SME Finance – Old paradigms, new evidence Thorsten Beck.
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Transcript of 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
0
5
10
15
20
25
30
35
40
2005 2006 2007 2008 2009 2010 2011 2012 2013
Ave
rag
e c
red
it g
row
th a
cro
ss e
me
rgin
g E
uro
pe
(%
)
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
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
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
3. Bank lending techniques
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
3. Bank lending techniques
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
Empirical methodology
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
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
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
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
Relationship lending and credit constraints
In 2005 (credit boom) no significant relationship between the local importance of relationship lending and firms’ financing 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
Relationship lending and credit constraints
In 2008 (credit crunch) firms in locality with more relationship banks less likely to be credit constrained
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
Relationship lending and credit constraints
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”)
Real economic impact
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
Real economic impact
Real economic impact
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 -
Real economic impact
When a high presence of relationship banks reduces credit constraints in a locality, this reduction is associated with stronger firm growth in 2008-2010
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 -
Real economic impact
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
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 -
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)