Cross-Sector Partnerships in Micro�nance:

On The Role of Motivated NGOs

Nurmukhammad Yusupov�

January 10, 2011

Abstract

The modern micro�nance industry can be viewed as an exem-

plary implementation of poverty alleviation programs involving dif-

ferent types of agents. However, it has been shown that when such

programs involve NGOs with altruistic motivations directed at bor-

rower welfare, borrower screening can produce suboptimal results in

terms of �nancial viability of such programs. In this paper I show

that the same holds in the context of post-contractual monitoring. I

focus on the issue of borrower moral hazard and show that optimality

of altruistic NGO�s involvement is restricted to a set of parametric

assumptions.

Keywords: Motivated agents; Micro�nance; Partnerships; Moral

Hazard

JEL Codes: G21, D82

�Chaire Banques Populaires, Audencia Nantes School of Management, 8 route de la

Jonelière, 44300 Nantes, France. nyusupov@audencia.com

1

1 Introduction

E¤ective delivery of many public services through partnerships between the

public and private sector has become a viable policy alternative to pure pub-

lic or private provision. At the same time, it is believed that the increased

involvement of nongovernmental organizations (NGOs) in such public-private

partnerships (henceforth PPPs) is bene�tial for the provision of public ser-

vices. PPPs are becoming ever more important in development-related issues

when the market fails to deliver public goods and services. In such situations

partnerships are often seen as a good way to implement socially important

projects e¢ ciently. While under traditional model of public services the

government has full control over the inputs and implementation, under the

PPP model the government speci�es the output to be produced and the con-

trol over the implementation is left to the private agent. PPPs are usually

de�ned in the literature as a long-term development and service contract be-

tween government and a private partner (Maskin and Tirole 2008; Iossa and

Martimort, 2008). The government typically o¤ers a contract to a private

agent both to develop the project and to operate and service it. The private

agent can raise private �nance in addition to receiving government payments

and consumer fees. Private agents driven by pro�t making opportunities

are expected to deliver on the e¢ cienies, while the public agent aiming at

maximizing the social welfare is expected to prioritize the quality.

One point of view for why such partnerships are globally gaining ground

in sectors of high social importantce stems from the perceived shortcomings

of the Structural Adjustment Programmes (SAPs) promoted by The World

Bank and International Monetary Fund since the 1980s. SAPs were meant to

advocate neo-liberal ideas of trickle down economics, reducing government�s

2

interference in the economy and promote competitive markets to achieve ef-

�ciencies. However, SAPs failed to deliver and have been criticized since

their early days because of their impact on the social sector resulting from

reduction in pro-social spending and little concern for inequality. The criti-

cism led to the so-called post-Washington consensus that promotes pro-social

expenditure of public funds. Partnerships between development agencies, na-

tional governments and businesses became the new way of doing business as

a more e¤ective and sustainable option in producing social impact alongside

economic reforms. At the World Summit on Sustainable Development in

Johannesburg in 2002 establishment of 240 new partnerships was announced

(Reed and Reed, 2009).

One of the spheres in which social service provision in partnerships has

gained a lot of ground lately is �nancial services for the people at the bot-

tom of the economic pyramid, or micro�nance. The modern micro�nance

industry can be viewed as an exemplary implementation of poverty allevia-

tion programs which brings together di¤erent types of agents in such part-

nerships (Matthäus-Maier and Pischke, 2008). Speci�cally, partnerships in

micro�nance can be observed in a variety of economic settings in which the

expansion of the industry is involving NGOs, big banks, international donors

and governmental agencies. Partnerships are naturally becoming a natural

form of collaboration between these di¤erent players, under which big players

usually provide capital while the NGOs supply their expertise (i) in screening

creditworthy borrowers to address adverse selection and/or (ii) in monitor-

ing those who receive the loans to reduce the cost of moral hazard. For

example, in India the SHG�bank, i.e. self help group and a bank, linkage

model has become the predominant form of micro�nance interventions and

accounts for tens of millions of clients (Ananth, 2005). Under this model,

3

the SHG is usually an NGO, helps groups of 15�20 individuals through an

incubation period after which time they are linked to banks, which lends to

the groups after the incubation period. The NGO does not provide any cap-

ital and its operating expenses are usually covered by external grant sources.

Once the bank-group linkage is established, the NGO often supervises the

loan portfolio without any speci�c incentives to perform follow-up supervi-

sion. A similar arrangement can be observed in the industrialized world as

well. A good example is France where the government has recently (in 2009)

launched a program called Nouveau dispositif d�Aide à la Création et à la

Reprise d�Entreprise (NACRE)1 which links commercial banks and special-

ized NGOs. The objective is to help microentrepreneurs to access �nancing

from traditional commercial banks and to provide them with the monitoring

services of specialized NGOs, like the boutiques de gestion2.

Participation of private agents that are driven not by pro�t motives

but by prosocial motivation or directed altruism, such as not-for-pro�t non-

governmental organizations (henceforth NGOs), make the traditional part-

nership model more complex. The complexity stems from the fact that, on

one side, the NGOs are driven by their mission to deliver public good (Besley

and Ghatak, 2001) and, on the other, they may not react to the �nancial

incentives designed to achieve e¢ ciencies as strongly as a purely pro�t-driven

agent would (Bennet and Iossa, 2010). With regard to reaction to incentives,

Benabou and Tirole (2003) show that under certain conditions �nancial in-

centives can be e¢ ciency-decreasing by crowding out intrinsic motivations.

These kinds of motivated agents are de�ned, according to Besley and Ghatak

(2005), as those �who pursue goals because they perceive intrinsic bene�ts

1See http://www.entreprises.mine�.gouv.fr/nacre/2See http://www.boutiques-de-gestion.com

4

from doing so�. It implies that NGO�s utility function has the �rst derivate

with respect to the utility of the consumer of the public good always strictly

positive. According to Selsky and Parker (2005), partnerships involving mul-

tiple agents with di¤erent motivations in the delivery of public goods are

termed cross-sector partnerships. Such partnerships occur in four �arenas�:

business-nonpro�t, business-government, government-nonpro�t, and trisec-

tor. The literature on such partnerships views them from the standpoint of

two analytic �platforms�: resource dependence (Gray and Wood, 1991) and

social issues (Gray, 1989; Trist, 1983; Waddell, 2005).

In this paper, I develop a model where the government sets up a partner-

ship program linking a big bank that does not have the capacity to address

agency problems when �nancing the poor and an NGO that does. The gov-

ernment then plays no role in the implementation stage of the project, which

is the focus of this paper. The motivated agent is the NGO based on the

assumption that it has superior knowledge about the target community of

poor households. Therefore it can do a better job in dealing with asymmetric

information, especially monitoring post-contractual behavior of the borrow-

ers. Motivation of the NGO is re�ected in its utility function which includes

an altruistic part, directed at borrower welfare, in an additive separable way

(Fehr and Schmidt, 2006; Francois and Vlassopulos, 2008). The problem

arises because by behaving in a morally hazardous way the borrower can in-

crease her utility by getting private bene�ts that only she can enjoy. Higher

utility of the borrower enters the altruistically motivated NGO�s utility with

a positive sign and may induce the NGO to support borrower moral hazard.

We assume that the NGO can also have ownership or �nancial stake as in

Bennet and Iossa (2010) where a not-for-pro�t maximizes a utility function

de�ned as a weighted sum of mission based bene�ts and �nancial pro�ts. We

5

then characterize conditions for the optimality of NGO�s involvement.

In a related paper, Roy and Chowdhury (2009) build a similar model

to show that such an NGO, if maximizes aggregate borrower utility, may

be inclined to attribute microloans to the less creditworthy who su¤er from

exclusion more than the more creditworthy. Although, in their model the

NGO can be involved with the borrower after the loan contract is signed to

help them increase their chances of successful repayment, the borrowers do

not make any choices that would a¤ect the outcome of their projects. Thus,

their model is that of adverse selection while our paper addresses the issue

of moral hazard and monitoring. Moreover, in contrast to their model we as-

sume that the NGO also has ownership or �nancial stake whereas in Roy and

Chowdhury (2009) the motivated agent is an NGO that only pursues proso-

cial objectives. Additionally, they do not consider action-oriented altruism

in contrast to our approach.

We show that in case where the NGO is motivated out of output-oriented

altruism the severity of borrower moral hazard, i.e. value of private bene�ts

when she decides to shirk instead of exerting e¤ort, as well the value of

NGO�s motivation may make the �rst best impossible and induce the NGO to

support borrower moral hazard. On the other hand, in case of action-oriented

altruism, if the e¤ect of NGO�s altruistic actions on its utility su¢ ciently high

the �rst best is implementable regardless of the severity of borrower moral

hazard.

The paper proceeds as follows. The following section describes the general

setup of the model. Then we study the partnership model to derive optimal

strategies for the players in Section 3. Section 4 discusses an alternative

speci�cation of the NGO�s altruism. Section 5 concludes the paper.

6

2 The Model

The economy consists of the government, a bank and an NGO which acts as

a motivated agent, and a pool of poor entrepreneurs each facing a business

project. The entrepreneurs plan to start a project each, which requires a

start-up capital of I units. The entrepreneurs do not have su¢ cient asset

ownership, and hence must borrow. Each entrepreneur owns A < I so the

loan amount is given by I�A > 0. Every borrower is protected by the limitedliability provision and after receiving the loan can choose to exert e¤ort to

produce uncertain output Y with high probability pH or shirk yielding private

bene�ts B that only she can enjoy along with a low probability of successful

output pL.

Whether the borrower exerts e¤ort or not is only observable to the NGO

and the borrower while not veri�able by the bank. This is based on the

assumption that the NGO has enough local knowledge and grass-root ex-

perience in the village, a natural assumption if the NGO has been already

active in this village, but perhaps in other spheres of activities. Under proper

circumstances, the NGO can be made to perform monitoring tasks over the

borrower, that come at a cost c, in order to induce the borrower to exert

e¤ort and forgo private bene�ts.

NGO�s monitoring can have two implications for the borrower. First, the

NGO can punish the borrower if she shirks by reducing her private bene�ts

from B to b. In reality it could imply a range of punishments that include

termination from further access to loans, seizure of whatever assets she may

have, or imposing social punishment in the form of substantially diminishing

defaulter�s reputation. Although, in literature a usual approach is to rule

out such punishment by imposing limited liability provision, in reality there

7

is evidence that defaulters in micro�nance receive quite severe, even violent,

punishments (Karim, 2008). Such form of monitoring will be termed as

passive monitoring according to corporate �nance literature3. Let us denote

the cost of such monitoring for the NGO by cp.

The NGO can also perform a di¤erent type of monitoring, with which

it can increase the probability of successful result of the borrower�s project.

A typical example is advising the borrowers on business administration is-

sues. This type of monitoring in corporate �nance is referred to as active

monitoring. Such monitoring comes at a cost ca for the NGO. Active moni-

toring adds to the probability of success pi measure qi, i 2 fH;Lg, such thatqH > qL. The value of qL can be normalized to zero (see Tirole (2006, p.364)

for example). Also, for further discussion I make a parametric assumption

that 0 < qH 6 1� pH .

Monitoring is targeted at reducing the cost of moral hazard which stems

from the choices made by the borrower, who can choose to either exert e¤ort

or shirk. Output is observable by all parties, so strategic default can be ruled

out. If the lending scheme includes the NGO, it may be only involved by

monitoring the borrowers. The NGO o¤ers monitoring services while the

bank issues IB to �nance the borrower�s needs. Upon the completion of the

project partnership members receive their shares of the output: YB goes to

the bank and YNGO to the NGO. At the same time, in addition to gaining

utility from �nancial payo¤, the NGO gains utility from the borrower welfare.

This is implied by the notion of directed altruism on part of the NGO that

weeks to reduce poverty. Following Roy and Chowdhury (2009) I introduce

the NGO�s motivation factor � that de�nes how much utility the NGO gets

3See Chapter X in Tirole (2006) for monitoring models in corporate �nance.

8

for a given level of the borrower�s utility.

If the bank were to �nance the microentrepreneur by itself, it would only

be repaid the full amount of debt YB + YNGO with probability, calculated

as a linear combination of pH and pL, depending on the a priori probability

that a given borrower exerts e¤ort. Nor would it be able to incentivize the

borrower to exert e¤ort. Also, in that case the problem would look like

a classical one-period moral hazard problem, where the principal only has

access to collateralization to resolve the problem.

Denote X = Y (1 + �) � (YB + YNGO)�, which implies aggregate socialoutput or the sum of all payo¤s with no regard to costs. Also de�ne 4p =pH � pL. I make the following parametric assumptions:

� Assumption 1: cp > ca, it implies that the cost of passive monitoringis higher in the sense that through excessive punishment the NGO can

hurt its public image within the target community and therefore lose

its ability to process soft information.

� Assumption 2: B < X= 4 p, this implies that the private bene�ts

from shirking are limited.

� Assumption 3: qH > B=X�4p, this implies that the e¤ect of activemonitoring is su¢ ciently high to be socially preferred.

� Assumption 4: B < 4p (Y � YB � YNGO), which implies the exis-tence of borrower moral hazard.

The government sets up (or approves) a partnership program that con-

nects a bank, an NGO and a borrower. In line with the PPP literature4,4See for example, Maskin and Tirole (2008).

9

Governmentlaunches apartnership

NGO announcesits type ofmonitoring

Borrowerchooses toexert effort orshirk

Output is realizedand payoffs aredistributed

Figure 1: Timing of events

the government maximizes aggregate social welfare. The NGO�s motivation

institutes itself in the altruistic part of its utility function in the form of

borrower welfare multiplied by �. Technically, altruism means that the �rst

derivative of the NGO�s utility function with respect to that of the borrower

is always strictly positive (Fehr and Schmidt 2006). Thus, a public moti-

vated NGO is willing to sacri�ce its own resources in order to improve the

well being of the borrowers.

More speci�cally, NGO has additively separable utility function that in-

cludes NGO�s own �nancial result and altruistic part. As in Roy and Chowd-

hury (2009), given the fact that the output of micro�nance PPP is poverty

alleviation, i.e. increase in the wellbeing of the poor through �nancing their

entrepreneurial endeavors, the altruistic part of the NGO�s utility function

includes the expected utility of the borrower with factor �.

Thus, we have the timing of events as shown in Figure 1.

10

3 Monitoring by Altruistic NGO

In this section I consider six possible scenarios: NGOmonitors actively/passively

or does not monitor at all while the borrower exerts e¤ort or shirks.

Case 1: NGO performs active monitoring and borrower exerts e¤ort.

The borrower succeeds with probability pH + qH to obtain residual income

Y �YB�YNGO while the bank and the NGO receive YB and YNGO respectively.With complementary probability every player gets zero. Thus the expected

payo¤ of the borrower is

UE = (pH + qH) (Y � YB � YNGO)

That of the NGO is composed of its expected net pro�t plus the terminal

wealth of the borrower multiplied by the NGO�s motivation factor �. While

the expected payo¤ YNGO takes place with probability pH + qH the NGO�s

monitoring costs ca are incurred with certainty. Thus the expected payo¤ is

computed as

UNGO = (pH + qH)YNGO + �UE � ca

The bank, following similar reasoning, gets

UB = (pH + qH)YB � IB

The aggregate social welfareP

ae, when the borrower exerts e¤ort and

the NGO performs active monitoring, is computed as the sum of the three

payo¤s:

�ae = (pH + qH) [Y (1 + �)� (YB + YNGO)�]� (IB + ca)

Similarly I compute the individual payo¤s for other 5 scenarios.

Case 2: Consider the case when the NGO performs active monitoring

and borrower shirks. Shirking by the borrower generates private bene�ts B

11

that only she can enjoy while reducing the probability of her success to pL.

Therefore, her payo¤ is computed as:

UE = pL (Y � YB � YNGO) +B

The NGO gets its revenue YNGO with probability pL from which it has to

cover its monitoring costs. Additionally, the terminal utility of the borrower

creates value for the NGO through the altruistic factor:

UNGO = pLYNGO + �UE � ca

The bank only enjoys �nancial pro�ts:

UB = pLYB � IB

Thus, the aggregate social welfare in case when the NGO performs active

monitoring and borrower shirks is given by:

�as = pL [Y (1 + �)� (YB + YNGO)�]� (IB + ca) +B

Case 3: Consider the case when the NGO performs passive monitoring

and borrower exerts e¤ort. E¤ort increases the probability of her success to

pH . Therefore, her payo¤ is computed as:

UE = pH (Y � YB � YNGO)

The NGO gets its revenue YNGO with probability pL from which it has

cover its monitoring costs. Additionally, it bene�ts from the terminal utility

of the borrower through the altruistic factor:

UNGO = pHYNGO + �UE � cp

12

The bank only has �nancial results:

UB = pHYB � IB

Thus, the aggregate social welfare in case when the NGO performs active

monitoring and borrower shirks is given by:

�pe = pH [Y (1 + �)� (YB + YNGO)�]� (IB + cp)

Case 4: Consider the case when the NGO performs passive monitoring

and borrower shirks. Shirking by the borrower generates private bene�ts

b < B that only she can enjoy while reducing the probability of her success

to pL. Therefore, her payo¤ is computed as:

UE = pL (Y � YB � YNGO) + b

The NGO gets its revenue YNGO with probability pL from which it has

cover its monitoring costs. Additionally, it bene�ts from the terminal utility

of the borrower through the altruistic factor:

UNGO = pLYNGO + �UE � cp

The bank only has �nancial results:

UB = pLYB � IB

Thus, the aggregate social welfare in case when the NGO performs active

monitoring and borrower shirks is given by:

�ps = pL [Y (1 + �)� (YB + YNGO)�]� (IB + cp) + b

Case 5: Consider the case when the NGO performs no monitoring and

borrower exerts e¤ort. By exerting e¤ort the borrower has probability pH of

success and no private bene�ts. Therefore, her payo¤ is computed as:

UE = pH (Y � YB � YNGO)

13

The NGO gets its revenue YNGO with probability pH as well as the addi-

tional altruistic bene�ts from the borrower�s utility:

UNGO = pHYNGO + �UE

The bank only has �nancial results:

UB = pHYB � IB

Thus, the aggregate social welfare in case when the NGO performs no

monitoring and borrower exerts e¤ort is given by:

�ne = pH [Y (1 + �)� (YB + YNGO)�]� IB

Case 6: Consider the case when the NGO performs no monitoring and

borrower shirks. By shirking the borrower has low probability pL of success

and private bene�ts B. Therefore, her payo¤ is computed as:

UE = pL (Y � YB � YNGO) +B

The NGO gets its revenue YNGO with probability pL as well as the addi-

tional altruistic bene�ts from the borrower�s utility:

UNGO = pLYNGO + �UE

The bank only has �nancial results:

UB = pLYB � IB

Thus, the aggregate social welfare in case when the NGO performs no

monitoring and borrower shirks e¤ort is given by:

�ns = pL [Y (1 + �)� (YB + YNGO)�]� IB +B

14

Following the assumptions 1-3 made earlier, it follows that social optimum

requires that the NGO performs active monitoring, so that the borrower�s

chances of successful repayment are increased, and borrower exerts e¤ort.

Hence, the following proposition:

Proposition 1 Let Assumptions 1-3 and the following condition hold:

ca < qHX

then the �rst best requires the NGO to be involved in active monitoring and

borrower exert e¤ort.

Proof. The upper bound on ca implies that it does not outweigh its added

social value. The proof then follows from the direct comparison of the 6 values

of aggregate welfare given the parametric assumptions 1-3.

Moreover, if B is su¢ ciently large it translates into the NGO�s utility and

can induce the NGO not to monitor. Hence, proposition 2:

Proposition 2 The �rst-best can be implemented only if the following in-

equalities hold: B is not so large, cost of monitoring ci; i 2 fa; pg, not solarge, motivation � not so large.

Proof. We are in a sequential game setting with the sequence of moves

given in Figure 1. Let us follow backward induction to determine possible

equilibria. Consider the case when the NGO announces active monitoring.

The borrower�s incentive constraint is then given by

(pH + qH) (Y � YB � YNGO) > pL (Y � YB � YNGO) +B

which translates into

qH >B

(Y � YB � YNGO)�4p > 0

15

So for given levels of B and 4p the NGO can induce the borrower to

exert e¤ort by generating su¢ ciently high value of qH . However, since the

value of qH is bounded from above, i.e. qH < 1 � pH , too high value of B ,

i.e. B > (1� pH) (Y � YB � YNGO) +4p, makes it impossible for the NGOto induce the borrower to exert e¤ort via active monitoring.

Now consider the NGO�s utility. In case of borrower e¤ort it is UNGO =

(pH + qH)YNGO + � (pH + qH) (Y � YB � YNGO) � ca, while in case of bor-rower shirking it is UNGO = pLYNGO + �pL (Y � YB � YNGO) + �B � ca. Inorder for the NGO to prefer that the borrower exerts e¤ort the following must

hold

(pH + qH)X > pLX + �B= (Y � YB � YNGO)

where X = YNGO= (Y � YB � YNGO) + �. It is straightforward to see

that for this inequality to hold � must be such that it o¤sets the high utility

stemming from possible high values of B.

Now consider the case when the NGO announces passive monitoring. The

borrower incentive constraint is given by

4p (Y � YB � YNGO) > b

so the NGO must su¢ ciently lower private bene�ts from shirking from B

to b in order to induce the borrower to exert e¤ort. The NGO�s utility in

turn is UNGO = pHYNGO +�pH (Y � YB � YNGO)� cp vs UNGO = pLYNGO +�pL (Y � YB � YNGO) + �b � cp when the borrower exerts e¤ort and shirksrespectively. The NGO prefers that the borrower exerts e¤ort only if the

following holds:

4p (YNGO=�+ (Y � YB � YNGO)) > b

which imposes an upper boundary on the value of private bene�ts b under

NGO�s passive monitoring.

16

When the NGO decides not to monitor the model becomes a conventional

model of corporate �nance (see Tirole, 2006) with an exception that both

NGO and borrower gain from borrower shirking. So the borrower incen-

tive constraint 4p (Y � YB � YNGO) > B and the NGO�s preferences are

given by UNGO = pHYNGO + �pH (Y � YB � YNGO) vs UNGO = pLYNGO +

�pL (Y � YB � YNGO) + �B when the borrower exerts e¤ort and shirks re-

spectively. The NGO will prefer that the borrower exerts e¤ort only if

4p > � (B �4p (Y � YB � YNGO))

which implies that both � and B must be low in order to address borrower

moral hazard.

Finally, to complete the proof we must compare conditions for e¤ectively

dealing with moral hazard under NGO�s three decisions: active monitoring,

passive monitoring and no monitoring.

(pH + qH)X > pLX + �B= (Y � YB � YNGO)

4p (YNGO=�+ (Y � YB � YNGO)) > b

4p > � (B �4p (Y � YB � YNGO))

it follows that the �rst one is the dominant strategy for the NGO when

the above conditions hold.

4 Action vs. Output Oriented Altruism

So far this paper�s analysis, as well as that of Roy and Chowdhury (2009), has

been based on the view that all agents care about outcomes, so the preference

ordering can be simply derived via Net Present Value (NPV) computation in

Bayesian framework. However, there exists a di¤erent, but important, side

17

to the speci�cation of preferences in the literature termed as "procedural

utility" (Benz, 2007).

Consider, for example, the case where the agents are involved in con-

tract enforcement disputes through an arbitration process. At the end of the

arbitration process agents have to choose whether or not to accept a court-

ordered but non-binding award or proceed to have a more costly formal trial.

From the traditional NPV perspective, there is an obvious rule to predict

how the agents will behave in such a situation: compute NPVs in all possible

scenarios and go with the one that o¤ers the higher expected monetary pay-

o¤. Yet in reality a di¤erent behavior is often observed. The seminal study

of Lind et al. (1993) revealed that such features of dispute resolution as the

fairness of the procedure was more crucial to the agents than the outcomes

themselves. When the participants perceived respectfulness, impartiality and

trustworthiness within the process they were more likely to accept the court

order, irrespective of the amounts up to US$ 800,000.

Thus, utility gained from the actions and procedures themselves, or pro-

cedural utility should be incorporated as they can make a di¤erence in the

resulting outcomes. The general concept of procedural utility implies that,

in addition to outcomes, agents value the processes and conditions leading

to those outcomes. More speci�cally, in the context of our model that is

centered around the NGO, the altruistic part of its utility function can be

of procedural type in the sense that the NGO gains utility from monitoring

itself. The reason is that the mission of the NGO can be viewed as to link big

banks that do not serve the poor by o¤ering monitoring services that reduce

moral hazard and enable banks to engage with the poor. The MFI gets some

private bene�ts from performing monitoring with the afore-mentioned objec-

18

tives of reducing borrower moral hazard and facilitating sustainble lending

by the bank. This e¤ect of gaining utility from doing the job is referred to

as "warm glow" (Andreoni, 1989).

The NGO�s altruism de�ned in such a way would �t the de�nition of

action-oriented altruism (Francois and Vlassopoulos, 2008). An NGO with

action-oriented altruism gains utility from the involvement in its expertise

per se, that is from monitoring the opaque and poor borrowers. In other

words, the altruistic bene�t depends not on the outcome, i.e. welfare on

the borrower, but rather on the NGO�s action choices. Thus, when NGO�s

motivations stem from action-oriented altruism its utility is higher when it

performs its monitoring regardless of the outcome. However, an assump-

tion must be made with regard to the NGO preferences over which type of

monitoring yields higher utility. Since micro�nance is delivered as a poverty

alleviation mechanism the preference should be given to the kind of mon-

itoring that provides aid to the borrower rather than additional distress5.

Obviously, such type of monitoring is the active one, which increases the

borrower�s probability of success without imposing any punishments.

In general, following Francois and Vlassopoulos (2008), a linearly separa-

ble utility function of the MFI would be given as:

UMFI = y � c(m) + �(g) + (m)

where y is the �nancial revenue, and c(m) is the cost of monitoring.

The third term of the MFI�s utility function, �(g), is a positive function

5Indeed the idea that micro�nance should be based on cooperation rather than pun-

ishment has gained ground in recent years. See for example the literature on Grameen

II.

19

of g which denotes the public good to which the MFI has to contribute

according to its mission, i.e. poverty alleviation. Therefore, a good proxy

to measure g is the welfare of the borrower UE: if the borrower is better o¤

poverty is reduced. Thus, following earlier discussion, one can de�ne de�ne:

�(g) = �(g = UE) = �UE, where � is a positive constant that serves as

the measure of the MFI�s output-oriented motivation. Finally, (m) is the

utility gained frommonitoring itself, for simplicity (and additive separability)

with no regard to the borrower welfare. In the earlier discussion of this

paper this last term was set to zero. However, should it not be zero it can

be de�ned as some factor � multiplied by some measure of the amount of

monitoring. One can introduce it in our model in the following way. Assume

the e¤ect of monitoring is monotonically increasing in the cost of monitoring.

Consider a simple linear form of �ca for active monitoring and �cp for passive

monitoring such that �; � > 1 so that any kind of monitoring is preferred

to no monitoring. To re�ect our assumption about the preference for active

monitoring we need to impose the constraint � > �. Then we can consider

the 6 scenarios as in Section 3.

Recall that suboptimality of NGO involvement in the earlier discussion

with output-oriented altruism stems from the fact that the NGO�s utility is

correlated with that of the borrower. Thus if the borrower gains su¢ cient pri-

vate bene�ts from shirking the NGO would be better o¤. This circumstance

undermines the NGO�s role as the reducer of asymmetric information, and

the consequential agency costs, between the bank and the borrower. How-

ever, should the NGO possess action-oriented altruism without the output-

oriented altruism there would no longer be this kind of problem as the NGO

would always �nd it a dominating startegy to engage in monitoring. Further

investigations of these implications is an important and promising next step

20

in this line of research.

5 Conclusion

In this paper I modelled the implementation stage of a partnership program

where the problem arises because by behaving in a morally hazardous way

the borrower can increase her utility by getting private bene�ts that only she

can enjoy. Higher utility of the borrower enters the NGO�s utility with a pos-

itive sign in the case of NGO�s output-oriented altruism. Therefore, NGO�s

motivation in the form of borrower-directed altruism may create a problem

for the sustainability. The analysis speci�es under which circumstances the

involvement of prosocially motivated NGOs is optimal and when it must be

limited as it can lead to suboptimal results for the viability of micro�nance

programs.

The next logical step in further investigation of the questions raised in

this paper could focus on the action-oriented altruism by the NGO which

may potentially eliminate agency costs.

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