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The Home Bias and the Credit Crunch:
A Regional Perspective
Andrea F. Presbitero Gregory F. Udell Alberto Zazzaro∗
February 24, 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 and whyits severity has been different across markets and firms. In this paper, we tackle this issueby looking at the Italian case. We take advantage of a dataset on a large sample of manu-facturing firms, observed quarterly between January 2008 and September 2009. Thanks todetailed information about loan applications and lending decisions, we are able to identifythe occurrence of a credit crunch in Italy which has been found to be harsher in provinceswith a large share of branches owned by distantly-managed banks. Inconsistent with theflight to quality hypothesis, however, we do not find evidence that economically weaker andsmaller firms suffered more during the crisis period than during tranquil periods. By con-trast, we find that large and healthy firms, the segment of borrowers which, according totheoretical predictions, are cream-skimmed by distantly-headquartered banks, were moreintensely hit by the credit tightening in functionally distant credit markets than in the onespopulated by less distant banks. This last result is consistent with the hypothesis of a homebias on the part of nationwide banks.
JEL Classification: F33, F34, F35, O11
Key words: Banking; Credit crunch; Distance; Home bias; Flight to quality.
∗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: [email protected]; 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: [email protected] Alberto Zazzaro, Department of Economics – Universita Politecnica delle Marche(Italy) and Money and Finance Research group (MoFiR). E-mail: [email protected]; personal web page:http://utenti.dea.univpm.it/zazzaro/. We thank Raoul Minetti and the participants at the MoFiR workshop onbanking (Ancona, 2012) and at seminars held at the Universita di Milano Bicocca and Universita Politecnicadelle Marche for valuable suggestions.
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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.; 2011; 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 – the least likely to be affected by the crunch – 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.; 2011;Puri et al.; 2011). The evidence also suggests that younger, smaller and informationally moreopaque firms may have been more severely affected (e.g. Artola and Genre; 2011; Canton et al.;
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.
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2011; Holton et al.; 2011; Popov and Udell; 2012).Our paper adds to this growing empirical literature on the determinants of the credit crunch
in 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 small and risky enterprises in more functionally distant banking systems are more(flight to quality) or less (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; 2011; 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 concerning a large sample of manufacturingfirms. This allows us to separate demand and supply effects, and to identify the existence andseverity 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 to provinces at a great distance from the home provincewhere they are headquartered. This makes Italy a representative case study, having broaderimplications for the analysis of the current credit crunch and the relevance of the home biaseffect 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 demandfor 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 significantly
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more 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. In sections 3 and 4 we present our data and variables anddiscuss our identification strategy. In section 5 we provide a descriptive analysis of the creditcrunch, while sections 6 and 7 present the results of our model estimations and the robustnessexercises. Section 8 offers a discussion of 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 sociallyembedded. As a result, it can be conveniently recovered and processed only by loan officersworking and living in the same neighborhoods where the borrowers operate, but they can alsobe only imperfectly (and at some cost) transmitted to the senior managers at the upper layersof the parent bank. Accordingly, loan officers benefit from informational rents and banks bearagency costs in order to align the interests of the former with those of bank shareholders andmitigate moral hazard in communication (Agarwal and Wang; 2009; Agarwal and Hauswald;2010; Hertzberg et al.; 2010; Uchida et al.; 2012). The fact is that the more hierarchicallyorganized and (physically and culturally) distant from the local economy is the parent bank,the greater the shortfalls in communication channels (Stein; 2002). For example, costs anduncertainty of loan reviews increase with physical distance from the bank’s headquarters whereloan reviewers report (Udell; 1989), as well as trust between bank’s managers and local loanofficers tends to be lower when the cultural distance between the geographical areas wherethe staff of the bank’s decisional centres and local offices work and live is great (Cremer et al.;2007). For such reasons, distant banks have an incentive to constrain local branches from lendingto soft-information-intensive borrowers, such as small and innovative enterprises (Dell’Aricciaand Marquez; 2004). Similarly, career-concerned loan officers can be induced to assume a
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).
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conservative attitude towards loans to small and new borrowers based on soft, uncommunicableinformation and a too liberal approach towards large and well-established borrowers who areevaluated with hard, easily transferable information (Hirshleifer and Thakor; 1992; Berger andUdell; 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;2011; 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).
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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 feeble links of functionally distant banks with the local economic community and theweight of the home bias and corporate politics for internal capital allocation can be thoughtto become more pronounced in times of global crisis when the amount of loanable funds islower. Consistently, Giannetti and Laeven (2011) find that the portfolio share of syndicatedloans issued by a bank in the home country is larger than that issued in foreign countries, andthat the home bias tends to significantly increase in periods when the home country undergoesa banking crisis. At the same time, when it is the host banking system to experience a crisis,foreign banks contract loans to local borrowers less than domestic banks, but to a much smallerextent than when they face negative shocks at home. Similarly, de Haas and van Horen (2011)find that during the 2007-2009 financial crisis international banks participating in cross-bordersyndicated loans reduced their lending exposure to countries far away from their headquartermore than their exposure to geographically close countries. Galindo et al. (2010) find insteadsome evidence to suggest that also cultural distance matters. They show that, while duringthe global crisis foreign banks generally amplified external financial shocks in Latin Americancountries, contracting credit more than domestic banks, Spanish banks behaved similarly todomestic banks. In the same vein, and with reference to the same period, Aiyar (2011) showsthat foreign subsidiaries and branches in the United Kingdom decreased their lending to localbusinesses by a larger amount than domestically owned banks. Likewise, using data on emergingeastern European countries, Popov and Udell (2012) find that small firms located in citieswhere the majority of lending banks are headquartered abroad are more likely to be credit-rationed, especially if banks in the area are financially distressed, while de Haas et al. (2011)find that foreign banks reduced their loan supply to local firms earlier and faster than domesticbanks. Finally, Gambacorta and Mistrulli (2011) show that during the global crisis Italian firmsborrowing from distant banks experienced a larger increase in interest rates and decrease in loansupply, while Barboni and Rossi (2012) find that firms mainly borrowing from local banks wereless credit rationed.
3 Data and variables
3.1 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 average
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size 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 focus onthe traditional sectors of Italian specialization (Malgarini et al.; 2005). A specific section of thesurvey was added in March 2008 to cover bank-firm relationships, providing useful informationto distinguish firms’ demand for credit and the banks’ rationing decisions. Additional datainclude information on the firm’s industrial sector, labor costs, status of liquidity and the levelof (domestic and foreign) orders, production and unsold goods.
Since data on the openings and closures of branches are at the bank-province level, it ispossible to calculate the stock of branches per bank per province in any quarter. These dataare complemented with information on bank governance (i.e. mutual, cooperatives, listed) andasset size (large, medium, small), on the location of their headquarters and holding companystructure (when applicable).
Considering that we only have the ISAE survey data for the months of March, June, Septem-ber and December and excluding observations with missing values and outliers, we end up withan unbalanced panel made by 3,623 firms and 23,140 observations, observed quarterly between2008:1 to 2009:3. 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),from 2008:4 to 2009:3.
In the following subsections we will describe in detail the data and the construction of thevariables (see also Appendix A).
3.2 Firm-specific variables
3.2.1 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.
Using information on the demand for and supply of credit we build a variable measuringthe financial health of firms’ in the pre-Lehman period. PRE − LEHMAN RATIONED isa time-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.
3.2.2 Other firms’ characteristics
The survey provides other useful information about firms’ characteristics, their economic activityand financial condition that allows us to take into account several factors that might influencethe demand for credit by firms and the availability of banks to satisfy such demand.
First, we consider the firms’ size (SIZE), the capacity to operate abroad (EXPORT ) andthe labor costs they incur (LABOR COST ). SIZE is measured, for each period, by thelogarithm of the average number of employees in each quarter. Enterprises with no more than
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20 employees are identified as small firms (SMALL). The export status is measured, in eachquarter, by a dummy variable equal to one for firms which sold part of the production abroad(EXPORT ). As a measure of the cost of production, we can take advantage of a specificquestion asking for the percentage change in labor cost per employee in the previous 12 months(LABOR COST ).
Second, we build variables measuring the trend of the economic activity of the surveyedfirms and their financial condition. In particular, the level of production and potential demand(LOW DEMAND) is measured by a dummy variable that assumes the value 1 for firmsthat answer “low” to the question “How are the level of orders and the general demand forproducts?” and zero otherwise (i.e., for firms answering “normal” or “high”). Similarly, firms’financial health is captured by three dummy variables which are constructed on the basis of aquestion about the level of liquidity with respect to operational needs, which respondents canevaluate as good, neither good nor bad, or bad (LIQUIDITY ).
Finally, the industry a firm belongs to and its location in the South of Italy4 are taken intoaccount for the possible effect that a specific industry or geographic location could have uponaccess to credit.
3.3 Credit market variables
We match firm-level data with aggregate indicators of the structure of local credit markets,defined at provincial level.5
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).6
All the banking-system variables are calculated at the end of each quarter from September2007 to December 2009.7
The credit market variables shows a great heterogeneity across provinces, as visually con-firmed by the maps reported in Figure 1. This variability is not exclusively driven by thetraditional Italian dichotomy between more financially developed Northern regions and a lessfinancially developed South, as shown by the between component of the standard deviationof the credit market structure variables within Northern and Southern provinces (Table A.2).
4As 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).
5In Italy there are currently 110 administrative provinces, with some being recently established. For reasonsof data availability, we refer to the standard classification into 103 provinces.
6Alternative measures of the presence of banks severely affected by the liquidity crisis are discussed in therobustness, see Section 7.
7For robustness, we have also taken the values of banking variables at the beginning of the two periods, findingvery similar results.
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By contrast, the quarterly frequency and the short duration of the data set seriously limit thevariability of credit market structure variables over time.
Figure 1: The spatial heterogeneity of credit market structure variables
Quintiles[.773,2.5](2.5,3.02](3.02,3.76](3.76,4.45](4.45,5.87]
Functional distance
Quintiles[.0355,.0855](.0855,.105](.105,.117](.117,.141](.141,.563]
Herfindhal-Hirschman index
Quintiles[.14,.412](.412,.528](.528,.579](.579,.639](.639,.878]
Share of branches held by top-5 banking groups
The maps report the provincial distribution of the time-average values of DISTANCE, HHI and LARGE BANKS overthe period 2008:1 – 2009:3.
4 The identification strategy
The critical problem in order to correctly identify a credit crunch effect on the likelihood offirms being credit-rationed is the selection bias arising from the fact that not all firms in thesample had a positive demand for credit and that those that are observed to be rationed mightnot be randomly drawn from the population of Italian firms. Such a bias may be especiallystrong in times of financial crisis, when many firms can decide not to apply for bank loans eitherbecause they have limited financing needs or because they are discouraged from applying bythe worsening lending conditions in the local credit market and the high probability of seeingtheir application rejected (Popov and Udell; 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.8 Since also the dependentvariable in the outcome equation is dichotomous, the presence of a credit crunch is tested esti-mating a binary response model with sample selection with maximum likelihood (Wooldridge;2011).
To estimate the impact of the functional distance of the local banking system from the localeconomy and of the flight to quality and home bias in banks’ lending decisions on the intensityof the crunch after Lehman’s collapse, we proceed in two steps.
8Specifically, only firms which stated, in a previous question, they had had direct contact with banks(DEMAND = 1) were asked the question “Did you get from the bank the requested amount of credit?”.
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4.1 Credit crunch, banks’ functional distance and flight to quality
In the 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 bankingsystems are more functionally distant, and whether it was the result of a generalized flight toquality on the part banks. We measure firms’ quality in terms of economic prospects, produc-tivity and informational transparency. The former is proxied by the expected level of productdemand (LOW DEMAND), while EXPORT and SIZE are taken as proxies for firms’ pro-ductivity and informational transparency as usual 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∑h=1
γ2kPOST−LEHMAN ×Qkijt +
m∑h=1
δhXhijt + εijt > 0]
DEMANDijt = 1[aPOST−LEHMANit + bDISTANCEjt +
3∑k=1
ckQkijt +
m∑h=1
dhXhijt +
2∑r=1
grIRit + 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,Qk are the firm quality variables conditioning the severity of the credit crunch with k ={SIZE, EXPORT, LOW DEMAND} and X is the set of bank-market-structure and firm-level control variables. The second equation is the selection equation, where the firms’ liquidityneeds and the variation in labor costs are included as identifying restrictions (IR), while thedependent variable in the rationing equation is observed only for firms which applied for bankcredit (DEMAND = 1). The error terms in the two equations, εi,j and ηi,j , are assumed to beindependent of the explanatory variables, with a zero-mean normal distribution, but possiblyreciprocally correlated.9
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.
4.2 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 portfolio
9Given the limited variability of the credit market structure variable over time (see Table A.2), 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.
10
away 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. To the bestof our knowledge, there is no direct evidence about these contrasting effects. A partial exceptionsupporting the flight-to-quality view is Albertazzi and Marchetti (2010), who document thatafter Lehman’s collapse, banks belonging to the five largest banking groups reduced outstandingloans to riskier borrowers significantly more than other banks. This evidence, however, doesnot consider credit rationing, is limited to banks with a low risk-weighted capital ratio (lowerthan 10%), does not control for the distance between the lending branch and the parent bankheadquarters and does not explicitly model the demand of 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 inthe pre-crisis period (PRE − LEHMAN RATIONED). Hence, we estimate the followingbivariate model:
RATIONEDijt = 1[αDISTANCEjt +4∑k=1
βkQ′hijt +
4∑k=1
γkDISTANCE ×Q′kijt + (2)
+
n∑h=1
δhXhijt + εijt > 0]
DEMANDijt = 1[aDISTANCEjt +
4∑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 fourk-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’ liquidity needs and labor costs as identifying restrictions (IR) anderror terms in the two equations are assumed independent of the explanatory variables, butpossibly 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.
5 Descriptive analysis
From firm-level data available in the ISAE/ISTAT survey it is possible to have clear descriptiveevidence of the intensity of the credit crunch. In the last quarter of 2008, the share of firmsjudging as restrictive the condition to access bank credit increased to 41%, while during thefirst nine months of 2008 one firm out of every four had the same perception (Figure 2, left
11
panel).10 The firms’ perception of banks’ lending behavior is strongly correlated with theirproduct demand and liquidity levels. In the right-hand panel in figure 2, we consider thedifference between the share of firms assessing their demand as high less the share assessing itas low, and the difference between the share of firms assessing their liquidity as good less theshare assessing it as bad. Both indicators follow a similar trend, with a decline in the businessdemand and liquidity from the second half of 2008 and the bottom reached in the first quarterof 2009. However, the climate of the product market is judged to evolve worse than liquidityconditions by a larger share of firms: on average, during the sample period 47% of firms faceda low level of demand, while only 6% stated that the level of demand was high.
Figure 2: 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: Elaborations based on the ISAE/ISTAT Survey of Manufacturing Firms. In any quarter, the business climate isdefined, alternatively: as the share of firms assessing their demand as high less the share assessing it as low (productdemand), and as the share of firms assessing their liquidity as good less the share assessing it as bad (liquidity).
In Figure 3 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 firmslocated in provinces where the banking system is functionally close (DISTANCE below the 75◦
percentile of its 2008:3 provincial distribution) and functionally distant (DISTANCE belowthe 75◦ 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 the geographical differences in the access to bank credit.On average, over the sample period, firms located in provinces densely populated by distantbanks are less likely to seek credit. The share of firms asking for fresh flows of bank creditin each quarter is 30.1% in provinces where the functional distance of the banking system is
10Similar findings are reported by Costa and Margani (2009).
12
particularly high, while the same share increases to 32.7% in provinces where the banking systemis functionally closer; this difference is statistically significant at the usual level of confidence.The opposite trend is observable with the share of rationed firms. Just before the onset of thecrisis (2008:3), the share of credit-rationed firms is 11.6%, irrespective of the functional distanceof local banking systems. In the first quarter after the Lehman collapse, the tightening of creditconditions is found everywhere, but the increase in credit rationing is statistically higher inprovinces dominated by distant banks.11
Figure 3: 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: Elaborations based on the sample of 3,631 firms (24,651 observations); source: ISAE/ISTAT Survey on Manu-facturing Firms. Provinces are classified as functionally close (distant) whether DISTANCE is below (above) the 75◦
percentile of its 2008:3 provincial distribution.
Table A.1 reports the descriptive statistics for the sample of 3,623 firms (23,140 observations)used in the first empirical exercise (Table B.1). From the descriptive analysis of quarterly data,it emerges that firms asking for bank credit do not differ significantly from the non-applicantsin terms 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. Bycontrast, 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 locatedin provinces where the banking system is functionally distant (Table A.1). This preliminaryevidence on the heterogeneity of the credit crunch is formally tested in the first empirical exercisein the next Section.
6 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 been
11The share of rationed firms is 26.4% (20.9%) in provinces where DISTANCE is above (below) the 75◦
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.
13
a (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, financing constraints proved relatively more binding for larger and exporting firms,and for enterprises with a higher expected product demand and financially healthier than theaverage. This suggests that the harsher credit crunch in functionally distant credit marketshas not been the result of a stronger, generalized flight from risk on the part of bank brancheslending locally, but of a contraction of the engagement by nationwide banks in provinces faraway from their headquarter due to a home bias effect.
6.1 Firms’ financing constraints pre- and post-Lehman
Table B.1 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.
6.1.1 Demand equation
Looking at the demand equation, there is clear evidence that, with the onset of the globalcrisis, there has been a significant contraction of the demand for credit by firms, whose loanapplications are 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.
6.1.2 Rationing equation
Consistent with previous literature, the estimation results of the rationing equation show thatfirms’ financing constraints decrease with their size, export attitude and the level of demand fortheir products (Alessandrini et al.; 2009; Minetti and Zhu; 2011). As for the demand side, aswith the supply of credit there is no significant difference between the North and the South ofItaly, once firm-specific characteristics and the structure of provincial credit markets are takeninto 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.
14
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).12 This result is robust to the inclusion of the degree of marketconcentration 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 coefficients on SIZE,EXPORT and LOW DEMAND are slightly lower in magnitude in the post-crisis than in thepre-crisis period (columns 2-4), even if the difference between the respective coefficients in thetwo periods is statistically not different than zero, as the t-tests at the bottom of Table B.1indicate.
By contrast, we 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 4 we provide a visual representation of the severity of the credit crunch afterthe Lehman’s collapse. The diagram shows the predicted probability of being credit rationed(and the 95% confidence intervals), conditional on having applied for bank credit, in the pre-crisis and in the post-crisis period as a function of functional distance. As one can see, therelationship between the likelihood of credit rationing and the functional distance of the localbanking system is positive in both periods, however it is clearly steeper after Lehman’s collapse.The vertical difference between the two lines is a measure of the tightness of credit supply inprovinces whose banking systems have a certain functional distance from the local economy.
The average partial effect of the POST LEHMAN dummy on the probability of creditrationing varies from 4.6% to 11.6% 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 9.5
12It 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.
15
percentage 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.
Figure 4: Credit crunch intensity and functional distance
.05
.1
.15
.2
.25
.3P
rob(
RA
TIO
NE
D =
1 |
DE
MA
ND
= 1
)
.5 11.
5 22.
5 33.
5 44.
5 55.
5 6
Funtional Distance
Pre-crisis Post-crisis
Calculations based on Table B.1, column (5).
6.2 The post-Lehman credit crunch
In Table B.2 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.
6.2.1 Demand equation
First of all, like in model (1) for the whole sample, the ρ coefficient indicates the existence of anegative and significant correlation between the demand and the supply equations, suggestingthat the neglecting of borrowers’ selection on the demand side would bias the results of a simplerationing model.
Column (1) shows that, ceteris paribus, firms which were credit rationed in the first threequarters of 2008 are 15.6% more likely to apply for a loan in the crisis period than previouslynon-rationed firms. Even after controlling for access to credit in the pre-crisis period, we findrobust evidence that the size and export attitude of firms are positively correlated with theircredit demand, while firms with a low expected product demand are no less likely to apply for aloan. Finally, like in the pre-Lehman period, increasing labor costs and the low level of liquiditycontribute to explain the demand for credit, while the structure of the local credit markets hasno significant influence on the decision to apply for a loan.
16
6.2.2 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 22.3%higher if it was already credit-rationed in one of the three quarters before Lehman. However,the introduction of this persistence effect does not cancel out the effects of SIZE, EXPORTand LOW DEMAND, with large and exporting firms, experiencing high levels of demand fortheir products, significantly less likely to be credit rationed.
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), with ahigh level of product demand, with more employees and exporting part of their productionabroad, are, on average, at an advantage with respect to firms with the opposite character-istics. However, in provinces with a predominance of distantly-managed banks, it is exactlythese “good-quality” and ”highly transparent” firms, the market segment typically served byfunctionally 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. Similar findings hold in the case of firmswith a high product demand (column 3) and of internationalized enterprises (column 4). Withregard to SIZE, the effect of DISTANCE seems to be constant across firms if we consider thenumber of employees continuously (column 5). However, if we split the effect of DISTANCEbetween small and large firms (column 6), once again we find that, contrary to the conventionalpriors, functionally distant banks do not further penalize opaque firms with 20 or less employees.By contrast, it seems that the market segment of medium-large firms, while generally less hitby the credit crunch, suffers it more if located in areas dominated by distantly-managed banks.
Summing up, the presence of distant banks in a particular area is associated with moresevere credit tightening, but this is not explicitly targeted at low quality firms. This is incontrast with the hypothesis of a flight to quality by functionally distant banks, according towhich the latter would have reacted to the global crisis by reducing the amount of credit toriskier, informationally opaquer and less productive borrowers. However, our results supportthe hypothesis that functionally distant banks shy away from lending in provinces which are ata distance from their headquarters.
7 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 collapse the quarterly datasetinto just two periods – the tranquil period and the crisis one – to estimate a more structuralrelationship between distance and access to credit, avoiding potential short-term effects due tothe quarterly frequency of the data set. In this exercise we also focus exclusively on firms which
17
were financially unconstrained in the pre-crisis period, either because they were not seekingcredit or because they obtained the credit they sought. Thus, we can evaluate the significanceof the home bias effect in driving the credit retrenchment to the flow of ”newly” credit-rationedborrowers. The second exercise introduces an alternative definition of functional distance basedon cultural differences across provinces. In the third exercise we limit the analysis to the very(culturally and economically) homogeneous centre-northern provinces to verify whether the ef-fect of distance may spuriously capture the traditional divide between the North and South ofItaly.
Finally, a number of other robustness exercises on the set of control variables have beenrun, but they are not reported for the sake of brevity.13 In particular, the effect of functionaldistances survives even including other measures of local credit market structure, such as theshare of bank branches held by cooperative or mutual banks. In addition, we controlled forthe possibility that LARGE BANKS might not capture the exposure of the local bankingsystem to the global financial crisis. Thanks to bank balance-sheet data retrieved from theBilbank database, published by the Italian Banking Association (ABI), we calculate alternativeprovincial indicators. Specifically, for each province we calculate the branch-weighted share ofbanks with, alternatively: (i) total capital ratio lower than 10, (ii) ratio of interbank liabilitiesover total assets in the first quartile of the national distribution, (iii) ratio of liquid assets overtotal assets in the first quartile of the national distribution, and (iv) ratio of charge-offs overtotal assets in the first quartile of the national distribution. Results do not show any robustevidence that credit rationing during the crisis has been higher in provinces with a dominanceof banks which are less capitalized, less liquid, more dependent on interbank markets, and withhigher charge-offs.
7.1 Pre-crisis non-rationed firms
The reported estimates based on quarterly data might be partially driven by short-term fluctu-ations in demand for credit, as showed by the spike in demand during 2009:q2 with respect tothe whole period (Figure 3, left panel). To address this issue we collapse the original quarterlydataset into two periods, the pre-Lehman (2008:q1 - 2008:q3) and the post-Lehman (2008:q4- 2009:q2), each consisting of three quarters. In the collapsed dataset the i -th firm is con-sidered as having demanded credit and/or being rationed whether it has applied for credit(DEMAND = 1) and/or has been rationed (RATIONED = 1) in at least one of the quar-ters included in the two periods. With regard to the firm-specific control variable, SIZE ismeasured as the logarithm of the average number of employees over each sub-period. Similarly,LABOR COST is the average percentage change in labor cost per employee over each sub-period. The i -th firm is considered as exporter if it exported in at least half of the quartersconsidered in each of the two periods. For the two categorical variables (LOW DEMAND andLIQUIDITY ), the most frequent answer in each sub-period is selected as the one characterizingthe firm in the pre- and post-Lehman periods.
The second aim of this robustness exercise is to focus on credit retrenchment. Hence, wezoom in on firms which were not credit constrained before the onset of the crisis to assesswhether their likelihood of being rationed during the crisis depends on the presence of distantbanks inside the province.
Results, reported in Table C.1, are consistent with those found using quarterly data, broadlyconfirming the home bias effect. The basic specification reported in column 1 shows that, for afirm which was not rationed in the pre-crisis period, the probability of becoming credit rationedduring at least one of the quarters of the entire post-Lehman period is higher in provinces where
13Results are available upon request from the authors.
18
functional distance is greater. Furthermore, there is no evidence that size and export statusmatters for the firm’s probability of becoming rationed during the crisis, while firms facing lowdemand are more likely to be rationed.
The decomposition of the effect of distance according to firm characteristics (columns 2 to5) does not validate the flight to quality hypothesis, since the adverse effect of DISTANCEon access to credit is limited to (more productive) exporting firms and it is the same for firmswith a low and high demand and for small and large enterprises.
7.2 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 model (2), in which we look at the possible differentiated effect of func-tional distance in the crisis period, shows that a large presence of culturally distant banks ispositively correlated with the likelihood of a firm being credit rationed in the post-Lehman pe-riod (Table C.2).14 As regards the possible heterogeneous effect of distance in the crisis period,we again find evidence consistent with the hypothesis that the credit crunch in functionallydistant banking systems was mainly driven by a home bias rather than a flight to quality effect.In other words, our estimations show that in provinces with a larger share of culturally distantbanks, previously unconstrained firms are relatively more likely to be rationed than in areaswith a lesser presence of distant banks. By contrast, no differences emerge according to firms’size, export status and expected demand.
7.3 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 model (2) excluding the South and exploiting the cross-sectional variability of functionaldistance across the more socio-economically homogeneous provinces of the North.
14Results reported in Table B.1 are also confirmed on measuring functional distance in terms of social capitalbut, given their limited informational content, are not reported for the sake of brevity.
19
Results are reported in Table C.3 and corroborate the fact that a large presence of distantly-managed banks is associated with a higher than average probability of credit rationing (column1) and that in functionally distant credit markets a home bias effect is at work.
8 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.
The paper looks at the Italian case, taking advantage of a dataset on a large sample of firms,observed quarterly between January 2008 and September 2009, matched with indicators of thelocal credit market structure, constructed at the provincial level. In particular, a large marketshare of branches of banks headquartered at a geographical distance from the province mayinfluence the availability of credit to local firms because of more severe asymmetric informationand agency costs, and because of internal capital markets and corporate politics issues.
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, butit has not been harsher for small and economically weak firms. In addition, we find that thecredit contraction to large and “good-quality” firms in functionally distant credit markets hasbeen 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 evidencecorroborates the hypothesis of a home bias on the part of distantly-headquartered banks.
20
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25
A Descriptive Tables
Table A.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 0.89
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***
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%.
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Table A.2: 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.
27
B Regression Tables
Table B.1: The post-Lehman credit crunch: whole sample
(1) (2) (3) (4) (5)Dependent variable: Prob (RATIONED)
POST − LEHMAN (0, 1) 0.361*** 0.361*** 0.352*** 0.341*** 0.305***[0.027] [0.048] [0.031] [0.030] [0.058]
SIZE -0.067*** -0.067*** -0.067*** -0.067***[0.011] [0.011] [0.011] [0.011]
EXPORT (0, 1) -0.138*** -0.138*** -0.137*** -0.138***[0.026] [0.026] [0.026] [0.026]
LOW DEMAND (0, 1) 0.108*** 0.108*** 0.108*** 0.108***[0.025] [0.025] [0.025] [0.025]
DISTANCE 0.025* 0.025* 0.025* 0.025*[0.014] [0.014] [0.014] [0.014]
HHI 0.236 0.236 0.236 0.235 0.232[0.221] [0.221] [0.221] [0.221] [0.221]
LARGE BANKS 0.084 0.084 0.085 0.085 0.084[0.117] [0.117] [0.117] [0.117] [0.117]
PRE − LEHMAN × SIZE -0.067***[0.014]
POST − LEHMAN × SIZE -0.067***[0.012]
PRE − LEHMAN × EXPORT -0.147***[0.030]
POST − LEHMAN × EXPORT -0.129***[0.029]
PRE − LEHMAN × LOW DEMAND 0.085***[0.030]
POST − LEHMAN × LOW DEMAND 0.125***[0.028]
PRE − LEHMAN ×DISTANCE 0.016[0.016]
POST − LEHMAN ×DISTANCE 0.034**[0.016]
SOUTH (0, 1) -0.024 -0.024 -0.024 -0.024 -0.024[0.031] [0.031] [0.031] [0.031] [0.031]
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 -0.067 -0.067 -0.067 -0.068 -0.067[0.191] [0.191] [0.191] [0.191] [0.191]
LARGE BANKS 0.067 0.067 0.067 0.067 0.067[0.104] [0.104] [0.104] [0.104] [0.104]
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***[0.009] [0.009] [0.009] [0.009] [0.009]
EXPORT (0, 1) 0.169*** 0.169*** 0.169*** 0.169*** 0.169***[0.020] [0.020] [0.020] [0.020] [0.020]
LOW DEMAND (0, 1) -0.065*** -0.065*** -0.065*** -0.065*** -0.065***[0.020] [0.020] [0.020] [0.020] [0.020]
LIQUIDITY (neither bad nor good) 0.169*** 0.169*** 0.170*** 0.169*** 0.169***[0.019] [0.019] [0.019] [0.019] [0.019]
LIQUIDITY (bad) 0.521*** 0.521*** 0.521*** 0.519*** 0.520***[0.029] [0.029] [0.029] [0.030] [0.029]
LABOR COST 0.014*** 0.014*** 0.014*** 0.014*** 0.014***[0.003] [0.003] [0.003] [0.003] [0.003]
SOUTH (0, 1) 0.005 0.005 0.005 0.005 0.005[0.029] [0.029] [0.029] [0.029] [0.029]
ρ -0.970 -0.970 -0.970 -0.970 -0.970
Wald test (χ2) 268.629 295.602 299.952 290.122 295.017Wald test (p-value) 0.000 0.000 0.000 0.000 0.000t-test on equality pre and post Lehman (p-value) 0.997 0.538 0.193 0.284Observations 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 isestimated using Stata 12 SE package with the HECKPROB command. Each regression includes 13 industry (2-digits)dummies not showed for reasons of space. The table reports at the bottom: 1) the results of a Wald test on the nullhypothesis ρ = 0, 2) 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 thepre- and post-crisis periods, and 3) the number of total and censored observations.
28
Table B.2: The probability of being credit rationed, post-Lehman period
(1) (2) (3) (4) (5) (6)Dependent variable: Prob (RATIONED)
DISTANCE 0.042* -0.003[0.023] [0.047]
DISTANCE × PRE − LEHMAN RATIONED -0.019[0.044]
DISTANCE × PRE − LEHMAN NON RATIONED 0.052**[0.024]
DISTANCE × LOW DEMAND 0.030[0.025]
DISTANCE ×HIGH DEMAND 0.060**[0.030]
DISTANCE × EXPORT 0.055*[0.029]
DISTANCE ×NON EXPORT 0.031[0.027]
DISTANCE × SIZE 0.015[0.014]
DISTANCE × SMALL 0.038[0.026]
DISTANCE × LARGE 0.054*[0.029]
PRE LEHMAN RATIONED (0, 1) 0.113* 0.342** 0.114* 0.113* 0.114* 0.120**[0.058] [0.156] [0.058] [0.058] [0.058] [0.058]
SIZE -0.069*** -0.069*** -0.069*** -0.068*** -0.115**[0.017] [0.017] [0.017] [0.017] [0.046]
SMALL (0, 1) 0.186*[0.102]
EXPORT (0, 1) -0.089** -0.091** -0.089** -0.165 -0.089** -0.107***[0.038] [0.039] [0.038] [0.112] [0.038] [0.038]
LOW DEMAND (0, 1) 0.133*** 0.136*** 0.228** 0.133*** 0.135*** 0.134***[0.041] [0.041] [0.106] [0.041] [0.041] [0.041]
SOUTH (0, 1) -0.007 -0.008 -0.006 -0.008 -0.007 -0.009[0.048] [0.048] [0.048] [0.048] [0.048] [0.048]
HHI 0.218 0.223 0.225 0.218 0.215 0.203[0.304] [0.306] [0.302] [0.305] [0.306] [0.305]
LARGE BANKS 0.126 0.132 0.128 0.132 0.133 0.124[0.177] [0.178] [0.177] [0.178] [0.178] [0.178]
Dependent variable: Prob (DEMAND)
DISTANCE -0.003 -0.004 -0.003 -0.003 -0.003 -0.004[0.017] [0.017] [0.017] [0.017] [0.017] [0.017]
HHI 0.097 0.098 0.098 0.097 0.096 0.106[0.234] [0.234] [0.234] [0.234] [0.234] [0.236]
LARGE BANKS 0.042 0.042 0.042 0.042 0.042 0.044[0.126] [0.126] [0.126] [0.126] [0.126] [0.127]
PRE LEHMAN RATIONED (0, 1) 0.501*** 0.501*** 0.501*** 0.501*** 0.501*** 0.495***[0.038] [0.038] [0.038] [0.038] [0.038] [0.038]
SIZE 0.055*** 0.055*** 0.055*** 0.055*** 0.055***[0.012] [0.012] [0.012] [0.012] [0.012]
SMALL (0, 1) -0.138***[0.029]
EXPORT (0, 1) 0.108*** 0.108*** 0.108*** 0.108*** 0.108*** 0.111***[0.027] [0.027] [0.027] [0.027] [0.027] [0.027]
LOW DEMAND (0, 1) -0.019 -0.019 -0.019 -0.019 -0.019 -0.020[0.028] [0.028] [0.028] [0.028] [0.028] [0.028]
LIQUIDITY (neither bad nor good) 0.224*** 0.224*** 0.224*** 0.223*** 0.224*** 0.222***[0.030] [0.030] [0.030] [0.030] [0.030] [0.030]
LIQUIDITY (bad) 0.630*** 0.630*** 0.630*** 0.629*** 0.630*** 0.628***[0.040] [0.040] [0.040] [0.040] [0.040] [0.039]
LABOR COST 0.012*** 0.012*** 0.012*** 0.012*** 0.012*** 0.012***[0.004] [0.004] [0.004] [0.004] [0.004] [0.004]
SOUTH (0, 1) -0.037 -0.037 -0.037 -0.037 -0.037 -0.036[0.032] [0.032] [0.032] [0.032] [0.032] [0.033]
ρ -0.911 -0.910 -0.911 -0.911 -0.910 -0.912
Wald test (χ2) 155.973 155.723 156.690 154.254 154.831 159.096Wald test (p-value) 0.000 0.000 0.000 0.000 0.000 0.000Observations 12,734 12,734 12,734 12,734 12,734 12,734Censored 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 provincial× 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, and 2)the number of total and censored observations.
29
C Robustness Tables
C.1 A focus on pre-crisis non rationed firms
Table C.1: Credit retrenchment in the post-Lehman periods
(1) (2) (3) (4) (5)Dependent variable: Prob (RATIONED)
DISTANCE 0.068*** 0.009[0.025] [0.052]
DISTANCE × LOW DEMAND 0.075**[0.031]
DISTANCE ×HIGH DEMAND 0.060*[0.031]
DISTANCE × EXPORT 0.113***[0.039]
DISTANCE ×NON EXPORT 0.035[0.025]
DISTANCE × SIZE 0.020[0.017]
DISTANCE × SMALL 0.053**[0.026]
DISTANCE × LARGE 0.068**[0.032]
SIZE -0.026 -0.026 -0.022 -0.086[0.020] [0.020] [0.020] [0.057]
SMALL (0, 1) 0.086[0.104]
EXPORT (0, 1) 0.073 0.073 -0.179 0.074 0.060[0.054] [0.054] [0.127] [0.054] [0.055]
LOW DEMAND (0, 1) 0.127*** 0.079 0.122** 0.127*** 0.127***[0.049] [0.120] [0.050] [0.049] [0.048]
SOUTH (0, 1) 0.005 0.004 0.000 0.006 0.003[0.063] [0.064] [0.063] [0.063] [0.064]
HHI 0.065 0.055 0.040 0.054 0.123[0.305] [0.310] [0.289] [0.303] [0.307]
LARGE BANKS -0.050 -0.050 -0.044 -0.053 -0.020[0.193] [0.193] [0.190] [0.192] [0.194]
Dependent variable: Prob (DEMAND)
DISTANCE -0.027 -0.027 -0.027 -0.027 -0.020[0.026] [0.025] [0.026] [0.025] [0.025]
HHI 0.309 0.306 0.311 0.304 0.264[0.264] [0.261] [0.263] [0.264] [0.264]
LARGE BANKS 0.219 0.219 0.224 0.220 0.198[0.178] [0.178] [0.176] [0.176] [0.179]
SIZE 0.023 0.023 0.022 0.024[0.022] [0.022] [0.022] [0.022]
SMALL (0, 1) -0.057[0.055]
EXPORT (0, 1) -0.028 -0.028 -0.026 -0.028 -0.024[0.047] [0.047] [0.048] [0.047] [0.048]
LOW DEMAND (0, 1) -0.083* -0.083* -0.082* -0.083* -0.085*[0.049] [0.049] [0.049] [0.049] [0.049]
LIQUIDITY (neither bad nor good) 0.190*** 0.189*** 0.179*** 0.190*** 0.191***[0.057] [0.057] [0.065] [0.056] [0.055]
LIQUIDITY (bad) 0.577*** 0.576*** 0.564*** 0.578*** 0.579***[0.084] [0.084] [0.095] [0.082] [0.079]
LABOR COST 0.011* 0.011* 0.010 0.010 0.010[0.006] [0.007] [0.006] [0.007] [0.007]
SOUTH (0, 1) -0.030 -0.030 -0.030 -0.030 -0.028[0.050] [0.050] [0.050] [0.050] [0.050]
ρ -0.975 -0.975 -0.980 -0.975 -0.975
Wald test (χ2) 25.604 25.039 15.656 26.846 30.161Wald test (p-value) 0.000 0.000 0.000 0.000 0.000Observations 3,317 3,317 3,317 3,317 3,317Censored 1,396 1,396 1,396 1,396 1,396
The table reports the regression coefficients and, in brackets, the associated robust standard errors clustered at provincial× 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) dummies not showed for reasonsof space. The table reports at the bottom: 1) the results of a Wald test on the null hypothesis ρ = 0, and 2) the numberof total and censored observations.
30
C.2 Cultural distance
Table C.2: The probability of being credit rationed, post-Lehman period andDISTANCE SOCIAL CAPITAL
(1) (2) (3) (4) (5) (6)Dependent variable: Prob (RATIONED)
DISTANCE SOCIAL CAPITAL 0.113*** 0.082[0.042] [0.093]
DISTANCE SOC. CAP.× PRE − LEHMAN RATIONED 0.014[0.085]
DISTANCE SOC. CAP.× PRE − LEHMAN NON RATIONED 0.131***[0.044]
DISTANCE SOCIAL CAPITAL× LOW DEMAND 0.100**[0.048]
DISTANCE SOCIAL CAPITAL×HIGH DEMAND 0.135**[0.056]
DISTANCE SOCIAL CAPITAL× EXPORT 0.120**[0.056]
DISTANCE SOCIAL CAPITAL×NON EXPORT 0.108**[0.049]
DISTANCE SOCIAL CAPITAL× SIZE 0.010[0.027]
DISTANCE SOCIAL CAPITAL× SMALL 0.119**[0.048]
DISTANCE SOCIAL CAPITAL× LARGE 0.118**[0.054]
PRE LEHMAN RATIONED (0, 1) 0.114* 0.251** 0.114** 0.114* 0.114** 0.120**[0.058] [0.113] [0.058] [0.058] [0.058] [0.058]
SIZE -0.069*** -0.069*** -0.069*** -0.069*** -0.080**[0.017] [0.017] [0.017] [0.017] [0.034]
SMALL (0, 1) 0.133*[0.076]
EXPORT (0, 1) -0.087** -0.090** -0.087** -0.102 -0.087** -0.106***[0.039] [0.039] [0.039] [0.083] [0.039] [0.038]
LOW DEMAND (0, 1) 0.134*** 0.137*** 0.176** 0.134*** 0.135*** 0.135***[0.041] [0.041] [0.081] [0.041] [0.041] [0.041]
SOUTH (0, 1) 0.020 0.020 0.019 0.020 0.020 0.019[0.049] [0.049] [0.048] [0.049] [0.049] [0.049]
HHI 0.143 0.142 0.147 0.141 0.138 0.130[0.296] [0.297] [0.294] [0.296] [0.297] [0.295]
LARGE BANKS 0.090 0.093 0.091 0.091 0.091 0.087[0.169] [0.170] [0.169] [0.170] [0.169] [0.169]
Dependent variable: Prob (DEMAND)
DISTANCE SOCIAL CAPITAL -0.049 -0.051 -0.050 -0.049 -0.049 -0.052[0.034] [0.034] [0.034] [0.034] [0.034] [0.034]
HHI 0.218 0.219 0.218 0.217 0.217 0.227[0.229] [0.228] [0.228] [0.229] [0.228] [0.230]
LARGE BANKS 0.106 0.106 0.106 0.106 0.106 0.108[0.125] [0.125] [0.125] [0.125] [0.124] [0.125]
PRE LEHMAN RATIONED (0, 1) 0.502*** 0.501*** 0.502*** 0.502*** 0.502*** 0.496***[0.038] [0.038] [0.038] [0.038] [0.038] [0.038]
SIZE 0.055*** 0.055*** 0.055*** 0.055*** 0.055***[0.012] [0.012] [0.012] [0.012] [0.012]
SMALL (0, 1) -0.137***[0.029]
EXPORT (0, 1) 0.106*** 0.106*** 0.106*** 0.106*** 0.106*** 0.109***[0.027] [0.027] [0.027] [0.027] [0.027] [0.027]
LOW DEMAND (0, 1) -0.021 -0.021 -0.021 -0.020 -0.021 -0.022[0.028] [0.028] [0.028] [0.028] [0.028] [0.028]
LIQUIDITY (neither bad nor good) 0.224*** 0.224*** 0.224*** 0.224*** 0.224*** 0.223***[0.030] [0.030] [0.030] [0.030] [0.030] [0.030]
LIQUIDITY (bad) 0.632*** 0.632*** 0.632*** 0.632*** 0.632*** 0.631***[0.040] [0.040] [0.040] [0.040] [0.040] [0.039]
LABOR COST 0.012*** 0.012*** 0.012*** 0.012*** 0.012*** 0.012***[0.004] [0.004] [0.004] [0.004] [0.004] [0.004]
SOUTH (0, 1) -0.052 -0.052 -0.052 -0.052 -0.052 -0.052[0.032] [0.032] [0.032] [0.032] [0.032] [0.033]
ρ -0.909 -0.908 -0.909 -0.909 -0.909 -0.911
Wald test (χ2) 156.077 156.860 156.636 155.925 155.993 158.869Wald test (p-value) 0.000 0.000 0.000 0.000 0.000 0.000Observations 12,734 12,734 12,734 12,734 12,734 12,734Censored 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 provincial× 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, and 2)the number of total and censored observations.
31
C.3 Exclusion of southern provinces
Table C.3: The probability of being credit rationed, post-Lehman period and without Southernprovinces
(1) (2) (3) (4) (5) (6)Dependent variable: Prob (RATIONED)
DISTANCE 0.043* -0.003[0.026] [0.053]
DISTANCE × CONSTRAINED -0.022[0.047]
DISTANCE × UNCONSTRAINED 0.054**[0.027]
DISTANCE × LOW DEMAND 0.029[0.028]
DISTANCE ×HIGH DEMAND 0.063*[0.033]
DISTANCE × EXPORT 0.065*[0.034]
DISTANCE ×NON EXPORT 0.028[0.030]
DISTANCE × SIZE 0.016[0.016]
DISTANCE × SMALL 0.037[0.029]
DISTANCE × LARGE 0.057*[0.033]
PRE LEHMAN CONSTRAINED (0, 1) 0.110* 0.358** 0.111* 0.112* 0.113* 0.119*[0.062] [0.160] [0.062] [0.062] [0.062] [0.062]
SIZE -0.079*** -0.079*** -0.079*** -0.078*** -0.128**[0.019] [0.019] [0.019] [0.019] [0.054]
SMALL (0, 1) 0.201*[0.111]
EXPORT (0, 1) -0.101** -0.104** -0.101** -0.217* -0.100** -0.126***[0.042] [0.042] [0.042] [0.121] [0.042] [0.041]
LOW DEMAND (0, 1) 0.166*** 0.170*** 0.276** 0.166*** 0.167*** 0.168***[0.045] [0.045] [0.114] [0.045] [0.045] [0.044]
HHI 0.222 0.227 0.238 0.213 0.210 0.208[0.340] [0.343] [0.336] [0.342] [0.343] [0.340]
LARGE BANKS 0.097 0.107 0.101 0.105 0.099 0.099[0.203] [0.206] [0.202] [0.205] [0.205] [0.204]
Dependent variable: Prob (DEMAND)
DISTANCE 0.002 0.001 0.001 0.002 0.002 0.001[0.019] [0.019] [0.019] [0.019] [0.019] [0.019]
HHI 0.141 0.143 0.142 0.140 0.140 0.141[0.261] [0.261] [0.261] [0.261] [0.261] [0.263]
LARGE BANKS 0.063 0.062 0.063 0.063 0.062 0.058[0.142] [0.142] [0.142] [0.142] [0.142] [0.142]
PRE LEHMAN CONSTRAINED (0, 1) 0.478*** 0.477*** 0.478*** 0.478*** 0.478*** 0.470***[0.041] [0.041] [0.041] [0.041] [0.041] [0.042]
SIZE 0.057*** 0.057*** 0.057*** 0.057*** 0.057***[0.014] [0.014] [0.014] [0.014] [0.014]
SMALL (0, 1) -0.138***[0.032]
EXPORT (0, 1) 0.095*** 0.095*** 0.095*** 0.095*** 0.095*** 0.100***[0.029] [0.029] [0.029] [0.029] [0.029] [0.029]
LOW DEMAND (0, 1) -0.036 -0.036 -0.036 -0.036 -0.036 -0.038[0.029] [0.029] [0.029] [0.029] [0.029] [0.029]
LIQUIDITY (neither bad nor good) 0.226*** 0.227*** 0.226*** 0.225*** 0.226*** 0.226***[0.035] [0.035] [0.035] [0.035] [0.035] [0.034]
LIQUIDITY (bad) 0.633*** 0.634*** 0.634*** 0.633*** 0.634*** 0.633***[0.046] [0.046] [0.046] [0.046] [0.046] [0.045]
LABOR COST 0.015*** 0.015*** 0.015*** 0.015*** 0.015*** 0.015***[0.004] [0.005] [0.004] [0.004] [0.004] [0.005]
ρ -0.911 -0.909 -0.911 -0.911 -0.910 -0.912
Wald test (χ2) 131.121 130.243 131.820 129.054 131.000 135.783Wald test (p-value) 0.000 0.000 0.000 0.000 0.000 0.000Observations 10,484 10,484 10,484 10,484 10,484 10,484Censored 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 provincial× 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, and 2)the number of total and censored observations.
32
