26.05.2018 The real effects of relationship lending Ryan Banerjee (BIS) Leonardo Gambacorta (BIS & CEPR) Enrico Sette (Bank of Italy) Banque de France 9 June 2017 The views expressed are the authors’ only and not necessarily those of the BIS, the Bank of Italy or the Eurosystem

26.05.2018 Motivation (1) Understand factors favoring the resilience of economies during and after a crisis “The real effects of relationship lending” – Banerjee, Gambacorta and Sette – 09.06.17

Focus on extent to which banks rely on Relationship Lending (RL) Motivation (2) Focus on extent to which banks rely on Relationship Lending (RL) RL=lending technology based on acquisition of soft information about the borrower, through repeated / close interaction. Long term implicit contract. RL helps access to finance for small / opaque firms (Degryse et al. 2009 for a review). RL contributed to soften the transmission of shocks (Bolton et al. 2016; Sette and Gobbi, 2015, Beck et al. 2015; Gambacorta and Mistrulli, 2015) “The real effects of relationship lending” – Banerjee, Gambacorta and Sette – 09.06.17

Not clear that RL really helps if the crisis is protracted / systemic Motivation (3) Is RL a good banking technology when also banks are under stress? What are the real effects of higher reliance on RL? Effect of RL seems to depend on banks’ balance sheet strength (Bolton et al. 2014; Sette and Gobbi, 2015; Gambacorta and Mistrulli, 2015) Not clear that RL really helps if the crisis is protracted / systemic “The real effects of relationship lending” – Banerjee, Gambacorta and Sette – 28.2.2017

Motivation (4) Debate on change in bank business models and diversification of financing mix of firms Extensive use of credit scoring by banks: more importance to hard (quantitative) vs soft (qualitative) information Diminish importance of bank funding for firms, but possible trade-off if RL provides insurance during crises

Test for impact on credit quantity and its cost 26.05.2018 Motivation (5) In this paper: do firms that rely more on relationship lending experience higher investment and employment growth during the crisis? Test for impact on credit quantity and its cost Distinguish different types of loans Exploit detailed micro data for identification and measurement of RL Test for heterogeneous effects “The real effects of relationship lending” – Banerjee, Gambacorta and Sette – 09.06.17

Relationship lending in good and bad times Related Literature Relationship lending in good and bad times (Sette and Gobbi JEEA 2015, Gobbi and Sette RF 2015, Bolton et al. RFS 2016, Beck et al. 2016; Gambacorta and Mistrulli, 2015) Real effects of credit shocks (during crises: Chodorow-Reich QJE 2014, Cingano, Manaresi, Sette, RFS 2016, Bentolila et al. 2015, Acharya et al. 2015, Amiti, Weinstein 2017 JPE forth.) Novelties of this paper: impact on investment and employment transmission mechanism: different types of credit (working capital loans versus term loans) focus on different crisis periods “The real effects of relationship lending” – Banerjee, Gambacorta and Sette – 09.06.17

2011-2014: sovereign debt crisis 26.05.2018 Empirical Strategy (1) Show that RL leads to better access to credit, distinguish between the 2 phases of the crisis 2008-2010: global financial crisis but Italian banks heterogeneously affected (Panetta et al, 2010) 2011-2014: sovereign debt crisis 2. Test whether firms’ RL intensity has an effect on investment and employment “The real effects of relationship lending” – Banerjee, Gambacorta and Sette – 09.06.17

Need to tackle possible endogeneity of RL: Empirical Strategy (2) Need to tackle possible endogeneity of RL: Firms relying more on RL may be different along several dimensions Point 1 (RL ensures better access to credit): rely on data at the bank-firm relationship-level. Control for firm observable and unobservable characteristics (time-varying). Point 2 (Real effects of RL): Control for observables and firm-fixed effects. Show balancing of covariates IV Several robustness

Data 3 main datasets 1. Credit Register: includes all exposures of a banks towards an individual if these are above 30k euros (75k euros until 2008) Distinguish 3 types of loans: revolving credit lines (overdraft), term loans (mortgages, leasing), loans backed by receivables For a representative sample of banks includes information on interest rates 2. Cerved (firm register): balance sheet information of incorporated firms 3. Supervisory reports: bank balance sheet data, useful for heterogeneous effects

Select random sample of 10% of CR firms Data Merge Credit Register, Firm Register (CERVED) using unique firm tax identifier and Supervisory reports for bank characteristics using unique bank identifier Select random sample of 10% of CR firms Data span 2004-2014 Non-financial firms Multiple bank relationships “The real effects of relationship lending” – Banerjee, Gambacorta and Sette – 09.06.17

Descriptive stats I

Descriptive stats II ∆Log (Total credit) Investment Rate (growth rate of fixed assets) Intangible investment/ Total investment ∆Log (Employment costs) Credit weighted log relationship duration in 2006 Credit weighted log relationship duration Return on assets Leverage (Debt / total assets) EBITDA/ Interest expense Log (total assets) Mean 2.019 17.98 19.31 4.039 1.400 1.461 0.550 80.26 6.717 8.042 Median -0.775 3.3 3.574 1.479 1.512 0.432 84.33 3.354 7.885 Standard deviation 31.14 75.82 71.91 24.45 0.511 0.583 4.923 16.59 13.92 1.264 25th percentile -12.49 -9.442 30.43 -5.082 1.062 1.060 -0.281 70.97 1.652 7.149 75th percentile 16.70 15.62 12.41 1.811 1.900 1.869 93.11 6.972 8.788 No. of observations 82692 82314 68064 79420 65398 80551 82633 82294 82689

Results on credit at the bank-firm relationship level 26.05.2018 Results on credit at the bank-firm relationship level Use Khwaja-Mian-type identification (2008, AER) ∆𝑌 𝑖,𝑗,𝑡 = 𝑅𝐿 𝑖,𝑗,𝑡 + 𝑅𝐿 𝑖,𝑗,𝑡 *D(Crisis 1)+ 𝑅𝐿 𝑖,𝑗,𝑡 *D(Crisis 2)+b X+ 𝛾 𝑖,𝑡 + 𝜀 𝑖,𝑗,𝑡 where Y is ∆ (log credit) or interest rate, X vector of controls, 𝛾 fixed effects RL measure of relationship lending is (log) length of the relationship (standard in the literature) Potentially endogenous, so important to control for firm time*varying unobservables compare credit by different banks to the same firm at the same time “The real effects of relationship lending” – Banerjee, Gambacorta and Sette – 09.06.17

Effects of relationship banking on lending 26.05.2018 Effects of relationship banking on lending “The real effects of relationship lending” – Banerjee, Gambacorta and Sette – 09.06.17

Effects of relationship banking on interest rates 26.05.2018 Effects of relationship banking on interest rates “The real effects of relationship lending” – Banerjee, Gambacorta and Sette – 09.06.17

Extensions & Robustness Heterogeneous effects by firm and by bank Effect of RL is stronger if banks have higher capital Effect of RL has limited heterogeneity across firm characteristics (leverage and profitability) Robustness: Include interactions of dummy crisis with all controls Subsample of relationships which are more intensely used (drawn/granted > 50%) Control for the presence of past-due loans

𝑦 𝑖,𝑡 =𝛽 𝑅𝑒𝑙𝐿𝑒𝑛𝑑 𝑖,𝑡 +𝛾 𝐹𝑖𝑟𝑚 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 𝑖,𝑡 + 𝛿 𝑡 + 𝜃 𝑖 Real effects Get to the firm level Construct credit-weighted average length of relationships We estimate the following model 𝑦 𝑖,𝑡 =𝛽 𝑅𝑒𝑙𝐿𝑒𝑛𝑑 𝑖,𝑡 +𝛾 𝐹𝑖𝑟𝑚 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 𝑖,𝑡 + 𝛿 𝑡 + 𝜃 𝑖 3 main dep vars: credit growth, investment rate, change in labor cost RelLend is credit-weighted average log duration of relationships (weights are share of credit of each banks). Firm-level controls include: leverage, roa, ebitda/interest expenses, firm size, z-score (prob default) Include firm fixed effects and time fixed effects

Problem: potentially endogenous 26.05.2018 Real effects Problem: potentially endogenous Test for sorting Fix RL at 2006 (before the crisis) and add interactions with crisis dummies + firm fe Use IV: instrument is the difference between average length and the average length of relationships with banks involved in M&As in 2006 (Hong and Kacperckyk, 2009) Intuition: Change in average length of relationship, conditional on firm FE and firm time varying controls uncorrelated with firm unobservables “The real effects of relationship lending” – Banerjee, Gambacorta and Sette – 09.06.17

A test for the presence of sorting in bank-firm relationship 26.05.2018 A test for the presence of sorting in bank-firm relationship Standardized difference (Imbens-Wooldridge 2009) in parentheses “The real effects of relationship lending” – Banerjee, Gambacorta and Sette – 09.06.17

Real effects of relationship lending at the firm level   ∆Log (Total credit) Average interest rate on total credit Investment Rate Intangible investment/ Total investment ∆Log (Labour costs) VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Weighted relationship duration*D(Post 2008) 4.435*** 4.260*** -26.02*** -27.01*** 3.893*** 4.440*** -8.222*** -8.989*** 4.314*** 4.289*** (0.661) (2.515) (2.490) (1.267) (1.282) (1.690) (1.674) (0.514) (0.507) Weighted relationship duration*D(Post 2011) 1.158* 1.011* -10.40*** -10.75*** 1.811 2.015* 12.50*** 13.57*** 0.457 0.531 (0.616) (0.611) (2.526) (2.519) (1.172) (1.163) (1.568) (1.564) (0.499) (0.497) Return on assets 0.301*** -0.547*** 0.245*** -1.371*** 0.455*** (0.0435) (0.153) (0.0817) (0.0998) (0.0351) Firm leverage -0.135*** 0.459*** -0.0910** -1.227*** -0.0515*** (0.0193) (0.0869) (0.0388) (0.0588) (0.0160) EBITDA/interest expenses 0.149*** -0.375*** 0.280*** -0.102*** 0.0395*** (0.0191) (0.0532) (0.0358) (0.0310) (0.0104) Log (firm total assets) -10.73*** 7.131*** -28.98*** 21.28*** -4.436*** (0.628) (2.635) (1.357) (1.500) (0.506) Z-Score -2.695*** 4.345** -1.872** -1.186 -0.229 (0.451) (1.723) (0.854) (1.168) (0.360) Time fixed effects Yes Firm fixed effects Observations 62995 62797 62837 62644 35013 61110 60987 R-squared 0.194 0.653 0.245 0.244 0.401 0.274 0.275

Instrumental variable estimation 26.05.2018 Instrumental variable estimation Intuition of instrument: M&A destroy some relationships and this changes the average duration. Change exogenous conditional on firm fixed effects. “The real effects of relationship lending” – Banerjee, Gambacorta and Sette – 09.06.17

IV: supporting the validity of the instrument

year by year regressions 26.05.2018 Other results Robustness checks: year by year regressions weighted regs by value added (aggregate effects) Firm heterogeneity: some evidence that highly-leveraged and highly-profitable firms receive more insulation effects on credit, investment and employment. Results on highly leveraged firms are weaker. Bank heterogeneity: well-capitalised RL banks protect more their clients “The real effects of relationship lending” – Banerjee, Gambacorta and Sette – 09.06.17

Alternative interpretation? Loan evergreening? Could this be evidence of «loan evergreening»? Banks captured by firms keep lending to avoid that these default. Boundary between RL and evergreening may be fuzzy. Yet: 1. results on bank heterogeneity: RL stronger if banks more capitalized. Stronger incentives to loan evergreening for low capital banks 2. limited evidence on firm heterogeneity, but stronger incentives to evergreen loans to weaker firms

Conclusion - Main points to take away 26.05.2018 Conclusion - Main points to take away Relationship lending ensured firms a steadier flow of credit during both crises Firms more reliant on RL invest and increase employment (relatively) more than other firms during both crises Insulation effects of RL remained pretty stable in the 2nd phase of the financial crisis (sovereign debt crisis) Bank capital influences the effect of relationship lending in affecting credit supply, investment and employment Some evidence of heterogeneous effects across firms in firm-level regressions. “The real effects of relationship lending” – Banerjee, Gambacorta and Sette – 09.06.17

Thanks for your attention. Enrico Sette Bank of Italy Email: enrico Thanks for your attention! Enrico Sette Bank of Italy Email: enrico.sette@bancaditalia.it my research page: https://sites.google.com/site/settenrico/home/research 27 “The real effects of relationship lending” – Banerjee, Gambacorta and Sette – 09.06.17