Extent of SME Credit Rationing | EU 2013-14 EIF-LSE Capstone Project - PowerPoint PPT Presentation

Extent of SME Credit Rationing | EU 2013-14 EIF-LSE Capstone Project 2018 Wen Chen Nicolas Nardecchia Venu Mothkoor Jay Patel Coordinator (LSE) : Prof. S. Jenkins Coordinators (EIF)*: Salome Gvetadze, Simone Signore and Elitsa Pavlova 1 * Many thanks to Patrick Sevestre, Elizabeth Krem p and Lionel Nesta for the crucial inputs. Luxembourg, February 27 th 2018

Agenda ▪ Introduction Objectives o Definitions o Previous European credit rationing studies o ▪ Methods Sample o Model o ▪ Results Partial credit rationing estimates o Heterogeneity analysis by SME size o ▪ Conclusion Relevance and limitations o 2

Introduction 3

We complete the following objectives as set out in the Terms of Reference. Review Literature ▪ Review equilibrium and disequilibrium credit rationing theories ▪ Review credit rationing empirical studies Estimate Credit Rationing ▪ Follow Kremp and Sevestre (2013) approach ▪ Use firm-level financial data for EU SMEs Extend Kremp and Sevestre (2013) ▪ Compare results with 2013-14 ECB SAFE surveys ▪ Estimate heterogeneity of partial credit rationing by SME size Introduction Methods Results Conclusion 4

The market clears at an equilibrium interest rate Market Equilibrium ▪ Credit demand and supply clear at an equilbrium interest rate in each period Interest rate ▪ Interest rates serve as an efficient allocation mechanism i* i* = equilibrium interest rate ∗ Loans 𝑹 𝒖 ∗ = equilibrium quantity of 𝑹 𝒖 loans There is no excess demand Introduction Methods Results Conclusion 5

The market does not clear under disequilibrium conditions Market Disequilibrium ▪ Interest rates may not freely adjust o Rate ceiling o Rate stickiness Interest rate i* i’ i* = equilibrium interest rate i’ = prevailing interest rate Excess Demand ∗ = equilibrium quantity of 𝑹 𝒖 loans 𝑹 𝒖 = observed quantity of loans ∗ Loans 𝑬 𝒖 𝑹 𝒖 = 𝑻 𝒖 𝑹 𝒖 𝑻 𝒖 = supply of loans 𝑬 𝒖 = latent demand for loans Excess demand results as the latent demand for loans exceeds supply Introduction Methods Results Conclusion 6

Country-level credit rationing studies Key Findings ▪ 6 country-level studies o 4 use firm data o 2 use bank data ▪ Each study uses United Kingdom – 1989 to 1999 France – 2000 to 2010 Atanasova and Wilson (2004) Kremp and Sevestre (2013) different explanatory variables ▪ The studies take different empirical Spain – 1994 to 2002 Croatia – 2000 to 2009 approaches Carbo-Valverde et al. (2009) Čeh et al. (2011) Portugal – 2005 to 2012 Greece – 2003 to 2011 Farinha and Felix (2015) European Central Bank (2015) No studies consider EU-wide SME credit rationing using firm-level data Introduction Methods Results Conclusion 7

Methods 8

Orbis and SAFE survey data: 14,270 SMEs using five-year panel data Firm Size Loan Information Other Sample 2013-14 2013-14 Characteristics ▪ 24 out of 28 EU countries, ex. Cyprus, Estonia, Lithuania, and Malta ▪ Industries: use 7 sub- Medium, groups of NACE rev.2 with a 23.76% Micro, classification Loan, 34.54% o Retail, Transportation, 36.46% Tourism, and Other without a Loan, (41.10%) 63.54% o Manufacturing Small, (28.54%) 41.70% 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 Introduction Methods Results Conclusion 9

Expected direction of explanatory variables in our model Latent demand for loans Latent supply of loans ′ 𝜸 𝟐 + 𝒗 𝟐,𝒖 ′ 𝜸 𝟑 + 𝒗 𝟑,𝒖 𝑻 𝒖 = 𝒀 𝟑,𝒖 𝑬 𝒖 = 𝒀 𝟐,𝒖 (?) SME size (+) SME size ( – ) Interest rate (+) Age (+) Short-term financing needs (+) Collateral (+) Long-term financing needs (+) Liquidity on hand ( – ) Internal resources available ( – ) Leverage (+) Credit rating Control factors: Industry, country, year Control factors: Industry, country, year Introduction Methods Results Conclusion 10

Market disequilibrium condition Observable Unobservable Interest rate Disequilibrium Condition 𝑹 𝒖 = 𝒏𝒋𝒐 𝑬 𝒖 , 𝑻 𝒖 Loans Introduction Methods Results Conclusion 11

Main results 12

Observed direction of explanatory variables in our model Latent demand for loans Latent supply of loans ′ 𝜸 𝟐 + 𝒗 𝟐,𝒖 ′ 𝜸 𝟑 + 𝒗 𝟑,𝒖 𝑬 𝒖 = 𝒀 𝟐,𝒖 𝑻 𝒖 = 𝒀 𝟑,𝒖 ( – ) Small-size (relative to Micro-size)*** ( – ) Small-size (relative to Micro-size)*** ( – ) Medium-size (relative to Micro-size)*** ( – ) Medium-size (relative to Micro-size)*** (+) Interest rate*** (+) Age ( – ) Short-term financing needs* (+) Collateral (+) Long-term financing needs ( – ) Liquidity on hand* ( – ) Internal resources available*** ( – ) Leverage*** ( – ) Credit rating** Control factors: Industry, country, year Control factors: Industry, country, year 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 13

Probability of partial credit rationing Interest rate Observable Unobservable Probability of partial credit rationing 𝑸𝒔 𝑬 𝒖 > 𝑻 𝒖 𝑹 𝒖 ) ▪ Only firms that have a loan can experience partial credit rationing ▪ We do not estimate full credit rationing Loans Introduction Methods Results Conclusion 14

Orbis and SAFE survey data: 14,270 SMEs using five-year panel data Firm Size Loan Information Other Sample 2013-14 2013-14 Characteristics ▪ 24 out of 28 EU countries, ex. Cyprus, Estonia, Lithuania, and Malta ▪ Industries: use 7 sub- Medium, groups of NACE rev.2 with a 23.76% Micro, classification Loan, 34.54% o Retail, Transportation, 36.46% Tourism, and Other without a Loan, (41.10%) 63.54% o Manufacturing Small, (28.54%) 41.70% 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 Introduction Methods Results Conclusion 15

Heterogeneity Analysis | Partial credit rationing by SME size Key Findings Probability that SMEs experience partial credit rationing ▪ On average, the Model Estimates* probability of partial 2013-14 SAFE Survey credit rationing for EU SMEs in our sample is 4.15% 4.15% All SMEs 14.78% ▪ The probability of partial Heterogeneity Analysis (by SME size) credit rationing is highest 6.96% for micro-size firms, Micro-size firms 15.20% followed by small- and medium size-firms. This is consistent with SAFE 4.26% survey results Small-size firms 14.28% ▪ Our sample is not representative of EU 3.43% SMEs after dropping firms Medium-size firms 13.20% with missing Orbis data; our results likely 0% 5% 10% 15% 20% 25% underestimate partial credit rationing * Among SMEs that applied for a loan Self-reported SAFE results suggest greater extent of rationing than model estimates Introduction Methods Results Conclusion 16

Conclusion 17

Understanding the nature of credit rationing is key to inform policy 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 characteristics ▪ o Individual lending capacity of banks o Market power of a bank in local markets Availability of EU-wide data ▪ Technical challenges ▪ Introduction Methods Results Conclusion 18

Appendix 19

Appendix Items 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 ▪ 20

Country-level credit rationing studies United Kingdom – 1989 to 1999 France – 2000 to 2010 Atanasova and Wilson (2004) Kremp and Sevestre (2013) 42.7% of the firms are constrained 6.4% of firms are partially constrained and 4.6% of firms are fully constrained Spain – 1994 to 2002 Croatia – 2000 to 2009 Carbo-Valverde et al. (2009) Čeh et al. (2011) 33.93% of firms are financially Identifies three distinct sub- constrained periods of bank credit activity Portugal – 2005 to 2012 Greece – 2003 to 2011 Farinha and Felix (2015) European Central Bank (2015) 15% of firms are partially Demand constraints for short-term constrained and 32% firms are business loans; Supply constraints fully constrained for long-term business loans, consumer loans and mortgages 21

Demand-side financial indicator variables Financial Expenses Interest rate Loans + Long term debt 1 𝑈𝑏𝑜𝑕𝑗𝑐𝑚𝑓 𝐺𝑗𝑦𝑓𝑒 𝐵𝑡𝑡𝑓𝑢𝑡 Long-term financing needs 𝑈𝑝𝑢𝑏𝑚 𝐵𝑡𝑡𝑓𝑢𝑡 𝑋𝑝𝑠𝑙𝑗𝑜𝑕 𝐷𝑏𝑞𝑗𝑢𝑏𝑚 Short-term financing needs 𝑈𝑝𝑢𝑏𝑚 𝐵𝑡𝑡𝑓𝑢𝑡 𝐷𝑏𝑡ℎ𝑔𝑚𝑝𝑥 2 Internal resources available 𝑈𝑝𝑢𝑏𝑚 𝐵𝑡𝑡𝑓𝑢𝑡 1. We use Noncurrent Liabilities when Loans + Long Term Debt data are not available 2. We use EBITDA when Cashflow data are not available 22