SME Finance – Old paradigms, new evidence

1. SME Finance – Old paradigms, new evidence Thorsten Beck

2. Access to finance – the size gap Source: Beck, Maimbo, Faye and Triki (2011)

3. Financing is an important obstacle Source: Beck, Maimbo, Faye and Triki (2011)

4. …and these obstacles are more binding for SMEs Source: Beck, Demirguc-Kunt and Maksimovic (2005)

5. …which can result in a missing middle Growth differential following initial size (average growth of Ivorian firms minus average growth of Germanfirms). Source: Sleuwagen and Goedhuys (2002)

6. Why are SMEs left out? • Transaction costs • Fixed cost component of credit provision effectively impedes outreach to “smaller” and costlier clients • Inability of financial institutions to exploit scale economies • Risk • Related to asymmetric information • Adverse selection: High risk borrowers are the ones most likely to look for external finance • Increases in the risk premium raise the risk of the pool of interested borrowers • Lenders will use non-price criteria to screen debtors/projects • Moral hazard: The agent (borrower) has incentives that are inconsistent with the principal’s (lender) interests • Agents may divert resources to riskier activities, loot assets, etc. • These challenges arise both on the country- and bank-level • SMEs therefore often squeezed between retail (large number!) and large enterprise finance (more manageable risk, scale)

7. SME Finance over the business cycle • SMEs typically hurt more during economic downturns and even more during financial crises • Opacity and limited collateral increase agency conflicts between lenders and borrowers during the crisis • Balance sheet channel of monetary policy – stronger effect of monetary policy changes on small firms • Lending retrenchment finds an easy target: high-cost borrowers • Additional crowding out effects in Eurozone: government funding

8. Who finances SMEs and how? • Limited financing sources – mostly banks, limited if any access to capital market • Demand-side constraints: resistance again sharing control • Supplier credit, internal finance • Bank lending: relationship vs. transaction based lending • Relationship: bank repeatedly interacts with clients in order to obtain and exploit proprietary borrower information (“soft” information) • Relationship lending traditionally seen as appropriate tool for lending to SMEs as they tend to be more opaque and less able to post collatera • Transaction: typical one-off loans where bank bases its lending decisions on verifiable information and assets (“hard” information) • Recently transaction lending proposed as alternative lending technique, especially useful for larger and non-local banks

9. Relationship vs. transaction-based lending – evidence from Bolivia

10. Our identification strategy Same firm, same month (5,137 loans to 287 firms) Foreign bank borrowers Domestic bank borrowers

11. Bank ownership and loan pricing • Foreign banks use credit ratings and collateral for pricing of their loans, especially for larger firms

12. Bank ownership and loan pricing Domestic banks based their pricing on the strength of the lending relationship, particularly for smaller firms

13. When Arm’s Length is Too Far.Relationship Banking over the Credit Cycle Thorsten Beck (Cass Business School)Hans Degryse (KU Leuven)Ralph De Haas (EBRD, Tilburg University)Neeltje Van Horen (De Nederlandsche Bank) Any views expressed are those of the authors and should not be attributed to the EBRD, De Nederlandsche Bank or the Eurosystem

14. Aftermath of the Great Recession: SMEs continue to experience credit constraints, potentially delaying the economic recovery Policy makers to the rescue… President Obama signs the Small Business Jobs Act Bank of England launches a subsidized SME funding and guarantee scheme ECB initiates targeted LTRO aimed at increasing lending to SMEs in Europe Etc. Etc. Motivation

15. These initiatives may alleviate firms’ funding constraints in the short term but are unlikely to be a long-term panacea Open question: how to protect entrepreneurs in a more structural way from the cyclicality in credit? Possible answers: Countercyclical fiscal and monetary policy (Aghion et al., 2010) Countercyclical capital buffers (Drehmann et al., 2010; Repullo, 2013) We ask: Do banks’ lending techniques contribute to the cyclicality of credit? Motivation

16. Banks themselves seem to think so… Institute for International Finance (2013): “The screening of loan applicants became more challenging as the credit cycle turned” “Banks can rely less on collateral and hard information and need to take a deeper look at firms’ prospects” “This requires a more subtle judgment, including about the ability and commitment of firms’ owners and management” Some banks may be better equipped to produce such judgments during an economic downturn… Motivation

17. Two core lending techniques: Relationship: bank repeatedly interacts with clients to obtain and exploit “soft” proprietary borrower information Transaction: typical one-off loans based on “hard” verifiable information and collateralizable assets Bank business models and SME lending

18. Relationship lending more appropriate for SMEs? Yes: SMEs are more opaque and have less collateral (Petersen and Rajan, 1994; Berger and Udell, 1995) No: banks can apply transaction lending too (Berger and Udell, 2006) Cross-country and country-specific evidence shows banks use both methods (De la Torre, Martinez Peria and Schmukler, 2010; Beck Demirguc-Kunt and Martinez Peria 2011) These cross-sectional studies cannot examine differences in the impact of lending techniques over the credit cycle Bank business models and SME lending

19. “Learning model” (Bolton, Freixas, Gambacorta and Mistrulli, 2013) Relationship banks compete with transaction banks R-banks incur higher costs due to monitoring and the need to hold more capital. Charge higher lending rates than T-banks in normal times R-banks learn about the borrower over time, so can continue to lend at more favorable terms when a crisis hits R-banks relax firms’ credit constraints more than T-banks in crisis times Test model using Italian credit-registry data and confirm these theoretical predictions Note: R-bank is defined as bank whose headquarter is located in the same province as the firm Relationship lending over the credit cycle

20. Identify relationship banks in a novel way: ask bank CEOs! No need for (simplifying) assumptions about lending technologies Merge with new data on geographic location of bank branches Detailed picture of bank branches in the vicinity of each firm Use direct measure of whether a firm is credit constrained Observe whether firms were turned down or discouraged Information for 2005 and 2008/09 Observe credit constraints in credit boom and bust Banks and firms active in 21 countries in Eastern Europe and Caucasus Broadens external validity This paper

21. Credit cycle in emerging Europe

22. During a credit boom, SME access to credit does not depend on the local presence of relationship lenders But the presence of relationship lenders alleviates firms’ credit constraints during a cyclical downturn Positive impact is strongest for opaque firms, firms with no other sources of external finance, and firms that lack tangible assets Firms in regions where the economic downturn is more severe also benefit more from the presence of relationship banks Reduction in credit constraints due to relationship lending helps mitigate the negative impact of financial crisis on firm growth (not an evergreening story) Main take away

23. Data

24. Dataset • We merge and combine three datasets: • Firm characteristics • Overview of bank branches • Bank characteristics incl. lending techniques

25. 1. Firms • EBRD-World Bank Business Environment and Enterprise Performance Survey (BEEPS): • 21 countries in Eastern Europe and Caucasus • Different levels of economic and financial development • Purpose of survey: gauge the extent to which different features of the business environment constitute obstacles to firms’ operations • Information on whether firm is credit constrained • Large number of firm characteristics • Geographical location of each firm • Sample • 2005 (6,948 firms): credit boom • 2008-09 (6,901 firms): turn of the credit cycle

26. 1. Firms • Survey data allow us to directly observe • Firms that do not need at loan: not credit constrained • Firms that need a loan and got it: not credit constrained • Firms that need a loan but are discouraged: credit constrained • Firms that need a loan but are rejected: credit constrained • Allows for the identification of the impact of credit-supply shocks

27. Firms • Identify credit constrained firms (follow Popov and Udell 2012) • BEEPS question K16 “Did the establishment apply for any loans or lines of credit in the last fiscal year?” • If NO, question K17 “What was the main reason that the establishment did not apply?” • If YES, question K18a “In the last fiscal year, did this establishment apply for any new loans or credit lines that were rejected?” • Needs loan and not credit constrained: • K16: “Yes” and K18a: “No” • Needs loan and credit constrained: • K18a: “Yes” (= rejected firms) • K17: “Interest rates not favorable”; “Collateral requirements too high”; “Size of loan and maturity are insufficient”; “Did not think would be approved” (= discouraged firms) • Does not need loan: • K16: No and K17: “does not need loan”

28. Firms • Substantial share of firms constrained • Tightening of financing constraints in 2008 • Substantial variation across countries: • Slovenia: 12% firms credit constrained in 2005 and 17% in 2008 • Azerbaijan: 64% in 2005 and 78% in 2008

29. 2. Bank branches • Data hand-collected • Directly contacting banks, bank websites, central banks • Cross-checked with (more limited) information in SNL database • Sample • Geo-coordinates of 38,310 branches of 422 banks (96.8% of bank assets) • Opening and closing dates so time-varying information (2005, 2008, and historical)

30. Connection between banks and firms • Combine geographic location of the firm with location of branches to determine which banks are physically present in vicinity of the firm • Firms tend to do business with nearby banks (< 6 km) (Petersen and Rajan, 2002; Degryse and Ongena, 2005; Agarwal and Hauswald, 2010) • Two methods: • Locality (main method) • E.g. link all BEEPS firms in Czech city of Brno to all bank branches in Brno • If firm in locality without bank branches, we link to branches in nearest locality • Total 2,478 localities with on average 21 bank branches • Circles with 5 (10) km radius • Draw circles with a radius of 5 or 10 kilometers around the geo-coordinates of each firm and then link the firm to only those branches inside circle • A 5 (10) km radius contains on average 18 (30) branches

35. 3. Bank lending techniques • Duration of bank-firm relationship • Petersen and Rajan (1994) and others building on their paper • Distance between borrowers and lenders • Bolton, Freixas, Gambacorta and Mistrulli (2013) • Implicit assumption: all foreign banks are transaction lenders • Infer from borrower population • Mian (2004): compare borrowers of different banks that are assumed to use different lending technologies • Infer from loan contracts • Beck, Ioannidou and Schaefer (2012): variables that can explain pricing; different loan conditionalities

36. 3. Bank lending techniques • Duration of bank-firm relationship • Petersen and Rajan (1994) and others building on their paper • Distance between borrowers and lenders • Bolton, Freixas, Gambacorta and Mistrulli (2013) • Implicit assumption: all foreign banks are transaction lenders • Infer from borrower population • Mian (2004): compare borrowers of different banks that are assumed to use different lending technologies • Infer from loan contracts • Beck, Ioannidou and Schaefer (2012): variables that can explain pricing; different loan conditionalities • Ask the banks • This paper

37. 3. Bank lending techniques • Use 2nd Banking Environment and Performance Survey (BEPS II) • Face-to-face interviews with almost 400 CEOs of the banks operating in our sample of countries (80.1% of bank assets) • BEPS II question Q6 “Rate on a five point scale the frequency of use of the following lending techniques when dealing with SMEs”: • Relationship lending • Fundamental and cash-flow analysis • Business collateral • Personal collateral • Bank is relationship bank if it considers “relationship lending” very important. • Other definitions in robustness tests

38. 3. Bank lending techniques • For both 2005 and 2008-09, we identify for each branch in the vicinity (circle or locality) of each firm whether it is a relationship bank or not • Variable Share relationship banks: • Number of branches of relationship banks to total number of branches in the locality of the firm • On average, 52% in 2005 and 50% in 2008

39. Bank lending techniques Substantial differences across countries ...

40. 3. Bank lending techniques … and substantial variation within-country

41. Empirical methodology

42. Dependent variable: D=1 if firm i in locality j of country k in industry l is credit constrained (rejected or discouraged), 0 otherwise Share relationship banks: Share of bank branches in locality j of country k that belong to banks for which relationship lending is “very important” when dealing with SMEs β3: Impact of the intensity of relationship banking on credit constraints Sample period: 2005 and 2008-09 Evaluate importance of relationship banking over the credit cycle Empirical methodology

43. Covariates: Firm variables: small firm, large firm, publicly listed, sole proprietorship, privatized, exporter, audited  control for observable firm-level heterogeneity Locality variables: bank solvency (Tier 1), share foreign banks, wholesale funding, economic activity locality (capital or city)  control for bank and locality characteristics Constructed analogously to bank relationship variable Country and industry fixed effects  control for (un)observable variation at country and industry level Empirical methodology

44. Probit with and without first-stage Heckman selection: To control for the fact that being credit constrained is only observable if the firm needs a loan Heckman first-stage dependent variable: D=1 if firm needs a loan, 0 otherwise Selection variables: Competitive pressure and Applied for subsidy(Popov and Udell, 2012; Hainz and Nakobin 2013) Empirical methodology

45. Results

46. First stage of Heckmanselection model withDemandfor credit as dependent variable Relationship lending and demand for credit

47. No relationship between share of relationship banks and the demand for credit Unlikelythat relationship lending is endogenoustolocaldemandconditions Relationship lending and demand for credit

48. Relationship lendingand credit constraints

49. In 2005 (credit boom) no significant relationship between the local importance of relationship lending and firms’ financing constraints Relationship lendingand credit constraints

50. In 2008 (credit crunch) firms in locality with more relationship banks less likely to be credit constrained Relationship lendingand credit constraints