THE HOME BIAS AND THE CREDIT CRUNCH: A REGIONAL PERSPECTIVE

Transcription

1 THE HOME BIAS AND THE CREDIT CRUNCH: A REGIONAL PERSPECTIVE Andrea F. Presbitero Gregory F. Udell Alberto Zazzaro Working paper no. 60 November 2012

2 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 bankruptcy of Lehman Brothers in September 2008 have spurred a credit crunch and, if so, how and why its severity has been different across markets and firms. In this paper, we tackle this issue by looking at the Italian case. We take advantage of a dataset on a large sample of manufacturing firms, observed quarterly between January 2008 and September Using detailed information about loan applications and lending decisions, we are able to identify the occurrence of a credit crunch in Italy that has been harsher in provinces with a large share of branches owned by distantly-managed banks. Inconsistent with the flight to quality hypothesis, however, we do not find evidence that economically weaker and smaller firms suffered more during the crisis period than during tranquil periods. By contrast, we find that financially healthier firms were more intensely hit by the credit tightening in functionally distant credit markets than in the ones populated by less distant banks. This result is consistent 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 at the MoFiR workshop on banking (Ancona, 2012), at the Post-Crisis Banking Conference (Amsterdam, 2012), and at seminars held at the Università di Milano Bicocca and Università Politecnica delle Marche for valuable suggestions. Andrea F. Presbitero (corresponding author), Department of Economics Università Politecnica delle Marche (Italy), Money and Finance Research group (MoFiR) and Centre for Macroeconomic and Finance Research (Ce- MaFiR). a.presbitero@univpm.it; personal web page: Gregory F. Udell, Indiana University, Kelley School of Business and Money and Finance Research group (MoFiR). E- mail: gudell@indiana.edu. Alberto Zazzaro, Department of Economics Università Politecnica delle Marche (Italy) and Money and Finance Research group (MoFiR). a.zazzaro@univpm.it; personal web page: 1

3 1 Introduction The financial crisis that began in the third quarter of 2007 originated with the bursting of a real estate bubble in the US and hit its peak in the quarters immediately after the collapse of Lehman Brothers in September This led to massive capital shocks to the US banking system that quickly propagated to Europe as global interbank loan markets seized up. The contagion from the US shock was subsequently exacerbated by Europe s own problems in the real estate sector in countries like Ireland and Spain compounded by sovereign debt problems particularly in the southern Euro zone. One of the most feared and debated consequences of the crisis in both Europe and the US has been the possible credit crunch caused by the contraction of banks capital and the adverse liquidity shocks in interbank markets. However, identifying the existence of a credit crunch during a global crisis, disentangling the shrinking of credit supply from the parallel reduction in credit demand, and distinguishing the factors that may have driven differences in the severity of the crunch across firms and markets are major concerns to policymakers and one of the biggest challenges facing empirical work. In the absence of unusual natural experiments that create an 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 exploit credit 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; Jiménez et al.; 2012; Gobbi and Sette; 2012). Another approach is to apply a disequilibrium model to identify credit constrained firms (e.g. Carbò-Valverde, Rodriguez-Fernandez and Udell; 2011; Kremp and Sevestre; 2011). An alternative approach to identify constrained firms, that we will follow in this paper, is to use survey data that contain information on loan applications and bank decisions (e.g. Popov and Udell; 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 where the crisis began (i.e., the US). These studies have found evidence of significant shocks to the supply 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 the impact of the credit crunch in the US is an analysis of the impact across different categories of borrowers and regions. Virtually all of this research on credit in the US during the crisis either focuses on large firms (e.g. Ivashina and Scharfstein; 2010) the least likely to be affected by the crunch, or bank balance sheet information (e.g. DeYoung et al.; 2012), or on indirect evidence such as the Federal Reserve s Senior Loan Officer Survey (e.g. Udell; 2009) 1. As a consequence of data limitations in the US 2, firm level analysis of the effect of the current crisis on small and medium enterprises (SMEs) has been substantially limited to Europe. In general these studies have confirmed a credit crunch in the European credit markets (e.g. Albertazzi and Marchetti; 2010; Carbò-Valverde, Degryse and Rodriguez-Fernandez; 2011; Carbò-Valverde, Rodriguez- Fernandez and Udell; 2011; Ferrando and Mulier; 2011; Iyer et al.; 2010; Jiménez et al.; 2012; Puri et al.; 2011). The evidence also indicates that younger, smaller and informationally more 1 One 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 of indirect evidence include a study of how large firms supplied trade credit during the crisis, some of which likely went 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 economic downturn during the crisis. 2 Unlike many European countries the US does not have a public credit registry. In addition, the best available firm level data on SME finance in the US, the Federal Reserve s Survey of Small Business Finance (SSBF), was discontinued just before the crisis began. While the SSBF data were not panel data, they did contain extensive data on firm characteristics, financial statements and loan terms. Moreover, the next survey would have been conducted in the middle of the crisis, had it not been discontinued. 2

4 opaque firms may have been more severely affected, suggesting the existence of a flight to quality on the part of banks during the crunch (e.g. Artola and Genre; 2011; Canton et al.; 2010; 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 local market affects the severity of the credit crunch in that market. Second, we look deeper into the question of which type of firms are more exposed to credit tightening by investigating the common conjecture that small and risky firms suffer most if operating in credit markets largely populated by nationwide, distantly-managed banks rather than by local banks. Our specific focus on the hierarchical structure of the local banking market centers on the issue of whether borrowers whose banks are less local are more vulnerable. The theoretical and empirical literature on commercial lending suggests that hierarchical banks are less able to provide relationship lending to SMEs because of difficulties associated with producing and transmitting soft information (Stein; 2002; Berger et al.; 2005; Liberti and Mian; 2009). This implies that as the functional distance between the loan officer and the headquarters where final lending decisions are made increases, banks are less able to make relationship-based loans and 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 retract disproportionally from markets which are distant from their headquarters. If this actually occurrs, then the adverse effect of functional distance on firms access to credit should be observed to be more pronounced in the months following the collapse of Lehman Brothers. In addition, we investigate whether the withdrawal of banks from local markets is the result of a flight to quality or a home bias effect. To establish which of the two effects prevails, we test whether 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 s collapse. Our study is closely related to studies that have examined the foreign ownership of banks and whether shocks to parent banks are propagated across borders affecting the lending activities of their foreign operations (e.g. Cetorelli and Goldberg; 2011; Popov and Udell; 2012). A few recent contributions have considered the existence of a home bias in banks lending reactions to adverse shocks to their own financial conditions at times of global crisis, by looking at the behavior of international banks in syndicated loan market (Galindo et al.; 2010; de Haas and van Horen; 2012; Giannetti and Laeven; 2011). In this paper, we take a national perspective by studying the credit crunch in Italian provinces during the present financial crisis. To this end, we exploit detailed survey information 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 severity of credit crunch across firms and markets. In particular, we merge firm-level data with information on the spatial distribution of bank branches in order to assess the effect of the organizational structure of the local banking systems on access to credit to local firms. Data availability apart, Italy, like many other countries in Europe and elsewhere, is characterized by a large number of small firms which are strongly dependent on loans from local banks to finance their investments and business activity, and by a number of nationwide banks which spread their subsidiaries and branches across provinces often located at a great distance from the 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 of the home bias effect in shaping the supply of loans. By way of preview we find that the shock to global liquidity surrounding the Lehman collapse was transmitted to the real sector in Italy in terms of a significant contraction in both demand 3

5 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 significantly 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 was not significantly higher than in tranquil periods. By contrast, we find robust evidence that the penetration 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 credit markets, while they are consistent with the hypothesis of a home bias on the part of nationwide banks. In fact, we find that the contraction of credit supply has targeted large and healthy firms the ones that, according to theory, are likely to borrow from distantly-headquartered banks relatively more in functionally distant credit markets than in the ones populated by banks functionally close to the local economy. The remainder of the paper is structured as follows. In the next section we offer a brief review of the related literature. Section 3 discusses the extent of the banking crisis in Italy. In sections 4 and 5 we present our data and variables, and a descriptive analysis of the credit crunch. In section 6 we discuss our identification strategy, while sections 7 and 8 present the results of our model estimations and the robustness exercises. Section 9 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 in functionally distant local credit markets and firms access to credit. There are several reasons why the presence of subsidiaries and branches of (foreign or domestic) banks headquartered at a geographical distance may adversely influence the availability of credit to local firms, resulting in 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 very local activity. 3 A crucial part of information about the firm s creditworthiness is soft and socially embedded. As a result, it can be conveniently recovered and processed only by loan officers working and living in the same neighborhoods where the borrowers operate, who cannot easily and perfectly transmit this information to senior managers at the upper layers of the parent bank. Accordingly, loan officers benefit from informational rents and banks bear agency costs in order to align the interests of the former with those of bank shareholders and to mitigate 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 (physically and culturally) distant from the local economy is the parent bank, the greater the shortfalls in communication channels (Stein; 2002). For example, costs and uncertainty of loan reviews increase 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 when the cultural distance between the geographical areas where the staff of the bank s decisional centres and local offices work and live is great (Cremer et al.; 2007). For such reasons, distant 3 With regard to lines of credit, in the US, the median distance between the firm and the lending bank branch was 4 miles in 1993 and 3 miles in 2003 (Brevoort and Wolken; 2009), while it was still lower in Europe: 1.58 miles in Italy (Bellucci et al.; 2010), 1.4 in Belgium (Degryse and Ongena; 2005) and 0.62 in Sweden (Carling and Lundberg; 2005). 4

6 banks have an incentive to constrain local branches from lending to soft-information-intensive borrowers, 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 to small and new borrowers based on soft, uncommunicable information and a too liberal approach towards large and well-established borrowers who are evaluated with hard, easily transferable information (Hirshleifer and Thakor; 1992; Berger and Udell; 2002). To the extent that the cost of funds is lower for branches of large and functionally distant banks, local credit markets tend to segment, with distant banks cream-skimming highreturn, informationally transparent borrowers, and local banks specializing in lending to softinformation borrowers. Market segmentation may result in higher overall lending to the economy or in extensive credit rationing to small firms, according to whether local banks are more or less efficient 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 subsidiaries of functionally distant banks tend to be less engaged in loans to small businesses and other soft-information-based investment projects, have a comparative disadvantage in relationship lending, ask for lower collateral, have a lower share of bad loans, are less prone to assist firms 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; Jiménez et al.; 2009; Micucci and Rossi; 2010). In addition, a number of studies provide evidence in support of the hypothesis that firms in 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 the private sector (normalized to GDP) and the rate of credit growth are negatively correlated with the 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 in India firms in districts with a foreign bank, especially if they are small sized and endowed with low tangible assets, have a significantly lower probability of obtaining long-term loans. Using Italian data, Alessandrini et al. (2009, 2010) show that firms are more likely to be financially constrained and less inclined to introduce innovations if they are located in provinces where a large share of branches belong to banks headquartered in physically distant provinces and in provinces with different social and economic environments. Similar findings with regard to France are documented by Djedidi (2010), while Presbitero and Zazzaro (2011), again with Italian data, find that in highly competitive markets the presence of functionally distant banks is detrimental to relationship lending. Finally, Özyildirim and Önder (2008) show that in Turkish provinces whose bank branches are distant from their headquarter the credit-to-gdp ratio has a low or even negative impact on local growth, suggesting that local branches of distant banks tend 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 by branches of banks headquartered at a distance (Morgan et al.; 2004). On the one hand, capital inflows from parent banks allow branches and affiliate banks to promptly increase lending in response to a boom in the local economy (de Haas and van Lelyveld; 2010) and to be partly insulated from idiosyncratic liquidity shocks (Houston et al.; 1997; Dahl et al.; 2002). On the 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

7 2012; Cetorelli and Goldberg; 2011) and may be more inclined to reduce local lending when the local economy and deposit growth slow down (Campello; 2002; Cremers et al.; 2010). However, capital allocation and the internal liquidity flows across bank branches and affiliates are 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, social and political importance of the local economy for the CEOs and other top managers at the bank s headquarter (Meyer et al.; 1992; Scharfstein and Stein; 2000). Once again, the headquarter-tobranch distance tends to affect both the local managers ability to attract internal resources and the bank s favoritism attitude towards local economy needs (the home bias). For example, it is reasonable to assume that the managers of branches located at a great distance from the center have little power to attract internal resources and influence capital budgeting decisions (Carlin et al.; 2006). In the same vein, the more physically and culturally close the bank s headquarters and top management are to a region, the greater is the incentive to favor the local economy and local firms (Landier et al.; 2009). The weak links of functionally distant banks with the local economic community and the weight of the home bias and corporate politics for internal capital allocation may be more pronounced in times of global crisis when the amount of loanable funds is lower. Consistent with this, Giannetti and Laeven (2011) find that the portfolio share of syndicated loans issued by a bank in the home country is larger than that issued in foreign countries, and that the home bias tends to significantly increase in periods when the home country is in a banking crisis. At the same time, when a host banking system experiences a crisis, foreign banks contract loans to local borrowers less than domestic banks, and to a much smaller extent than when they face negative shocks at home. Similarly, de Haas and van Horen (2012) find that during the financial crisis international banks participating in cross-border syndicated loans reduced their lending exposure to countries further away from their headquarters more than their exposure to geographically close countries. Galindo et al. (2010) find some evidence to suggest that cultural distance also matters. They show that, while foreign banks in Latin America during the crisis generally amplified external financial shocks, Spanish banks operating in 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 lending to local businesses by a larger amount than domestically owned banks. Likewise, using data on emerging eastern European countries, Popov and Udell (2012) find that small firms located in cities where the majority of lending banks are headquartered abroad are more likely to be credit-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 than domestic banks. Finally, Gambacorta and Mistrulli (2011) show that during the global crisis Italian firms borrowing from distant banks experienced a larger increase in interest rates and decrease in loan supply, and Barboni and Rossi (2012) find that firms borrowing primarily from local banks were less credit rationed. 3 Bank credit in Italy during the financial crisis The bursting of the U.S. housing bubble in 2007 had only a limited impact on banks lending activities in Italy compared with those in other European countries, and it was only in the third quarter of 2008, soon after the failure of Lehman Brothers that the crisis started to show its contractionary effects on local credit markets (Aisen and Franken; 2010). A more traditional intermediation model 4, coupled with strict prudential supervision by the 4 A banking system that does not speak English, according to the colorful metaphor used by Giulio Tremonti, 6

8 national regulatory authority, had restrained Italian banks from holding excessive quantities of 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 exposure of Italian banks to structured financial products amounted to only 4.9 billion Euros (2% of supervisory capital of Italian banks), and the value of securitization transactions in Italy was only 7% of total transactions in Europe (OECD; 2009). Consequently, the capital and liquidity positions of Italian banks, as well as their profitability, experienced little deterioration during the first phase of the crisis between July 2007 and September 2008, such that only a small fraction 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-onquarter annual growth rate (see Figure 1), with banks broadly maintaining unchanged credit approval 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 loans to households and small firms decelerated, although it continued to be at largely positive rates. Figure 1: Loan growth by bank size: Percentage change in bank loans Lehman's collapse 31/12/ /06/ /12/ /06/ /12/ /06/ /12/ /06/ /12/ /06/ /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 in loans is calculated from quarterly data on a year-on-year basis. Things drastically changed after Lehman s default, when the problems of accessing interbank funds exacerbated and deterioration of the quality of loan portfolios weakened the profitability and the capital position of Italian banks (Angelini et al.; 2011; Bonaccorsi Di Patti and Sette; 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 overdrawn 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% in the Italian Minister for the Economy and Finance of the time, in a TV interview in October See the Euro Area Lending Survey for the Euro area and Italian banks available at 6 See Bollettino Statistico, Bank of Italy, various issues ( mon cred fin/) and Panetta and Signoretti (2010). 7

9 Figure 2: Credit standards on loan approval by Italian banks Diffusion index 0.20 Net percentage Lehman's collapse 0-50 Lehman's collapse Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-03 Jan-04 Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Over the past three months Over the next three months Over the past three months Over the next three months (a) Diffusion index of credit tightening (b) Net percentage of banks that tightened credit standards Notes: These figures are constructed from answers to questions 1 and 6 of the ECB Bank Lending Survey on Italian Banks. Question 1: Over the past three months, how have your bank s credit standards as applied to the approval of loans or credit lines to enterprises changed?. Question 6: Please indicate how you expect your bank s credit standards as applied to the approval of loans or credit lines to enterprises to change over the next three months. The five possible answers 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 mean of answers (i)-(v), where the values attributed to each answer are 1, 0.5, 0, 0.5 and 1 and the weights are the observed frequencies. 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 of credit standards answers (iv) and (v). See: By the end of 2008, the growth rate of loans by large banks became negative (-2.9%), while that of small banks decelerated from 14 to 10 percent between December 2007 and December 2008 (Figure 1), to further decrease to 2.4% at the end of The percentage of Italian banks participating in the ECB Bank Lending Survey which stated they had tightened credit standards was 87.5% and 100% over the last two quarters of But what is more interesting is that from April 2008 to April 2009 these banks systematically underestimated the evolution of the credit supply over the next quarter (Figure 2, panel (b)), suggesting that the bankruptcy of Lehman Brothers coincided with and, at least partially, contributed to an unexpected shock to 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 of bank branches from two sources: 1) a monthly survey of about 3,800 Italian manufacturing firms, interviewed from March 2008 to February 2010 by the ISAE (Institute of Studies and Economic Analysis), recently becoming part of the ISTAT (Italian Institute of Statistics); and 2) 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 size is 74 employees, while the median is 18) and was recently updated and re-engineered with the main aim of increasing the comparability of the Italian data with those released by the other European institutions, such as the Ifo Business Climate Survey, while still maintaining a focus on the traditional sectors of Italian specialization (Malgarini et al.; 2005). The representative sample is stratified by geographical areas, economic activities and number of employees. A 8

10 specific section of the survey was added in March 2008 on firm access to credit that provides information on firms demand for credit and its rationing decisions. These data do not provide the identity of the bank and information about the strength of the banking relationship. The data, however, do contain information that allow us to control for some specific firm characteristics which we discuss below. Further, the data include information on the firm s location at the administrative province level 7. Our bank data include information on the openings and closures of branches are at the bank-province level, allowing us to calculate the stock of branches per bank per province in any 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 headquarters and holding company structure (when applicable). Because we only have the ISAE survey data for the months of March, June, September and December and because of observations with missing values and outliers, we end up with an unbalanced panel consisting of 3,623 firms and 23,140 observations, observed quarterly between 2008:1 to 2009:3. 8 Within this dataset, we distinguish two main periods: the pre-lehman period (P RE LEHM AN), from 2008:1 to 2008:3, and the post-lehman (P OST LEHM AN), from 2008:4 to 2009:3. In the following subsections we will describe in detail the data and the construction of the variables (their summary statistics are reported in Table 1 in Appendix A). 4.2 Firm-specific variables Access to credit The two main dependent variables regarding firms access to bank credit distinguish the demand and supply of credit. DEMAND is an indicator variable which assumes value one for firms which report direct contacts with one or more banks in the previous quarter in order to seek credit (i.e., we exclude firms stating that they just went to the bank to ask for information). RAT IONED, 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 desired amount of bank credit. Lacking loan-level data, our variables about the loan demand and supply do not refer to a specific bank-firm relationship, but they indicate firms which demanded credit to 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 measuring a firm s access to credit in the pre-lehman period. P RE LEHMAN RAT IONED is a time-invariant dummy variable observed only in the pre-lehman quarters which is equal to one for firms which were quantity-rationed at least in one quarter in the pre-lehman period (RAT IONED = 1) and zero for firms which were not rationed (RAT IONED = 0) or nonapplicants (DEMAND = 0). In other words, this variable identifies firms which had problems in accessing bank credit in the tranquil period, compared to firms which either obtained the desired amount of credit or did not apply for a bank loan between 2008:1 and 2008:3. 7 Italy is currently divided into 110 provinces (corresponding to the third level NUTS as coded by the European Union, which are grouped into 20 administrative regions (corresponding to NUTS2, with the exception of Trentino-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. 8 Data 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: 9

11 4.2.2 Other firms characteristics As noted above, the survey provides some useful information about characteristics of respondent firms, their economic activity and their financial condition. This allows us to take into account several factors that might influence the demand for credit by firms and the willingness of banks to satisfy such demand. Regarding firm characteristics, we include variables on the firms size (SIZE), the capacity to operate abroad (EXP ORT ) and the labor costs they incur (LABOR COST ). SIZE is measured, 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 export status is measured, in each quarter, by a dummy variable equal to one for firms which sold part of the production abroad (EXP ORT ). As a measure of the cost of production, we can take advantage of a specific question asking for the percentage change in labor cost per employee in the previous 12 months (LABOR COST ). Regarding economic activity, we capture the level of production and potential product demand (LOW DEMAND) with a dummy variable that assumes the value 1 for firms that answer low to the question How are the level of orders and the general demand for products? and zero otherwise (i.e., for firms answering normal or high ). Firm financial health is captured by three dummy variables which are constructed on the basis of a question about the level of liquidity with respect to operational needs, which respondents can evaluate as good, neither good nor bad, or bad (LIQUIDIT Y ). 10 Finally, the industry a firm belongs to and its location in the South of Italy are taken into account for the possible effect that a specific industry or geographic location could have upon access to credit. 11 Figure 3 clearly shows that that firms characteristics before the onset of the global financial crisis are not homogeneously distributed across Italian provinces. In line with the geographical distribution of the population of Italian firms, large and exporting firms in our sample are concentrated in the more developed Northern provinces. By contrast, firm financial health and firm access to bank credit follow less clear geographical patterns, although the former appears more severe in southern provinces. Firms heterogeneity is reflected also in their relationship with the banking system. As showed in Table 2, exporters and firms in worse financial health (BAD LIQUIDIT Y ) are generally 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 in the quarter before the Lehman bankruptcy. Firm size, instead, is neither statistically correlated with the demand for credit, nor with credit rationing. The latter is a much more frequent status among enterprises facing a low product demand and severe liquidity shortages than among healthy firms. This is true in tranquil and crisis periods alike. 9 The threshold of 20 employees for small firms has been used in the empirical literature on the credit crunch in Italy, among others, by Gambacorta and Mistrulli (2011). We also consider the more traditional threshold of 50 employees obtaining similar results. 10 The survey question is: Currently, the level of liquidity with respect to operational needs is good, neither good nor bad, or bad?. In order to test for a home bias effect, below we measure the firm s financial health by a two dummies. The first is equal to one for enterprises which state that liquidity is at a bad level with respect to operational needs (BAD LIQUIDIT Y ). The second is equal to one for firms which declare that their liquidity is good or neither good nor bad (GOOD LIQUIDIT Y ). 11 As is well documented in the banking literature, Italy s southern regions are economically and financially less developed, and local firms have greater difficulties in accessing bank credit (Lucchetti et al.; 2001; Guiso et al.; 2004a; Alessandrini et al.; 2009). 10

12 Figure 3: The spatial heterogeneity of firms characteristics Number of employees Exporting firms Quintiles [8.87,28.5] (28.5,47] (47,63.6] (63.6,101] (101,304] Quintiles [.0789,.308] (.308,.403] (.403,.496] (.496,.626] (.626,.77] Bad liquidity needs Credit rationed firms Quintiles [0,.0909] (.0909,.112] (.112,.149] (.149,.199] (.199,.4] Quintiles [0,.0417] (.0417,.0833] (.0833,.111] (.111,.174] (.174,.667] Notes: The maps report the provincial distribution of the time-average values of SIZE, EXP ORT, LARGE, LIQU IDIT Y and RAT ION ED over the period 2008:1 2008: Credit market variables Because we do not have information on the identity of lending banks but information on the location of the firm we match firm-level data with aggregate indicators of the structure of local credit markets, defined at provincial level.12 First, we consider the organizational structure of the local banking systems as proxied by the headquarter-to-branch functional distance (DIST AN CE). Following Alessandrini et al. (2009), we measure functional distance at the province level as the ratio of the number of branches in the province weighted by the logarithm of 1 plus the kilometric distance between the province of the branch and the province where the parent bank is headquartered, over total branches in the province. Second, we include the degree of credit market concentration in the province by building the Herfindhal-Hirschman index computed on the share of branches held by banks operating in the province (HHI). Finally, in order to take into account the financial crisis and how it has hurt the local banking system, we follow by considering the share of branches belonging to the five largest Italian banking groups (LARGE BAN KS) which were most seriously affected by the crisis, slowing down their lending activity (Albertazzi and Marchetti; 2010; Gobbi and Sette; 2012) Our matching of firm-level survey data to local bank market structure in an analysis of credit access during the crisis has been used in other studies (e.g. Popov and Udell; 2012). In Italy there are currently 110 administrative provinces, with some being recently established. For reasons of data availability, we refer to the standard classification into 103 provinces. 13 Alternative measures of the presence of banks severely affected by the liquidity crisis are discussed in the 11

13 All the banking-system variables are calculated at the end of each quarter from September 2007 to December The credit market variables exhibit significant heterogeneity across provinces, as visually confirmed by the maps reported in Figure 4. This variability is not exclusively driven by the traditional Italian dichotomy between the more financially developed Northern regions and the less financially developed South, as shown by the between component of the standard deviation 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 seriously limit the variability of credit market structure variables over time. Figure 4: The spatial heterogeneity of credit market structure variables Functional distance Quintiles [.777,2.45] (2.45,3.06] (3.06,3.67] (3.67,4.46] (4.46,5.91] Herfindhal-Hirschman index Quintiles [.0359,.0819] (.0819,.0985] (.0985,.113] (.113,.136] (.136,.544] Share of branches held by top-5 banking groups Quintiles [.14,.418] (.418,.527] (.527,.581] (.581,.649] (.649,.875] Notes: The maps report the provincial distribution of the time-average values of DIST AN CE, HHI and LARGE BAN KS over the period 2008:1 2009:3. While the effects of functional distance during the credit crunch (see Section 2) are bank-firm specific, in this paper we follow a market-based approach by measuring the average functional distance of bank branches located in each of the provinces.16 One limitation of this approach is that local firms could actually borrow from branches located outside the province where they are domiciled. In this case, the correlation between the functional distance of the local banking system and firms financing constraints would be spurious: rationed firms are those borrowing from out-of-province branches and not from inprovince branches. This spurious relation may be particularly relevant for larger and exportoriented 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 the credit 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-province and out-of-province branches, in Italy about 80% of loans are provided by branches located in robustness, see Section For robustness, we have also taken the values of banking variables at the beginning of the two periods, finding very similar results. 15 It 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. 16 The 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

14 the same province where the borrower is domiciled. 17 In addition, it seems likely that firms that switch to non-local lenders will be switching to bank branches that are more functionally distant, not less functionally distant. 5 Descriptive analysis Firm-level data from the ISAE/ISTAT survey provide a clear descriptive evidence of the intensity of the credit crunch. In the last quarter of 2008, the share of firms that judged access bank 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 their product demand and liquidity levels. In the right-hand panel in figure 5, we consider the difference 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) as good less the share assessing it as bad. Both indicators follow a similar trend, with a decline in the business demand and financial health from the second half of 2008 and the bottom reached in the first quarter of However, the climate of the product market is judged to evolve worse than liquidity conditions by a larger share of firms: on average, during the sample period 47% of firms faced a low level of demand, while only 6% stated that the level of demand was high. Figure 5: Access to bank credit and business climate: Access to credit (share of firms) Business climate q1 2008q2 2008q3 2008q4 2009q1 2009q2 2009q q1 2008q2 2008q3 2008q4 2009q1 2009q2 2009q3 Accomodating Restrictive Product demand Liquidity needs (a) Conditions of access to credit (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 assessing it 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 the third quarter of 2009 of the two dependent variables used in the bivariate probit model. In the 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 (RAT ION ED). To take into account possible differences in the severity of the credit crunch according to the structure of local credit markets, we calculate these shares separately for firms 17 The detailed data on the share of loans granted by branches in a province to resident in that province are not publicly available. We thank Riccardo De Bonis for providing us the aggregate figures both at the provincial and regional levels. 18 Similar findings are reported by Costa and Margani (2009). 13

15 located in provinces where the banking system is functionally close (DIST AN CE below the 75 th percentile of its 2008:3 provincial distribution) and functionally distant (DIST ANCE below the 75 th percentile of its 2008:3 provincial distribution). Two main patterns emerge from the diagrams. The first concerns timing and shows that while the demand for credit remained quite stable before and after Lehman s collapse (apart from a temporary and sharp increase in the second quarter of 2009), the restraining response of the banking system to the reduction in global liquidity was evident and immediately transmitted to the real sector. The share of rationed firms increased from 11.6 percent in the third quarter of 2008 to 21.6 percent in the last quarter of the year and further to 25.5 and 27.5 percent respectively in the first and third quarters of The second pattern is related to geographical differences in the access to bank credit. On average, over the sample period, firms located in provinces densely populated by distant banks are less likely to seek credit. The share of firms asking for new bank credit in each quarter is 30.1% 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 functionally closer; this difference is statistically significant at the usual level of confidence. The opposite trend is observable in the share of rationed firms. Just before the onset of the crisis (2008:3), the share of credit-rationed firms is 11.6%, irrespective of the functional distance of local banking systems. In the first quarter after the Lehman collapse, the tightening of credit conditions is found everywhere, but the increase in credit rationing is statistically higher in provinces dominated by distant banks. 19 Figure 6: Demand and supply of bank credit: 2008:1 2009:3.4.4 Share of firms demanding bank credit Share of rationed firms q1 2008q2 2008q3 2008q4 2009q1 2009q2 2009q q1 2008q2 2008q3 2008q4 2009q1 2009q2 2009q3 Functionally close provinces Functionally distant provinces Functionally close provinces Functionally distant provinces (a) Demand for bank credit (b) Credit rationing Notes: These figures are constructed on the sample of 3,631 firms (24,651 observations). Source: ISAE/ISTAT Survey on Manufacturing Firms. Provinces are classified as functionally close (distant) whether DIST ANCE is below (above) the 75 th 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 in terms of size and product demand, while they are more likely to export. Moreover, the two groups of firms are not located in provinces with different degrees of functional distance. By 19 The share of rationed firms is 26.4% (20.9%) in provinces where DIST ANCE is above (below) the 75 th percentile of its 2008:3 provincial distribution, and this difference is statistically significant at the 10 percent level of confidence. Over the whole 2008:1 2009:3 period, 21.6% of firms located in provinces where the banking system is functionally distant are credit-rationed, while this share drops to 16.7% in provinces with a closer banking system. Also in this case the difference is statistically significant. 14