Glosarium ANP

• NETWORK – A network is a structure of clusters and nodes and connections between the nodes that appears in a single window.   A network usually has feedback (lines going both ways between clusters) and may have inner dependence (a loop on a cluster indicating nodes in that cluster are connected to each other).  A hierarchy is a simple kind of network that has a goal node in a goal cluster that connects to criteria in a criteria cluster and so on, downwards, but no feedback.
• MODEL – A decision model is a logical structure for making a decision.  Your decision model will have two files with a .mod extension.  The first is the main model that has the Benefits, Opportunities, Costs and Risks nodes, called the merits, in the top level network and a waterfall of subnetworks beneath.  The networks at the very bottom contain the decision alternatives.  This is the part of the model directly concerned the decision.  The second is the strategic criteria ratings model that is used to establish priorities for the B, O, C, and R in this particular decision.
• NODES AND CLUSTERS – A cluster is a window inside a network that contains nodes.  A cluster is a logical grouping of factors or elements in the decision being considered.  The factors or elements are the nodes.  Clusters are a device to help you in structuring the decision problem.  All connections are made between nodes.  Clusters end up linked by default when a node in one cluster is linked to some nodes in another cluster.
• CONNECTIONS – Connections are made by linking nodes together.  The way to decide on what connections to make is to pick a node and examine the nodes in another cluster (or its own) to see if they influence it.  If some of them do, link it to them.  This node will become the parent and the nodes it links to in a cluster will become its children for a pairwise comparison set.  A link will appear from the parent’s cluster to the children’s cluster.  Depending on how many clusters there are with children belonging to that node, a parent may have many comparison sets.  Inner dependence is when a node is linked to other nodes in its own cluster, and a loop will appear on the cluster.
• BOCR – the Benefits, Opportunities, Costs and Risks nodes, referred to as the BOCR merits, are in the top level network in the main model. There is a goal node linked to them so at the end of the decision process you can insert their priorities.  To evaluate their importance one creates a separate strategic criteria model with ratings where the BOCR are the alternatives in the ratings spreadsheet.  You must know the most dominant alternative for the B, O, C, and R respectively as determined in your main decision model, and keep it in mind as you make the ratings.  For example, think of the impact of the dominant alternative for Benefits on the strategic criteria as you select the ratings across the Benefits row.  Finally the priorities derived in the ratings are inserted as direct data into the main model by selecting the Goal node and Compare/Misc./Data and typing them.
• STRATEGIC CRITERIA – These are the invariant criteria or objectives of the individual or organization making the decision that always need to be satisfied and are external to the actual decision model of the day.
• SUBNETWORK (OR SUBNET)  – a subnetwork is any network attached to a node in a network above it.  The BOCR nodes are in the top-level or main network.  Each of them has a subnetwork containing its control criteria nodes which will contain a hierarchical model of potential control criteria nodes.
• CONTROL CRITERIA SUBNETWORK – there is a control criterion subnetwork for each of the BOCR merits.  In it one establishes a hierarchial structure of goal, criteria and perhaps subcriteria, pairwise compared to establish priorities.  These criteria or subcriteria are potential control criteria nodes. Select the nodes with the most priority to build decision subnets under (you should pick nodes with about 70% of the total priority).  To see the overall derived priorities, select Computations/Priorities and read the limiting priorities column.  These become the control criteria and you build decision subnets under them.  [If you have only criteria, their limiting priorities sum to 1.  If you have both criteria and sub-criteria you will need to multiply the limiting priorities by 2 to make them sum to 1 for the criteria and 1 for the sub-criteria, and determine the actual overall priorities (because of the way the limiting supermatrix “stochasticizes” its columns to make them sum to 1). 
• DECISION SUBNET – the network attached to each control criterion node. You must create a decision subnet for each selected control criterion, so for benefits, for example, you may select 3 or 4 control criteria and end up with 3 or 4 decision subnets.  These are the bottom level networks, called decision subnets because they must contain a cluster named Alternatives that contains the nodes that are the decision alternatives.  In general, the other clusters and nodes you put into the decision subnet are the forces influencing the decision.
• DECISION SUBNET TEMPLATE – it is often wise to build a general decision subnet containing the alternatives and all the other clusters and nodes you may need. You can build it in the first decision subnet in your model, and save that network, only, as a “template”.  You can then import the template by going into another decision subnet and opening the saved template.  It will overwrite whatever is there.  Modify as necessary by deleting clusters and nodes not needed, or adding new ones.  The original template will remain as it was and you can re-use it over and over again.  You may be able to make the node connections in the original template, but you will usually need to make the comparisons each time you  after importing as they will be different for different decision subnets.
• FORMULAS – formulas are used to combine the results synthesized in the B, O, C and R subnets.  Formulas are only used in the top level model and can be selected, and changed, with the Design/Formulas command.  The first listed formula, the Additive (Negative) is automatically selected when you use the File/New command for a full model template.
• SYNTHESIS – can be performed in any network.  The Computation/Synthesis  (or use the Syn icon) command always rolls up the results for all networks beneath the current network you are in when the command is invoked and combines them.  For the top level network the results from below are combined according to the selected formula.  Control criteria networks weight results rolling up from the decision subnets by multiplying by the the priorities of the respective control criteria nodes times the alternative values and adding them.
• SENSITIVITY – can be launched using the Computation/Sensitivity command.  In a complex model, one usually wishes to do sensitivity for the B, O, C, and R nodes.  Look at the Tutorial file that is in Word on your CD to find out how to use synthesis.

IN SEARCH OF SUSTAINABLE CONVENTIONAL AND ISLAMIC MICROFINANCE MODEL FOR MICRO ENTERPRISES

Ascarya and Widodo Cahyono

ABSTRACT

The role of Micro Enterprises (MEs) in Indonesia, especially after monetary crisis, considered as a safety valve in the process of national economic recovery both in enhancing economic growth and reducing unemployment rate. MEs have always been in difficulties to access loan or financing from the banking industry (conventional as well as Islamic financial institutions) for a number of reasons. This study is aimed to determine and evaluate several existing models of conventional and Islamic microfinance for MEs to find the best existing microfinance model.

The results show that the best conventional Grameen model is Koperasi Mitra Dhuafa, the best Islamic Grameen model is KUBE Sejahtera No.21, the best conventional rural bank model is BKK Purwodadi, the best Islamic rural bank model is Amanah Ummah, the best conventional micro banking unit model is BRI Unit, while the best Islamic micro banking unit model is BSM Warung Mikro. The overall best MFIs are Koperasi Mitra Dhuafa (cGrameen), BRI Unit (cMBU) and UGT (BMT). Moreover, the best financing program is held by KUBE (iGrameen), the best social-development program is held by KUBE (iGrameen), the best MFI performance is held by Amanah Ummah (Islamic Rural Bank), while the best outreach is held by BRI Unit (cMBU).

The most important sustainability criteria are: 1) Aid Independent (MFI Performance); 2) Coverage (Outreach);  3) Savings Program (Soc-Dev. Program); 4) Profitability (MFI Performance); 5) Risk Mitigation (Financing Program); 6) Social Services (Soc-Dev. Program); 7) Pick-Up Service (Financing Program); and 8) Avg. Financing (Outreach). Moreover, Cooperative-BMT model is the most balanced sustainable model, which is operated as a more social business institution.

JEL Classifications: G21, G28, O17

Keywords: Microfinance, Islamic Microfinance, Microfinance Institution, Micro Enterprises

1.    INTRODUCTION

1.1  Background

There are plenty researches and papers that have been conducted and discussed with regard to the positive relationship between micro, small and medium enterprises (MSMEs) and economic growth, their role in poverty alleviation, as well as their role in reducing unemployment rate. Compared to the large scale of business or enterprise, MSMEs are proven to be endured and resistant to the financial crises or macro-economic shocks.

The role of MSMEs, especially after monetary crisis, considered as a safety valve in the process of national economic recovery both in enhancing economic growth and reducing unemployment rate. In the research held by Central Bank of Indonesia in 2001 titled “Credit Crunch in Indonesia After Crisis: The Fact, Causal Factor and Policy Implication” published by Department of Economy and Monetary Policy Bank Indonesia in 2001, there existed an acknowledgment from our industrial Banking that credit distributed to SMEs[1]had minimum risk and – compare to large enterprises – SMEs has sound and better business performance.

Several data show the significance of MSMEs’ contribution towards Growth Domestic Product for about 58.17% in 2009. It is more than that of the contribution of large enterprises towards GDP. MSMEs sectors could absorb for about 96 million labors (97.30% labor force).

Table 1.1 Statistics of Micro, Small and Medium Enterprises in 2009

	Number		GDP	Labor		Export
Micro	52,176,795	98.88%	32.68%	90,012,694	91.03%	1.51%
Small	546,675	1.04%	10.80%	3,521,073	3.56%	3.87%
Medium	41,133	0.08%	14.69%	2,677,565	2.71%	11.65%
Large	4,677	0.01%	41.83%	2,674,671	2.70%	82.98%
MSMEs	52,764,603	99.99%	58.17%	96,211,332	97.30%	17.02%

Source: Ministry of Cooperation and SMEs

Furthermore, the contribution of MSMEs to the national income through export activities reaches Rp162.25 trillion or 17.02% from the total national export. With its specialties – especially with its low financial capital –, MSMEs could produce in the short-term process. Having simple management and huge unit volume scattered in the whole nation, brought about MSMEs to have better resistant toward the fluctuation of business cycle. 

During 2005 – 2009, the MSMEs sectors have increased significantly in number (12.22%), in share of GDP (15.10%), in labor absorption (24.01%) as well as in share of export (47.05%), while on the contrary large enterprises have decreased in all segments. More interestingly, the biggest part of MSMEs is micro enterprises (MEs), which counted for 98.88% in number of enterprises, 32.68% share of GDP and 91.03% of labor force. Furthermore, the biggest increases in number (15.36%) and in employment (28.65%) have been in MEs, while these figures in SMEs have been decreased.

Tambunan (2004) stated seven years after economic crisis, the most valuable lessons that should be taken into account are: (1) Indonesian economy cannot depend mostly on large enterprises, (2) SMEs has more resistant compare to the large one and (3) there is no clear industrial policy that enhances economic growth and creates vocation for poor and unemployed people.

Despite historical success of MSMEs, there exist unresolved issues that need to be further discussed whereby MSMEs, especially micro enterprises (MEs) have always been in difficulties to access loan or financing from the banking industry (conventional as well as Islamic financial institutions) for a number of reasons.

Figure 1.1 Conventional Loan and Islamic Financing to MSMEs

Figure 1.1 shows that conventional loan share to MSMEs has been stable at 22%, while Islamic financing share to MSMEs has been declining. At the end of June 2011, productive conventional loan to MSMEs reached 22% of conventional bank portfolio or Rp.428.8 trillion, while productive Islamic financing to MSMEs reached 38% or Rp.31.3 trillion.

Table 1.2 Some Definitions of Micro, Small and Medium Enterprises

	ASSET*	WORKING CAPITAL**	YEARLY REVENUE*	CREDIT LIMIT***	LABOR**
Micro	<50	85	< 300	<50	3.6
Small	50 -500	514	300-2500	50 – 500	16.6
Medium	500-10000	2000	2000-50000	500 – 5000	38.2

Source: * MSME Act No.20/2008; ** Survey 2005; *** Bank Indonesia Regulation

Note: in IDR Million, except Labor in number; USD1=Rp9000.

In addition, as shown in table 1.2, credit limit to micro enterprises is less than Rp.50 million, while commercial bank (conventional and Islamic) usually has minimum credit limit of Rp.50 million. Therefore, loan and financing portfolio of conventional and Islamic banking mostly have been extended to small and medium enterprises. Commercial banks usually serve MEs through their micro-banking unit, such as BRI Unit and BSM Warung Mikro, which according to the ministry of cooperative and SME (for 2010 up to October 2010) has been amounted to 18% of MSMEs portfolio or Rp.28.1 trillion.

Furthermore, within Rp50 million credit limit, there are wide ranges of MEs variations. Mohamad (2011) finds that 70 percent of 52.18 million MEs need loan below Rp5 million with low penetration, while 30 percent of MEs need loan up to Rp50 million.

Certainly, limit credit formula ranging < Rp.50 million for the micro-enterprise should be reviewed carefully as so many poor or low-income society only need credit for about Rp300.000 - Rp1 million, as also suggested by Ascarya and Sanrego (2007). Compare to Grameen Bank established by Dr Muhammad Yunus, they do give credit for micro-enterprises not more than US$200 (Rp1.80 million with exchange rate Rp9000 = US$1).

Nevertheless, there are various conventional and Islamic microfinance institutions that specifically serve micro enterprises for-profit or not-for-profit motives, ranging from Grameen model, cooperation model, rural bank model, as well as micro unit of commercial bank model. These institutions, however, still could not meet the need of 52.18 million MEs. Millions of MEs still could not access credit or financing from conventional or Islamic microfinance institutions.

It is based on these issues; this study is trying to analyze and discover the real problem of MEs, specifically to find a sustainable conventional and Islamic Bank microfinance model for them, which can be replicated throughout Indonesia, to be able to serve all financial services needed by all MEs.   

1.2  Objective

The objective of this study is first, to identify the salient characteristics of sustainable Islamic microfinance model which able to support MEs financing problems systematically and progressively. Second, this study will determine and evaluate several existing models of Islamic microfinance for MEs to find the best existing Islamic microfinance model, and third, this study will propose sustainable Islamic microfinance model which is suitable for MEs to progress gradually, feasible for Islamic Financial Institutions, as well as sustainable in the long run.

1.3  Methodology

For the first and second objectives, this study will apply Analytic Network Process (ANP) method with three steps. First, focus group discussion (FGD) and/or in-depth interview will be conducted with various stakeholders, such as micro-enterprises, Islamic banks, experts, academicians, as well as regulators of MSMEs and Islamic bank, to fully understand the real problems and to identify factors causing financing problems of MEs. Second, the results of the first steps will be used to develop an appropriate ANP network and its questionnaires to obtain proper data from MEs and Islamic bank. Third, ANP analysis will be applied to set priority on alternative solutions as well as policies and strategies to formulate optimal policy recommendations. Moreover, for the third objective, this study will apply descriptive analysis based on ANP results, in-depth interviews and Focus Group Discussions, to formulate microfinance model for MEs.

1.4  Importance of the Study

The results of this study are expected to be beneficial for various stakeholders, especially for authorities and regulators of micro-enterprises and Islamic financial institutions (Islamic banks, Islamic rural banks and BMTs), to formulate policies and strategies to improve access and availability of Islamic microfinance for MEs. This study will also be beneficial for micro-enterprises to improve from their weaknesses. Moreover, this study will also become an addition to the academic literatures on Islamic microfinance.  

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[1] Micro enterprises sometimes are grouped together with Small enterprises, so that MSMEs sometime is termed as SMEs.