Financial viability and social utility of microfinance
institutions in Cameroon: a multi criteria and DEA
combined analysis.
By
Joseph NZONGANG1,
Faculty of Economics and Management,
University of Dschang,
Cameroon
Abstract
This paper measures the social performance and the financial performance of the
institutions of microfinance within a network. It provides a practical tool to the network's
manager for decision making. The DEA approach is used jointly with the PROMETHEEGAIA approach. The results show that the IMF located in Cameroon member of the MC²
network fulfil the objective of providing financial and social objectives with a waste of
resources. Furthermore, the existence of a linkage between the efficiency of the IMF and the
level of their assets is highlighted.
Keywords: Microfinance –Efficiency- MC2 – DEA- PROMETHEE GAIA.
1
E-mail : jnzongang@yahoo.fr
Microfinance like many other instruments of development provokes debates on its
capacity to alleviate poverty. It is defined as the provision (supplying) of financial services to
the fringe of population excluded from the formal system. According to many professionals,
microfinance should go together with other actions in order to be able to effectively improve
the living conditions of the beneficiaries. Some institutions of microfinance (IMF) positioning
themselves by the side of the poorer have promoted the idea of “complete” microfinance
services (Hickson, 1999). According to this approach, the IMF should provide to their
beneficiaries at the same time financial services and non financial services. These services
aimed at strengthening the beneficiaries’ capacity to develop activities that produce durable
incomes. Other practitioners have challenged this approach. Several arguments have been put
forward, like the lack of competence or the absence of mandate of the IMF concerning the
provision of non financial services. Such services can divert them from their main objective
which is the provision of financial services and by so doing give contradictory messages to
the clients, in particular if these non financial services are free. Since the non financial
services lead to some important costs which are added to those of the microfinance
operations, the non financial services can also affect the capacity of the IMF to get to financial
self-sufficiency.
Microfinance has the particularity of trying to accomplish a social mission while
functioning as a classical financial institution. The consequence of this dualism is that a
financial success tends to lead to a social failure and vice-versa. This gives rise to a debate as
to the determination of the aspect which would have priority. The divergence of views
becomes more pronounced due to the fact that the different participants of the microfinancial
sector have different objectives. The donors are interested in the social impact of the
institutions that they finance while the investors care about the financial durability
(sustainability). However, whatever may be the orientation, many questions remain. Have the
resources put at the disposal of the institutions of microfinance (IMF) been used in an optimal
manner or have they been wasted? Has the institution been managed in an efficient way?
In developing countries, microfinance is presented as a mean of poverty alleviation. The
objective of IMFs in these countries is to reach the best possible performance (efficiency);
this could be achieved when they are able to reconcile two requirements: social performance
(SP) by reducing poverty and financial performance (FP) by ensuring a sustainable
profitability. However, these two requirements provoke a debate between two conflicting
schools of thought: the school of welfare highlights the social requirement of the targeting of
the poor and their living conditions; the institutionalist approach stands up for the economic
requirement of profitability and of the institutions’ viability. The IMFs of the “Mutuelles
Communautaires de Croissance “ network (MC2) in Cameroon provide a pertinent illustration
of this debate and the analysis of their activities permits to answer the following question: is
there any compatibility between social performance and financial performance?
The search for the complementarity between SP and FP becomes inevitable inasmuch
as it conditions the durability (sustainability) of the institution, gives confidence to the
potential partners and permits to build fame. However, the question of SP versus FP in the
IMF has not attracted much attention in the literature (Navajas et al., 1998; Paxton, 2000;
Woller and Shreiner, 2002; Gutiérrez-Niéto et al., 2005 ; Polanco, 2005 ; Cul, Demirguc-Kunt
and Morduch, 2006 ; Cornée, 2006). These works measure the degree of efficiency of the
structures studied. But, the issue of the efficiency does not only concern the investors and the
donors. It is equally of interest for the managers, the operators of the IMF on a daily basis. In
the Cameroonian context, most of the IMF try to gather together into networks2. To improve
the performance of a network, the manager needs an instrument panel in order to improve
his/her management. It is precisely this problem that we try to solve in the scope of this study.
Our study tries to reconcile the SP and the FP of the IMF gathered within a network and to
provide some concrete elements of assistance to the managers’ decision taking. For that, we
are going to combine two methodologies (DEA3 and PROMETHEE-GAIA4).
In order to achieve this objective, the work has been structured into three parts. The first
part reviews the related literature on the different contrasting points of view of the two
schools of thought on the social performance and the financial performance of the
microfinance institutions. The second part brings out the concept of the efficiency frontier as
well as the different steps of the empirical analysis. Finally, the third part presents the results
and proposes some recommendations in order to ameliorate the performance of the IMFs
studied.
1.
Social performance versus financial performance of the IMF
Microfinance is a mean of poverty alleviation in developing countries through the
financing of the activities productive of incomes for poor households. However, the best way
of helping the poor to have access to the financial services contrasts the welfare approach with
the approach of the institutionalists. Though they all share the objective of poverty alleviation,
these two approaches put microfinance at the crossroads.
1.1. The welfare approach
This school of thought has been identified as a school of poverty measurement
(Asselin and Anyck, 2000). According to this trend, an individual is considered as poor when
he/she is situated below a minimum economic well-being. The advocates of this school of
thought are based on the theory of social responsibility vis-à-vis the clientele in order to meet
their expectations (Caroll, 1979). They assess the performance of an IMF in the point of view
of the client through the social scope « Outreach » (Lafourcade; Isern; Mwangi and Brown,
2005) and the assessment impact (Harper; Hill; Horn; Salib and Walen, 2005). It targets the
poorest people whose incomes are at 50% inferior to the poverty threshold ($1 per day) and
aims at improving their living conditions. It is essentially made up of solidarity institutions –
NGOs or cooperatives – which consider microfinance as an important means of the fight
against poverty for the poor (Hamed, 2004). Even though it insists on the rational
management of the resources and does not exclude the fact that the IMF could run a profitable
activity at the end of 5 to 12 years, this school of thought advocates a supply of financial
services at relatively low rates of interest and a large resort to grants (Olszyma-Marzys,
2006).
This welfare approach has however created the rates of settlement below 50% as well
as very high functioning costs leading to the failure and to the disappearance of some IMF
though it is based on the logic of grants and the dependence of the beneficiaries. As a matter
of fact, these IMFs are faced with obstacles (the viability and durability problem) which are
limits to their development and to their capacity to contribute to the development of the
person they help and to a poor performance. In this manner, the welfare approach has been the
2
3
4
In Cameroon, more than 75% of the IMF are affiliated to a network (Creusor, 2006).
Data Envelopment Analysis.
PROMETHEE = Preference Ranking Organization Method for Enrichment Evaluation; GAIA = Geometrical Analysis for Interactive
Assistance.
subject of many criticisms on the grounds of its subjectivity, its costs and the methodological
difficulties that it brings about (De Briey, 2005). A revival of the financial and economic
thought was necessary in order to re-assess the conditions of success of the IMF where the
interest shown by the economists and the practitioners in the study of the IMF efficiency in
poverty alleviation paves more and more the way for an efficiency analysis in financial and
accounting terms.
1.2. The institutionalist approach
Supported by international organisations such as the United Nations and the World
Bank, the institutionalist approach came to birth (Woller, Dunford and Woodworth, 1999). It
is based on the theory of contracts which considers that the non-fulfilment of contracts can
lead to some opportunist behaviours of those who need loans (Ghatak and Guinane, 1999). Its
protagonists consider that the unique manner to reach the great majority of the poor without
access to the financial services is to increase the activity of the microfinance through its
integration into the formal financial system. In this way, they try to put the IMF within the
logic of market by insisting on the will of the setting up of a sustainable microfinance system
as well as the will of rendering loans within the reach of the majority of the population (De
Briey, 2005). Each IMF should aim at financial sustainability by maximising its efficiency
and its productivity. They have conceived a set of “best banking practices” in order to
increase the efficiency of the management systems, whose adoption is an essential step to get
at financial self-sufficiency on the industrial scale and to have access to financial market and
to reach the maximum number of poor clients (Morduch, 2000). Consequently, the
sustainability necessarily passes through the access to the financial autonomy which is a
criterion that better fulfills the social mission (Cornée, 2006). The measurement of the social
impact passes through a proxy, profitability, while they appreciate success through the selfsufficiency of the programme (Otero and Ryhne, 1994).
The interest for self-sufficiency emerged from the recognition of the scarcity of funds.
As a matter of fact, the institutionalists believe in the necessity of large scale intervention
which demands financial resources above what the bankers can provide. They are afraid of the
changing nature of these national and international sponsors because an IMF which wants to
operate in the long run, by becoming structurally dependent on grants, would risk being a
short-lived programme. But the only way to get the financial resources which they need is to
resort to private sources (saving, commercial debts, capital outlays and venture capital).
This approach has not only been adopted by most of the literature edited in the scope
of microfinance but it can also be observed at the moment through two great trends. We find
the upgrading process where certain regulated IMFs are now created in countries offering a
regulation process of the specialised institutions in microfinance on the one hand. These IMF
are NGOs which give rise to regulated financial institutions having the status of limited
companies and which come clearly within the scope of profitability the logic (De Briey,
2005). We find the downgrading process where certain traditional commercial banks which
are looking for new markets have most recently entered into the microfinance sector. These
banks have not only been convinced of the potentialities of micro credit, but they also have
easy access to the funds and to better tools of marketing. They can directly grant loans to the
micro entrepreneurs or can acquire shares in the IMF on the other hand.
However, this institutionalist approach has been the object of many criticisms. At the
level of the targeted population, it has as a preferred clientele the micro entrepreneurs very
close to the poverty line, geographically concentrated, having highly profitable activities with
short production cycle. Moreover, it requires from its clients very high interest rates in order
to ensure financial autonomy. Finally, this approach recommends the attainment of the
institutional and financial viability for long term microfinance programmes.
The welfare and the institutionalist approaches have been the object of few criticisms.
The first approach is confronted with the problem of viability and that of sustainability
induced by the grants, the low rates of reimbursement and the increase of the functioning
costs; the second approach has as preferred clientele the micro entrepreneurs very close to the
poverty line on which are applied very high interest rates in order to ensure the financial
autonomy of the IMF. This contrast leads to trade-offs between the two paradigms.
2. The methodology of the efficiency frontiers
The complementarity between SP and FP can be analysed through the notion of
efficiency. Efficiency is a relative notion which permits to estimate the performance as well as
the development potential of a company and to place it in comparison with its competitors. It
implies that the resources of a company should be used at best according to its objectives. For
an IMF, it is a matter of getting a social and financial result without wasting the given micro
financings.
The efficiency methods, deriving from the production theory, permit to characterise
the performances of the firms (Kopp and Diewert, 1982; Kumbhakar, 1988). The companies
are considered as technically efficient when they can supply their products from a minimum
level of resources without changing their combination of factors (Atkinson et Cornwell,
1994).
Two methods can be used to calculate the efficiency scores (Berger and Humphrey,
1997). The parametric methods specify the structural relations between the variables with the
help of a function which characterises the production function and resorts to econometrics to
estimate it. The nonparametric methods, to which belongs the Data Envelopment Analysis
(DEA) approach, do not a priori set hypotheses on the relations which exist between the
variables but directly construct a frontier from the observations thanks to linear programming.
The measure of efficiency is obtained by comparing the results of a unit of production to those
it would have obtained if it adopted the practices of the best which are located at the efficiency
frontier. Its efficiency is measured by calculating the distance which separates it from the
frontier. This distance is expressed with the help of an efficiency score.
In the present work, we have adopted the DEA nonparametric approach for it permits
to “reveal” the inquired information (the efficiency) from the observed data by specifying a
few hypotheses on the structure of the efficiency frontier.
The DEA and the PROMETHEE-GAIA methods are first presented. The data used as
well as the selected variables are then described.
2.1. DEA and PROMETHEE-GAIA : tools of estimation of efficiency and of
aid to decision
2.1.1. The DEA method
The DEA method constructs an efficiency frontier which envelopes the observations
of the sample. In this way, the information located on this frontier corresponds to the 100%
efficient entities. The entities located outside this frontier are considered as not being totally
efficient. The score of efficiency corresponds to the measure of the gap existing between the
observations and the frontier. When we want to reduce wasting of resources, this score gives
us some information on the possibility of reduction of all the inputs without any modification
of the initial combination of factors and of the level of outputs supplied.
The DEA method is adapted for the study of the efficiency of small samples of
observations. It analyses each unit separately and measures its efficiency in comparison with
the set of units of the sample. Its important adjustment to the available data represents an
indisputable asset in the domain of analysis of the IMF efficiency. However, this advantage is
also a limit for it is impossible to draw conclusions as to the absolute values of the efficiency
analysed. On the other hand, it is not easy to know the contribution of each variable in the
realisation of efficiency. The DEA approach does not estimate the coefficients to describe the
function characterising the frontier of efficiency5. In the absence of such information, a
certain degree of precision is lost; this can reduce the possibilities of operational use of the
results. It is to circumvent this limit that we combine the PROMETHEE-GAIA method with
the DEA approach.
2.1.2. The PROMETHEE-GAIA method
PROMETHEE-GAIA is a method of aid to the multi criteria decision (Brans and
Mareschal, 1994, 2002). It permits to treat multi criteria alternatives in view of an
arrangement of variables and observations. It is applied according to a process which
comprises four steps:
- comparisons of pairs of alternatives from variables in order to determine an index
of aggregate preferences;
- calculations of streams of out classification;
- the arrangement of the alternatives from the streams of out classification;
- the visualisation of the alternatives in a plane called GAIA.
2.1.3. Justification of the combination: DEA - PROMETHEE-GAIA
In the DEA model, the researchers decide a priori on the model to use in selecting the
variables which will be used in measuring the efficiency of the sample of observations
(Pedraja, 1999). Serrano et al. (2001, p.3) draw attention on the fact that a variable can be
included in the model when it does not contribute to the calculation of efficiency. Inversely,
it is possible that a variable not included in the model could be very important for the
analysis. Serrano et al. suggest circumventing this problem by calculating some DEA scores
of all the possible combinations of the variables. The use of the Principal Component
Analysis (PCA) permits afterwards to analyse the results (Gutiérrez et al., 2007 ; Cornée,
2006).
We want to bring in new concrete elements of help to the decision making of the
manager of the IMF network in order that those evolving below their potentialities should be
identified and pushed towards the efficiency. We can not then make a combination of
different DEA models since each model proposes its own corrective measures. The man ager
would find himself/herself with as many propositions as models, without any selection
criterion for retaining the most adapted to the IMF network that he/she manages. The solution
is then to stick at one model and to analyse the results obtained in using a method of help to
the multi criteria decision.
5
To get specific scores per input and output, two approaches are proposed in the literature on the measure of the
efficiency. The first one is based on some ex post calculations combining the efficiency scores and the gap
variables of the linear programme (Torgensen et al., 1996). The second one uses a non radical efficiency measure
in the sense where a score is obtained for each output. The total measure describing the firm efficiency is then
the average of the set of the specific scores calculated (Färe and Lovell, 1978).
One of the advantages of the combination of the DEA and the PROMETHEE-GAIA
models is to allow making some comparisons between the different IMF and the variables
chosen. This permits us to know what IMF is “good” or “bad” on such or such input or
output.
2.2. Data and variables selected
2.2.1. Sources of data
Launched in 1992, the activity of the “MC2” aims at endowing the village
communities with rural development micro banks created and managed by their members, in
the respect of the sociocultural values. The “MC2” propose to the populations adapted
solutions in order to overcome their problems of access to the financial services and permit
them to improve their living conditions in a sustainable manner. It is a question of an
endogenous approach of development which permits the underprivileged populations to create
wealth. As any microfinance institution, the “MC2” have a twofold objective. An economic
objective which concerns their financial viability and a social objective which is that of
reaching the poorest levels of the populations by financing small and micro activities.
The “MC2” are institutions of first category6 sponsored by Afriland First Bank which
plays at the same time the role of a commercial bank and provides the technical assistance in
partnership with the NGO “ADAF” (Appropriate Development for Africa Foundation).
On the 31st December 2007, there were 66 operational “MC2”. On this same date, the
network deals directly with 82 280 individuals, 9 844 groups and associations and indirectly
with about 574 480 persons. The total amount of deposits is 11, 87 billions of CFA Francs,
the capital raised in the “MC2” amounts to 2, 36 billions of CFA Francs. A total amount of 25,
43 billions of CFA Francs has been granted in a form of loans since 1992 (ADAF, 2008). The
flexibility of the “MC2” as well as its adaptability to each sociocultural context permits its fast
introduction in the different milieu which experience poverty problems and which the
populations have chosen to become members in order to emerge from poverty.
On the basis of the data at our disposal, we have retained the 20 “MC2”of the network
having more than 10 years of experience. This selection guarantees a financial sustainability
of the IMF as well as an effective social settling. The encoded data are stemming from the
financial statements of the 2006 financial year.
2.2.2. Selection of the variables
The role of a financial institution can be described according to the production and the
intermediation models. In the first case, the institution uses some factors (capital, labour) to
proceed to the financial transactions (saving and credit). On the other hand, if we consider the
role of intermediation, the financial institution collects the deposits and grants loans, the
deposits are considered as inputs while the loans are considered as outputs.
To carry out our study, we have retained three inputs (the total asset, the number of the
employees, the exploitation expenses) and three outputs (the outstanding loans, the
exploitation revenues, the number of women membership). The choice of the first two inputs
(personnel and assets) has been done according to the production approach. The variables
“exploitation expenses” and “exploitation revenues” synthesize the information on the
operational and the financial self-sufficiency of the “MC2”. The outstanding loans measure
6
The regulation n°01/02/CEMAC/UMAC/COBAC relative to the exercise of the microfinance activities in
Central Africa states in its section 7 that the IMF of the first category treat only with their members.
the accessibility of the IMF. Furthermore, to represent the interaction between the financial
and social objective of the “MC2”, the number of women members is introduced as an output
of the DEA model. Women are the targets having priority of the IMF (Montalieu, 2002).
3. The results and the recommendations
We are now going to interpret the results stemming from the combination of the DEA
and PROMETHEE-GAIA models and we shall give some recommendations in order to
improve the efficiency of the “MC2”.
3.1. Interpretation of the results of the DEA model7
Table1: Efficiency and pair of reference of the MC2
MC²
Babouantou
Bafia_Makenen
e
Bafou
Bafoussam_Rur
al
Baham
Bambalang_Ba
munka
Bamendjou
Bandja
Bandjoun
Bangangté
Bangouu
Batoufam
Bayangam
Doumbouo
Manjoo
Melonggg
Muyuka
Ngaoundal
Penka_Michel
Batié
Source: author
Bab
BMk
Baf
B_r
Bah
B_B
Bam
Bja
Bjn
Bté
Bng
Bat
Bay
Dou
Man
Mel
Muy
Nga
Pen
TPD
Efficiency
100.00%
100.00%
85.83%
100.00%
93.28%
60.79%
100.00%
100.00%
100.00%
100.00%
100.00%
87.81%
100.00%
100.00%
82.80%
97.94%
100.00%
78.42%
94.31%
62.00%
The ‘’MC2’’ of reference
Muy
Dou
Bjn
Bjn
Bay
Bng
Bab
Muy
Bam
Bjn
Bng
Bab
Bjn
Bté
Muy
B_r
Dou
Bab
Bam
Dou
Muy
Bté
Muy
Dou
Bng
Bté
Muy
BMk
Dou
Table 1 above represents the efficiency scores obtained by every one of the “MC2”
retained in the sample of this study. A score of 100% means that the “MC2” is efficient. It
uses its resources in an optimal manner to supply the financial outputs (loans and products)
and the social output (membership of women). These “MC2” define the efficiency frontier.
The “MC2” having a score inferior to 100% presents a technical inefficiency. They waste
productive resources in the process of supplying the outputs retained in the model. They can
refer to the practices of the “MC2” of reference which are associated to them in order to
become more efficient in the management of their inputs.
Proximity of the efficiency scores emerges from table 1. This can be the result of two
elements. These “MC2” have a certain mastery of their activity due to their length of service
7
For the calculation of the scores of the efficiency, we have used the “DEA Frontier” software of Joe Zhu.
and they also have a neighbouring geographical localisation (14 are located in the West
Region), and this leads to some similarities in the habits and customs of the local populations.
Table 2: Excesses of inputs, lacks of outputs of every “MC2”
Excesses of inputs
Lacks of outputs
Act
Em
Ch
Cr
Pro Fem
Bab
0
0
0
0
0
0
BMk
0
0
0
0
0
0
Baf
0
1
2 591 923
3 744 268
0
0
B_r
0
0
0
0
0
0
Bah
53 908 741
0
0
0
0
0
B_B
0
2
0
0
0
0
Bam
0
0
0
0
0
0
Bja
0
0
0
0
0
0
Bjn
0
0
0
0
0
0
Bté
0
0
0
0
0
0
Bng
0
0
0
0
0
0
Bat
0
3
0
16 617 727
0
1
Bay
0
0
0
0
0
0
Dou
0
0
0
0
0
0
Man
0
1
0
24 117 892
0
0
Mel
0
0
2 623 586
0
0
0
Muy
0
0
0
0
0
0
Nga
0
3
2 890 797
0
0
19
Pen
76 245 493
0
0
975 565
0
0
TPD
13 185 386
1
0
0
0
0
Act : stands for assets Ch : stands for charges
Fem : stands for women
Cr : stands for credits
Em : stands for employees Pro : stands for products
Source: author
MC2
Table 2 illustrates for each “MC2” the existing possibilities of reduction on certain
inputs or of possible increase of certain outputs after the correction of the efficiency. These
values indicate the situations where the “MC2” is not efficient in the sense of Pareto. There
still remains some unused resources or inversely some outputs not produced which would
permit to the concerned “MC2” to go farther in the improvement of its performance8.
As an example, the “MC2” of Bafou has an efficiency score of 85, 83%. In comparison
with its “MC2” of reference, it can produce the same level of outputs by reducing its
consumption of inputs by 14, 17%. However, in addition to the proportional reduction of all
its inputs, it is still possible to reduce some of them. In this way, the costs can still be more
reduced by 2 591 923 CFA Francs. Similarly, the number of employees can be reduced by
one unit. Originally, Bafou had 5 employees. After the correction by the score of efficiency,
the number of employees can be reduced by one person. After taking into account the
information given by the model and relative to the surpluses of inputs of each “MC2”, there
subsists one employee too many. Hence the optimal number of the employees is 3. As to the
8
On a technical plane, these values describe the non saturation of the constraints of the linear programme from
which the DEA efficiency frontier is constructed.
outputs, this “MC2” has at its disposal an outstanding loans that 3 744 268 CFA Francs less
than what it could manage with the means it has.
In this manner, thanks to the information relative to the efficiency scores and to the
different surpluses of inputs and lacks of outputs, the manager of the network has an idea of
the objective to achieve in order to make every “MC2” more efficient. The table 3 below gives
the target values to achieve for each IMF in order to render them completely efficient. These
values can be used by the manager as a reference instrument in the management of each
“MC2”
Table 3: Aims in term of inputs and of outputs of each ‘’MC²’’
Aims in term of inputs
Act
Em
Ch
Bab
375 502 748
5
20 200 287
BMk 1 071 805 827
8
77 096 702
Baf
117 390 471
3
10 423 811
B_r
457 158 397
6
32 415 904
Bah
635 248 630
6
39 892 770
B_B
38 006 503
1
4 064 586
Bam
349 085 862
5
29 197 031
Bja
259 717 413
5
15 099 086
Bjn
600 347 781
6
38 692 016
Bté
795 791 217
5
53 871 251
Bng
105 977 137
4
12 981 132
Bat
145 677 594
2
8 460 042
Bay
443 629 053
5
23 634 979
Dou
272 508 929
7
24 229 070
Man
162 837 309
3
12 950 687
Mel
688 230 016
7
47 185 232
Muy
511 028 672
8
44 753 666
Nga
56 383 497
2
6 112 931
Pen
336 993 985
6
22 902 824
TPD
68 315 719
4
11 729 846
Source :author
MC2
Aims in term of outputs
Cr
Pro
133 464 101
34 455 607
356 571 255
80 284 044
47 483 443
8 604 928
170 369 043
42 233 800
184 317 763
44 901 834
21 533 690
2 322 565
138 583 833
33 025 642
105 878 847
15 004 364
290 395 356
45 123 366
142 538 493
63 318 936
64 942 423
5 504 700
53 459 124
13 597 319
132 493 808
22 864 692
109 147 135
19 180 835
63 547 199
14 777 842
278 229 980
54 974 556
228 199 671
53 265 644
30 773 402
4 117 056
117 597 415
27 856 699
70 172 568
10 321 838
Fem
333
869
295
543
617
65
467
319
571
664
209
127
520
728
235
662
425
66
509
252
The DEA provides elements for identifying the inefficient and efficient “MC2”, the
target values for the construction of dashboards for every “MC2” and the identification of the
IMF of reference in the management of inputs. However, it reveals itself insufficient in a
managerial perspective of the IMF. As a matter of fact, it is very important to know the
behaviour of every “MC2” with respect to the different variables of the model in order to
facilitate the taking of corrective measures.
The combination of the DEA and the PROMETHEE-GAIA approaches will permit to
characterise the relation existing between the “MC2” and the different variables at our
disposal.
3.2. The combination of the DEA and the PROMETHEE-GAIA approaches
The most significant element in the DEA analysis is the relation which exists
between the “MC2” and the variables of the DEA model. This information can be obtained in
analysing the corresponding GAIA plane.
3.2.1. The results of the PROMETHEE-GAIA approach9
The links which exist between the “MC2” and the variables of the DEA model are
synthesized in the GAIA plane represented in Figure 1 below. The different “MC2” are
represented by the triangles and the different variables by the squares. The horizontal axis
contrasts the information relative to the inputs (green squares) and to the outputs (red squares)
of the DEA model. The vertical axis contrasts the social objective with the financial objective
(the women membership) of the considered IMF.
Figure 1 : Typology of the ‘’MC²’’ in the GAIA plane
Source :author
On figure 1 above, the criteria of PROMETHEE decision is represented by the circles.
They permit to identify four groups of “MC2”. The “MC2” located in the each group are
similar.
In this manner, we observe that the groups 1 and 3 are made up of very different IMF
as to their behaviour relative to the different variables of the model. The group 2 is presented
as a median position between the two extremes that are the groups 1 and 3. On the other hand,
the “MC2” of Muyuka and Doumbouo isolate themselves from groups 1 and 2 and define a
larger group (group 4) comprising the ellipses 1 and 2 and contrasting with group 3.
The interpretation of the variables is based on the measure of the angle made by
vectors which link them to the origin. In this way, the figure 1 shows that there exists an angle
– which is almost 90 degrees – between the variables “women membership” and the other
outputs of the model (loans and exploitation revenues). This means that the social output is
independent from the financial outputs. The fact that an “MC2” enjoys some important
exploitation revenues does not influence the number of women membership.
On the other hand, there exists an angle with about 180 degrees between the variables
exploitation revenues (outputs) and the assets (inputs). This means that they are contrasting. If
one “MC2” is successful on one of these two variables, then it is inefficient on the other one.
The group 3 is next to the criterion “active”. It gathers the “MC2” which have modest assets.
It contrasts with group 4, which “MC2” have more important assets. For example, the “MC2”
9
For the calculations of the PROMETHEE-GAIA method, we have used the “Decision Lab” software.
of Bafia-Makenene (group 4) has outstanding loans of more than one billion while the “MC2”
of Bambalang-Bamunka (group 3) has a level of assets which is less than 63 millions of CFA
Francs.
The group 4 represents institutions which have important assets. However, most of
these “MC2” have average performance in the maximisation of the exploitation revenues and
the number of women membership, that is to say, in the achievement of the social and
financial objective of the “MC2”. In this group, the “MC2” which have the highest assets are
in group 1 while those having average sizes are in group 2. Within the group 4, two “MC2”
distinguish themselves from the other. Muyuka is more interested in the financial objectives
than Doumbouo which favours the social objective.
Generally, the positioning of these “MC2” which have been studied does not result from
a decision of their companies’ managers, but rather from the facts. Consequently, the
knowledge of the situation of the IMF with respect of the social and financial objectives can
help the network managers identify the measures to be taken in order to ameliorate the IMF
results about the neglected aspect.
3.2.2. Combined interpretation DEA- GAIA
Table 4 below presents the efficiency scores deriving from the DEA approach when we
keep as “MC2” of reference Doumbouo and Muyuka. All the “MC2” are then evaluated with
respect to these two IMF in order to provide an efficiency score which situates each institution
of micro credits in relation to two objectives (social and financial) recognised from their
activity. The results are presented by ranking the “MC2” in a decreasing order of efficiency.
Table 4 : Scores et benchmarks of the MC² of the reduced DEA model
MC2
DEA scores
Benchmarks
Doumbouo
100.00%
-
-
Muyuka
100.00%
Bamendjou
99.48%
Dou
Bafoussam-rural
95.57%
Dou
Muy
Babouantou
90.41%
Dou
Muy
Bafou
85.83%
Dou
Muy
Manjo
81.26%
Dou
Muy
Batoufam
80.35%
Dou
Muy
Melong
79.95%
Dou
Muy
Bangangté
79.41%
Dou
Muy
Bandjoun
77.52%
Dou
Muy
Bafia-makenene
75.14%
Dou
Muy
Muy
Bangou
73.82%
Dou
Muy
Penka-michel
71.06%
Dou
Muy
Bandja
67.24%
Dou
Muy
Baham
63.70%
Dou
Muy
Ngaoundal
62.03%
Dou
Muy
Bayangam
61.16%
Dou
Muy
Batié
47.13%
Dou
Muy
47.08%
Dou
Muy
Bambalang-bamunka
Source :author
Using the scores given in table 4 and the GAIA plane, it is possible to identify some
general trends. The most successful institutions have a score which is situated around 90%
and belong to group 2; the median group on figure 1. These institutions have assets comprised
between 200 and 400 millions of CFA Francs. The group 1 is constituted of institutions whose
efficiency varies between 75% and 80%, apart from the “MC2” of Bafoussam rural (score of
95%) and of Baham (score of 63%). The value of the assets of that group amounts to 200
millions. The group 3 is the most disparate. It groups together “MC2” having good scores like
Bafou (86%) and Manjo (81%) and “MC2” having the poorest scores: Batié and BambalangBamunka (47%). The value of the assets for this group exceeds 400 millions of CFA Francs.
These results show that there exists a link between the level of efficiency of a “MC 2”
and the value of its assets. It seems that a “MC2” is “mature” in terms of the management of
its resources when it passes the 200 millions CFA Francs. Below this threshold, it falters out
and can get all the levels of efficiency. However, when its assets are superior to 400 millions
CFA Francs, its efficiency declines; the most important level of resources becoming probably
more difficult to manage.
The GAIA plane has then permitted us to deepen the understanding of the efficiency
scores. It has appeared judicious not to limit ourselves to only the results given by the DEA
approach. The exploitation of the GAIA plane has permitted to understand the “MC2” scores.
Not understanding the DEA results could lead to some hesitations and experimentations that a
manager should not allow. This is even more important if we should run a whole network.
3.3. Recommendations for the optimisation of the management of the “MC2”
network
The efficiency scores have allowed us rank the IMF according to their level of
performance. The target values given by the DEA approach can be used to lead the “MC2”
towards a higher level of performance, serving as objective to the “MC2”.
Our first recommendation is to carry out the classification of the IMF according to
their level of performance in a regular way. The evolution of the scores obtained each year
could give an idea on the progress achieved by each IMF and globally by the network and
could allow the updating of the objectives to be reached.
In the Cameroonian context where there is a great cultural diversity, it is interesting for
the network manager to proceed to a deeper analysis. From a more important sample, it could
be possible to carry out a similar study per region. As a matter of fact, we have noticed that
among the “MC2” used, that of Ngaoundal which is located in Muslim region is the IMF of
our sample which produces the lowest social output (women membership). This level of
analysis goes beyond the managerial scope to integrate the sociocultural context of the
“MC2”.
Concerning the characterisation of the “MC2” of reference, we suggest a twofold
approach to be able to capitalise the obtained information. At first, it is a question of creating
an exchange between the inefficient “MC2” and the persons in charge of “ADAF” in order to
identify the weak points of this “MC2” and to agree on the elements coming from the “MC2”
of reference which could serve as examples and then work in collaboration with the managers
of the efficient “MC2” for the setting up of more efficient management methods. Secondly, it
is conceivable to favour exchanges between the efficient “MC2”, the inefficient “MC2” and
“ADAF” by organising, for instance, seminars during which the different participants could
learn from one another.
Conclusion
Microfinance is characterised by a consensus on its objective which is poverty
alleviation. However, there exists a divergence on the manner to help the poor through the
access to financial services. Two trends emerge. The client approach considers microfinance
as a means for reducing the poverty of the poorest people. It stresses on the amelioration of
the welfare of the targeted populations, that is to say, on the social impact of microfinance.
The institutionalist approach considers that the only way to reach the great majority of the
poor is to increase microfinance activity through a policy favouring the granting of loans to
the masses, the search for the financial autonomy and the integration of the IMF into the
formal financial system.
The aim of this study was to measure the performance of the IMF of the “MC2”
network. As a matter of fact, for the cohesion of the network, their managers have all interest
to make sure that the units which constitute it should be efficient in their management in order
that the resources of the whole network should not be wasted.
The data analysis has allowed getting the efficiency scores for each “MC2”. They
constitute a synthetic measure of their relative performance, that is to say, of their capacity to
limit wastage in the provision of social and financial services. The DEA approach used in this
study has led to the identification of the inefficient and efficient “MC2” as well as to the
definition of the target values permitting to the network administrator to define dashboards for
each “MC2”. Afterwards, the use of the PROMETHEE-GAIA approach has permitted a better
understanding of the behaviour of each IMF with regards to the different inputs and outputs of
the DEA model. The additional information can be used by the administrator and the
concerned “MC2” to define the process of amelioration of its activity towards a higher social
and financial level of performance.
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Annex : Data relative to each “MC2”
MC2
Assets
Bab
375 502 748
BMk
1 071 805 827
Baf
136 774 713
B_r
457 158 397
Bah
738 843 766
B_B
62 519 267
Bam
349 985 862
Bja
259 717 413
Bjn
600 347 781
Bté
795 791 217
Bng
105 977 137
Bat
165 903 426
Bay
443 629 053
Dou
272 508 929
Man
196 660 112
Mel
702 699 016
Muy
511 028 672
Nga
71 901 702
Pen
438 172 022
TPD
200 169 585
Source : ADAF (2006)
Inputs
Emp
5
8
5
6
6
5
5
5
6
5
4
6
5
7
4
7
8
6
6
5
Costs
20 200 287
77 096 702
15 164 973
32 415 904
42 768 932
6 686 091
29 197 031
15 099 086
38 692 016
53 871 251
12 981 132
9 634 632
23 634 979
24 229 070
15 640 663
50 855 974
44 753 666
11 481 788
24 284 652
11 729 846
Credits
133 464 101
356 571 255
43 739 175
170 369 043
184 317 763
21 533 690
138 583 833
105 878 847
290 395 356
142 538 493
64 942 423
36 841 397
132 493 808
109 147 135
39 429 307
278 229 980
228 199 671
30 773 402
116 621 850
70 172 568
Outputs
Products
34 455 607
80 284 044
8 604 928
42 233 800
44 901 834
2 322 565
33 025 642
15 004 364
45 123 366
63 318 936
5 504 700
13 597 319
22 864 692
19 180 835
14 777 842
54 974 556
53 265 644
4 117 056
27 856 699
5 504 700
Wom
333
869
295
543
617
65
467
319
571
664
209
127
520
728
235
662
425
66
509
252