Information technology and banking organization

Information technology and banking organization

Sauro Mocetti*, Marcello Pagnini* & Enrico Sette**

Conference on “The Economics of Small Businesses in the Aftermath of the Crisis. Cross-Country Analyses”

Urbino, October 20th – 21st, 2010

*Bank of Italy, Regional Economic Research Division, Bologna branch**Bank of Italy, Research Department, Rome

Resesarch project on bank organization and local credit markets

The present paper is part of a research project of the Bank of Italy based on a survey on 300 Italian banks

The final report describing the main results of the survey and several papers that were also part of the project can be found in the volume:

Cannari, Pagnini and Rossi (eds) ,2010, Banks, Local Credit Markets and Credit Supply, n.5, Seminar and conferences, Bank of Italy

(downlodable at: www.bancaditalia.it/pubblicazioni/seminari_convegni/banche-mercati-territoriali)

CONTENTSIntroductionLuigi Cannari, Marcello Pagnini and Paola Rossi ……………………………….…………. 5Session 1NEW TECHNOLOGIES AND BANKING ORGANIZATION1. The organization of lending and the use of credit scoring techniques in Italianbanks: Results of a sample surveyGiorgio Albareto, Michele Benvenuti, Sauro Mocetti, Marcello Pagnini andPaola Rossi ………….…………………………………………………….……………. 112. Information technology and banking organizationSauro Mocetti, Marcello Pagnini and Enrico Sette ………………………….…………. 493. Bank acquisitions and decentralization choicesEnrico Beretta and Silvia Del Prete …………………………………………….………. 77DiscussionElena Beccalli …….………………………………………………..……..…….………. 103Barbara Chizzolini ….…………………………………………………….…………….. 107Session 2BANKING ORGANIZATION AND SMALL BUSINESS LENDING4. The retail activity of foreign banks: Effects on credit supply to households andfirmsLuigi Infante and Paola Rossi ………….…………………….….….…………………... 1135. Debt restructuring and the role of lending technologiesGiacinto Micucci and Paola Rossi …………………….……………….……….……… 1436. Loan officer authority and small business lending. Evidence from a surveyMichele Benvenuti, Luca Casolaro, Silvia Del Prete and Paolo Emilio Mistrulli .……. 175DiscussionAngelo Baglioni …….…………………………………………………..…………....…. 193Paola Bongini …….……………………………………………….……………….....…. 197

MAIN GOALS OF THE PROJECT

A metrics for the bank internal organization in small business lending (power delegation, Credit scoring adoption, LBM tenure,…)

Determinants of the internal organization: ICT, M&A

Effects of organizational choices on credit allocation (financial distress, credit availability for sme, interbank competition)

MAJOR FINDINGS IN A NUTSHELL

sharp differences in bank organization even beyond the traditional divides (small vs large banks)

Bank organization does matter for credit allocation, although in a non trivial way

What comes next? New survey on 2009 Main topic: how did heterogeneous bank organizations react to the crisis?

INTRODUCTION AND MOTIVATIONS (1)New technologies and the role of the LBM

The aim of the paper is to empirically investigates whether and how the introduction of new technologies in banking industry affected the degree of power delegation to the local branch manager (LBM) in small business lending

The issue is relevant because:

• primacy of the LBM in small business lending (particularly for firms that are more opaque and difficult to evaluate)

• worries about the introduction of ICT & credit scoring and their effects on LBM’s initiative and small business credit conditions

INTRODUCTION AND MOTIVATIONS (2)Literature

The paper is at the crossing of three strands of literature:

• Role of LBM in lending activity (Stein 2002; Liberti 2005; Liberti&Mian 2006; Uchida, Udell, Yamori,2009; Agarwal and Hauswald, 2010, Cannari, Pagnini,Rossi, 2010,Hertzberg, Liberti, Paravisini, 2010)

• Effects of new technologies – including credit scoring – in banking industry and lending activity (Berger 2003; Berger et al. 2005; Casolaro&Gobbi 2006; Felici&Pagnini 2008)

• Impact of new technologies on firm organization (Brynjolfsson&Hitt 1998; Bresnahan et al. 2002, Colombo&Delmastro 2004, Bloom et al. 2009)

LBM

THEORETICAL FRAMEWORK (1)A graphical illustration

HEADQUARTER

Decentralization(power delegation)

vs.

Centralization(transmission of information)

THEORETICAL FRAMEWORK (2)Centralization vs. decentralization

THEORETICAL FRAMEWORK (3)(ambiguous) impact of new technologies

IT reduce monitoring coststhus leading to more delegation

IT reduce costs for acquiring and processing information for the CEO thus leading to more centralization

Further channels:

• IT might reduce the costs of acquiring information locally

• IT might substitute LBM in manual activities thus increasing efficiency of cognitive interactive tasks

• Other channels…

monc

deccc inf

THE MODEL (1)Aim of the model

• Sketch these ideas in a (super) simple principal-agent model

• Show the ambiguity in IT-delegation relationship

• Get an estimable (linear) equation

THE MODEL (4)Estimable equation

sizeBHmm

distBHmm

ITBHmmBHmm

mBHx

12

)(

12

)(

12

)(

12333222111*

iiiii XSCORINGCAPICTPD 21 _

Delegation ICT capital stock per employee in 2003

Adoption of credit scoring

Bank size, distance, etc.

DATA AND VARIABLES (1)Data sources

Sample: about 300 banks in 2006

Sources:

• Bank of Italy survey on the distribution of power delegation across hierarchical levels within the bank organization and credit scoring adoption in small business lending (Albareto el al. 2010)

• Bank of Italy Supervisory reports: balance sheet data on bank size and profitability, hardware and software investment flows, etc.

DATA AND VARIABLES (2)IT variables

• ICT capital stock per employee

Hardware, software and premises for computing equipment. It has been computed using the perpetual inventory method

• Adoption of credit scoring

Discrete variable: 1=no adoption of CS; 2=adoption of CS in the last 3 years; 3=adoption of CS since at least 3 years

DATA AND VARIABLES (3)LBM’s power delegation

0

5

10

15

20

25

30

0 1-20 21-50 51-100 100-200 201-500 oltre 500

Absolute power delegation

0

5

10

15

20

25

30

0 0-5 5-10 10-20 20-30 30-50 oltre 50

Relative power delegation

Maximum amount of loan that can be granted in autonomy by the LBM. Power delegation refers to applicants exhibiting a risk level that a bank judge a priori as normal

Maximum amount of loan that can be granted in autonomy by the LBM normalized with respect that of the CEO

DATA AND VARIABLES (4)Further variables

Other controls

Variable Description (1) Mean St. dev.

SIZE Log of total assets. 20.69 (1.476) DISTANCE Log of average distance (in kilometres) between the headquarters of the

bank and the local markets where the bank has at least one branch. The distance is weighted by the amount of loans borrowed in the market where the local branches are situated.

3.10 (0.895)

LOAN SIZE Log of the average loan size in the bank’s portfolio. 11.57 (0.548) BRANCH SIZE Log of the number of employees per branch. 1.66 (0.350) ROA Returns on assets. 0.01 (0.008) BAD LOANS Ratio of bad loans to total lending. 0.04 (0.044) NUMLEV (Log of the) number of hierarchical levels between the LBM and the CEO

(2006). 1.19 (0.355)

TURNOVER (Log of the) average permanence (in months) of the LBM within the same branch (2006).

3.71 (0.430)

CEO EDU Dummy equal to 1 if the CEO has a university degree. 0.38 (0.487) CEO AGE Age of the CEO. 53.48 (6.123)

(1) Data refer to 2003 if not otherwise specified. Source: Supervisory Report of the Bank of Italy, Survey on Banking internal organization.

EMPIRICAL ANALYSIS (1)The econometric set-up

• Cross section of approx. 300 banks in 2006

• ‘Static’ analysis: Do differences in the intensity of IT adoption across banks explain the variation in the amount of power delegated to the LBM in SME lending?

EMPIRICAL ANALYSIS (2)Main findings

Impact of new technologies on relative delegation

ICT capital stock per employee 0.013*** 0.012** 0.012*** (0.004) (0.005) (0.004)

▲ CS in the last 3 years -0.012 -0.018 -0.022 (0.017) (0.017) (0.018) ═ CS since at least 3 years 0.069*** 0.060** 0.070*** (0.024) (0.024) (0.024) ▲ BANK SIZE YES YES YES YES

Other controls (distance, loan size, branch size)

- - - YES

Impact of new technologies on absolute delegation

ICT capital stock per employee 0.135** 0.119** 0.110** (0.057) (0.060) (0.056)

▲ CS in the last 3 years 0.336 0.302 0.324 (0.213) (0.216) (0.218) ═ CS since at least 3 years 0.412* 0.352 0.393* (0.212) (0.215) (0.221) ▲ BANK SIZE YES YES YES YES

Other controls (distance, loan size, branch size)

- - - YES

Observations 291 296 291 289

EMPIRICAL ANALYSIS (3)Robustness checks

I II III IV

ICT capital stock per employee 0.012*** 0.011*** 0.014*** 0.014*** (0.004) (0.004) (0.004) (0.004)

▲ CS in the last 3 years -0.020 -0.017 -0.034* -0.026 (0.019) (0.018) (0.020) (0.021) ═ CS since at least 3 years 0.072*** 0.081*** 0.070*** 0.083*** (0.025) (0.025) (0.026) (0.026) ▲ BASELINE CONTROLS Bank size, distance, loan size, branch size

YES YES YES YES

BANK PERFORMANCE ROA, bad loans

YES - - YES

ORGANIZATION VARIABLES Hierarchical levels, turnover

- YES - YES

CEO CHARACTERISTICS CEO education, CEO age

- - YES YES


EMPIRICAL ANALYSIS (4)IV estimates

I II III IV

ICT_CAP 0.029** 0.029** 0.011*** 0.011*** (0.012) (0.012) (0.004) (0.004)

▲ SCORING 0-2 -0.026 -0.026 -0.022 -0.023 (0.025) (0.024) (0.024) (0.023) ═ SCORING 3+ 0.066* 0.064* 0.061 0.060* (0.039) (0.033) (0.039) (0.033) ▲ BASELINE CONTROLS Bank size, distance, loan size, branch size

YES YES YES YES

Instrumental variables ICT_ 95 SCORE_CC

ICT_ 95 SCORE_MO

ICT_ 00 SCORE_CC

ICT_ 00 SCORE_MO


IV variables:

• Lagged variable for ICT capital stock

• “Household” credit scoring for “small business” credit scoring

EMPIRICAL ANALYSIS (5)A synthesis of the results

• Banks equipped with more ICT capital delegate more• Banks adopting credit scoring also enlarge the level of delegation to LBM• Results are robust to many additional controls including IV estimates

Further results (see the paper):• New technologies take time to exert their effects (because of learning costs and uncertainty surrounding their returns)• Impact of ICT capital is stronger for banks adopting credit scoring (positive complementarities)• Impact of new technologies on delegation is stronger for banks that are more specialized in small business lending

CONCLUSIONSOur interpretation of the results

• ICT forces pushing toward decentralization (falling monitoring costs) prevail over those moving in the opposite direction (falling information transmission costs)

• Worries about the introduction of ICT and its negative effects on bank-firm relationships were exaggerated.

• Re-definition of the role of LBM: thanks to the pre-screening activity of the scoring and the computerization of routine tasks, the LBM can focus on cognitive and interactive tasks (e.g. analysis of “border” lending practices)

What comes next? Lessons for and from the crisis

•How did heterogeneous bank organizations face the recent crisis?

•Which consequences for sme credit availability and costs?

• Had the crisis an impact on bank organization? And If so, how?

• …

Thanks for your attention!

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Information technology and banking organization