SME Finance – Old paradigms, new evidence Thorsten Beck

SME Finance – Old paradigms, new evidence

Thorsten Beck

Access to finance – the size gap

Source: Beck, Maimbo, Faye and Triki (2011)

Financing is an important obstacle

Source: Beck, Maimbo, Faye and Triki (2011)

…and these obstacles are more binding for SMEs

Growth constraints across firms of different sizes

-12.0%

-10.0%

-8.0%

-6.0%

-4.0%

-2.0%

0.0%

Financing Legal Corruption

Small

Medium

Large

Source: Beck, Demirguc-Kunt and Maksimovic (2005)

…which can result in a missing middle

Growth differential following initial size (average growth of Ivorian firms minus average growth of German firms). Source: Sleuwagen and Goedhuys (2002)

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)

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

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

Relationship vs. transaction-based lending – evidence from Bolivia

0

2

4

6

8

10

12

14

Interest rate Collateral probability (times ten)

Maturity (in months)

Domestic

Foreign

Our identification strategy

Domestic bank borrowers

Foreign bank borrowers

Same firm, same month

(5,137 loans to 287 firms)

10/20

Bank ownership and loan pricing

11/20

Foreign banks use credit ratings and collateral for pricing of their loans, especially for larger firms

Bank ownership and loan pricing

12/20

• Domestic banks based their pricing on the strength of the lending relationship, particularly for smaller firms

When Arm’s Length is Too Far.Relationship Banking over the Credit Cycle

Any views expressed are those of the authors and should not be attributed to the EBRD, De Nederlandsche Bank or the Eurosystem

Thorsten Beck (Cass Business School)

Hans Degryse (KU Leuven)

Ralph De Haas (EBRD, Tilburg University)

Neeltje Van Horen (De Nederlandsche Bank)

Motivation

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

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

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…

Bank business models and SME lending

Two core lending techniques:

① Relationship: bank repeatedly interacts with clients to obtain and exploit “soft” proprietary borrower information

② Transaction: typical one-off loans based on “hard” verifiable information and collateralizable assets

Bank business models and SME lending

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

Relationship lending over the credit cycle

“Learning model” (Bolton, Freixas, Gambacorta and Mistrulli, 2013)

① Relationship banks compete with transaction banks

② 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

③ R-banks learn about the borrower over time, so can continue to lend at more favorable terms when a crisis hits

④ 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

This paper

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

Credit cycle in emerging Europe

-5

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2005 2006 2007 2008 2009 2010 2011 2012 2013

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Total credit Corporate credit

1. During a credit boom, SME access to credit does not depend on the local presence of relationship lenders

2. But the presence of relationship lenders alleviates firms’ credit constraints during a cyclical downturn

3. Positive impact is strongest for opaque firms, firms with no other sources of external finance, and firms that lack tangible assets

4. Firms in regions where the economic downturn is more severe also benefit more from the presence of relationship banks

5. 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

Data

We merge and combine three datasets:

① Firm characteristics

② Overview of bank branches

③ Bank characteristics incl. lending techniques

Dataset

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

1. Firms

Survey data allow us to directly observe

1. Firms that do not need at loan: not credit constrained

2. Firms that need a loan and got it: not credit constrained

3. Firms that need a loan but are discouraged: credit constrained

4. Firms that need a loan but are rejected: credit constrained

Allows for the identification of the impact of credit-supply shocks

1. 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”

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

Firms

Loan needed Constrained

2005 2008 2005 2008

Share firms 0.70 0.62 0.34 0.40

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)

2. Bank branches

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:

① 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

② 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

Connection between banks and firms

1. Duration of bank-firm relationship Petersen and Rajan (1994) and others building on their paper

2. Distance between borrowers and lenders Bolton, Freixas, Gambacorta and Mistrulli (2013) Implicit assumption: all foreign banks are transaction lenders

3. Infer from borrower population Mian (2004): compare borrowers of different banks that are assumed to use

different lending technologies

4. Infer from loan contracts Beck, Ioannidou and Schaefer (2012): variables that can explain pricing;

different loan conditionalities

3. Bank lending techniques

1. Duration of bank-firm relationship Petersen and Rajan (1994) and others building on their paper

2. Distance between borrowers and lenders Bolton, Freixas, Gambacorta and Mistrulli (2013) Implicit assumption: all foreign banks are transaction lenders

3. Infer from borrower population Mian (2004): compare borrowers of different banks that are assumed to use

different lending technologies

4. Infer from loan contracts Beck, Ioannidou and Schaefer (2012): variables that can explain pricing;

different loan conditionalities

5. Ask the banks This paper


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


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


Substantial differences across countries ...

Bank lending techniques

Share relationship

banks 2005 2008

Albania 0.92 0.83Armenia 0.35 0.46Azerbaijan 0.36 0.45Belarus 0.26 0.27Bosnia 0.59 0.56Bulgaria 0.84 0.77Croatia 0.74 0.71Czech Republic 1.00 0.90Estonia 0.57 0.53Georgia 0.18 0.19Hungary 0.60 0.58Latvia 0.49 0.45Lithuania 0.61 0.59Macedonia 0.40 0.39Moldova 0.27 0.28Poland 0.60 0.59Romania 0.58 0.55Serbia 0.81 0.79Slovak Republic 0.27 0.31Slovenia 0.67 0.64

Ukraine 0.11 0.27

… and substantial variation within-country


Empirical methodology

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


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


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)


Results

Relationship lending and demand for credit

First stage of Heckman selection model with Demand for credit as dependent variable

2005 2008Locality 5 km 10 km Locality 5 km 10 km

[1] [2] [3] [4] [5] [6]Share relationship banks -0.082 0.024 0.028 0.046 0.051 0.089

(0.157) (0.141) (0.163) (0.139) (0.122) (0.138)Competition 0.317*** 0.309*** 0.311*** 0.250*** 0.246*** 0.239***

(0.045) (0.044) (0.042) (0.043) (0.041) (0.042)Subsidized 0.264*** 0.278*** 0.266*** 0.297*** 0.294*** 0.288***

(0.084) (0.083) (0.084) (0.086) (0.086) (0.081)Firm controls Yes Yes Yes Yes Yes YesLocality controls Yes Yes Yes Yes Yes YesCountry FE Yes Yes Yes Yes Yes YesIndustry FE Yes Yes Yes Yes Yes YesNumber of obs. 6,451 6,739 6,631 6,616 6,670 6,821

Pseudo R2 0.052 0.052 0.052 0.054 0.055 0.054

Relationship lending and demand for credit

No relationship between share of relationship banks and the demand for credit

Unlikely that relationship lending is endogenous to local demand conditions

2005 2008Locality 5 km 10 km Locality 5 km 10 km

[1] [2] [3] [4] [5] [6]Share relationship banks -0.082 0.024 0.028 0.046 0.051 0.089

(0.157) (0.141) (0.163) (0.139) (0.122) (0.138)Competition 0.317*** 0.309*** 0.311*** 0.250*** 0.246*** 0.239***

(0.045) (0.044) (0.042) (0.043) (0.041) (0.042)Subsidized 0.264*** 0.278*** 0.266*** 0.297*** 0.294*** 0.288***

(0.084) (0.083) (0.084) (0.086) (0.086) (0.081)Firm controls Yes Yes Yes Yes Yes YesLocality controls Yes Yes Yes Yes Yes YesCountry FE Yes Yes Yes Yes Yes YesIndustry FE Yes Yes Yes Yes Yes YesNumber of obs. 6,451 6,739 6,631 6,616 6,670 6,821

Pseudo R2 0.052 0.052 0.052 0.054 0.055 0.054

Relationship lending and credit constraints

2005 2008

Probit Heckman Probit Heckman

Locality Locality 5 km 10 km Locality Locality 5 km 10 km

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

Share relationship banks 0.017 0.191 0.169 0.240 0.159 -0.431*** -0.470*** -0.439*** -0.427*** -0.403**

(0.246) (0.270) (0.244) (0.200) (0.202) (0.134) (0.152) (0.156) (0.162) (0.182)

Firm controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Locality controls No Yes Yes Yes Yes No Yes Yes Yes Yes

Country FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Number of obs. 4,162 4,059 4,059 3,655 4,055 4,052 4,022 4,022 3,868 4,126

Pseudo R2 0.14 0.15 0.15 0.14 0.15 0.10 0.10 0.10 0.10 0.10

2005 2008



[1] [2] [3] [4] [5] [6] [7] [8] [9] [10]


(0.246) (0.270) (0.244) (0.200) (0.202) (0.134) (0.152) (0.156) (0.162) (0.182)





Number of obs. 4,162 4,059 4,059 3,655 4,055 4,052 4,022 4,022 3,868 4,126

Pseudo R2 0.14 0.15 0.15 0.14 0.15 0.10 0.10 0.10 0.10 0.10


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

2005 2008



[1] [2] [3] [4] [5] [6] [7] [8] [9] [10]


(0.246) (0.270) (0.244) (0.200) (0.202) (0.134) (0.152) (0.156) (0.162) (0.182)





Number of obs. 4,162 4,059 4,059 3,655 4,055 4,052 4,022 4,022 3,868 4,126

Pseudo R2 0.14 0.15 0.15 0.14 0.15 0.10 0.10 0.10 0.10 0.10


In 2008 (credit crunch) firms in locality with more relationship banks less likely to be credit constrained

2005 2008



[1] [2] [3] [4] [5] [6] [7] [8] [9] [10]


(0.246) (0.270) (0.244) (0.200) (0.202) (0.134) (0.152) (0.156) (0.162) (0.182)





Number of obs. 4,162 4,059 4,059 3,655 4,055 4,052 4,022 4,022 3,868 4,126

Pseudo R2 0.14 0.15 0.15 0.14 0.15 0.10 0.10 0.10 0.10 0.10


In 2008 (credit crunch) firms in locality with more relationship banks less

likely to be credit constrained: A move from a locality with 20% relationship lenders to one with 80%

relationship lenders reduces the probability of being credit constrained by 26 percentage points

Robustness

Controlling for local competition and small banks

Additional controls local credit markets

2005 2008-09 2005 2008-09 2005 2008-09

[1] [2] [3] [4] [5] [6]

Share Relationship Banks 0.182 -0.421*** 0.182 -0.436*** 0.169 -0.425***

(0.259) (0.149) (0.263) (0.157) (0.247) (0.153)

HHI -0.167 0.348**

(0.141) (0.153)

Lerner index -0.415 0.504

(0.846) (1.084)

Share small banks -0.072 -0.100

(0.404) (0.165)

Firm controls Yes Yes Yes Yes Yes Yes

Locality controls Yes Yes Yes Yes Yes Yes

Country FE Yes Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes Yes

Number of obs. 4,527 4,085 4,519 4,084 4,525 4,083

Pseudo R2 0.132 0.100 0.132 0.099 0.132 0.099

Continuous variable: takes each bank’s score (0 to 4) on importance of relationship lending and then takes the branch-weighted average of this score by locality

Relative variable I: takes each bank’s score (0 to 4) on importance of relationship lending divided by its score (0 to 4) on importance fundamental/cash flow analysis and then takes the branch-weighted average of this ratio by locality

Relative variable II: no. branches of banks for whom relationship lending is "Very important" for SME but not for retail lending/total no. bank branches in the locality.

Transaction bank: banks for which fundamental/cash flow analysis is “very important”, while relationship lending is not “very important”

Alternative relationship-lending measures

2005 2008-09 2005 2008-09 2005 2008-09 2005 2008-09

[1] [2] [3] [4] [5] [6] [7] [8]

0.089 -0.233** -0.115 -0.520* 0.209 -0.455** -0.203 0.413**

(0.153) (0.116) (0.268) (0.279) (0.242) (0.219) (0.256) (0.192)

Firm controls Yes Yes Yes Yes Yes Yes Yes Yes

Locality controls Yes Yes Yes Yes Yes Yes Yes Yes

Country FE Yes Yes Yes Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes Yes Yes Yes

Number of obs. 4,527 4,085 4,527 4,085 4,527 4,083 4,527 4,085

Pseudo R2 0.131 0.098 0.131 0.098 0.132 0.099 0.132 0.099

Share relationship banks

Relationship banks (continuous)

Relationship banks (relative to other

lending techniques)

Relationship banks (relative to retail

borrowers)

Share transaction banks

Clustering standard errors at the locality level or using wild cluster bootstrap-t procedure (Cameron, Gelbach and Miller, 2008)

Use of linear probability OLS instead of probit

Pooling 2005 and 2008 observations and including 2008 interaction

Excluding Ukraine

Excluding banks with ownership change

Also robust to…

Endogeneity

Results unlikely driven by R-banks self-selecting into certain localities:

1. Share relationship banks does not affect demand for loan

2. Results unchanged if we use share R-banks in 1995/2000 as regressor

3. Results hold in IV procedure with R-banks in 1995 as instrument

4. Average characteristics of firms in locality independent of share R-banks

5. Negative Altonji ratio (Altonji et al. 2005; Bellows and Miguel 2008)

Self-selection banks

Share relationship banks: 1995 Share relationship banks: 2000

2005 2008 2005 2008

[7] [8] [9] [10]

Share relationship banks 0.044 -0.346*** 0.178 -0.299**

(0.211) (0.073) (0.146) (0.128)

Firm controls Yes Yes Yes Yes

Locality controls Yes Yes Yes Yes

Country FE Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes

Number of obs. 4,063 3,537 4,137 3,683

Pseudo R2 0.134 0.099 0.134 0.100

Results unlikely driven by firms self-selecting into certain localities:

1. Results also hold for old(er) firms

Self-selection firms

Firms 5 years and

olderFirms 10 years and

olderFirms 12 years and

older

2005 2008 2005 2008 2005 2008[1] [2] [3] [4] [5] [6]

Share relationship banks 0.125 -0.478*** 0.202 -0.390** 0.147 -0.464**

(0.237) (0.157) (0.254) (0.193) (0.262) (0.212)

Firm controls Yes Yes Yes Yes Yes Yes

Locality controls Yes Yes Yes Yes Yes Yes

Country FE Yes Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes Yes

Number of obs. 4,174 3,738 2,776 2,904 2,153 2,525

Pseudo R2 0.134 0.103 0.150 0.106 0.158 0.111

Differences across firms and countries

Firm heterogeneity

Virtually none of the main or interaction effects significant in 2005

2005Firm type → Employees Age Exporter Audited External

fundingPublicly

listedAsset

tangibility

[1] [2] [3] [5] [6] [7] [8]Share relationship banks 0.055 0.089 0.148 0.296 0.102 0.173 -0.008

(0.380) (0.514) (0.262) (0.240) (0.265) (0.244) (0.347)

Share relationship banks * Firm type

0.028 0.032 0.082 -0.278** 0.304 -0.233 -0.034

(0.070) (0.165) (0.295) (0.138) (0.270) (0.579) (0.243)

Firm type -0.262*** 0.088 -0.269* -0.116 0.094 -0.002 -0.339**

(0.080) (0.076) (0.157) (0.076) (0.153) (0.381) (0.144)

Firm controls Yes Yes Yes Yes Yes Yes Yes

Locality controls Yes Yes Yes Yes Yes Yes Yes

Country FE Yes Yes Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes Yes No

Number of obs. 4,527 4,520 4,527 4,527 4,527 4,527 1,929

Pseudo R2 0.146 0.134 0.132 0.132 0.136 0.132 0.168

Firm heterogeneity

In downturn, especially smaller and opaque firms benefit from the presence of relationship banks

2008Firm type → Employees Age Exporter Audited External

fundingPublicly

listedAsset

tangibility

[9] [10] [12] [13] [14] [15] [16]Share relationship banks -1.040*** -1.065*** -0.572*** -0.598*** -0.532*** -0.535*** -0.431*

(0.312) (0.364) (0.192) (0.182) (0.170) (0.165) (0.257)

Share relationship banks * Firm type

0.181** 0.244** 0.409* 0.333* 0.448*** 0.594** 0.448**

(0.078) (0.123) (0.219) (0.188) (0.167) (0.250) (0.205)

Firm type -0.282*** -0.139* -0.391*** -0.363*** -0.184** -0.045 -0.372***

(0.062) (0.073) (0.116) (0.115) (0.089) (0.132) (0.090)

Firm controls Yes Yes Yes Yes Yes Yes Yes

Locality controls Yes Yes Yes Yes Yes Yes Yes

Country FE Yes Yes Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes Yes No

Number of obs. 4,085 4,023 4,085 4,085 4,085 4,085 1,652

Pseudo R2 0.107 0.101 0.100 0.100 0.101 0.100 0.122

Geographical heterogeneity

Stronger impact in countries and regions hit harder by the Great Recession

Consistent with interpretation that collecting soft information by relationship banks enables them to continue lending when a crisis hits

Country GDP growth Regional GDP growth Regional GDP growth if available;

country GDP growth otherwise

[1] [2] [3] [4] [5] [6]

Share relationship banks -0.324* -0.400*** -0.546*** -0.631*** -0.362** -0.444***(0.189) (0.151) (0.206) (0.198) (0.153) (0.150)

Share relationship banks*Output growth 2008-09 1.869 2.510** 2.451**(1.464) (1.237) (1.093)

Share relationship banks*Output growth 2007-09 1.711** 1.151** 1.229**(0.863) (0.576) (0.481)

Firm controls Yes Yes Yes Yes Yes YesLocality controls Yes Yes Yes Yes Yes YesCountry FE Yes Yes Yes Yes Yes YesIndustry FE Yes Yes Yes Yes Yes YesNumber of obs. 4,085 4,085 3,099 3,099 4,085 4,085Pseudo R2 0.099 0.099 0.095 0.093 0.101 0.100

Real economic impact

Results indicate that the presence of relationship banks alleviates local firms’ credit constraints during an economic downturn:

A. Does the presence of relationship banks help sound firms to bridge difficult times and recover more quickly? (Chemmanur and Fulghieri, 1994: “helping hand”)

B. Or does this reflect an evergreening story where banks roll over loans to underperforming firms? (Cabellero, Hoshi and Kashyap, 2008: “zombie lending”)


Need information on firms’ balance sheets: Match firms in BEEPS 2008-09 sample with Orbis database Match almost 50% of firms (2,966 firms)

Analyze growth in assets, operational revenue and number of employees in 2008-2010 and 2005-2007 (placebo)

2SLS: Credit constrained instrumented by Share relationship banks: First stage: explain firm-level credit constraints (in 2008) by relationship

lending at the locality level (and other covariates) Second stage: explain real firm growth through the exogenous variation in

credit constraints Identifying assumption: Presence relationship banks only affects firm

growth through its impact on these firms’ ability to access credit



Growth total assets Growth operating revenues Growth number of employees

2008-2010 2005-2007 2008-2010 2005-2007 2008-2010 2005-2007

2SLS OLS 2SLS 2SLS OLS 2SLS 2SLS OLS 2SLS

[1] [2] [3] [4] [5] [6] [7] [8] [9]

Credit constrained 2008 -1.149** -0.039 0.036 -3.202** -0.061 -1.058 -1.322* -0.076 -0.348

(0.525) (0.038) (0.420) (1.564) (0.059) (1.056) (0.746) (0.059) (0.733)

Firm controls Yes Yes Yes Yes Yes Yes Yes Yes Yes

Locality controls Yes Yes Yes Yes Yes Yes Yes Yes Yes

Country FE Yes Yes Yes Yes Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes Yes Yes Yes Yes

F-stat 4.74 - 5.17 3.13 - 1.66 3.32 - 2.78

P-value F-stat 0.046 - 0.037 0.095 - 0.216 0.088 - 0.114

R2 (first stage) 0.110 - 0.122 0.118 - 0.135 0.117 - 0.132

Share Relationship Banks -0.215** - -0.211** -0.162* - -0.133 -0.162* - -0.181*(first-stage) (0.096) - (0.090) (0.089) - (0.101) (0.087) - (0.106)

Number of obs. 877 886 759 967 977 822 765 938 765

Pseudo R2 - 0.023 - - 0.031 - - 0.026 -


When a high presence of relationship banks reduces credit constraints in a locality, this reduction is associated with stronger firm growth in 2008-2010


2008-2010 2005-2007 2008-2010 2005-2007 2008-2010 2005-2007


[1] [2] [3] [4] [5] [6] [7] [8] [9]


(0.525) (0.038) (0.420) (1.564) (0.059) (1.056) (0.746) (0.059) (0.733)





F-stat 4.74 - 5.17 3.13 - 1.66 3.32 - 2.78

P-value F-stat 0.046 - 0.037 0.095 - 0.216 0.088 - 0.114

R2 (first stage) 0.110 - 0.122 0.118 - 0.135 0.117 - 0.132


Number of obs. 877 886 759 967 977 822 765 938 765

Pseudo R2 - 0.023 - - 0.031 - - 0.026 -


But higher presence of relationship banks did not “cause” stronger firm growth in 2005-2007

Hence: unlikely that we pick up a selection effect in 2008-2010


2008-2010 2005-2007 2008-2010 2005-2007 2008-2010 2005-2007


[1] [2] [3] [4] [5] [6] [7] [8] [9]


(0.525) (0.038) (0.420) (1.564) (0.059) (1.056) (0.746) (0.059) (0.733)





F-stat 4.74 - 5.17 3.13 - 1.66 3.32 - 2.78

P-value F-stat 0.046 - 0.037 0.095 - 0.216 0.088 - 0.114

R2 (first stage) 0.110 - 0.122 0.118 - 0.135 0.117 - 0.132


Number of obs. 877 886 759 967 977 822 765 938 765

Pseudo R2 - 0.023 - - 0.031 - - 0.026 -

How to shield SMEs from credit cycle downturns?

We examine the impact of relationship lending on firms’ credit constraints at different parts of the cycle

We find that: During ‘good times’, relationship and transaction lending act as substitutes During ‘bad times’, relationship lending helps alleviate credit constraints

Especially for opaque firms

Especially in regions that suffer more during the crisis

Reduction in credit constraints has a positive impact on firm growth during the downturn (not an evergreening story)

Conclusions

Possible policy implications

Banks and their shareholders should be careful with excessive reductions in front-line staff and branches

Build infrastructure to collect better ‘hard’ information Credit registries: transparent, low-cost hard information to assess credit risk Can provide incentive for banks to invest in the collection of soft information

as way to compete (Karapetyan and Stacescu, 2014)

Thorsten Beck

www.thorstenbeck.com

Thank you

http://www.thorstenbeck.com/

Documents

SME Finance – Old paradigms, new evidence Thorsten Beck