Add Journal to My Library
The Review of Financial Studies
, Volume Advance Article (8) – Apr 24, 2018

40 pages

/lp/ou_press/lending-standards-over-the-credit-cycle-QjfsLoFyi2

- Publisher
- Oxford University Press
- Copyright
- © The Author(s) 2018. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
- ISSN
- 0893-9454
- eISSN
- 1465-7368
- D.O.I.
- 10.1093/rfs/hhy023
- Publisher site
- See Article on Publisher Site

Abstract We analyze how firms’ segmentation into credit classes affects the lending standards applied by banks to small and medium enterprises over the cycle. We exploit an institutional feature of the Italian credit market that generates a discontinuity in the allocation of comparable firms into the performing and substandard classes of credit risk. In the boom period, segmentation results in a positive interest rate spread between substandard and performing firms. In the bust period, the increase in banks’ cost of wholesale funds implies that substandard firms are excluded from credit. These firms then report lower values of production and capital investments. Received January 22, 2016; editorial decision December 18, 2017 by Editor Robin Greenwood. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online. A growing empirical literature shows that segmentation between investment-grade and speculative-grade firms can have important implications for their access to capital markets (e.g., Kisgen and Strahan 2010; Lemmon and Roberts 2010; Chernenko and Sunderam 2012). Segmentation implies that firms of different credit quality have access to different pools of investor capital, and that the price and quantity available of this capital vary over time. An unanswered question is whether the effects of such asset class segmentation extend into bank lending policies, and lead to substantially different access to credit for otherwise similar small- and medium-sized enterprises (SME). This question is relevant not only because SME account for up to 70% of jobs in most Organisation for Economic Co-operation and Development (OECD) countries, but also because they nearly exclusively rely on bank financing (OECD 1997). In this paper, then, we study whether segmentation influences the bank lending standards applied to SME, and, relatedly, how the consequences of firm segmentation vary over the credit cycle. The empirical identification of the link between SME segmentation and bank lending standards is a challenging one. The reason is that the adjustment of lending standards can conform to different mechanisms. In neoclassical theories of financial intermediation, banks tighten credit by raising the credit spread and quantity drops along the credit demand. Alternatively, for given price, lenders can tighten standards by rationing risky firms’ quantity of credit—as in models with informational frictions. Consequently, to distinguish between these mechanisms requires detailed contract-level information on price and quantity of bank credit.1 To address this challenge, our analysis relies on a unique loan-level data set collected by the Italian central bank. This data set allows us to observe the total quantity of credit granted and the per-loan interest rate charged by financial intermediaries to SME. Our sample is composed of 144,000 firm-year observations in the manufacturing sector and 253,000 funding contracts covering the period between 2004 and 2011. Like other OECD economies, Italy was experiencing a credit cycle that reached its peak between 2006 and 2007 (Drehmann, Borio, and Tsatsaronis 2012) and then culminated with the Great Recession. To study the consequences of segmentation for firms’ real decisions, we also use a comprehensive data set containing information on firms’ balance sheet statements. Our full data sets then give us an untapped opportunity to study how firm segmentation shapes the relationship between banks and SME over the cycle. An additional empirical challenge to the analysis is how to isolate changes in banks’ lending supply from changes in firms’ desire to borrow. To do so, we exploit the institutional features of the Italian credit market for SME. First, for historical reasons, the credit risk assessment of SME performed by Italian banks uses a common credit rating (the Score) that banks purchase from an external agency (Centrale dei Bilanci, or CEBI). Unlike U.S. corporate credit ratings, the Score is unsolicited, available for all SME, and computed based only on firms’ past balance sheets. Second, within this rating methodology, firms are allocated into two main rating classes—performing and substandard—based on the value of a continuous variable. Importantly, the bank has access to information on both the risk class and the continuous value of the firm’s rating when making its decisions, but, when reporting its loan portfolio to financial markets, it classifies firms based only on their rating classes. The structure of the rating, and its construction, give us the opportunity to follow an intuitive empirical strategy to identify the effects of segmentation on financial contracts. Specifically, we exploit the sharp discontinuity in the allocation of firms into the performing and substandard classes of credit risk. As our measure of lending standards, then, we take the differences in the credit conditions between a firm marginally classified as performing and one that is marginally classified as substandard based on the value of the rating’s continuous variable. These threshold differences inform us about how banks’ supply of credit is affected by segmentation, while holding constant the demand for credit. The classification between substandard and performing risks is important for bank lending choices because it affects the banks’ cost of financing. The national banking regulator adopts a conservative definition of nonperforming loans (NPL), which also includes loans of substandard credit quality (World Bank 2002; Bank of Italy 2013; Barisitz 2013; Jassaud and Kang 2015; Bholat et al. 2016).2 Banks then allocate loans in the category of nonperforming loans based, among others, on the risk status provided by their credit scoring (e.g., Intesa 2015). This has implications for bank capital and investor assessment of bank balance sheets. Indeed, NPL absorb valuable bank capital (Jassaud and Kang 2015), and their volume is often referenced as the major indicator of banks’ asset quality by rating agencies (Moody’s 2015; Fitch 2016). We empirically confirm the importance of banks’ choice of exposure toward performing and substandard credit quality by relating the cost of funding borne by Italian banks to the composition of their loan portfolio. Our main findings on the impact of segmentation on lending conditions follow. In the boom period, the substandard and performing firms at the threshold are treated differently mainly in terms of the interest rates applied to new loans. Indeed, we find an interest rate spread of about 4% (or 20 basis points), and a positive but not statistically significant difference in the amount of granted credit. As a consequence of the financial crisis that hit the Italian banking sector, in the bust period banks tightened their lending standards mainly by acting on the quantity margin: specifically, the performing firms obtain 39% more financing than comparable substandard firms (at a similar interest rate). For the final years in our sample (2010–2011), our estimates point to a reduction in the differences of bank lending at the threshold, and an increase in the interest rate spread. All these results are consistent with those arising from a model of financial contracting in the presence of informational frictions and market segmentation. To quantify the importance of segmentation for bank lending, we compare the estimates of our threshold analysis to those arising from a naïve specification that analyzes differences in the lending conditions between all performing and substandard firms. We find that, in the bust period, segmentation can account for a significantly larger part of the observed naïve differential in the amount of credit than in the boom period. Another key insight arising from our discontinuity strategy relates to the patterns of the interest rate spread. While the naïve interest rate differences are increasing throughout the cycle, we show that, during the crisis, the threshold spread is close to zero—reflecting the implementation of lending standards’ adjustment primarily via a restriction of substandard firms’ access to credit. We then trace the implications of lending standards for firms’ real activity. The production choices of the firms at the threshold significantly diverge during the crisis, to the point that the marginally performing firms report up to 50% larger values of production than the marginally substandard ones. After decomposing production values into firms’ investment in inputs, we find that an increase in the interest rate spread induces firms to adjust their expenditures in variable inputs (i.e., intermediates and employment). Instead, in the bust period, when banks act on the quantity margin to adjust lending standards, firms respond by cutting capital investments, which typically have a long-run nature. The richness of our contract-level data allows us to study the economic mechanism driving the sensitivity of bank lending to segmentation. Specifically, we test for the relative importance of bank capitalization and bank investor composition in explaining the relationship between segmentation and lending policies. In line with, among others, Ivashina and Scharfstein (2009) and Iyer, Puri, and Ryan (2016), we show that the degree of exposure to funding from short-term investors is quantitatively more important than bank capitalization to explain our threshold differences. Finally, we compare the lending conditions applied to two comparable firms, one of which is downgraded to the substandard class as a result of a small change in the value of its continuous rating (which is observed only by the bank, not by its investors). This analysis shows that the negative impact of a downgrade on credit allocations becomes progressively larger and statistically significant in crisis and recovery. We confirm the internal validity of our results by presenting the following robustness checks to our empirical design. First, we find no systematic evidence of manipulation of the rating, which confirms the fact that it is very difficult for firms to manipulate the Score. Second, we show that, close to the threshold, firms feature comparable economic characteristics, and are thus “as if” randomly sampled. Third, we confirm the relevance of the threshold that assigns firms to the performing and substandard classes. In particular, we run our threshold analysis at all the other six thresholds associated with the categorical value of the rating, and find that most of the estimates are not statistically significant. This suggests that our results capture a form of market segmentation, not a simple rating effect. In addition to the literature on the consequences of market segmentation for financial contracts, our paper also contributes to the macrofinance literature studying the dynamics of credit over the cycle.3 Specifically, Greenwood and Hanson (2013) show that the deterioration of credit quality during booms forecasts low excess returns to bondholders. Similarly, in their historical account of credit cycles, Lopez-Salido, Stein, and Zakrajšek (2017) find that elevated credit sentiment is associated with a more aggressive pricing of risk and a subsequent contraction in economic activity. Consistent with these studies, we provide evidence of how the 2004–2011 cycle affected the transmission of market segmentation into bank lending policies. Our paper is also related to the body of work on empirical banking (e.g., Jiménez et al. 2012, 2014; Chodorow-Reich 2014). We extend this literature by showing that, to understand the dynamics of bank lending standards, one needs to jointly analyze the price and quantity of lending.4 1. Documenting Segmentation in the Credit Market The goal of this section is to establish the presence of segmentation in the Italian credit market for SME. We will first present the institutional features of this market that generate segmentation, and then document the relationship between segmentation and the banks’ cost of wholesale funds. 1.1 The Score rating system Evidence from the 2006 Bank of Italy survey of Italian banks indicates that 90% of the banks using a firm’s rating find it important when deciding on whether to process a loan application, 76% of them use the rating to set the amount of lending, and 62% use it to formulate an interest-rate offer. For historical reasons, Italian banks use a common credit rating produced by Centrale dei Bilanci (CEBI) when making decisions about lending to SME. CEBI is a credit agency founded in 1983 as a joint initiative of the Italian Central Bank and the Italian Banking Association to record and process firms’ financial statements. According to Standard & Poor’s (2004), “Banks are the main users of the outputs of CEBI,” referring to the Score rating produced by CEBI as the major tool used to assess SME credit risk. In 2004, the share of credit granted to SME by banks subscribing to the Score rating system was 73%. The following features of the Score are of particular interest to our research design: The Score is unsolicited by firms and is computed based on firms’ past balance sheets. Although its exact algorithm is a business secret of CEBI, information provided to the regulator by the agency that produces the Score shows that the construction of the rating is based on multiple discriminant analyses of past firm balance sheet information (Altman 1968).5 These features make the manipulation of the rating very unlikely. The system generates two continuous variables that determine the assignment to discrete rating categories. Based on predetermined thresholds, the first continuous variable is used to allocate the firms among the first five rating categories (1–5), the second to allocate the firms among categories 6 to 9. The Score therefore ranges from 1, for firms that are the least likely to default, to 9, for those most likely to default.6 We obtained from CEBI direct access to the information on the values of the continuous and discrete variables for the manufacturing firms rated by the agency. We also have access to the exact thresholds that determine the allocation of firms into the different rating categories. This means that we can reconstruct the exact firm allocation mechanism implemented within the Score rating system. Figure 1 illustrates some of the key empirical features of the Score. The left panel of Figure 1 plots the Score variable of firms in year $$t$$ against the share of delinquent firms in year $$t+1$$. To construct this figure, we combine information from Italian chambers of commerce and the credit register of the Italian central bank for the period 2004–2011. We define a firm as delinquent if it entered a formal bankruptcy process, or if its loan was flagged as late/defaulted in the credit register. Finally, we decompose the informativeness of the rating variable across three periods: boom (2004–2007), bust (2008–2009), and recovery (2010–2011). The panel suggests a monotonic relationship between the rating variable and future credit events. Indeed, the share of delinquent firms with a Score of up to 4 in a given year hovers around 4%. This share rises to about 10% for firms with a Score of 7. At the same time, the decomposition of default rates across subperiods indicates that the informativeness of the rating variable is relatively stable over the cycle. More specifically, the increase in delinquency rates between the boom period and the bust period for a Score of 7 is less than one percentage point. Figure 1. View largeDownload slide Characteristics of the Score assignment variable The left panel plots the Score variable against the share of defaults within the next year in the boom (dashed), crisis (solid), and recovery (dotted). The right panel plots the average loan rate by Score category for the first quarter of 2005. Figure 1. View largeDownload slide Characteristics of the Score assignment variable The left panel plots the Score variable against the share of defaults within the next year in the boom (dashed), crisis (solid), and recovery (dotted). The right panel plots the average loan rate by Score category for the first quarter of 2005. The right panel of Figure 1 plots the rating variable against the interest rate on loans for the first quarter of 2005. A strong positive relationship exists between the rating variable and interest rates on loans. The best (lowest) Score, in terms of creditworthiness, is on average associated with a loan interest rate of 4%, and the worst (highest) category pays an average loan interest rate of around 5%.7 Figure 1 therefore suggests that the Score rating provides a reliable estimate of the expected likelihood of a firm’s delinquency, which is then taken into account by the banks for their lending decisions. 1.2 Segmentation of SME in the Italian credit market Within the Score rating methodology, the distinction between the performing and substandard classes of credit risk stands out as particularly relevant for banks and their stakeholders. The performing class consists of the firms with a Score category between 1 and 6, and the substandard class comprises firms with a Score between 7 and 9.8 The importance of this classification stems from its implications for bank disclosure and reporting of their loan portfolio. National regulators decide on the loan categories that enter the class of NPL: this is relevant for our purposes because the Bank of Italy adopts a conservative definition of NPL, which includes loans of substandard credit quality (World Bank 2002; Bank of Italy 2013; Barisitz 2013; Jassaud and Kang 2015; Bholat et al. 2016).9 NPL absorb valuable bank capital: the capital charge for NPL amounts on average to 12% of banks’ risk-weighted assets, and are estimated to tie up more than 6% of bank capital (Jassaud and Kang 2015).10 Moreover, a bank’s exposure to NPL is often referenced as the major indicator of asset quality by the bank’s rating agencies.11 Banks then allocate loans in the category of nonperforming loans based, among others, on the risk status provided by their credit scoring (e.g., Intesa 2015). Moreover, in their annual reports, they clearly distinguish between their exposure to the firms classified as substandard and performing by the rating (e.g., Unicredit 2008). As a consequence, investors monitor the volume of substandard lending to assess a bank’s risk profile. The presence of such segmentation gives rise to clear, testable implications. First, one expects outside investors to charge a higher cost of funding to those banks that carry a higher volume of substandard loans in their loan book. Second, one should find that the continuous variables should not contain any useful information to explain the bank cost of funding on wholesale funding markets. 1.3 Segmentation and bank cost of financing We now provide evidence consistent with the presence of segmentation in the Italian credit market. We use three confidential data sets from the Bank of Italy. The first provides us with information on the amount and interest rate at which Italian banks raise financing from repo markets, households, and firms at a monthly frequency between 2004 and 2011. The second data set contains yearly bank balance sheets between 2006 and 2011, and provides us with information about a bank’s size, capitalization, and liquidity. Finally, we use information from the credit register to determine the composition of each bank’s SME portfolio based on the categorical and continuous variables of the rating system. To estimate the relationship between a bank’s cost of financing and its lending portfolio, we use the following ordinary least squares (OLS) specification: \begin{align} r_{b,t} &= \alpha_{0} +\alpha_{1} \mbox{Substandard to Total Credit}_{b,t-1} +\alpha_{2} \mbox{Continuous Score 1}_{b,t-1} \nonumber \\ &\quad +\alpha_{3} \mbox{Continuous Score 2}_{b,t-1} + X_{b,t-1}\Psi + I_{b,t}\Phi + \pi_{t} +\epsilon_{b,t}. \end{align} (1) In Equation (1), $$b$$ denotes a bank in our data set, and $$t$$ is taken at the monthly level. The dependent variable, $$r_{b,t}$$, is the (volume) weighted average interest rate paid by banks across all investors. Substandard to Total Credit$$_{b,t-1}$$ is the share of a bank’s volume of lending to SME in the substandard rating class relative to total lending. Continuous Variable 1$$_{b,t-1}$$ and Continuous Variable 2$$_{b,t-1}$$ characterize the SME portfolio of the bank in terms of the average continuous ratings. $$X_{b,t-1}$$ denotes a vector of bank characteristics; $$I_{b,t}$$ denotes issuance characteristics such as amounts, maturity, and investor composition; and $$\pi_{t}$$ are month-year fixed effects. All explanatory variables, except for issuance characteristics, are measured before the issuance. Standard errors are clustered at the bank level. In Columns (1) and (2) of Table 1, we show that external investors monitor banks by pricing lending portfolios based on banks’ exposure to the substandard and performing classes. The estimate in Column (1) implies that a 25% higher share of substandard lending in the bank portfolio is associated with an increase in the bank’s interest rate of approximately 28%, or 31 basis points. Column (2) extends the baseline specification in Equation (1) by including the continuous values produced by the rating system. The coefficient on the share of substandard loans remains significant and economically identical to the first specification. Instead, the coefficients on the values of the continuous variables are neither statistically nor economically significant. Our evidence is therefore consistent with the presence of market-driven segmentation in the Italian credit market for SME. Investors observe the distribution of loans into rating classes, and set a higher interest-rate premium to compensate for a larger exposure to substandard loans. Table 1 Banks’ cost of financing and rating segmentation Pre-2008 Post-2008 (1) (2) (3) (4) Substandard to Total Credit 1.26*** 1.24* –0.37 1.34** (0.46) (0.66) (0.29) (0.68) Continuous Variable 1 –0.2 (0.15) Continuous Variable 2 0.09 (0.31) Bank & Issuance Characteristics Yes Yes Yes Yes Bank Fixed Effects No No Yes Yes Time Fixed Effects Yes Yes Yes Yes $$R^2$$ 0.76 0.76 0.85 0.54 N 4,788 4,728 2,233 2,212 Pre-2008 Post-2008 (1) (2) (3) (4) Substandard to Total Credit 1.26*** 1.24* –0.37 1.34** (0.46) (0.66) (0.29) (0.68) Continuous Variable 1 –0.2 (0.15) Continuous Variable 2 0.09 (0.31) Bank & Issuance Characteristics Yes Yes Yes Yes Bank Fixed Effects No No Yes Yes Time Fixed Effects Yes Yes Yes Yes $$R^2$$ 0.76 0.76 0.85 0.54 N 4,788 4,728 2,233 2,212 The table reports the estimates of the specification in Equation (1), using as a dependent variable the interest rate at which Italian banks raise financing. In Columns (1) and (2), the dependent variable is the (volume) weighted average interest rate at which banks raised financing across different types of investors (repo markets, households, firms) between 2004 and 2011. In Columns (3) and (4), we reestimate our pricing equation for the periods before and after 2008, respectively. Accordingly, the dependent variable is the interest rate at which banks raised financing on repurchase markets before 2008 in Column (3) and after 2008 in Column (4). Substandard to Total Credit is the share of a bank’s volume of lending to SME in the substandard rating class relative to total lending. Continous Variable 1 denotes the mean of the continuous variable of firms in rating categories 1 to 5. Continous Variable 2 denotes the mean of the continuous variable of firms in rating categories 6 to 9. The specification includes a vector of bank and issuance characteristics. Issuance characteristics include amounts raised, maturity, and investor composition. Bank characteristics include size (in terms of total assets), the value of the tier 1 capitalization ratio, and the bank’s liquidity ratio. The specification includes monthly fixed effects, with standard errors clustered at the bank level. One star denotes significance at the 10% level, two stars denote significance at the 5% level, and three stars denote significance at the 1% level. Table 1 Banks’ cost of financing and rating segmentation Pre-2008 Post-2008 (1) (2) (3) (4) Substandard to Total Credit 1.26*** 1.24* –0.37 1.34** (0.46) (0.66) (0.29) (0.68) Continuous Variable 1 –0.2 (0.15) Continuous Variable 2 0.09 (0.31) Bank & Issuance Characteristics Yes Yes Yes Yes Bank Fixed Effects No No Yes Yes Time Fixed Effects Yes Yes Yes Yes $$R^2$$ 0.76 0.76 0.85 0.54 N 4,788 4,728 2,233 2,212 Pre-2008 Post-2008 (1) (2) (3) (4) Substandard to Total Credit 1.26*** 1.24* –0.37 1.34** (0.46) (0.66) (0.29) (0.68) Continuous Variable 1 –0.2 (0.15) Continuous Variable 2 0.09 (0.31) Bank & Issuance Characteristics Yes Yes Yes Yes Bank Fixed Effects No No Yes Yes Time Fixed Effects Yes Yes Yes Yes $$R^2$$ 0.76 0.76 0.85 0.54 N 4,788 4,728 2,233 2,212 The table reports the estimates of the specification in Equation (1), using as a dependent variable the interest rate at which Italian banks raise financing. In Columns (1) and (2), the dependent variable is the (volume) weighted average interest rate at which banks raised financing across different types of investors (repo markets, households, firms) between 2004 and 2011. In Columns (3) and (4), we reestimate our pricing equation for the periods before and after 2008, respectively. Accordingly, the dependent variable is the interest rate at which banks raised financing on repurchase markets before 2008 in Column (3) and after 2008 in Column (4). Substandard to Total Credit is the share of a bank’s volume of lending to SME in the substandard rating class relative to total lending. Continous Variable 1 denotes the mean of the continuous variable of firms in rating categories 1 to 5. Continous Variable 2 denotes the mean of the continuous variable of firms in rating categories 6 to 9. The specification includes a vector of bank and issuance characteristics. Issuance characteristics include amounts raised, maturity, and investor composition. Bank characteristics include size (in terms of total assets), the value of the tier 1 capitalization ratio, and the bank’s liquidity ratio. The specification includes monthly fixed effects, with standard errors clustered at the bank level. One star denotes significance at the 10% level, two stars denote significance at the 5% level, and three stars denote significance at the 1% level. In Columns (3) and (4), we focus on the cost of financing on the repurchase market, the primary source of funds for the securitized banking system. This market is of particular interest, because Gorton and Metrick (2012) describe the crisis as a “run on repo” that was triggered by concerns about bank solvency. We therefore reestimate our pricing equation in Equation (1) separately for the period before and after 2008, and augment our specification with bank fixed effects. In the boom phase of the credit cycle, the correlation between interest rates on the repurchase market and the composition of banks’ lending portfolio is low and statistically nonsignificant. In the bust period, the correlation is positive and economically significant, implying an increase in the interest rate premium required by investors from banks that are relatively more exposed to substandard credit risk. 2. Theoretical Framework To motivate our empirical analysis, we propose a model of credit with market segmentation and moral hazard. Specifically, we extend the basic framework in Tirole (2006, chap. 3), to accommodate the institutional features of the Italian credit market for SME. As in Tirole (2006), entrepreneurs need capital to fund a project. The bank-firm relationship is frustrated by moral hazard: by putting effort, the firm succeeds with positive probability. By shirking, the firm defaults with certainty, but the entrepreneur then gains private benefits. Finally, we allow firms to use the bank’s monitoring technology. Differently from the standard setup, firms are segmented into two rating classes, performing and substandard. Moreover, we assume that the conditions at which banks receive funding depend on the phase of the credit cycle. We show that a bank’s ability to tame the firm’s moral hazard problem can be impaired when funding conditions on the wholesale market heat up (i.e., in the crisis period). This can push the bank to reduce lending at the expense of the substandard firms.12 The model features three categories of agents: the bank, its investors, and two firms. The two firms are allocated by the rating system used by the bank into the performing and substandard classes of credit risk. We consider the case in which the two firms fall exactly at the threshold between the two classes. The bank knows this, and understands that they are economically identical. The cost of funds to the bank is set by external investors who, consistent with our empirical evidence, observe only the firms’ rating class. The existence of market segmentation has then two main implications for bank lending.13 First, the bank’s cost of funding to a firm will reflect the composition of demand in the credit class. Second, the cost of financing paid by the bank will vary over the cycle according to the conditions on the wholesale funding market. In the boom period, the low cost at which the bank raises financing in the wholesale market implies that both firms can obtain access to unmonitored credit. More specifically, both firms receive the same amount of lending, but the substandard firm pays a higher interest rate—mirroring the higher risk in their class. In the bust period, worse conditions on the market for wholesale funds erode firms’ net worth, and imply that lending is not viable for the bank. Then, firms have two options: the first is to use the bank’s monitoring technology, which comes at a cost, but also alleviates the moral hazard problem. Alternatively, they are (partially) rationed from credit. Assume that monitoring works with the performing firm, so that the bank can break even on this firm’s project. Instead, the monitoring technology does not work for the substandard firm: that is, the rise in the cost of wholesale funding for the bank, combined with the cost of monitoring, implies that the net present value of the substandard firm’s project remains negative. Then, the substandard firm is credit rationed at equilibrium. To sum up: in the bust period, quantity differences arise in the credit contracts offered to the two firms at the threshold. As we will further discuss later, these results will guide the interpretation of the credit differences arising in our empirical analysis. 3. Data Preview and Economic Environment To test the link between segmentation of firms and bank lending standards, we use a confidential data sets from the Bank of Italy that contain information on bank balance sheets and the financial contracts signed between banks and SME. We instead obtain firm balance sheets and rating information from CEBI. Our final sample is composed of about 144,000 firm-year observations in the manufacturing sector and 253,000 funding contracts signed between the first quarter of 2004 and the last quarter of 2011. Further details on the data set and its organization can be found in Online Appendix A. This section first documents the presence of substantial heterogeneity across rating classes. This heterogeneity suggests that a naïve comparison between the credit conditions of firms in different rating classes is likely to yield misleading conclusions on the pattern of lending standards, because the resulting credit differences could simply reflect differences in firms’ demand for credit. Then, we show the patterns of firms’ financial contracts over time, which document how the phases of the credit cycle that Italy experienced between 2004 and 2011 affect financial allocations. Finally, we present key developments in the Italian banking environment that occurred during our sample period, illustrating the significant effects of the financial crisis on the wholesale funding and capitalization of Italian banks. 3.1 Firm financing environment We begin by presenting the sources of cross-sectional heterogeneity in our data set and the time-series variation in firm financial contracts. 3.1.1 Cross-sectional descriptive statistics Table 2 provides the cross-sectional characteristics of the full sample in Column (1). Columns (2) and (3) show corresponding results for the group of performing and substandard firms, and Columns (4) and (5) show the same for categories 6 and 7. Finally, Column (6) reports the mean difference between the values of the variables in categories 6 and 7. Table 2 Descriptive statistics All Performing Substandard Score 6 Score 7 6–7 Comparison (1) (2) (3) (4) (5) (6) Panel A: Loan information Term Loans: Interest Rate 4.57 4.32 5.3 4.79 5.29 –0.48*** (1.62) (1.56) (1.6) (1.58) (1.59) Term Loans: Amount 816 885 617 451 569 –118 (9850) (5156) (17300) (1623) (17700) Term Loans: Maturity 0.66 0.66 0.65 0.77 0.72 0.05*** (0.47) (0.47) (0.48) (0.44) (247) N 253,502 188,026 65,475 49,265 60,326 109,591 Panel B: Aggregate financing information All Bank Financing Granted 8,503 9,237 6,167 7,542 6,392 1,150*** (37,200) (40,600) (23,100) (24,600) (21,100) Share of Term Loans Granted 0.35 0.35 0.36 0.33 0.35 –0.02*** (0.25) (0.25) (0.25) (0.21) (0.25) Share of Write-downs 0.01 0.01 0.03 0.00 0.01 –0.01*** (0.09) (0.04) (0.17) (0.05) (0.09) N 543,855 414,041 129,754 63,722 104,253 167,975 Panel C: Balance sheet information Employment 92 95 76 73 72 1 (294) (295) (290) (170) (207) Investment to Assets 0.05 0.05 0.04 0.04 0.039 0.001** (0.06) (0.06) (0.06) (0.06) (0.06) Return to Assets 0.05 0.07 0.00 0.05 0.03 0.02*** (0.10) (0.08) (0.13) (0.07) (0.07) Leverage 0.67 0.61 0.86 0.79 0.85 –0.06*** (0.19) (0.18) (0.10) (0.10) (0.09) N 143,953 108,353 35,600 16,432 27,350 43,782 All Performing Substandard Score 6 Score 7 6–7 Comparison (1) (2) (3) (4) (5) (6) Panel A: Loan information Term Loans: Interest Rate 4.57 4.32 5.3 4.79 5.29 –0.48*** (1.62) (1.56) (1.6) (1.58) (1.59) Term Loans: Amount 816 885 617 451 569 –118 (9850) (5156) (17300) (1623) (17700) Term Loans: Maturity 0.66 0.66 0.65 0.77 0.72 0.05*** (0.47) (0.47) (0.48) (0.44) (247) N 253,502 188,026 65,475 49,265 60,326 109,591 Panel B: Aggregate financing information All Bank Financing Granted 8,503 9,237 6,167 7,542 6,392 1,150*** (37,200) (40,600) (23,100) (24,600) (21,100) Share of Term Loans Granted 0.35 0.35 0.36 0.33 0.35 –0.02*** (0.25) (0.25) (0.25) (0.21) (0.25) Share of Write-downs 0.01 0.01 0.03 0.00 0.01 –0.01*** (0.09) (0.04) (0.17) (0.05) (0.09) N 543,855 414,041 129,754 63,722 104,253 167,975 Panel C: Balance sheet information Employment 92 95 76 73 72 1 (294) (295) (290) (170) (207) Investment to Assets 0.05 0.05 0.04 0.04 0.039 0.001** (0.06) (0.06) (0.06) (0.06) (0.06) Return to Assets 0.05 0.07 0.00 0.05 0.03 0.02*** (0.10) (0.08) (0.13) (0.07) (0.07) Leverage 0.67 0.61 0.86 0.79 0.85 –0.06*** (0.19) (0.18) (0.10) (0.10) (0.09) N 143,953 108,353 35,600 16,432 27,350 43,782 All panels use data for the period 2004.Q1–2011.Q4, and monetary values expressed in KE (1,000 euro). Standard deviations are reported in brackets. The last column reports the difference in means of each variable between categories 6 and 7. One star denotes significance at the 10% level, two stars denote significance at the 5% level, and three stars denote significance at the 1% level. Panel A uses pooled loan-level data with observations at the loan-quarter level. Interest Rate is the gross annual interest rate inclusive of participation fees, loan origination fees, and monthly service charges. Amount is the granted amount of the issued term loan. Maturity is a binary variable indicating whether the maturity of the newly issued loans is up to one year, or longer. Panel B uses credit register data with observations at the firm-quarter level. All Bank Financing Granted is the firms’ total amount of bank financing granted summing across all categories (loans, credit lines, backed loans). Share of Term Loans Granted is the firms’ total amount of term loans granted, divided by the total amount of bank financing granted for all categories. Share of Write-downs is a binary variable indicating whether the firms’ total amount of bank financing granted for all categories has experienced write-downs by banks. Panel C uses balance sheet and cash-flow statements at the firm-year level. Employment is defined as the firms’ average employment over the year. Investment to Assets is the firms’ investment in material fixed assets over total fixed assets. Returns to Assets is defined as the firms’ earnings before interest and taxes over total assets. Leverage is the firms’ ratio of debt (both short and long term) over total assets. In all panels, $$N$$ corresponds to the pooled number of observations in our sample. Table 2 Descriptive statistics All Performing Substandard Score 6 Score 7 6–7 Comparison (1) (2) (3) (4) (5) (6) Panel A: Loan information Term Loans: Interest Rate 4.57 4.32 5.3 4.79 5.29 –0.48*** (1.62) (1.56) (1.6) (1.58) (1.59) Term Loans: Amount 816 885 617 451 569 –118 (9850) (5156) (17300) (1623) (17700) Term Loans: Maturity 0.66 0.66 0.65 0.77 0.72 0.05*** (0.47) (0.47) (0.48) (0.44) (247) N 253,502 188,026 65,475 49,265 60,326 109,591 Panel B: Aggregate financing information All Bank Financing Granted 8,503 9,237 6,167 7,542 6,392 1,150*** (37,200) (40,600) (23,100) (24,600) (21,100) Share of Term Loans Granted 0.35 0.35 0.36 0.33 0.35 –0.02*** (0.25) (0.25) (0.25) (0.21) (0.25) Share of Write-downs 0.01 0.01 0.03 0.00 0.01 –0.01*** (0.09) (0.04) (0.17) (0.05) (0.09) N 543,855 414,041 129,754 63,722 104,253 167,975 Panel C: Balance sheet information Employment 92 95 76 73 72 1 (294) (295) (290) (170) (207) Investment to Assets 0.05 0.05 0.04 0.04 0.039 0.001** (0.06) (0.06) (0.06) (0.06) (0.06) Return to Assets 0.05 0.07 0.00 0.05 0.03 0.02*** (0.10) (0.08) (0.13) (0.07) (0.07) Leverage 0.67 0.61 0.86 0.79 0.85 –0.06*** (0.19) (0.18) (0.10) (0.10) (0.09) N 143,953 108,353 35,600 16,432 27,350 43,782 All Performing Substandard Score 6 Score 7 6–7 Comparison (1) (2) (3) (4) (5) (6) Panel A: Loan information Term Loans: Interest Rate 4.57 4.32 5.3 4.79 5.29 –0.48*** (1.62) (1.56) (1.6) (1.58) (1.59) Term Loans: Amount 816 885 617 451 569 –118 (9850) (5156) (17300) (1623) (17700) Term Loans: Maturity 0.66 0.66 0.65 0.77 0.72 0.05*** (0.47) (0.47) (0.48) (0.44) (247) N 253,502 188,026 65,475 49,265 60,326 109,591 Panel B: Aggregate financing information All Bank Financing Granted 8,503 9,237 6,167 7,542 6,392 1,150*** (37,200) (40,600) (23,100) (24,600) (21,100) Share of Term Loans Granted 0.35 0.35 0.36 0.33 0.35 –0.02*** (0.25) (0.25) (0.25) (0.21) (0.25) Share of Write-downs 0.01 0.01 0.03 0.00 0.01 –0.01*** (0.09) (0.04) (0.17) (0.05) (0.09) N 543,855 414,041 129,754 63,722 104,253 167,975 Panel C: Balance sheet information Employment 92 95 76 73 72 1 (294) (295) (290) (170) (207) Investment to Assets 0.05 0.05 0.04 0.04 0.039 0.001** (0.06) (0.06) (0.06) (0.06) (0.06) Return to Assets 0.05 0.07 0.00 0.05 0.03 0.02*** (0.10) (0.08) (0.13) (0.07) (0.07) Leverage 0.67 0.61 0.86 0.79 0.85 –0.06*** (0.19) (0.18) (0.10) (0.10) (0.09) N 143,953 108,353 35,600 16,432 27,350 43,782 All panels use data for the period 2004.Q1–2011.Q4, and monetary values expressed in KE (1,000 euro). Standard deviations are reported in brackets. The last column reports the difference in means of each variable between categories 6 and 7. One star denotes significance at the 10% level, two stars denote significance at the 5% level, and three stars denote significance at the 1% level. Panel A uses pooled loan-level data with observations at the loan-quarter level. Interest Rate is the gross annual interest rate inclusive of participation fees, loan origination fees, and monthly service charges. Amount is the granted amount of the issued term loan. Maturity is a binary variable indicating whether the maturity of the newly issued loans is up to one year, or longer. Panel B uses credit register data with observations at the firm-quarter level. All Bank Financing Granted is the firms’ total amount of bank financing granted summing across all categories (loans, credit lines, backed loans). Share of Term Loans Granted is the firms’ total amount of term loans granted, divided by the total amount of bank financing granted for all categories. Share of Write-downs is a binary variable indicating whether the firms’ total amount of bank financing granted for all categories has experienced write-downs by banks. Panel C uses balance sheet and cash-flow statements at the firm-year level. Employment is defined as the firms’ average employment over the year. Investment to Assets is the firms’ investment in material fixed assets over total fixed assets. Returns to Assets is defined as the firms’ earnings before interest and taxes over total assets. Leverage is the firms’ ratio of debt (both short and long term) over total assets. In all panels, $$N$$ corresponds to the pooled number of observations in our sample. The table shows that there is significant heterogeneity among firms with different risk profiles, not only with respect to financial characteristics, but also in terms of balance sheet characteristics. More specifically, panel A of Table 2 shows that in the full sample, the average nominal interest rate charged for a loan is 4.57%. However, the interest rates applied to performing and substandard firms are 4.32% and 5.3%, respectively. Although the average loan in the sample is approximately 816,000 euro, it is about 617,000 euro for a firm in the substandard class. Moreover, the loans in our sample are mostly short term, as these account for around two-thirds of the total value of granted loans. Panel B reports the aggregate financing characteristics of the firms in our sample. On average, total bank lending amounts to 8.5 million euro (ME) per firm, 35% of which is in the form of loans. While firms in the performing class receive bank financing that adds up to about 9.2ME, firms in the substandard class receive an average of 6ME. Panel C provides an overview of the main balance sheet characteristics of Italian manufacturing firms based on unique firm-year observations. Firms in our sample are relatively small. On average, they employ 92 workers, with firms in the performing class being relatively larger than those in the substandard class. While the investment-to-asset ratio is stable across classes, the values of leverage and return to assets are not. The leverage ratio increases from 0.61 for firms in the performing class to 0.86 for those in the substandard class. Moreover, return on assets decreases from 0.07 to 0 for firms in these two classes. Finally, Column (6) of panel C shows that the heterogeneity in firm characteristics extends to rating categories 6 and 7. The cost and availability of bank financing suggests significantly tighter conditions for firms in category 7 as opposed to category 6. For instance, interest rates for firms in category 6 are 50 points lower than those of firms in category 7. At the same time, these firms are significantly different in terms of characteristics related to the demand for credit, such as the value of investment and profitability. Taken together, the descriptive statistics show the importance of obtaining a measure of lending standards that is not biased by demand heterogeneity. 3.1.2 Time-series descriptive statistics In Figure 2, we document the variation in financial contracts across time. Figure 2. View largeDownload slide Descriptive statistics across time In the left panel, we plot the per-firm aggregate value of bank financing for different rating categories across time. In the right panel, we plot the nominal average interest rates applied to firms in different rating categories across time. Figure 2. View largeDownload slide Descriptive statistics across time In the left panel, we plot the per-firm aggregate value of bank financing for different rating categories across time. In the right panel, we plot the nominal average interest rates applied to firms in different rating categories across time. The left panel illustrates that, like other OECD economies (Drehmann, Borio, and Tsatsaronis 2012), between 2004 and 2011 Italy was experiencing a credit cycle that reached its peak in 2007. The right panel focuses on firms’ nominal average interest rates, showing that nominal rates mirrored the pattern of the indicators for the monetary policy of the European Central Bank. More specifically, the left panel shows that the time series of the amount of bank financing to Italian SME features a humped shape. From the first quarter of 2004 to the fourth quarter of 2007, bank financing increased by 18%, on average. It then decreased by 11% through the end of the sample period. Although this pattern is qualitatively similar across risk classes, the variation in bank financing is larger for substandard firms: between 2004 and 2008 bank financing to performing firms increased by 13%, while it rose by 29% for substandard firms. This evidence is consistent with the historical account of credit booms by Greenwood and Hanson (2013), who show that the quality of credit deteriorates as aggregate credit increases. Finally, the right panel of Figure 2 shows that nominal interest rates increased from 4.3% in 2004 to 6.11% in late 2008. Similar to the patterns in the left panel, the levels of the interest rate spreads are consistent with the risk categories in our rating system. 3.2 Banking environment In Figure 3, we illustrate the key developments in the Italian banking environment that occurred during our sample period. We use bank balance sheet data between 2006 and 2011 from Bank of Italy. Figure 3. View largeDownload slide Bank capital and credit risk In the top panel, we plot the ratio of the volume of repo financing over total assets for the five largest banks in our data set. In the middle panel, we plot the tier 1 capital ratio for the five largest banks in our data set across time. In the bottom panel, we use data from the European Central Bank statistical data warehouse to plot the credit risk capital allocations over total capital requirements (black line), the fraction of capital allocations computed using the standardized approach (gray line), and the fraction computed using the internal rating-based (IRB) approach (dashed black line). Figure 3. View largeDownload slide Bank capital and credit risk In the top panel, we plot the ratio of the volume of repo financing over total assets for the five largest banks in our data set. In the middle panel, we plot the tier 1 capital ratio for the five largest banks in our data set across time. In the bottom panel, we use data from the European Central Bank statistical data warehouse to plot the credit risk capital allocations over total capital requirements (black line), the fraction of capital allocations computed using the standardized approach (gray line), and the fraction computed using the internal rating-based (IRB) approach (dashed black line). The top panel of Figure 3 plots the share of repo financing of banks relative to their total assets for the five largest banks in our sample. In the expansionary phase of the cycle, the dependence of banks on repo financing grew from 5% in 2005 to nearly 12% at the beginning of 2008. During the financial crisis, this source of financing plummeted to 2.5% and remained at low levels until the end of our sample period. The middle panel of Figure 3 illustrates the capitalization of Italian banks: we compute the tier 1 capital ratio for the five largest banks in our sample by dividing banks’ tier 1 capital by their total assets. The figure shows that the average value of banks’ capital ratio at the beginning of the financial crisis period was approximately 4.5%. In 2008 the ratio fell to around 3.6%, before rising above 5% toward the end of the sample period. The patterns in these two panels are shared by the banking systems of other European countries during the same time interval. The bottom panel of Figure 3 provides evidence on the implementation of the Basel II agreements. Credit risk capital allocations account for more than 100% of total capital requirements through 2008 and 2010, implying that credit risk management was critical for Italian banks during our sample period. Moreover, the transition from Basel I to Basel II is unlikely to drive the evolution of lending standards in our sample. Indeed, the total fraction of capital allocations calculated using internal rating systems oscillates around 20%. 4. The Empirical Model 4.1 Identification strategy Empirically identifying how segmentation influences bank lending standards is challenging for two reasons. First, it requires a setup where the econometrician observes the exact information held by the bank about the firm credit risk profile. Then, to isolate demand from supply considerations, the econometrician would like to compare firms that are identical from the perspective of the loan officer, but classified into different classes of credit risk. To address these challenges, we exploit the institutional features of the Italian credit market for SME introduced in Section 1. The structure of the rating, and its construction, give us the opportunity to follow an intuitive empirical strategy to identify the effects of segmentation on financial contracts. Specifically, we exploit the sharp discontinuity in the allocation of firms into the performing and substandard classes of credit risk. As our measure of lending standards, then, we take the differences in the credit conditions between a firm marginally classified as performing and one that is marginally classified as substandard based on the value of the rating’s continuous variable. These threshold differences inform us about how banks’ supply of credit is affected by segmentation, while holding constant the demand for credit. The support of the continuous variable for categories 6 and 7 ranges between –0.6 and 1.5, and the threshold is 0.15. Below this threshold, a firm’s Score is 7 and thus the firm falls into the substandard class. Above the threshold, a firm’s Score is 6 and it is in the performing class. Throughout the analysis, we normalize the threshold to 0 and only use the support of the continuous variable that spans between categories 6 and 7. Thus, if $$s_{i}$$ is the value of firm $$i$$’s continuous variable, the allocation of this firm into a rating class takes place according to the following sharp mechanism: \begin{eqnarray} {\textit{Score}}_{i,t} = \left\{ \begin{array}{@{}llll} 6\ \text{(Performing)} & \quad \text{If $0 \leq s_{i,t} <1.35$} \\\ \\ 7\ \text{(Substandard)} & \quad \text{If $-0.75 \leq s_{i,t}<0$} \end{array} \right. . \end{eqnarray} (2) 4.2 Main specification Let $$\bar{s}$$ denote the normalized threshold that allocates firms into rating categories 6 and 7. Our main specification follows: \begin{align} y_{i,t} &= \beta_{0} +\beta_{1}(\mbox{Performing}_{i,t}\times\mbox{Boom}_{t})+\beta_{2} (\mbox{Performing}_{i,t}\times\mbox{Crisis}_{t})\nonumber\\ & \quad +\beta_{3} (\mbox{Performing}_{i,t}\times\mbox{Recovery}_{t})+f_{t}(s_{i,t}-\bar{s})\nonumber\\ & \quad +\mbox{Performing}_{i,t}\times g_{t}(s_{i,t}-\bar{s}) + \pi_{t} + u_{i,t}. \end{align} (3) The dependent variable capturing the supply of bank financing is the (log) total value of bank lending granted to firm $$i$$ in quarter $$t$$. This measure accounts for the possibility that firms obtain credit from multiple banks. The variable capturing the cost of bank financing is the (log) value of the interest rate applied to a new loan granted to firm $$i$$ in quarter $$t$$. The indicator Performing$$_{i,t}$$ takes a value of 1 if a firm is in the performing class (i.e., $$s_{i,t} \geq 0$$ implying a Score of 6), and 0 otherwise. It is interacted with three indicator variables associated with the phases of the credit cycle. Boom$$_{t}$$ takes a value of 1 between the first quarter of 2004 until the last quarter of 2007, and 0 otherwise. Crisis$$_{t}$$ takes a value of 1 between the first quarter of 2008 until the last quarter of 2009, and 0 otherwise. Recovery$$_{t}$$ takes a value of 1 between the first quarter of 2010 until the last quarter of 2011, and 0 otherwise. Functions $$f_{t}(\cdot)$$ and $$g_t(\cdot)$$ correspond to flexible sixth-order polynomials whose goal is to fit the smoothed curves on either side of the cutoff as closely to the data as possible. Function $$f_{t}(\cdot)$$ is estimated from $$0$$ to the left, whereas the Performing$$_{i,t}\times g_{t}(\cdot)$$ term is estimated from $$0$$ to the right. The subscript $$t$$ for $$f_{t}(\cdot)$$ and $$g_{t}(\cdot)$$ indicates that the polynomials are separately estimated for each time period through interactions with $$\pi_{t}$$, the quarter-year fixed effects. $$u_{i,t}$$ is a mean-zero error term clustered at the firm level.14 As a robustness check, we will estimate a version of the specification that also includes the past value of the rating, and its interaction with each time period. The interpretation of Equation (1) is the following. First, note that, at the cutoff, the $$f_{t}(\cdot)$$ and $$g_{t}(\cdot)$$ polynomials are evaluated at 0 and drop out of the calculation. This allows us to interpret the parameters $$(\beta_{1}, \beta_{2}, \beta_{3})$$ as capturing the magnitude of the discontinuity in credit conditions at the threshold $$\bar s$$. The null hypothesis of our framework is that if a bank uses all its information on the borrowing firm, there should be no discontinuity in lending contracts at the threshold. In other words, under our null hypothesis, segmentation should not matter for lending decisions. Second, the estimated discontinuity parameters $$(\beta_{1}, \beta_{2}, \beta_{3})$$ have an intuitive interpretation. The estimate of $$\beta_{1}$$ measures differences in credit allocations between marginally performing firms and substandard firms in the period between 2004 and 2007. The estimate of $$\beta_{2}$$ measures differences in credit allocations in the period between 2008 and 2009. Finally, the estimate of $$\beta_{3}$$ captures the difference between marginally performing firms and substandard firms in the period between 2010 and 2011. In the main specification, we restrict our attention to the sample of firms that remain in the same rating category for at least two consecutive years. This condition limits two potential concerns. The first is that the bank reports to investors a firm as performing on the basis of its rating in $$t-1$$, even though it is already downgraded in $$t$$. The second is related to the possibility that large variations in the value of the continuous rating that then lead to downgrades might themselves be correlated to the firms’ demand for credit. We then separately study the source of variation coming from a firm downgrade for financial contracting, and provide evidence based on downgrades caused by small changes in the value of the continuous rating. We extend our main specification in two directions. First, we study whether, via its impact on lending standards, segmentation is relevant for firms’ real choices. Specifically, we estimate Equation (3) using as dependent variables firms’ expenditures in production inputs and the value of production. The balance sheet information we use for this analysis is reported in end-of-the-year statements; thus, it reflects a firm’s lending conditions throughout the year. Second, we look at the differences between the lending conditions at the threshold within each phase of the credit cycle. To this end, we estimate Equation (4) separately for each quarter-year cross-section of firms at the threshold in our sample period: \begin{equation} y_{i,.}=\beta_{0}+\beta_{1} \mbox{Performing}_{i,.}+f(s_{i,.} -\bar{s})+\mbox{Performing}_{i,.}\times g(s_{i,.}-\bar{s}) + u_{i,.}. \end{equation} (4) In Equation (4), the dot indicates that we fix the time period. This exercise is meant to understand whether there are distinct credit dynamics within each of the subperiods of the credit cycle. 4.3 Mechanism for the transmission of market segmentation In this section, we first exploit the heterogeneity of the banks in our data set to study how banks’ financial structure affects the sensitivity of lending to market segmentation. Then, we analyze the implications of segmentation for marginally downgraded firms over the cycle. 4.3.1 Banks’ financial structure We consider two channels through which financial structure can affect banks’ sensitivity to market segmentation: capital requirements and investor composition. Intuitively, low levels of regulatory capital can help explain a bank’s greater sensitivity to market segmentation. Similarly, investor composition can account for the sensitivity of banks to market segmentation: certain investor categories are more responsive than others to bank solvency risk, and update their assessment of bank loan quality over the cycle (Ivashina and Scharfstein 2009; Iyer, Puri, and Ryan 2016). To explore the relative merits of these two channels in determining bank sensitivity to segmentation, we compute the following measures of bank heterogeneity. To study the role played by capital requirements, we compute, for the pre-crisis period, each bank’s tier 1 capital ratio. To study heterogeneity in investor composition, we focus on the importance of repo markets for a bank funding structure. As we show in Table 1, during the crisis, investors in repo markets updated their interest rate conditions based on banks’ exposure to substandard firms. We therefore measure each banks’ pre-crisis share of financing from repo markets. We augment our main specification with interactions between the Performing$$_{it}$$ indicator and these bank-specific characteristics: \begin{align} y_{i,b,t} & = \beta_{0} +\beta_{1}(\mbox{Performing}_{i,t}\times\mbox{Boom}_{t})+\beta_{2}(\mbox{Performing}_{i,t}\times\mbox{Crisis}_{t})\nonumber\\ & \quad +\beta_{3}(\mbox{Performing}_{i,t}\times\mbox{Recovery}_{t})\nonumber\\ & \quad +\gamma_{1}(\mbox{Performing}_{i,t}\times\mbox{Tier1}_{b}\times\mbox{Boom}_{t})\nonumber\\ & \quad +\gamma_{2} (\mbox{Performing}_{i,t}\times\mbox{Tier1}_{b}\times\mbox{Crisis}_{t})\nonumber\\ & \quad +\gamma_{3} (\mbox{Performing}_{i,t}\times\mbox{Tier1}_{b}\times \mbox{Recovery}_{t})\nonumber\\ & \quad +\delta_{1} (\mbox{Performing}_{i,t}\times\mbox{Repo}_{b}\times\mbox{Boom}_{t})\nonumber\\ & \quad +\delta_{2} (\mbox{Performing}_{i,t}\times\mbox{Repo}_{b}\times\mbox{Crisis}_{t})\nonumber\\ & \quad +\delta_{3} (\mbox{Performing}_{i,t}\times\mbox{Repo}_{b}\times\mbox{Recovery}_{t})\nonumber\\ & \quad +f_{t}(s_{i,t}-\bar{s})+\mbox{Performing}_{i,t}\times g_{t}(s_{i,t}-\bar{s}) + X_{i,b,t}\Psi + \pi_{t} + u_{i,t}. \end{align} (5) In Equation (5), Tier1$$_b$$ is defined as a bank $$b$$’s core equity capital divided by its total assets, and Repo$$_{b}$$ is defined as the share of the bank’s total financing from repo markets. $$X_{i,b,t}$$ is a vector that includes the levels and interactions of all the variables in the set of triple interactions. Standard errors are clustered at the firm-bank level. As an additional robustness check, we augment Equation (5) by including firm-year fixed effects. 4.3.2 Analysis of downgraded firms Finally, we study how market segmentation affects the lending policies set on firms that are marginally downgraded over the cycle. More specifically, we ask what is the implication of a downgrade to substandard quality for credit conditions over the cycle, and whether the bank exploits its superior information on the company’s downgrade. We compare two firms that fall in the performing class until year $$t-1$$, but differ in their rating class in year $$t$$.15 The specification follows: \begin{align} y_{i,b,t} & = \beta_{0} + \beta_{1} (\mbox{Down}_{i,t}\times\mbox{Boom}_{t})+\beta_{2} (\mbox{Down}_{i,t}\times\mbox{Crisis}_{t})\nonumber\\ &\quad +\beta_{3} (\mbox{Down}_{i,t}\times\mbox{Recovery}_{t})\nonumber\\ &\quad +f_{t}(s_{i,t}-s_{i,t-1})+\mbox{Down}_{i,t}\times g_{t}(s_{i,t}-s_{i,t-1})\nonumber\\ &\quad +m_{t}(s_{i,t-1})+\mbox{Down}_{i,t}\times n_{t}(s_{i,t-1}) + \pi_{t} + u_{i,t}. \end{align} (6) Down$$_{i,t}$$ is a binary variable equal to 1 if the firm is downgraded from category 6 to category 7 in year $$t$$, and is 0 otherwise. In Equation (5), the polynomials in $$f_{t}(\cdot)$$ and $$g_{t}(\cdot)$$ are a function of the change in the continuous variable between $$t-1$$ and $$t$$. By evaluating these polynomials close to 0, our analysis considers those firms that were downgraded as a consequence of a similar and small change in the value of the continuous rating. To make sure that we implement a local identification of downgraded and non-downgraded firms around the threshold, we augment the specification by including also polynomials for the continuous assignment variable in $$t-1$$, $$m_{t}(\cdot)$$ and $$n_{t}(\cdot)$$. Consequently, we compare firms that not only experienced a similar small change in the continuous variable, but were also close to the threshold.16 5. Results In this section, we present the results on the differences in credit conditions—specifically, differences in the interest rates and in the total amount of bank financing—for firms at the threshold between the performing and the substandard classes. We then decompose the changes in lending standards within each phase by estimating differences in credit allocations separately for each quarter. Finally, we explore whether differences in credit conditions give rise to differences in firms’ production and input choices. 5.1 Results on credit allocations Table 3 reports the estimates related to credit allocations. The dependent variable in Columns (1) to (3) is the log amount of total bank financing granted to the firm, while in Columns (4) to (6) the dependent variable is the log interest rate on new bank loans. In Columns (1) and (4) we report the estimates of the main specification in Equation (3), while Columns (2) and (5) augment this specification by interacting past ratings with quarter-year fixed effects. Finally, Columns (3) and (6) report the results of a naïve specification comparing lending conditions to all performing and substandard firms. Table 3 Credit effects Quantity Price Dependent variable (1) (2) (3) (4) (5) (6) Boom $$\times$$ Performing 0.11 0.11 0.35*** –0.04** –0.04** –0.15*** (0.09) (0.09) (0.02) (0.02) (0.02) (0.01) Crisis $$\times$$ Performing 0.33*** 0.33*** 0.32*** 0.03 0.03 –0.15*** (0.11) (0.11) (0.02) (0.03) (0.03) (0.01) Recovery $$\times$$ Performing 0.20* 0.20* 0.30*** –0.08** –0.08** –0.27*** (0.12) (0.11) (0.02) (0.04) (0.04) (0.01) Lagged Rating –0.01*** 0.00*** 0.00*** 0.00*** (0.00) (0.00) (0.00) (0.00) Polynomial Yes Yes No Yes Yes No Quarter $$\times$$ Year Fixed Effects Yes Yes Yes Yes Yes Yes $$R^2$$ 0.06 0.06 0.01 0.38 0.38 0.43 N 157,775 157,775 518,047 105,865 105,865 246,240 Quantity Price Dependent variable (1) (2) (3) (4) (5) (6) Boom $$\times$$ Performing 0.11 0.11 0.35*** –0.04** –0.04** –0.15*** (0.09) (0.09) (0.02) (0.02) (0.02) (0.01) Crisis $$\times$$ Performing 0.33*** 0.33*** 0.32*** 0.03 0.03 –0.15*** (0.11) (0.11) (0.02) (0.03) (0.03) (0.01) Recovery $$\times$$ Performing 0.20* 0.20* 0.30*** –0.08** –0.08** –0.27*** (0.12) (0.11) (0.02) (0.04) (0.04) (0.01) Lagged Rating –0.01*** 0.00*** 0.00*** 0.00*** (0.00) (0.00) (0.00) (0.00) Polynomial Yes Yes No Yes Yes No Quarter $$\times$$ Year Fixed Effects Yes Yes Yes Yes Yes Yes $$R^2$$ 0.06 0.06 0.01 0.38 0.38 0.43 N 157,775 157,775 518,047 105,865 105,865 246,240 In Columns (1), (2), (4), and (5) the table reports OLS estimates of the threshold specification in Equation (3). Instead, Columns (3) and (6) estimate a simple mean difference specification using data for all firms in the rating system. The dependent variable in the first three columns is the (log) total value of bank lending granted to firm $$i$$ in quarter $$t$$. The dependent variable in the last three columns is the (log) value of the interest rate applied to a new loan granted to firm $$i$$ in quarter $$t$$. The indicator Performing$$_{i,t}$$ takes a value of 1 if a firm is in the performing class (i.e., $$s_{i,t} \geq 0$$ implying a Score of 6), and 0 otherwise. It is interacted with three indicator variables associated with the phases of the credit cycle. Boom$$_{t}$$ takes a value of 1 from the first quarter of 2004 to the last quarter of 2007, and 0 otherwise. Crisis$$_{t}$$ takes a value of 1 from the first quarter of 2008 to the last quarter of 2009, and 0 otherwise. Recovery$$_{t}$$ takes a value of 1 from the first quarter of 2010 to the last quarter of 2011, and 0 otherwise. Functions $$f_{t}(\cdot)$$ and $$g_{t}(\cdot)$$ correspond to flexible sixth-order polynomials estimated separately for each quarter-year. Function $$f_{t}(\cdot)$$ is estimated from $$0$$ to the left, whereas the Performing$$_{i,t}\times g_{t}(\cdot)$$ term is estimated from $$0$$ to the right. Finally, Lagged Rating controls for the past value of the rating. In Columns (2) and (5) the variable is interacted with quarter-year fixed effects. Standard errors, clustered at the firm level, are reported in brackets. One star denotes significance at the 10% level, two stars denote significance at the 5% level, and three stars denote significance at the 1% level. Table 3 Credit effects Quantity Price Dependent variable (1) (2) (3) (4) (5) (6) Boom $$\times$$ Performing 0.11 0.11 0.35*** –0.04** –0.04** –0.15*** (0.09) (0.09) (0.02) (0.02) (0.02) (0.01) Crisis $$\times$$ Performing 0.33*** 0.33*** 0.32*** 0.03 0.03 –0.15*** (0.11) (0.11) (0.02) (0.03) (0.03) (0.01) Recovery $$\times$$ Performing 0.20* 0.20* 0.30*** –0.08** –0.08** –0.27*** (0.12) (0.11) (0.02) (0.04) (0.04) (0.01) Lagged Rating –0.01*** 0.00*** 0.00*** 0.00*** (0.00) (0.00) (0.00) (0.00) Polynomial Yes Yes No Yes Yes No Quarter $$\times$$ Year Fixed Effects Yes Yes Yes Yes Yes Yes $$R^2$$ 0.06 0.06 0.01 0.38 0.38 0.43 N 157,775 157,775 518,047 105,865 105,865 246,240 Quantity Price Dependent variable (1) (2) (3) (4) (5) (6) Boom $$\times$$ Performing 0.11 0.11 0.35*** –0.04** –0.04** –0.15*** (0.09) (0.09) (0.02) (0.02) (0.02) (0.01) Crisis $$\times$$ Performing 0.33*** 0.33*** 0.32*** 0.03 0.03 –0.15*** (0.11) (0.11) (0.02) (0.03) (0.03) (0.01) Recovery $$\times$$ Performing 0.20* 0.20* 0.30*** –0.08** –0.08** –0.27*** (0.12) (0.11) (0.02) (0.04) (0.04) (0.01) Lagged Rating –0.01*** 0.00*** 0.00*** 0.00*** (0.00) (0.00) (0.00) (0.00) Polynomial Yes Yes No Yes Yes No Quarter $$\times$$ Year Fixed Effects Yes Yes Yes Yes Yes Yes $$R^2$$ 0.06 0.06 0.01 0.38 0.38 0.43 N 157,775 157,775 518,047 105,865 105,865 246,240 In Columns (1), (2), (4), and (5) the table reports OLS estimates of the threshold specification in Equation (3). Instead, Columns (3) and (6) estimate a simple mean difference specification using data for all firms in the rating system. The dependent variable in the first three columns is the (log) total value of bank lending granted to firm $$i$$ in quarter $$t$$. The dependent variable in the last three columns is the (log) value of the interest rate applied to a new loan granted to firm $$i$$ in quarter $$t$$. The indicator Performing$$_{i,t}$$ takes a value of 1 if a firm is in the performing class (i.e., $$s_{i,t} \geq 0$$ implying a Score of 6), and 0 otherwise. It is interacted with three indicator variables associated with the phases of the credit cycle. Boom$$_{t}$$ takes a value of 1 from the first quarter of 2004 to the last quarter of 2007, and 0 otherwise. Crisis$$_{t}$$ takes a value of 1 from the first quarter of 2008 to the last quarter of 2009, and 0 otherwise. Recovery$$_{t}$$ takes a value of 1 from the first quarter of 2010 to the last quarter of 2011, and 0 otherwise. Functions $$f_{t}(\cdot)$$ and $$g_{t}(\cdot)$$ correspond to flexible sixth-order polynomials estimated separately for each quarter-year. Function $$f_{t}(\cdot)$$ is estimated from $$0$$ to the left, whereas the Performing$$_{i,t}\times g_{t}(\cdot)$$ term is estimated from $$0$$ to the right. Finally, Lagged Rating controls for the past value of the rating. In Columns (2) and (5) the variable is interacted with quarter-year fixed effects. Standard errors, clustered at the firm level, are reported in brackets. One star denotes significance at the 10% level, two stars denote significance at the 5% level, and three stars denote significance at the 1% level. The estimates related to the period between 2004 and 2007 in Columns (1) and (4) suggest that segmentation mainly results in a positive interest rate spread between substandard and performing firms at the threshold. Firms in the substandard class are charged up to 4%,17 or 20 basis points, higher interest rates on new bank loans than similar firms in the performing class. The difference in the total amount of lending granted to these firms, instead, is positive (11%) but not statistically significant. The size of this coefficient reflects the large within-period dynamics occurring in the boom period, as we discuss later. Through 2008 and 2009, the financial crisis that hit the Italian banking sector led to an exacerbation of the consequences of segmentation for lending policies. Importantly, we find that tighter lending standards essentially translate into differences in the quantity of lending for the firms at the threshold. Indeed, marginally performing firms obtain 39% more bank financing than similar firms across the threshold. Instead, interest rate differences remain stable and close to zero (in economic and statistical terms). These results are consistent with the prediction of our theoretical framework. A rise in the interest rates paid by banks to outside investors, together with the increase in the opportunity cost of lending to substandard firms, translate into an equilibrium in which banks monitor the performing firms and (partly) exclude substandard firms from lending. Between 2010 and 2011, our estimates are in line with an incomplete recovery of bank lending. During this period, segmentation means a reduction in the differences in the quantity of credit from 39% to 22%. However, this reduction is accompanied by an increase in the interest rate spread to approximately 8%, or 40 basis points. To better understand the dynamics of credit within each phase, we report, in Figure 4, the quarterly estimates obtained with the specification in Equation (4). Figure 4. View largeDownload slide Discontinuity estimates for quantity and price The figure plots the estimates and 90% confidence intervals of the threshold specification in Equation (4), run on each distinct quarter in our sample period (2004.Q1–2011.Q4). The dependent variable in the top panel is the (log) total value of bank lending granted to firm $$i$$ in quarter $$t$$ (top panel). The dependent variable in the bottom panel is the (log) value of the interest rate applied to a new loan granted to firm $$i$$ in quarter $$t$$ (bottom panel). The plotted discontinuity estimates refer to Performing$$_{i,t}$$, an indicator variable that takes a value of 1 if a firm is in the performing class (i.e., $$s_{i,t} \geq 0$$ implying a Score of 6), and 0 otherwise. Figure 4. View largeDownload slide Discontinuity estimates for quantity and price The figure plots the estimates and 90% confidence intervals of the threshold specification in Equation (4), run on each distinct quarter in our sample period (2004.Q1–2011.Q4). The dependent variable in the top panel is the (log) total value of bank lending granted to firm $$i$$ in quarter $$t$$ (top panel). The dependent variable in the bottom panel is the (log) value of the interest rate applied to a new loan granted to firm $$i$$ in quarter $$t$$ (bottom panel). The plotted discontinuity estimates refer to Performing$$_{i,t}$$, an indicator variable that takes a value of 1 if a firm is in the performing class (i.e., $$s_{i,t} \geq 0$$ implying a Score of 6), and 0 otherwise. The top panel of the figure shows that early in the boom period (2004 and 2005), differences in the total amount of lending to firms at the threshold are positive and large, but progressively vanish in 2006 and 2007. These patterns then are likely to explain the economically large differences in total lending for the boom period in Column (1) of Table 3. Similarly to total lending, the interest rate spreads between firms at the threshold (bottom panel) narrow throughout the boom phase and disappear at the peak of the cycle. Differences in credit allocations are relatively stable within the crisis period, but vary again during the recovery period. Specifically, the estimates for 2010 and 2011 imply a gradual decrease in the differences in the amount of bank lending. To quantify the impact of segmentation on bank lending, we contrast the results obtained with our threshold analysis to those arising from a naïve specification that compares lending conditions to all performing and substandard firms (Columns (3) and (6)). First, segmentation is relatively more important to explain the naïve specification’s differences in total lending in the bust than in the boom period. In the boom period, the naïve estimates imply a 42% differential in the amount of bank credit. During that period, the threshold differences amount to only 12%, suggesting that segmentation alone cannot explain the large estimate in the naïve specification. In 2008–2009, the overall differential between the quantity of lending across rating classes remains stable, while Column (1) indicates a 39% differential for the firms at the threshold. In the bust period, then, segmentation can account for a larger part of the observed differential in the amount of credit than in the boom period. Second, the analysis of the interest rate spreads arising from a naïve comparison would lead to misleading conclusions, not only quantitatively but also qualitatively. Indeed, the results of the naïve regression suggest that the interest rate differences are persistently large in economic terms, and increasing throughout the cycle. Instead, we show that, within our discontinuity design, the interest rate spread narrows over the boom phase, and disappears during the crisis. This reflects the fact that, in the bust period, bank lending standards’ adjustments are implemented primarily by changing the quantity of credit. 5.2 Implications for firms’ real activity Table 4 reports the results of our baseline regression in Equation (3) using as dependent variables the log of firms’ sales and expenditures in investment, employment, and intermediates. The balance sheet reports contain only partial information about employment choices; thus, to fill this data gap, we obtain employment figures from firms’ mandatory contributions to the Italian pension system, and merge this information based on the firms’ fiscal identifier. Table 4 Real effects Production Investment Intermediates Employment Dependent variable (1) (2) (3) (4) (5) (6) (7) (8) Boom $$\times$$ Performing 0.16* 0.16* 0.14 0.14 0.11 0.11 0.14* 0.14* (0.09) (0.09) (0.15) (0.15) (0.09) (0.09) (0.08) (0.08) Crisis $$\times$$ Performing 0.44*** 0.44*** 0.55** 0.55** 0.43*** 0.43*** 0.39*** 0.38*** (0.12) (0.12) (0.23) (0.23) (0.13) (0.13) (0.16) (0.11) Recovery $$\times$$ Performing 0.31** 0.31** 0.12 0.12 0.28* 0.28* 0.28** 0.28** (0.14) (0.14) (0.23) (0.23) (0.15) (0.15) (0.15) (0.14) Lagged Rating –0.01*** –0.00* –0.01*** –0.01*** (0.00) (0.00) (0.00) (0.00) Polynomial Yes Yes Yes Yes Yes Yes Yes Yes Quarter $$\times$$ Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes $$R^2$$ 0.09 0.09 0.07 0.07 0.09 0.09 0.02 0.02 N 41,157 41,157 33,889 33,889 40,585 40,585 39,041 39,041 Production Investment Intermediates Employment Dependent variable (1) (2) (3) (4) (5) (6) (7) (8) Boom $$\times$$ Performing 0.16* 0.16* 0.14 0.14 0.11 0.11 0.14* 0.14* (0.09) (0.09) (0.15) (0.15) (0.09) (0.09) (0.08) (0.08) Crisis $$\times$$ Performing 0.44*** 0.44*** 0.55** 0.55** 0.43*** 0.43*** 0.39*** 0.38*** (0.12) (0.12) (0.23) (0.23) (0.13) (0.13) (0.16) (0.11) Recovery $$\times$$ Performing 0.31** 0.31** 0.12 0.12 0.28* 0.28* 0.28** 0.28** (0.14) (0.14) (0.23) (0.23) (0.15) (0.15) (0.15) (0.14) Lagged Rating –0.01*** –0.00* –0.01*** –0.01*** (0.00) (0.00) (0.00) (0.00) Polynomial Yes Yes Yes Yes Yes Yes Yes Yes Quarter $$\times$$ Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes $$R^2$$ 0.09 0.09 0.07 0.07 0.09 0.09 0.02 0.02 N 41,157 41,157 33,889 33,889 40,585 40,585 39,041 39,041 The table reports the estimates of the threshold specification in Model (3) using as dependent variables the (log) sales, investment, employment, and intermediates of firm $$i$$ in year $$t$$. The indicator Performing$$_{i,t}$$ takes a value of 1 if a firm is in the performing class (i.e., $$s_{i,t} \geq 0$$ implying a Score of 6), and 0 otherwise. It is interacted with three indicator variables associated with the phases of the credit cycle. Boom$$_{t}$$ takes a value of 1 from the first quarter of 2004 to the last quarter of 2007, and 0 otherwise. Crisis$$_{t}$$ takes a value of 1 from the first quarter of 2008 to the last quarter of 2009, and 0 otherwise. Recovery$$_{t}$$ takes a value of 1 from the first quarter of 2010 to the last quarter of 2011, and 0 otherwise. Functions $$f_{t}(\cdot)$$ and $$g_{t}(\cdot)$$ correspond to flexible sixth-order polynomials estimated separately for each year. Function $$f_{t}(\cdot)$$ is estimated from $$0$$ to the left, whereas the Performing$$_{i,t}\times g_{t}(\cdot)$$ term is estimated from $$0$$ to the right. Finally, Lagged Rating controls for the past value of the rating. In Columns (2), (4), (6), and (8) the variable is interacted with quarter-year fixed effects. Standard errors, clustered at the firm level, are reported in brackets. One star denotes significance at the 10% level, two stars denote significance at the 5% level, and three stars denote significance at the 1% level. Table 4 Real effects Production Investment Intermediates Employment Dependent variable (1) (2) (3) (4) (5) (6) (7) (8) Boom $$\times$$ Performing 0.16* 0.16* 0.14 0.14 0.11 0.11 0.14* 0.14* (0.09) (0.09) (0.15) (0.15) (0.09) (0.09) (0.08) (0.08) Crisis $$\times$$ Performing 0.44*** 0.44*** 0.55** 0.55** 0.43*** 0.43*** 0.39*** 0.38*** (0.12) (0.12) (0.23) (0.23) (0.13) (0.13) (0.16) (0.11) Recovery $$\times$$ Performing 0.31** 0.31** 0.12 0.12 0.28* 0.28* 0.28** 0.28** (0.14) (0.14) (0.23) (0.23) (0.15) (0.15) (0.15) (0.14) Lagged Rating –0.01*** –0.00* –0.01*** –0.01*** (0.00) (0.00) (0.00) (0.00) Polynomial Yes Yes Yes Yes Yes Yes Yes Yes Quarter $$\times$$ Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes $$R^2$$ 0.09 0.09 0.07 0.07 0.09 0.09 0.02 0.02 N 41,157 41,157 33,889 33,889 40,585 40,585 39,041 39,041 Production Investment Intermediates Employment Dependent variable (1) (2) (3) (4) (5) (6) (7) (8) Boom $$\times$$ Performing 0.16* 0.16* 0.14 0.14 0.11 0.11 0.14* 0.14* (0.09) (0.09) (0.15) (0.15) (0.09) (0.09) (0.08) (0.08) Crisis $$\times$$ Performing 0.44*** 0.44*** 0.55** 0.55** 0.43*** 0.43*** 0.39*** 0.38*** (0.12) (0.12) (0.23) (0.23) (0.13) (0.13) (0.16) (0.11) Recovery $$\times$$ Performing 0.31** 0.31** 0.12 0.12 0.28* 0.28* 0.28** 0.28** (0.14) (0.14) (0.23) (0.23) (0.15) (0.15) (0.15) (0.14) Lagged Rating –0.01*** –0.00* –0.01*** –0.01*** (0.00) (0.00) (0.00) (0.00) Polynomial Yes Yes Yes Yes Yes Yes Yes Yes Quarter $$\times$$ Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes $$R^2$$ 0.09 0.09 0.07 0.07 0.09 0.09 0.02 0.02 N 41,157 41,157 33,889 33,889 40,585 40,585 39,041 39,041 The table reports the estimates of the threshold specification in Model (3) using as dependent variables the (log) sales, investment, employment, and intermediates of firm $$i$$ in year $$t$$. The indicator Performing$$_{i,t}$$ takes a value of 1 if a firm is in the performing class (i.e., $$s_{i,t} \geq 0$$ implying a Score of 6), and 0 otherwise. It is interacted with three indicator variables associated with the phases of the credit cycle. Boom$$_{t}$$ takes a value of 1 from the first quarter of 2004 to the last quarter of 2007, and 0 otherwise. Crisis$$_{t}$$ takes a value of 1 from the first quarter of 2008 to the last quarter of 2009, and 0 otherwise. Recovery$$_{t}$$ takes a value of 1 from the first quarter of 2010 to the last quarter of 2011, and 0 otherwise. Functions $$f_{t}(\cdot)$$ and $$g_{t}(\cdot)$$ correspond to flexible sixth-order polynomials estimated separately for each year. Function $$f_{t}(\cdot)$$ is estimated from $$0$$ to the left, whereas the Performing$$_{i,t}\times g_{t}(\cdot)$$ term is estimated from $$0$$ to the right. Finally, Lagged Rating controls for the past value of the rating. In Columns (2), (4), (6), and (8) the variable is interacted with quarter-year fixed effects. Standard errors, clustered at the firm level, are reported in brackets. One star denotes significance at the 10% level, two stars denote significance at the 5% level, and three stars denote significance at the 1% level. Columns (1) and (2) yield three main findings. First, between 2004 and 2007, marginally performing firms on average produce 17% more than marginally substandard firms. A yearly decomposition of these estimates, which is reported in the Online Appendix, suggests that, consistent with the economically large differences in credit allocations arising in 2004 and 2005 (Figure 4), production differences are mainly concentrated in the early years of the boom and vanish in 2007. Our second finding is that production choices of firms at the threshold diverge significantly during the period in which access to credit is limited for the marginally substandard firms: in 2008 and 2009, the marginally performing firms report about 50% larger values of production than the marginally substandard ones. Finally, consistent with the partial recovery of lending taking place between 2010 and 2011, we find that, in this period, production differences gradually decrease but remain larger than the pre-crisis ones. To further the analysis of the implications of shifts in lending standards for firm real activity, we report the differences in input choices made by the firms at the threshold. We estimate our discontinuity design using as dependent variables the value of firms’ investment in capital, expenditures in intermediates, and employment. The main finding is that the divergence in production outcomes during the crisis is driven mainly by investment choices. During the most acute phase of the financial crisis, on average, performing firms invest about 70% more than substandard firms. In recovery, instead, lower values of production are essentially driven by reduced expenditures in intermediate and labor inputs. 6. The Economic Mechanism In this section, we investigate the economic mechanism driving the transmission of segmentation onto bank lending standards. 6.1 Bank heterogeneity Table 5 investigates the possible channels through which bank heterogeneity can explain how segmentation affects credit supply. In Columns (1) and (2), we jointly test for the relative importance of bank capitalization and investor composition in determining the sensitivity of bank lending to segmentation. Recall that, to proxy bank capitalization, we measure banks’ tier 1 capital ratio. Instead, as a measure of investor composition, we take the banks’ dependence on fundings from repo markets. Both measures are taken as a pre-2008 average at the bank level. Table 5 Bank heterogeneity Quantity Price Dependent variable (1) (2) (3) (4) Boom $$\times$$ Performing 0.08 –0.05* (0.07) (0.03) Crisis $$\times$$ Performing 0.26** –0.02 (0.09) (0.04) Recovery $$\times$$ Performing 0.08 –0.12* (0.11) (0.07) Boom $$\times$$ Performing $$\times$$ Tier1 0.16 –0.07 0.09 0.11 (0.29) (0.23) (0.16) (0.14) Crisis $$\times$$ Performing $$\times$$ Tier1 –0.90** –0.83*** –0.00 0.29 (0.43) (0.33) (0.27) (0.27) Recovery$$\times$$Performing$$\times$$Tier1 –0.71 –0.96** 0.15 0.33 (0.55) (0.41) (.39) (.41) Boom $$\times$$ Performing $$\times$$ Repo 0.02 –0.02 –0.21** 0.06 (0.15) (0.11) (0.09) (0.08) Crisis $$\times$$ Performing $$\times$$ Repo 0.39* 0.31* 0.25* 0.16 (0.23) (0.19) (0.13) (0.16) Recovery $$\times$$ Performing $$\times$$ Repo 0.58* 0.36 –0.03 0.04 (0.35) (0.28) (0.25) (0.30) Polynomial Yes Yes No Yes Quarter $$\times$$ Year Fixed Effects Yes Yes Yes Yes Firm $$\times$$ Year Fixed Effects No Yes No Yes $$R^2$$ 0.02 0.55 0.37 0.77 N 787,634 787,634 89,140 89,140 Quantity Price Dependent variable (1) (2) (3) (4) Boom $$\times$$ Performing 0.08 –0.05* (0.07) (0.03) Crisis $$\times$$ Performing 0.26** –0.02 (0.09) (0.04) Recovery $$\times$$ Performing 0.08 –0.12* (0.11) (0.07) Boom $$\times$$ Performing $$\times$$ Tier1 0.16 –0.07 0.09 0.11 (0.29) (0.23) (0.16) (0.14) Crisis $$\times$$ Performing $$\times$$ Tier1 –0.90** –0.83*** –0.00 0.29 (0.43) (0.33) (0.27) (0.27) Recovery$$\times$$Performing$$\times$$Tier1 –0.71 –0.96** 0.15 0.33 (0.55) (0.41) (.39) (.41) Boom $$\times$$ Performing $$\times$$ Repo 0.02 –0.02 –0.21** 0.06 (0.15) (0.11) (0.09) (0.08) Crisis $$\times$$ Performing $$\times$$ Repo 0.39* 0.31* 0.25* 0.16 (0.23) (0.19) (0.13) (0.16) Recovery $$\times$$ Performing $$\times$$ Repo 0.58* 0.36 –0.03 0.04 (0.35) (0.28) (0.25) (0.30) Polynomial Yes Yes No Yes Quarter $$\times$$ Year Fixed Effects Yes Yes Yes Yes Firm $$\times$$ Year Fixed Effects No Yes No Yes $$R^2$$ 0.02 0.55 0.37 0.77 N 787,634 787,634 89,140 89,140 The table reports OLS estimates of the threshold specification in Equation (5). The dependent variable in the first two columns is the (log) total value of bank lending granted by bank $$b$$ to firm $$i$$ in quarter $$t$$. The dependent variable in the last two columns is the (log) value of the interest rate applied to a new loan granted by bank $$b$$ to firm $$i$$ in quarter $$t$$. The indicator Performing$$_{i,t}$$ takes a value of 1 if a firm is in the performing class (i.e., $$s_{i,t} \geq 0$$ implying a Score of 6), and 0 otherwise. It is interacted with three indicator variables associated with the phases of the credit cycle. Boom$$_{t}$$ takes a value of 1 from the first quarter of 2004 to the last quarter of 2007, and 0 otherwise. Crisis$$_{t}$$ takes a value of 1 from the first quarter of 2008 to the last quarter of 2009, and 0 otherwise. Recovery$$_{t}$$ takes a value of 1 from the first quarter of 2010 to the last quarter of 2011, and 0 otherwise. Tier1$$_b$$ is defined as a bank $$b$$’s core equity capital divided by its total assets, and Repo$$_{b}$$ is defined as the share of the bank’s total financing from repo markets. All of the bank specific variables are measured pre-crisis. Functions $$f_{t}(\cdot)$$ and $$g_{t}(\cdot)$$ correspond to flexible sixth-order polynomials estimated separately for each quarter-year. Function $$f_{t}(\cdot)$$ is estimated from $$0$$ to the left, whereas the Performing$$_{i,t}\times g_{t}(\cdot)$$ term is estimated from $$0$$ to the right. Finally, Lagged Rating controls for the past value of the rating and is interacted with quarter-year fixed effects. Standard errors, clustered at the firm-bank level, are reported in brackets. One star denotes significance at the 10% level, two stars denote significance at the 5% level, and three stars denote significance at the 1% level. Table 5 Bank heterogeneity Quantity Price Dependent variable (1) (2) (3) (4) Boom $$\times$$ Performing 0.08 –0.05* (0.07) (0.03) Crisis $$\times$$ Performing 0.26** –0.02 (0.09) (0.04) Recovery $$\times$$ Performing 0.08 –0.12* (0.11) (0.07) Boom $$\times$$ Performing $$\times$$ Tier1 0.16 –0.07 0.09 0.11 (0.29) (0.23) (0.16) (0.14) Crisis $$\times$$ Performing $$\times$$ Tier1 –0.90** –0.83*** –0.00 0.29 (0.43) (0.33) (0.27) (0.27) Recovery$$\times$$Performing$$\times$$Tier1 –0.71 –0.96** 0.15 0.33 (0.55) (0.41) (.39) (.41) Boom $$\times$$ Performing $$\times$$ Repo 0.02 –0.02 –0.21** 0.06 (0.15) (0.11) (0.09) (0.08) Crisis $$\times$$ Performing $$\times$$ Repo 0.39* 0.31* 0.25* 0.16 (0.23) (0.19) (0.13) (0.16) Recovery $$\times$$ Performing $$\times$$ Repo 0.58* 0.36 –0.03 0.04 (0.35) (0.28) (0.25) (0.30) Polynomial Yes Yes No Yes Quarter $$\times$$ Year Fixed Effects Yes Yes Yes Yes Firm $$\times$$ Year Fixed Effects No Yes No Yes $$R^2$$ 0.02 0.55 0.37 0.77 N 787,634 787,634 89,140 89,140 Quantity Price Dependent variable (1) (2) (3) (4) Boom $$\times$$ Performing 0.08 –0.05* (0.07) (0.03) Crisis $$\times$$ Performing 0.26** –0.02 (0.09) (0.04) Recovery $$\times$$ Performing 0.08 –0.12* (0.11) (0.07) Boom $$\times$$ Performing $$\times$$ Tier1 0.16 –0.07 0.09 0.11 (0.29) (0.23) (0.16) (0.14) Crisis $$\times$$ Performing $$\times$$ Tier1 –0.90** –0.83*** –0.00 0.29 (0.43) (0.33) (0.27) (0.27) Recovery$$\times$$Performing$$\times$$Tier1 –0.71 –0.96** 0.15 0.33 (0.55) (0.41) (.39) (.41) Boom $$\times$$ Performing $$\times$$ Repo 0.02 –0.02 –0.21** 0.06 (0.15) (0.11) (0.09) (0.08) Crisis $$\times$$ Performing $$\times$$ Repo 0.39* 0.31* 0.25* 0.16 (0.23) (0.19) (0.13) (0.16) Recovery $$\times$$ Performing $$\times$$ Repo 0.58* 0.36 –0.03 0.04 (0.35) (0.28) (0.25) (0.30) Polynomial Yes Yes No Yes Quarter $$\times$$ Year Fixed Effects Yes Yes Yes Yes Firm $$\times$$ Year Fixed Effects No Yes No Yes $$R^2$$ 0.02 0.55 0.37 0.77 N 787,634 787,634 89,140 89,140 The table reports OLS estimates of the threshold specification in Equation (5). The dependent variable in the first two columns is the (log) total value of bank lending granted by bank $$b$$ to firm $$i$$ in quarter $$t$$. The dependent variable in the last two columns is the (log) value of the interest rate applied to a new loan granted by bank $$b$$ to firm $$i$$ in quarter $$t$$. The indicator Performing$$_{i,t}$$ takes a value of 1 if a firm is in the performing class (i.e., $$s_{i,t} \geq 0$$ implying a Score of 6), and 0 otherwise. It is interacted with three indicator variables associated with the phases of the credit cycle. Boom$$_{t}$$ takes a value of 1 from the first quarter of 2004 to the last quarter of 2007, and 0 otherwise. Crisis$$_{t}$$ takes a value of 1 from the first quarter of 2008 to the last quarter of 2009, and 0 otherwise. Recovery$$_{t}$$ takes a value of 1 from the first quarter of 2010 to the last quarter of 2011, and 0 otherwise. Tier1$$_b$$ is defined as a bank $$b$$’s core equity capital divided by its total assets, and Repo$$_{b}$$ is defined as the share of the bank’s total financing from repo markets. All of the bank specific variables are measured pre-crisis. Functions $$f_{t}(\cdot)$$ and $$g_{t}(\cdot)$$ correspond to flexible sixth-order polynomials estimated separately for each quarter-year. Function $$f_{t}(\cdot)$$ is estimated from $$0$$ to the left, whereas the Performing$$_{i,t}\times g_{t}(\cdot)$$ term is estimated from $$0$$ to the right. Finally, Lagged Rating controls for the past value of the rating and is interacted with quarter-year fixed effects. Standard errors, clustered at the firm-bank level, are reported in brackets. One star denotes significance at the 10% level, two stars denote significance at the 5% level, and three stars denote significance at the 1% level. We begin by interpreting our results in Column (1). First, notice that the baseline differences remain qualitatively very similar to those obtained with the main specification. Second, in the pre-crisis period, bank heterogeneity does not seem to affect how banks set the amount of total lending. This is intuitive: in the boom period, banks expect favorable financing conditions on wholesale markets. This means that they can lend “as if” unconstrained by segmentation, and make full use of their information on the firms’ risk profile. These patterns change dramatically during the crisis. The negative sign on the interaction (Crisis$$\times$$Performing$$\times$$Tier1) indicates that highly capitalized banks are less likely to offer different amounts of credit to borrowers at the threshold. Similarly, those banks that are less dependent on short-term investors are also less likely to cut on lending as a consequence of market segmentation. Interestingly, the sensitivity of bank lending to these factors remains high even in the recovery period. Column (2) augments the discontinuity design by including firm-year fixed effects. This means that we exploit heterogeneity in the amount of lending to the same firm and in the same year from different banks. The estimates remain similar despite the increase in the number of estimated parameters. Columns (3) and (4) repeat the analysis by looking at the differences in interest rates. Our estimates suggest that bank heterogeneity is not particularly helpful to explain banks’ price differences at the threshold. For instance, there is no evidence of significant differences in the spreads set by highly and lowly capitalized banks. Moreover, although, in principle, investor composition could affect the interest rate spreads, the evidence arising from the estimated parameters in Table 5 is rather mixed and, thus, inconclusive. To analyze the quantitative importance of bank capitalization and investor composition, we relate the results in the table to the drop in capitalization and repo financing that happened between 2007 and 2009. During that period, Italian banks’ tier 1 capitalization fell by almost one percentage point. If we take the implied cumulative effect of segmentation and multiply it by the drop in capitalization, we obtain a differential tightening at the threshold of only $$0.6\%$$ (or $$\left(\exp\left\{-0.90\right\}-1\right)\times0.01$$). Instead, the share of repo financing by banks went from 10% in 2007, to approximately 2% at the end of 2009. This suggests that the investor composition channel can account for a differential quantity tightening of approximately $$3.8\%$$ (or $$\left(\exp\left\{0.39\right\}-1\right)\times0.08$$). This represents 10% of the observed threshold difference during the crisis, and indicates that investor composition is quantitatively an important channel to explain the consequences of segmentation on lending policies.18 6.2 Evidence from downgrades In Table 6, we report estimates of lending conditions to downgraded firms. Table 6 Downgrades from performing to substandard Quantity Price Production Dependent variable (1) (2) (3) (4) (5) (6) Boom$$\times$$Down 0.10*** 0.04 0.03*** –0.02 0.08*** 0.06 (0.03) (0.14) (0.00) (0.03) (0.03) (0.12) Crisis$$\times$$Down 0.03 –0.12 0.01 –0.06 –0.00 –0.14 (0.04) (0.21) (0.01) (0.06) (0.04) (0.19) Recovery$$\times$$Down –0.12*** –0.33* 0.03** 0.12** –0.01 –0.31* (0.04) (0.18) (0.01) (0.06) (0.04) (0.16) Polynomial No Yes No Yes No Yes Quarter $$\times$$ Year Fixed Effects Yes Yes Yes Yes Yes Yes $$R^2$$ 0.01 0.04 0.39 0.40 0.02 0.07 N 88,830 88,830 70,848 70,848 22,978 22,978 Quantity Price Production Dependent variable (1) (2) (3) (4) (5) (6) Boom$$\times$$Down 0.10*** 0.04 0.03*** –0.02 0.08*** 0.06 (0.03) (0.14) (0.00) (0.03) (0.03) (0.12) Crisis$$\times$$Down 0.03 –0.12 0.01 –0.06 –0.00 –0.14 (0.04) (0.21) (0.01) (0.06) (0.04) (0.19) Recovery$$\times$$Down –0.12*** –0.33* 0.03** 0.12** –0.01 –0.31* (0.04) (0.18) (0.01) (0.06) (0.04) (0.16) Polynomial No Yes No Yes No Yes Quarter $$\times$$ Year Fixed Effects Yes Yes Yes Yes Yes Yes $$R^2$$ 0.01 0.04 0.39 0.40 0.02 0.07 N 88,830 88,830 70,848 70,848 22,978 22,978 The table reports OLS estimates of the threshold specification in Equation (6) using the sample of firms downgraded from Score 6 to 7. The dependent variable in the first two columns is the (log) total value of bank lending granted to firm $$i$$ in quarter $$t$$. The dependent variable in the third and fourth columns is the (log) value of the interest rate applied to a new loan granted to firm $$i$$ in quarter $$t$$. The dependent variable in the fifth and sixth columns is the (log) value of sales of firm $$i$$ in year $$t$$. The indicator Down$$_{i,t}$$ is a binary variable equal to 1 if the firm is downgraded from category 6 to category 7 in year $$t$$, and is 0 otherwise. It is interacted with three indicator variables associated with the phases of the credit cycle. Boom$$_{t}$$ takes a value of 1 from the first quarter of 2004 to the last quarter of 2007, and 0 otherwise. Crisis$$_{t}$$ takes a value of 1 from the first quarter of 2008 to the last quarter of 2009, and 0 otherwise. Recovery$$_{t}$$ takes a value of 1 from the first quarter of 2010 to the last quarter of 2011, and 0 otherwise. Polynomial functions $$f_{t}(\cdot)$$, $$g_{t}(\cdot)$$, $$m_{t}(\cdot)$$, $$n_{t}(\cdot)$$ correspond to flexible sixth-order polynomials estimated separately for each quarter-year. The polynomials in $$f_{t}(\cdot)$$ and $$g_{t}(\cdot)$$ are a function of the change in the continuous variable between $$t-1$$ and $$t$$. The polynomials in $$m_{t}(\cdot)$$ and $$n_{t}(\cdot)$$ are a function of the continuous variable in $$t-1$$. Standard errors, clustered at the firm level, are reported in brackets. In addition, we include polynomials for the level of the continuous variable in $$t-1$$. One star denotes significance at the 10% level, two stars denote significance at the 5% level, and three stars denote significance at the 1% level. Table 6 Downgrades from performing to substandard Quantity Price Production Dependent variable (1) (2) (3) (4) (5) (6) Boom$$\times$$Down 0.10*** 0.04 0.03*** –0.02 0.08*** 0.06 (0.03) (0.14) (0.00) (0.03) (0.03) (0.12) Crisis$$\times$$Down 0.03 –0.12 0.01 –0.06 –0.00 –0.14 (0.04) (0.21) (0.01) (0.06) (0.04) (0.19) Recovery$$\times$$Down –0.12*** –0.33* 0.03** 0.12** –0.01 –0.31* (0.04) (0.18) (0.01) (0.06) (0.04) (0.16) Polynomial No Yes No Yes No Yes Quarter $$\times$$ Year Fixed Effects Yes Yes Yes Yes Yes Yes $$R^2$$ 0.01 0.04 0.39 0.40 0.02 0.07 N 88,830 88,830 70,848 70,848 22,978 22,978 Quantity Price Production Dependent variable (1) (2) (3) (4) (5) (6) Boom$$\times$$Down 0.10*** 0.04 0.03*** –0.02 0.08*** 0.06 (0.03) (0.14) (0.00) (0.03) (0.03) (0.12) Crisis$$\times$$Down 0.03 –0.12 0.01 –0.06 –0.00 –0.14 (0.04) (0.21) (0.01) (0.06) (0.04) (0.19) Recovery$$\times$$Down –0.12*** –0.33* 0.03** 0.12** –0.01 –0.31* (0.04) (0.18) (0.01) (0.06) (0.04) (0.16) Polynomial No Yes No Yes No Yes Quarter $$\times$$ Year Fixed Effects Yes Yes Yes Yes Yes Yes $$R^2$$ 0.01 0.04 0.39 0.40 0.02 0.07 N 88,830 88,830 70,848 70,848 22,978 22,978 The table reports OLS estimates of the threshold specification in Equation (6) using the sample of firms downgraded from Score 6 to 7. The dependent variable in the first two columns is the (log) total value of bank lending granted to firm $$i$$ in quarter $$t$$. The dependent variable in the third and fourth columns is the (log) value of the interest rate applied to a new loan granted to firm $$i$$ in quarter $$t$$. The dependent variable in the fifth and sixth columns is the (log) value of sales of firm $$i$$ in year $$t$$. The indicator Down$$_{i,t}$$ is a binary variable equal to 1 if the firm is downgraded from category 6 to category 7 in year $$t$$, and is 0 otherwise. It is interacted with three indicator variables associated with the phases of the credit cycle. Boom$$_{t}$$ takes a value of 1 from the first quarter of 2004 to the last quarter of 2007, and 0 otherwise. Crisis$$_{t}$$ takes a value of 1 from the first quarter of 2008 to the last quarter of 2009, and 0 otherwise. Recovery$$_{t}$$ takes a value of 1 from the first quarter of 2010 to the last quarter of 2011, and 0 otherwise. Polynomial functions $$f_{t}(\cdot)$$, $$g_{t}(\cdot)$$, $$m_{t}(\cdot)$$, $$n_{t}(\cdot)$$ correspond to flexible sixth-order polynomials estimated separately for each quarter-year. The polynomials in $$f_{t}(\cdot)$$ and $$g_{t}(\cdot)$$ are a function of the change in the continuous variable between $$t-1$$ and $$t$$. The polynomials in $$m_{t}(\cdot)$$ and $$n_{t}(\cdot)$$ are a function of the continuous variable in $$t-1$$. Standard errors, clustered at the firm level, are reported in brackets. In addition, we include polynomials for the level of the continuous variable in $$t-1$$. One star denotes significance at the 10% level, two stars denote significance at the 5% level, and three stars denote significance at the 1% level. In Columns (1), (3), and (5), we estimate a naïve version of the specification in Equation (6), without the polynomial terms in the continuous variables ($$f_t(\cdot),g_t(\cdot),m_t(\cdot),n_t(\cdot)$$). The estimate obtained with this specification relative to the boom phase (Column (1)) suggests that downgraded firms obtain 10% more bank financing than non-downgraded firms. This puzzling result is most likely caused by some unobserved heterogeneity across these groups. Indeed, if banks were to use the information on the change in the rating’s value to shape their response to downgrades, we would at most expect the absence of negative effects or the existence of small effects. In Columns (2), (4), and (6), we estimate the full specification, and thus compare firms that not only experienced a similar small change in the continuous variable, but were also close to the threshold. Consistent with our intuition earlier, we find that downgraded firms do not obtain higher volumes of credit than non-downgraded firms in the boom period. In crisis and recovery, the negative impact of a downgrade on credit allocations becomes progressively larger and statistically significant. During the recovery period, downgraded firms obtain 39% less bank financing than non-downgraded firms. The estimates in Column (4) also show that the restricted access to credit during the recovery period is accompanied by a higher cost of funds for downgraded firms. Finally, Column (6) shows that differences in the amount of production between marginally downgraded and non-downgraded firms are small and not statistically significant during the boom period. Intuitively, consistent with the credit patterns, these production differences are reversed during the subsequent phases of the cycle. 7. Empirical Tests In this section, we test the three identifying assumptions underlying our empirical setting. First, we show that firms do not seem to manipulate their ratings to self-select into more favorable categories. Second, we show that firms at the threshold are balanced in terms of their economic characteristics. Finally, we present placebo tests to provide further evidence on the relevance of the threshold between the substandard and performing classes of credit risk. Given that the Score is computed on a yearly basis, we perform these tests on the yearly cross-section of firms, unless otherwise stated. 7.1 Manipulation of the Score and self-selection Given the importance of the Score in bank credit decisions, a natural question to ask is whether firms are able to manipulate their credit rating and self-select into a better category. Manipulation of the rating is very unlikely, not only because the Score is unsolicited by firms and is computed based on firms’ past balance sheets, but also because its exact algorithm is a business secret. Nevertheless, manipulation can be detected empirically: it would result in a systematic discontinuity of firms’ distribution at the threshold, due either to the absence of observations near the threshold or to the presence of clusters of observations on the side of the threshold assigning a firm to the safer category. In Table 7, we test for the presence of a discontinuity in firm density at that threshold. Table 7 Self-selection into rating categories 6 and 7 Year 2004 2005 2006 2007 2008 2009 2010 2011 McCrary Density Estimate 0.10 0.13 0.02 0.08 0.3*** –0.00 0.08 0.17 (0.06) (0.07) (0.07) (0.06) (0.07) (0.08) (0.10) (0.10) N 5,951 5,876 6,098 6,514 5,551 5,360 4,307 4,110 Year 2004 2005 2006 2007 2008 2009 2010 2011 McCrary Density Estimate 0.10 0.13 0.02 0.08 0.3*** –0.00 0.08 0.17 (0.06) (0.07) (0.07) (0.06) (0.07) (0.08) (0.10) (0.10) N 5,951 5,876 6,098 6,514 5,551 5,360 4,307 4,110 The table reports, at a yearly level, the McCrary density estimates of the continuous variable’s distribution. For each year, we run a kernel local linear regression of the log of the density on both sides of the threshold separating substandard firms in category 7 from performing firms in category 6. One star denotes significance at the 10% level, two stars denote significance at the 5% level, and three stars denote significance at the 1% level. Table 7 Self-selection into rating categories 6 and 7 Year 2004 2005 2006 2007 2008 2009 2010 2011 McCrary Density Estimate 0.10 0.13 0.02 0.08 0.3*** –0.00 0.08 0.17 (0.06) (0.07) (0.07) (0.06) (0.07) (0.08) (0.10) (0.10) N 5,951 5,876 6,098 6,514 5,551 5,360 4,307 4,110 Year 2004 2005 2006 2007 2008 2009 2010 2011 McCrary Density Estimate 0.10 0.13 0.02 0.08 0.3*** –0.00 0.08 0.17 (0.06) (0.07) (0.07) (0.06) (0.07) (0.08) (0.10) (0.10) N 5,951 5,876 6,098 6,514 5,551 5,360 4,307 4,110 The table reports, at a yearly level, the McCrary density estimates of the continuous variable’s distribution. For each year, we run a kernel local linear regression of the log of the density on both sides of the threshold separating substandard firms in category 7 from performing firms in category 6. One star denotes significance at the 10% level, two stars denote significance at the 5% level, and three stars denote significance at the 1% level. Following McCrary (2008), for each year we run a kernel local linear regression of the log of the density on both sides of the threshold separating substandard firms in category 7 from performing firms in category 6. Table 7 shows that, with the exception of 2008, there is no evidence of significant discontinuities in the distribution of firms at the threshold. The discontinuity in 2008 is most likely coincidental for two reasons. First, if firms had discovered the exact formula of the Score and how to manipulate their assignment, a discontinuity should emerge systematically in every year following 2008. Second, had strategic manipulation occurred, it would mean that firms had anticipated by at least one year the financial crisis and the associated benefits of being classified as marginally performing entities.19 7.1.1 Policy experiment We also exploit a policy experiment to address the potential concern that the discontinuity arising in the McCrary tests for 2008 reflects firms’ strategic manipulation of the Score. In November 2008, Law 185 (decreto legislativo n. 185) granted firms the possibility to revaluate fixed assets. Crucially, differently from previous laws with the same goal, Law 185 does not require the firm to pay taxes on the higher values of the assets in its balance sheet. We exploit this policy experiment in the following way: we run our main specification in Equation (3) using as dependent variable the (log) value of revalued assets. If the Score was manipulated, then we should observe that those firms that marginally fall in the performing class during the crisis were also those that revaluated assets disproportionally more than the marginally substandard firms. Table 8 shows that there is no significant difference in the outcome variable across the three phases of the credit cycle. This evidence further confirms that manipulation of the assignment variable is highly unlikely. Table 8 Manipulation: Revaluations Log revaluations Dependent variable (1) (2) Boom $$\times$$ Performing –0.04 –0.05 (.05) (0.06) Crisis $$\times$$ Performing –0.01 –0.03 (0.06) (0.07) Recovery $$\times$$ Performing 0.00 –0.02 (0.05) (0.06) Polynomial Yes Yes Quarter $$\times$$ Year Fixed Effects Yes Yes Control Variables No Yes $$R^2$$ 0.12 0.21 N 77,079 57,243 Log revaluations Dependent variable (1) (2) Boom $$\times$$ Performing –0.04 –0.05 (.05) (0.06) Crisis $$\times$$ Performing –0.01 –0.03 (0.06) (0.07) Recovery $$\times$$ Performing 0.00 –0.02 (0.05) (0.06) Polynomial Yes Yes Quarter $$\times$$ Year Fixed Effects Yes Yes Control Variables No Yes $$R^2$$ 0.12 0.21 N 77,079 57,243 The table reports OLS estimates of the threshold specification in Model 3. The dependent variable is the (log) value of revalued assets of firm $$i$$ in year $$t$$. The indicator Performing$$_{i,t}$$ takes a value of 1 if a firm is in the performing class (i.e., $$s_{i,t} \geq 0$$ implying a Score of 6), and 0 otherwise. It is interacted with three indicator variables associated with the phases of the credit cycle. Boom$$_{t}$$ takes a value of 1 from the first quarter of 2004 to the last quarter of 2007, and 0 otherwise. Crisis$$_{t}$$ takes a value of 1 from the first quarter of 2008 to the last quarter of 2009, and 0 otherwise. Recovery$$_{t}$$ takes a value of 1 from the first quarter of 2010 to the last quarter of 2011, and 0 otherwise. Functions $$f_{t}(\cdot)$$ and $$g_{t}(\cdot)$$ correspond to flexible sixth-order polynomials estimated separately for each quarter-year. Function $$f_{t}(\cdot)$$ is estimated from $$0$$ to the left, whereas the Performing$$_{i,t}\times g_{t}(\cdot)$$ term is estimated from $$0$$ to the right. Standard errors, clustered at the firm level, are reported in brackets. One star denotes significance at the 10% level, two stars denote significance at the 5% level, and three stars denote significance at the 1% level. Table 8 Manipulation: Revaluations Log revaluations Dependent variable (1) (2) Boom $$\times$$ Performing –0.04 –0.05 (.05) (0.06) Crisis $$\times$$ Performing –0.01 –0.03 (0.06) (0.07) Recovery $$\times$$ Performing 0.00 –0.02 (0.05) (0.06) Polynomial Yes Yes Quarter $$\times$$ Year Fixed Effects Yes Yes Control Variables No Yes $$R^2$$ 0.12 0.21 N 77,079 57,243 Log revaluations Dependent variable (1) (2) Boom $$\times$$ Performing –0.04 –0.05 (.05) (0.06) Crisis $$\times$$ Performing –0.01 –0.03 (0.06) (0.07) Recovery $$\times$$ Performing 0.00 –0.02 (0.05) (0.06) Polynomial Yes Yes Quarter $$\times$$ Year Fixed Effects Yes Yes Control Variables No Yes $$R^2$$ 0.12 0.21 N 77,079 57,243 The table reports OLS estimates of the threshold specification in Model 3. The dependent variable is the (log) value of revalued assets of firm $$i$$ in year $$t$$. The indicator Performing$$_{i,t}$$ takes a value of 1 if a firm is in the performing class (i.e., $$s_{i,t} \geq 0$$ implying a Score of 6), and 0 otherwise. It is interacted with three indicator variables associated with the phases of the credit cycle. Boom$$_{t}$$ takes a value of 1 from the first quarter of 2004 to the last quarter of 2007, and 0 otherwise. Crisis$$_{t}$$ takes a value of 1 from the first quarter of 2008 to the last quarter of 2009, and 0 otherwise. Recovery$$_{t}$$ takes a value of 1 from the first quarter of 2010 to the last quarter of 2011, and 0 otherwise. Functions $$f_{t}(\cdot)$$ and $$g_{t}(\cdot)$$ correspond to flexible sixth-order polynomials estimated separately for each quarter-year. Function $$f_{t}(\cdot)$$ is estimated from $$0$$ to the left, whereas the Performing$$_{i,t}\times g_{t}(\cdot)$$ term is estimated from $$0$$ to the right. Standard errors, clustered at the firm level, are reported in brackets. One star denotes significance at the 10% level, two stars denote significance at the 5% level, and three stars denote significance at the 1% level. 7.2 Balancing tests In Table 9, we analyze whether firms close to the threshold are as if randomly sampled, a critical identification assumption within regression discontinuity models. If firms are nonrandomly sorted into specific rating classes, we would expect firm characteristics to differ systematically across the threshold. Following the regression discontinuity literature, the firm characteristics we test are those logically unaffected by the threshold but plausibly related to firm financing. Table 9 Model diagnostics: Balancing checks Year 2004 2005 2006 2007 2008 2009 2010 2011 Panel A: Presample characteristics Leverage 0.00 0.01 –0.04 –0.03 0.05 –0.01 –0.04 0.01 (.03) (.04) (.03) (.03) (.04) (.04) (.05) (.06) N 3,967 3,636 3,595 3,678 2,888 2,705 2,168 2,024 Return to Assets 0.00 0.00 0.00 –0.01 –0.02 0.00 0.00 0.00 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.02) (0.02) N 5,306 4,844 4,750 4,836 3,776 3,504 2,721 2,508 Investment to Assets 0.02 0.02 0.01 0.02 0.02 –0.02 –0.03 –0.02 (0.01) (0.02) (0.01) (0.02) (0.02) (0.02) (0.03) (0.02) N 4,501 4,136 4,083 4,174 3,353 3,100 2,414 2,237 Panel B: Bank balancing characteristics Credit Event 0.01 0.00 0.01 0.00 –0.01 0.00 –0.03 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.03) N 5,736 5,944 6,358 5,411 5,276 4,235 4,045 Asked 0.02 0.00 –0.02 –0.07 –0.03 0.04 0.03 –0.07 (0.04) (0.05) (0.04) (0.05) (0.04) (0.04) (0.05) (0.05) N 5,687 5,677 5,889 6,306 5,370 5,264 4,217 4,030 Bank Size –0.12 –0.05 –0.02 0.23** 0.1 0.09 0.04 0.23 (0.14) (0.14) (0.11) (0.12) (0.14) (0.17) (0.19) (0.18) N 5,652 5,641 5,855 6,287 5,356 5,108 4,105 3,937 Panel C: Time-invariant characteristics Activity: Food Industry 0.03 –0.04 0.03 –0.01 0.05 0.04 0.06 –0.06 (0.04) (0.05) (0.04) (0.04) (0.04) (0.04) (0.06) (0.06) N 5,951 5,876 6,098 6,514 5,551 5,360 4,307 4,110 Location: Top 5 Cities 0.06 0.03 0.05 –0.06 0.02 –0.01 0.07 0.05 (0.06) (0.06) (0.06) (0.06) (0.06) (0.06) (0.08) (0.07) N 5,951 5,876 6,098 6,514 5,551 5,360 4,307 4,110 Year 2004 2005 2006 2007 2008 2009 2010 2011 Panel A: Presample characteristics Leverage 0.00 0.01 –0.04 –0.03 0.05 –0.01 –0.04 0.01 (.03) (.04) (.03) (.03) (.04) (.04) (.05) (.06) N 3,967 3,636 3,595 3,678 2,888 2,705 2,168 2,024 Return to Assets 0.00 0.00 0.00 –0.01 –0.02 0.00 0.00 0.00 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.02) (0.02) N 5,306 4,844 4,750 4,836 3,776 3,504 2,721 2,508 Investment to Assets 0.02 0.02 0.01 0.02 0.02 –0.02 –0.03 –0.02 (0.01) (0.02) (0.01) (0.02) (0.02) (0.02) (0.03) (0.02) N 4,501 4,136 4,083 4,174 3,353 3,100 2,414 2,237 Panel B: Bank balancing characteristics Credit Event 0.01 0.00 0.01 0.00 –0.01 0.00 –0.03 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.03) N 5,736 5,944 6,358 5,411 5,276 4,235 4,045 Asked 0.02 0.00 –0.02 –0.07 –0.03 0.04 0.03 –0.07 (0.04) (0.05) (0.04) (0.05) (0.04) (0.04) (0.05) (0.05) N 5,687 5,677 5,889 6,306 5,370 5,264 4,217 4,030 Bank Size –0.12 –0.05 –0.02 0.23** 0.1 0.09 0.04 0.23 (0.14) (0.14) (0.11) (0.12) (0.14) (0.17) (0.19) (0.18) N 5,652 5,641 5,855 6,287 5,356 5,108 4,105 3,937 Panel C: Time-invariant characteristics Activity: Food Industry 0.03 –0.04 0.03 –0.01 0.05 0.04 0.06 –0.06 (0.04) (0.05) (0.04) (0.04) (0.04) (0.04) (0.06) (0.06) N 5,951 5,876 6,098 6,514 5,551 5,360 4,307 4,110 Location: Top 5 Cities 0.06 0.03 0.05 –0.06 0.02 –0.01 0.07 0.05 (0.06) (0.06) (0.06) (0.06) (0.06) (0.06) (0.08) (0.07) N 5,951 5,876 6,098 6,514 5,551 5,360 4,307 4,110 The table estimates differences in presample firm characteristics at the threshold. In all rows, the dependent variable is measured in 2003. The estimates refer to the indicator variable Performing$$_{i,t}$$ takes a value of 1 if a firm is in the performing class (i.e., $$s_{i,t} \geq 0$$ implying a Score of 6), and 0 otherwise. Credit Event is a binary variable equal to 1 if any of a given firm’s banks classified the firm’s credit as nonperforming. Asked is a binary variable equal to 1 if any non-current bank requested information on the firm during the year. Bank Size corresponds to the value of a bank’s total assets. Food Industry is a binary variable indicating whether the firms’ SIC code belongs to the food industry. Top 5 Cities is a binary variable indicating whether the firms’ headquarters zip code is in one of the largest five cities. See Tables 2 for the definition of the other variables. One star denotes significance at the 10% level, two stars denote significance at the 5% level, and three stars denote significance at the 1% level. Table 9 Model diagnostics: Balancing checks Year 2004 2005 2006 2007 2008 2009 2010 2011 Panel A: Presample characteristics Leverage 0.00 0.01 –0.04 –0.03 0.05 –0.01 –0.04 0.01 (.03) (.04) (.03) (.03) (.04) (.04) (.05) (.06) N 3,967 3,636 3,595 3,678 2,888 2,705 2,168 2,024 Return to Assets 0.00 0.00 0.00 –0.01 –0.02 0.00 0.00 0.00 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.02) (0.02) N 5,306 4,844 4,750 4,836 3,776 3,504 2,721 2,508 Investment to Assets 0.02 0.02 0.01 0.02 0.02 –0.02 –0.03 –0.02 (0.01) (0.02) (0.01) (0.02) (0.02) (0.02) (0.03) (0.02) N 4,501 4,136 4,083 4,174 3,353 3,100 2,414 2,237 Panel B: Bank balancing characteristics Credit Event 0.01 0.00 0.01 0.00 –0.01 0.00 –0.03 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.03) N 5,736 5,944 6,358 5,411 5,276 4,235 4,045 Asked 0.02 0.00 –0.02 –0.07 –0.03 0.04 0.03 –0.07 (0.04) (0.05) (0.04) (0.05) (0.04) (0.04) (0.05) (0.05) N 5,687 5,677 5,889 6,306 5,370 5,264 4,217 4,030 Bank Size –0.12 –0.05 –0.02 0.23** 0.1 0.09 0.04 0.23 (0.14) (0.14) (0.11) (0.12) (0.14) (0.17) (0.19) (0.18) N 5,652 5,641 5,855 6,287 5,356 5,108 4,105 3,937 Panel C: Time-invariant characteristics Activity: Food Industry 0.03 –0.04 0.03 –0.01 0.05 0.04 0.06 –0.06 (0.04) (0.05) (0.04) (0.04) (0.04) (0.04) (0.06) (0.06) N 5,951 5,876 6,098 6,514 5,551 5,360 4,307 4,110 Location: Top 5 Cities 0.06 0.03 0.05 –0.06 0.02 –0.01 0.07 0.05 (0.06) (0.06) (0.06) (0.06) (0.06) (0.06) (0.08) (0.07) N 5,951 5,876 6,098 6,514 5,551 5,360 4,307 4,110 Year 2004 2005 2006 2007 2008 2009 2010 2011 Panel A: Presample characteristics Leverage 0.00 0.01 –0.04 –0.03 0.05 –0.01 –0.04 0.01 (.03) (.04) (.03) (.03) (.04) (.04) (.05) (.06) N 3,967 3,636 3,595 3,678 2,888 2,705 2,168 2,024 Return to Assets 0.00 0.00 0.00 –0.01 –0.02 0.00 0.00 0.00 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.02) (0.02) N 5,306 4,844 4,750 4,836 3,776 3,504 2,721 2,508 Investment to Assets 0.02 0.02 0.01 0.02 0.02 –0.02 –0.03 –0.02 (0.01) (0.02) (0.01) (0.02) (0.02) (0.02) (0.03) (0.02) N 4,501 4,136 4,083 4,174 3,353 3,100 2,414 2,237 Panel B: Bank balancing characteristics Credit Event 0.01 0.00 0.01 0.00 –0.01 0.00 –0.03 (0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.03) N 5,736 5,944 6,358 5,411 5,276 4,235 4,045 Asked 0.02 0.00 –0.02 –0.07 –0.03 0.04 0.03 –0.07 (0.04) (0.05) (0.04) (0.05) (0.04) (0.04) (0.05) (0.05) N 5,687 5,677 5,889 6,306 5,370 5,264 4,217 4,030 Bank Size –0.12 –0.05 –0.02 0.23** 0.1 0.09 0.04 0.23 (0.14) (0.14) (0.11) (0.12) (0.14) (0.17) (0.19) (0.18) N 5,652 5,641 5,855 6,287 5,356 5,108 4,105 3,937 Panel C: Time-invariant characteristics Activity: Food Industry 0.03 –0.04 0.03 –0.01 0.05 0.04 0.06 –0.06 (0.04) (0.05) (0.04) (0.04) (0.04) (0.04) (0.06) (0.06) N 5,951 5,876 6,098 6,514 5,551 5,360 4,307 4,110 Location: Top 5 Cities 0.06 0.03 0.05 –0.06 0.02 –0.01 0.07 0.05 (0.06) (0.06) (0.06) (0.06) (0.06) (0.06) (0.08) (0.07) N 5,951 5,876 6,098 6,514 5,551 5,360 4,307 4,110 The table estimates differences in presample firm characteristics at the threshold. In all rows, the dependent variable is measured in 2003. The estimates refer to the indicator variable Performing$$_{i,t}$$ takes a value of 1 if a firm is in the performing class (i.e., $$s_{i,t} \geq 0$$ implying a Score of 6), and 0 otherwise. Credit Event is a binary variable equal to 1 if any of a given firm’s banks classified the firm’s credit as nonperforming. Asked is a binary variable equal to 1 if any non-current bank requested information on the firm during the year. Bank Size corresponds to the value of a bank’s total assets. Food Industry is a binary variable indicating whether the firms’ SIC code belongs to the food industry. Top 5 Cities is a binary variable indicating whether the firms’ headquarters zip code is in one of the largest five cities. See Tables 2 for the definition of the other variables. One star denotes significance at the 10% level, two stars denote significance at the 5% level, and three stars denote significance at the 1% level. In panel A of Table 9, the dependent variables are a broad set of firm financing, investment, and profitability measures taken in 2003. In the first row, we show that firms at the threshold do not differ in terms of leverage choices in the pre-sample period. Moreover, we find no significant difference in firms’ return on assets or investments. Panel B tests for differences in bank-firm relationships at the threshold. The first row in the table focuses on the banks’ probability of reporting a delinquent loan. If there were a discontinuity in the probability of a firm’s credit event at the threshold, then our results could be explained by the fact that banks correctly price this difference. However, we find no statistically or economically significant differences at the threshold. In the second row, the variable $$Asked$$ is a binary indicator equal to 1 if a bank requests information on a new loan applicant. The estimates suggest that firms at the threshold do not display a different propensity to apply for loans to new banks. The last row of the panel tests for the presence of assortative matching between banks and firms at the threshold (Paravisini et al. 2014). For each firm, we compute its bank’s average size.20 Again, we find no evidence of a systematic difference at the threshold. Panel C focuses on differences in time-invariant firm characteristics. In the first row, the dependent variable is the firm’s activity sector proxied by its SIC code. The yearly estimates indicate no statistically or economically significant evidence of firms clustering into sectors such as food industries. Next, we look at time-invariant characteristics related to firms’ geographic locations. This is a particularly interesting dimension to study within this setting because Italian geography is correlated with heterogeneity in economic development, crime rates, and political accountability (Brollo et al. 2013) and could thus be associated with opportunistic manipulation. The variable capturing location in the largest cities or the most entrepreneurial areas does not display a statistically significant discontinuity.21 7.3 Empirical relevance of the threshold We now provide further evidence on the relevance of the threshold between performing and substandard firms. First, we confirm the local interpretation of our estimates by providing nonparametric plots of the outcome variable as a function of the continuous assignment variable. Second, we implement placebo tests in which we randomly re-label the value of the threshold. Finally, we investigate whether banks use alternative ratings’ cutoffs to formulate lending standards. 7.3.1 Nonparametric plots In the left panel of Figure 5, we focus on data from the second quarter of 2009, when our results at the threshold feature quantity differences and no interest rate differences. We divide the domain of $$s$$ into mutually exclusive bins of size $$0.03$$.22 For each bin, we compute the average and the 90% confidence interval of the outcome variable, and plot these values at the bin’s midpoint. The fitted gray line shows how close the sixth-order polynomial approximates the variation in bank financing conditions at the threshold. Figure 5. View largeDownload slide Second quarter of 2009 The figure focuses on the second quarter of 2009. We divide the domain of $$s_i$$ into mutually exclusive bins with a size of $$0.03$$. For each bin, we compute the average and the 90% confidence interval of the outcome variable, and plot these values at the bin’s midpoint. The fitted gray line shows how closely the sixth-order polynomial approximates the variation in bank financing conditions at the threshold. Figure 5. View largeDownload slide Second quarter of 2009 The figure focuses on the second quarter of 2009. We divide the domain of $$s_i$$ into mutually exclusive bins with a size of $$0.03$$. For each bin, we compute the average and the 90% confidence interval of the outcome variable, and plot these values at the bin’s midpoint. The fitted gray line shows how closely the sixth-order polynomial approximates the variation in bank financing conditions at the threshold. The top panel of Figure 5 shows that a clear discontinuity arises in the total amount of bank financing close to the threshold. The magnitude of this discontinuity can be quantified by comparing the mean value of the variable of interest in the two bins next to the threshold. Immediately to the left of the threshold, the average value of (log) granted credit is approximately 14.6, whereas immediately to the right this value is 15, implying that the estimated value of $$\beta$$ captures the variation arising directly at the threshold. The bottom panel of Figure 5 repeats this exercise for the interest rates on new bank loans. It shows that when there is no discontinuity in the value of the conditional regression function at the threshold, the polynomial fit does not display any significant discontinuity. Figure 6 repeats this analysis by focusing on the second quarter of 2011, when our results at the threshold feature significant interest rate differences and no quantity differences.23 Figure 6. View largeDownload slide Second quarter of 2011 The figure focuses on the second quarter of 2011. We divide the domain of $$s_i$$ into mutually exclusive bins with a size of $$0.03$$. For each bin, we compute the average and the standard deviation of the outcome variable, and plot these values at the bin’s midpoint. The fitted gray line shows how closely the sixth-order polynomial approximates the variation in bank financing conditions at the threshold. Figure 6. View largeDownload slide Second quarter of 2011 The figure focuses on the second quarter of 2011. We divide the domain of $$s_i$$ into mutually exclusive bins with a size of $$0.03$$. For each bin, we compute the average and the standard deviation of the outcome variable, and plot these values at the bin’s midpoint. The fitted gray line shows how closely the sixth-order polynomial approximates the variation in bank financing conditions at the threshold. 7.3.2 Placebo tests Finding a significant discontinuity in lending conditions at the threshold, as shown in Figure 4, might not necessarily establish a causal relationship between the threshold and the design of financial contracts. For example, analogous results might arise when comparing financing conditions borne by firms whose Score lies further away from the true threshold. We thus implement the following falsification tests: we draw approximately 100 randomly distributed placebo thresholds along the support of Score categories 6 and 7, and rerun our specification on the cross-section of firms at the threshold in all the quarters in our sample. We plot in Figure 7 the distribution of the placebo estimates for the second quarters of 2009 and 2011. Figure 7. View largeDownload slide Placebo estimates: Second quarters of 2009 and 2011 The figure plots the empirical distribution of discontinuity estimates based on approximately 100 randomly drawn placebo thresholds. The vertical dotted line represents the estimate obtained from the true threshold. The top panel figures focus on the second quarter of 2009, while the bottom panel focuses on the second quarter of 2011. Figure 7. View largeDownload slide Placebo estimates: Second quarters of 2009 and 2011 The figure plots the empirical distribution of discontinuity estimates based on approximately 100 randomly drawn placebo thresholds. The vertical dotted line represents the estimate obtained from the true threshold. The top panel figures focus on the second quarter of 2009, while the bottom panel focuses on the second quarter of 2011. Figure 7 illustrates that the contractual differences identified by the true threshold estimates (vertical dotted line) are not due to a coincidental discontinuity. If this were the case, then we should observe similar estimates arising when considering randomly placed thresholds. In the top-left panel, we find that the 100 placebo estimates for the differences in the quantity of bank financing are approximately normally distributed around 0. Similarly, the bottom-right panel shows that in the second quarter of 2011 the interest rate differences of 20% that we find in the main analysis are well outside the normal variation arising from randomly placed thresholds.24 This evidence demonstrates the relevance of the categorical value of the Score for Italian banks’ lending decisions. If banks were not using the categorical rating when making their credit choices, then the threshold should not yield financial outcomes that are significantly and systematically different from those obtained using a randomly set threshold along the support of the continuous variable. Our evidence rejects this claim on the basis of the distribution of placebo estimates within and across the sample period. 7.3.3 Other rating thresholds Finally, as in Agarwal et al. (Forthcoming), we investigate whether banks use alternative ratings’ cutoffs to formulate lending standards. We estimate our specification on the cross-section of firms at all the other six thresholds associated with the categorical value of the rating system.25 In Table 10, the reported dummy variable is equal to 1 for firms in the better—that is, lower-value–rating category, and 0 otherwise. Table 10 Yearly discontinuity estimates: Other thresholds Year 2004 2005 2006 2007 2008 2009 2010 2011 Threshold between categories 1 and 2 Quantity –0.3 –0.15 0.07 0.17 –0.28 –0.19 –0.3 –0.32 (0.24) (0.26) (0.26) (0.31) (0.27) (0.25) (0.23) (0.21) N 2,555 2,693 2,648 2,684 2,886 2,975 2,677 2,773 Price 0.04 0.13 0.08 0.03 –0.12 –0.23 –0.04 –0.22 ( 0.11) (0.12) (0.11) (0.08) (0.08) (0.2) (0.18) (0.22) N 583 716 782 815 715 712 832 775 Threshold between categories 2 and 3 Quantity –0.12 –0.19 –0.45 –0.3 –0.25 –0.2 –0.45 –0.51 ( 0.39) (0.4) (0.39) (0.35) (0.41) (0.34) (0.36) (0.35) N 2,311 2,508 2,480 2,383 2,265 2,243 2,243 2,375 Price 0.00 0.16 –0.1 0.01 –0.02 –0.1 –0.23 0.7*** ( 0.13) (0.12) (0.11) (0.08) (0.14) (0.27) (0.22) (0.22) N 1,099 1,427 1,595 1,702 1,475 1,260 1,406 1,825 Threshold between categories 3 and 4 Quantity –0.24 –0.03 –0.14 0.29 0.11 –0.29 –0.15 0.29 ( 0.31) (0.3) (0.35) (0.29) (0.33) (0.32) (0.29) (0.3) N 6,087 6,361 6,371 6,526 6,040 5,968 5,840 6,128 Price –0.03 0.03 0.09 –0.03 –0.08 –0.01 –0.12 –0.03 ( 0.08) (0.09) (0.08) (0.04) (0.06) (0.13) (0.15) (0.12) N 7,197 9,359 10,255 10,547 9,033 8,625 11,153 13,158 Threshold between categories 4 and 5 Quantity –0.33 0.22 –0.44* –0.18 –0.2 –0.06 –0.26 –0.41* ( 0.24) (0.24) (0.24) (0.21) (0.24) (0.24) (0.24) (0.23) N 7,019 7,359 7,437 7,616 6,960 6,878 6,711 7,058 Price 0.00 –0.05 0.03 –0.01 0.00 –0.02 –0.23*** 0.07 ( 0.05) (0.06) (0.04) (0.03) (0.03) (0.1) (0.08) (0.07) N 11,072 14,972 16,561 17,056 14,662 13,505 17,687 19,743 Threshold between categories 7 and 8 Quantity –0.25 –0.28 –0.29 –0.06 –0.36 –0.63 1.44* 1.01 ( 0.48) (0.51) (0.55) (0.55) (0.63) (0.66) (0.73) (0.88) N 4,160 4,136 4,256 4,602 3,752 3,472 2,875 2,688 Price .00 –0.2 0.1 –0.22** –0.08 0.35* –0.56 –0.12 ( 0.19) (0.17) (0.11) (0.09) (0.1) (0.2) (0.56) (0.27) N 6,058 8,394 10,412 13,192 8,280 6,047 5,883 5,791 Threshold between categories 8 and 9 Quantity –0.9 0.18 0.51 –1.31 –1.26 –0.42 –0.97 –1.68 ( 1.4) (1.16) (1.12) (1.36) (1.09) (1.24) (0.95) (1.2) N 596 649 598 646 595 668 517 616 Price –1.29 –0.01 0.21 0.09 –0.02 0.07 0.4 –0.31 ( 54.98) (0.53) (0.26) (0.27) (0.13) (0.5) (0.47) (0.4) N 380 494 655 761 518 701 471 489 Year 2004 2005 2006 2007 2008 2009 2010 2011 Threshold between categories 1 and 2 Quantity –0.3 –0.15 0.07 0.17 –0.28 –0.19 –0.3 –0.32 (0.24) (0.26) (0.26) (0.31) (0.27) (0.25) (0.23) (0.21) N 2,555 2,693 2,648 2,684 2,886 2,975 2,677 2,773 Price 0.04 0.13 0.08 0.03 –0.12 –0.23 –0.04 –0.22 ( 0.11) (0.12) (0.11) (0.08) (0.08) (0.2) (0.18) (0.22) N 583 716 782 815 715 712 832 775 Threshold between categories 2 and 3 Quantity –0.12 –0.19 –0.45 –0.3 –0.25 –0.2 –0.45 –0.51 ( 0.39) (0.4) (0.39) (0.35) (0.41) (0.34) (0.36) (0.35) N 2,311 2,508 2,480 2,383 2,265 2,243 2,243 2,375 Price 0.00 0.16 –0.1 0.01 –0.02 –0.1 –0.23 0.7*** ( 0.13) (0.12) (0.11) (0.08) (0.14) (0.27) (0.22) (0.22) N 1,099 1,427 1,595 1,702 1,475 1,260 1,406 1,825 Threshold between categories 3 and 4 Quantity –0.24 –0.03 –0.14 0.29 0.11 –0.29 –0.15 0.29 ( 0.31) (0.3) (0.35) (0.29) (0.33) (0.32) (0.29) (0.3) N 6,087 6,361 6,371 6,526 6,040 5,968 5,840 6,128 Price –0.03 0.03 0.09 –0.03 –0.08 –0.01 –0.12 –0.03 ( 0.08) (0.09) (0.08) (0.04) (0.06) (0.13) (0.15) (0.12) N 7,197 9,359 10,255 10,547 9,033 8,625 11,153 13,158 Threshold between categories 4 and 5 Quantity –0.33 0.22 –0.44* –0.18 –0.2 –0.06 –0.26 –0.41* ( 0.24) (0.24) (0.24) (0.21) (0.24) (0.24) (0.24) (0.23) N 7,019 7,359 7,437 7,616 6,960 6,878 6,711 7,058 Price 0.00 –0.05 0.03 –0.01 0.00 –0.02 –0.23*** 0.07 ( 0.05) (0.06) (0.04) (0.03) (0.03) (0.1) (0.08) (0.07) N 11,072 14,972 16,561 17,056 14,662 13,505 17,687 19,743 Threshold between categories 7 and 8 Quantity –0.25 –0.28 –0.29 –0.06 –0.36 –0.63 1.44* 1.01 ( 0.48) (0.51) (0.55) (0.55) (0.63) (0.66) (0.73) (0.88) N 4,160 4,136 4,256 4,602 3,752 3,472 2,875 2,688 Price .00 –0.2 0.1 –0.22** –0.08 0.35* –0.56 –0.12 ( 0.19) (0.17) (0.11) (0.09) (0.1) (0.2) (0.56) (0.27) N 6,058 8,394 10,412 13,192 8,280 6,047 5,883 5,791 Threshold between categories 8 and 9 Quantity –0.9 0.18 0.51 –1.31 –1.26 –0.42 –0.97 –1.68 ( 1.4) (1.16) (1.12) (1.36) (1.09) (1.24) (0.95) (1.2) N 596 649 598 646 595 668 517 616 Price –1.29 –0.01 0.21 0.09 –0.02 0.07 0.4 –0.31 ( 54.98) (0.53) (0.26) (0.27) (0.13) (0.5) (0.47) (0.4) N 380 494 655 761 518 701 471 489 The table reports estimates from our baseline specification at all the seven thresholds associated with the categorical value of the rating system. We report standard errors in brackets. The dependent variable is either All Bank Financing Granted or Interest Rate for each year between 2004.Q1–2011.Q4. We estimate the discontinuity $$\left( s_{i}\geq 0 \right)$$ using a flexible sixth-order polynomial on either side of each normalized threshold between each contiguous Score category, allowing for a discontinuity at 0. The reported estimates refer to $$S_{i}$$, a binary variable that takes a value of 1 if the continuous variable $$s_i \geq 0$$, that is, if the firm is allocated to the lower credit risk category as opposed to the higher credit risk category. See Table 2 for other variable definitions. One star denotes significance at the 10% level, two stars denote significance at the 5% level, and three stars denote significance at the 1% level. Table 10 Yearly discontinuity estimates: Other thresholds Year 2004 2005 2006 2007 2008 2009 2010 2011 Threshold between categories 1 and 2 Quantity –0.3 –0.15 0.07 0.17 –0.28 –0.19 –0.3 –0.32 (0.24) (0.26) (0.26) (0.31) (0.27) (0.25) (0.23) (0.21) N 2,555 2,693 2,648 2,684 2,886 2,975 2,677 2,773 Price 0.04 0.13 0.08 0.03 –0.12 –0.23 –0.04 –0.22 ( 0.11) (0.12) (0.11) (0.08) (0.08) (0.2) (0.18) (0.22) N 583 716 782 815 715 712 832 775 Threshold between categories 2 and 3 Quantity –0.12 –0.19 –0.45 –0.3 –0.25 –0.2 –0.45 –0.51 ( 0.39) (0.4) (0.39) (0.35) (0.41) (0.34) (0.36) (0.35) N 2,311 2,508 2,480 2,383 2,265 2,243 2,243 2,375 Price 0.00 0.16 –0.1 0.01 –0.02 –0.1 –0.23 0.7*** ( 0.13) (0.12) (0.11) (0.08) (0.14) (0.27) (0.22) (0.22) N 1,099 1,427 1,595 1,702 1,475 1,260 1,406 1,825 Threshold between categories 3 and 4 Quantity –0.24 –0.03 –0.14 0.29 0.11 –0.29 –0.15 0.29 ( 0.31) (0.3) (0.35) (0.29) (0.33) (0.32) (0.29) (0.3) N 6,087 6,361 6,371 6,526 6,040 5,968 5,840 6,128 Price –0.03 0.03 0.09 –0.03 –0.08 –0.01 –0.12 –0.03 ( 0.08) (0.09) (0.08) (0.04) (0.06) (0.13) (0.15) (0.12) N 7,197 9,359 10,255 10,547 9,033 8,625 11,153 13,158 Threshold between categories 4 and 5 Quantity –0.33 0.22 –0.44* –0.18 –0.2 –0.06 –0.26 –0.41* ( 0.24) (0.24) (0.24) (0.21) (0.24) (0.24) (0.24) (0.23) N 7,019 7,359 7,437 7,616 6,960 6,878 6,711 7,058 Price 0.00 –0.05 0.03 –0.01 0.00 –0.02 –0.23*** 0.07 ( 0.05) (0.06) (0.04) (0.03) (0.03) (0.1) (0.08) (0.07) N 11,072 14,972 16,561 17,056 14,662 13,505 17,687 19,743 Threshold between categories 7 and 8 Quantity –0.25 –0.28 –0.29 –0.06 –0.36 –0.63 1.44* 1.01 ( 0.48) (0.51) (0.55) (0.55) (0.63) (0.66) (0.73) (0.88) N 4,160 4,136 4,256 4,602 3,752 3,472 2,875 2,688 Price .00 –0.2 0.1 –0.22** –0.08 0.35* –0.56 –0.12 ( 0.19) (0.17) (0.11) (0.09) (0.1) (0.2) (0.56) (0.27) N 6,058 8,394 10,412 13,192 8,280 6,047 5,883 5,791 Threshold between categories 8 and 9 Quantity –0.9 0.18 0.51 –1.31 –1.26 –0.42 –0.97 –1.68 ( 1.4) (1.16) (1.12) (1.36) (1.09) (1.24) (0.95) (1.2) N 596 649 598 646 595 668 517 616 Price –1.29 –0.01 0.21 0.09 –0.02 0.07 0.4 –0.31 ( 54.98) (0.53) (0.26) (0.27) (0.13) (0.5) (0.47) (0.4) N 380 494 655 761 518 701 471 489 Year 2004 2005 2006 2007 2008 2009 2010 2011 Threshold between categories 1 and 2 Quantity –0.3 –0.15 0.07 0.17 –0.28 –0.19 –0.3 –0.32 (0.24) (0.26) (0.26) (0.31) (0.27) (0.25) (0.23) (0.21) N 2,555 2,693 2,648 2,684 2,886 2,975 2,677 2,773 Price 0.04 0.13 0.08 0.03 –0.12 –0.23 –0.04 –0.22 ( 0.11) (0.12) (0.11) (0.08) (0.08) (0.2) (0.18) (0.22) N 583 716 782 815 715 712 832 775 Threshold between categories 2 and 3 Quantity –0.12 –0.19 –0.45 –0.3 –0.25 –0.2 –0.45 –0.51 ( 0.39) (0.4) (0.39) (0.35) (0.41) (0.34) (0.36) (0.35) N 2,311 2,508 2,480 2,383 2,265 2,243 2,243 2,375 Price 0.00 0.16 –0.1 0.01 –0.02 –0.1 –0.23 0.7*** ( 0.13) (0.12) (0.11) (0.08) (0.14) (0.27) (0.22) (0.22) N 1,099 1,427 1,595 1,702 1,475 1,260 1,406 1,825 Threshold between categories 3 and 4 Quantity –0.24 –0.03 –0.14 0.29 0.11 –0.29 –0.15 0.29 ( 0.31) (0.3) (0.35) (0.29) (0.33) (0.32) (0.29) (0.3) N 6,087 6,361 6,371 6,526 6,040 5,968 5,840 6,128 Price –0.03 0.03 0.09 –0.03 –0.08 –0.01 –0.12 –0.03 ( 0.08) (0.09) (0.08) (0.04) (0.06) (0.13) (0.15) (0.12) N 7,197 9,359 10,255 10,547 9,033 8,625 11,153 13,158 Threshold between categories 4 and 5 Quantity –0.33 0.22 –0.44* –0.18 –0.2 –0.06 –0.26 –0.41* ( 0.24) (0.24) (0.24) (0.21) (0.24) (0.24) (0.24) (0.23) N 7,019 7,359 7,437 7,616 6,960 6,878 6,711 7,058 Price 0.00 –0.05 0.03 –0.01 0.00 –0.02 –0.23*** 0.07 ( 0.05) (0.06) (0.04) (0.03) (0.03) (0.1) (0.08) (0.07) N 11,072 14,972 16,561 17,056 14,662 13,505 17,687 19,743 Threshold between categories 7 and 8 Quantity –0.25 –0.28 –0.29 –0.06 –0.36 –0.63 1.44* 1.01 ( 0.48) (0.51) (0.55) (0.55) (0.63) (0.66) (0.73) (0.88) N 4,160 4,136 4,256 4,602 3,752 3,472 2,875 2,688 Price .00 –0.2 0.1 –0.22** –0.08 0.35* –0.56 –0.12 ( 0.19) (0.17) (0.11) (0.09) (0.1) (0.2) (0.56) (0.27) N 6,058 8,394 10,412 13,192 8,280 6,047 5,883 5,791 Threshold between categories 8 and 9 Quantity –0.9 0.18 0.51 –1.31 –1.26 –0.42 –0.97 –1.68 ( 1.4) (1.16) (1.12) (1.36) (1.09) (1.24) (0.95) (1.2) N 596 649 598 646 595 668 517 616 Price –1.29 –0.01 0.21 0.09 –0.02 0.07 0.4 –0.31 ( 54.98) (0.53) (0.26) (0.27) (0.13) (0.5) (0.47) (0.4) N 380 494 655 761 518 701 471 489 The table reports estimates from our baseline specification at all the seven thresholds associated with the categorical value of the rating system. We report standard errors in brackets. The dependent variable is either All Bank Financing Granted or Interest Rate for each year between 2004.Q1–2011.Q4. We estimate the discontinuity $$\left( s_{i}\geq 0 \right)$$ using a flexible sixth-order polynomial on either side of each normalized threshold between each contiguous Score category, allowing for a discontinuity at 0. The reported estimates refer to $$S_{i}$$, a binary variable that takes a value of 1 if the continuous variable $$s_i \geq 0$$, that is, if the firm is allocated to the lower credit risk category as opposed to the higher credit risk category. See Table 2 for other variable definitions. One star denotes significance at the 10% level, two stars denote significance at the 5% level, and three stars denote significance at the 1% level. Table 10 shows that most of our estimates on the other thresholds of the Score are not statistically significant. This confirms that our results capture a form of market segmentation, not a simple rating effect, as the only rating values that matter are those moving firms between the performing and substandard classes of credit. 8. Conclusions In this paper, we ask whether the effects of firm segmentation into performing and substandard rating classes can affect the lending policies of banks across the credit cycle. We take advantage of the institutional features of the Italian credit market for SME in order to obtain a quasi-random assignment of firms into these classes of credit risk. The resulting patterns of lending differences give us a new, contract-level measure for the bank lending standards. In this setting, bank lending standards are driven by market segmentation and reflect banks’ sensitivity to the markets for banks’ capital. While our analysis focuses on the single credit cycle that interested the Italian economy between 2004 and 2011, there are two considerations that support both the external validity and the interest of our results. First, the aggregate financing patterns of the Italian economy during this period were similar to those of other OECD economies. Second, the credit cycle in our data culminates with the great recession. This renders the analysis particularly interesting, as it allows us to provide implications for the qualitative and quantitative features of lending standards before and during those years, and the consequences for real allocations. Finally, we discuss the implications of our analysis for the allocative efficiency of banks’ credit policies. By construction, firms in our empirical design are ex ante identical and should, absent the threshold, receive the same credit conditions. This means that, whenever we observe differences in the credit terms at the threshold, there is an inefficiency caused by segmentation in the relative allocation of credit. We thank the editor (Robin Greenwood) and two anonymous referees for insightful comments. The paper also benefited from comments by Klaus Adam, Allen Berger, Steve Bond, Elena Carletti, Antonio Ciccone, Decio Coviello, Matteo Crosignani, Andrew Ellul, Carlo Favero, Nicola Gennaioli, Simon Gilchrist, Martin Hellwig, Victoria Ivashina, Rajkamal Iyer, Nobuhiro Kiyotaki, Augustin Landier, Rocco Macchiavello, Tommaso Nannicini, Steven Ongena, Marco Pagano, Nicola Pavanini, Nicola Persico, José-Luis Peydró, Andrea Polo, Andrea Pozzi, Manju Puri, Antoinette Schoar, Amit Seru, Enrico Sette, Andrei Shleifer, Jeremy Stein, Javier Suárez, Adi Sunderam, Michele Tertilt, David Thesmar, Franco Varetto, Egon Zakrajšek, and participants in the Banque de France (ACPR), Bank of Italy, Bank of Spain, Bocconi, CSEF, Danmarks Nationalbank, EIEF, Goethe University (Frankfurt), HEC Montreal, IFN (Stockholm), Italian Treasury Department, University of Mannheim, Max Planck Institute (Bonn), Tilburg University, Università Tor Vergata (Rome) seminars and in the NBER Summer Institute (Capital Markets and the Economy), Swiss Conference on Financial Intermediation, Annual Bank Research Conference FDIC/JFSR, European Winter Finance Summit, ESSFM, Csef-Igier Symposium on Economics and Institutions, First Young Scholars Finance Consortium (Texas A&M), Petralia Workshop and 4Nations Cup conferences for helpful comments. The views expressed are those of the authors and do not necessarily reflect those of the Bank of Italy. Emanuele Tarantino thanks the EIEF for its hospitality. Supplementary data can be found on The Review of Financial Studies web site. Footnotes 1 A possibility would be to look at the U.S. syndicated loan market, which allows us to use a long time series of data within a well-known environment. However, borrowers in this market tend to be significantly larger than a typical SME (Sufi 2007; Ivashina 2009). 2 Specifically, the definition of NPL includes bad loans, past due, and loans to insolvent firms other than substandard credits. The latter are defined as exposures to counterparties facing temporary difficulties defined on the basis of objective factors. 3 This literature finds that the flow of credit (e.g., Covas and Den Haan 2011; Jermann and Quadrini 2012; Becker and Ivashina 2014) and the value of credit spreads (Gilchrist, Yankov, and Zakrajšek 2009) are both highly procyclical. 4 Our results also inform the (growing) theoretical literature on lending standards over the cycle (e.g., Dell’Ariccia and Marquez 2006; Martin 2008; Kovbasyuk and Spagnolo 2017; Gete 2017). 5 While the formula in the original Altman’s model is publicly known, the agency uses its own version. Specifically, to our knowledge, CEBI’s version of the model uses approximately fifteen factors taken from firms’ balance sheets; however, the exact composition and weights in the formula are a business secret. That is, they are not shared with the regulator or the banks. 6 The continuous variables are difficult to interpret because their value is industry specific. Moreover, differently from the discrete value of the rating, by construction, they do not provide the bank with a direct estimate of the firm default probability (Altman 2004). 7 Descriptive statistics on firms’ distribution in the rating categories can be found in Online Appendix B (Figure B1). 8 To understand the consequences for firms of this classification in terms of S&P’s ratings, note that a Score of 6 corresponds to class B, and a Score of 7 to class CCC (Altman 2004). 9 Specifically, the definition of NPL includes bad loans, past due, and loans to insolvent firms other than substandard credits. The latter are defined as exposures to counterparties facing temporary difficulties defined on the basis of objective factors. 10 Additionally, NPL weigh in the banks’ balance sheets for two main reasons. The first is that there are very limited fiscal and accounting incentives for banks to write off and sell NPL. The second is related to the lengthy Italian bankruptcy system (Rodano, Serrano-Velarde, and Tarantino 2016), and the small number of asset management companies willing to buy these assets. 11 For example, in their banks’ rating guidelines, (Moody’s 2015, 33) reports that “[asset] risks are captured, to a considerable degree, by a single financial ratio, problem loans/gross loans (which we term the problem loan ratio),” and Fitch (2016) specifies that the “core metric” to measure asset quality is the problem loan ratio. 12 In this section, we present the model’s main insights. The full derivation can be found in Online Appendix D. While this theoretical framework relies on “ex post monitoring,” the intuition extends to models of “ex ante screening.” A previous version of the paper explored this mechanism and showed the robustness of the conclusions. In the boom period, when screening is costly and bank liquidity is aplenty, the bank pools the firms at the threshold with the other firms in the same asset class. This means that all borrowers receive lending at a return that reflects the average degree of risk in a class (thus leading to price differences at the threshold). In the bust period, the exacerbation of the adverse selection problem, combined with a shortage of the banking sector’s liquidity, implies that the bank engages in screening at equilibrium. Screening then leads to differences in the quantity of credit offered to the firms at the threshold that penalize those borrowers falling in the substandard class. 13 In the absence of segmentation, the two firms would always obtain the same contract with the bank at equilibrium. 14 We estimate alternative specifications in which we scale the supply of bank financing by assets or express interest rates in terms of basis point differences, and we obtain the same results. To simplify the analysis, we restrict $$f_{t}(\cdot)$$ and $$g_{t}(\cdot)$$ to be of the same polynomial order. However, our results are not sensitive to this choice. Finally, we also use local-linear functions to estimate differences in credit conditions at the threshold. Our results remain robust to these additional checks. 15 Clearly, one limitation of this analysis is that the reason for the downgrade might itself be correlated to the demand for credit of the firm. 16 We thank the anonymous referee for very helpful suggestions on this point. 17 To obtain the exact percentage changes, we compute $$\left[\left(\exp\left\{\hat{\beta}\right\}-1\right)\times100 \right]$$, where $$\hat\beta$$ is the per-period coefficient. 18 We also explored the sensitivity of bank lending to other sources of bank heterogeneity. For instance, consistent with the previous results, we find that the banks that were highly exposed to the interbank market significantly cut lending to the substandard firms at the threshold in 2008 and 2009. Similarly, during that period, intermediaries putting more weight on soft information when setting credit policies were less likely to cut their lending to substandard borrowers. One needs to be careful when interpreting this last result, as bank organizational structure is likely to be correlated with differences in size and investor composition. 19 Figure C1 in Online Appendix C provides the year-by-year plots associated with these tests. We also plot the distribution of firms that enter rating categories 6 or 7 in any given year. If firms were able to determine the value of their own continuous variable, then we should observe a disproportionate number of new firms clustering just above the threshold, in category 6. Confirming the lack of manipulation, Figure C2 of Online Appendix C shows that a significant mass of firms enters the sample with a value of the continuous variable that lies just below the threshold, in category 7. Finally, we also jointly test for manipulation across the entire cycle and find no evidence of bunching. 20 This evidence is important since small banks are typically seen as more efficient in generating private information about borrowers. Thus, one possibility would be that differences in lending are due to borrowers self-selecting into different bank relations. 21 Table C3 of Online Appendix C shows the results of additional balancing tests. 22 Our results remain the same when plotting bins of different size, like $$0.02$$ or $$0.01$$. 23 Note that, around the threshold, the relationship between credit outcomes and the continuous value of the rating is not necessarily monotonic. Two comments are in order here. First, deriving the identification of the estimates from the units closest to the threshold is precisely the focus of the applied literature on discontinuity designs. Second, on average, the relationship between the value of the rating and the interest rates of the loans is monotonic. To address potential concerns on the sensitivity of our results with respect to bandwidth choices, we reestimate our specification using lower polynomial orders, and local linear methods. Our results are robust to these changes, and can be found in Table C5 of Online Appendix C. 24 In Online Appendix C, Table C4 reports the descriptive statistics about the mean, median, and statistical significance of these placebo tests across all quarters. The estimated values are about zero and are not significant in most of the quarters. Finally, Figure C3 illustrates that a randomly drawn placebo threshold is also unlikely to yield an economically sensible pattern of estimates across time. 25 Due to the construction of the CEBI rating, the threshold between categories 5 and 6 cannot be used (see Section 1). References Agarwal, S., Chomsisengphet, S. Mahoney, N. and Stroebel. J. Forthcoming. Do banks pass through credit expansions? The marginal profitability of consumer lending during the Great Recession. Quarterly Journal of Economics . Altman, E. I. 1968 . Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance 23 ( 4 ): 589 – 609 . Google Scholar CrossRef Search ADS Altman, E. I. Managing credit risk: the challenge for the new millennium. Presentation updated through 2004, New York University Stern School of Business, http://people.stern.nyu.edu/ealtman/2-%20CopManagingCreditRisk.pdf. CrossRef Search ADS Ashcraft, A. B. 2005 . Are banks really special? New evidence from the FDIC-induced failure of healthy banks. American Economic Review 95 ( 5 ): 1712 – 30 . Google Scholar CrossRef Search ADS Bank of Italy . 2013 . The recent asset quality review on non-performing loans conducted by the Bank of Italy: Main features and results. July 29 . https://www.bancaditalia.it/media/approfondimenti/2013/analisi-prestiti-deteriorati/Asset_quality_review.pdf?language_id=1. Barisitz, S. 2013 . Nonperforming loans in Western Europe: A selective comparison of countries and national definitions. Focus on European Economic Integration, Oesterreichische Nationalbank ( Austrian Central Bank ), no. 1 , pp. 28 – 47 . Becker, B., and Ivashina V. 2014 . Cyclicality of credit supply: Firm level evidence. Journal of Monetary Economics 62 ( C ): 76 – 93 . Google Scholar CrossRef Search ADS Bholat, D., Lastra, R. Markose, S. Miglionico, A. and Sen. K. 2016 . Non-performing loans: Regulatory and accounting treatments of assets. Bank of England Working Papers, no. 594 . Brollo, F., Nannicini, T. Perotti, R. and Tabellini. G. 2013 . The political resource curse. American Economic Review 103 ( 5 ): 1759 – 96 . Google Scholar CrossRef Search ADS Calonico, S., Cattaneo, M. D. and Titiunik. R. 2014 . Robust nonparametric confidence intervals for regression discontinuity designs. Econometrica 82 ( 6 ): 2295 – 2326 . Google Scholar CrossRef Search ADS Chernenko, S., and Sunderam. A. 2012 . The real consequences of market segmentation. Review of Financial Studies 25 ( 7 ): 2041 – 69 . Google Scholar CrossRef Search ADS Chodorow-Reich, G. 2014 . The employment effects of credit market disruptions: Firm-level evidence from the 2008–2009 financial crisis. Quarterly Journal of Economics 129 ( 1 ): 1 – 59 . Google Scholar CrossRef Search ADS Covas, F., and Den Haan. W. J. 2011 . The cyclical behavior of debt and equity finance. American Economic Review 101 ( 2 ): 877 – 99 . Google Scholar CrossRef Search ADS Dell’Ariccia, G., and Marquez. R. 2006 . Lending booms and lending standards. Journal of Finance 61 ( 5 ): 2511 – 46 . Google Scholar CrossRef Search ADS Drehmann, M., Borio, C. and Tsatsaronis. K. 2012 . Characterising the financial cycle: Don’t lose sight of the medium term! BIS Working Papers, no. 380 , Bank for International Settlements . Fitch . 2016 . Global bank rating criteria. July 15 . https://www.fitchratings.com/site/re/884135. Gete, P. 2017 . Banking crises, lending standards and misallocation. http://dx.doi.org/10.2139/ssrn.2905308. Gilchrist, S., Yankov, V. and Zakrajšek. E. 2009 . Credit market shocks and economic fluctuations: Evidence from corporate bond and stock markets. Journal of Monetary Economics 56 ( 4 ): 471 – 93 . Google Scholar CrossRef Search ADS Gorton, G. B., and Metrick. A. 2012 . Securitized banking and the run on repo. Journal of Financial Economics 104 ( 3 ): 425 – 51 . Google Scholar CrossRef Search ADS Greenwood, R., and Hanson. S. G. 2013 . Issuer quality and corporate bond returns. Review of Financial Studies 26 ( 6 ): 1483 – 1525 . Google Scholar CrossRef Search ADS Imbens, G., and Kalyanaraman. K. 2014 . Optimal bandwidth choice for the regression discontinuity estimator. Review of Economic Studies 79 ( 3 ): 933 – 59 . Google Scholar CrossRef Search ADS Intesa . 2015 . Consolidated financial statements (Part E). http://www.group.intesasanpaolo.com/scriptIsir0/si09/contentData/view/20150408_RischiCredito_uk.pdf?id=CNT-05-000000025BE48&ct=application/pdf. Ivashina, V. 2009 . Asymmetric information effects on loan spreads. Journal of Financial Economics 92 ( 2 ): 300 – 319 . Google Scholar CrossRef Search ADS Ivashina, V., and Scharfstein. D. 2009 . Bank lending during the financial crisis of 2008. Journal of Financial Economics 97 ( 3 ): 319 — 38 . Google Scholar CrossRef Search ADS Iyer, R., Puri, M. and Ryan. N. 2016 . A tale of two runs: Depositor responses to bank solvency risk. Journal of Finance 71 ( 6 ): 2687 – 2726 . Google Scholar CrossRef Search ADS Jassaud, N., and Kang. K. H. 2015 . A strategy for developing a market for nonperforming loans in Italy. IMF Working Paper, no. 15/24 , International Monetary Fund. Google Scholar CrossRef Search ADS Jermann, U., and Quadrini. V. 2012 . Macroeconomic effects of financial shocks. American Economic Review 102 ( 1 ): 238 – 71 . Google Scholar CrossRef Search ADS PubMed Jiménez, G., Ongena, S. Peydró, J.-L. and Saurina. J. 2012 . Credit supply and monetary policy: Identifying the bank balance-sheet channel with loan applications. American Economic Review 102 ( 5 ): 2301 – 26 . Google Scholar CrossRef Search ADS Jiménez, G., Ongena, S. Peydró, J.-L. and Saurina. J. 2014 . Hazardous times for monetary policy: What do 23 million loans say about the impact of monetary policy on credit risk-taking? Econometrica 82 ( 2 ): 463 – 505 . Google Scholar CrossRef Search ADS Kisgen, D. J., and Strahan. P. E. 2010 . Do regulations based on credit ratings affect a firm’s cost of capital. Review of Financial Studies 23 ( 12 ): 4324 – 47 . Google Scholar CrossRef Search ADS Khwaja, A. I., and Mian. A. 2008 . Tracing the impact of bank liquidity shocks: Evidence from an emerging market. American Economic Review 98 ( 4 ): 1413 – 42 . Google Scholar CrossRef Search ADS Kovbasyuk, S., and Spagnolo. G. 2017 . Memory and markets. EIEF Working Papers, no. 1606, Einaudi Institute for Economics and Finance. Lemmon, M., and Roberts. M. R. 2010 . The response of corporate financing and investment to changes in the supply of credit. Journal of Financial and Quantitative Analysis 45 ( 3 ): 555 – 87 . Google Scholar CrossRef Search ADS Lopez-Salido, D., Stein, J. C. and Zakrajšek. E. 2017 . Credit-market sentiment and the business cycle. Quarterly Journal of Economics 132 ( 3 ): 1373 – 1426 . Google Scholar CrossRef Search ADS McCrary, J. 2008 . Manipulation of the running variable in the regression discontinuity design: a density test. Journal of Econometrics 142 ( 2 ): 698 – 714 . Google Scholar CrossRef Search ADS Moody’s. 2015 . Rating methodology: Banks. March 16 . www.moodys.com/methodologies. Martin, A. 2008 . Endogenous credit cycles. Economics Working Papers, no. 916, Department of Economics and Business, Universitat Pompeu Fabra. Google Scholar CrossRef Search ADS OECD . 1997 . Small Businesses, Job Creation and Growth: Facts, Obstacles and Best Practices. Paravisini, D., Rappaport, V. Schnabl, P. and Wolfenzon. D. 2014 . Dissecting the effect of credit supply on trade: Evidence from matched credit-export data. Review of Economic Studies 82 ( 1 ): 333 – 359 . Google Scholar CrossRef Search ADS Rajan, R. G. 2005 . Has financial development made the world riskier? Proceedings of the Economic Policy Symposium, Jackson Hole, Federal Reserve Bank of Kansas City , August , 313 – 69 . Rodano, G., Serrano-Velarde, N. and Tarantino. E. 2016 . Bankruptcy law and bank financing. Journal of Financial Economics 120 ( 2 ): 363 – 82 . Google Scholar CrossRef Search ADS Stein, J. C. 2002 . Information production and capital allocation: Decentralized versus hierarchical firms. Journal of Finance 57 ( 5 ): 1891 – 1921 . Google Scholar CrossRef Search ADS Standard & Poor’s. 2004 . Credit risk tracker Italy. Standard & Poor’s Risk Solutions. Sufi, A. 2007 . Information asymmetry and financing arrangements: Evidence from syndicated loans. Journal of Finance 62 ( 2 ): 629 – 68 . Google Scholar CrossRef Search ADS Tirole, J. 2006 . The theory of corporate finance . Princeton, NJ : Princeton University Press. Unicredit Bank. 2008 . Unicredit S.p.A. 2008 Annual Report. World Bank . 2002 . Bank loan classification and provisioning practices in selected developed and emerging countries (a survey of current practices in countries represented on the Basel Core Principles Liaison Group). © The Author(s) 2018. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

The Review of Financial Studies – Oxford University Press

**Published: ** Apr 24, 2018

Loading...

personal research library

It’s your single place to instantly

**discover** and **read** the research

that matters to you.

Enjoy **affordable access** to

over 18 million articles from more than

**15,000 peer-reviewed journals**.

All for just $49/month

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Read from thousands of the leading scholarly journals from *SpringerNature*, *Elsevier*, *Wiley-Blackwell*, *Oxford University Press* and more.

All the latest content is available, no embargo periods.

## “Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”

Daniel C.

## “Whoa! It’s like Spotify but for academic articles.”

@Phil_Robichaud

## “I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”

@deepthiw

## “My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”

@JoseServera

DeepDyve ## Freelancer | DeepDyve ## Pro | |
---|---|---|

Price | FREE | $49/month |

Save searches from | ||

Create lists to | ||

Export lists, citations | ||

Read DeepDyve articles | Abstract access only | Unlimited access to over |

20 pages / month | ||

PDF Discount |