extent of sme credit rationing, eu 2013-14 · extent of sme credit rationing | eu 2013-14 eif-lse...
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Extent of SME Credit Rationing | EU 2013-14
EIF-LSE Capstone Project 2018
1
Wen Chen
Venu Mothkoor
Nicolas Nardecchia
Jay Patel
Luxembourg, February 27th 2018
Coordinator (LSE) : Prof. S. Jenkins
Coordinators (EIF)*: Salome Gvetadze, Simone Signore and Elitsa Pavlova
* Many thanks to Patrick Sevestre, Elizabeth Kremp and Lionel Nesta for the crucial inputs.
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Agenda
▪ Introductiono Objectives
o Definitions
o Previous European credit rationing studies
▪ Methodso Sample
o Model
▪ Resultso Partial credit rationing estimates
o Heterogeneity analysis by SME size
▪ Conclusiono Relevance and limitations
2
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Introduction
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We complete the following objectives as
set out in the Terms of Reference.
4
▪ Review equilibrium and disequilibrium credit rationing theories▪ Review credit rationing empirical studies
▪ Follow Kremp and Sevestre (2013) approach▪ Use firm-level financial data for EU SMEs
▪ Compare results with 2013-14 ECB SAFE surveys
▪ Estimate heterogeneity of partial credit rationing by SME size
Review Literature
Estimate Credit Rationing
Extend Kremp and Sevestre (2013)
Introduction Methods Results Conclusion
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The market clears at an equilibrium
interest rate
5
Market Equilibrium
Inte
rest
ra
te
i*
Loans
▪ Credit demand and
supply clear at an
equilbrium interest rate
in each period
▪ Interest rates serve as an
efficient allocation
mechanism
There is no excess demand
𝑸𝒕∗
Methods Results ConclusionIntroduction
i* = equilibrium interest rate𝑸𝒕
∗ = equilibrium quantity of loans
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The market does not clear under
disequilibrium conditions
6
▪ Interest rates may not
freely adjust
o Rate ceiling
o Rate stickiness
Excess demand results as the latent demand for loans exceeds supply
i*
Inte
rest
ra
te
Loans
i’
Excess Demand
𝑸𝒕 = 𝑺𝒕 𝑸𝒕∗ 𝑫𝒕
Methods Results ConclusionIntroduction
i* = equilibrium interest ratei’ = prevailing interest rate𝑸𝒕
∗ = equilibrium quantity of loans𝑸𝒕 = observed quantity of loans𝑺𝒕 = supply of loans𝑫𝒕 = latent demand for loans
Market Disequilibrium
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Country-level credit rationing studies
7
No studies consider EU-wide SME credit rationing using firm-level data
Key Findings
▪ 6 country-level studies
o 4 use firm data
o 2 use bank data
▪ Each study uses
different explanatory
variables
▪ The studies take
different empirical
approaches
United Kingdom – 1989 to 1999Atanasova and Wilson (2004)
Spain – 1994 to 2002Carbo-Valverde et al. (2009)
Croatia – 2000 to 2009Čeh et al. (2011)
France – 2000 to 2010Kremp and Sevestre (2013)
Portugal – 2005 to 2012Farinha and Felix (2015)
Greece – 2003 to 2011European Central Bank (2015)
Methods Results ConclusionIntroduction
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Methods
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Orbis and SAFE survey data:
14,270 SMEs using five-year panel data
9
Micro,
34.54%
Small,
41.70%
Medium,
23.76%
Firm Size2013-14
Loan Information2013-14
Other Sample Characteristics
▪ 24 out of 28 EU countries,
ex. Cyprus, Estonia,
Lithuania, and Malta
▪ Industries: use 7 sub-
groups of NACE rev.2
classification
o Retail, Transportation,
Tourism, and Other
(41.10%)
o Manufacturing
(28.54%)
o Real Estate, Education,
and Admin (14.72%)
o Other 4 sub-groups
(15.64%)
Due to data availability issues, our sample is skewed towards bigger firms
with a
Loan,
36.46%
without a
Loan,
63.54%
Introduction Methods Results Conclusion
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Expected direction of explanatory
variables in our model
10
𝑫𝒕 = 𝑿𝟏,𝒕′ 𝜷𝟏 + 𝒖𝟏,𝒕
(?) SME size
(–) Interest rate(+) Short-term financing needs(+) Long-term financing needs(–) Internal resources available
Latent demand for loans
𝑺𝒕 = 𝑿𝟐,𝒕′ 𝜷𝟐 + 𝒖𝟐,𝒕
(+) SME size
(+) Age(+) Collateral(+) Liquidity on hand(–) Leverage(+) Credit rating
Latent supply of loans
Control factors: Industry, country, year Control factors: Industry, country, year
Introduction Results ConclusionMethods
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Market disequilibrium condition
11
Disequilibrium Condition
𝑸𝒕 = 𝒎𝒊𝒏 𝑫𝒕, 𝑺𝒕
Introduction Results ConclusionMethods
Observable
Inte
rest
ra
te
Loans
Unobservable
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Main results
12
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Observed direction of explanatory
variables in our model
13
Green font indicates alignment with our hypothesis for variable direction
* Statistically significant at the 10% level
** Statistically significant at the 5% level
*** Statistically significant at the 1% level
Introduction Methods Results Conclusion
𝑫𝒕 = 𝑿𝟏,𝒕′ 𝜷𝟏 + 𝒖𝟏,𝒕
(–) Small-size (relative to Micro-size)***(–) Medium-size (relative to Micro-size)***
(+) Interest rate***(–) Short-term financing needs*(+) Long-term financing needs(–) Internal resources available***
Latent demand for loans
𝑺𝒕 = 𝑿𝟐,𝒕′ 𝜷𝟐 + 𝒖𝟐,𝒕
(–) Small-size (relative to Micro-size)***(–) Medium-size (relative to Micro-size)***
(+) Age(+) Collateral(–) Liquidity on hand*(–) Leverage***(–) Credit rating**
Latent supply of loans
Control factors: Industry, country, year Control factors: Industry, country, year
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Observable
Inte
rest
ra
te
Loans
Unobservable
14
Probability of partial credit rationing
𝑷𝒓 𝑫𝒕 > 𝑺𝒕 𝑸𝒕)
Introduction Methods ConclusionResults
Probability of partial credit rationing
▪ Only firms that have a loan can experience partial credit rationing
▪ We do not estimate full credit rationing
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Orbis and SAFE survey data:
14,270 SMEs using five-year panel data
15
Micro,
34.54%
Small,
41.70%
Medium,
23.76%
Firm Size2013-14
Loan Information2013-14
Other Sample Characteristics
▪ 24 out of 28 EU countries,
ex. Cyprus, Estonia,
Lithuania, and Malta
▪ Industries: use 7 sub-
groups of NACE rev.2
classification
o Retail, Transportation,
Tourism, and Other
(41.10%)
o Manufacturing
(28.54%)
o Real Estate, Education,
and Admin (14.72%)
o Other 4 sub-groups
(15.64%)
Due to data availability issues, our sample is skewed towards bigger firms
with a
Loan,
36.46%
without a
Loan,
63.54%
Introduction Methods Results Conclusion
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13.20%
14.28%
15.20%
14.78%
3.43%
4.26%
6.96%
4.15%
0% 5% 10% 15% 20% 25%
Medium-size firms
Small-size firms
Micro-size firms
All SMEs
Probability that SMEs experience partial credit rationing
Model Estimates*
2013-14 SAFE Survey
Heterogeneity Analysis | Partial credit
rationing by SME size
16
Self-reported SAFE results suggest greater extent of rationing than model estimates
Key Findings
▪ On average, the
probability of partial
credit rationing for EU
SMEs in our sample is
4.15%
▪ The probability of partial
credit rationing is highest
for micro-size firms,
followed by small- and
medium size-firms. This is
consistent with SAFE
survey results
▪ Our sample is not
representative of EU
SMEs after dropping firms
with missing Orbis data;
our results likely
underestimate partial
credit rationing
Heterogeneity Analysis (by SME size)
* Among SMEs that applied for a loan
Introduction Methods ConclusionResults
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Conclusion
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Understanding the nature of credit
rationing is key to inform policy
18
The model can be used to determine:
▪ Extent of credit rationing at an aggregate level▪ Differential probabilities of credit rationing for subgroupings including, but not limited to,
by firm size and country group
Limitations:
▪ Non-bank SME financing options not evaluated
▪ Bank characteristicso Individual lending capacity of bankso Market power of a bank in local markets
▪ Availability of EU-wide data▪ Technical challenges
Introduction Methods Results Conclusion
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Appendix
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Appendix Items
20
Other▪ European credit rationing studies (detail)
Demand▪ -side variable details
Supply▪ -side variable details
Altman▪ and Sabato (2007) Z-score
Sample▪ 1 | Summary statistics
References▪
Acknowledgements▪
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21
United Kingdom – 1989 to 1999Atanasova and Wilson (2004)42.7% of the firms are constrained
Spain – 1994 to 2002Carbo-Valverde et al. (2009)33.93% of firms are financially constrained
Croatia – 2000 to 2009Čeh et al. (2011)Identifies three distinct sub-periods of bank credit activity
France – 2000 to 2010Kremp and Sevestre (2013)6.4% of firms are partiallyconstrained and 4.6% of firms are fully constrained
Portugal – 2005 to 2012Farinha and Felix (2015)15% of firms are partially constrained and 32% firms are fully constrained
Greece – 2003 to 2011European Central Bank (2015)Demand constraints for short-term business loans; Supply constraints for long-term business loans, consumer loans and mortgages
Country-level credit rationing studies
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Demand-side financial indicator variables
22
1. We use Noncurrent Liabilities when Loans + Long Term Debt data are not available
2. We use EBITDA when Cashflow data are not available
Financial Expenses
Loans + Long term debt1
𝑇𝑎𝑛𝑔𝑖𝑏𝑙𝑒 𝐹𝑖𝑥𝑒𝑑 𝐴𝑠𝑠𝑒𝑡𝑠
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠
𝑊𝑜𝑟𝑘𝑖𝑛𝑔 𝐶𝑎𝑝𝑖𝑡𝑎𝑙
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠
𝐶𝑎𝑠ℎ𝑓𝑙𝑜𝑤2
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠Internal resources available
Short-term financing needs
Long-term financing needs
Interest rate
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Altman Z-score3 CategoriesRelatively Safe Zone | Z-score > ҧ𝑥 + 1σ
Relatively Grey Zone | ҧ𝑥 - 1σ < Z-score < ҧ𝑥 + 1σRelatively Distressed Zone | Z-score < ҧ𝑥 - 1σ
23
1. We use Noncurrent Liabilities when Loans + Long Term Debt data are not available2. We use EBITDA when Cashflow data are not available3. Z-score based on Altman and Sabato (2007) model
Supply-side financial indicator variables
Physical non-cash collateral
Liquidity on hand
Leverage
Credit rating
Age category
𝑇𝑎𝑛𝑔𝑖𝑏𝑙𝑒 𝐹𝑖𝑥𝑒𝑑 𝐴𝑠𝑠𝑒𝑡𝑠 + 𝐼𝑛𝑣𝑒𝑛𝑡𝑜𝑟𝑦 𝑆𝑡𝑜𝑐𝑘
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠
𝑁𝑒𝑡 𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝐴𝑠𝑠𝑒𝑡𝑠
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠
𝐿𝑜𝑎𝑛𝑠 + 𝐿𝑜𝑛𝑔 𝑇𝑒𝑟𝑚 𝐷𝑒𝑏𝑡1
𝐶𝑎𝑠ℎ𝑓𝑙𝑜𝑤2
SAFE (2013-14) Size Categories< 2 yrs. | 2-5 yrs. | 5-10 yrs. | > 10 yrs.
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+1σ
Altman and Sabato (2007) Z-score model
24
Adapted Model
Log[ PD / (1-PD)]=+ 53.48- 4.09 * Log[ (1-Cashflow) / Total Assets]- 1.13 * Log[ Current Liabilities / Equity Book Value]- 4.32 * Log[ (1-Retained Earnings) / Total Assets]+ 1.84 * Log[ (Balance Sheet Cash / Total Assets]+ 1.97 * Log[ Cashflow / Financial Expenses]Sample
Financial data for 2,010 SMEs from the United States between 1994 and 2002
Source
Altman, E. and Sabato, G. (2007). Modelling Credit Risk for SMEs: Evidence from the U.S. Market. Abacus, 43(3), pp.332-357.
Rationale
Sample consists of SMEs from a well-diversified economy, which may serve as a valid proxy for the EU economy
Relative Credit Rating Method
ҧ𝑥-1σ
Grey Zone
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Sample 1 | Summary Statistics
25
30.37%
42.73%
26.90%
Micro Small Medium
with
loans
36.93%
41.10%
21.97%
Micro Small Medium
without
loans
0
2,000
4,000
6,000
8,000
10,000
with loans without loans
Total Assets (th euros)
Firm size proportions
0%
2%
4%
6%
8%
10%
12%
with loans without loans
Interest Rate
(observed / imputed)
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
Internal
resources
ST
financing
needs
LT
financing
needs
Collateral Liquidity
on hand
with loans without loans
Main variable averages(over total assets)
25th, 50th, 75th percentiles and mean
25%
Median
75%
Mean
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References
26
Atanasova▪ , C. V., & Wilson, N. (2004). Disequilibrium in the UK corporate loan market. Journal of Banking & Finance 28, 595-614.
Carbo▪ -Valverde, S., Rodriguez-Fernandez, F., & F.Udell, G. (2009). Bank Market Power and SME Financing Constraints. Review of Finance (13), 309–340.
▪ C eh, A. M., Dumic ic , M., & Krznar, I. (2011). A Credit Market Disequilibrium Model And Periods of Credit Crunch. Croatian National Bank, Working Papers W − 28.
European Central Bank. (▪ 2015). Credit market disequilibrium in Greece (2003-2011): A Bayesian approach. (Working Paper Series No 1805).
European Investment Bank. (▪ 2014). Unlocking lending in Europe. EIB’s Economics Department.
Farinha▪ , L. s., & Felix, S. n. (2015). Credit rationing for Portuguese SMEs. Finance Research Letters (14), 167-177.
Ferreira, M., Mendes, D., & Pereira, J. (▪ 2016). Non-Bank Financing of European Non-Financial Firms. EFFAS.
Kremp▪ , E., & Sevestre, P. (2013). Did the crisis induce credit rationing for French SMEs? Journal of Banking & Finance (37), 3757-3772.
World Bank. (▪ 2013). European Bank Deleveraging and Global Credit Conditions. Policy Research Working Paper 6388.
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Acknowledgments
EIF Research team & EIB Institute
▪ Simone Signore
▪ Salome Gvetadze
▪ Elitsa Pavlova
▪ Antonia Botsari
LSE Team
▪ Prof. Stephen Jenkins
▪ 2017 LSE – EIF Capstone Team
Other Acknowledgments
▪ Patrick Sevestre and Elizabeth Kremp
▪ Lionel Nesta
27