1) The study analyzes the network structure of the French syndicated loan market and how lenders' positions within this network impact borrowing costs. 2) It finds that the French syndicated loan market exhibits "small world" properties where lenders are clustered yet the average path between any two lenders is small. 3) More central lenders within the network, as measured by betweenness, closeness, and degree receive lower loan spreads, indicating their experience and reputation mitigate agency costs.

1. Bank lending networks, experience,
reputation and borrowing costs
Christophe J. Godlewski
Bulat Sanditov
Thierry Burger-Helmchen
Journée Financement PME/TPE 2012
Université de Valenciennes
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2. Outline
1. Background
2. Data
3. Methodology
4. Results
5. Discussion
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3. Background
• Network structure of bank lending markets & its
impact on borrowing costs
• Focus: syndicated loans (1.500 BLN USD in 2009; 1/3
of external financing for companies)
• Issues: informational frictions + agency costs + loan
pricing
• Social capital & networks
• Experience & reputation  collaboration &
reciprocity  trust & reciprocity
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4. Background
• Analyze small world properties of the French
syndicated lending market
• Contribution to developing literature on small world
features of financial intermediation
• Evaluate the impact of network structure on
borrowing costs
• Contribution to literature on experience &
reputation impact on loan pricing
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5. Background
• Syndicated lending agency costs
• Adverse selection & moral hazard problems => loan
pricing
• Role of lenders’ experience, reputation… 
interaction, reciprocity, trust…
• Financial intermediation networks
• Information, capital, liquidity networks
• Compensation for asymmetric information &
reduction of opportunistic behavior
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6. Data
• Dealscan (LPC, Reuters) database
• Loan agreement & bank syndicate data
• Amount, spread, maturity…
• Identify country, role, name of lenders
• Period: 1992-2006
• French market
• 924 deals to 776 borrowers involving 436 lenders
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7. Data
Variables Description Mean Std. dev.
Spread Loan spread in bps over benchmark rate 120.25 119.42
Loan amount Loan amount in millions $ 1890.00 3160.00
Maturity Loan maturity in months 70.62 36.62
Guarantors =1 if guarantors are present 0.06 0.24
Covenants =1 if financial covenants are present 0.10 0.30
Number of arrangers Number of arrangers in the syndicate 5.34 5.40
Number of lenders Number of lenders in the syndicate 14.37 10.59
Local lenders % of French lenders in the syndicate 0.40 0.29
Syndicate Average % of the loan held by arrangers in the syndicate 0.11 0.14
concentration
League table % of league tables lenders in the syndicate 0.34 0.21
League table (local) % of league table French lenders in the syndicate 0.14 0.17
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8. Methodology
(a)
Loans A B C D
Lenders 1 2 3 4 5 6 7 8 9 10 11
(b)
5 7 11
3
4 6
2 8
10
1 9
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9. Methodology
• Network structure analysis
• Cf. Baum et al. (2003) + Watts & Strogatz (1998)
• Clustering coefficient (C) = share of bank’s neighbors
having direct contact with each other
• Average path length (L) = average distance between
all pairs of banks in a network
• Small world = short L + high C
• Comparing observed & benchmark network
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10. Methodology
• Randomly reconnect deals & lenders controlling for:
lender’s participation ; number of lenders / deal ;
number of arrangers / deal
• Ratios: LL (L / L random) ; CC (C / C random) ; SW =
CC / LL
• Network participant characteristics
• Lenders’ degree, closeness, betweenness
centralities => social capital
• Proxies for experience & reputation
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11. Methodology
• Betweenness = actor’s position within the network
=> control over flow of information, resources…
• Closeness = actor’s position with respect to other
actors => social distance to other actors
• Degree = number of closest neighbors => local
measure of social involvement
• Loan spread = f(lenders’ network
characteristics, controls: main loan & syndicate
characteristics)
• OLS with s.e. clustered at deal level 11

12. Results
• Is the French market a small world ?
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13. Results
• Does lender’s centrality matter for loan pricing ?
Loan + Number of + Local + Syndicate + League + Local league
variables lenders lenders concentration table table
Betweenness -24.220*** -23.438*** -23.750*** -18.381** -1.866*** -2.405***
(5.795) (5.462) (5.738) (8.065) (0.651) (0.707)
R² 0.529 0.533 0.529 0.503 0.520 0.517
F 29.142 29.669 30.613 14.829 32.693 33.652
Closeness -3.1359** -3.1626** -3.0618** -2.8145 -1.1202 -2.2503
(1.4858) (1.4301) (1.4684) (1.9891) (1.8355) (1.5748)
R² 0.5156 0.5209 0.5155 0.4969 0.5209 0.5185
F 28.0483 29.6803 30.5641 14.9921 31.0070 31.5757
Degree -0.0014** -0.0014** -0.0014** -0.0012* -0.0007 -0.0011*
(0.0006) (0.0006) (0.0006) (0.0007) (0.0008) (0.0006)
R² 0.5176 0.5227 0.5175 0.4988 0.5215 0.5197
F 27.1606 29.1862 29.8898 14.7081 30.8546 30.8622
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14. Results
• Robustness checks
• Split sample => more / less (information)
problematic deals => more / less impact of lenders’
centrality
• Local lenders only / large loans / long maturities /
large syndicates / concentrated syndicates / many
league tables lenders…
• Main results remain robust
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15. Discussion
• Analyze small world properties of the French
syndicated lending market
• => social capital, experience, reputation
• French market dynamics => small world
• Evaluate the impact of network structure on
borrowing costs
• Lenders’ centrality = proxy for experience &
reputation => mitigate agency costs
• Lower loan spread when “central” bank syndicates
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