institutional design as a commitment device in credit markets with asymmetric information:...

Institutional Design as a Commitment Devicein Credit Markets with AsymmetricInformation: Experimental Evidence

Daniela Di Cagno - Emanuela Sciubba�

The aim of this experiment is to test the role of institutional design incredit markets as a commitment device against renegotiation: whenthere is asymmetric information does a lower degree of centralizationenhance ef®ciency? Does decentralization alleviate the adverse selec-tion problem in credit markets? We run a large-scale computerizedexperiment involving 12 different data sets and 3 different uncertaintyscenarios on a sample of 120 subjects. The results obtained con®rmthe superiority of a decentralized institutional framework: the numberof poor projects undertaken in a decentralized market was signi®c-antly smaller than the number of poor projects undertaken in centra-lized markets in all the scenarios. This experimental evidence showsthat the institutional design is crucial in seeking ®nancial disciplineand therefore can shed some light on the debate on `Anglo-Saxon'versus `German±Japanese' credit practices.

(J.E.L.: C90, D82, G21, L10).

1. Introduction

1.1. Motivation

The world of banking is changing under the conjunction of a multitude of

forces.

Until the early 1990s, commercial banks in Europe were relatively

protected from competition, through formal or informal barriers to entry into

the market. Deregulation, the single market programme and, above all, the

abolition of capital controls which occurred in the late 1980s, were all recipes

for an increase in competitive pressure in the European banking industry.

At the same time, the world banking industry has been shaped by a

massive consolidation. The signs of such world-wide transformations are

Economic Notes by Banca Monte dei Paschi di Siena SpA, vol. 29, no. 2-2000, pp. 281±313

# Banca Monte dei Paschi di Siena SpA, 2000. Published by Blackwell Publishers,108 Cowley Road, Oxford OX4 1JF, UK and 350 Main Street, Malden, MA 02148, USA.

� Address for correspondence: Daniela Di Cagno, Universita' LUISS, Via di Villa Massimo

n.56, 00198 Roma, Italy. E-mail: [email protected]; Emanuela Sciubba, Tinbergen Institute

Rotterdam, Burg. Oudlaan 50, 3062 PA Rotterdam, The Netherlands. E-mail: [email protected]

plenty. One telling illustration is provided by the following facts (Danthine et

al., 1999): in 1990, the world's six largest banks (measured by their market

capitalization) were all Japanese.1 In 1998, only Tokyo-Mitsubischi has

survived in the top-ten list, and it has fallen to number nine. Ten years ago

there was only one US bank in that group, JP Morgan; in 1988 there were six.2

No EURO-based bank makes it to the top-ten group. Deutsche Bank, which

was the highest non-Japanese bank in the top-ten list in 1990, has disappeared

from the list.

Consolidation in the US commercial banking industry has taken the form

of a massive reduction in the number of banks. Between 1979 and 1997, the

number of commercial banking organizations in the US has fallen from 12,463

to 7,234.3 Bankruptcies have played only a relatively small role in this

development; most of the consolidation was due to mergers and acquisitions.

The ten largest mergers in the US history, in any industry, occurred during

1998 and four out of these occurred in banking: Citicorp-Travelers, Bank-

America-Nationsbank, Bank One-First Chicago and Northwest-Wells Fargo.

The volume of European banking mergers almost quadrupled in the same

period. In Switzerland, UBS and SBC merged to create the world's largest bank

by total assets. In Bavaria, Bayerische Hypobank and Bayerische Vereinsbank

merged to create the number four bank in Germany. In Austria, Bank Austria

acquired Creditanstalt. In Spain, Banco Santander completed the acquisition of

Banesto and announced the merger with Banco Central Hispanoamericano. In

Italy, IMI and San Paolo, and Cariplo and Ambroveneto, merged to create the

country's two largest banks. And these are only a few examples.

There is very little doubt on the fact that the world banking industry is

changing. Especially inside the EURO, the practice of banking and the process

of ®nancial intermediation is becoming more uniform, but at what speed and

on which model are we converging?

These issues have forcefully brought back the interest of economists and

practitioners alike in the debate on the `Anglo-Saxon' versus the `German±

Japanese' corporate ®nancing practices.

From a theoretical standpoint, one might think of different reasons for

supporting one or the other. Starting point for any analysis that aims at tracing

ef®ciency conditions for the one or the other institutional design, is the

presence of severe asymmetric information in any banking relationship.

1 International Bank of Japan, Fuji, Sumitomo, Dai-Ichi, Tokyo-Mitsubischi and Sanwa.2 BankAmerica, Citigroup, Wells Fargo, First Union, Bank One, Chase Manhattan.3 The clean-up has been concentrated among the smallest institutions: in this period, the

number of banks with total assets in excess of US$ 100 billion has actually increased from 3 to 6;

the number of medium-sized banks (with total assets between US$ 100 million and US$ 100

billion) has remained relatively stable; the number of banks with total assets below US$ 100

million has fallen from 10,014 to 5,636. As a result, the market share of the top eight ®rms has

increased from 22.3% in 1988 to 35.5% in 1997.

282 Economic Notes 2-2000

# Banca Monte dei Paschi di Siena SpA, 2000.

In banking, asymmetric information acts in two directions. First, at the

beginning of a credit relationship, creditors do not possess as good information

as their clients on the quality of the projects they are asked to ®nance. Second,

during the credit relationship, bankers do not have the same extent of control

over the success of the project as entrepreneurs have. This gives rise to

problems both of adverse selection and of moral hazard. In particular, the

adverse selection problem has been extensively studied within the literature on

credit rationing; see, for example, Stiglitz and Weiss (1981).

The debate on centralization versus decentralization of credit markets has

therefore been centred on the relevance of asymmetric information. Econo-

mists have asked under which institutional framework informational asymmet-

ries can be better dealt with. The answers that the literature has given so far do

not unanimously support one institutional design as opposed to the other. The

lesson that one can draw from the debate is, therefore, that the preference for

one system rather than the other has to be based on the speci®c context. The

role of the economic theorist is to highlight precisely which institutional

framework will work ef®ciently in which context.

1.2. Related Literature

Several contributors have suggested reasons why a centralized credit

market of the `German±Japanese' style might result in a more ef®cient

selection of investment projects.

For example, Gehrig (1998) shows how loan markets competition can

reduce monitoring and screening incentives of banks with the result that a

higher degree of market fragmentation has a negative effect on the average

quality of the investment projects ®nanced. In fact competition tends to reduce

oligopoly rents and, at the same time, banks' incentives to devote resources to

creditworthiness tests.4 As a result competition might worsen the adverse

selection problem.

Petersen and Rajan (1995) highlight how competition might be incompat-

ible with `relationship banking'. They believe that a fragmented credit market

may be inimical to the formation of stable relationships between ®rms and

banks. Stable relationships are, on the contrary, bene®cial precisely because

they work towards a reduction of informational asymmetries and alleviate the

moral hazard problem.

Other contributors have suggested reasons why a decentralized credit

market might favour a more ef®cient investment project selection.

4 The basic argument dates back at least to Schumpeter who suggested that a monopolistic

economy offers better incentives for innovation because an inventor can better recoup his

investment in R&D through future rents.

283D. Di Cagno and E. Sciubba: Experimental Evidence


In the theory of banking, decentralization has been shown to improve upon

project selection according to at least three different approaches: multiple

banking, access to trade credit and diffuse ownership of capital. These contexts

all share the common feature that the problems posed by informational

asymmetries can be alleviated when third parties enter the picture.5 More

generally, in contract theory Dewatripont (1988) among others has highighted

the usefulness of contracts with third parties as a means of strategic commit-

ment.

In a recent paper, Detragiache et al. (1997) show how multiple versus

single banking relationships can help to overcome severe adverse selection

problems. They consider an economy where only banks that have established a

relationship with an entrepreneur can extract information on the quality of his

project. They assume that lending can be terminated in two cases: if the bank

learns that the quality of the project is bad or if the bank is hit by a liquidity

crisis. In the latter case, a good interrupted project might experience severe

dif®culties in ®nding a new creditor as the termination by previous bankers

will be perceived as a bad signal on its quality. To overcome this problem,

entrepreneurs might ®nd it pro®table to establish multiple banking relation-

ships.

Bias and Gollier (1997) suggest that trade credit can alleviate the adverse

selection problem by incorporating in the lending relation the private informa-

tion held by suppliers about their customers. They believe that trade credit

serves a non-®nancial role: if suppliers have private information about their

customers, their lending can serve a signalling role and alleviate the informa-

tion asymmetry which would otherwise preclude ®nancing of pro®table

projects.

Finally, Dewatripont and Maskin (1995) argue that decentralization of

credit markets may promote a more ef®cient project selection when creditors

are not fully informed ex-ante about projects' quality. Diffuse ownership of

capital and competition among banks work as a commitment device against

renegotiation between banks and entrepreneurs.6 If banks can credibly commit

not to renegotiate the terms of lending contracts, then entrepreneurs will screen

their project quality and only pursue investment opportunities which are

pro®table. Dewatripont and Maskin (1995) conclude their analysis suggesting

that a qualitatively different result might arise if one assumes that some of the

most pro®table projects are also very slow and do not pay in the short run. In

such a circumstance, decentralized credit markets display the drawback of

being more prone that centralized economies to short-termism.

5 This kind of approach is also largely employed in the literature on bankruptcies, where

bankruptcy law serves as a commitment device and is required to enforce the bargaining structure

ex-post. See, for example, Berkovitch et al. (1997).6 Decentralization as an enforcement mechanism has been studied also in other subdiscip-

lines of economics. For example, Dewatripont (1989) looks at the renegotiation issue in the context

of labour contract theory.



1.3. Our Contribution

The theoretical debate on which of the two institutional designs leads to a

more ef®cient project selection is, therefore, very lively and still very open.

The answer will depend on the initial assumptions and therefore on the speci®c

context one has in mind.

Most of the contributors to this debate focus on the role of third parties in

the process of project selection. For example: in Dewatripont and Maskin

(1995), the fact that there are third parties involved implies that entrepreneurs

®nd the threat of termination of their lending contracts more credible and

therefore operate a better screening on the quality of their projects. The aim of

this paper is to test experimentally whether this is actually true: we ask whether

a diffuse ownership of capital can really work as an enforcement mechanism

that prevents agents from pursuing unpro®table investment opportunities.

We consider a stylized banking industry, modelled according to a simpli-

®ed version of Dewatripont and Maskin (1995) and Detragiache et al. (1997).

In particular, we look at a credit market in which creditors are not fully

informed ex-ante about the quality of the projects that entrepreneurs submit to

®nancing. Project managers know their projects' quality, but creditors acquire

this information only gradually. As a result, it may happen that projects that

looked worth ®nancing in the ®rst place, after a while, turn out to be poor

deals. Nevertheless, re®nancing them once that they have begun, becomes

sequentially optimal because of the sunk costs which the bank has incurred.

In this economy, if the threat of termination deterred entrepreneurs from

undertaking poor projects in the ®rst place, creditors would wish to commit

themselves ex-ante not to re®nance. However, sunk costs may well make this

threat not credible: ex-post both creditors and entrepreneurs can be better off

re®nancing.

In such a circumstance, decentralization works as a commitment device to

terminate poor projects. In fact, if the ownership of capital is diffuse, the initial

creditor may not have suf®cient resources to continue to fund the poor project:

re®nancing will require new creditors to have additional costs and/or to pay an

informational rent to initial creditors. As a result, later creditors will not be

able to capture all the surplus from re®nancing. Decentralization, thus, reduces

incentives to re®nance and, making the threat to terminate more credible,

enhances an ef®cient project selection preventing unpro®table projects from

ever being undertaken.

We ran a large-scale computerized experiment involving 12 different data

sets and 3 different uncertainty scenarios on a sample of 120 subjects. The

results obtained con®rm the superiority of a decentralized institutional frame-

work: the number of poor projects undertaken in a decentralized market was

signi®cantly smaller than the number of poor projects undertaken in central-

ized markets in all the scenarios. This experimental evidence shows that



forcing a contract with a third party through a diffuse ownership of capital is

indeed perceived as a strong enforcement mechanism by economic agents.

1.4. Overview

The structure of the paper is as follows. In section 2, we present the model

and discuss its predictions for centralized and decentralized credit markets.

Section 3 describes the experiment. In particular, in section 3.1, we present the

experimental design; in section 3.2, we describe the data set; in section 3.3, we

introduce three different probabilistic scenarios; and, ®nally, in sections 3.4

and 3.5, we brie¯y comment respectively on the pilot we ran and the software

we adopted. Section 4 summarizes the experimental results. In particular, we

comment on the results we obtain for projects' selection in section 4.1 and for

earnings in section 4.2. Section 5 concludes the paper. The appendix contains

the full set of pay-offs and the instructions for the experiment. A complete set

of experimental data can be obtained from the authors upon request.

2. The Model

We consider a simple dynamic investment model, very close to Dewatri-

pont and Maskin (1995) and to Detragiache et al. (1997).

There are two time periods, one entrepreneur and either one or two banks;

players negotiate a lending contract at the beginning of the ®rst period and the

investment project is carried out in periods 1 and 2; if a project is incomplete

at the end of period 1, players can renegotiate the terms of the lending contract.

All parties are risk-neutral and maximize expected pro®ts.

The story is as follows. Entrepreneurs have ideas for investment projects,

but no capital of their own to ®nance them; bankers do not have ideas of their

own for investment projects but own all the capital. In such an economy, a

lending contract is clearly mutually bene®cial. The entrepreneur describes his

investment project to the banker and asks for ®nancing: in particular, the

entrepreneur suggests a completion date for the project. We normalize the

length of time needed for completion to one period. The two parties agree on a

lending contract that speci®es an interest payment that the entrepreneur will

correspond to the bank upon completion of the investment project at the end of

period 1.

There are two types of investment projects: good investment projects and

bad investment projects. Good projects are indeed completed within the

expected time and are pro®table in period 1; bad projects are not pro®table and

they do not produce any wealth in period 1. To recoup some of the resources

spent on a bad project in period 1, additional ®nancing is required for period 2.

We normalize the amount of capital needed to carry on the project to one



unit of capital per period: good projects will only require one unit of capital;

bad projects will require two units of capital to be accomplished.

At the beginning of period 1, when the parties sign the lending contract,

there is asymmetric information on the quality of the investment project: the

entrepreneur knows whether his project is good or bad, while the banker does

not. If the entrepreneur obtains a positive pay-off out of a bad quality project,

then he might still want to pursue his investment opportunity and ask for

®nancing, without revealing the quality of his project to the banker. If bankers

are not capable of screening good from bad projects and if the likelihood of a

bad quality project is high, then entrepreneurs with good projects might be

refused ®nancing. This is an adverse selection problem.

To analyse the problem more formally, we need to introduce some

notation. Let á be the prior probability that the investment project is good and

(1ÿ á) be the prior probability that the investment project is bad. Clearly á is

such that 0 < á < 1 and we assume that its value is common knowledge. Let

Eg be the pay-off to an entrepreneur when he has a good quality project and

his project is ®nanced and carried out to completion. Let Et be the pay-off to

an entrepreneur when he has a bad quality project and his project is ®nanced

for period 1, but then interrupted and not carried on to completion, i.e. the

project is terminated. Finally let Eb be the pay-off to an entrepreneur when he

pursues a bad quality project and his project is ®nanced in both period 1 and 2,

and is therefore carried out to completion.

We are interested in studying the case: Eb > Et. Namely, we assume that,

should an entrepreneur pursue a bad investment opportunity and obtain

®nancing in the ®rst place, then his pro®t is higher if his investment project is

carried out to completion rather than terminated in period 1. This assumption

is needed if we want bad quality projects ever to be completed. In fact, should

the entrepreneur's pay-off be higher in case of termination, he would always

choose to interrupt his project and not to reapply for ®nancing. In other words,

without this `preference for completion' assumption, the prediction of the

model would become trivial.

2.1. Centralized Credit Market

We model the centralized credit market as a bilateral monopoly: there is a

single creditor endowed with two units of capital.

At the beginning of period 1, the entrepreneur observes the type of his

investment opportunity and decides whether to pursue the project or not. If he

decides to undertake the project, he requests a loan of one unit of capital to the

banker, who can either accept or refuse the ®nancing. If the banker refuses,

since the entrepreneur has no capital of his own, the project cannot be

undertaken and the game reaches a terminal node where zero pay-offs are

distributed both to the entrepreneur and the banker. If the banker accepts to



®nance the project, the parties sign a lending contract where the entrepreneur

agrees to pay an interest R . 0 upon completion of the project and the bank

agrees to lend one unit of capital.

At the end of period 1, the uncertainty on the type of the project is

revealed. The banker `naturally' learns if he ®nanced a good or bad investment

opportunity: in fact, a good project is completed in time (i.e. by the end of

period 1) and yields a (net) payoff of Eg to the entrepreneur and the agreed

interest payment R to the banker.

If the project is bad, its observable return at the end of period 1 is zero and

the banker obtains nothing, unless he agrees to devote additional resources to

the project re®nancing it for period 2. However, the entrepreneur might still

obtain a positive pay-off out of a terminated project (think of his salary, for

example, or of his own private bene®ts in being in charge of an investment

project for a period, and the like): Et is the (possibly positive) pay-off to an

entrepreneur when his bad quality project is terminated at the end of period 1.

The banker, on the contrary, if the investment project is not carried to

completion, will not obtain the agreed interest payment and will lose the initial

capital invested: his pay-off will be negative and equal to ÿ1.

On the other hand, if the banker agrees on re®nancing, at the end of period

2, the entrepreneur obtains a payoff Eb > Et and the creditor obtains the

agreed interest payment. It is clear that, for some pay-off values, both banker

and entrepreneur will be better off if the bad quality project is carried out to

completion.

To summarize, the sequential form of the game can be represented as in

Figure 1.

Nature

Bank

Entrepreneur Entrepreneur

(0,0)

(0,0) (0,0)(Eg, R 2 1)

(Eb, R 2 2)(Et , 21)

Bank

nb b b nb

(0,0)

α 1 2 α

nf nff f

nrf rf

Figure 1: Centralized Credit MarketNotes: b � borrow; nb � not borrow; f � ®nance; nf � not ®nance; rf � re®nance; nrf � not

re®nance



2.2. Decentralized Credit Market

Following Dewatripont and Maskin (1995), we model decentralization as

a market where the ownership of capital is diffuse, so that the capital resources

of a single banker are not suf®cient to ®nance the project for two periods. As a

result, the entrepreneur who has pursued a bad project has to turn to a different

banker if he wants to obtain ®nancing for a second period.

The model is similar to the one we described for a centralized market, but

we now allow for two creditors, each with only one unit of capital. At the

beginning of period 1, an entrepreneur who wants to undertake an investment

opportunity, asks for a loan to one of the two bankers, whom we call Bank 1. If

the project is a good one, the story is exactly as in the centralized market case.

On the other hand, if the project is a bad one and needs re®nancing the

entrepreneur must now turn to Bank 2 as his ®rst creditor has no capital left.

If Bank 2 agrees on re®nancing and the bad project is carried to

completion, the entrepreneur obtains a pay-off Eb. Bank 1 loses the unit of

capital ®nanced, but obtains an informational rent from Bank 2: his pay-off at

the end of period 2 will be IRÿ 1, where IR > 0 is a payment from Bank 2 to

Bank 1. Finally, the Bank 2 obtains Rÿ IRÿ 1.

The presence of additional costs in establishing a multiple banking

relationship ± here referred to as informational rent ± is essential for the

qualitative results that one can draw from this type of models. In the next

section, we show that, as long as the informational rent is substantial, namely

IR . Rÿ 1, decentralization promotes a more ef®cient project selection; if

0 < IR < Rÿ 1, the two institutional frameworks are essentially the same and

enforce the same choice of investment opportunities. As soon as the value for

IR is above Rÿ 1, the set of investment projects that is carried out and

®nanced in equilibrium in the two market designs changes dramatically. We

believe that one can think of IR as of additional costs which are needed to

establish a new banking relationship and/or of the actual informational rent

that the second banker pays to the ®rst one, such as handling fees to transfer

the bank accounts, and so on.

To summarize, the sequential form of the game can be represented as in

Figure 2.

2.3. Equilibria

We are interested in comparing the perfect Bayesian equilibria of this

game under centralization and decentralization. In particular, we focus on

investigating how these two institutional designs may serve to deter poor

entrepreneurs by making lending termination more credible.

Clearly both number and kind of equilibria crucially depend on our

assumptions about the relative magnitudes of pay-offs.



Assume that entrepreneurs attach negative utility to a bad quality project

that is interrupted, but positive utility to a bad quality project that is carried out

to completion. Formally: Eb . 0 . Et. We can now distinguish different cases

according to the magnitude of the interest payment. If the interest payment is

small, namely if R , 1, then bankers will not want to re®nance bad projects.

This is true under centralization and, a fortiori, under decentralization. Thus,

since they obtain negative pay-offs out of interrupted projects (Et , 0),

entrepreneurs will not pursue bad projects and only good projects will be

undertaken and ®nanced under both centralization and decentralization.

If the interest payment is very large, namely Rÿ IR . 1, then regardless

of what was initially agreed, it is advantageous to all parties to re®nance a slow

project under decentralization and, a fortiori, centralization. Because entrepre-

neurs obtain a positive pay-off out of a completed bad quality project (Eb . 0),

entrepreneurs will pursue bad projects as well as good ones and so, under both

systems, both types of projects will be undertaken and ®nanced.

The most interesting case is when the interest payment is large, but not

large enough relative to the informational rent, namely when Rÿ IR , 1 , R:

in this case, in fact, re®nancing becomes ef®cient under centralization but not

under decentralization. Entrepreneurs anticipate that bad quality projects will

not be re®nanced and since they attach negative utility to interrupted projects,

poor investment opportunities will only be undertaken in a centralized market

and not in a decentralized one. Bankers, in turn, anticipate the ef®cient project

selection operated by entrepreneurs and always agree on ®nancing projects.

The fact that an entrepreneur has accepted the investment opportunity serves a

signalling role for the banker, who updates his prior beliefs on the quality of

the project and attaches probability 1 to the investment opportunity being of

good quality.

Nature

Bank 1

Entrepreneur Entrepreneur

(0,0,0)

(0,0,0) (0,0,0)(Eg, R 2 1,0)

(Eb, IR 2 1, R-IR 2 1)(Et , 21,0)

Bank 2

nb b b nb

(0,0,0)

α 1 2 α

nf nff f

nrf rf

Figure 2: Decentralized Credit MarketNotes: b � borrow; nb � not borrow; f � ®nance; nf � not ®nance; rf � re®nance; nrf � not

re®nance



Assume now that entrepreneurs obtain positive pay-offs out of interrupted

projects, so that Et . 0. If this is the case, then termination does not deter

entrepreneurs from seeking ®nancing, and both types of projects are under-

taken by entrepreneurs under either system.

Finally, assume that the pay-off that entrepreneurs obtain out of completed

bad quality projects is negative, namely: Eb , 0. If this is the case, then only

good projects are undertaken and ®nanced under either centralization and

decentralization.

The main qualitative conclusion of this analysis is, therefore, that either

centralization and decentralization lead to the same project selection in

equilibrium or decentralization is strictly better, i.e. it selects more ef®ciently.

3. The Experiment

This section of the paper describes the experimental design, the data set,

the probabilistic scenarios, the pilot and the software used in the experiment.

3.1. The Experimental Design

We ran a large-scale computerized experiment with 12 different data sets

(six for centralized markets and six for decentralized markets) run three times

each, under different stochastic frameworks.

The experiment was conducted at L.U.I.S.S. University in Rome (hence

the Italian text in later ®gures and in the Appendices) with a sample of ten

undergraduate students in Economics for each session. The total number of

participants to the experiment was 120 people.

We adopted an experimental design that closely reproduces the theoretical

model. However, we chose not to label projects as of good and bad quality so

as to avoid inducing decisions in experimental subjects. We labelled them

instead respectively as short-term and long-term projects. Subjects were aware

of the fact that long-term projects needed longer ®nancing and yielded lower

pay-offs.

Before starting the experiment, written instructions ± also containing the

structure and the value of the monetary incentives ± were distributed to the

subjects and read aloud, allowing time for questions. Soon after the instruc-

tions, a practical session was run so that subjects could familiarize with the

software.

At the end of the practical session, the real experiment started.

From the initial sample of ten people, the computer selected at random

®ve subjects to be entrepreneurs and ®ve subjects to be bankers.

The software showed each experimental subject one of the two following

screens: screen E1 tells the subject that he will play the role of entrepreneur



and prompts him to wait for the start of a new round. At the bottom of the

screen, at this point (and continuously throughout the experiment), subjects

can keep track of their cumulated pro®ts. Screen B1 is the analogous screen

for the banker. See Figure 3.

Each session of the experiment consisted of 60 rounds (20 for each

probability framework). The subjects were paid in real money (Italian lire),

immediately at the end of the experiment on the basis of the results performed.

Each round developed as follows: One entrepreneur is selected at random to

start the round (see computer screen E2 in Figure 4). The player selected can

immediately visualize the probability structure that he is facing: he can

observe on his screen a row of Ls and Bs in the same ratio as long-term and

short-term projects. Moreover a moving arrow points at the row of Ls and Bs

and ®nally stops under one of them. At this point, the entrepreneur sees screen

E3 (Figure 4) that speci®es the type of investment selected and asks him to

decide whether to accept it, pressing S (yes) or N (no) on the keyboard. This

kind of presentation of the random selection procedure will hopefully convince

subjects of the randomness of the selection. In the meanwhile, all the other

agents observe a waiting screen.

Figure 3: (a) Screen E1: Role is assigned to entrepreneur; (b) Screen B1: Role is assigned tobanker

Figure 4: (a) Screen E2: Player selection and probability screen; (b) Screen E3: Selection ofinvestment type and entrepreneur's decision



· If the entrepreneur rejects the opportunity, a new round starts.

· If the entrepreneur accepts the investment project, the computer

randomly selects a banker.

The banker selected observes screen B2 (Figure 5). Screen B2 informs the

banker that an entrepreneur is asking for ®nancing. The screen of the banker

also displays the row of Ls and Bs in the relevant ratio, however he does not

observe the moving arrow selecting the type of the project. The computer asks

the banker to decide whether to ®nance the project.

· If the banker refuses to ®nance, the round is over and a new round

starts.

· If the banker accepts to ®nance the project, the nature of the

investment opportunity is revealed.

If it is a short-term investment, the banker observes screen B3b (Figure 5).

Both the entrepreneur and the banker receive the corresponding payoffs. If it is

a long-term project, the experiment develops differently in the centralized and

decentralized environments.

· In centralized markets, the banker observes screen B3lc (Figure 6),

where he is informed that he ®nanced a long-term project and there-

fore he is asked whether he wants re®nance it.

· In decentralized markets, the banker observes screen B3ld (Figure 6)

that informs him that he ®nanced a long-term project. Then a second

banker is randomly selected to observe screen B4 (Figure 6) that asks

him whether he wants to re®nance a long-term project.

On the basis of the different decisions taken, corresponding pay-offs are

assigned to the involved parties and a new round begins.

Throughout the experiment, subjects not directly involved in decisions

observe waiting screens and are not informed on the current round.

We experienced that agents were able to take their decisions rather

Figure 5: (a) Screen B2: Player's selection, probability screen and banker's decision; (b) ScreenB3b: Revelation of type ± short



quickly, so that each round lasted only a few seconds. As a result, even if not

all participants were directly taking decisions in every round, they still felt

involved in the whole experiment.

3.2. The Data Set

The full data set is in Appendix A. Each subject was initially endowed

with 6,000 lire. We used six different pay-offs structures both for the

centralized and the decentralized version of the experiment: data sets 1 to 6

apply to a centralized context and data sets 7 to 12 to a decentralized one. To

avoid the dif®culty of evaluating the effects of parameters' changes, we

allowed for a single change of pay-offs for each pair of data sets. For example,

we can easily compare the results from data set 1 and 2 (or 7 and 8) as they

only differ with respect to the payoff that the entrepreneur obtains when his

long-term project is interrupted. Similarly, data set 3 can be compared to data

set 6 (or 9 to 12). Data set 2 and data set 5 (or 8 and 11) only differ for the pay-

off that the banker obtains when he re®nances a long-term project, and so on.

Figure 6: (a) Screen B3lc: Revelation of type ± long ± and banker's decision in centralizedmarkets; (b) Screen B3ld: Revelation of type ± long ± in decentralized markets; (c) Screen B4:

Player's selection and banker's decision



3.3. The Probabilistic Scenarios

We run each data set with three different probability scenarios:

· Equal probability of long- and short-term projects

· Higher probability of long-term projects (ratio of short-term to long-

term of 3 to 7)

· Higher probability of short-term projects (ratio of short-term to long-

term of 7 to 3)

Experimental sessions were organized so that each group of 10 subjects

experienced a single data set in all the three different probabilistic frameworks.

This allowed us to check for the effect of changes in probabilities.

3.4. The Pilot

The computerized experiment was preceded by a pilot run by hand.

The results of the pilot are published in Di Cagno and Sciubba (1997).

With respect to the pilot, the computerized version of the experiment features

several advantages. First of all, a clear advantage is related to the anonymity of

the players that the computerized experiment guarantees. In the pilot in fact,

we detected reputation effects and this caused interdependencies in subjects'

(investment but especially ®nancing) decisions.

A second advantage is given by the fact that the probabilistic environment

and the monetary incentives were more clearly highlighted in the computerized

version: at any time during the experiment, players' screens displayed probabil-

ities and pro®ts.

Finally, computerized running made processing of the results easier to

handle.

3.5. The Software

The software for the experiment has been developed at EXEC (University

of York, UK) in FORTRAN. We thank Norman Spivey for his help.

4. The Results

This section of the paper describes the project selection, and earnings for

all three sessions.



4.1. Project Selection

In Tables 1 and 2, we present the `optimal' behaviour (i.e. the behaviour

that the theory predicts) for both entrepreneurs and bankers in the two

institutional frameworks and in the three probabilistic sessions.

4.1.1. Session 1

In session 1, short-term and long-term investment opportunities are

equally likely to occur.

In session 1, the predicted behaviour for centralized markets is that all

projects are undertaken for all data sets except for data set 4, where only short-

term projects should be accepted (see Table 1). The experimental evidence

Table 2: Optimal Behaviour: Decentralized Credit Markets

Data set Session 1 Session 2 Session 3

E I B II B E I B II B E I B II B

7 Y Y Y Y N Y Y Y Y8 Y Y Y Y N Y Y Y Y9 Y (short) Y N Y (short) Y N Y (short) Y N

N (long) N (long) N (long)10 Y (short) Y N Y (short) Y N Y (short) Y N

N (long) N (long) N (long)11 Y Y N Y N N Y Y N12 Y (short) Y N Y (short) Y N Y (short) Y N

N (long) N (long) N (long)

E � entrepreneur; B � banker; II B � second banker

Table 1: Optimal Behaviour: Centralized Credit Markets

Data set Session 1 Session 2 Session 3

E B RIF B E B RIF B E B RIF B

1 Y Y Y Y N Y Y Y Y2 Y Y Y Y N Y Y Y Y3 Y Y Y Y N Y Y Y Y4 Y (short) Y N Y (short) Y N Y (short) Y N

N (long) N (long) N (long)5 Y Y N Y N N Y Y N6 Y Y Y Y N Y Y Y Y

Notes: E � entrepreneur; B � banker; RIF B � re®nancing banker; Y � yes; N � no



shows that, at least to a certain extent, entrepreneurs succeeded in anticipating

the fact that bankers would have terminated long-term projects. In fact, the

percentage of long-term projects accepted by entrepreneurs is 50 per cent,

which is the lowest acceptance ratio among data sets in session 1 (Table 3).

As far as bankers are concerned, the model predicts that they should

always ®nance projects (see Table 1). Results suggest that this optimal

behaviour has been followed in most data sets except for data set 5. Comparing

data sets 4 and 5, we ®nd that they display the same pay-offs and probabilities

for the banker. However, in data set 5, the entrepreneur has a higher incentive

to undertake long-term projects. The evidence shows that experimental sub-

jects playing the role of bankers were aware of this higher incentive and

accepted projects in 55 per cent of the cases as opposed to 93 per cent in data

set 4 (see Table 4).

In decentralized markets, the model predicts that entrepreneurs should

undertake both types of projects for data sets 7, 8 and 11. In data sets 9, 10 and

12, they should screen between projects and accept only short-term ones (see

Table 2).

Table 3: Entrepreneur's Decisions: Centralized Credit Markets

Data set ST projects ST projectsundertaken

LT projects LT projectsundertaken

Number % Number %

Session 1 (Prob. 50±50%)1 9 9 100 11 9 822 12 12 100 8 6 753 11 11 100 9 9 1004 10 10 100 10 5 505 8 8 100 12 12 1006 13 13 100 7 7 100

Session 2 (Prob. 30±70%)1 6 6 100 14 12 862 6 6 100 14 13 933 7 7 100 13 9 694 5 5 100 15 14 935 7 7 100 13 13 1006 5 5 100 15 14 93

Session 3 (Prob. 70±30%)1 11 11 100 9 7 782 15 15 100 5 4 803 14 14 100 6 6 1004 15 15 100 5 4 805 17 17 100 3 3 1006 17 17 100 3 3 100



The evidence suggests that in data sets 9, 10 and 12 the acceptance rates

of long-term project have been respectively 82 per cent, 20 per cent and 55 per

cent. Entrepreneurs' behaviour seems not to be fully rational except for data

set 10 where the model predicts very well (see Table 5).

Comparing these results to those obtained within the centralized frame-

work, we ®nd evidence of a more ef®cient project selection in the former one.

In fact, assuming identical pay-offs and probabilities we ®nd that the accept-

ance rates of long-term projects fall from 100 per cent in centralized markets

to 82 per cent in decentralized markets in data sets 3 and 9; from 50 per cent to

20 per cent in data sets 4 and 10 and from 100 per cent to 55 per cent in data

sets 6 and 12 (compare tables 3 and 5).

As far as bankers are concerned, if risk neutral they should have ®nanced

every project, except for data sets 9, 10 and 12 where they should have selected

only short-term projects (see Table 2).

Most decisions were taken optimally, but not all of them. The second

bankers failed to implement optimal behaviour (see Tables 6 and 7).

Table 4: Centralized Credit Markets: Banker's decisions

Data set Financingopportunities

Financingopportunities

accepted

Re®nancingopportunities

Re®nancingopportunities

accepted

Number % Number %

Session 1 (Prob. 50±50%)1 18 17 94 8 7 872 18 14 78 4 4 1003 20 18 90 9 8 894 15 14 93 5 2 405 20 11 55 8 1 126 20 19 95 6 4 67

Session 2 (Prob. 30±70%)1 18 14 78 7 3 432 19 15 79 9 7 783 16 12 75 6 5 834 19 13 68 10 3 305 20 13 65 8 0 06 19 13 68 10 10 100

Session 3 (Prob. 70±30%)1 18 17 94 7 4 572 19 14 74 2 1 503 20 18 90 6 6 1004 19 17 89 2 0 05 20 11 55 1 0 06 20 15 75 1 1 100



4.1.2. Session 2

In Session 2, long-term projects are more likely to occur than short-term

projects.

In centralized markets, the optimal behaviour predicted by the model for

entrepreneurs does not differ from session 1 (see Table 1).

Results show that there is a higher acceptance rate of long-term projects

(see Table 3). The probabilistic framework probably affected experimental

subjects' behaviour in that they were afraid that accepting a long-term project

was their only chance to actively participate in the experiment.

Bankers behaved more optimally: they always re®nanced projects in data

set 6 and did not re®nance projects in data sets 4 and 5 (see Table 4).

In decentralized markets, as expected, in data sets 9, 10 and 12 entrepren-

eurs were less prone to accept long-term projects (see Table 5). Acceptance

rates with respect to the centralized framework fall from 93 per cent to 7 per

cent in data set 10 and from 93 per cent to 38 per cent in data set 12 (compare

Tables 3 and 5).

First bankers in data sets 10 and 11 recognized the different incentive for

entrepreneurs to carry on long-term projects. In fact they ®nanced 85 per cent

Table 5: Decentralized Credit Markets: Entrepreneur's decisions

Data set ST projects ST projectsundertaken

LT projects LT projectsundertaken

Number % Number %

Session 1 (Prob. 50±50%)7 10 10 100 10 7 708 9 9 100 11 10 919 9 9 100 11 9 82

10 10 10 100 10 2 2011 10 10 100 10 10 10012 9 9 100 11 6 55

Session 2 (Prob. 30±70%)7 3 3 100 17 8 478 8 8 100 12 11 929 7 7 100 13 9 69

10 6 6 100 14 1 711 8 8 100 12 11 9212 7 7 100 13 5 38

Session 3 (Prob. 70±30%)7 13 13 100 7 3 438 12 12 100 8 8 1009 13 13 100 7 4 57

10 11 11 100 9 3 3311 10 10 100 10 6 6012 16 16 100 4 3 75



of the projects in data set 10 and 68 per cent of projects in data set 11, where

the incentive for the entrepreneurs to accept long-term projects was higher (see

Table 6).

Second bankers behaved rationally (see Table 7).

4.1.3. Session 3

In session 3, short-term projects are more likely to occur. In the central-

ized market framework, the model predicts that all projects should be under-

taken by entrepreneurs, except for data set 4, where long-term projects should

be rejected (see Table 1).

The experimental evidence shows that, in data sets 3, 5 and 6, all projects

were undertaken, as expected. However in data set 4 only 20 per cent of the

long-term projects were rejected (see Table 3).

On the other hand, most of the bankers behaved as prescribed by the

model. On average they ®nanced 80 per cent of the projects, whereas the

model predicted that they should ®nance them all (see Table 4).

Table 6: Decentralized Credit Markets: First Banker's Decisions

Data set Financing opportunities Financing opportunities accepted

Number %

Session 1 (Prob. 50±50%)7 17 14 828 19 16 849 18 12 67

10 12 10 8311 20 16 8012 15 11 73

Session 2 (Prob. 30±70%)7 11 5 458 19 13 689 16 9 56

10 7 6 8611 19 13 6812 12 7 58

Session 3 (Prob. 70±30%)7 16 15 948 20 15 759 17 15 88

10 14 11 7711 16 14 8712 19 18 95



Failure to compute the expected value or non-neutrality to risk might

account for these discrepancies.

In their re®nancing decisions, bankers were closer to optimality. This was

particularly true for data sets 3, 4, 5 and 6; in data sets 3 and 6, they should

have optimally re®nanced and, in 4 and 5, they should not have: their

re®nancing rates for data sets 3, 4, 5 and 6 were respectively 100 per cent, 0

per cent, 0 per cent and 100 per cent (see Table 4).

In the decentralized market framework, we expected only short-term

projects to be undertaken by entrepreneurs in data sets 9, 10 and 12 (see Table

2). Data set 10 was the closer to our theoretical prediction (see Table 5).

Comparing the experimental results in centralized and decentralized

markets, we ®nd that, also in this third probabilistic framework, a diffuse

ownership of capital enhances the ef®ciency of project selection. The accept-

ance rates of long-term projects were considerably lower in decentralized

markets. In particular, they fall from 100 per cent to 57 per cent in data set 9;

from 80 per cent to 33 per cent in data set 10; ®nally from 100 per cent to 75

per cent in data set 12 (compare Tables 3 and 5).

Table 7: Decentralised Credit Markets: Second Banker's Decisions

Data set Re®nancing opportunities Re®nancing opportunities accepted

Number %

Session 1 (Prob. 50±50%)7 6 3 508 9 6 679 6 2 33

10 1 0 011 8 4 5012 4 1 25

Session 2 (Prob. 30±70%)7 4 2 508 8 6 759 4 0 0

10 1 0 011 7 1 1412 2 0 0

Session 3 (Prob. 70±30%)7 3 2 678 6 5 839 4 2 50

10 2 1 5011 6 0 012 2 0 0



Bankers ®nanced on average 86 per cent of the projects, whereas the

model predicted 100 per cent acceptance. Second bankers, as well, were quite

close to optimality (see Tables 6 and 7).

4.2. Earnings

Average earnings per undertaken project were consistently higher in the

decentralized market framework than in the centralized one, both for entrepren-

eurs and for bankers (compare Tables 8 and 9). Since we controlled for pay-

offs and probabilities, this result suggests that the selection of investment

opportunities operated by entrepreneurs was more ef®cient under decentraliza-

tion.

In both frameworks, however, actual earnings were sensibly lower than

those predicted by the theory. There are a few outliers, however, that are worth

commenting. In some cases, especially in the riskiest probabilistic framework

± session 2 ± experimental subjects earned higher pro®ts that they would have,

if they had behaved according to the model. We believe that this result is

justi®ed by the fact that they did not assess risks correctly and therefore

behaved more daringly and luck rewarded them.

The total cost of the experiment was 2,577,810, Italian lire equivalent to

1332 euro.

5. Centralization versus Decentralization: Concluding Remarks

The experimental evidence we provide suggests that decentralized credit

markets promote a more ef®cient project selection: the number of bad quality

projects undertaken was signi®cantly smaller in decentralized than in central-

ized markets.

We also ®nd that the theory has more explanatory power when markets are

decentralized. We believe that this results from the fact that having a third

party involved might serve to focus players' attention on the decision process

ever more and we suggest this as a tentative explanation for the fact that agents

behaved more rationally in decentralized markets than in centralized ones.

The statistics on average earnings show that subjects' performances and

pro®ts were higher in decentralized than in centralized markets. This provides

us with additional evidence of the fact that a diffuse ownership of capital acts

as a commitment device in this context and promotes a more ef®cient project

selection, succeeding to deter entrepreneurs from undertaking bad quality

projects in the ®rst place.

In the experiment, entrepreneurs seemed to be able to anticipate their

opponents' behaviour to a greater extent than bankers. We suggest two

tentative explanations for this result.



Table 8: Earnings in Centralized Credit Markets

Data set Average earnings Optimal earnings�

Sessions 1 Sessions 2 Sessions 3 Sessions 1 Sessions 2 Sessions 3

E B E B E B E B E B E B

1 8,180 6,620 7,180 5,780 8,120 6,840 18,300 7,300 6,000 6,000 19,700 10,7002 8,240 7,440 6,100 5,420 8,500 7,860 20,400 12,400 6,000 6,000 22,500 17,5003 8,480 6,720 7,490 6,200 8,760 7,440 19,700 9,800 6,000 6,000 21,800 15,2004 7,800 6,720 6,500 4,480 8,440 7,800 16,000 16,000 11,000 11,000 21,000 21,0005 6,940 4,960 7,320 5,400 8,200 8,200 16,400 2,000 6,000 6,000 23,600 20,0006 8,760 7,560 7,200 5,000 8,860 8,640 21,100 13,400 6,000 6,000 23,900 20,600

Average earnings 8.067 6,670 6,965 5,380 8,480 7,797 18,650 10,150 6,833 6,833 22,083 17,500Average number of projects

undertaken18.5 18.5 18.5 18.5 19.3 19.3 18.3 18.3 0.8 0.8 19.2 19.2

Average return 436 360 374 291 439 404 1,019 555 8,541 8,541 1,150 911

�Including L. 6,000 of initial endowment; E � entrepreneur; B � banker

303


Table 9: Earnings in Decentralized Credit Markets

Data set Average earnings Optimal earnings�

Sessions 1 Sessions 2 Sessions 3 Sessions 1 Sessions 2 Sessions 3

E B E B E B E B E B E B

7 7,360 6,430 6,040 5,420 8,280 7,820 19,000 9,000 6,000 6,000 21,100 14,1008 7,760 5,660 7,480 5,860 8,040 6,650 18,300 7,300 6,000 6,000 20,400 12,4009 7,140 5,980 6,750 5,800 8,160 7,380 15,000 15,000 13,000 13,000 19,000 19,000

10 8,960 7,600 6,950 6,800 7,720 7,310 16,000 16,000 12,000 12,000 17,000 17,00011 7,920 5,640 7,480 5,710 7,840 6,400 18,000 6,000 6,000 6,000 18,000 4,00012 7,240 6,590 6,720 6,600 9,120 8,800 15,000 15,000 13,000 13,000 22,000 22,000

Average earnings 7,730 6,317 6,903 6,032 8,193 7,393 16,880 8,883 9,333 9,333 19,583 14,750Average number of projects

undertaken16.8 16.8 14.0 14.0 17.0 17.0 13.3 13.3 3.3 3.3 16.6 16.6

Average return 460 376 493 431 482 435 1,269 668 2,828 2,828 1,180 883

�Including L. 6,000 of initial endowment; E � entrepreneur; B � banker

30

4


First, entrepreneurs are the ®rst ones to move: they might ®nd themselves

in a better position to consider the game as a whole and to try to behave

strategically with respect to bankers.

Second, the different information structure might have a role in this:

entrepreneurs information set is less coarse. They know whether they were

presented with a good or bad investment opportunity and they might, therefore,

®nd it easier to associate different actions to different projects types, irrespect-

ive of the probabilistic structure. Bankers, on the contrary, are forced to

compute expected values.

This consideration is also backed up by the fact that, in the experimental

results, the three different probabilistic structures seemed to have a larger

impact on bankers' rather than on entrepreneurs'decisions.

Finally, we would like to comment on a possible weakness implicit in this

kind of analysis, and on the way we adopted to overcome it. Clearly, one might

suspect that some of the experimental results could be driven by subjects'

eagerness to participate actively to the experiment rather than by their pro®t

maximizing behaviour. For example, if we had agents fearing to play only

once, then a likely scenario might have been the following: both bankers and

entrepreneurs accept all projects so as to take an active part in their only

chance to participate in the experiment. They might have just tried their luck.

We believe that having 20 different rounds for every data set, we have

alleviated this problem.

Each experimental session consisted of 60 rounds and therefore for 120

different times (180 times for the sessions on decentralized markets) roles were

assigned among the 10 participants. Until the last round, subjects knew that

they stood very good chances of playing again and were alert and motivated

during the whole experiment.

It happened only once, during the pilot, that one out of the 10 experi-

mental subject was never randomly selected to participate.

Moreover, we endowed subjects with an initial amount of money so that a

`just trying luck' behaviour could have been costly for them.

Finally, the average earning that the individual subject received from

participating to the experiment was 7,160 Italian lire, the equivalent of a pizza

meal for a student in a budget restaurant.



REFERENCES

E. Berkovitch - R. Israel - J. F. Zender (1997), `̀ Optimal Bankruptcy Law and

Firm-Speci®c Investment'', European Economic Review, 41, 487±97.

B. Bias - C. Gollier (1997), `̀ Trade Credit and Credit Rationing'', Review of

Financial Studies, 10, 903±37.

J. Danthine - F. Giavazzi - X. Vives - E.-L. von Thadden (1999), The Future

of European Banking, Center for Economic Policy Research Working Paper,

MEI n. 9.

E. Detragiache - P. G. Garella - L. Guiso (1997), Multiple versus Single Banking

Relationships, Center for Economic Policy Research Working Paper, DP n. 1649.

M. Dewatripont (1988), `̀ Commitment Through Renegotiation ± Proof Contracts

with Third Parties'', Review of Economic Studies, 60, 377±90.

M. Dewatripont (1989), `̀ Renegotiation and Information Revelation over Time: The

Case of Optimal Labor Contracts'', Quarterly Journal of Economics, 104, 589±

619.

M. Dewatripont - E. Maskin (1995), `̀ Credit and Ef®ciency in Centralised and

Decentralised Economies'', Review of Economic Studies, 62, 541±55.

D. Di Cagno - E. Sciubba (1997), `̀ The Link between Economic Ef®ciency and

Decentralisation in Credit Markets: Experimental Evidence'', Quaderno di ricerca

dell' Osservatorio e Centro Studi Monetari della Luiss Guido Carli, n. 86.

T. Gehrig (1998), Screening, Cross-Border Banking and the Allocation of Credit,

Center for Economic Policy Research Working Paper, DP n. 1973.

M. A. Petersen - R. G. Rajan (1995), `̀ The Effect of Credit Market Competition on

Lending Relationships'', Quarterly Journal of Economics, 110, 407±43.

J. E. Stiglitz - A. Weiss (1981), `̀ Credit Rationing in Markets with Imperfect

Information'', American Economic Review, 71, 393±410.

APPENDIX A: DATA SET

Tables A1 and A2 show the pay-offs in centralized and decentralized

markets, respectively.



APPENDIX B: INSTRUCTIONS FOR THE EXPERIMENT

Istruzioni per l'Esperimento (MC)

Benvenuti!

State per partecipare ad un esperimento relativo alle scelte di investimento

da parte delle imprese e di ®nanziamento da parte delle banche.

Vi sono 10 partecipanti a questa sessione dell'esperimento: 5 imprenditori

e 5 banchieri. Per prima cosa il computer assegneraÁ in modo casuale il ruolo di

imprenditore o di banchiere che ciascuno di voi giocheraÁ nell'esperimento.

Questa sessione sperimentale si compone di 3 sets da 20 rounds l'uno.

I primi a giocare sono gli imprenditori. All'inizio di ogni round, il

computer sorteggeraÁ tra i cinque un imprenditore cui offriraÁ una opportunitaÁ di

investimento che potraÁ essere a lungo o a breve termine.

In ogni round il computer determina in modo casuale il tipo di investi-

mento che vi viene proposto. Il numero di possibili investimenti a lungo e a

breve termine tra cui il computer seleziona varia tra un set e l'altro.

L'imprenditore selezionato puoÁ accettare o ri®utare l'opportunitaÁ offerta-

Table A1: Pay-offs in Centralized Markets

Ses. n. Entrepreneur Banker

ST LT Interrupted LT Re®nanced ST LT Interrupted LT Re®nanced

1 1000 ÿ200 300 1000 ÿ1000 ÿ7002 1000 200 300 1000 ÿ1000 ÿ7003 1000 ÿ50 300 1000 ÿ1000 ÿ8004 1000 ÿ200 300 1000 ÿ1000 ÿ12005 1000 200 300 1000 ÿ1000 ÿ12006 1000 ÿ200 300 1000 ÿ1000 ÿ800

Table A2: Pay-offs in Decentralized Markets

Ses. n. Entrepreneur First Banker Second Banker

ST LTInterrupted

LTRe®nanced

ST LTInterrupted

LTRe®nanced

LT Financed

7 1000 ÿ200 300 1000 ÿ1000 ÿ750 508 1000 200 300 1000 ÿ1000 ÿ750 509 1000 ÿ50 300 1000 ÿ1000 ÿ750 ÿ50

10 1000 ÿ200 300 1000 ÿ1000 ÿ750 ÿ45011 1000 200 300 1000 ÿ1000 ÿ750 ÿ45012 1000 ÿ200 300 1000 ÿ1000 ÿ750 ÿ50



gli. Se ri®uta, l'imprenditore perde il proprio turno e deve aspettare di essere

nuovamente selezionato per poter giocare di nuovo. Il gioco riprende come da

principio (i.e. il computer sorteggeraÁ nuovamente un imprenditore). Se l'im-

prenditore accetta, allora dovraÁ rivolgersi ad uno dei banchieri per il ®nanzia-

mento.

E' qui che i banchieri entrano in gioco. Il computer selezioneraÁ, sempre a

caso, il banchiere cui viene chiesto il ®nanziamento. Il banchiere sorteggiato

deve decidere se concedere il ®nanziamento oppure no.

Se il banchiere ri®uta di ®nanziare il progetto, il gioco ricomincia come

da principio (i.e. viene sorteggiato un imprenditore). Se il banchiere accetta, il

gioco prosegue e il computer rivela al banchiere il tipo di investimento

®nanziato. Se si tratta di un investimento a breve, il progetto di ®nanziamento

viene portato a termine ed inizia un nuovo round.

Se si tratta di un investimento a lunga, il banchiere in gioco deve decidere

se continuare a ®nanziarlo oppure no. Se il banchiere si ri®uta di ri®nanziare,

il progetto di investimento viene interrotto ed inizia un nuovo round. Se il

banchiere accetta di ri®nanziare, il progetto di investimento viene portato a

termine ed inizia un nuovo round.

Il guadagno o la perdita che imprenditori e banchieri ottengono in ogni

round nelle varie situazioni descritte, sono indicati nella tabella dei payoffs che

vi eÁ stata distribuita. La tabella che vedete eÁ la stessa per tutti i partecipanti a

questa sessione dell'esperimento. Consultatela quindi con attenzione prima di

prendere ogni decisione di investimento o di ®nanziamento. Alla ®ne

dell'esperimento ciascuno di voi riceveraÁ la somma (in lire) corrispondente ai

risultati da voi ottenuti nel corso dell'esperimento stesso proprio sulla base di

tale tabella.

Tutti i giocatori hanno una dotazione iniziale di 6000 unitaÁ di moneta

sperimentale. Il computer registreraÁ tutti i vostri guadagni ed il vostro pro®tto

totale appariraÁ sempre in basso, sul vostro schermo. Il guadagno complessivo

dell'esperimento saraÁ dato dalla somma totale dei guadagni e delle perdite

ottenuti nei tre sets da 20 rounds.

Prima di iniziare l'esperimento vero e proprio, vi verraÁ mostrato a titolo

esempli®cativo cosa potraÁ apparire sui vostri schermi nel corso del gioco.

Durante questa dimostrazione, non premete alcun tasto a meno che non vi

venga chiesto esplicitamente di farlo dagli sperimentatori.

Per qualsiasi problema, rivolgetevi agli sperimentatori.

Buon lavoro!

Sessione di Pratica (MC)

Premete il tasto C adesso.

Quello che vedete eÁ lo schermo che appare all'imprenditore che viene

selezionato per prendere una decisione di investimento. L'indicatore a freccia



seleziona in modo casuale uno dei progetti di investimento. Se l'indicatore si

arresta sulla lettera L il progetto selezionato eÁ a lungo termine; se si arresta

sulla lettera B il progetto selezionato eÁ a breve termine. Per accettare

l'opportunitaÁ di investimento premete il tasto S, per ri®utare premete il tasto

N.

Premete il tasto S adesso per andare avanti con la dimostrazione.

Questo eÁ lo schermo che appare al banchiere che viene selezionato per

®nanziare l'investimento. Per accettare premete il tasto S, per ri®utare premete

il tasto N.


Lo schermo che vedete indica al banchiere che l'investimento ®nanziato eÁ

a breve termine.

Vediamo, invece, cosa accade se l'investimento ®nanziato eÁ a lungo

termine. Per far questo torniamo allo schermo della scelta di ®nanziamento per

il banchiere.

Premete ora il tasto S.

In questo caso l'investimento ®nanziato eÁ a lungo termine. Il banchiere

deve decidere se ri®nanziarlo o meno.

Premete il tasto S per terminare la dimostrazione.

E' essenziale che prendiate tutte le decisioni da soli ed in modo autonomo.

Non dovete, quindi, consultarvi nel corso dell'esperimento.

Diamo inizio, adesso, all'esperimento vero e proprio.

Istruzioni per l'Esperimento (MD)

Benvenuti!

State per partecipare ad un esperimento relativo alle scelte di investimento

da parte delle imprese e di ®nanziamento da parte delle banche.

Vi sono 10 partecipanti a questa sessione dell'esperimento: 5 imprenditori

e 5 banchieri. Per prima cosa il computer assegneraÁ in modo casuale il ruolo di

imprenditore o di banchiere che ciascuno di voi giocheraÁ nell'esperimento.

Questa sessione sperimentale si compone di 3 sets da 20 rounds l'uno.

I primi a giocare sono gli imprenditori. All'inizio di ogni round, il

computer sorteggeraÁ tra i cinque un imprenditore cui offriraÁ una opportunitaÁ di

investimento che potraÁ essere a lungo o a breve termine.

In ogni round il computer determina in modo casuale il tipo di investi-

mento che vi viene proposto. Il numero di possibili investimenti a lungo e a

breve termine tra cui il computer seleziona varia nel corso dell'esperimento.

L'imprenditore selezionato puoÁ accettare o ri®utare l'opportunitaÁ offerta-

gli. Se ri®uta, l'imprenditore perde il proprio turno e deve aspettare di essere

nuovamente selezionato per poter giocare di nuovo. Il gioco riprende come da

principio (i.e. il computer sorteggeraÁ nuovamente un imprenditore). Se l'im-



prenditore accetta, allora dovraÁ rivolgersi ad uno dei banchieri per il ®nanzia-

mento.

E' qui che i banchieri entrano in gioco. Il computer selezioneraÁ, sempre a

caso, il banchiere cui viene chiesto il ®nanziamento. Il banchiere sorteggiato

deve decidere se concedere il ®nanziamento oppure no.

Se il banchiere ri®uta di ®nanziare il progetto, il gioco ricomincia come

da principio (i.e. viene sorteggiato un imprenditore). Se il banchiere accetta, il

gioco prosegue e il computer rivela al banchiere il tipo di investimento

®nanziato. Se si tratta di un investimento a breve, il progetto di ®nanziamento


Se si tratta di un investimento a lunga, il computer seleziona in modo

casuale un secondo banchiere al quale viene offerta la opportunitaÁ di ri®nan-

ziare il progetto di investimento.

Se il secondo banchiere si ri®uta di ri®nanziare, il progetto di investimento

viene interrotto ed inizia un nuovo round.

Se il secondo banchiere accetta di ri®nanziare, il progetto di investimento


Il guadagno o la perdita che imprenditori e banchieri ottengono in ogni

round nelle varie situazioni descritte, sono indicati nella tabella dei payoffs che

vi eÁ stata distribuita. La tabella che vedete eÁ la stessa per tutti i partecipanti a

questa sessione dell'esperimento. Consultatela quindi con attenzione prima di

prendere ogni decisione di investimento o di ®nanziamento. Alla ®ne

dell'esperimento ciascuno di voi riceveraÁ la somma (in lire) corrispondente ai

risultati da voi ottenuti nel corso dell'esperimento stesso proprio sulla base di

tale tabella.

Tutti i giocatori hanno una dotazione iniziale di 6000 unitaÁ di moneta

sperimentale. Il computer registreraÁ tutti i vostri guadagni ed il vostro pro®tto

totale appariraÁ sempre in basso, sul vostro schermo. Il guadagno complessivo

dell'esperimento saraÁ dato dalla somma totale dei guadagni e delle perdite

ottenuti nei tre sets da 20 rounds.

Prima di iniziare l'esperimento vero e proprio, vi verraÁ mostrato a titolo

esempli®cativo cosa potraÁ apparire sui vostri schermi nel corso del gioco.

Durante questa dimostrazione, non premete alcun tasto a meno che non vi

venga chiesto esplicitamente di farlo dagli sperimentatori.

Per qualsiasi problema, rivolgetevi agli sperimentatori.

Buon lavoro!

Sessione di Pratica (MD)

Premete il tasto C adesso.

Quello che vedete eÁ lo schermo che appare all'imprenditore che viene

selezionato per prendere una decisione di investimento. L'indicatore a freccia

seleziona in modo casuale uno dei progetti di investimento. Se l'indicatore si



arresta sulla lettera L il progetto selezionato eÁ a lungo termine; se si arresta

sulla lettera B il progetto selezionato eÁ a breve termine. L'imprenditore accetta

l'opportunitaÁ di investimento premendo il tasto S e ri®uta premendo il tasto N.


Questo eÁ lo schermo che appare al primo banchiere che viene selezionato

per ®nanziare l'investimento. Il banchiere accetta premendo il tasto S e ri®uta

premendo il tasto N.


Lo schermo che vedete indica al primo banchiere che l'investimento

®nanziato eÁ a breve termine.

Vediamo, invece, cosa accade se l'investimento ®nanziato eÁ a lungo

termine. Per far questo torniamo allo schermo della scelta di ®nanziamento per

il banchiere.

Premete ora il tasto S.

In questo caso l'investimento che il primo banchiere ha ®nanziato eÁ a

lungo termine. Il computer seleziona un secondo banchiere. Questo eÁ lo

schermo che appare al secondo banchiere selezionato. Il secondo banchiere

accetta premendo il tasto S e ri®uta premendo il tasto N.

Premete il tasto S per terminare la dimostrazione.

E' essenziale che prendiate tutte le decisioni da soli ed in modo autonomo.

Non dovete, quindi, consultarvi nel corso dell'esperimento.

Diamo inizio, adesso, all'esperimento vero e proprio.

Non-technical Summary

There is very little doubt that the world of banking is changing under the

conjunction of a multitude of forces. Until the early 1990s, commercial banks

in Europe were relatively protected from competition, through formal or

informal barriers to entry into the market. Deregulation, the single market

programme and, above all, the abolition of capital controls which occurred in

the late 1980s, were all recipes for an increase in competitive pressure in the

European banking industry. At the same time, the world banking industry has

been shaped by a massive consolidation. The ten largest mergers in the US

history, in any industry, occurred during 1998 and four out of these occurred in

banking. The volume of European banking mergers almost quadrupled in the

same period.

These issues have forcefully brought back the interest of economists and

practitioners alike in the debate on the `Anglo-Saxon' versus the `German±

Japanese' corporate ®nancing practices.

From a theoretical standpoint, one might think of different reasons for

supporting one or the other. Starting point for any analysis that aims at tracing

ef®ciency conditions for the one or the other institutional design, is the

presence of severe asymmetric information in any banking relationship.



In banking, asymmetric information acts in two directions. First, at the

beginning of a credit relationship, creditors do not possess as good information

as their clients on the quality of the projects they are asked to ®nance. Second,

during the credit relationship, bankers do not have the same extent of control

over the success of the project as entrepreneurs have. This gives rise to

problems both of adverse selection and of moral hazard.

The debate on centralization versus decentralization of credit markets has

therefore been centred on the relevance of asymmetric information. Econo-

mists have asked under which institutional framework informational asymmet-

ries can be better dealt with. The answers that the literature has given so far do

not unanimously support one institutional design as opposed to the other. The

theoretical debate on which of the two institutional designs leads to a more

ef®cient project selection is, therefore, very lively and still very open. The

answer will depend on the initial assumptions and therefore on the speci®c

context one has in mind.

Most of the contributors to this debate focus on the role of third parties in

the process of project selection. For example: in Dewatripont and Maskin

(1995) the fact that there are third parties involved implies that entrepreneurs

®nd the threat of termination of their lending contracts more credible and

therefore operate a better screening on the quality of their projects. The aim of

this paper is to test experimentally whether this is actually true: we ask whether

a diffuse ownership of capital can really work as an enforcement mechanism

that prevents agents from pursuing unpro®table investment opportunities.

We consider a stylized banking industry, modelled according to a simpli-

®ed version of Dewatripont and Maskin (1995). In particular, we look at a

credit market in which creditors are not fully informed ex-ante about the

quality of the projects that entrepreneurs submit to ®nancing. Project managers

know their projects' quality, but creditors acquire this information only

gradually. As a result, it may happen that projects that looked worth ®nancing

in the ®rst place, after a while turn out to be poor deals. Nevertheless

re®nancing them once that they have begun, becomes sequentially optimal

because of the sunk costs which the bank has incurred.

In this economy, if the threat of termination deterred entrepreneurs from

undertaking poor projects in the ®rst place, creditors would wish to commit

themselves ex-ante not to re®nance. However, sunk costs may well make this

threat not credible: ex-post both creditors and entrepreneurs can be better off

re®nancing.

In such a circumstance, decentralization works as a commitment device to

terminate poor projects. In fact, if the ownership of capital is diffuse, the initial

creditor may not have suf®cient resources to continue to fund the poor project:

re®nancing will require new creditors to have additional costs and/or to pay an

informational rent to initial creditors. As a result, later creditors will not be

able to capture all the surplus from re®nancing. Decentralization, thus, reduces

incentives to re®nance and, making the threat to terminate more credible,



enhances an ef®cient project selection preventing unpro®table projects from

ever being undertaken.

We ran a large-scale computerized experiment involving 12 different data

sets and 3 different uncertainty scenarios on a sample of 120 subjects.

The experimental evidence we provide suggests that decentralized credit

markets promote a more ef®cient project selection: the number of bad quality

projects undertaken by experimental subjects was signi®cantly smaller in

decentralized than in centralized markets.

We also ®nd that the theory has more explanatory power when markets are

decentralized. We believe that this results from the fact that having a third

party involved might serve to focus players' attention on the decision process

ever more and we suggest this as a tentative explanation for the fact that agents

behaved more rationally in decentralized markets than in centralized ones.

We also provide statistics on average earnings and these show that

subjects' performances and pro®ts were higher in decentralized than in

centralized markets. We believe that this constitutes additional evidence of the

fact that a diffuse ownership of capital acts as a commitment device in this

context.

We conclude that the results obtained in our experimental setting con®rm

the superiority of a decentralized institutional framework: forcing a contract

with a third party through a diffuse ownership of capital indeed works as an

enforcement mechanism and promotes a more ef®cient project selection,

succeeding to deter subjects from undertaking bad quality projects in the ®rst

place.



institutional design as a commitment device in credit markets with asymmetric information:...

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