehman period. PRE − LEHMAN RATIONED is atime-invariant dummy variable observed only in the pre-Lehman quarters which is equal toone for firms which were quantity-rationed at least in one quarter in the pre-Lehman period(RATIONED = 1) and zero for firms which were not rationed (RATIONED = 0) or non-applicants (DEMAND = 0). In other words, this variable identifies firms which had problemsin accessing bank credit in the tranquil period, compared to firms which either obtained thedesired amount of credit or did not apply for a bank loan between 2008:1 and 2008:3.

7Italy is currently divided into 110 provinces (corresponding to the third level NUTS as coded by the Euro-pean Union, which are grouped into 20 administrative regions (corresponding to NUTS2, with the exception ofTrentino-Alto Adige that European Union, unlike the Italian administrative law, split into two NUTS2 regions).Since some provinces were recently constituted, we use the old classification of 103 provinces.

8Data are collected on a monthly basis, but only aggregate indicators are publicly available.We were able to have access to firm-level variables, but only on a quarterly basis (the March,June, September and December releases. Additional information on the survey are available here:http://siqual.istat.it/SIQual/visualizza.do?id=888894.

9

4.2.2 Other firms’ characteristics

As noted above, the survey provides some useful information about characteristics of respondentfirms, their economic activity and their financial condition. This allows us to take into accountseveral factors that might influence the demand for credit by firms and the willingness of banksto satisfy such demand.

Regarding firm characteristics, we include variables on the firms’ size (SIZE), the capacityto operate abroad (EXPORT ) and the labor costs they incur (LABOR COST ). SIZE ismeasured, for each period, by the logarithm of the average number of employees in each quarter.Enterprises with no more than 20 employees are identified as small firms (SMALL).9 The exportstatus is measured, in each quarter, by a dummy variable equal to one for firms which sold partof the production abroad (EXPORT ). As a measure of the cost of production, we can takeadvantage of a specific question asking for the percentage change in labor cost per employee inthe previous 12 months (LABOR COST ).

Regarding economic activity, we capture the level of production and potential product de-mand (LOW DEMAND) with a dummy variable that assumes the value 1 for firms thatanswer “low” to the question “How are the level of orders and the general demand for prod-ucts?” and zero otherwise (i.e., for firms answering “normal” or “high”). Firm financial healthis captured by three dummy variables which are constructed on the basis of a question aboutthe level of liquidity with respect to operational needs, which respondents can evaluate as good,neither good nor bad, or bad (LIQUIDITY ).10

Finally, the industry a firm belongs to and its location in the South of Italy are taken intoaccount for the possible effect that a specific industry or geographic location could have uponaccess to credit.11

Figure 3 clearly shows that that firms’ characteristics before the onset of the global financialcrisis are not homogeneously distributed across Italian provinces. In line with the geographicaldistribution of the population of Italian firms, large and exporting firms in our sample areconcentrated in the more developed Northern provinces. By contrast, firm financial health andfirm access to bank credit follow less clear geographical patterns, although the former appearsmore severe in southern provinces.

Firms’ heterogeneity is reflected also in their relationship with the banking system. Asshowed in Table 2, exporters and firms in worse financial health (BAD LIQUIDITY ) aregenerally more likely to apply for bank credit than non-exporters and financially healthy firms.A lower product demand is associated with a lower propensity to demand credit, but only inthe quarter before the Lehman bankruptcy. Firm size, instead, is neither statistically correlatedwith the demand for credit, nor with credit rationing. The latter is a much more frequentstatus among enterprises facing a low product demand and severe liquidity shortages thanamong healthy firms. This is true in tranquil and crisis periods alike.

9The threshold of 20 employees for small firms has been used in the empirical literature on the credit crunchin Italy, among others, by Gambacorta and Mistrulli (2011). We also consider the more traditional threshold of50 employees obtaining similar results.

10The survey question is: “Currently, the level of liquidity with respect to operational needs is good, neithergood nor bad, or bad?”. In order to test for a home bias effect, below we measure the firm’s financial health by atwo dummies. The first is equal to one for enterprises which state that liquidity is at a bad level with respect tooperational needs (BAD LIQUIDITY ). The second is equal to one for firms which declare that their liquidityis good or neither good nor bad (GOOD LIQUIDITY ).

11As is well documented in the banking literature, Italy’s southern regions are economically and financially lessdeveloped, and local firms have greater difficulties in accessing bank credit (Lucchetti et al.; 2001; Guiso et al.;2004a; Alessandrini et al.; 2009).

10

Figure 3: The spatial heterogeneity of firms’ characteristics

Quintiles[8.87,28.5](28.5,47](47,63.6](63.6,101](101,304]

Number of employees

Quintiles[.0789,.308](.308,.403](.403,.496](.496,.626](.626,.77]

Exporting firms

Quintiles[0,.0909](.0909,.112](.112,.149](.149,.199](.199,.4]

Bad liquidity needs

Quintiles[0,.0417](.0417,.0833](.0833,.111](.111,.174](.174,.667]

Credit rationed firms

Notes: The maps report the provincial distribution of the time-average values of SIZE, EXPORT , LARGE, LIQUIDITYand RATIONED over the period 2008:1 – 2008:3.

4.3 Credit market variables

Because we do not have information on the identity of lending banks — but information on thelocation of the firm — we match firm-level data with aggregate indicators of the structure oflocal credit markets, defined at provincial level.12

First, we consider the organizational structure of the local banking systems as proxied by theheadquarter-to-branch functional distance (DISTANCE). Following Alessandrini et al. (2009),we measure functional distance at the province level as the ratio of the number of branches inthe province weighted by the logarithm of 1 plus the kilometric distance between the provinceof the branch and the province where the parent bank is headquartered, over total branches inthe province.

Second, we include the degree of credit market concentration in the province by buildingthe Herfindhal-Hirschman index computed on the share of branches held by banks operating inthe province (HHI).

Finally, in order to take into account the financial crisis and how it has hurt the localbanking system, we follow by considering the share of branches belonging to the five largestItalian banking groups (LARGE BANKS) which were most seriously affected by the crisis,slowing down their lending activity (Albertazzi and Marchetti; 2010; Gobbi and Sette; 2012).13

12Our matching of firm-level survey data to local bank market structure in an analysis of credit access during thecrisis has been used in other studies (e.g. Popov and Udell; 2012). In Italy there are currently 110 administrativeprovinces, with some being recently established. For reasons of data availability, we refer to the standardclassification into 103 provinces.

13Alternative measures of the presence of banks severely affected by the liquidity crisis are discussed in the

11

All the banking-system variables are calculated at the end of each quarter from September2007 to December 2009.14

The credit market variables exhibit significant heterogeneity across provinces, as visuallyconfirmed by the maps reported in Figure 4. This variability is not exclusively driven by thetraditional Italian dichotomy between the more financially developed Northern regions andthe less financially developed South, as shown by the “between” component of the standarddeviation of the credit market structure variables within Northern and Southern provinces(Table 3).15 By contrast, the quarterly frequency and the short duration of the data set seriouslylimit the variability of credit market structure variables over time.

Figure 4: The spatial heterogeneity of credit market structure variables

Quintiles[.777,2.45](2.45,3.06](3.06,3.67](3.67,4.46](4.46,5.91]

Functional distance

Quintiles[.0359,.0819](.0819,.0985](.0985,.113](.113,.136](.136,.544]

Herfindhal-Hirschman index

Quintiles[.14,.418](.418,.527](.527,.581](.581,.649](.649,.875]

Share of branches held by top-5 banking groups

Notes: The maps report the provincial distribution of the time-average values ofDISTANCE, HHI and LARGE BANKSover the period 2008:1 – 2009:3.

While the effects of functional distance during the credit crunch (see Section 2) are bank-firmspecific, in this paper we follow a market-based approach by measuring the average functionaldistance of bank branches located in each of the provinces.16

One limitation of this approach is that local firms could actually borrow from brancheslocated outside the province where they are domiciled. In this case, the correlation betweenthe functional distance of the local banking system and firms’ financing constraints would bespurious: rationed firms are those borrowing from out-of-province branches and not from in-province branches. This spurious relation may be particularly relevant for larger and export-oriented firms that are more likely to borrow from out-of-market global banks during a crisis.When rationing occurs in a local market those firms switch to a different province in which thecredit supply is less restricted (Berger et al.; 2005; Uchida et al.; 2008; Gopalan et al.; 2011).Although our data do not allow us to distinguish between firms borrowing from in-provinceand out-of-province branches, in Italy about 80% of loans are provided by branches located in

robustness, see Section 8.14For robustness, we have also taken the values of banking variables at the beginning of the two periods, finding

very similar results.15It is worthy to note that the spatial distribution of the organizational and competitive structures of credit

markets mirror the spatial distribution of firms’ riskiness and size reported in figure 3 only partially.16The market-based approach has been widely employed in the literature to investigate the effects of banking

consolidation processes on firms’ access to credit (Berger et al.; 1999, 2007; Bonaccorsi Di Patti and Gobbi; 2007).

12

the same province where the borrower is domiciled.17 In addition, it seems likely that firmsthat switch to non-local lenders will be switching to bank branches that are more functionallydistant, not less functionally distant.

5 Descriptive analysis

Firm-level data from the ISAE/ISTAT survey provide a clear descriptive evidence of the in-tensity of the credit crunch. In the last quarter of 2008, the share of firms that judged accessbank credit as restrictive increased to 41%, during the first nine months of 2008 (Figure 5,left panel).18 The firms’ perception of banks’ lending behavior is strongly correlated with theirproduct demand and liquidity levels. In the right-hand panel in figure 5, we consider the differ-ence between the share of firms assessing their demand as high less the share assessing it as low,and the difference between the share of firms assessing their financial health (i.e., liquidity) asgood less the share assessing it as bad. Both indicators follow a similar trend, with a decline inthe business demand and financial health from the second half of 2008 and the bottom reachedin the first quarter of 2009. However, the climate of the product market is judged to evolveworse than liquidity conditions by a larger share of firms: on average, during the sample period47% of firms faced a low level of demand, while only 6% stated that the level of demand washigh.

Figure 5: Access to bank credit and business climate: 2008-2009

0

.1

.2

.3

.4

Acc

ess

to c

redi

t (sh

are

of fi

rms)

2008q1 2008q2 2008q3 2008q4 2009q1 2009q2 2009q3

Accomodating Restrictive

(a) Conditions of access to credit

−.6

−.4

−.2

0

.2

Bus

ines

s cl

imat

e

2008q1 2008q2 2008q3 2008q4 2009q1 2009q2 2009q3

Product demand Liquidity needs

(b) Business climate

Notes: These figures are constructed from data from the ISAE/ISTAT Survey of Manufacturing Firms. In any quarter,the business climate is defined, alternatively: as the share of firms assessing their demand as high less the share assessingit as low (product demand), and as the share of firms assessing their liquidity as good less the share assessing it as bad(liquidity).

In Figure 6 we look directly at the evolution between the first quarter of 2008 and thethird quarter of 2009 of the two dependent variables used in the bivariate probit model. Inthe left-hand panel we plot the share of firms which applied for bank credit (DEMAND),while in the right-hand panel we focus on the share of firms which have been credit-rationed(RATIONED). To take into account possible differences in the severity of the credit crunchaccording to the structure of local credit markets, we calculate these shares separately for firms

17The detailed data on the share of loans granted by branches in a province to resident in that province arenot publicly available. We thank Riccardo De Bonis for providing us the aggregate figures both at the provincialand regional levels.

18Similar findings are reported by Costa and Margani (2009).

13

located in provinces where the banking system is functionally close (DISTANCE below the75th percentile of its 2008:3 provincial distribution) and functionally distant (DISTANCEbelow the 75th percentile of its 2008:3 provincial distribution).

Two main patterns emerge from the diagrams. The first concerns timing and shows thatwhile the demand for credit remained quite stable before and after Lehman’s collapse (apartfrom a temporary and sharp increase in the second quarter of 2009), the restraining response ofthe banking system to the reduction in global liquidity was evident and immediately transmittedto the real sector. The share of rationed firms increased from 11.6 percent in the third quarterof 2008 to 21.6 percent in the last quarter of the year and further to 25.5 and 27.5 percentrespectively in the first and third quarters of 2009.

The second pattern is related to geographical differences in the access to bank credit. Onaverage, over the sample period, firms located in provinces densely populated by distant banksare less likely to seek credit. The share of firms asking for new bank credit in each quarter is30.1% in provinces where the functional distance of the banking system is particularly high,while the same share increases to 32.7% in provinces where the banking system is functionallycloser; this difference is statistically significant at the usual level of confidence. The oppositetrend is observable in the share of rationed firms. Just before the onset of the crisis (2008:3), theshare of credit-rationed firms is 11.6%, irrespective of the functional distance of local bankingsystems. In the first quarter after the Lehman collapse, the tightening of credit conditionsis found everywhere, but the increase in credit rationing is statistically higher in provincesdominated by distant banks.19

Figure 6: Demand and supply of bank credit: 2008:1 – 2009:3

0

.1

.2

.3

.4

Sha

re o

f firm

s de

man

ding

ban

k cr

edit

2008q1 2008q2 2008q3 2008q4 2009q1 2009q2 2009q3

Functionally close provinces Functionally distant provinces

(a) Demand for bank credit

0

.1

.2

.3

.4

Sha

re o

f rat

ione

d fir

ms

2008q1 2008q2 2008q3 2008q4 2009q1 2009q2 2009q3

Functionally close provinces Functionally distant provinces

(b) Credit rationing

Notes: These figures are constructed on the sample of 3,631 firms (24,651 observations). Source: ISAE/ISTAT Survey onManufacturing Firms. Provinces are classified as functionally close (distant) whether DISTANCE is below (above) the75th percentile of its 2008:3 provincial distribution.

Table 1 reports the descriptive statistics for the sample of 3,623 firms (23,140 observations)used in the first empirical exercise (Table 4). From the descriptive analysis of quarterly data,we see that firms demanding bank credit do not differ significantly from the non-applicants interms of size and product demand, while they are more likely to export. Moreover, the twogroups of firms are not located in provinces with different degrees of functional distance. By

19The share of rationed firms is 26.4% (20.9%) in provinces where DISTANCE is above (below) the 75th

percentile of its 2008:3 provincial distribution, and this difference is statistically significant at the 10 percent levelof confidence. Over the whole 2008:1 – 2009:3 period, 21.6% of firms located in provinces where the bankingsystem is functionally distant are credit-rationed, while this share drops to 16.7% in provinces with a closerbanking system. Also in this case the difference is statistically significant.

14

contrast, significant differences emerge in the sub-sample of applicants between the ones whichwere credit-rationed and the others which were untouched by credit restrictions. The formerare smaller, less internationalized, with a lower product demand and predominantly located inprovinces where the banking system is functionally distant (Table 1). This preliminary evidenceon the heterogeneity of the credit crunch is formally tested in the first empirical exercise in thenext Section.

6 The identification strategy

The critical problem in correctly identifying a credit crunch effect on the likelihood of firmsbeing credit-rationed is the selection bias arising from the fact that not all firms in the samplehad a positive demand for credit and that these firms might not be randomly drawn from thepopulation of Italian firms. Such a bias may be especially strong during a financial crisis, whenmany firms may decide not to apply for bank loans either because they have limited financingneeds or because they are discouraged from applying by the worsening lending conditions inthe local credit market and the high probability of seeing their application rejected (Popov andUdell; 2012).

To address the left-truncation of the sample, our identification strategy is based on a sampleselection model a la Heckman, in which the selection mechanism results from sampled firmsnot responding to the survey questions about access to bank credit.20 Since also the dependentvariable in the outcome equation is dichotomous, the presence of a credit crunch is tested esti-mating with maximum likelihood a binary response model with sample selection (Wooldridge;2011).

To estimate the impact of the functional distance of the local banking system from the localeconomy and to assess the importance of the flight to quality and home bias in banks’ lendingdecisions on the intensity of the crunch after Lehman’s collapse, we proceed in two steps.

6.1 The credit crunch, bank functional distance and the flight to quality

In our first step, we test whether the crisis has actually produced a credit crunch in Italy.Then, we investigate whether the credit crunch was harsher for firms in provinces whose bank-ing structures are more functionally distant, and whether it was the result of a generalizedflight to quality on the part banks. We measure firms’ quality in terms of economic prospects,productivity and informational transparency. The former is proxied by the expected level ofproduct demand (LOW DEMAND), while EXPORT and SIZE are taken as proxies forfirms’ productivity and informational transparency, respectively, as is common in the literature(Berger and Udell; 2002; Wagner; 2007).

Hence, we estimate the following bivariate model:

RATIONEDijt = 1[αPOST−LEHMANt + β1DISTANCEjt + β2POST−LEHMAN ×DISTANCEjt +(1)

+3∑k=1

γ1kQkijt +3∑k=1

γ2kPOST−LEHMAN ×Qkijt +m∑h=1

δhXhijt + εijt > 0]

DEMANDijt = 1[aPOST−LEHMANit + bDISTANCEjt +

3∑k=1

ckQkijt +

m∑h=1

dhXhijt + gIRit + ηijt > 0]

where i, j and t indicate firms, provinces and quarters respectively, POST -LEHMAN isa dummy variable which is equal to 1 for the quarters 2008:4 – 2009:3 and 0 otherwise,

20Specifically, only firms that stated in a previous question that they had had direct contact with banks(DEMAND = 1) were asked the question “Did you get from banks the requested amount of credit?”.

15

Qk are the firm quality variables conditioning the severity of the credit crunch with k ={SIZE, EXPORT, LOW DEMAND}. Finally, X is the set of bank-market-structure andfirm-level control variables, including a measure of financial health (LIQUIDITY ) to cap-ture credit risk, a set of industry dummies, a dummy for forms located in Southern provinces(SOUTH) and two measures of credit market structure: the Herfindhal-Hirschman index ofconcentration and the share of branches belonging to the five largest Italian banking groups.The second equation is the selection equation, where the firms’ variation in labor costs is in-cluded as identifying restriction (IR), while the dependent variable in the rationing equation isobserved only for firms which applied for bank credit (DEMAND = 1). The error terms in thetwo equations, εi,j and ηi,j , are assumed to be independent of the explanatory variables, with azero-mean normal distribution, but possibly reciprocally correlated.21

The sign and significance of coefficients for POST -LEHMAN and its interaction terms inthe rationing equation capture the impact of the crisis on the supply of loans and its heterogene-ity across markets and firms. Namely, a value of α significantly greater than zero would indicatethat Italian firms experienced a credit crunch after Lehman, β2 > 0 suggests that the crunchwas harsher in credit markets mostly populated by functionally distant banks, while γ2k > (<)0provides evidence of a flight to quality by banks which contracted loans disproportionally more(less) to small and risky firms.

6.2 Who is hurt by functional distance? Home bias vs flight to quality

Since distantly-managed banks are usually found to be at a disadvantage in soft-informationproduction and relationship lending, a common conjecture is that during crisis periods small,risky and informationally opaque firms would be the most hurt by banking systems with alarge presence of branches belonging to functionally distant banks. However, to the extentthat the penetration of distant banks produces a segmentation of local credit markets intosafe/transparent borrowers served by distantly-managed banks and risky/opaque borrowersserved by local lenders, and if nationwide banks have actually rebalanced their loan portfolioaway from distant provinces, credit retrenchment in functionally distant banking systems shouldprove to be relatively more pronounced for the former type of borrower than for the latter.

Therefore, in this second model we focus on the post-Lehman period to test whether inprovinces populated by functionally distant banks the flight to quality effect was severer orwhether it was the “good-quality” firms, the market segment typically served by nationwidebanks, which suffered relatively more than in provinces with a close banking system.22

To the best of our knowledge, there is no direct evidence about these contrasting effects.A partial exception supporting the flight-to-quality view is Albertazzi and Marchetti (2010),who document that after Lehman’s collapse, banks belonging to the five largest banking groups

21Given the limited variability of the credit market structure variable over time (see Table 3), we can notinclude provincial fixed effects. However we include a dummy for firms located in Southern provinces and, in therobustness section, we show that our results are confirmed considering exclusively the sub-sample of Centre-Northprovinces.

22As we already noted, “good-quality” firms are also those that more frequently borrow from global banksoutside the province. Therefore, even if “good-quality” firms were to be proved to suffer the credit crunchrelatively more in provinces where the banking system is functionally distant than in other provinces, in principlewe cannot exclude that this is due to the fact that global banks are those most heavily hurt during the crisis ratherthan to a home bias on the part of functional distant banks operating in the province. However: (1) the shareof residents borrowing from out-of-province branches is small (less than 20 percent); (2) global out-of-provincebanks can be reasonably assumed to be functionally distant from the province and hence susceptible to the samehome bias; (3) since the share of resident firms borrowing from in-region branches is more than 90% (regions arethe first administrative level in Italy, covering a much greater geographical area than provinces; see footnote 7), as a robustness exercise we introduce a variable measuring the share of branches of problematic banks in theregion where the firm is domiciled (results are available upon request).

16

reduced outstanding loans to riskier borrowers significantly more than other banks. This evi-dence, however, does not consider credit rationing, is limited to banks with a low risk-weightedcapital ratio (lower than 10%), does not control for the distance between the lending branchand the parent bank headquarters and does not explicitly model the demand for credit.

As in model (1), we proxy firms quality by LOW DEMAND, EXPORT and SIZE. Inaddition we consider the firm’s financial risk by using the status of being credit-rationed in thepre-crisis period (PRE − LEHMAN RATIONED), and the firm’s financial health, proxiedby a dummy variable equal to one for firms with a bad liquidity level (BAD LIQUIDITY ).Hence, we estimate the following bivariate model:

RATIONEDijt = 1[αDISTANCEjt +

5∑k=1

βkQ′hijt +

5∑k=1

γkDISTANCE ×Q′kijt + (2)

+

n∑h=1

δhXhijt + εijt > 0]

DEMANDijt = 1[aDISTANCEjt +

5∑k=1

bkQ′hijt +

m∑h=1

dhXhijt +

2∑r=1

grIRit + ηijt > 0]

where i, j and t indicate firms, provinces and the post-Lehman quarters, Q′

includes the fivek-characteristics for firms’ quality mentioned above and X is the set of bank-market-structure,firm-level control variables, and four time dummies. As in the previous model, the demandequation includes the firms’ labor costs as identifying restriction (IR) and error terms in thetwo equations are assumed independent of the explanatory variables, but possibly correlated.

In the rationing equation, a coefficient α > 0 indicates that the credit crunch is more severein provinces dominated by nationwide banks, while coefficients on the variables included in Q

′

identify the flight-to-quality effect. The interaction terms DISTANCE × Q′ allow us to testwhich type of borrower was hurt by functionally distant banking systems. A coefficient γk > 0indicates that a large presence of distantly-managed bank branches in a province spurs flightfrom risky, less productive and opaque firms. Conversely, a γk not significantly greater thanzero would suggest that the strength of flight to quality is not affected by the structure of thelocal banking system and possibly (if γk < 0) that safe and transparent firms are relativelymore harmed in areas with functionally distant banking systems. This would imply that thecredit crunch was the result of the home bias by nationwide banks.

7 Econometric results

In line with the recent empirical evidence about the credit crunch in Italy (Costa and Margani;2009; Gambacorta and Mistrulli; 2011; Gobbi and Sette; 2012), our regression results show thatItalian banks significantly reduced credit supply after the collapse of Lehman Brothers. Inaddition, they clearly show that the organizational structure of local credit markets has beena (statistically and economically) major determinant of the severity of the credit crunch andthe spatial heterogeneity across Italian provinces. The greater the share of branches held byout-of-market, distantly-managed banks, the higher the probability of local enterprises beingcredit rationed in the crisis period. In particular, in provinces where the banking system isfunctionally distant, even firms which before the onset of the financial crisis had full accessto bank credit increased their likelihood of being rationed after Lehman’s collapse. In thoseprovinces, large firms, exporters and firms facing a high product demand are not relatively lesslikely to be credit rationed. By contrast, financing constraints proved relatively more bindingfor financially healthier enterprises. This suggests that the harsher credit crunch in functionallydistant credit markets has not been the result of a stronger, generalized flight from risk on the

17

part of bank branches lending locally, but of a contraction of the engagement by nationwidebanks in provinces far away from their headquarter possibly due to a home bias effect. We turnnow to a more detailed discussion of our results.

7.1 Firms’ financing constraints pre- and post-Lehman

Table 4 reports the coefficients for the model (1) estimated over the pooled sample using pre-and post-Lehman quarters. The negative and significant ρ confirms the presence of a negativecorrelation between the equations modeling the demand and supply of credit, supporting thediscouraged borrower hypothesis according to which, in anticipation of a high probability ofcredit rationing, a self-selection mechanism is at work leading riskier firms to stay out of thecredit market.

7.1.1 Demand equation

Looking at the demand equation, there is evidence that, with the onset of the global crisis, therehas been a significant contraction of the demand for credit by firms, whose loan applicationsare 8.7% less frequent on average.

The coefficients for the firm-level variables are generally significant and with signs consistentwith the hypothesis that applicants tend to self-select. Namely, we find that large firms, withpart of their production being exported, increasing labor costs, a stronger demand for theirproducts and with lower levels of liquidity, are significantly more likely to apply for a bank loan.By contrast, the credit market structure variables (DISTANCE, HHI and LARGE BANKS)are not significantly correlated with the firm-level demand for bank credit, indicating that thefirms’ credit demand depends on their own characteristics more than that of banks operatingin the local market. Finally, the demand for credit in 2008-2010 has not been affected by thegeographical location of firms in the less-developed Southern regions.

7.1.2 Rationing equation

Consistent with previous literature, the estimation results of the rationing equation showthat firms’ financing constraints decrease with their size, export attitude and the level of de-mand for their products (Alessandrini et al.; 2009; Minetti and Zhu; 2011). Financial health(LIQUIDITY ) is not significantly associated with financing constraints.23 As for the demandside, as with the supply of credit there is no significant difference between the North and theSouth of Italy, once firm-specific characteristics and the structure of provincial credit marketsare taken into account.

The significant and positive coefficient for the dummy POST LEHMAN reported in col-umn (1) suggests that there has been a tightening in the supply of bank credit since the collapseof Lehman Brothers. The calculation of the average partial effects shows that, other things be-ing equal, in the post-Lehman period Italian firms have had a 7.8% higher probability of beingcredit rationed.

Moving to the credit market structure variables, column (1) shows that firms located inprovinces where the banking system is functionally distant are more likely to be credit rationed(Alessandrini et al.; 2009).24 This result is robust to the inclusion of the degree of market

23Given that, once controlled for the demand for credit, liquidity is not significantly correlated with theprobability of being rationed, we used this variable to identify the model in alternative to or together with thechange in labor costs. The main results are unaffected.

24It is worth noting that in this paper we use a different dataset from the one analyzed by Alessandrini et al.(2009), covering smaller firms over a different time period.

18

concentration and of the share of branches belonging to the five largest banking groups. Inaddition, neither of these measures of the structure of local credit markets are significantlyassociated with local firms’ financing constraints. In particular, the lack of a higher probabilityof credit rationing in provinces with a larger share of branches held by the five largest bankinggroups, the most exposed to the global financial crisis, does not support the hypothesis thatlarge banks, on average, have acted as a channel transmitting the financial crisis to the realeconomy (Albertazzi and Marchetti; 2010; Gobbi and Sette; 2012).

According to our findings, on average, (i) financing constraints depend on informationalfrictions between the borrower and the banking system, and (ii) firms experienced a significantcredit crunch after the default of Lehman Brothers. In columns (2) to (5), we assess thepossibility that the tightness of the credit crunch has been heterogeneous with respect to thebank-firm informational frictions. In particular, we test whether the marginal effect of the creditcrunch depends on the organizational structure of the local banking system, the informationalproblems that characterize the credit relationships and the quality of borrowers and, accordingly,we split the SIZE, EXPORT , LOW DEMAND and DISTANCE variables in the two pre-and post-Lehman subperiods.

Rather surprisingly, our results do not corroborate the narrative that, during the globalcrisis, small, exporting and low demand firms have been especially hurt by a lack of bankcredit (Artola and Genre; 2011; Ferrando and Mulier; 2011). In fact, the difference between thecoefficients on SIZE, EXPORT and LOW DEMAND in the post-crisis and in the pre-crisisperiods (columns 2-4) is statistically not different than zero, as the t-tests at the bottom ofTable 4 indicate.

However, we do find that the negative impact of the functional distance of local creditmarkets on the likelihood of local firms being credit rationed is larger and statistically significantexclusively in the crisis period. This result suggests that the intensity of the credit crunch hasbeen greater in provinces more densely populated by distantly-managed banks, headquarteredoutside the province (column 5).

In Figure 7 we provide a visual representation of the severity of the credit crunch afterLehman’s collapse. The diagram shows the predicted probability of being credit rationed (andthe 95% confidence intervals), conditional on having applied for bank credit, in the pre-crisis andin the post-crisis period as a function of functional distance. As one can see, the relationshipbetween the likelihood of credit rationing and the functional distance of the local bankingsystem is positive in both periods, however it is clearly steeper in the quarters after the failureof Lehman Brothers. The vertical difference between the two lines is a measure of the tightnessof credit supply in provinces whose banking systems have a certain functional distance from thelocal economy.

The average partial effect of the POST LEHMAN dummy on the probability of creditrationing varies from 4.6% to 11.3% as long as DISTANCE increases (for memory, it was -7.8%on average; see column (1)). For concreteness, consider the comparison between two provinces,such as Siena and Bari. The former is characterized by a functionally close banking system, itbeing home to one of the largest Italian banking groups, the Monte dei Paschi (DISTANCE =1.5), while the latter predominantly comprises distantly-managed banks (DISTANCE = 4.5).According to our estimates (column 4), credit tightening has been almost twice as severe in theprovince of Bari, where the probability of rationing for the average firm has increased by 9.3percentage points in the post-Lehman period, as in the province of Siena, where the averagecredit crunch has raised the likelihood of being credit rationed by 5.7 percentage points.

19

Figure 7: Credit crunch intensity and functional distance

.05

.1

.15

.2

.25

.3

Prob

(RAT

IONE

D =

1 | D

EMAN

D =

1)

.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6

Funtional Distance

Pre-crisis Post-crisis

Notes: This figure is constructed from the estimates reported in Table 4, column (5).

7.2 The post-Lehman credit crunch

In Table 5 we present the estimates of the bivariate probit with selection, focusing exclusivelyon the crisis period. With this specification, we are able to control for the initial conditions offirms in the credit market, adding the time-invariant dummy PRE−LEHMAN RATIONED,which identifies firms having being credit rationed in the pre-Lehman period. Furthermore, wetest whether the tighter credit crunch experienced in provinces where the banking system isfunctionally distant has been the result of a harsher flight to quality by local branches or of ahome bias by functionally distant bank branches.

7.2.1 Demand equation

As in model (1) for the whole sample, the ρ coefficient indicates the existence of a negativeand significant correlation between the demand and the supply equations, suggesting that theneglecting of borrowers’ selection on the demand side would bias the results of a simple rationingmodel.

The results in column (1) shows that, ceteris paribus, firms which were credit rationed in thefirst three quarters of 2008 are 14.8% more likely to apply for a loan in the crisis period thanpreviously non-rationed firms. Even after controlling for access to credit in the pre-crisis period,we find robust evidence that the size and export attitude of firms are positively correlated withtheir credit demand, while firms with a low expected product demand are no less likely to applyfor a loan. Finally, like in the pre-Lehman period, increasing labor costs and the low level ofliquidity contribute to explain the demand for credit, while the structure of the local creditmarkets has no significant influence on the decision to apply for a loan.

20

7.2.2 Rationing equation

Moving on to the rationing equation, we find that credit rationing is a persistent phenomenon:the probability of a firm loan application being rejected in the post-Lehman period is 19.7%higher if it was already credit-rationed in one of the three quarters before Lehman. Poorfinancial health (BAD LIQUIDITY ) and low product demand increases the likelihood ofbeing credit rationed by 16.8 and 5.4 percentage points, respectively. The coefficients on SIZEand EXPORT are not statistically significant. Since firms’ size and export orientation arelikely to be persistent, their effect could be washed out by the status of credit rationed in thepre-crisis period.

With regard to the effect of DISTANCE, the estimates reported in column (1) confirmthat enterprises located in provinces where the banking system is functionally distant exhibit ahigher probability of being credit rationed. However, it is interesting to note that, during thecrisis, this negative average effect on credit availability is the result of a differentiated impacton specific groups of firms.

Firms which were previously unconstrained before the Lehman collapse (either because theyhad full access to the credit market or because they had not applied for bank credit) and firmswith good or normal financial health are, on average, at an advantage with respect to firmswith the opposite characteristics. However, in provinces with a predominance of distantly-managed banks, it is exactly these “good-quality” firms, the market segment typically servedby functionally distant and hierarchically organized banks, that are relatively more likely to becredit rationed after Lehman’s collapse.

The estimates reported in column (2), for instance, show that firms which have been creditconstrained in the pre-Lehman period are more likely to be constrained in the crisis period. Thiseffect is not exacerbated in areas dominated by distant banks. Instead, unconstrained firms havea better average access to credit markets, although the greater the presence of functional distantbanks in the province, the smaller is their advantage. The same result holds when consideringthe level of financial health (column 7). The effect of distance is not equal across firms withbad and good liquidity levels, and the advantage of the latter in terms of access to bank creditdiminishes the larger the share of distantly-managed banks in the province.

Similar findings hold in the case of firms with a high product demand (column 3) andof internationalized enterprises (column 4). However, the coefficients on DISTANCE arenot statistically different between firms with a low and a high product demand, and betweenexporters and non-exporters. With regard to SIZE, the effect of DISTANCE seems to beconstant across firms either if we consider the number of employees continuously (column 5),or if we split the effect of DISTANCE between small and large firms (column 6). In thelatter case, once again the point estimates suggest that, contrary to the conventional priors,functionally distant banks do not further penalize opaque firms with 20 or less employees. Bycontrast, it appears that medium-large firms, while generally less hit by the credit crunch, suffermore if located in areas dominated by distantly-managed banks.

Summing up, the presence of distant banks in a particular area is associated with more severecredit tightening, but this is not explicitly targeted at low quality firms. This is in contrastwith the hypothesis of a flight to quality by functionally distant banks, according to whichthe latter would have reacted to the global crisis by reducing the amount of credit to riskier,informationally opaquer and less productive borrowers. However, our results are consistent withthe hypothesis that functionally distant banks shy away from lending in provinces which are ata distance from their headquarters.

21

8 Robustness

In this Section we conduct a number of additional econometric exercises to check for the ro-bustness of our main findings on the existence of a home bias in the Italian credit marketsduring the post-Lehman credit crunch. In the first exercise we control for the possibility thatLARGE BANKS might not capture the exposure of local banking systems to the global finan-cial crisis. Thus, we augment our model adding several indicators of the average performanceand business model of provincial credit markets. The second exercise introduces an alternativedefinition of functional distance based on cultural differences across provinces. In the third exer-cise we limit the analysis to the very (culturally and economically) homogeneous centre-northernprovinces to verify whether the effect of distance may spuriously capture the traditional dividebetween the North and South of Italy.

Finally, a number of other robustness exercises on the set of control variables have beenrun.25 In particular, the effect of functional distances survives even including other measures oflocal credit market structure, such as the share of bank branches held by cooperative or mutualbanks. In addition, we further try to control for the change in the firm’s risk profile duringthe sample period. In model (1) we interact the industry dummies and the SOUTH dummywith the POST −LEHMAN dummy, so that we could allow for industry- and spatial-specificchanges in risk profile before and after Lehman’s collapse.

8.1 Banks’ exposure to the crisis

So far we have controlled for the exposure of the local banking systems to the global financialcrisis using the share of branches belonging to the five largest Italian banking groups. Whilethere is evidence suggesting that larger banks have been more heavily affected during the crisis(Albertazzi and Marchetti; 2010; Gobbi and Sette; 2012), it is also possible that bank size doesnot fully capture banks’ funding difficulties and liquidity stress. Therefore, in this section weundertake a number of robustness exercises replacing LARGE BANKS with several alternativeindicators which measure the exposure of the provincial banking systems to the global crisis.

Using bank balance-sheet yearly data retrieved from the Bilbank database, published bythe Italian Banking Association (ABI), we calculate different provincial indicators of the size,capitalization, funding structure and performance of local banking systems. Specifically, startingfrom individual bank balance sheet information, for each province we calculate the branch-weighted average of: (i) the logarithm of bank total assets (ASSETS), (ii) the growth rateof total assets (ASSET GROWTH), (iii) the TIER1 capital ratio (TIER1 CAPITAL), (iv)the ratio of loan loss provisions over total interest income (CREDIT RISK), (v) the share ofwholesale funding over total funding (WHOLESALE FUNDING), (vi) the non-performingloans ratio (NON − PERFORMING LOANS), and (vii) the returns-on-asset (ROA).

Results are reported in Tables 6 and 7. In the former we replicate the model estimated incolumn (5) of Table 4. On the whole, the effect of distance on credit rationing is robust tothe inclusion of several other measures of the possible exposure of in-province branches to theglobal financial crisis. In particular, we find that firms are more likely to be credit rationed inprovinces where there is a larger share of branches owned by banks which are less capitalized, lessprofitable, and less dependent on interbank markets (Table 6). This result is consistent with thehypothesis that banks more heavily hit by the crisis were likely to contract their loan supply, butit does not wash out the effects of the presence of distantly-managed banks on credit availabilityto local firms after the collapse of Lehman Brothers. In addition, we find confirmation that firmssuffered from the credit crunch regardless of their size, export-orientation and the strength of

25Results are not reported for the sake of brevity, but they are available upon request from the authors.

22

demand for their products, suggesting the absence of a flight to quality reaction by banks.26

In Table 7 we replicate the estimation of model (2) as specified in columns 2-4 and 6-7 of Table 5. For the sake of space, we report only results for the rationing equation: inparticular in each column we report the coefficients on the interaction terms with DISTANCEand the specific bank performance indicator reported in the column headings and included inthe estimated specification. Focusing on the crisis period, we find that the exposure of localbanking systems to the crisis is not significantly correlated with the likelihood of firms beingcredit rationed. However, we find that the correlation between distance and credit rationingis robust to the inclusion of indicators of health status of local banking systems and that,consistent with the home bias hypothesis, the firms which were most damaged by the presenceof functionally distant banks during the crisis were the ”good-quality” firms.27

Since we do not have information on the identity of lending banks, we cannot control for thepossibility that firms were rationed by branches located out of their province. In this case, thenegative impact of functional distance on access to credit of “good-quality” firms could capturethe fact that these firms received credit from global banks located outside their province andthat these global banks were more heavily affected during the crisis. In order to control for thispossibility, we exploit the fact that more than 90% of credit to firms comes from branches locatedin their region of residence. Therefore, we build the same indicators of bank performance at theregional level (instead of the smaller provincial level) and test whether the coefficients on theinteraction terms between DISTANCE and a firm’s credit rationing status in the pre-crisisperiod (PRE − LEHMAN RATIONED), information transparency (SIZE), productivity(EXPORT ), economic and financial health (DEMAND and LIQUIDITY ) maintain theirstatistical significance. The results, reported in Table 8, broadly confirm our main findings andare consistent with the home bias hypothesis.

8.2 Cultural distance

Apart from the physical distance between the bank headquarters and the local branches, theorganizational diseconomies may depend on the cultural distance which separates the thinkingcentre and the operational periphery of the bank (Alessandrini et al.; 2008, 2009; Galindo et al.;2010; Giannetti and Yafeh; 2012). In this view, differences in cultural values between loanofficers and bank managers would make the communication of information more noisy andcostly, putting multi-market banks at a disadvantage with respect to local-embedded banks.

Building on an established literature showing that social capital matters for local financialdevelopment (Guiso et al.; 2004b), we proxy cultural distance between two provinces as theabsolute difference in social capital, measured at the average voter turnout at the 21 referendaheld in Italy in 1993, 1995 and 2001. Then we calculate (DISTANCE SOCIAL CAPITAL)as the ratio of the number of branches in the province weighted by the logarithm of 1 plus thecultural distance between the province of the branch and the province where the parent bankis headquartered, over total branches in the province.

The estimation of models (1) and (2), in which we look at the possible differentiated effectof functional distance in the crisis period, shows that a large presence of culturally distantbanks is positively correlated with the likelihood of a firm being credit rationed in the post-Lehman period (Tables 9 and 10).As regards the possible heterogeneous effect of distance inthe crisis period, we again find evidence consistent with the hypothesis that the credit crunchin functionally distant banking systems is consistent with a flight to quality effect, rather thanwith a home bias effect. In other words, our estimations show that in provinces with a larger

26Once again, for brevity we do not report these estimation results which are available upon request27The coefficients not reported in Table 7 are qualitatively identical to those reported in Table 5

23

share of culturally distant banks, financially healthier firms are relatively more likely to berationed than in areas with a lesser presence of distant banks. No significant differences emergeaccording to firms’ size, export status and expected demand.

8.3 Exclusion of southern provinces

The third exercise deals with potential concerns that the adverse effect of functional distancemight capture some other confounding factor connected with the socio-economic divide be-tween richer, more financially developed northern provinces and poorer, less financially devel-oped provinces in southern Italy. In addition, given that the intense process of mergers andacquisitions which affected the southern banking system in the late 1990s, a large share of bankbranches located in the South are owned by out-of-market banks, predominantly located inthe North. Hence, the overall effect of DISTANCE might capture either differences in GDPper capita across provinces or it might be due to a large extent to the difference in functionaldistance between northern and southern provinces. To rule out these possibilities, we esti-mate models (1) and (2) excluding the South and exploiting the cross-sectional variability offunctional distance across the more socio-economically homogeneous provinces of the North.

Results are reported in Tables 11 and 12 and corroborate the fact that a large presence ofdistantly-managed banks is associated with a higher than average probability of credit rationingand that in functionally distant credit markets there is no evidence of a flight to quality effect,since previously unconstrained and financially healthier firms are relatively more likely to berationed in areas with a greater presence of distant banks.

9 Discussion and Conclusion

A major policy issue is whether the financial crisis, culminating in the bankruptcy of LehmanBrothers in September 2008, has spurred a credit crunch and, if so, whether its severity has beenaffected by the organizational structure of local banking systems. To answer these questions,a key challenge is disentangling demand and supply effects. In the absence of unusual naturalexperiments that create an easily identifiable supply shock, we identify constrained firms usingsurvey data that contain information on loan applications and allow us to observe whether firmsapplied for loans and to observe the outcome of this application.

Our paper looks at the Italian case, taking advantage of a dataset on a large sample offirms, observed quarterly between January 2008 and September 2009, matched with indicatorsof the local credit market structure, constructed at the provincial level. In particular, weanalyze whether a large market share of branches of banks headquartered at a geographicaldistance from the province may influence the availability of credit to local firms because ofmore severe asymmetric information and larger agency costs, problems associated with internalcapital markets and corporate politics.

Our results confirm the severity of credit tightening: in the post-Lehman period Italian firmshad a 7.8% higher probability of being credit rationed. Moreover, the credit crunch has beenmore severe in provinces with larger shares of branches owned by distantly-managed banks.However, it has not been harsher for small and economically weak firms. In addition, assumingthat our firm-specific variables are good proxies for firm quality and financial health, we find thatthe credit contraction to large and “good-quality” firms in functionally distant credit marketshas been relatively stronger than in credit markets largely populated by functionally close banks.Thus, our results are inconsistent with the common idea that the credit crunch was the resultof a flight to quality by banks and especially by nationwide banks. By contrast, the evidence isconsistent with the hypothesis of a home bias on the part of distantly-headquartered banks.

24

REFERENCES

Agarwal, S. and Hauswald, R. (2010). Authority and information, Technical report, availableat http://ssrn.com/abstract=1566062.

Agarwal, S. and Wang, F. H. (2009). Perverse incentives at the banks? evidence from a naturalexperiment, Working Paper Series WP-09-08, Federal Reserve Bank of Chicago.

Aisen, A. and Franken, M. (2010). Bank credit during the 2008 financial crisis: A cross-countrycomparison, IMF Working Paper 10/47, International Monetary Fund.

Aiyar, S. (2011). How did the crisis in international funding markets affect bank lending?Balance sheet evidence from the United Kingdom, Working paper 424, Bank of England.

Albertazzi, U. and Marchetti, D. J. (2010). Credit supply, flight to quality and evergreening:an analysis of bank-firm relationships after Lehman, Temi di discussione (Working Papers)756, Banca d’Italia.

Alessandrini, P., Calcagnini, G. and Zazzaro, A. (2008). Asset restructuring strategies in bankacquisitions: Does distance between dealing partners matter?, Journal of Banking & Finance32(5): 699–713.

Alessandrini, P., Presbitero, A. F. and Zazzaro, A. (2009). Banks, distances and firms’ financingconstraints, Review of Finance 13(2): 261–307.

Alessandrini, P., Presbitero, A. F. and Zazzaro, A. (2010). Bank Size or Distance: WhatHampers Innovation Adoption by SMEs?, Journal of Economic Geography 10(6): 845–881.

Angelini, P., Nobili, A. and Picillo, C. (2011). The interbank market after august 2007: Whathas changed and why?, Journal of Money, Credit and Banking 43(5): 923–958.

Artola, C. and Genre, V. (2011). Euro Area SMEs Under Financial Constraints: Belief orReality?, Working Paper Series 3650, CESifo.

Barboni, G. and Rossi, C. (2012). Does your neighbour know you better? local banks andcredit tightening in the financial crisis. Bank of Italy.

Bellucci, A., Borisov, A. and Zazzaro, A. (2010). Does gender matter in bank-firm relationships?Evidence from small business lending, Journal of Banking & Finance 34(12): 2968–2984.

Berger, A. and DeYoung, R. (2001). The effects of geographic expansion on bank efficiency,Journal of Financial Services Research 19(2): 163–184.

Berger, A. N., Demsetz, R. S. and Strahan, P. E. (1999). The consolidation of the financialservices industry: Causes, consequences, and implications for the future, Journal of Banking& Finance 23(2-4): 135–194.

Berger, A. N., Klapper, L. F. and Udell, G. F. (2001). The ability of banks to lend to informa-tionally opaque small businesses, Journal of Banking & Finance 25(12): 2127–2167.

Berger, A. N., Miller, N. H., Petersen, M. A., Rajan, R. G. and Stein, J. C. (2005). Doesfunction follow organizational form? Evidence from the lending practices of large and smallbanks, Journal of Financial Economics 76(2): 237–269.

Berger, A. N., Rosen, R. J. and Udell, G. F. (2007). Does market size structure affect compe-tition? The case of small business lending, Journal of Banking & Finance 31(1): 11–33.

25

Berger, A. N. and Udell, G. F. (2002). Small business credit availability and relationship lending:The importance of bank organisational structure, The Economic Journal 112(477): F32–F53.

Berrospide, J., Black, L. and Keeton, W. (2011). The cross-market spillover of economic shocksthrough multi-market banks. Board of Governors of the Federal Reserve System.

Bonaccorsi Di Patti, E. and Gobbi, G. (2007). Winners or losers? the effects of bankingconsolidation on corporate borrowers, Journal of Finance 62(2): 669–695.

Bonaccorsi Di Patti, E. and Sette, E. (2012). Bank balance sheets and the transmission of finan-cial shocks to borrowers: evidence from the 2007-2008 crisis, Temi di discussione (WorkingPapers) 848, Banca d’Italia.

Brevoort, K. P. and Wolken, J. D. (2009). Does distance matter in banking?, in P. Alessan-drini, M. Fratianni and A. Zazzaro (eds), The Changing Geography of Banking and Finance,Springer, chapter 3, pp. 27–56.

Campello, M. (2002). Internal capital markets in financial conglomerates: Evidence from smallbank responses to monetary policy, Journal of Finance 57(6): 2773–2805.

Canton, E., Grilob, I., Monteagudob, J. and van der Zwan, P. (2010). Investigating the percep-tions of credit constraints in the EU, Research Paper ERS-2010-001-ORG, Erasmus ResearchInstitute of Management.

Carbo-Valverde, S., Degryse, H. and Rodriguez-Fernandez, F. (2011). Lending relationships andcredit rationing: the impact of securitization, CentER Discussion Paper 2011-128, TilburgUniversity - CentER.

Carbo-Valverde, S., Rodriguez-Fernandez, F. and Udell, G. F. (2011). Trade credit, the financialcrisis, and firm access to finance. University of Grenada.

Carlin, W., Charlton, A. and Mayer, C. (2006). Capital markets, ownership and distance,Discussion Papers 5764, CEPR.

Carling, K. and Lundberg, S. (2005). Asymmetric information and distance: an empiricalassessment of geographical credit rationing, Journal of Economics and Business 57(1): 39–59.

Cetorelli, N. and Goldberg, L. S. (2011). Global banks and international shock transmission:Evidence from the crisis, IMF Economic Review 59(1): 41–76.

Contessi, S. and Francis, J. L. (2010). U.S. commercial bank lending through 2008:Q4: Newevidence from gross credit flows, Economic Inquiry forthcoming.

Costa, S. and Margani, P. (2009). Credit crunch in italy: Evidence on new isae survey data.paper presented at the Fourth joint EU-OECD Workshop on Business and Consumer OpinionSurveys.

Cremer, J., Garicano, L. and Prat, A. (2007). Language and the theory of the firm, TheQuarterly Journal of Economics 122(1): 347–407.

Cremers, K. J. M., Huang, R. and Sautner, Z. (2010). Internal capital markets and corporatepolitics in a banking group, Review of Financial Studies 24(2): 358–401.

26

Dahl, D., Shrieves, R. and Spivey, M. (2002). Financing loan growth at banks, Journal ofFinancial Services Research 22(3): 189–202.

de Haas, R., Korniyenko, Y., Loukoianova, E. and Pivovarsky, A. (2011). Foreign banks duringthe crisis: Sinners or saints? European Bank of Reconstruction and Development.

de Haas, R. and van Horen, N. (2012). Running for the exit: International banks and crisistransmission, Review of Financial Studies forthcoming.

de Haas, R. and van Lelyveld, I. (2010). Internal capital markets and lending by multinationalbank subsidiaries, Journal of Financial Intermediation 19(1): 1–25.

Degryse, H. and Ongena, S. (2005). Distance, lending relationships, and competition, TheJournal of Finance LX(1): 231–265.

Dell’Ariccia, G. and Marquez, R. (2004). Information and bank credit allocation, Journal ofFinancial Economics 72(1): 185–214.

Detragiache, E., Tressel, T. and Gupta, P. (2008). Foreign banks in poor countries: Theory andevidence, Journal of Finance 63(5): 2123–2160.

DeYoung, R., Glennon, D. and Nigro, P. (2008). Borrower-lender distance, credit scoring, andloan performance: Evidence from informational-opaque small business borrowers, Journal ofFinancial Intermediation 17(1): 113–143.

DeYoung, R., Gron, A., Torna, G. and Winton, A. (2012). Risk overhang and loanportfolio decisions: Small business loan supply before and during the financial crisis.http://dx.doi.org/10.2139/ssrn.2140952.

Djedidi, S. (2010). Distances and small business credit constraints: the French case. Universityof Paris 9 Dauphine.

Ferrando, A. and Mulier, K. (2011). Firms’ financial constraints: do perceptions match theactual situation? European Central Bank.

Galindo, A., Izquierdo, A. and Rojas-Suarez, L. (2010). Financial integration and foreign banksin latin america: How do they impact the transmission of external financial shocks?, RESWorking Papers 4651, Inter-American Development Bank, Research Department.

Gambacorta, L. and Mistrulli, P. E. (2011). Bank heterogeneity and interest rate setting: whatlessons have we learned since lehman brothers?, Temi di discussione (Working papers) 829,Bank of Italy.

Garcia-Appendini, E. and Montoriol-Garriga, J. (2011). Firms as liquidity providers: Evidencefrom the 2007-2008 financial crisis, Carefin working paper 5/11, Bocconi University.

Giannetti, M. and Laeven, L. (2011). The flight home effect: Evidence from the syndicatedloan market during financial crises, Journal of Financial Economics 104(1): 23–43.

Giannetti, M. and Yafeh, Y. (2012). Do cultural differences between contracting parties matter?evidence from syndicated bank loans, Management Science 58(2): 365–383.

Gobbi, G. and Sette, E. (2012). Relationship lending in a financial turmoil, Mo.Fi.R. WorkingPapers 59, Money and Finance Research group (Mo.Fi.R.) - Univ. Politecnica Marche - Dept.Economic and Social Sciences.

27

Gopalan, R., Udell, G. and Yeramilli, V. (2011). Why do firms form new bank relationships?,Journal of Financial and Quantitative Analysis 46(5): 1335–1365.

Gormley, T. A. (2010). The impact of foreign bank entry in emerging markets: Evidence fromindia, Journal of Financial Intermediation 19(1): 26–51.

Gormley, T. A. (2011). Costly information, foreign entry, and credit access. available athttp://ssrn.com/abstract=896000.

Gozzi, J. C. and Goetz, M. (2010). Liquidity shocks, local banks, and economic activity:Evidence from the 2007-2009 crisis. Brown University.

Guiso, L., Sapienza, P. and Zingales, L. (2004a). Does local financial development matter?, TheQuarterly Journal of Economics 119(3): 929–969.

Guiso, L., Sapienza, P. and Zingales, L. (2004b). The role of social capital in financial develop-ment, The American Economic Review 94(3): 526–556.

Hertzberg, A., Liberti, J. M. and Paravisini, D. (2010). Information and incentives inside thefirm: Evidence from loan officer rotation, Journal of Finance 65(3): 795–828.

Hirshleifer, D. and Thakor, A. V. (1992). Managerial conservatism, project choice, and debt,Review of Financial Studies 5(3): 437–70.

Holton, S., Lawless, M. and McCann, F. (2011). Firm access to finance in the european crisis.Central Bank of Ireland.

Houston, J., James, C. and Marcus, D. (1997). Capital market frictions and the role of internalcapital markets in banking, Journal of Financial Economics 46(2): 135–164.

Imai, M. and Takarabe, S. (2011). Bank integration and transmission of financial shocks:Evidence from japan, American Economic Journal: Macroeconomics 3(1): 155–83.

Ivashina, V. and Scharfstein, D. (2010). Bank lending during the financial crisis of 2008, Journalof Financial Economics 97(3): 319–338.

Iyer, R., Lopes, S., Peydro, J. and Schoar, A. (2010). Interbank liquidity crunch and the firmcredit crunch: Evidence from the 2007-2009 crisis. MIT.

Jimenez, G., Ongena, S., Peydro, J. and Saurina, J. (2012). Credit supply and monetary pol-icy: identifying the bank-balance sheet channel with loan applications, American EconomicReview 102(5): 2121–2165.

Jimenez, G., Salas, V. and Saurina, J. (2009). Organizational distance and use of collateral inbusiness loans, Journal of Banking & Finance 33(2): 234–243.

Khwaja, A. I. and Mian, A. (2008). Tracing the impact of bank liquidity shocks: Evidence froman emerging market, American Economic Review 98(4): 1413–42.

Kremp, E. and Sevestre, P. (2011). Did the crisis induce credit rationing for french SMEs?Banque de France.

Landier, A., Nair, V. B. and Wulf, J. (2009). Trade-offs in Staying Close: Corporate DecisionMaking and Geographic Dispersion, Review of Financial Studies 22(3): 1119–1148.

28

Liberti, J. M. and Mian, A. R. (2009). Estimating the effect of hierarchies on information use,Review of Financial Studies 22(10): 4057–4090.

Lucchetti, R., Papi, L. and Zazzaro, A. (2001). Banks’ inefficiency and economic growth: Amicro-macro approach, The Scottish Journal of Political Economy 48(4): 400–424.

Malgarini, M., Margani, P. and Martelli, B. M. (2005). New design of the isae manufacturingsurvey, Journal of Business Cycle Measurement and Analysis 5(1): 125–142.

Meyer, M., Milgrom, P. and Roberts, J. (1992). Organizational prospects, influence costs, andownership changes, Journal of Economics & Management Strategy 1(1): 9–35.

Mian, A. (2006). Distance constraints: The limits of foreign lending in poor economies, TheJournal of Finance LXI(3): 1465–1505.

Micucci, G. and Rossi, P. (2010). Debt restructuring and the role of lending technologies, Temidi discussione (Working Papers) 763, Bank of Italy.

Minetti, R. and Zhu, S. C. (2011). Credit constraints and firm export: Microeconomic evidencefrom italy, Journal of International Economics 83(2): 109–125.

Morgan, D., Rime, B. and Strahan, P. E. (2004). Bank integration and state business cycles,The Quarterly Journal of Economics 119(4): 1555–1584.

OECD (2009). OECD Economic Surveys. Italy, OECD, Paris.

Ozyildirim, S. and Onder, Z. (2008). Banking activities and local output growth: does distancefrom centre matter?, Regional Studies 42(2): 229–244.

Panetta, F. and Signoretti, F. M. (2010). Domanda e offerta di credito in Italia durante la crisifinanziaria, Questioni di Economia e Finanza (Occasional Papers) 63, Banca d’Italia.

Peek, J. and Rosengren, E. S. (1997). The international transmission of financial shocks: Thecase of japan, American Economic Review 87(4): 495–505.

Peek, J. and Rosengren, E. S. (2000). Collateral damage: Effects of the japanese bank crisis onreal activity in the united states, American Economic Review 90(1): 30–45.

Popov, A. and Udell, G. F. (2012). Cross-border banking, credit access, and the financial crisis,Journal of International Economics 87(1): 147–161.

Presbitero, A. F. and Zazzaro, A. (2011). Competition and relationship lending: Friends orfoes?, Journal of Financial Intermediation 20(3): 387–413.

Puri, M., Rocholl, J. and Steffen, S. (2011). Global retail lending in the aftermath of theus financial crisis: Distinguishing between supply and demand effects, Journal of FinancialEconomics 100(3): 556–578.

Quaglia, L. (2009). The response to the global financial turmoil in Italy: ’a financial systemthat does not speak english’, South European Society and Politics 14(1): 7–18.

Santos, J. A. C. (2010). Bank corporate loan pricing following the subprime crisis, Review ofFinancial Studies 24(6): 1916–1943.

Scharfstein, D. S. and Stein, J. C. (2000). The dark side of internal capital markets: Divisionalrent-seeking and inefficient investment, Journal of Finance 55(6): 2537–2564.

29

Schnabl, P. (2012). The international transmission of bank liquidity shocks: Evidence from anemerging market, Journal of Finance 67(3): 897–932.

Sengupta, R. (2007). Foreign entry and bank competition, Journal of Financial Economics84(2): 502–528.

Stein, J. C. (2002). Information production and capital allocation: Decentralized versus hier-archical firms, Journal of Finance 57(5): 1891–1921.

Uchida, H., Udell, G. F. and Watanabe, W. (2008). Bank size and lending relationships inJapan, Journal of the Japanese and International Economies 22(2): 242–267.

Uchida, H., Udell, G. F. and Yamori, N. (2012). Loan officers and relationship lending to SMEs,Journal of Financial Intermediation 21(1): 97–122.

Udell, G. F. (1989). Loan quality, commercial loan review and loan officer contracting, Journalof Banking & Finance 13(3): 367–382.

Udell, G. F. (2009). Wall street, main street and a credit crunch: Thoughts on the currentfinancial crisis, Business Horizons 52(2): 117–125.

Wagner, J. (2007). Exports and productivity: A survey of the evidence from firm-level data,The World Economy 30(1): 60–82.

Winston Smith, S. and Robb, A. M. (2011). Who seeks and who receives? implications ofdemand for and access to financial capital by young firms. Kauffman Foundation.

Wooldridge, J. M. (2011). Econometric Analysis of Cross Section and Panel Data, second edn,MIT Press, Cambridge, MA.

30

A Descriptive Tables

Table 1: Descriptive Statistics: whole sample

Obs. Mean Std. Dev. Min. Max. t-test

Dependent variables

RATIONED 6,616 0.17 0.38 0 1DEMAND 23,140 0.29 0.45 0 1

Credit market structure variables

DISTANCE 23,140 3.18 0.96 0.77 5.91DISTANCE | DEMAND = 1 6,616 3.17 0.93 0.77 5.91 0.126DISTANCE | RATIONED = 1 1,150 3.26 0.95 0.77 5.91 0.000***HHI 23,140 0.11 0.05 0.03 0.60LARGE BANKS 23,140 0.52 0.13 0.14 0.89ASSETS 23,140 16.22 0.75 14.05 17.70ASSETS GROWTH 23,140 0.11 0.54 -0.13 5.07TIER1 CAPITAL 23,140 10.31 4.75 8.06 120.36CREDIT RISK 23,140 0.27 0.07 0.14 0.53WHOLESALE FUNDING 23,140 0.18 0.05 0.06 0.29NON − PERFORMING LOANS 23,140 4.44 1.09 2.17 9.89ROA 23,140 0.66 0.18 0.18 1.20

Firm-specific variables

SIZE 23,140 3.15 1.24 1.61 8.88eSIZE 23,140 74.44 285.16 5 7,151eSIZE | DEMAND = 1 6,616 76.71 294.05 5 7,151 0.443eSIZE | RATIONED = 1 1,150 49.97 154.17 5 2,849 0.000***SMALL 23,140 0.55 0.50 0 1EXPORT 23,140 0.47 0.50 0 1EXPORT | DEMAND = 1 6,616 0.53 0.50 0 1 0.000***EXPORT | RATIONED = 1 1,150 0.48 0.50 0 1 0.000***LABOR COST 23,140 1.31 2.78 -20 20SOUTH 23,140 0.18 0.38 0 1PRE − LEHMAN RATIONED 23,140 0.08 0.28 0 1PRE − LEHMAN RATIONED | DEMAND = 1 6,616 0.15 0.36 0 1 0.000***PRE − LEHMAN RATIONED | RATIONED = 1 1,150 0.54 0.50 0 1 0.000***LOW DEMAND 23,140 0.47 0.50 0 1LOW DEMAND | DEMAND = 1 6,616 0.46 0.50 0 1 0.338LOW DEMAND | RATIONED = 1 1,150 0.66 0.47 0 1 0.000***BAD LIQUIDITY (0, 1) 23,140 0.20 0.40 0 1

LIQUIDITY Obs. % Cum. %

Good 5,617 24.27 24.27Neither good nor bad 12,936 55.90 80.18Bad 4,587 19.82 100

Notes: The table reports the descriptive statistics for the whole sample, consisting of an unbalanced panel of 3,623 firms,observed quarterly between 2008:1 to 2009:3. The last column reports the p-values of the t-test on the null hypothesisthat the average values of DISTANCE, eSIZE , EXPORTS, PRE − LEHMAN RATIONED, and LOW DEMANDare equal for firms demanding or not bank credit and for firms which are credit rationed or not; *** indicates a statisticalsignificance at 1%.

31

Table 2: Descriptive Statistics: firms’ characteristics

Pre-Lehman Post-Lehman

Did the firm demand for bank credit?No Yes t-test No Yes t-test

eSIZE 72.70 73.89 73.61 69.83EXPORT 0.455 0.563 *** 0.451 0.497 **BAD LIQUIDITY 0.140 0.173 ** 0.211 0.290 ***LOW DEMAND 0.391 0.352 ** 0.560 0.575

Has been the firm credit rationed?No Yes t-test No Yes t-test

eSIZE 78.380 39.920 75.400 49.080EXPORT 0.575 0.473 ** 0.506 0.460BAD LIQUIDITY 0.123 0.550 *** 0.222 0.545 ***LOW DEMAND 0.334 0.489 *** 0.544 0.690 ***

Notes: The table reports the descriptive statistics for a balanced sample, consisting of a panel of 3,439 firms, observed in2008:3 (pre-Lehman) and in 2008:4 (post-Lehman). Columns 4 and 7 report the p-values of the mean comparison t-test. *significant at 10%; ** significant at 5%; *** significant at 1%.

Table 3: Descriptive Statistics: credit market structure variables

Variable All provinces Northern provinces Southern provinces

Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

DISTANCE Overall 3.437 1.088 2.962 0.849 4.321 0.921Between 1.086 0.848 0.920Within 0.123 0.110 0.146

HHI Overall 0.127 0.073 0.114 0.043 0.151 0.103Between 0.072 0.043 0.104Within 0.008 0.007 0.010

LARGE BANKS Overall 0.531 0.136 0.536 0.135 0.522 0.136Between 0.134 0.135 0.135Within 0.022 0.020 0.026

Notes: The table refers to 721 observations, corresponding to 103 administrative provinces (67 in the North and 36 in theSouth) over 7 quarters, from 2008:1 to 2009:3.

32

B Regression Tables

Table 4: The post-Lehman credit crunch

(1) (2) (3) (4) (5)Dependent variable: Prob (RATIONED)

POST − LEHMAN (0, 1) 0.371*** 0.363*** 0.361*** 0.349*** 0.313***[0.031] [0.054] [0.034] [0.034] [0.066]

SIZE -0.070*** -0.070*** -0.070*** -0.070***[0.012] [0.012] [0.012] [0.012]

EXPORT (0, 1) -0.131*** -0.131*** -0.132*** -0.132***[0.029] [0.029] [0.028] [0.028]

LOW DEMAND (0, 1) 0.111*** 0.111*** 0.111*** 0.110***[0.028] [0.028] [0.028] [0.028]

LIQUIDITY (neither bad nor good) -0.016 -0.014 -0.015 -0.024 -0.018[0.059] [0.060] [0.059] [0.058] [0.059]

LIQUIDITY (bad) 0.071 0.075 0.073 0.048 0.063[0.128] [0.132] [0.128] [0.127] [0.130]

DISTANCE 0.027* 0.027* 0.027* 0.026*[0.015] [0.015] [0.015] [0.015]

HHI 0.263 0.265 0.263 0.256 0.256[0.235] [0.235] [0.235] [0.231] [0.234]

LARGE BANKS 0.107 0.109 0.108 0.101 0.104[0.127] [0.128] [0.127] [0.126] [0.127]

PRE − LEHMAN × SIZE -0.072***[0.016]

POST − LEHMAN × SIZE -0.069***[0.013]

PRE − LEHMAN × EXPORT -0.143***[0.032]

POST − LEHMAN × EXPORT -0.121***[0.034]

PRE − LEHMAN × LOW DEMAND 0.087***[0.034]

POST − LEHMAN × LOW DEMAND 0.126***[0.032]

PRE − LEHMAN ×DISTANCE 0.017[0.018]

POST − LEHMAN ×DISTANCE 0.035**[0.017]

SOUTH -0.025 -0.025 -0.025 -0.025 -0.025[0.033] [0.033] [0.033] [0.032] [0.032]

Dependent variable: Prob (DEMAND)

DISTANCE -0.008 -0.008 -0.008 -0.008 -0.008[0.013] [0.013] [0.013] [0.013] [0.013]

HHI -0.069 -0.069 -0.069 -0.070 -0.069[0.190] [0.190] [0.190] [0.190] [0.190]

LARGE BANKS 0.069 0.069 0.069 0.069 0.069[0.104] [0.104] [0.104] [0.104] [0.104]

POST − LEHMAN (0, 1) -0.261*** -0.261*** -0.261*** -0.261*** -0.261***[0.024] [0.024] [0.024] [0.024] [0.024]

SIZE 0.044*** 0.044*** 0.044*** 0.044*** 0.044***[0.008] [0.008] [0.008] [0.008] [0.008]

EXPORT (0, 1) 0.168*** 0.168*** 0.168*** 0.168*** 0.168***[0.020] [0.020] [0.020] [0.020] [0.020]

LOW DEMAND (0, 1) -0.062*** -0.062*** -0.062*** -0.062*** -0.062***[0.020] [0.020] [0.020] [0.020] [0.020]

LIQUIDITY (neither bad nor good) 0.194*** 0.194*** 0.194*** 0.194*** 0.194***[0.022] [0.022] [0.022] [0.022] [0.022]

LIQUIDITY (bad) 0.519*** 0.519*** 0.519*** 0.519*** 0.519***[0.031] [0.031] [0.031] [0.031] [0.031]

SOUTH 0.005 0.005 0.005 0.005 0.005[0.029] [0.029] [0.029] [0.029] [0.029]

LABOR COST 0.015*** 0.015*** 0.015*** 0.015*** 0.015***[0.003] [0.003] [0.003] [0.003] [0.003]

ρ -0.951 -0.950 -0.950 -0.957 -0.953

Wald test (χ2) 26.518 24.808 26.713 24.880 24.916Wald test (p-value) 0.000 0.000 0.000 0.000 0.000

t-test on equality pre and post Lehman (χ2) 0.033 0.397 1.309 0.978t-test on equality pre and post Lehman (p-value) 0.856 0.528 0.253 0.323Observations 23,140 23,140 23,140 23,140 23,140Censored 16,524 16,524 16,524 16,524 16,524

Notes: The table reports the regression coefficients and, in brackets, the associated robust standard errors clustered atprovincial and time (quarter) level. * significant at 10%; ** significant at 5%; *** significant at 1%. The model is estimatedusing Stata 12 SE package with the HECKPROB command. Each regression includes 13 industry (2-digits) dummies notshowed for reasons of space. The table reports at the bottom: 1) the results of a Wald test on the null hypothesis ρ = 0,2) the χ2 and the p-value of a t-test on the null hypothesis that the coefficients on SIZE (column 2), EXPORT (column3), LOW DEMAND (column 4) and DISTANCE (column 5) in the credit rationing equation are equal in the pre- andpost-crisis periods, and 3) the number of total and censored observations.

33

Table 5: The probability of being credit rationed after Lehman’s collapse

(1) (2) (3) (4) (5) (6) (7)Dependent variable: Prob (RATIONED)

DISTANCE 0.058* -0.029[0.033] [0.080]

DISTANCE × PRE − LEHMAN RATIONED -0.025[0.057]

DISTANCE × PRE − LEHMAN NON RATIONED 0.078**[0.035]

DISTANCE × LOW DEMAND 0.038[0.038]

DISTANCE ×HIGH DEMAND 0.093*[0.048]

DISTANCE × EXPORT 0.077[0.047]

DISTANCE ×NON EXPORT 0.044[0.041]

DISTANCE × SIZE 0.029[0.024]

DISTANCE × SMALL 20 0.045[0.040]

DISTANCE × LARGE 20 0.083*[0.045]

DISTANCE × BAD LIQUIDITY -0.008[0.042]

DISTANCE ×GOOD LIQUIDITY 0.113***[0.044]

PRE − LEHMAN RATIONED (0, 1) 0.918*** 1.250*** 0.922*** 0.919*** 0.918*** 0.925*** 0.918***[0.071] [0.210] [0.070] [0.070] [0.071] [0.070] [0.071]

SIZE -0.032 -0.031 -0.032 -0.031 -0.123 -0.031[0.024] [0.024] [0.024] [0.024] [0.081] [0.024]

SMALL 20 (0, 1) 0.129[0.184]

EXPORT (0, 1) 0.025 0.021 0.026 -0.081 0.026 -0.003 0.026[0.056] [0.056] [0.056] [0.191] [0.056] [0.056] [0.056]

LOW DEMAND (0, 1) 0.211*** 0.216*** 0.387** 0.211*** 0.213*** 0.212*** 0.216***[0.057] [0.057] [0.185] [0.057] [0.057] [0.057] [0.057]

BAD LIQUIDITY (0, 1) 0.772*** 0.769*** 0.774*** 0.772*** 0.771*** 0.776*** 1.160***[0.058] [0.058] [0.058] [0.058] [0.058] [0.058] [0.187]

HHI 0.605 0.606 0.615 0.607 0.600 0.604 0.557[0.462] [0.467] [0.457] [0.464] [0.466] [0.466] [0.462]

LARGE BANKS 0.286 0.297 0.288 0.295 0.300 0.292 0.282[0.254] [0.258] [0.253] [0.255] [0.257] [0.257] [0.255]

SOUTH -0.068 -0.069 -0.067 -0.070 -0.069 -0.071 -0.073[0.069] [0.069] [0.069] [0.069] [0.069] [0.069] [0.069]

Dependent variable: Prob (DEMAND)

DISTANCE -0.005 -0.005 -0.005 -0.005 -0.005 -0.007 -0.005[0.017] [0.017] [0.017] [0.017] [0.017] [0.017] [0.017]

HHI 0.150 0.150 0.150 0.150 0.150 0.159 0.150[0.237] [0.237] [0.237] [0.237] [0.237] [0.238] [0.237]

LARGE BANKS 0.052 0.052 0.052 0.052 0.052 0.055 0.052[0.127] [0.127] [0.127] [0.127] [0.127] [0.127] [0.127]

PRE − LEHMAN RATIONED (0, 1) 0.540*** 0.540*** 0.540*** 0.540*** 0.540*** 0.535*** 0.540***[0.039] [0.039] [0.039] [0.039] [0.039] [0.040] [0.040]

SIZE 0.048*** 0.048*** 0.048*** 0.048*** 0.048*** 0.048***[0.012] [0.012] [0.012] [0.012] [0.012] [0.012]

SMALL 20 (0, 1) -0.126***[0.028]

EXPORT (0, 1) 0.103*** 0.103*** 0.103*** 0.103*** 0.103*** 0.104*** 0.103***[0.027] [0.027] [0.027] [0.027] [0.027] [0.027] [0.027]

LOW DEMAND (0, 1) 0.002 0.002 0.002 0.002 0.002 0.001 0.002[0.028] [0.028] [0.028] [0.028] [0.028] [0.028] [0.028]

BAD LIQUIDITY (0, 1) 0.429*** 0.429*** 0.429*** 0.429*** 0.429*** 0.428*** 0.429***[0.032] [0.032] [0.032] [0.032] [0.032] [0.032] [0.032]

LABOR COST 0.009 0.009 0.009 0.009 0.009 0.009* 0.009[0.005] [0.005] [0.005] [0.005] [0.005] [0.005] [0.005]

SOUTH -0.039 -0.039 -0.039 -0.039 -0.039 -0.038 -0.039[0.032] [0.032] [0.032] [0.032] [0.032] [0.032] [0.032]

ρ 0.380 0.379 0.389 0.381 0.382 0.380 0.384

Wald test (χ2) 2.327 2.320 2.419 2.396 2.183 2.333 2.233Wald test (p-value) 0.127 0.128 0.120 0.122 0.140 0.127 0.135

t-test on DISTANCE coeff. (χ2) 2.756 1.034 0.324 0.540 4.650t-test on DISTANCE coeff. (p-value) 0.098 0.309 0.569 0.462 0.031Observations 12,734 12,734 12,734 12,734 12,734 12,734 12,734Censored 9,531 9,531 9,531 9,531 9,531 9,531 9,531

The table reports the regression coefficients and, in brackets, the associated robust standard errors clustered at provincialand time level. * significant at 10%; ** significant at 5%; *** significant at 1%. The model is estimated using Stata 12 SEpackage with the HECKPROB command. Each regression includes 13 industry (2-digits) and 4 time dummies not showedfor reasons of space. The table reports at the bottom: 1) the results of a Wald test on the null hypothesis ρ = 0, 2) the χ2

and the p-value of a t-test on the null hypothesis that the coefficients on DISTANCE in the credit rationing equation areequal across pre-Lehman rationed and non-rationed firms (column 2), firms with low and high product demand (column3), exporters and non-exporters (column 4), small and large firms (column 6), firms with good and bad liquidity levels(column 7), and 3) the number of total and censored observations.

34

C Robustness Tables

C.1 Banks’ exposure to the crisis

C.2 Cultural distance

35

Table 6: The post-Lehman credit crunch: controlling for banks’ characteristics at the provincialmarket level

(1) (2) (3) (4) (5) (6) (7)Dependent variable: Prob (RATIONED)

POST − LEHMAN (0, 1) 0.307*** 0.313*** 0.312*** 0.312*** 0.300*** 0.316*** 0.293***[0.065] [0.066] [0.065] [0.066] [0.065] [0.068] [0.068]

SIZE -0.069*** -0.069*** -0.069*** -0.069*** -0.069*** -0.070*** -0.070***[0.012] [0.012] [0.012] [0.012] [0.012] [0.012] [0.012]

EXPORT (0, 1) -0.132*** -0.132*** -0.134*** -0.132*** -0.128*** -0.129*** -0.132***[0.028] [0.028] [0.028] [0.028] [0.028] [0.029] [0.028]

LOW DEMAND (0, 1) 0.110*** 0.110*** 0.108*** 0.109*** 0.109*** 0.112*** 0.109***[0.028] [0.028] [0.028] [0.028] [0.028] [0.028] [0.028]

LIQUIDITY (neither bad nor good) -0.020 -0.021 -0.023 -0.022 -0.025 -0.013 -0.022[0.059] [0.059] [0.061] [0.059] [0.059] [0.062] [0.060]

LIQUIDITY (bad) 0.059 0.057 0.050 0.054 0.047 0.078 0.053[0.129] [0.129] [0.133] [0.130] [0.127] [0.137] [0.130]

PRE − LEHMAN ×DISTANCE 0.023 0.023 0.024 0.023 0.026 0.024 0.020[0.019] [0.017] [0.017] [0.017] [0.017] [0.018] [0.017]

POST − LEHMAN ×DISTANCE 0.043** 0.040** 0.041*** 0.040** 0.043*** 0.041** 0.038**[0.017] [0.016] [0.016] [0.016] [0.016] [0.016] [0.016]

HHI 0.202 0.196 0.166 0.198 0.178 0.012 0.197[0.216] [0.215] [0.214] [0.247] [0.215] [0.271] [0.214]

SOUTH -0.014 -0.017 -0.016 -0.018 0.003 0.013 0.001[0.038] [0.031] [0.031] [0.034] [0.032] [0.038] [0.032]

ASSETS -0.002[0.025]

ASSET GROWTH 0.003[0.022]

TIER1 CAPITAL -0.005**[0.002]

CREDIT RISK -0.008[0.245]

WHOLESALE FUNDING -0.579**[0.281]

NON − PERFORMING LOANS 0.021[0.016]

ROA -0.144**[0.073]

Dependent variable: Prob (DEMAND)

POST − LEHMAN (0, 1) -0.262*** -0.261*** -0.263*** -0.262*** -0.247*** -0.259*** -0.239***[0.024] [0.024] [0.024] [0.024] [0.024] [0.024] [0.025]

SIZE 0.044*** 0.044*** 0.044*** 0.044*** 0.043*** 0.043*** 0.044***[0.008] [0.008] [0.008] [0.008] [0.008] [0.008] [0.008]

EXPORT (0, 1) 0.166*** 0.168*** 0.170*** 0.169*** 0.163*** 0.166*** 0.169***[0.020] [0.020] [0.020] [0.020] [0.020] [0.020] [0.020]

LOW DEMAND (0, 1) -0.062*** -0.062*** -0.061*** -0.061*** -0.062*** -0.063*** -0.061***[0.020] [0.020] [0.020] [0.020] [0.020] [0.020] [0.020]

LIQUIDITY (neither bad nor good) 0.194*** 0.194*** 0.193*** 0.194*** 0.195*** 0.195*** 0.194***[0.022] [0.022] [0.022] [0.022] [0.022] [0.022] [0.022]

LIQUIDITY (bad) 0.520*** 0.520*** 0.517*** 0.520*** 0.521*** 0.522*** 0.521***[0.031] [0.031] [0.031] [0.031] [0.031] [0.031] [0.031]

DISTANCE -0.013 -0.003 -0.005 -0.004 -0.007 -0.004 -0.001[0.014] [0.011] [0.011] [0.011] [0.012] [0.011] [0.011]

HHI -0.144 -0.108 -0.081 -0.104 -0.092 0.186 -0.112[0.176] [0.177] [0.178] [0.206] [0.178] [0.220] [0.177]

SOUTH -0.010 0.011 0.009 0.010 -0.012 -0.036 -0.011[0.033] [0.027] [0.027] [0.030] [0.029] [0.033] [0.028]

LABOR COST 0.015*** 0.015*** 0.015*** 0.015*** 0.015*** 0.015*** 0.015***[0.003] [0.003] [0.004] [0.003] [0.003] [0.003] [0.003]

ASSETS 0.025[0.021]

ASSET GROWTH 0.005[0.019]

TIER1 CAPITAL 0.005**[0.002]

CREDIT RISK -0.010[0.225]

WHOLESALE FUNDING 0.660***[0.238]

NON − PERFORMING LOANS -0.034**[0.015]

ROA 0.176***[0.064]

ρ -0.954 -0.955 -0.958 -0.956 -0.958 -0.949 -0.956

Wald test (χ2) 25.038 24.745 22.799 24.419 25.099 23.503 24.033Wald test (p-value) 0.000 0.000 0.000 0.000 0.000 0.000 0.000

t-test on equality pre and post Lehman (χ2) 1.162 0.922 0.973 0.934 0.923 0.845 0.941t-test on equality pre and post Lehman (p-value) 0.281 0.337 0.324 0.334 0.337 0.358 0.332Observations 23,140 23,140 23,140 23,140 23,140 23,140 23,140Censored 16,524 16,524 16,524 16,524 16,524 16,524 16,524

Notes: The table reports the regression coefficients and, in brackets, the associated robust standard errors clustered atprovincial and time (quarter) level. * significant at 10%; ** significant at 5%; *** significant at 1%. The model is estimatedusing Stata 12 SE package with the HECKPROB command. Each regression includes 13 industry (2-digits) dummies, notshowed for reasons of space. The table reports at the bottom: 1) the results of a Wald test on the null hypothesis ρ = 0,, 2) the χ2 and the p-value of a t-test on the null hypothesis that the coefficients on DISTANCE in the credit rationingequation are equal in the pre- and post-crisis periods, and 3) the number of total and censored observations.36

Table 7: The probability of being credit rationed after the Lehman’s collapse: controlling forbank performance indicators at the provincial market level

Banking indicators, at the provincial market level

ASSETS ASSET TIER1 CREDIT WHOLESALE NPL ROAGROWTH CAPITAL RISK FUNDING

Dependent variable: Prob (RATIONED)(1) (2) (3) (4) (5) (6) (7)

Table 5, column 2

DISTANCE × RATIONED -0.015 -0.008 -0.008 -0.009 -0.009 -0.009 -0.008[0.060] [0.057] [0.057] [0.057] [0.057] [0.057] [0.057]

DISTANCE ×NON RATIONED 0.085** 0.092*** 0.093*** 0.091*** 0.092*** 0.092*** 0.092***[0.037] [0.034] [0.034] [0.034] [0.034] [0.034] [0.034]

Provincial bank performance indicator 0.022 -0.002 -0.001 0.232 0.005 -0.019 -0.011[0.050] [0.014] [0.002] [0.451] [0.660] [0.029] [0.169]

t-test on DISTANCE coeff. (χ2) 2.487 2.491 2.506 2.478 2.501 2.551 2.483t-test on DISTANCE coeff. (p-value) 0.115 0.115 0.113 0.115 0.114 0.110 0.115

Table 5, column 3

DISTANCE × LOW DEMAND 0.046 0.053 0.053 0.052 0.053 0.053 0.053[0.039] [0.036] [0.036] [0.036] [0.036] [0.036] [0.036]

DISTANCE ×HIGH DEMAND 0.100** 0.107** 0.107** 0.106** 0.107** 0.107** 0.106**[0.049] [0.045] [0.045] [0.045] [0.045] [0.045] [0.045]

Provincial bank performance indicator 0.021 -0.001 -0.001 0.230 0.003 -0.018 -0.013[0.050] [0.014] [0.002] [0.447] [0.657] [0.029] [0.167]

t-test on DISTANCE coeff. (χ2) 1.018 1.014 1.030 1.019 1.017 1.028 1.020t-test on DISTANCE coeff. (p-value) 0.313 0.314 0.310 0.313 0.313 0.311 0.312

Table 5, column 4

DISTANCE × EXPORT 0.082* 0.089* 0.089* 0.088* 0.089* 0.089* 0.089*[0.049] [0.046] [0.046] [0.046] [0.046] [0.046] [0.046]

DISTANCE ×NON EXPORT 0.054 0.060 0.061 0.060 0.060 0.060 0.060[0.041] [0.038] [0.038] [0.038] [0.038] [0.038] [0.038]

Provincial bank performance indicator 0.020 -0.001 -0.001 0.228 0.028 -0.017 -0.019[0.050] [0.014] [0.002] [0.450] [0.658] [0.029] [0.168]

t-test on DISTANCE coeff. (χ2) 0.247 0.244 0.235 0.245 0.247 0.240 0.247t-test on DISTANCE coeff. (p-value) 0.620 0.622 0.628 0.621 0.619 0.624 0.619

Table 5, column 6

DISTANCE × SMALL 20 0.054 0.060* 0.061* 0.060* 0.060* 0.061* 0.061*[0.041] [0.036] [0.036] [0.036] [0.036] [0.036] [0.036]

DISTANCE × LARGE 20 0.090** 0.096** 0.097** 0.096** 0.096** 0.096** 0.096**[0.045] [0.044] [0.044] [0.044] [0.044] [0.044] [0.044]

Provincial bank performance indicator 0.021 -0.002 -0.001 0.217 0.037 -0.016 -0.024[0.050] [0.014] [0.002] [0.455] [0.659] [0.029] [0.169]

t-test on DISTANCE coeff. (χ2) 0.484 0.483 0.467 0.487 0.478 0.461 0.479t-test on DISTANCE coeff. (p-value) 0.487 0.487 0.495 0.485 0.489 0.497 0.489

Table 5, column 7

DISTANCE × BAD LIQUIDITY 0.001 0.005 0.006 0.005 0.005 0.006 0.006[0.043] [0.041] [0.041] [0.040] [0.040] [0.041] [0.040]

DISTANCE ×GOOD LIQUIDITY 0.121*** 0.127*** 0.127*** 0.126*** 0.127*** 0.127*** 0.127***[0.045] [0.041] [0.041] [0.041] [0.041] [0.042] [0.041]

Provincial bank performance indicator 0.016 -0.002 -0.000 0.228 -0.067 -0.016 -0.017[0.050] [0.014] [0.002] [0.451] [0.659] [0.029] [0.165]

t-test on DISTANCE coeff. (χ2) 4.659 4.718 4.691 4.721 4.779 4.645 4.718t-test on DISTANCE coeff. (p-value) 0.031 0.030 0.030 0.030 0.029 0.031 0.030

Observations 12,734 12,734 12,734 12,734 12,734 12,734 12,734Censored 9,531 9,531 9,531 9,531 9,531 9,531 9,531

Notes: The table replicates the estimation of model (2), as reported in columns 2, 3, 4, 6 and 7 of Table 5 adding,alternatively, seven bank performance indicators of the possible exposure of in-province branches to the global crisis.Each panel refers to a specific column of Table 5. Each column refers to a model specification augmented with the specificprovincial banking indicator indicated in the column headings. For the sake of space, we report only results for the rationingequation. Each panel reports: 1) the coefficients (and the associated robust standard errors clustered at provincial andtime level) on the interaction terms between DISTANCE and the firm-specific characteristics, 2) the coefficients (andthe associated robust standard errors clustered at provincial and time level) on the provincial bank performance indicatorreported in the column headings, and 3) the χ2 and the p-value of the t-test on equality of DISTANCE coefficients. *significant at 10%; ** significant at 5%; *** significant at 1%. The model is estimated using Stata 12 SE package with theHECKPROB command. Each regression includes 13 industry (2-digits) dummies and the standard set of control variables,as reported in Table 5.

37

Table 8: The probability of being credit rationed after the Lehman’s collapse: controlling forbank performane indicators at the regional market level

Banking indicators, at the regional market level

ASSETS ASSET TIER1 CREDIT WHOLESALE NPL ROAGROWTH CAPITAL RISK FUNDING

Dependent variable: Prob (RATIONED)(1) (2) (3) (4) (5) (6) (7)

Table 5, column 2

DISTANCE × RATIONED -0.012 -0.008 -0.012 -0.009 -0.012 -0.008 -0.009[0.059] [0.057] [0.057] [0.057] [0.057] [0.057] [0.057]

DISTANCE ×NON RATIONED 0.088** 0.093*** 0.087** 0.091*** 0.090*** 0.095*** 0.092***[0.035] [0.034] [0.035] [0.034] [0.034] [0.034] [0.034]

Regional bank performance indicator -0.002 0.034 0.015 0.145 -0.426 -0.042 0.053[0.030] [0.057] [0.028] [0.541] [0.836] [0.032] [0.215]

t-test on DISTANCE coeff. (χ2) 2.479 2.489 2.468 2.494 2.607 2.565 2.509t-test on DISTANCE coeff. (p-value) 0.115 0.115 0.116 0.114 0.106 0.109 0.113

Table 5, column 3

DISTANCE × LOW DEMAND 0.050 0.054 0.049 0.052 0.050 0.055 0.052[0.037] [0.036] [0.036] [0.036] [0.036] [0.036] [0.036]

DISTANCE ×HIGH DEMAND 0.103** 0.107** 0.101** 0.106** 0.106** 0.109** 0.107**[0.048] [0.045] [0.045] [0.045] [0.045] [0.045] [0.045]

Regional bank performance indicator -0.002 0.035 0.015 0.119 -0.407 -0.041 0.048[0.030] [0.056] [0.028] [0.536] [0.833] [0.031] [0.213]

t-test on DISTANCE coeff. (χ2) 0.986 1.021 0.967 1.021 1.080 1.020 1.044t-test on DISTANCE coeff. (p-value) 0.321 0.312 0.325 0.312 0.299 0.313 0.307

Table 5, column 4

DISTANCE × EXPORT 0.085* 0.090* 0.086* 0.088* 0.087* 0.091** 0.089*[0.048] [0.046] [0.046] [0.046] [0.046] [0.046] [0.046]

DISTANCE ×NON EXPORT 0.056 0.061 0.053 0.059 0.059 0.062* 0.060[0.040] [0.038] [0.039] [0.038] [0.039] [0.038] [0.038]

Regional bank performance indicator -0.002 0.034 0.017 0.119 -0.368 -0.040 0.034[0.030] [0.056] [0.028] [0.539] [0.836] [0.032] [0.214]

t-test on DISTANCE coeff. (χ2) 0.261 0.247 0.319 0.251 0.244 0.242 0.248t-test on DISTANCE coeff. (p-value) 0.610 0.620 0.572 0.616 0.621 0.623 0.618

Table 5, column 6

DISTANCE × SMALL 20 0.056 0.062* 0.054 0.060* 0.059 0.063* 0.060*[0.040] [0.037] [0.037] [0.036] [0.036] [0.036] [0.036]

DISTANCE × LARGE 20 0.093** 0.097** 0.092** 0.096** 0.094** 0.098** 0.096**[0.044] [0.044] [0.044] [0.044] [0.045] [0.044] [0.044]

Regional bank performance indicator -0.001 0.032 0.018 0.114 -0.380 -0.038 0.026[0.030] [0.056] [0.028] [0.544] [0.837] [0.032] [0.216]

t-test on DISTANCE coeff. (χ2) 0.490 0.468 0.553 0.485 0.467 0.448 0.476t-test on DISTANCE coeff. (p-value) 0.484 0.494 0.457 0.486 0.494 0.503 0.49

Table 5, column 7

DISTANCE × BAD LIQUIDITY 0.003 0.006 -0.001 0.005 0.001 0.008 0.005[0.042] [0.041] [0.042] [0.040] [0.041] [0.040] [0.040]

DISTANCE ×GOOD LIQUIDITY 0.124*** 0.127*** 0.122*** 0.126*** 0.126*** 0.128*** 0.126***[0.043] [0.042] [0.041] [0.041] [0.042] [0.041] [0.042]

Regional bank performance indicator -0.002 0.035 0.018 0.143 -0.496 -0.038 0.039[0.030] [0.057] [0.027] [0.539] [0.834] [0.032] [0.211]

t-test on DISTANCE coeff. (χ2) 4.660 4.719 4.861 4.738 4.949 4.566 4.734t-test on DISTANCE coeff. (p-value) 0.031 0.030 0.027 0.030 0.026 0.033 0.030

Observations 12,734 12,734 12,734 12,734 12,734 12,734 12,734Censored 9,531 9,531 9,531 9,531 9,531 9,531 9,531

Notes: The table replicates the estimation of model (2), as reported in columns 2, 3, 4, 6 and 7 of Table 5 adding,alternatively, seven bank performance indicators of the possible exposure of in-region branches to the global crisis. Eachpanel refers to a specific column of Table 5. Each column refers to a model specification augmented with the specificregional banking indicator indicated in the column headings. For the sake of space, we report only results for the rationingequation. Each panel reports: 1) the coefficients (and the associated robust standard errors clustered at provincial andtime level) on the interaction terms between DISTANCE and the firm-specific characteristics, 2) the coefficients (andthe associated robust standard errors clustered at provincial and time level) on the regional bank performance indicatorreported in the column headings, and 3) the χ2 and the p-value of the t-test on equality of DISTANCE coefficients. *significant at 10%; ** significant at 5%; *** significant at 1%. The model is estimated using Stata 12 SE package with theHECKPROB command. Each regression includes 13 industry (2-digits) dummies and the standard set of control variables,as reported in Table 5.

38

Table 9: The post-Lehman credit crunch, DISTANCE SOCIAL CAPITAL

(1) (2) (3) (4) (5)Dependent variable: Prob (RATIONED)

POST − LEHMAN (0, 1) 0.376*** 0.366*** 0.365*** 0.353*** 0.348***[0.031] [0.055] [0.034] [0.034] [0.053]

SIZE -0.070*** -0.070*** -0.070*** -0.070***[0.012] [0.012] [0.012] [0.012]

EXPORT (0, 1) -0.129*** -0.129*** -0.131*** -0.130***[0.029] [0.029] [0.028] [0.029]

LOW DEMAND (0, 1) 0.112*** 0.112*** 0.112*** 0.112***[0.028] [0.028] [0.028] [0.028]

LIQUIDITY (neither bad nor good) -0.013 -0.012 -0.012 -0.022 -0.014[0.059] [0.061] [0.059] [0.059] [0.060]

LIQUIDITY (bad) 0.080 0.085 0.083 0.057 0.076[0.129] [0.134] [0.130] [0.128] [0.132]

DISTANCE SOCIAL CAPITAL 0.075** 0.075** 0.075** 0.073**[0.030] [0.031] [0.030] [0.030]

HHI 0.216 0.218 0.216 0.210 0.211[0.237] [0.237] [0.237] [0.233] [0.236]

LARGE BANKS 0.085 0.087 0.086 0.079 0.085[0.125] [0.126] [0.125] [0.124] [0.125]

PRE − LEHMAN × SIZE -0.072***[0.016]

POST − LEHMAN × SIZE -0.069***[0.013]

PRE − LEHMAN × EXPORT -0.141***[0.033]

POST − LEHMAN × EXPORT -0.119***[0.034]

PRE − LEHMAN × LOW DEMAND 0.088***[0.034]

POST − LEHMAN × LOW DEMAND 0.127***[0.032]

PRE − LEHMAN ×DISTANCE SOC CAP 0.062*[0.037]

POST − LEHMAN ×DISTANCE SOC CAP 0.085**[0.034]

SOUTH -0.005 -0.005 -0.005 -0.006 -0.005[0.034] [0.034] [0.034] [0.033] [0.034]

Dependent variable: Prob (DEMAND)

DISTANCE SOCIAL CAPITAL -0.030 -0.030 -0.030 -0.030 -0.030[0.027] [0.027] [0.027] [0.027] [0.027]

HHI -0.028 -0.028 -0.028 -0.029 -0.028[0.192] [0.192] [0.192] [0.192] [0.192]

LARGE BANKS 0.090 0.090 0.090 0.090 0.090[0.103] [0.103] [0.103] [0.103] [0.103]

POST − LEHMAN (0, 1) -0.262*** -0.262*** -0.262*** -0.262*** -0.262***[0.024] [0.024] [0.024] [0.024] [0.024]

SIZE 0.044*** 0.044*** 0.044*** 0.044*** 0.044***[0.008] [0.008] [0.008] [0.008] [0.008]

EXPORT (0, 1) 0.168*** 0.168*** 0.168*** 0.168*** 0.168***[0.020] [0.020] [0.020] [0.020] [0.020]

LOW DEMAND (0, 1) -0.062*** -0.062*** -0.062*** -0.062*** -0.062***[0.020] [0.020] [0.020] [0.020] [0.020]

LIQUIDITY (neither bad nor good) 0.195*** 0.195*** 0.195*** 0.195*** 0.195***[0.022] [0.022] [0.022] [0.022] [0.022]

LIQUIDITY (bad) 0.521*** 0.521*** 0.521*** 0.521*** 0.521***[0.031] [0.031] [0.031] [0.031] [0.031]

SOUTH -0.005 -0.005 -0.005 -0.005 -0.005[0.030] [0.030] [0.030] [0.030] [0.030]

LABOR COST 0.015*** 0.015*** 0.015*** 0.015*** 0.015***[0.003] [0.003] [0.003] [0.003] [0.003]

ρ -0.948 -0.946 -0.947 -0.954 -0.949

Wald test (χ2) 26.451 24.584 26.340 25.040 25.232Wald test (p-value) 0.000 0.000 0.000 0.000 0.000

t-test on equality pre and post Lehman (χ2) 0.039 0.380 1.274 0.417t-test on equality pre and post Lehman (p-value) 0.844 0.538 0.259 0.519Observations 23,140 23,140 23,140 23,140 23,140Censored 16,524 16,524 16,524 16,524 16,524

Notes: The table reports the regression coefficients and, in brackets, the associated robust standard errors clustered atprovincial and time (quarter) level. * significant at 10%; ** significant at 5%; *** significant at 1%. The model is estimatedusing Stata 12 SE package with the HECKPROB command. Each regression includes 13 industry (2-digits) dummies notshowed for reasons of space. The table reports at the bottom: 1) the results of a Wald test on the null hypothesis ρ = 0,2) the χ2 and the p-value of a t-test on the null hypothesis that the coefficients on SIZE (column 2), EXPORT (column3), LOW DEMAND (column 4) and DISTANCE (column 5) in the credit rationing equation are equal in the pre- andpost-crisis periods, and 3) the number of total and censored observations.

39

Table 10: The probability of being credit rationed after Lehman’s collapse,DISTANCE SOCIAL CAPITAL

(1) (2) (3) (4) (5) (6) (7)Dependent variable: Prob (RATIONED)

DISTANCE SOCIAL CAPITAL 0.111* 0.037[0.063] [0.158]

DISTANCE SOC CAP × RATIONED -0.036[0.120]

DISTANCE SOC CAP ×NON RATIONED 0.147**[0.067]

DISTANCE SOC CAP × LOW DEMAND 0.085[0.074]

DISTANCE SOC CAP ×HIGH DEMAND 0.158*[0.090]

DISTANCE SOC CAP × EXPORT 0.118[0.088]

DISTANCE SOC CAP ×NON EXPORT 0.106[0.077]

DISTANCE SOC CAP × SIZE 0.025[0.047]

DISTANCE SOC CAP × SMALL 20 0.109[0.076]

DISTANCE SOC CAP × LARGE 20 0.127[0.085]

DISTANCE SOC CAP × BAD LIQUIDITY 0.003[0.082]

DISTANCE SOC CAP ×GOOD LIQUIDITY 0.198**[0.080]

PRE − LEHMAN RATIONED (0, 1) 0.916*** 1.128*** 0.918*** 0.916*** 0.917*** 0.924*** 0.917***[0.070] [0.152] [0.070] [0.070] [0.070] [0.069] [0.071]

SIZE -0.032 -0.031 -0.032 -0.031 -0.058 -0.031[0.024] [0.024] [0.024] [0.024] [0.057] [0.024]

SMALL 20 (0, 1) 0.026[0.131]

EXPORT (0, 1) 0.026 0.020 0.026 0.012 0.026 -0.004 0.023[0.056] [0.056] [0.056] [0.133] [0.056] [0.056] [0.056]

LOW DEMAND (0, 1) 0.209*** 0.213*** 0.294** 0.209*** 0.210*** 0.210*** 0.214***[0.057] [0.057] [0.137] [0.057] [0.057] [0.056] [0.057]

BAD LIQUIDITY (0, 1) 0.772*** 0.770*** 0.773*** 0.772*** 0.772*** 0.777*** 0.994***[0.058] [0.058] [0.057] [0.058] [0.058] [0.057] [0.131]

HHI 0.623 0.615 0.628 0.621 0.611 0.619 0.605[0.456] [0.461] [0.452] [0.457] [0.458] [0.458] [0.456]

LARGE BANKS 0.297 0.304 0.299 0.298 0.302 0.299 0.304[0.252] [0.256] [0.251] [0.253] [0.254] [0.253] [0.254]

SOUTH -0.045 -0.044 -0.046 -0.045 -0.044 -0.046 -0.046[0.072] [0.073] [0.072] [0.072] [0.072] [0.072] [0.073]

Dependent variable: Prob (DEMAND)

DISTANCE SOCIAL CAPITAL -0.046 -0.046 -0.046 -0.046 -0.046 -0.049 -0.046[0.034] [0.034] [0.034] [0.034] [0.034] [0.034] [0.034]

HHI 0.247 0.247 0.247 0.247 0.246 0.255 0.246[0.230] [0.230] [0.230] [0.230] [0.230] [0.232] [0.230]

LARGE BANKS 0.103 0.103 0.103 0.103 0.103 0.106 0.103[0.125] [0.125] [0.125] [0.125] [0.125] [0.126] [0.125]

PRE − LEHMAN RATIONED (0, 1) 0.541*** 0.540*** 0.541*** 0.541*** 0.541*** 0.536*** 0.541***[0.039] [0.039] [0.039] [0.039] [0.039] [0.040] [0.039]

SIZE 0.048*** 0.048*** 0.048*** 0.048*** 0.048*** 0.048***[0.012] [0.012] [0.012] [0.012] [0.012] [0.012]

SMALL 20 (0, 1) -0.125***[0.028]

EXPORT (0, 1) 0.102*** 0.102*** 0.102*** 0.102*** 0.102*** 0.102*** 0.102***[0.027] [0.027] [0.027] [0.027] [0.027] [0.027] [0.027]

LOW DEMAND (0, 1) 0.001 0.001 0.001 0.001 0.001 0.000 0.001[0.028] [0.028] [0.028] [0.028] [0.028] [0.028] [0.028]

BAD LIQUIDITY (0, 1) 0.430*** 0.430*** 0.430*** 0.430*** 0.430*** 0.430*** 0.430***[0.032] [0.032] [0.032] [0.032] [0.032] [0.032] [0.032]

LABOR COST 0.009 0.009 0.009 0.009 0.009 0.009* 0.009[0.005] [0.005] [0.005] [0.005] [0.005] [0.005] [0.005]

SOUTH -0.052 -0.052 -0.052 -0.052 -0.052 -0.052 -0.052[0.032] [0.032] [0.032] [0.032] [0.032] [0.032] [0.032]

ρ 0.388 0.382 0.392 0.388 0.390 0.387 0.380

Wald test (χ2) 2.501 2.343 2.529 2.500 2.483 2.547 2.258Wald test (p-value) 0.114 0.126 0.112 0.114 0.115 0.110 0.133

t-test on DISTANCE coeff. (χ2) 2.088 0.500 0.012 0.033 3.684t-test on DISTANCE coeff. (p-value) 0.148 0.480 0.912 0.855 0.055Observations 12,734 12,734 12,734 12,734 12,734 12,734 12,734Censored 9,531 9,531 9,531 9,531 9,531 9,531 9,531

The table reports the regression coefficients and, in brackets, the associated robust standard errors clustered at provincialand time level. * significant at 10%; ** significant at 5%; *** significant at 1%. The model is estimated using Stata 12 SEpackage with the HECKPROB command. Each regression includes 13 industry (2-digits) and 4 time dummies not showedfor reasons of space. The table reports at the bottom: 1) the results of a Wald test on the null hypothesis ρ = 0, 2) the χ2

and the p-value of a t-test on the null hypothesis that the coefficients on DISTANCE in the credit rationing equation areequal across pre-Lehman rationed and non-rationed firms (column 2), firms with low and high product demand (column3), exporters and non-exporters (column 4), small and large firms (column 6), firms with good and bad liquidity levels(column 7), and 3) the number of total and censored observations.

40

C.3 Exclusion of southern provinces

Table 11: The post-Lehman credit crunch, without Southern provinces

(1) (2) (3) (4) (5) (6)Dependent variable: Prob (RATIONED)

POST − LEHMAN (0, 1) 0.365*** 0.364*** 0.365*** 0.336*** 0.309***[0.034] [0.071] [0.039] [0.039] [0.083]

SIZE -0.080*** -0.080*** -0.080*** -0.080***[0.015] [0.015] [0.014] [0.014]

EXPORT (0, 1) -0.136*** -0.136*** -0.137*** -0.137***[0.033] [0.033] [0.032] [0.033]

LOW DEMAND (0, 1) 0.135*** 0.135*** 0.135*** 0.135***[0.033] [0.033] [0.033] [0.033]

LIQUIDITY (neither bad nor good) 0.044 0.044 0.044 0.039 0.043[0.066] [0.065] [0.066] [0.065] [0.066]

LIQUIDITY (bad) 0.196 0.197 0.197 0.181 0.193[0.131] [0.131] [0.132] [0.130] [0.133]

DISTANCE 0.033* 0.033* 0.033* 0.032*[0.018] [0.018] [0.018] [0.018]

HHI 0.270 0.270 0.270 0.267 0.264[0.279] [0.279] [0.279] [0.276] [0.279]

LARGE BANKS 0.065 0.065 0.065 0.063 0.065[0.149] [0.149] [0.149] [0.148] [0.149]

PRE − LEHMAN × SIZE -0.080***[0.020]

POST − LEHMAN × SIZE -0.080***[0.016]

PRE − LEHMAN × EXPORT -0.137***[0.040]

POST − LEHMAN × EXPORT -0.135***[0.039]

PRE − LEHMAN × LOW DEMAND 0.102**[0.042]

POST − LEHMAN × LOW DEMAND 0.157***[0.039]

PRE − LEHMAN ×DISTANCE 0.023[0.022]

POST − LEHMAN ×DISTANCE 0.040*[0.021]

Dependent variable: Prob (DEMAND)

DISTANCE -0.003 -0.003 -0.003 -0.003 -0.003[0.014] [0.015] [0.014] [0.014] [0.015]

HHI -0.052 -0.052 -0.052 -0.052 -0.052[0.216] [0.216] [0.216] [0.216] [0.216]

LARGE BANKS 0.094 0.094 0.094 0.094 0.094[0.118] [0.118] [0.118] [0.118] [0.118]

POST − LEHMAN (0, 1) -0.240*** -0.240*** -0.240*** -0.239*** -0.239***[0.027] [0.027] [0.027] [0.027] [0.027]

SIZE 0.045*** 0.045*** 0.045*** 0.045*** 0.045***[0.010] [0.010] [0.010] [0.010] [0.010]

EXPORT (0, 1) 0.159*** 0.159*** 0.159*** 0.159*** 0.159***[0.022] [0.022] [0.022] [0.022] [0.022]

LOW DEMAND (0, 1) -0.061*** -0.061*** -0.061*** -0.061*** -0.061***[0.021] [0.021] [0.021] [0.021] [0.021]

LIQUIDITY (neither bad nor good) 0.186*** 0.186*** 0.186*** 0.186*** 0.186***[0.025] [0.025] [0.025] [0.025] [0.025]

LIQUIDITY (bad) 0.494*** 0.494*** 0.494*** 0.494*** 0.494***[0.035] [0.035] [0.035] [0.035] [0.035]

LABOR COST 0.021*** 0.021*** 0.021*** 0.020*** 0.020***[0.004] [0.004] [0.004] [0.004] [0.004]

ρ -0.913 -0.913 -0.913 -0.919 -0.914

Wald test (χ2) 31.141 31.216 30.721 31.104 30.138Wald test (p-value) 0.000 0.000 0.000 0.000 0.000

t-test on equality pre and post Lehman (χ2) 0.000 0.001 1.468 0.530t-test on equality pre and post Lehman (p-value) 0.985 0.976 0.226 0.466Observations 19,037 19,037 19,037 19,037 19,037Censored 13,645 13,645 13,645 13,645 13,645

The table reports the regression coefficients and, in brackets, the associated robust standard errors clustered at provincialand time level. * significant at 10%; ** significant at 5%; *** significant at 1%. The model is estimated using Stata 12 SEpackage with the HECKPROB command. Each regression includes 13 industry (2-digits) and 4 time dummies not showedfor reasons of space. The table reports at the bottom: 1) the results of a Wald test on the null hypothesis ρ = 0, 2) theχ2 and the p-value of a t-test on the null hypothesis that the coefficients on SIZE (column 2), EXPORT (column 3),LOW DEMAND (column 4) and DISTANCE (column 5) in the credit rationing equation are equal in the pre- andpost-crisis periods, and 3) the number of total and censored observations.

41

Table 12: The probability of being credit rationed after Lehman’s collapse, without Southernprovinces

(1) (2) (3) (4) (5) (6) (7)Dependent variable: Prob (RATIONED)

DISTANCE 0.070* -0.025[0.040] [0.095]

DISTANCE × PRE − LEHMAN RATIONED -0.027[0.066]

DISTANCE × PRE − LEHMAN NON − RATIONED 0.093**[0.042]

DISTANCE × LOW DEMAND 0.045[0.045]

DISTANCE ×HIGH DEMAND 0.112**[0.055]

DISTANCE × EXPORT 0.105*[0.059]

DISTANCE ×NON EXPORT 0.047[0.046]

DISTANCE × SIZE 0.032[0.030]

DISTANCE × SMALL 20 0.054[0.046]

DISTANCE × LARGE 20 0.102*[0.054]

DISTANCE × BAD LIQUIDITY -0.005[0.042]

DISTANCE ×GOOD LIQUIDITY 0.114***[0.044]

PRE − LEHMAN RATIONED (0, 1) 0.815*** 1.202*** 0.823*** 0.818*** 0.817*** 0.824*** 0.918***[0.175] [0.271] [0.173] [0.179] [0.176] [0.159] [0.070]

SIZE -0.056 -0.057 -0.056 -0.056 -0.159 -0.031[0.035] [0.035] [0.036] [0.036] [0.103] [0.024]

SMALL 20 (0, 1) 0.194[0.216]

EXPORT (0, 1) -0.035 -0.042 -0.035 -0.221 -0.033 -0.071 0.020[0.074] [0.074] [0.074] [0.219] [0.073] [0.072] [0.056]

LOW DEMAND (0, 1) 0.258*** 0.265*** 0.475** 0.259*** 0.260*** 0.261*** 0.212***[0.065] [0.065] [0.209] [0.065] [0.065] [0.064] [0.057]

BAD LIQUIDITY (0, 1) 0.714*** 0.708*** 0.720*** 0.712*** 0.713*** 0.721*** 1.156***[0.144] [0.145] [0.141] [0.147] [0.145] [0.131] [0.187]

HHI 0.67 0.668 0.697 0.659 0.647 0.655 0.518[0.545] [0.550] [0.535] [0.550] [0.553] [0.551] [0.460]

LARGE BANKS 0.283 0.303 0.291 0.297 0.29 0.287 0.219[0.305] [0.309] [0.302] [0.307] [0.307] [0.306] [0.245]

Dependent variable: Prob (DEMAND)

DISTANCE -0.001 -0.001 -0.001 -0.001 -0.001 -0.001 -0.004[0.019] [0.020] [0.019] [0.019] [0.019] [0.020] [0.017]

HHI 0.189 0.189 0.188 0.189 0.189 0.190 0.127[0.263] [0.263] [0.263] [0.263] [0.263] [0.265] [0.237]

LARGE BANKS 0.08 0.08 0.08 0.08 0.08 0.078 0.052[0.143] [0.143] [0.143] [0.143] [0.143] [0.143] [0.124]

PRE − LEHMAN RATIONED (0, 1) 0.510*** 0.510*** 0.510*** 0.510*** 0.510*** 0.503*** 0.540***[0.043] [0.043] [0.043] [0.043] [0.043] [0.043] [0.040]

SIZE 0.050*** 0.050*** 0.050*** 0.050*** 0.050*** 0.048***[0.014] [0.014] [0.014] [0.014] [0.014] [0.012]

SMALL 20 (0, 1) -0.124***[0.032]

EXPORT (0, 1) 0.091*** 0.091*** 0.091*** 0.091*** 0.091*** 0.094*** 0.100***[0.030] [0.030] [0.030] [0.030] [0.030] [0.029] [0.027]

LOW DEMAND (0, 1) -0.015 -0.015 -0.015 -0.015 -0.015 -0.016 0.001[0.029] [0.029] [0.029] [0.029] [0.029] [0.029] [0.028]

BAD LIQUIDITY (0, 1) 0.434*** 0.434*** 0.434*** 0.434*** 0.434*** 0.433*** 0.430***[0.036] [0.036] [0.036] [0.036] [0.036] [0.035] [0.032]

LABOR COST 0.012* 0.013* 0.012* 0.012* 0.012* 0.013* 0.009[0.007] [0.007] [0.008] [0.008] [0.007] [0.007] [0.005]

ρ 0.105 0.101 0.120 0.102 0.104 0.102 0.384

Wald test (χ2) 0.040 0.036 0.050 0.036 0.039 0.046 2.233Wald test (p-value) 0.842 0.849 0.823 0.850 0.844 0.830 0.135

t-test on DISTANCE coeff. (χ2) 3.024 1.258 0.764 0.649 4.510t-test on DISTANCE coeff. (p-value) 0.082 0.262 0.382 0.420 0.034Observations 10,484 10,484 10,484 10,484 10,484 10,484 10,484Censored 7,845 7,845 7,845 7,845 7,845 7,845 7,845

The table reports the regression coefficients and, in brackets, the associated robust standard errors clustered at provincialand time level. * significant at 10%; ** significant at 5%; *** significant at 1%. The model is estimated using Stata 12 SEpackage with the HECKPROB command. Each regression includes 13 industry (2-digits) and 4 time dummies not showedfor reasons of space. The table reports at the bottom: 1) the results of a Wald test on the null hypothesis ρ = 0, 2) the χ2

and the p-value of a t-test on the null hypothesis that the coefficients on DISTANCE in the credit rationing equation areequal across pre-Lehman rationed and non-rationed firms (column 2), firms with low and high product demand (column3), exporters and non-exporters (column 4), small and large firms (column 6), firms with good and bad liquidity levels(column 7), and 3) the number of total and censored observations.

42