1. The Impact of Organizational Structure and Lending Technology On Banking Competition Hans Degryse Luc Laeven Steven Ongena Discussion by: Fabio Panetta Banca d’Italia

2. 2 INTRODUCTION Main goal of the paper is to study how banking competition (loan rates, market shares) are affected by transportation costs in local markets Main innovation with respect to Degryse and Ongena JF, 2005 (almost same dataset) is the inclusion of organizational variables: transportation costs affected by –distance between bank (A)-borrower (F), and F-rival bank (B) –organizational structure (complexity) of A and B Nice paper. Ideal for a discussant

3. 3 The theoretical model (1) Bank-specific transportation costs. Most simple case (linear transportation costs): –TC A = t A * x, where x= distance between A-F t A = A’s unit trans’n cost –Mkt Share A =t B /(t A + t B ) i.e. market share increases with TC of Rival and decreases with own TC –r A = t B - (t A + t B ) x slope (spatial pricing) increases with own and Rival’s TC

4. 4 Lending decision can be based on hard info (HI) or soft info (SI) –If HI used, transportation cost lower. Distance matters less (just “send a fax”) –If SI is used, transportation cost higher. More scope for spatial pricing “Identifying assumption”: banks more likely to use HI if “organizationally complex”: –large –foreign –hierarchical –IT-intensive (fax) The theoretical model (2)

5. 5 Empirical analysis (1) BRANCH REACH (BR): –BR A = a*(OF B ) + CONTROLS OF=Organizational Form: Rival is Large, Foreign, More Hierarchycal (Bank, Branch), Fax a<0 LOAN RATE (r): –r A = γ*distance A + θ*distance A *OF B + β*distance B + δ * distance B *OF B + controls γ 0 β > 0, δ <0

6. 6 –Model estimated using unique dataset with firm-specific loan rates info on location of firms, lending and rival banks data on rivals’ organizational form –Results of the estimation overall consistent with predictions Geographical reach narrower if rivals large, foreign, hierarchical and with fax Presence of large/foreign rivals reduces spatial pricing Empirical analysis (2)

7. 7 –Model: useful to illustrate predictions tested in the empirical analysis. Additional insights could be obtained extending the framework to endogenize the technology choice of the rivals and their branch network density –Analysis seems not to take into account possibility of endogenous matching bank-firm For ex. banks may be willing to lend to risky firms only if close to the lender, which allows a mitigation of risk. Then, higher rates to closer firms just reflect higher risk => risk-adjusted rates or firm fixed-effects (not possible in a cross-section) spatial pricing may disappear Comments (1): The Model

8. 8 –Model: r & Reach depend on OF B but also on OF A Empirical analysis: only OF B used –Omitted variable bias? –Why not use (i) A’s branch-specific hierarchycal pos.? (ii) Fax in A’s branch (but see below)? –Fax. Is this relevant? –SI difficult to transfer, whether or not you have a fax. HI easy to transfer even without fax (phone, mail) –Fax is OK if proxy for IT, but unlikely (IT policy at bank- level, not branch-level). Why not use info on IT intensity? –More details needed on empirical analysis –panel/cross section? How do you compute SEs? Empirical Analysis: General Comments

9. 9 Is Size of the mkt of A’s branches affected by OF B ? –Affected by characteristics of the area? Is traveling easier? Mountains /Quality of public transportation / Better highways? »Suggestion: postal-zone fixed-effects? –Do (some of) A’s branches specialize in specific groups of borrowers? (SIZE, but also sector/exporting)? »Suggestion: (i) analyze dn. of customers across branches (ii) use branch-specific fixed effects? –Reach affected by duration relationships? (Inertia: old customers not much affected by small changes in TC) »Suggestion: check robustness dropping longest relationships? Empirical Analysis: Branch Reach (1)

10. 10 –In tables 2 and 3, robust results seem to emerge when OF B is measured using hierarchy. Other variables significant occasionally. Interested to see a regression including both bank- and branch- specific hierarchy Empirical Analysis: Branch Reach (2)

11. 11 –Is spatial pricing attenuated by B’s use of HI? Model: level of r affected by differences in MC (μ). Why no controls for bank characteristics (Cost/Income)? Table 4: spatial pricing decreases if rival large, foreign, hierarchical, more efficient in using IT (fax) –Paper claims this reflects use of HI. However, an alternative expl’n is that the lending tech of these rivals is simply more efficient –Moreover, the interaction terms are rarely significant (only for Large banks) Relationship vars. likely correlated with degree of asy info and hence may change the impact of distance on loan rates. Would be useful to interact these vars with distance Empirical Analysis: Loan rates (1)

12. 12 Not sure Tab. 5 really consistent with model. Your regression: r A = a 0 * (distance lender) + a 1 * (distance lender * Large rival) + a 2 * (distance lender * Large rival * Small firm)+… Predictions (would be useful to clearly spell out the predictions of the model somewhere in the paper): 1.Spatial pricing: a 0 <0 OK 2.Spatial Pricing less sharp when rival large, hi tech….. a 1 >0 OK (but coefficients never significant) 3.Effect 2. less pronounced for soft borrowers. a 2 0 Empirical Analysis: Loan rates (2)