RISK MANAGEMENT IN MICROFINACE INSTITUITION

A dissertation submitted in partial fulfilment of the

requirements for the award of the degree of

MASTER OF BUSINESS ADMINISTRATION

By

SIRIPURAPU DEEPTHI

Register No 1120243

Under the guidance of

DR ANIRBAN GHATAK

Institute of Management

Christ University, Bangalore

March 2013

ii

DECLARATION

I, Siripurapu Deepthi, do hereby declare that the dissertation entitled Risk Management In

Microfinance Institutions. has been undertaken by me for the award of the degree of Master

of Business Administration. I have completed this study under the guidance of Prof. Anand

Aivalli, Associate Professor, Institute of Management, Christ University, Bangalore.

I also declare that this dissertation has not been submitted for the award of any degree,

diploma, associateship or fellowship or any other title in this University or any other

university.

Place: Bangalore (Name & Signature of the

Candidate)

Date: Siripurapu Deepthi

Register No 1120243

iii

CERTIFICATE

This is to certify that the dissertation submitted by Miss Siripurapuu Deepthi on the title

Risk Management In Microfinance Institutions is a record of research work done by him

during the academic year 2012 – 13 under my guidance and supervision in partial fulfillment

of degree of Master of Business Administration. This dissertation has not been submitted for

the award of any degree, diploma, associateship or fellowship or any other title in this

University or any other university.

Place: Bangalore (Name & Signature of the guide)

Date: Dr Anirban Ghatak

iv

ACKNOWLEDGEMENTS

I am indebted to many people who helped me accomplish this dissertation successfully.

First, I thank the Vice Chancellor Dr Fr Thomas C Matthew of Christ University for giving me the

opportunity to do my research.

I thank Prof. Ghadially Zoher, Associate Dean, Fr Thomas T V, Director, Prof. C K T

Chandrasekhara, Head-Administration, Dr S Jeevananda, Coordinator, Kengeri Campus and Prof T S

Ramachandran, Head-Finance of Christ University Institute of Management for their kind support.

I thank Dr Anirban Ghatak, for his support and guidance during the course of my research. I

remember him with much gratitude for his patience and motivation, but for which I could not have

submitted this work.

I thank my parents for their blessings and constant support, without which this dissertation would not

have seen the light of day.

Siripurapu Deepthi

Register No: 1120243

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ABSTRACT:

Inherently there is a high risk with the MFI segment. The small, medium and even larger

MFI find it difficult to manage risk or predict the outcome of credit transactions. In Indian

one can find various types of models with in micro financing, such as NGOs, NBFIs, Rural

Banking, Credit Union, these legal entities have different credit risk based on their business

focus, hence it becomes difficult for them, to be able to predict the credit risk that will be

involved. In this dissertation, I have tried to build a estimation model which can be used by

Micro financing institution in India. This model will project the credit risk based on

parameter such as operational self-sufficiency, operational efficiency, write offs, liquidity,

type of micro financing institution.

Apart from that, I have tried to analyze the level of credit risk management of NBFIs in

Bangalore and Hyderabad. And found that all NBFIs have almost the same kind of credit

management in place apart from some exceptional NFBIs, which have concentrated on

management quality along with the MIS in place, good reporting standards, good loan

portfolio management and etc.

vi

TABLE OF CONTENTS

Declaration ii

Certificate iii

Acknowledgement iv

Abstract v

Table of Contents vi

List of Tables vii

List of Charts viii

Abbreviations viii

CHAPTER I

INTRODUCTION

1.1 BACKGROUND OF THE STUDY 1

1.2 PHASES OF MICROFINANCE 4

1.3 PROBLEM STATEMENT 7

1.4 NEED FOR THE STUDY 7

1.5 PURPOSE OF THE STUDY 7

CHAPTER II

LITERATURE REVIEW

2.1 INTRODUCTION 8

2.2 MAJOR RISKS IN MICROFINANCE 9

2.3 HOW THE REVIEW HAVE BEEN CONDUCTED 10

2.4 STUDIES DONE IN THIS AREA 10

2.5 CONCLUSIONS 45

vii

CHAPTER III

RESEARCH METHODOLOGY

3.1 INTRODUCTION 46

3.2 STATEMENT OF THE PROBLEM 46

3.3 THE MODEL 46

3.3.1 SAMPLING METHOD 50

3.3.2 DATA COLLECTION 50

3.4 THE REGRESSION MODEL 50

3.4.1 THE VARIABLES 50

3.4.2 HYPOTHESIS 50

3.4.3 REGRESSION MODEL 52

CHAPTER IV

INDUSTRY OVERVIEW

4.1 MICROFINANCE INDUSTRY 53

CHAPTER V

DATA ANALYSIS AND INTERPRETATION

5.1 INTRODUCTION 55

5.2 MORGAN STANLEY CREDIT RISK ASSESSMENT 55

5.2.1 THE MODEL 55

5.2.2 ANALYSIS OF PRIMARY DATA 59

5.2.2.1 RESPONDENTS PROFILE 59

5.2.2.2 DATA CONSOLIDATION AND ANALYSIS 59

5.2.2.3 THE CRONBACH'S ALPHA TEST 62

5.2.3 ANALYSIS OF SECONDARY DATA 63

viii

5.2.3.1 CONSOLIDATION OF PRIMARY AND

SECONDARY DATA 65

5.2.3.2 INTERPRETATION OF DESCRIPTIVE AND

CORRELATION TABLES 71

5.3 ESTIMATION METHODOLOGY 73

5.3.1 THE ESTIMATION METHODOLOGY 73

5.3.2 ANALYSIS OF THE CORRELATION MATRIX 77

5.3.3 THE RANDOM EFFECT MODEL 77

5.3.3.1 HYPOTHESIS 78

5.3.3.2 DATA ANALYSIS 83

5.3.3.3 THE RANDOM EFFECT MODEL

BUILT BY ESTIMATION METHODOLOGY 84

CHAPTER VI

FINDINGS, SUGGESTIONS AND CONCLUSION

6.1 INTRODUCTION 85

6.2 DISCUSSION OF THE FINDINGS 85

6.3 CONCLUSIONS 85

6.4 SUGGESTIONS 86

6.5 SCOPE FOR FURTHER RESEARCH 86

BIBLIOGRAPHY 87

ANNEXURES 90

ix

LIST OF TABLES

Table 1.1 Phases of microfinance 4

Table 1.2 Risk categories 5

Table 2.1 Major risks to microfinance institutions 9

Table 2.2 Classification of the microfinance industry 10

Table 2.3 Morgan Stanley credit assessment model 13

Table 2.4 Ratings of microfinance institutions 17

Table 2.5 Camel's indicators 24

Table 2.6 Operational features of different MFI models in India 36

Table 2.7 Cost Benefits Of Option 1 and Option 2 38

Table 2.8 Business Model For The Banks 39

Table 3.1 Morgan Stanley credit assessment model 46

Table 5.1 Morgan Stanley credit assessment model 55

Table 5.2 consolidated view of grades given to qualitative parameters 60

Table 5.3 The Cronbach's Alpha Test 62

Table 5.4 consolidated view of grades given to quantitative parameters 64

Table 5.5 Morgan Stanley Credit Risk Assessment 65

Table 5.6 Final grades given obtained from the 65

Morgan Stanley credit risk Assessment

Table 5.7 Descriptive of the Independent And Dependent Variable 67

Used to Determine The Morgan Stanley Credit Risk Assessment

Table 5.8 Pearson Correlation between the parameter used in

Morgan Stanley credit risk assessment 69

Table 5.9 Descriptive of the Independent And

Dependent Variable Used to Determine Estimation Model 74

x

Table 5.10 Correlation Coefficient Matrix For Estimation Model 75

Table 5.11 Estimates of Fixed Effect 81

Table 5.12 F value and significance of fixed effects for random effect model 82

Table 5.13 Goodness of fit 82

Table 5.14 Covariance Parameters 83

APPENDIX 2 Responses to the Questionnaire 99

APPENDIX 3 Secondary Data for Morgan Stanley Credit Assessment Model 134

APPENDIX 4 Data For Random Effect Model 171

CHAPTER 1

INTRODUCTION

1.1 BACKGROUND OF THE STUDY :

According to “Fanie Jansen Van Vuuren in Risk management for microfinance institutions in

South Africa,” Risk is the probability that a decision will lead to a different outcome from the

one which is thought, due to the fact that the decisions are made under uncertainty with

imperfect information.

(Vijender, 2012)“Small-scale financial services primarily credit and savings, provided to

people who farm, fish or herd and adds that it refers to all types of financial services provided

to low-income households and enterprises.”

(Davis, 2006). ”Extension of small loans to entrepreneurs too poor to qualify for traditional

bank loans.”

The Reserve Bank of India defines, “microfinance is provision of thrift, credit and other

financial services and products of very small amount to the poor in rural, semi-urban and

urban areas for enabling them to raise their income levels and improve living standards.”

(khan) The practice of microfinance is not new and has probably been around for as long as

currency itself has. Informal credit and savings services probably formed around social

groups where the members got together to help one another as a community. Savings and

credit groups that have operated for centuries include the "susus" of Ghana, "chit funds" in

India, "tandas" in Mexico, "arisan" in Indonesia, "cheetu" in Sri Lanka, "tontines" in West

Africa, and "pasanaku" in Bolivia. One of the major concerns of microfinance is to increase

penetration so as to attain volumes and hence increase the number of people who can benefit.

Increasing penetration would raise the income levels of the people and hence improving the

living standards of people.

The interesting aspect of formal financial system is that, they can provide microcredit at low

interest rates and easy periodical installments, but this kind of facility is not available in

formal financial system. Microfinance operates mostly in an informal system since there

exist complex legal and operational procedures (such as collateral for microcredit, being able

to fulfill committee norms for working capital loans etc.). The problem gets complicated

2

when poor people apply for loans, since the poor people cannot inform the formal financial

system their creditworthiness or their requirement for savings, services, and loans.

Significant movement of microfinance has been seen in India. Most of the leading

practitioners of microfinance activities follow grameen model. Banks lean microcredit

through self-help groups(SHGs) , to local microfinance institutions that have contacts in

small villages, Business correspondence model.

RBI in its 2009-2010 annual report, talks about encouraging business correspondence model

for micro financing. “The lead banks were advised to provide banking services through a

banking outlet in every village having a population of over 2,000. The banking services could

be provided through any of the various forms of ICT-based models (such as BCs) and not

necessarily through a brick and mortar branch.” And hence the following were observes Out

of the 167 villages identified for transformation into „model villages‟, 160 are unbanked. A

total of 130 BCs/business facilitators (BFs) were appointed covering 111 villages, while ICT-

based financial inclusion was initiated in 88 villages by issue of 26,850 smart cards covering

59.6 per cent households in the villages. Of the 88 villages, 33 have achieved 100 per cent

BC-ICT based financial inclusion.

What services are provided by the micro financing in India?

Typically MFIs in India provide services such as- savings, credit and insurance.

The loans provided by the MFIs serve low income population in various ways: (comparison

of performance of microfinance institutions with commercial banks in India- prof zohra bi,

shyam lal dev pandey)

a) Loans for working capital

b) Alternatives the loans provided by money lenders

The major components of microfinance are

a) Deposits

b) Loans

c) Payment services

3

d) Money transfer

e) Insurance to the poor

From the reports submitted by RBI, sub-committee of central broad of directors of RBI who

were studying on the issues and concerns of MFI sector pointed out the following points

a) Out of the total loans outstanding of 45600 crores, under the Micro Financing sector at

the end out March 2010 , MFI segment accounted for about Ra 18344crores i.e. 40

percent. Also the incremental growth of advances is high

b) Hence there is a setback between SHG-bank linkage segment

c) The committee pointed out that the apart from interest rate, other incidental charges such

as processing free, interest free security deposits have hiked the effective interest rate

d) For larger MFI effective rates of interest calculated on the mean outstanding portfolio

during 2009-2010 and has ranges between 31 percent to 51 percent with an average of 35

percent. For smaller MFI the average interest rate was about 29 percent. The main

e) Problem identified was multiple lending, over financing and ghost borrowers. The

presence of ring leaders who acted as intermediaries between the MFI and the potential

customers.

f) The committee also noticed coercive methods of recovery of MFI , lack of grace period.

g) The committee pointed out that for larger MFIs the overhead costs as a percentage of

outstanding was higher that of smaller MFIs, hence smaller the MFI the efficient is the

operation.

h) Only 25% of the credit was used for income generating activities

Suggestions from the committee:

a) A new regulation act for NBFC-MFI

b) The minimum capital requirement of the NBFC- MFI should be enhanced from Rs 2

crores to Rs.15crores.

4

1.2 PHASES OF MICROFINANCE :

Table 1.1: Phases of microfinance

Phases Year Features

First Phase:

Social Banking

1960-1990 1) Nationalization of commercial banks.

Fourteen commercial banks were nationalized

in 1969 and 8 commercialized banks were

nationalized in 980

2) Lead bank scheme was initiated with district

credit plans

3) Expansion of the network of rural banking.

RRBs were set up in 1976. NABARD was

formed in 1982. Cooperative banking was

structured and developed. SIDBI was

established

4) Extension disbursement of subsidized credits

Second Phase:

Financial

Systems

Approach

1990-2000 1) NGO-based FIs were developed to provide

Microfinance products and services on not for

profit basis

2) SHG-bank linkage programme was initiated

and rapidly replicated

3) Innovative credit lending mechanisms based

on “peer pressure” and “moral collateral”

were developed.

Third Phase:

Financial

Inclusion

2000 onwards 1) Microfinance is seen as a business

proposition and has been commercialized

2) Development of for profit MFIs like Non

banking finance companies(NBFCs) and non

banking financial institutions

5

3) NGO-MFIs are been legitimized

4) Customers‟- centric/ client centric

microfinance products and services are given

importance

5) Policy regulations are increased

Source: Understanding Microfinance ,Debadutta K Panda.

Microfinancing is inherently a high risk business when compared to commercial bank(wright

and Haynes) and the agendas of a commercial bank are not aligned with the funding the poor.

Hence the with the risk return tradeoffs, higher the risk higher the return, the loans in a

microfinance are usually have interest rates ranging from 15%-48%( now regulated by RBI

with a cap of 24% effect from april 1st 2012).

According to “managing risk and creating value with microfinance”,”mike Goldberg and

eric palladini” risks in microfinance can be categorized at follows

Table 1.2: Risk categories

Risk category Subcategories Specific risks

Financial risk Credit Loan portfolio(internal)

Interest rate (internal/external)

Loan enforcement

practices(internal)

Loan rescheduling and

refinancing practices

Market Prices(external)

Markets(external)

Exchange

6

rate(currency)(external)

Value chain(external)

Liquidity Cash flow management

issues(internal)

Operational Risk Transaction( internal)

Fraud and integrity(internal) Branch level authority limits on

lending

Technological (internal) Information technology

Human resource(internal) Staff training, operational

manuals

Legal and compliance(internal) Operational audits, financial

audits

Environment (external) Specific environmental impacts

Strategic risks Performance(internal) Generating profits and returns

on assets and on equity to attract

investors

External business(external) New financial sector laws

Reputational(external) Competitive pressures(existing,

new actors)

Governance (internal) Changes in regulatory

practices(licensing and reporting

requirements)(external)

Lack of board consistency and

direction(internal)

7

Country (external) Relationships with donors and

government programs(eternal)

Producer risks Experience

Technology

Management ability

Source : understanding microfinance, debadutta panda

According to “understanding microfinance- debadutta k panda,” Risks in indian context can

be classified as

1) Functional risks

2) Financial risks

3) External risks.

In the following research we have mainly focused on the credit risk of the microfinance

industry and building a quantitative model in order forecast the credit risk based on some

independent predictable variables.

1.3 PROBLEM STATEMENT:

a) There Is No Proper Credit Grading System For An MFI.

b) There is no forecasting tool for credit risk measurement

1.4 NEED FOR THE STUDY:

The need for the study was that, there has been no study like Morgan Stanley credit risk

management or quantitative modeling on the microfinance sector of India. Lately RBI has

been pushing financial inclusion reforms onto the cooperate banks which are going to learn

the microfinance fundamentals from the existing MFIs.

1.5 PURPOSE OF THE STUDY:

8

The purpose of the study is to assess the credit risk management structure of an MFI based

on parameters mentioned in chapter 3 also quantify and project the credit risk using a

quantitative model.

Inherently there is a high risk with the MFI segment. The small, medium and even larger

MFI find it difficult to manage risk or predict the outcome of credit transactions.

The probable reason could be due to the fact that the customer base is volatile or

intermediaries between the MFI and the customers who hide the customer details or lack of

risk management tools.

CHAPTER 2

LITERATURE REVIEW

2.1 INTRODUCTION

By the risk management framework for micro financing institutes published by microfinance

network,

The document focuses on helping senior managers and directors of MFIs design a

comprehensive and systematic approach for identifying, anticipating and responding to the

major risks faced by the MFIs. This document identifies that risk management is an essential

element of long term success and hence for financial institutions, to effectively management

risk they have to keep the following points in mind.

a) They have to have systematic approach to evaluate and measure risk so as to identify

the risk in the early stage and hence fix it.

b) A good risk management framework allows management to quantify the risk and fine

tune to the capital allocation and liquidity needs to match the on and off balance sheet

risks faced by the institutions and to evaluate the impact of potential shocks to

financial system or institution.

c) Having a good information on potential consequences for both positive and negative.

There has been a significant increase in the emphasis on risk management, hence the bank

managers and regulators are able to better anticipate risks, than just to react to them.

Therefore to foster stronger financial institutions the revised camels approach among US

regulators emphasizes the quality of internal systems to identify and address potential

problems quickly.

For MFIs proper internal risk management yields to practices designed to limit risk associate

with individual product lines and systematic, quantitative methods to identify, monitor and

control aggregate risks across financial institutions.

MFIs have been growing and serving large base of customers and also attract more

mainstream investment capital and funds, hence they have to strengthen their internal

capacity to identify and anticipate potential risks to avoid unexpected losses and surprises.

Creating a risk management framework and culture with in an MFI in the next step after

mastering the fundamental of individual risks, such as credit risk, treasury risk, and liquidity

9

risk. A risk management framework is a guide for MFI managers to design an integrated and

comprehensive risk management system that helps them focus on most important risks in an

effective and efficient manner. Hence according to the paper risk management framework is

a consciously designed system to protect the organization from undesirable surprised

(downside risks) and enable it to the advantage of opportunities (upside risks).

2.2 THE MAJOR RISKS TO MICROFINANCE INSTITUTIONS:

Many risks are common to all financial institutions, from banks to unregulated MFIs, these

include credit risk, liquidity risk, market or pricing risk, operational risk, compliance and

legal risk and strategic risk.

Hence most risks can be classified as

a) Financial risks

b) Operational risks

c) Strategic risks.

Table 2.1: Major risks to microfinance institutions

FINANCIAL RISKS OPERATIONAL RISKS STRATEGIC RISKS

Credit Risk

Transaction Risk

Portfolio Risk

Liquidity Risk

Market Risk

Interest Rate Risk

Foreign Exchange Risk

Investment Portfolio Risk

Transaction Risk

Human Resource Risk

Information And Technology

Risk

Fraud Risk

Legal And Compliance

Governance Risk

Ineffective Oversight

Poor Governance Structure

Reputation Risk

External Business Risk

Event Risk

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Hence considering one risk at a time for literature review, we would get a better idea on

various aspects of risk management

2.3 HOW THE REVIEW HAS BEEN CONDUCTED:

The review has been conducted by looking up in different journals and data

bases of universities which have published relevant models to detect the credit

risk and other risks such as operational risk, market risk , foreign risk and then

they have been reporting in this dissertation.

2.4 STUDIES DONE IN THIS AREA:

(vurren, 2011) The main objective of this study was to combine and analyses different risks

in the microfinance environment in order to create a framework which can assist in the

effective management of these risks.Find out the optimal risk balance.The effective

management of risk in the microfinance environment.Prediction of the outcome of

microfinance credit transactions .The average profile of a microfinance client in south Africa.

The research was empirical based on primary and secondary data. The data was collected

through questionnaires combined with qualitative data analysis procedures.it is a cross

sectional study of a particular phenomenon at a particular time. The study was based on

small medium and large companies in the microfinance industry of south Africa. Post the

implementation of national credit act in June 2007.

Table 2.2: Classification of the microfinance industry

size Characteristics

Small <R5 million in turnover- between one and 10 branches

Medium <R250 million in turnover between 10 and 100 branches

Large >R250 million in turnover listed entities

11

The target population has been divided into 4 categories The first category is unlisted entities

with less than 10 branches. The second category is unlisted entities with more than 10

branches The third entity is with banking license .The fourth category includes the

microfinance division of some of the traditional banks. According to the category the

questionnaires were designed. Data analysis was done through pie charts and bar charts and

then analysed. The following were the findings of the author. Five risk tools where to be

analyzed and the respondents gave “credit granting policy and customer affordability

calculations” the highest priority followed by “internal controls”, “debt controls”, “debt

collecting”, “staff training creating loyalty and integrity”, “credit scoring models.” The risks

that can be involved in non-bank microfinance institutions in south Africa where analyzed

and the respondents answered “internal and external fraud”, “bad debts”, customer migration

to competitors or the commercial banks” “regulation of the industry” and “lack of affordable

funding.” How well the risk tools used in banks can be applied to the micro financing

industry. Most effective way to lower the overall microfinance risk in south Africa. And the

respondents answered “conservative credit granting policy”, “improved internal controls”,

“better loan management system”, “better educated staff” , “better collecting on arrears

clients.” The biggest predictors of non-payment of new client in microfinance institution in

south Africa are “ disposable income” “number of loans” “ judgments” “employment

industry” credit enquiries” “gender”, “age”, “race”. The biggest contributor to minimize

credit risk in a microfinance institution in south Africa is accurate affordability calculation,

shorter term loans instead of longer ones , the use of a credit score model, small loan

amounts, the analysis of credit bureau information. The most efficient way to optimize client

service in a microfinance institution in south Africa, and the most efficient way to reduce risk

in microfinance institutions in south Africa are “real time loan management system”,”

decentralized credit decisions”, “cash disbursements to clients”, “ a call center function”,

centralized credit decision.” The items on which MFI would spend the most in a financial

year could be “ staff training, internal audit, independent review on the loan management

system”, “rewards for fraud tip offs” The biggest misperception in south Africa regarding

microfinance institutions. Are MFI were no affected negatively by the national credit act,

MFIs don‟t relieve poverty in SA, MFI in SA don‟t realy compete with the 4 major banks,

MFI in SA is an extremely high risk industry. The most efficient options to pro- actively

12

manage risk in a microfinance institution in SA are a credit scoring model, build customer

relationship with shorter products, extensive training for new staff, to only disburse 30 day

loans. The best predictors of on time payment of clients are correct affordability calculations

, a shorter term loan, work reference, a credit score model, a proper and signed credit

agreement. The findings from client information of 3000 microfinance clients in south

Africa: A good client means not in arrears for more than 2 installments And a bad client

means some on who is in arrears for more than 2 installments. The following table was

constructed for 2009 and 2010 year

In the paper the author identifies through literature review, identifies various ways to identify

the risks related to MFI, ie. The debt equity ratio( gearing risks), interest cover, liquidity risk,

market risk(beta) company specific risk, growth, management team, industry comparative

performance, theft and fraud and the non-performance of loans.

Then he identifies the relation between the business and credit risk. According to the author,

to lower the risk of loans not performing the emphasis should be on quality loans and a risk

portfolio not exceeding 5%. The quality of a loan is determined by the probability that the

credit decision is right. Hence usually the following are the ways for a proper credit decision.

Rationing credit ,Requiring collateral ,Screening applicants, Monitoring borrowers, Credit

scoring In this paper he takes up screening of applicants and monitoring borrowers. By

effectively managing the risk in the industry, south Africa has a good market where in

business models can be sustainable. By being able to service the poor through credit lending

it is creating opportunities to help build the economy. A combination of risk tools need to be

applied effectively in order to reduce material risks, predict good customer and also real time

loan management system with integrated credit scoring models, accurate affordability

calculation combined with well trained staff forms the basis of risk management . even

though there was a thorough examination of the MFI industry, the author did not look into

each risk and tools that need to be used to mitigate the risk.

(Ayayi, 2012) Credit risk assessment in the microfinance industry: an application to a

selected group of Vietnamese microfinance institutions and an extension to east Asian pacific

microfinance institutions. The objective of this is to access credit risk in order to determine

internal global scale rating for Vietnamese MFI. Particular attention is paid to conventional

13

and special credit evaluation metrics due to the unique institutional arrangement of MFIs and

the socioeconomic environment in which they operate. Also this research is to provide an

analysis to the Vietnamese MFI so that the donors and investors try making decisions with

respect to providing .The other important aspect of this paper is to help the MFI management

teams to evaluate their institution‟s performance and hence identify and correct the

weakness.To achieve the objective, the author has used to Morgan Stanley approach to

assessing credit risk in the microfinance industry. The approach was supplemented with his

numerical grading system, and hence converted the quantitative and qualitative risk factors

on the same schedule hence providing a comparative analysis of the MFIs understudy.He

used Morgan Stanley approach since, it was tailor made for to institutions that are providing

microfinance products. Whose business model mainly revolved around providing micro-

loans as financing or micro-entrepreneurs, It addressed the challenges faced by microfinance

industry such as country risk, data availability and minimal default history among FI. It

draws up a methodology of rating the major pioneers in micro financing industry.Morgan

Stanley credit analysis indicators are tabulated as below.

Table 2.3 : Morgan Stanley credit assessment model

RATING

FACTOR

INDICATOR DEFINITIONS GRADES

Loan portfolio A1: portfolio at risk=( outstanding

loans with arrears over 30 days+

rescheduled or restructured loans)/

total gross loan portfolio

<3;<6;<9;<12;<15; above 15

A2: write offs=total write offs

over the last 12

months/average gross

lolan portfolio

<2;<3.5;<5;<7;<10; above 10

A3: size of portfolio=gross loan

portfolio

>300M;>350M;>100M;>50M;

>10>;<10M

A4: loan loss reserves= loan

reserves/PAR30

>85;>75;>65;>60;>55; below

55

14

Profitability,

sustainability,

operating

efficiency

B1:Sustainability= operating

income/(financial expenses+loan

loss provisions+write

offs+operating expenses)

>120;>115;>110;>100;>90;belo

w 90

B2: ROAA=net income/average

assets

>3;>2;>1;>0;>-2;below -2

B3: operating efficiency= total

operating expenses/average gross

loan portfolio

<20;<25;<30;<40;<50; above

50

B4: productivity= number of

borrowers/total head count

>200;>190;>170;>145;>130

below 130

Asset and

Liability

management

C1: leverage= total

liabilities/(networth+subordinate

debt)

<5x;<6x;<7x;<8x;<9x; above

9x

C2: exposure to foreign

currency=(financial debt in non-

hedged foreign currency)/total

financial debt

<15;<20;<35;<50;<65; above

65

C3: liquidity= (cash+short term

inverment)/(gross loan portfolio)

>15;>12;>9;>6;>3 below 3

Management

and strategy

D1: quality of senior management

and board

D2: strategy and business plan

( including competitive landscape)

D3: quality and support from

shareholders and network

D4: HR management

Systems and

reporting

E1: quality of management

information systems

E2:quality and speed of data feed

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Loan portfolio

I. Portfolio at risk: PAR30 value below 3% is ranked best by Morgan Stanley. Low

PAR30 value may indicate that the MFI have decided that they don‟t want the

bad loans in their books, hence they must have written-off any loans that are not

being paid for more than 30 days.

II. Write-offs: the low values of write offs remove the doubt about the good

portfolios that have been concluded in the PAR30.lower the write offs, better it is

for the ratio according to morgan Stanley rankings. Because write offs of a loan

affects the gross loan portfolio and loan loss reserves.

III. Size of portfolio: the overall growth of the loan portfolio is MFI is a due to the

increasing rate of expansion of their number of active borrowers.

IV. Loan loss reserves: the evaluation of MFI‟s loan loss reserve levels and policies

allows a credit analyst to determine how well an MFI can cope with estimated

loan loss and hence gives one an understanding an MFi‟s level of financial

responsibility. An MFI‟s loan loss reserves should ideally cover any anticipated

losses. Also an MFI has to satisfy the regulatory standards applied to

provisioning as dictated by its legal status.

E3: quality of reports and

distribution/analysis of reports

Internal and

operational

controls

F1: operational procedures

F2: internal controls

Growth potential G1: regulatory environment and

government involvement

G2: Number and density of micro-

entrepreneurs

G3: behavior of micro-

entrepreneurs towards microloans

16

i) Profitability, sustainability and operational efficiency: this parameter gives the

idea of the financial viability of the MFI. One has to set minimum expected

levels of profitability and cash flow sustainability, while taking into account

the MFI‟s ability to leverage its operational platform and flexibility In the

event of deteriorating margins.

I. Sustainability: this measures the free cash flows, there by reflecting the extent

of an MFI‟s financial cushion against margin or top line shocks.

II. ROAA: takes into account taxes and other sources of revenues, including

income earned on cash in the bank there by providing a more measure for

profitability.

III. Operational efficiency: this indicated the MFI‟s ability to operate efficiently and

leverage its infrastructure.

a) Econometric analysis: for the econometric analysis the MFI for east asia and pacific

were analyzed and correlation matrix for 118 different MFI with 14 variable was

made and conclusions were drawn. Econometric analysis showed that there was no

statistical difference in terms of risk management among different types of MFI.There

was no significant conclusion made even after the econometric testing, morgan

Stanley approach to credit assessment was used to understand the credit risk of the

MFI, the research gap is even though the econometric analysis was done, It was

compared with few MFI in limited to East Asia and Pacific rather than comparing

with the global players in MFI. It indirectly means the researcher narrowed down his

interests to one particular region.

(GUTHRIE, 2010)Determinants of Credit Ratings of Microfinance Institutions in the

Former Soviet Union.This study primarily seeks to explore two questions. First,

whether ratings respond to individual indicators as the existing literature on both the

traditional financial sector and the microfinance sector predict. This is important to

determine perception of credit risk of microfinance benchmarking it with other

financial institutions. It tries to determine the optimal model for predicting the credit

rating of a MFI given number of independent variable This tries to use the traditional

rating agencies and financial institutions to MFI and specialized rating agencies. Also

it expands little work that has been done on determining contributors to a strong

17

credit rating of MFI and fills a gap in the knowledge regarding the optimal model for

predicting an institution‟s credit rating.This research is based on the work from

Gutierrez and Serrano.The work from Gutierrez and Serrano finds 5 key components

to credit rating.Size was found to positively impact the credit rating and is consistent

with the research on contributors to ratings of Russian financial institutions.

Profitability and efficiency also were identified as positive contributors to credit

ratings.Increased risk and lower portfolio quality harmed a firm‟s rating. The work

for Gutierrez and Serrano showed that metrics or social performance have no bearings

on ratings of MFI.The rating agencies are primarily concerned with identifying

probability of default, not a firm‟s impact on poverty alleviation or economic

development. This analysis has replicated the model proposed by Gutierrez and

Serrano , to establish the validity of the results for MFI. But the paper also expands

to identify the specific model that best predicts the rating of an MFI.The paper

surveys the rating agencies of the MFIs and identifies the following

Table 2.4: Ratings of microfinance institutions

Ratings ECA LA MENA SA SSAf %

total

Apoyo and associados

internacionales S.A.C

1 0 1 0 0 0 0.19

Class and asociados

S.A.

3 0 3 0 0 0 0.57

CRISIL 24 0 1 0 21 0 4.56

Ecuability 2 0 2 0 0 0 .38

Equilibrium 8 0 8 0 0 0 1.52

Feller Rate 1 0 1 0 0 0 0.19

Fitch Ratings 10 0 10 0 0 0 1.9

JCR-VIS credit rating

company LTD

1 0 0 0 1 0 .19

M-CRIL 46 6 0 0 21 0 8.75

Microfinanza rating Sri 134 61 52 3 1 14 25.9

18

MicroRate 131 0 93 2 0 36 24.9

Planet rating SAS 163 23 56 19 1 57 30.99

S&P‟s 2 0 2 0 0 0 0.38

Total 526 90 229 24 45 107

Percent total 100 17 44 5 9 20

Planet rating was created in 1999 as a specialized MFI rating agency. It operates in over sixty

countries and is headquarters in Paris, France. Planet rating offer pre-rating assesments,

credit ratings, social ratings and consulting services to help MFIs improve their performance

and management Planet Ratings uses a Proprietary GIRAFFE methodology that assessed

i) Governance

ii) Information

iii) Risk management

iv) Activities and Services

v) Financing and Liquidity and

vi) efficiency and profitability

Represents a modification of the typical CAMELS system for evaluating banks that measures

Capital Adequacy, Asset Management, Management quality, earnings Liquidity and

Sensitivity to market risk. The paper also discusses the ordered probit model methodology

used for credit rating: Using a standard ordinary least square regression was rejected as this

method includes inappropriate assumptions about the underlying parameters. It assumes that

that interval between possible ratings captures differences that are of the same absolute

magnitude. This is equivalent to saying that the risk differential between a AA- rated agency

and a AAA- rated agency is the same as that between a BB and BBB- rated agency. Rating

agencies frequently define levels above which a rating indicates investment quality and

below which an institution or security is non-investment grade The difference between these

categories, therefore, cannot be considered discrete, equally spaced intervals. Credit ratings

are ordinal. Hence the appropriate credit rating analysis tool would be multiple discriminant

analysis. This is an improvement on the ordinary least square method. As it takes into point

19

the ordinal nature of the credit rating and treats each rating as a separate category and

requires more significant assumptions about the distribution of the independent variables.

The coefficients on the parameters will differ in interpretation from thos associated with the

standard ordinary least square method The positive sign indicates a positive impact on the

dependent variable. The magnitude of impact is not a direct linear relationship.

P(yt = 1) = F(c1 – xt*β),

P(yt = 2) = F(c2 – xt*β) - F(c1 – xt*β)

. . .

P(yt = k - 1) = F(ck-1 – xt‟*β) - F(ck-2 – xt*β)

P(yt = k) = 1 - F(c k-1 – xt*β)

The function F is cumulative distribution on function of a standard normal random variable.

Parameters are the vector of slop coefficients β and the threshold values c.This study has

contributed to the literature on microfinance in a number of ways. Donors and lenders can

also use the results to target specific areas .He attempted to apply the existing research to

some other area, which he was focusing on former soviet union ,using the research from

latin America.

(Muriu, 2011) what explains the low profitability of Microfinance Institutions In Africa? To

find out why MFIs of other regions have positive profits and those operating in sub-Sahara

Africa(SSA) economies continue to post negative profits. Also finds out the determinants of

MFI profitability Find the relation between credit risk, managerial efficiency, capitalization

with profitability. Corruption effect on the profitability. There are few observations in the

paper that the author has made. Even though there is a high loan repayment rates, only few of

the MFIs are profitable. The MFIs in Africa have on an average consistently posted negative

profits compared to other regions. Hence the two goals of the paper are:Identify on the basis

of empirical evidence and in a single static framework, significant determinants of MFI‟s

profitability.Investigate if the MFIs can maximize profits or whether they are pursuing

additional objective as well. The research was based on determinants of profitability in MFI

sector hence the author has built a model based on the same.MFI industry is characterized by

a different function to that of retail banks of any other profit seeking corporate entity. Hence

20

multivariate regression model was used to for the same. The linear regression model that was

predicted was based on the literature reviews. Hence the determinants are

Size: this variable was used to capture the economies of scale or diseconomies of scale in the

market.

Age: age is introduced in model to capture the learning effects. From the literature review of

the author, older firms have more amount of experience in the same industry hence enjoy

higher profits

Capital assets ratio (CAP): high CAP ratio signifies that the MFI is operating over cautiously

and ignoring profitable investment opportunities. On the contrary the cost of insurance

against bankruptcy can be high for MI with low CAP ratio. The gearing ratio defines the

source of business finance to boost financial performance.

Credit risk: this is another determinant in MFI industry. Poor quality of credit reducs the

profitability of the MFI. Hence the negative relationship between credit risk and the

profitability. This is calculated by taking sum of the level of loans past due 30 days or more

and still accruing interest hence portfolio at risk( PAR30) . write off ratio which is the value

of loans written off during the year as uncollectible as a percentage of average gross portfolio

over the year. Other measure for credit risk is risk coverage(RC) ratio which is measure as

the adjusted impaired loss allowance/PAR30. Loan loss reserve ratio this is measured by

ratio of loan loss reserves to gross loans.

Efficiency: is expenses management should ensure a more effective use of MFI‟s loanable

resources. Higher ratios of operating expenses to gross loan portfolio imply a less efficient

management. From the literature review we can say that microfinance is a costly business

since it has high transaction cost and information cost. This is measured by operating

expense/average gross loan portfolio and in robustness tests, cost per borrower can be used

The other two proxies , Macroeconomic environment, inflation and real GNI per capita

growth. Dependent variable is ROA or ROE. Efficiency in delivering microfinance is an

important determinant of profitability.A major drawback of the negative profitability in SA

could be due to the fact that the managerial practices have come down due to the increase in

21

technological innovations. Higher spending could be due to the same reasons. the main

research gap is the analysis was based on literature review rather than actually coming up

with original work.

(Venkataraman, 2006)To measure each kind of risk in the Basel II norm through a

comprehensive IT solution. Risk identification, Quantitative risk measurement, Risk

mitigation, Minimum capital allocation. The 3 pillars of Basel II are

a) Pillar I: minimum capital requirement

b) Pillar II: supervisory review process

c) Pillar III: market discipline requirements

Types of risk

a) Credit risk; default by the borrower to repay the borrowings

b) Market risk: volatility of the bank‟s portfolio due to change in market factors

c) Operational risk: risk arising out of banks inefficient internal processes, systems,

people or external events like natural disasters, robbery,etc

Minimum capital allocation for credit risk: Standardized approach: external credit rating

agencies , capital allocation and credit rating are inversely proportional. Internal rating,

Foundation IR approach, Advanced IR approach, In both the methods capital allocated is

based on the following 3 factors ,EAD exposure at default: amount of facility that is likely to

be drawn in default,LGD loss given at default: measure the proportion of lost exposure n

default Probability of default(PD) chances of default in terms of percentage (default- fails to

repay borrowings) Minimum capital allocation for market risk: VAR is used to measure

market risk. VAR measures the likely loss in value of a portfolio over a iven time period with

specified probability. Minimum capital allocation for operational risk: These three methods

are used to measure and allocate operational risk.Basic indicator approach: capital charge

should be 15% banks average annual positive gross income over previous years.

Standardized indicator approach: in this approach the bank activities are classified into 8

business line. Each business line is having an exposure indicator which is multiplied by the

factor( beta) will give the capital charge for operational risk. Advanced measurement

approach: loss distribution approach is of the advanced versions in this approach, in which

22

the impact of significant operation events on various business lines of banks and frequency of

occurrences of these events are captured in the form of normal distribution.

(I.B., 2007) performance of microfinance providers in karnataka. Objective of the study To

study the growth and pattern of microfinance in Karnataka.To evaluate the business

performance of the Microfinance providers.To study the impact of micro financial

institutions on member enterprises .To identify the constraints faced by the microfinance

providers. The data for the research was collected from the primary source with respect to

amount lent, portfolio lending by microfinance providers, cost and returns involved in each

activities, recovery performance under micro financial activities in selected districts was

collected with the help of a questionnaire. Analytical techniques used are.Triennium

averages: the 1st three years average and the last three years averages was calculated because

of plausibility of large number of continuous time series data . the annual average growth in

percentages calculated by dividing the changes during the period by number of years in the

study period.this is done to study the performance of microfinance activities undertaken by

non government microfinance providers Compounding growth rate analysis: the growth in

the number of SHGs credit link, banks loan and refinance of microfinance providers can be

assessed by taking for 14 year period.And the compound growth were computed by using

exponential function of the form.

Yt=ABtUt

where

Yt is SHG credit linked/bank loans/refinance/ number of family assistd/recovery/over dues

A is the time period

Ut= error term

B= 1+G where g is the growth rate

By taking logarithm

We see that log(Yt)=log A+t log B+log Ut

23

Which is of the form

Qt=a+bt+Ut

Hence g=antilog(b)-1*100

Paired t test: to find out the impact of NGOs on the SHGs the paired t test was done. Which

is statistical test for finding the differences in performance of SHGs before and after joining

the NGOs who are involved in microfinance. Impact index: the impact of the NGO on the

SHGs was also assessed using the scoring pattern Impact index=(average scored

obtained)/(average maximum scored to be obtained).The pattern of growth of SHGs in the

state 1992-1993 to 2005-2006 and that the importance of SHGs has increased in the lives of

the poor people and that the microfinance may also be possible because of refinance support

provided by the apex level institutions involved in microfinance. The total amount of loans as

expanded considerably through NABARD especially from selected villages.

(Saltzman, 1998)Capital Adequacy. The objective of the capital adequacy analysis is to

measure the financial solvency of an MFI by determining whether the risks it has incurred

are adequately offset with capital and reserves to absorb potential losses. There are three

indicators:First one is leverage, explains the relationship between the risk-weighted assets of

the MFI and its equity. Second one is ability to raise equity, a qualitative assessment of an

MFI‟s ability to respond to a need to replenish or increase equity at any given time. the third,

is adequacy of reserves, is a quantitative measure of the MFI‟s loan loss reserve and the

degree to which the institution can absorb potential loan losses.

Asset Quality. The analysis of asset quality is divided into three components

PORTFOLIO QUALITY: Portfolio quality includes two quantitative indicators: portfolio at

risk, which measures the portfolio past due over 30 days; and write-offs/write-off policy,

which measures the MFI‟s adjusted write-offs based on CAMEL criteria

PORTFOLIO CLASSIFICATION SYSTEM: entails reviewing the portfolio‟s aging

schedules and assessing the institution‟s policies associated with assessing portfolio risk.

24

FIXED ASSETS: fixed assets, one indicator is the productivity of long-term assets, which

evaluates the MFI‟s policies for investing in fixed assets.

MANAGEMENT: Five qualitative indicators make up this area of analysis:

Governance, human resources, processes, controls, and audit, information technology

system, strategic planning and budgeting. EARNINGS: Three quantitative and one

qualitative indicator to measure the profitability of MFIs: Adjusted Return On Equity:

measures the ability of the institution to maintain and increase its net worth through earnings

from operations. Operational Efficiency: measures the efficiency of the institution and

monitors its progress toward achieving a cost structure that is closer to the level achieved by

formal financial institutions. Adjusted Return On Assets: measures how well the MFI‟s

assets are utilized, or the institution‟s ability to generate earnings with a given asset base.

Interest Rate Policy: to assess the degree to which management analyzes and adjusts the

institution‟s interest rates on microenterprise loans (and deposits if applicable), based on the

cost of funds, profitability targets, and macroeconomic environment. Liquidity

Management:evaluates the MFI‟s ability to accommodate decreases in funding sources and

increases in assets and to pay expenses at a reasonable cost. Indicators in this area are

liability structure, availability of funds to meet credit demand, cash flow projections, and

productivity of other current assets. Under liability structure, CAMEL analysts review the

composition of the institution‟s liabilities, including their tenor, interest rate, payment terms,

and sensitivity to changes in the macroeconomic environment.

The paper also drafted the CAMEL‟s indicators with weightings

Table 2.5 camel’s indicators

Quantitative Indicators Qualitative Indicators

Capital Adequacy (15%

Leverage (5%)

Adequacy Of Reserves(5%)

Weightings (%)

Ability To Raise Equity(5%)

Asset Quality (21%)

Portfolio At Risk(8%)

Write Offs/Write Off Policy(7%)

Portfolio Classification System (3%)

Productivity Of Long Term Assets(1.5%)

Infrastructure(1.5%)

25

Management(23%) Governance/Management (6%)

Human Resources (4%)

Processes, Controls, And Audit (4%)

Information Technology System (5%)

Strategic Planning And Budgeting( 4%)

Earnings (24%)

Return On Equity (5%)

Operational Efficiency( 8%)

Return On Assets (7%)

Interest Rate Policy (4%)

Liquidity Management (17%)

Productivity Of Other Current Assets (2%)

Liability Structure( 8%)

Availability Of Funds To Meet Credit

Demand (4%)

Cash Flow Projections( 3%)

Total(100) 47% 53%

(Barman, 2009) Role Of Microfinance Interventions In Financial Inclusion: A Comparative

Study Of Microfinance Models.To study the relationship between the level of indebtedness

to moneylenders and the type of microfinance model through a case study in Varanasi, U.P.

Comparing two microfinance models prevalent in the research area.This survey was

conducted among 59 households of twelve villages covering four blocks of the selected

district. Primary data on different socio-economic aspects of the households and details of

micro-financial services availed by them were collected directly from the clients through the

structured questionnaire and personal interview. Qualitative information was collected

through Focus Group Discussions (FGDs) and semi-structured interviews of the bankers,

NGOs and MFIs operating in the area to understand the supply-and demand sides of the

problem of microcredit in the selected research area. The collected data are subjected with

the chi-square statistical test in order to determine if there is significant variation in the

tendency to borrow from the moneylenders among clients of SHG and MFI model of

microfinance. The test is applied when one has two categorical variables from a single

population. It is used to determine whether there is a significant association between the two

variables i.e. indebtedness to moneylender and being client of particular type of microfinance

26

model.The authors conclude that the level of indebtedness to moneylenders is higher in the

case of clients of Microfinance Institutions (MFI) model and without complete information

on the credit-worthiness of borrowers, MFIs may contribute to the over-indebtedness of their

clients as well as damage in their performance. there could be more number of variables

which could affect the indebtedness to money lenders.

(Khan, 2012)The main aim of this paper is to provide with a literature review on previous

work n transaction costs including operating costs, in microfinance.The second part of the

paper describes the research modalities followed by a section which provides the findings

based on empirical evidenvr.The depth into one case study of lean cost management .

Provides managerial recommendations. The data was collected from Microfinance

information exchange(MIX).the parameters considered were Average loan balance

outstanding per borrower in USD,Gross loan portfolio in USD,Number of depositors, Cost

per borrower in USD,Operating expenses as a percent of the gross loan portfolio, Nominal

yield on gross loan portfolio , And based on these data longitudinal analysis was conducted

from the data from MIX and analysis of top 10 MFIs, which accounted for about 92% of the

clients over the past 10 years. Time series data for outreach was presented and the top 3 mFIs

are in the league of their own and are about equal in size of growth rates. There are number

of factors that attribute to an MFI having lean operation and being cost effective. The

operating costs differ significantly for different institutions and can be attributed to achieving

economics of scale in operations .They saw that it is possible to adopt cost effective

operating structure while operating in same service space as other less efficient MFIs. they

used the existing literature to find out the costs that the MFis incur rather than using primary

data to find out about the different types of costs.

(Karlan, 2008) Credit Elasticities in Less-Developed Economies: Implications for

Microfinance.Test the assumption of price inelastic demand using randomized trials

conducted by a consumer lender in South Africa.identify demand curves for consumer credit

by randomizing both the interest rate offered to each of more than 50,000 past clients on a

direct mail solicitation, and the maturity of an example loan.The sample frame consisted of

all individuals from 86 predominantly urban branches who had borrowed from the Lender

27

within the past 24 months, were in good standing, and did not currently have a loan from the

Lender as of 30 days prior to the mailer. pilot-tested in three branches during July 2003

(wave 1), and then expanded the experiment to the remaining 83 branches in two additional

waves that started with mailers sent in September 2003 (wave 2) and October 2003 (wave

3).the randomized field experiment to estimate price and maturity elasticities of demand for

consumer credit. The sample includes former borrowers from a major, for-profit, South

African consumer micro lender to the working poor. In the Lender‟s case, the cost of

reducing interest rates (lost gross interest revenue on infra marginal loans) slightly exceeded

the benefits (increased gross revenue from marginal borrowing, increased net revenue from

higher repayment rates)

(Eversole, 2003)help, risk and deceit: micro entrepreneurs talk about microfinance. To find

the relation between the ostensibly commercial transactions which converted into complex

assumptions about the social development, external assistance and power? To illustrate the

divide between developed and developed in their shared quest to help business grow and

concludes that building strong lending institutions does not automatically translate into broad

based benefits for micro entrepreneurs of their businesses. While international agencies

priorities the development of sustainable microfinance organization to provide loans to the

micro and small businesses, the business people themselves may see their own interests as

quite different for those of the organizations meant to serve them. The reasons for this were

many such as loan products that were suited to only certain kinds of businesses, businesses

which were ill equipped to take out loans. Expectations that help equated to short term

assistance and flexible repayment schedules and assumptions that corruption was likely to be

rampant whenever development money arrived.

(Barone, 2011)Exploring Household Microfinance Decisions: An Econometric Assessment

For The Case Of Ghana. To analyze the relationship between household financial

instruments by determining the link between insurance coverage and household savings. The

data set used for the purposes of this paper uses data from 351 households captured at one

period in time. Because the data is not dynamic, a two-step approach is used to analyze the

relationship between insurance coverage and savings at the household level .

Variables:

28

a) Insurance purchase:

i) Health Insurance

ii) Life Insurance

iii) Old age Insurance

iv) Other Insurance

b) Savings:

i) Total HH savings

c) Shocks to house holds

i) Weather shock

ii) Crime shock

iii) Business shock

iv) Loss of job

v) Death of worker

vi) Illness of worker

vii) Family shock

viii) Severity of shock

d) Risk perception

i) Share of ill

ii) Share of injured

e) Additional risk measures:

i) Share of employed

ii) Share of dependents

iii) Avg HH age

iv) Life expectancy

v) Risk aversion measure

vi) Risk aversion measure

f) Income

g) Controls:

1) Female head

2) Age (in years)

3) Education (in years)

29

4) HH earnings (occupational)

5) HH additional earnings

6) Distance to health provider (in km)

7) Vaccinations

8) Private Hospital

9) Health center1

10) Chemist/Pharmacist

11) Government Hospital

12) Mission Hospital

The sample mean , std dev of each of the variables was taken and analyzed based on the data.

Regression model of the nature:

P( Y=1, Health insurance) = α+β1 savings+β2 life insurance + β3 old age insurance +µ

Was constructed and regression analysis was done There are a variety of reasons to support

this claim. Financial tools, when used in unison, provide households with options for

managing assets. Prior to a shock, households can allocate income between savings and

insurance products to help protect against potential risks. The findings of this paper suggest

expanding access to products increases use through simple exposure. Households use saving

mechanisms and insurance products, they appear to increase their use of both products.

(crabb, 2007) foreign exchange risk management practices of microfinance institutions. to

review the current practices in the management of forex risk for and by MFIs.The advantages

and disadvantages of these practices The standard framework of the Forex risk measurements

are ,MeasuringVAR to exchange rate fluctuations,Purchasing derivatives of adjusting

portfolios to offset this risk, Continuously monitor the risk position.Diversify both the source

of debt capital and the use of debt capital, Insuring the risk of devaluation in the network,

Using currency swaps. Three general conclusions can be drawn from this study of Forex

exchange risk and MFIs.First need additional funding to meet demands and debt capital is

most likely source for funding. Second Forex exchange rate risk is significant and though it is

only one factor in a decision to lend to a MFI , it is a strong deterrent. The risk devaluation

against most major currencies such as the US dollar and the Euro is high and it is in these

30

currencies that any new debt capital is likely to be denominated. The existing Forex practices

are prohibitively expensive, either to the client or the institution. the potential intermediaries

or counter parties to any potential currency swap agreements were not discussed in the paper.

(Abiola, 2011)impact analysis of microfinance in Nigeria. To apply the financing constraints

approach to study whether microfinance institutions improve access to credit for

microenterprise in Nigeria or not. This paper is based on generating financial constraint

theory model thing or an event.

Pri = (1+ exp(-λi))-1, where λ is linearly dependent on the variables hypothesized to affect

the probability: λi = α + βXi.

The probability thus varies from 0 to 1 (λ = ±∞), and the model is simplified by rearranging it

into a log of the odds,

ln(Pi /(1 - Pi)) = α + βXi.

Which, for examples consists of individual outcomes, and can be estimated with maximum

likelihood. Interpretation of the coefficients can also be done by reverting back to the

probabilities. Thus,

Pr(IFA = 1) = f(α + β1IF + β2IO + y/Z)

where IFA is the decision to invest in fixed assets, IF is the variable for internal funds capital;

IO is the investment opportunity variable, and Z is a vector of variables that capture various

characteristics of the enterprise and the states in which it operates. Firms without investment

opportunities would not invest even if they had capital. Thus, control for investment

opportunity (IO) and separated it from the effect of internal funds (IF). The paper uses the

financing constraints approach to study the impact of microfinance on access to credit for

microenterprises in nigeria.The model contained ten independent variables (average profit,

market & skill, hired employee, asset loan, enterprise age, internally generated revenue,

business location, entrepreneur gender and availability of investment opportunity).They show

that MFBs improved access to credit in locations where more MFBs offered financial

products because investment in local microenterprises was less sensitive to availability of

internal funds in unconstrained location, than investment in microenterprises in locations

31

where microfinance activities were limited or non-existent and where micro entrepreneurs

had to rely more on internal funds for investment. Popularity of microfinance forces MFBs to

be more transparent and thereby decreases the cost of assembling a database with MFBs

branch distribution, therefore making the financing constraints approach more attractive for

use in the future.

(Rahman, 2011)The Development Perspective of Finance and Microfinance Sector in China:

How Far Is Microfinance Regulations? The paper reviews the development process of bank

and microfinance sector in China and presents their regulatory status. Research methodology:

since this paper is a review of existing literature there is so quantitative research

methodology. Microfinance structure and their services Since the first microfinance seed was

planted in China, a vast number of different types of microfinance operators have appeared

within the Chinese market. Generally, there are three broad categories of microfinance

service providers. These include,Micro-credit by financial institutes This category mostly

includes state own formal microfinance service providers i.e. ABC, ADBC, RCCs, Rural,

Commercial Bank, Rural Cooperative Bank, Postal Savings, China Development Bank

(CDB), MCC, VTB, LC, andRMCCs. The microfinance market share is dominated by these

providers.,Micro-credit by NGOs & international organizationsThe service providers are-

NGOs, international organizations and social organizations. The internationalorganizations

have been providing financial services as project based with the collaboration of government

agencies.They also incorporate different services beside micro-credit i.e savings, training in

project sites. NGO lending services have covered countrywide and large volume of business.

Micro-credit by Government agencies This category provides micro-credit focusing on the

government poverty reduction program. For instance, Urban Credit Bank (UCB) was

established to support laid-off workers which ultimately expanded micro-credit services to

urban areas.Only NGO-MFIs and MCCs are non-financial institutions and consequently not

allowed to work with savings or receive funding from commercial banks –thus, preventing

them from enjoying economies of scale Even the lending companies are also not allowed to

work with savings. In addition, the three newly created rural financial institutions (VTBs,

LCs, and RMCCs) as well as MCCs are subjected to geographical restriction. The traditional

collateral system for micro-financing still exists particularly for micro-lending companies,

lending companies, postal saving banks, MCCs, and VTBs. Even RCCs and UCCs have

32

followed a special kind of collateral to credit disbursement. RCCs required collateral for

large loan amounts and UCCs required companies guarantee. On the other hand, the donor

funded projects (UNDP, UNFPA, UNICEF, Heifer Project, World Vision, Oxfam Hong

Kong and CIDA) are allowed to providing micro-credit services by collaboration with

government departments or agencies having certain conditions. that the banking and

microfinance services have expanded and improved gradually. Hence, the banking sector is

close to the maturity stage while the microfinance sector is still at learning stage. CBRC is

the sole institute to deal with policy regulations for banks and microfinance service providers

which may contradict to handle different goal oriented institutes (Banks and MFIs run their

business in different perspectives).Author recommended to the concerned authorities to have

a balanced policy regulation for the microfinance

(Jiwani, 2007) Sustainable Microfinance: The Impact Of Pay For Performance On Key

Performance Indicators. This study investigated the relationships between pay-for-

performance incentive programs and loan officer productivity in microfinance institutions

(MFIs).

Loan officers‟ performance is measured by five key performance indicators:

1) new borrowers,

2) portfolio value,

3) average loan size,

4) arrear rate,

5) default rate.

The independent variable is the loan officer‟s financial incentive (the percentage of salary

that is based on performance). Five dependent measures (performance outcomes) have been

examined:

a) number of new borrowers,

b) value of portfolio,

c) average loan size of the borrowers,

d) number of borrowers in arrears (loans overdue > 30 days),

e) number of borrowers in default (loan overdue >90 days).

33

The second research question uses survey questions from supervisors of loan officers, and

loan officers to assess the impact of the productivity level of MFIs with financial incentives

and MFIs without financial incentives: Is there a difference between the productivity level of

loan officers at MFIs with financial incentives and MFIs without financial incentives All five

hypotheses suggested that there would be an increase in productivity with higher incentives.

Results indicated that the number of new borrowers was related to the size of the incentive

program. The negative correlation between the number of new borrowers and the size of the

incentive program indicated that MFIs with larger incentive programs had loan officers with

a smaller number of new borrowers in each month, and overall. There were no relationships

between the size of the incentive program and any of the other performance measures.

(Kundu, 2012)Savings, Lending Rate and Skill Improvement in Microfinance Operating

Through Public-Private Cooperation.microfinance program through joint liability credit

contract is explained with the help of a two-stage game when the program is operated by a

non-motivated NGO with the help of a commercial bank and government. Initially, the

author assume that two homogeneous members belong to the same village form SHG on the

basis of joint liability only for two periods. The group is formed by the initiative of an NGO

whose basic activities are:

1) Motivating local housewives to form SHG;

2) Collecting savings (contribution) from them in installment and giving them technical

knowledge for skill improvement of the participants at the initial stage;

3) Bridging the gap between the group and the bank as well as the government;

4) Maintaining the group corpus;

5) Collecting subsidy and cash credit from the DRDA and bank respectively;

6) Disbursing credit simultaneously to both the members and recovering credit from the

members

7) Generating profit after performing all these activities at the end of the second period.

Government Subsidized Microfinance Program in the Total Absence of Social Sanction:

Suppose each member of the group is willing to contribute (save) x amount in each

installment and each member has to contribute 2t times in each year. The amount saved by

each group member in each installment is deposited in the office of the NGO and the NGO

34

deposits the amount in the linked commercial bank. assume that before getting first credit

from her group, each member has to save t times regularly. During this period, she is also

getting skill-training from the NGO without spending any amount. Total amount

accumulated in the group after contributing for „t‟ times by each member is:

2tx(1 + i) = 2X(1 + i), where 2tx = X.

The NGO withdraws 2X amount from bank and distributes that equally among the group

members as credit against a rate of interest rˆ. The income earned by each member after

utilizing the microcredit as the working capital can be expressed as:

Ym = ƟX, where mϵ {1, 2} ...(1)

Here Ɵ is the degree of technical knowledge gained by each group member after group

formation from the NGO and Ɵ > 1. It is also assumed that the husbands of both the

members are earning members and ready to contribute their entire income for their family.

The annual earning of the husband of each group member is W and 2x < W. At the end of the

first stage, we have four possible levels of consumption of both the member households. If

the group member is well-behaved and is ready to repay her own loan with interest at the end

of the year, then the consumption of the non-defaulter member household will be:

CmGR

= W+ƟX- 2X+ X(1+ rˆ)

where m ϵ{1, 2}

It reestablishes the fact that even in the presence of government subsidy in microcredit

program under joint liability through formation of SHG, social sanction or depriving the

members from enjoying further benefits from the government still plays an important role of

security at the time of repayment of loan.It is also proved that if the group members are not

equally powerful in the society, then in the second stage of the game, the powerful member

applying her social influence and taking advantage of joint liability may force the less

powerful member to repay her loan with interest and enjoy a free ride. So positive assortative

matching, both from the economic as well as social point of view, is necessary at the time of

35

group formation and that should be maintained in both the periods to keep repayment rate

100%.

(Arch, 2005)Microfinance and development: risk and return for a policy outcome perspective

This paper address microfinance- financial services products including credit loans and

insurance which encourage productive and entrepreneurial activity for the marginalized often

unbanked also known as the poverty market. This paper provides the overview of the

microfinance market space, its industry players and it addresses current issues in

development policy. This is a descriptive paper hence the author has considered various

scenarios and analyzed the microfinance market The problem with the financial system of

Kenya is that it was built as if the structure of the economy was that o England or the US. In

reality all most all the people are small farmers, vendors and informal sector industrialists.

Hence a financial system that serves the reality should be created. The maturing of the

microfinance market has led to some spectacular successes.

(Stackel, 2010)Reducing Defaults In Microfinance: A Case Study Of Fundación Integral

Campesino (Finca) Costa Rica.This study seeks to determine why some microfinance

institutions have high default rates while other have low ones. Three literature-based

hypotheses regarding default reduction were tested on communal credit enterprises (CCEs)

of a poverty-focused microfinance program called FINCA Costa Rica. The hypothesis author

derived were from the literature review

a) Creating a highly-unified structure/group sentiment within MFIs,

b) implementing good quality training programs,

c) exerting discipline in financial administration.

These three methods were be explored here. The five CCEs also show differing

characteristics, related to default rates. Each CCE tracks their default rates on a document

called the credit profile, which shows all outstanding loans and the most current payment

status. The payment status can be: paid, between 1-30 days late, between 30-60 days late,

between 60-90 days late, and more than 90 days late. Starting with figure 3.5, the following

charts show the long-term default rates for each CCE during the months of August and

December. compare the group structures, training programs received, and amount of

36

discipline employed in each CCE in order to see if any of these factors are associated with

Bahia Ballena having high default rates. These hypotheses are not mutually exclusive, but

nevertheless they may present interesting findings on the potential causes of high default

rates.

(EDARURAL)White paper published by EDARURAL with collaboration of M-CRIL,to find

the various business models existing with MFIs .sample 20 MFIs were taken and primary

data was collected through interviews.M-CRIL rating reports, MFI annual reports.MFI use

groups as intermediaries for financial transactions, but there are different ways of working

with groups. They are broadly classified as SHGs and Grameen replicators. A small number

of MFIs have an individual banking approach (IB) while some SFMC patners are

cooperatives usually catering to a specific economic sector such as fishing,,

handlooms,dairying rather than MFI model.Most of the MFI associated with SIDBI follow

SHG model

Table 2.6: Operational features of different MFI models in India

Operational features SHG Grameen IB

Clients Primarily women Primarily women Primarily men

Groups 15-20 clients per

group

Usually 5 clients per

group

Individual clients

Service focus Savings and credit Credit-regular cycle Credit

Role of MFI staff Guide and facilitate Organize Organize

Meetings Monthly Weekly Individual

transactions-often

daily

Savings deposits Rs 20-100/month Rs 5-25 per week Flexible

Interest on savings Bank

rate(4.25%)+profit

share

6-9% 6%+

Initial loan size Rs 5-10,000 Rs 2-%5,000 Rs 5-15000

Effective interest

rate(usual range)

24-28% 32-38% 23-38%

37

Insurance : at a very preliminary stage:usually loan linked, some life and health some times,

links to national companies

Development

services

Some associated

programs

A few small social

projects

Enterprise support

(Bruett, 2004) The author starts to look at interest rate risk and suggests that the tool that is

already used by the banks i.e. ALM (asset liability management) should be used to calculate

the maturity gap and hence monitor it regularly. Set targets and limits for the maturity gap

ratio particularly aging categories. Then the author focuses on the foreign currency exposure

i.e. according to the literature review of the paper, MFI have proven to be more resilient than

larger banks after currency shocks not only because they have more diversified loan

portfolios , but also because they have less foreign currency exposures. Liquidity risk: it

refers to the risk that MFI is not able to meet its obligations due to lack of cash. The MFIs

lack the basic policies for liquidity management Liquidity target= 1 month cash expense+x%

gross loan portfolio Measuring liquidity can be difficult, since there could be a movement of

cash in the future. Cash position indicator= (cash+ short term investments)/assets, Dynamic

liquidity ratio= (cash+expected cash inflow)/(anticipated cash outflows).As MFI grow , it is

not enough to just manage credit risk and operational risk , risks such as fraud risk would

also come into play.The MFI managers and board members have to give importance to

macroeconomic and systemic trends and develop strategies to address them.

(CHIUMYA, 2006),The aim of the research was to contribute to the understanding of

regulatory and supervisory issues in relation to microfinance in order to inform the design of

regulatory policy in Zambia An evaluation of the potential impact of regulation on MFI .The

micro level analysis of impact of regulation and supervision on the MFI licensed by the

authority .Macro level analysis to study of the effect of regulation on the microfinance

sector.The research method that was mostly applied was Regulatory Impact Assessment;

regulation imposes costs and benefits, intended or otherwise, on stakeholders. RIA is an

empirical method of decision making, i.e. a decision which “is based on fact finding and

analysis that defines parameters of action according to established criteria”RIA is a rigorous

framework for policy making and analysis that helps to ensure policy decisions are as

38

soundly based as possible, and “can inform the decision process about the efficiency of the

policy and about the cost effectiveness of the instruments” RIA has been described as a

“decision tool, a method of, systematically and consistently examining selected potential

impacts from government action and of, communicating the information to decision-makers

defines RIA as a “method for analyzing the costs and benefits of regulatory change”, the RIA

provides a method for assessing the positive and negative impact of existing or potential

regulatory measures and can be used to ex ante assessment of proposed new or revised

regulation or the ex post assessment of existing regulation.

Data collection through Focused Group Discussion, Survey, Semi structured interviews and

documentary review.FGD were used to get stakeholder views, on whether the microfinance

sector should be regulated and supervised, the benefits of regulation and supervision and

who will be the most appropriate regulator. Option 1: do nothing maintains status quo, in

this situation it is assumed that the MFI sector would evolve and develop .Option 2:

introduce the draft MFI with BOZ as a supervisory authority.

Table 2.7: Cost Benefits Of Option 1 And Option 2

Option 1 Option2

Benefits Growth of the

microfinance sector

Increased competition

Access to financial

services

Lower changes and

interest rates

Provisions of the

BFSA not as strict as

those of the DMFRs

Clears up ambiguity

in regulatory

environment

Higher capital levels

Availabiltiy of

information

Increased consumer

protection

Increased access to

funding for MFIs

Costs Ambiguous regulatory

environment

Does not meet stated

objectives

39

2 tier system

Customer exploitation

Less competition

Reduced access to

financial services

Fewer services

Significant

compliance costs for

MFIs

Higher charges and

interest rates

BOZ supervisory

costs and costs

incurred in

establishing the

regulatory framework

Net benefit High Low

(managing microfinance risks, 2008) To introduce sophisticated systems and technical tools

of risk management. Institutional cultural issues related to cognitive biases in executive

decision making behavior . The paper looks at 8 different kinds of micro financing banks and

has calculated the PAR and risk coverage ratio for 3 years.PAR= outstanding balance, loans

overdue > 30days/adjusted gross loan portfolio.Risk coverage ratio = adjusted loan loss

reserve /PAR> 30 days if leans are based on adequate marketable collateral, this ratio doesn‟t

have to be high .

Table 2.8: Business Model For The Banks

Institution Type of institution

Cantilan bank Rural bank

ASKI NGO

Bangko Kabayan Rural bank

40

1st valley bank Rural bank

NWTF NGO

BASIX Non bank finance

Nirdhan Microfinance bank

Proshika NGO

Buro Tangali NGO

(Kundu, Savings, Lending Rate, 2011)In this paper, microfinance program through joint

liability credit contract is explained with the help of a two-stage game when the program is

operated by a non-motivated NGO with the help of a commercial bank and government. It is

observed that even in the presence of public-private cooperation and back-ended subsidy

provided by the government, both individual sanction as well as social sanction play an

important role of security against credit for proper functioning of the program. Non-

homogeneity among the group members may allow the socially powerful member to force

her less powerful co-member to repay her debt with interest and enjoy a free ride by taking

advantage of the joint liability. It is also proved that the non-motivated NGO, who itself

plays the function of the self-help group, can offer credit to the group members at lowest

possible rate of interest and arrange sufficient training for the group members for skill

improvement after group formation, if, and only if, it gets sufficient financial support from

the government in the initial period and if the linked commercial bank charges low lending

rate to the group in credit-linkage program. This will in turn encourage each group member

of the respective groups to enhance compulsory savings in each installment in both the

periods, which ultimately will help her to get a higher amount of credit in each period and

thus improve the consumption of the member household progressively.

(OGUNTOYINBO, 2011)The research report provides a credit risk assessment and

evaluation of Accion Microfinance Bank Limited (AMFB) for the period 2006 to 2010, using

Morgan Stanley‟s methodology for analysing the credits and performance ratings of

microfinance institutions (MFIs). Since MFIs are set up to provide credit and other financial

services to the poor, financially underserviced segment of the society, and since the credit

support granted to such micro businesses usually lacks collateral, it is imperative that the

41

management of such credit services be sound in order to mitigate the high risks involved.

Thus, credit risk management determines the success and survival of microfinance banks

(MFBs): weak credit management leads to capital erosion and eventual failure, whereas

sound credit risk management guarantees profitability and sustainability and, hence, the

realisation of the objectives of their setup – enhancing the welfare of micro-entrepreneurs.

The data for the research report were sourced from AMFB‟s financial statements for the

years 2006 to 2010 and from interviews that were conducted with principal officials of this

MFB. The research found that good regulatory corporate governance and management

practices, sound quantitative credit risk assessment and management, and quality and

maturity of management lead to low credit risk accompanied by high profitability and

sustainability for MFBs. As AMFB matured, the quality of portfolio, profitability,

sustainability and operating efficiency were seen to increase. The quality of shareholders,

board and management was found to be crucial for the sound management of the MFB. The

research report, therefore, recommends regular and continuous credit risk identification,

assessment and management, as well as sound corporate governance, if MFBs are to survive

and grow and achieve their developmental objectives

(Arvelo, 2008)The methodology addresses the specific challenges inherentvin microfinance

such as country risk, data availability and minimal default history among microfinance

institutions. Importantly, the methodology draws upon the work of major pioneers in

microfinance rating, including Standard and Poor‟s June 2007 report on assessing

microfinance risks, as well as the analysis of specialized rating agencies like Planet Finance,

MicroRate, M-CRIL and CRISIL. They also incorporated research insights made available

by important industry players like ACCION and the Consultative Group to Assist the Poor.2

Finally, our methodology builds on credit analysis processes used to assess established

emerging markets financial institutions and companies, applying the team‟s extensive

experience in emerging markets credit evaluation. the article describes the framework and

credit risk assessment process we use to determine internal global scale ratings for

microfinance institutions, including a detailed discussion of both conventional and

specialized credit evaluation metrics. The analysis has identified seven “rating factors” that

are important to consider when assessing the credit risk of these institutions: (1) loan

portfolio; (2) profitability, sustainability and operating efficiency; (3) management and

42

strategy; (4) systems and reporting; (5) operating procedures and internal controls; (6) asset-

liability management; and (7) growth potential. And before getting into the particulars, two

important .institutions that are (a) strictly dedicated to providing microfinance products and

(b) whose business model mainly revolves around providing microloans used to finance the

businesses of microentrepreneurs. Second, while it may be possible to make modifications to

or extrapolate from this model in the future, in its current form this framework considers the

industry only as it is today.

(Pearlman, 2007)This report explores the problems of low productivity in the microenterprise

sector and of low formal credit use, principally microfinance, by poor households.

Vulnerability to risk, defined as the inability to smooth consumption across negative income

shocks, as a new explanation for both phenomena. The limited ability to manage risk May

lead some poor households to choose low yield, low risk enterprises over higher yield but

more risky options. It also may lead them to forgo formal credit if this is used to finance high

yield/ high risk projects. Using both theoretical models and empirical evidence from

microentrepreneurs in Lima, Peru . Vulnerability is an important determinant of enterprise

choice and microfinance selection.

(Saad, 2009)Rural credit programs in developing countries are designed to help the poorest

of the poor by providing collateral-free loans at a low cost. In order to properly measure

The efficacy of these programs, one needs to examine not only the pecuniary benefits of the

programs but also the non-pecuniary benefits. The micro-loans are mandated for income-

generating purpose such as investing in a micro-enterprise. To elaborate, one way that credit

programs can benefit the poor is by providing them opportunities to increase their income.

Another way that these programs benefit is by empowering women. The credit programs tend

to target poor women, thereby providing them with income generating opportunities that they

otherwise lack. A woman's potential contribution to the household income may increase her

intra-household bargaining power and empower her. This may have far-reaching

consequences in terms of household investment in children's health and education, as well as

a woman's wellbeing. In the following thesis, the two different effects of credit programs.

The examines the effect of borrowing from credit and non-credit programs on self-

employment profits. The second chapter examines the effect of men's and women's self-

43

employment profits on woman's intra-household bargaining power and how it differs with

the gender of the primary borrower. The self-employment activities that are considered were

primarily funded by the credit programs or by noncredit sources such as commercial banks

and moneylenders.

(Kero, 2011)This paper analyzes two complementary macroprudential regulations that deal

with the problem of banks capital procyclicality; the countercyclical capital bu¤ers and

Spanish dynamic provisioning. The regulatory advances in relation to consultative

documents published by the Basel Commission in 2009 and 2011, known as the Basel III .In

the case of countercyclical capital buyers and concentrate in the discussion between Repullo

and Suarez (2011) and the Basel III proposal, if the gap of credit to Gdp is an appropriate

variable to activate the capital buyers. the reasons that show that Repullo and Suarez (2011)

is not a very well-founded critique against the Basel III and a number of issues that require

more research in this topic. The quantitative papers in the literature that try to account for the

efficiency of the Basel III regulations. The results of the literature show that the

countercyclical capital buyers contribute to the stabilization of the economy and the output

loss for the implementation of these regulations is not very big and in aggregate terms the

regulated economies perform much more better. Finally in the case of the Spanish dynamic

provisioning, both the regulatory authorities and the academic literature support its

implementation worldwide. The next step will be to build a theoretical framework that will

allow me to identify which is the most efficient regulation.

2.5 CONCLUSION:

The conclusion drawn from this literature review is that, there has been a lot of study on MFI

in Africa but not many in India. there is a definite regulatory body for MFIs in other

countries , but where as in India it has been up to RBI for registered MFI(NBFI) but the rest

of them work in the form of trust, which is not a regulated space. The type of customers

MFIs attract need high degree of customization since they have been and they also attract a

lot risks which is mostly linked to credit risks. The MFIs tend to cover their risks by

adjusting them to the interest rates which are so as to maintain the balance of risk and return.

There have been many models used for African nations but not in India which I see as

research gap and can be explored.

CHAPTER III

Research methodology

44

3.1 INTRODUCTION:

This chapter discusses the research methodology that has been undertaken while testing the

credit risk of microfinance industry in India. It contains the sampling method, the

questionnaire, the method of data collection, the models used and the method of analysis.

3.2 STATEMENT OF THE PROBLEM:

This study addresses the following problems:

a) To credit assess the Microfinance industry in India

b) To build a random effect model, in order to project the credit risk for a particular MFI

for the subsequent years.

3.3 THE MODEL:

The following is based on “Morgan Stanley credit risk assessment model.” Which gives an

idea of the credit risk of the MFI industry. And the variables are listed on in the table.

Table 3.1: Morgan Stanley Credit Assessment Model

RATING

FACTOR INDICATOR DEFINITIONS GRADES

Loan portfolio A1: portfolio at risk=( outstanding

loans with arrears over 30 days+

rescheduled or restructured loans)/

total gross loan portfolio. It is the

value of the loans which are

outstanding at the end of 30 days.

This assesses the credit risk that a

loan portfolio is carrying.

<3;<6;<9;<12;<15; above

15

A2: write offs=total write offs over

the last 12 months/average gross loan

portfolio. This is an independent

<2;<3.5;<5;<7;<10; above

10

45

variable which discusses the

historical data of write offs.

A3: size of portfolio=gross loan

portfolio

>300M;>350M;>100M;>5

0M;>10>;<10M

A4: loan loss reserves= loan

reserves/PAR30.

>85;>75;>65;>60;>55;

below 55

Profitability,

sustainability,

operating

efficiency

B1: Sustainability= operating

income/ (financial expenses+loan

loss provisions+write offs+operating

expenses). the sustainability is

assessed to check for the going

concern of the company, i.e. to check

if the company can honor is financial,

loan loss provisioning , write offs,

operating expenses by the operating

income it generates.

>120;>115;>110;>100;>90

;below 90

B2: ROAA=net income/average

assets

>3;>2;>1;>0;>-2;below -2

B3: operating efficiency= total

operating expenses/average gross

loan portfolio.

<20;<25;<30;<40;<50;

above 50

B4: productivity= number of

borrowers/total head count.

This is shows the efficiency of the

employees to process the loan

application.

>200;>190;>170;>145;>13

0 below 130

46

Asset and

Liability

management

C1: leverage= total

liabilities/(networth+subordinate

debt), this gives the idea of the

cushioning of the liabilities with

capital raising capacity of the MFI.

<5x;<6x;<7x;<8x;<9x;

above 9x

C2: exposure to foreign currency=

(financial debt in non-hedged foreign

currency)/total financial debt. This

variable covers the currency risk of a

MFI‟s in which they haven‟t hedged.

<15;<20;<35;<50;<65;

above 65

C3: liquidity= (cash+short term

investment)/(gross loan portfolio).

this variable is used to check the

MFI‟s cushioning with respect to the

probable losses it would make in its

loan portfolio.

>15;>12;>9;>6;>3 below 3

Management

and strategy

D1: quality of senior management

and board.

Credit risk is dependent on senior

management‟s decision on the credit

policy. And hence the decision is

dependent on the qualification of the

senior management

D2: strategy and business plan.

The business plan depicts the

attractiveness of the schemes that the

MFI proposes to borrowers and the

depositors.

( including competitive landscape)

47

D3: quality and support from

shareholders and network. This gives

the ability to raise capital from the

existing shareholders in order to scale

up the operations and achieve

economies of scale.

D4: HR management. this variable

gives the idea of quality of employees

hired by the HR department.

Systems and

reporting

E1: quality of management

information systems. MIS is most

important thing for credit decision.

This also emphasizes on the historical

borrowing patterns of the customer

E2:quality and speed of data feed.

This gives the speed of processing the

data of the borrower.

E3: quality of reports and

distribution/analysis of reports. Also

gives in-depth view if the MIS

reporting

Internal and

operational

controls

F1: operational procedures. If the

company has Standard operating

procedures in place to process the

loans of the customers.

F2: internal controls. This is most

important with respect to the

seriousness of the top management

on reduction of NPAs.

Growth

potential

G1: regulatory environment and

government involvement. This is the

48

3.3.1 Sampling method:

The sampling method used was stratification. The whole MFI industry was divided into

NBFI( non-banking financial institution, rural banks, banks which provide micro credit,

credit union. And the credit risk assessment model was applied to NBFIs in Hyderabad and

Bangalore. This hence the sample size was 15 NBFIs.

3.3.2 Data collection:

The quantitative data was collected from mixmarket.com i.e. secondary data and the

qualitative data was collected through questionnaires which were circulated to the 15 NBFIs.

The questionnaire is attached in the annexures.

3.4 THE REGRESSION MODEL:

3.4.1. The variables:

Dependent variable: PAR30 is the dependent variable which depicts the credit risk of the

MFI at time t. PAR30 is the total loans which would be outstanding at the end of 30 days.

Independent variables:

external control by the regulator to

control any kind of industrial crisis.

G2: Number and density of micro-

entrepreneurs. Gives the

attractiveness of the schemes of the

MFI in order to build on their loan

portfolio

G3: behavior of micro-entrepreneurs

towards microloans. The perception

of the borrowers about the MFI

Source : Ayi Gavriel Ayayi , (2012) ,P.47

49

Write offs: this is the ratio between total write offs over the last 12 months/average gross

loan portfolio, Log of gross loan portfolio, operating sustainability, return of assets, operating

efficiency, productivity, log of leverage, liquidity, bank dummy, credit union dummy,NBFI

dummy,rural banking dummy.

3.4.2 Hypothesis:

Hypothesis 1:

H0: write offs have no effect on the credit risk (PAR30) of the MFI

H1: write offs have effect on the credit risk

Hypothesis 2:

H0: gross loan portfolio have no effect on the credit risk ( PAR30)

H1: gross loan portfolio have effect on the credit risk(PAR30)

Hypothesis 3:

H0: operating sustainability have no effect on the credit risk(PAR30)

H1: operating sustainability have effect on the credit risk(PAR30)

Hypothesis 4:

H0: return on assets have no effect on credit risk(PAR30)

H1: return on assets has effect on credit risk (PAR30)

Hypothesis 5:

H0: productivity has no effect on credit risk (PAR30)

H1: productivity has an effect credit risk (PAR30)

Hypothesis 6:

H0: leverage has no effect on credit risk of the MFI

50

H1: leverage has an effect on the credit risk of the MFI

Hypothesis 7:

H0: liquidity has no effect on the credit risk of the MFI

H1: liquidity has an effect on the credit risk of the MFI

Hypothesis 8:

H0: the type of the MFI has no effect on the credit risk it faces.

H1: the type of MFI has an effect on the credit risk it faces.

3.4.3 REGRESSION MODEL:

The model is based on the random effect modeling of the data. This model is to project the

credit risk of a particular MFI i at time t,

Yit = β0+β1(writeoffs)it+β2(log gross loan portfolio)it+β3(operational self-

sufficiency)it+β4(operational efficiency)it+β5( productivity)it +β6(liquidity)it+β7(bank

dummy)it+β8(NBFI dummy)it+β9(rural banking dummy)it+β10(NGO dummy)it+β11(credit

union)it

Where Yit is the par30 value of an MFI at time t( year) The variables are as defined in the

model.

Chapter IV Industry overview

51

4.1 INDUSTRY OVERVIEW:

Microfinance industry in India has been on a rampant growth with high success rate and also

sustainable business model. These business models have made an attempt to overcome to

challenges faces by traditional micro crediting houses.As of march 2009 India has reached

about 22 million borrowers with about $2.3 billion lending. The microfinance business model

in India generates about 20% to 30% ROE, and usually financed by the commercial as well

as the public sector banks. This could be due to the RBI rule of40% of total loan portfolio

should consist of priority sector leading or investment in RIDF (rural infrastructure

development ) bonds. And hence the commercial banks look at a return of 14-16% from the

MFI over the 4% return from RIDF bonds. Also the statistics talk about the a CAGR of 86%

in loan portfolio over the last 5 years, with about an average of 95% of repayment from the

rural/urban poor community which has been commendable performance by the MFIs. one

reason for this kind of growth is the customization of the loan products with respect to the

villages. Also the microfinance institutions have tried to diversify their products from loans

to insurance, savings, remittance, low cost health care, educational services which gives them

not only the edge with respect to reliability but also sustainability in terms of customer

retention. They have also tried to set up competitive MIS systems which will give them a

customer loan history which will be usefull to price their loans according to the customer

rather than product generalization. Microfinance institutions have gotten the view that,

performance through spatial expansion would do no good to both the organization as well as

the customers. They have looked into existence through saturation in few areas and spatial

expansion in few areas. RBI has been promoting the idea of business correspondence model,

which will reduce the operational cost of the MFI and improve their reach through mobile

banking. the regulator has stressed importance on financial inclusion since there are a huge

chunk of untapped population in banking who haven‟t had the advantage of earning returns

through investments.

There are various business models followed by the microfinance institutions one of which

would be JLG i.e joint liability group, which work on the philosophy in sync with the self-

52

help group. In this JLG, women make a group of 5-10 in size according to their reliability on

each other and save in a joint account and loan each other money, the MFI automatically

becomes a member of the group and deposits certain amount of money along with the

women. Typically these group charges each other interest rate of 25-35% pa. this is a

profitable model since there is an automatic pear pressure from the other women on the

borrower.

The various government organizations which are involved in this microfinance industry are

National Bank for Agriculture and Rural Development (NABARD), Small Industries

Development Bank of India (SIDBI), Friends of Women‟s World Banking (FWWB),

Rashtriya Mahila Kosh (RMK), Council for Advancement of People‟s Action and Rural

Technologies (CAPART), Rashtriya Gramin Vikas Nidhi (RGVN), various donor funded

programs especially by the International Fund for Agricultural Development (IFAD), United

Nations Development Programs (UNDP), World Bank and Department for International

Development, UK (DFID).

But there has been a situation where the industry has been plunged into crisis during 2008,

due to Andhra Pradesh, about 25% of the sector is concentrated in this state. And the crisis

happened with most of the customers of Andhra Pradesh committed suicides due to harsh

recovery procedures followed by the MFIs . this caused to regulator RBI to come into picture

and cap the interest rates charged by the MFIs. apart from this the political parties had step

in, and wrote of the whole loan portfolio which eventually caused the MFIs to run into losses

in these states. Then ujjivan microfinance has faced this problem, they have diversified into

north and north western states to minimize their exposure to one area. The non-repayment

either happens due to adamant group of customers behavior or due to inability to pay. One

other reason was the MFI trying to push their interest spreads to earn profits , which was

perceived by the regulator.

As a whole MFIs have been playing a major part in financial inclusions. But there is a

necessity for a regulatory body which defines clear boundaries for them also customers have

been growing for this kind of inclusion and this would bring in a lot of diversified

opportunities and liquidity into the economy, along with growth in contribution of

agricultural sector, with rapid growth of information outreach.

CHAPTER V

DATA ANALYSIS AND

INTERPRETATION

53

5.1 INTRODUCTION:

As mentioned in the chapter 3, the data analysis and interpretation is continued into this

chapter. The primary aim of this chapter would be to assess the credit risk of the

microfinance institutions present in Hyderabad and Bangalore using Morgan Stanley credit

assessment model, later to develop a random effect model using the estimation methodology

which can be used to predict the credit risk.

5.2 MORGAN STANLEY CREDIT ASSESSMENT:

5.2.1 THE MODEL

The following is the table which describes the Morgan Stanley credit assessment matrix.This

matrix consists of both secondary data and primary data which has been used to predict the

credit risk of the MFI

Table 5.1: Morgan Stanley Credit Assessment Model Summary

RATING

FACTOR

INDICATOR DEFINITIONS GRADES

Loan portfolio(A) A1: portfolio at risk=( outstanding loans

with arrears over 30 days+ rescheduled

or restructured loans)/ total gross loan

portfolio. It is the value of the loans

which are outstanding at the end of 30

days. This assesses the credit risk that a

loan portfolio is carrying.

<3;<6;<9;<12;<15;

above 15

A2: write offs=total write offs over the

last 12 months/average gross loan

portfolio. This is an independent variable

which discusses the historical data of

write offs.

<2;<3.5;<5;<7;<10;

above 10

A3: size of portfolio=gross loan portfolio >300M;>350M;>100M;

54

>50M;>10>;<10M

A4: loan loss reserves= loan

reserves/PAR30.

>85;>75;>65;>60;>55;

below 55

Profitability,

sustainability,

operating

efficiency(B)

B1: Sustainability= operating income/

(financial expenses+loan loss

provisions+write offs+operating

expenses). the sustainability is assessed

to check for the going concern of the

company, i.e. to check if the company

can honor is financial, loan loss

provisioning , write offs, operating

expenses by the operating income it

generates.

>120;>115;>110;>100;>

90;below 90

B2: ROAA=net income/average assets >3;>2;>1;>0;>-2;below -

2

B3: operating efficiency= total operating

expenses/average gross loan portfolio.

<20;<25;<30;<40;<50;

above 50

B4: productivity= number of

borrowers/total head count.

This is shows the efficiency of the

employees to process the loan

application.

>200;>190;>170;>145;>

130 below 130

Asset and

Liability

management (C )

C1: leverage= total

liabilities/(networth+subordinate debt),

this gives the idea of the cushioning of

the liabilities with capital raising

capacity of the MFI.

<5x;<6x;<7x;<8x;<9x;

above 9x

C2: exposure to foreign currency=

(financial debt in non-hedged foreign

<15;<20;<35;<50;<65;

above 65

55

currency)/total financial debt. This

variable covers the currency risk of a

MFI‟s in which they haven‟t hedged.

C3: liquidity= (cash+short term

investment)/(gross loan portfolio). this

variable is used to check the MFI‟s

cushioning with respect to the probable

losses it would make in its loan portfolio.

>15;>12;>9;>6;>3 below

3

Management and

strategy(D)

D1: quality of senior management and

board.

Credit risk is dependent on senior

management‟s decision on the credit

policy. And hence the decision is

dependent on the qualification of the

senior management

D2: strategy and business plan.

The business plan depicts the

attractiveness of the schemes that the

MFI proposes to borrowers and the

depositors.

( including competitive landscape)

D3: quality and support from

shareholders and network. This gives the

ability to raise capital from the existing

shareholders in order to scale up the

operations and achieve economies of

scale.

D4: HR management. this variable gives

the idea of quality of employees hired by

the HR department.

56

Systems and

reporting

(E )

E1: quality of management information

systems. MIS is most important thing for

credit decision. This also emphasizes on

the historical borrowing patterns of the

customer

E2:quality and speed of data feed. This

gives the speed of processing the data of

the borrower.

E3: quality of reports and

distribution/analysis of reports. Also

gives in-depth view if the MIS reporting

Internal and

operational

controls(F)

F1: operational procedures. If the

company has Standard operating

procedures in place to process the loans

of the customers.

F2: internal controls. This is most

important with respect to the seriousness

of the top management on reduction of

NPAs.

Growth potential

(G)

G1: regulatory environment and

government involvement. This is the

external control by the regulator to

control any kind of industrial crisis.

G2: Number and density of micro-

entrepreneurs. Gives the attractiveness of

the schemes of the MFI in order to build

on their loan portfolio

G3: behavior of micro-entrepreneurs

towards microloans. The perception of

the borrowers about the MFI

Source: Ayi Gavriel Ayayi , (2012) ,P.47

57

5.2.2 ANALYSIS OF PRIMARY DATA:

The analysis of primary data was conducted through questionnaires (annexure) which has

been administered to various NBFI present in Bangalore and Hyderabad. The responses

hence collected were graded according to the qualitative parameters of the model i.e.

Management and strategy (D), Systems and reporting (E), Internal and operational controls

(F), Growth potential (G) .Table 5.1 gives the synopsis of the whole questionnaire according

the parameters used in Morgan Stanley credit assessment matrix.

5.2.2.1 RESPONDENTS PROFILE:

The respondents of the questionnaire were mainly middle level managers of the NBFIs who

are involved in the daily operational activity of the company; the responses from these NBFI

were recorded through interaction in Bangalore and online entry for Hyderabad.

5.2.2.2 DATA CONSOLIDATION AND ANALYSIS:

Table 5.1 matrix was formed through 4 step process

Step 1: Questionnaire administration to various NBFCs.

Step2: Grading each question with respect to the responses

Step3: Average the grades of the questionnaire to obtain

D1,D2,D3,D4,E1,E2,E3,F1,F2,G1,G2,G3

Step4: Average the grades obtained above to form D, E, F, G

58

TABLE 5.2: Consolidated view of grades given to qualitative parameters

NBFC name D1 D2 D3 D4 D E1 E2 E3 E F1 F2 F G1 G2 G3 G

Asmitha

microfinance

2.3

3

3.89 3.67 3.86 3.44 3.36 3.12 5.00 3.83 3.59 4.18 3.88 4.59 4.59 4.09 4.42

Basix India 4.6

7

4.22 4.67 4.00 4.39 4.30 4.54 4.00 4.28 4.41 4.15 4.28 4.07 4.07 4.24 4.13

BSS

microfinance

5.0

0

4.22 4.00 3.86 4.27 4.36 4.45 5.00 4.60 4.59 4.68 4.63 4.84 4.84 4.71 4.80

chaitanya

microfinance

4.6

7

4.44 4.00 4.29 4.35 4.47 4.38 5.00 4.61 4.53 4.73 4.63 4.87 4.87 4.70 4.81

Grameen

Financial

Services Pvt Ltd

5.0

0

4.44 5.00 4.75 4.80 4.73 4.91 6.00 5.21 5.18 5.37 5.27 5.68 5.68 5.43 5.60

janalakshmi

microfinance

5.0

0

4.00 4.00 4.29 4.32 4.43 4.48 6.00 4.97 4.86 5.21 5.04 5.61 5.61 5.23 5.48

KCIPL 3.0

0

4.22 4.00 3.86 3.77 3.69 3.56 5.00 4.09 3.92 4.35 4.13 4.67 4.67 4.30 4.55

KOPSA 3.0

0

4.22 4.00 4.00 3.81 3.74 3.58 4.00 3.77 3.69 3.87 3.78 3.94 3.94 3.81 3.89

nano 3.6

7

4.67 4.00 4.00 4.08 4.11 3.93 5.00 4.35 4.19 4.56 4.38 4.78 4.78 4.49 4.68

Samasta 3.6 4.11 4.00 4.29 4.02 4.02 3.90 5.00 4.31 4.17 4.51 4.34 4.76 4.76 4.46 4.66

59

7

share

microfinance

5.0

0

4.56 4.00 5.00 4.64 4.85 4.62 6.00 5.16 4.96 5.43 5.19 5.71 5.71 5.34 5.59

spandana

spoorti

4.6

7

4.56 4.00 4.86 4.52 4.69 4.45 6.00 5.05 4.84 5.35 5.09 5.67 5.67 5.26 5.53

tbf 2.6

7

4.11 2.00 3.43 3.05 3.40 2.69 1.00 2.36 2.27 2.20 2.23 1.60 1.60 1.93 1.71

trident

microfinance

5.0

0

4.33 4.00 11.8

6

6.30 7.06 5.35 6.00 6.14 5.52 6.53 6.02 6.27 6.27 5.89 6.14

ujjivan

microfinance

6.0

0

4.67 4.00 5.14 4.95 5.27 5.09 6.00 5.45 5.32 5.63 5.48 5.82 5.82 5.57 5.73

Source: consolidated from the questionnaire.

60

5.2.2.3 The Cronbach’s Alpha Test:

This test gives the reliability of the questionnaire used for the analysis of the quantitative

parameters and the following was observed

Table 5.3 : Results For Cronbach’s Alpha Test

Each of the following component variables has zero variance and is removed from the scale: Q4, Q5,

Q14, Q16, Q24, Q31, Q33, Q34, Q35, Q36

Reliability Statistics

Cronbach's Alpha Cronbach's Alpha Based on

Standardized Items

N of Items

.768 .861 29

Source: computed using the spss on the responses.

One can see that the cronbach‟s alpha on standardized items was found to be .861 which is

higher than 0.7(standard used in most social sciences researches), one can infer that the

responses have high internal consistency.

Hence one can conclude that the questionnaire which has been administered has been

accepted precisely for analysis of the current credit risk in a particular MFI.

61

5.2.3 ANALYSIS OF THE SECONDARY DATA:

the secondary data was obtained from mixmarket.com which was used for analysis of the

factors which represented loan portfolio(A) , Profitability/ sustainability/ operating

efficiency(B), Asset and Liability management (C ).

The following steps were followed to consolidate the grades for these factors

Step 1: obtain the data for the A1,A2,A3,A4,B1,B2,B3,B4,C1,C2,C3, for each of the years

and grade them.

Step 2: average the grade over the years to obtain the consolidated figure(annexure) .

Step 3: average A1,A2,A3,A4 to obtain A , B1,B2,B3,B4 to obtain B, C1,C2,C3 to obtain C.

Then the following table is obtained for the 15 NBFCs present in Hyderabad and Bangalore.

62

Table 5.4: Consolidated view of grades given to quantitative parameters

MFI A1 A2 A3 A4 B1 B2 B3 B4 C1 C2 C3

AML 2.00 1.75 1.00 1.88 6.00 2.88 1.00 1.38 3.13 1.00 2.63

BASIX 2.81 1.63 2.19 3.63 6.00 3.50 1.25 4.38 1.81 1.00 3.19

BSS 1.00 1.22 2.00 5.44 6.00 2.44 1.00 1.00 2.56 1.00 2.56

Chaitanya 1.00 1.00 4.00 2.67 6.00 3.33 1.67 3.67 1.00 1.00 2.67

GFSPL 1.00 1.00 3.00 3.08 6.00 3.50 2.33 3.58 2.00 1.00 6.00

Janalakshmi Financial Services

Pvt. Ltd.

1.67 1.17 2.33 5.17 6.00 5.00 1.50 1.00 3.00 1.00 2.00

KCIPL 1.00 1.00 3.00 6.00 6.00 5.00 1.33 2.67 1.33 1.00 2.00

KOPSA 3.67 1.00 4.67 6.00 6.00 5.00 3.67 2.67 1.00 1.00 2.00

Nano 1.00 1.00 4.00 2.67 6.00 5.00 1.00 6.00 1.00 1.00 4.00

Samasta 1.00 1.00 3.25 4.75 6.00 5.00 2.00 2.33 1.00 1.00 1.75

SHARE 3.11 1.67 1.00 5.44 6.00 5.00 1.00 1.33 2.67 1.00 2.00

Spandana 2.15 1.46 2.54 4.85 6.00 5.00 1.00 1.77 2.08 1.00 4.00

TBF 2.00 1.00 5.00 6.00 6.00 5.00 1.00 1.00 1.00 1.00 6.00

Trident Microfinance 2.60 2.00 1.80 5.00 6.00 5.00 1.00 1.00 1.80 1.00 2.60

Ujjivan 1.00 1.00 1.43 3.14 6.00 5.00 1.00 3.57 2.57 1.00 3.86

Source: computed from the data obtained from mixmarket.com

63

5.2.3.1 CONSOLIDATION OF PRIMARY AND SECONDARY DATA:

From the above two sections, consolidated table for credit risk grading can be determined,

this credit risk grading has been used to assess the credit risk of the NBFI. Quantitative

factors determine the historical results of the quality of loans, profitability/sustainability and

the assets-liability management of the MFI while the qualitative factors determine the quality

of management, the internal control, the quality of MIS and the growth potential of the

Microfinance. After grading each of the factors based on the secondary and primary data sets

respectively , morrgan Stanley benchmark matrix has been used to assign weights to

individual parameters. The following table gives us the idea of the benchmark

Table 5.5: Morgan Stanley Credit Risk Assessment

Sl.No Parameter Weights

1 Loan portfolio 24%

2 Sustainability/profitability 23%

3 Asset-liability management 7%

4 Management quality 19%

5 System and reporting 11%

6 Internal control 10%

7 Growth potential 6%

Source: Oguntoyinbo(2011), p-21

One applying the benchmark the following credit risk grading has be observed:

Table 5.6: final grades given obtained from the Morgan

Stanley credit risk assessment

MFI name grade for credit risk

Asmitha microfinance 2.93

Basix India 3.60

BSS microfinance 3.39

chaitanya microfinance 3.56

64

Grameen Financial Services Pvt

Ltd

3.93

janalakshmi microfinance 3.74

KCIPL 3.48

KOPSA 3.76

nano 3.67

Samasta 3.52

share microfinance 3.88

spandana spoorti 3.87

tbf 2.94

trident microfinance 4.40

ujjivan microfinance 3.90

Source: computed from the primary and secondary sources.

65

Table 5.7:Descriptives Of The Independent And Dependent Variables Used To Determine The Morgan Stanley

Credit Risk Assessment

Descriptive Statistics

N Range Minimum Maximum Mean Std.

Deviation Variance Skewness

A1 15 2.67 1 3.67 1.8007 0.23475 0.90918 0.827 0.743 0.58

A2 15 1 1 2 1.26 0.08953 0.34674 0.12 1.006 0.58

A3 15 4 1 5 2.7473 0.32452 1.25685 1.58 0.338 0.58

A4 15 4.12 1.88 6 4.3813 0.36105 1.39834 1.955 -0.429 0.58

A 15 2.19 1.64 3.83 2.5473 0.15413 0.59694 0.356 0.517 0.58

B1 15 0 6 6 6 0 0 0 . .

B2 15 2.56 2.44 5 4.3767 0.24406 0.94524 0.893 -1.043 0.58

B3 15 2.67 1 3.67 1.45 0.19187 0.74309 0.552 2.222 0.58

B4 15 5 1 6 2.49 0.38856 1.5049 2.265 0.911 0.58

B 15 1.89 2.61 4.5 3.578 0.13077 0.50646 0.257 -0.106 0.58

C1 15 2.13 1 3.13 1.8633 0.20255 0.78447 0.615 0.232 0.58

C2 15 0 1 1 1 0 0 0 . .

C3 15 4.25 1.75 6 3.1507 0.35588 1.37833 1.9 1.208 0.58

C 15 1.75 1.25 3 2.0047 0.12805 0.49594 0.246 0.292 0.58

D1 15 3.67 2.33 6 4.2233 0.27923 1.08147 1.17 -0.412 0.58

D2 15 0.78 3.89 4.67 4.3107 0.06185 0.23954 0.057 0.027 0.58

66

D3 15 3 2 5 3.956 0.16226 0.62842 0.395 -1.994 0.58

D4 15 8.43 3.43 11.86 4.766 0.52197 2.0216 4.087 3.499 0.58

D 15 3.25 3.05 6.3 4.314 0.19267 0.7462 0.557 1.038 0.58

E1 15 3.7 3.36 7.06 4.432 0.23381 0.90553 0.82 1.74 0.58

E2 15 2.66 2.69 5.35 4.2033 0.18961 0.73435 0.539 -0.535 0.58

E3 15 5 1 6 5 0.33806 1.30931 1.714 -2.203 0.58

E 15 3.78 2.36 6.14 4.5453 0.22765 0.88168 0.777 -0.706 0.58

F1 15 3.25 2.27 5.52 4.4027 0.21275 0.82399 0.679 -1.14 0.58

F2 15 4.33 2.2 6.53 4.7167 0.25416 0.98435 0.969 -0.795 0.58

F 15 3.79 2.23 6.02 4.558 0.23139 0.89618 0.803 -1 0.58

G1 15 4.67 1.6 6.27 4.8587 0.29071 1.1259 1.268 -1.736 0.58

G2 15 4.67 1.6 6.27 4.8587 0.29071 1.1259 1.268 -1.736 0.58

G3 15 3.96 1.93 5.89 4.63 0.2483 0.96166 0.925 -1.513 0.58

G 15 4.43 1.71 6.14 4.7813 0.27586 1.0684 1.141 -1.677 0.58

GRADE 15 1.47 2.93 4.4 3.638 0.09685 0.37508 0.141 -0.315 0.58

Valid N

(listwise) 15

Source: computed using spss with the data obtained from primary and secondary source

67

Correlation table:

Table 5.8: Pearson Correlation between the parameters used in Morgan Stanley credit risk assessment

Correlations

A B C D E F G GRADE

A

Pearson

Correlation 1 0.112 -0.323 -0.192 -0.393 -0.42 -0.478 0.63

Sig. (2-tailed) 0.691 0.24 0.494 0.147 0.119 0.072 0.015

N 15 15 15 15 15 15 15 15

B

Pearson

Correlation 0.112 1 -0.232 -0.009 -0.016 -0.006 -0.009 0.624

Sig. (2-tailed) 0.691 0.406 0.975 0.954 0.984 0.976 0.039

N 15 15 15 15 15 15 15 15

C

Pearson

Correlation -0.323 -0.232 1 0.041 0.029 0.024 -0.021 -0.678

Sig. (2-tailed) 0.24 0.406 0.885 0.919 0.932 0.939 0.783

N 15 15 15 15 15 15 15 15

D

Pearson

Correlation -0.192 -0.009 0.041 1 .922

** .897

** .798

** .895

**

Sig. (2-tailed) 0.494 0.975 0.885 0 0 0 0

N 15 15 15 15 15 15 15 15

68

E

Pearson

Correlation -0.393 -0.016 0.029 .922

** 1 .998

** .965

** .859

**

Sig. (2-tailed) 0.147 0.954 0.919 0 0 0 0

N 15 15 15 15 15 15 15 15

F

Pearson

Correlation -0.42 -0.006 0.024 .897

** .998

** 1 .978

** .844

**

Sig. (2-tailed) 0.119 0.984 0.932 0 0 0 0

N 15 15 15 15 15 15 15 15

G

Pearson

Correlation -0.478 -0.009 -0.021 .798

** .965

** .978

** 1 .770

**

Sig. (2-tailed) 0.072 0.976 0.939 0 0 0 0.001

N 15 15 15 15 15 15 15 15

GRADE

Pearson

Correlation 0.03 0.324 -0.078 .895

** .859

** .844

** .770

** 1

Sig. (2-tailed) 0.915 0.239 0.783 0 0 0 0.001

Source: computed using spss with the data obtained from primary and secondary source

69

5.2.3.2 INTERPRETATION OF THE DESCRIPTIVE AND

CORRELATION TABLES:

From the above Pearson correlation table one can see that, the credit risk grade is positively

correlated with the management quality with a significance level of 0.000, therefore one can

say that better the management quality better is the credit risk management in MFI which is

in sync with the theories; management plays an important role in determining the loan

forwarding decision. For example credit risk is higher in farm related activities during low

monsoons, hence the management should control the credit forwarding to such areas. This

kind of decisions has to be taken up by the management in order to reduce the overall credit

risk of the MFI.

There is a positive relationship between the credit risk grade and the

profitability/sustainability , hence one can conclude that, better the credit risk management

better the profitability which is obviously in sync with the theory of MFI. In case of MFIs

their profitability is directly dependent on the credit forwarding, and the economies of scale

they achieve. Unlike the banking industry which has other related products, MFIs major

business is in loan disbursement hence better the credit risk management higher the

profitability, sustainability can be observed.

Loan portfolio is dependent on the write offs, PAR, gross loan portfolio, loan loss reserves.

The rationale behind the positive relationship with the credit risk management is that higher

the loan loss reserve better the cushioning to the risk which could be experienced, but gross

loan portfolio adjusted to the write offs gives the actual profit making portfolio and this

directly related to the credit risk management of the MFI.

Internal controls, which includes, regulatory control as well as the internal/external audit has

a positive relation with the credit risk management. These controls give a boundaries of

operations which are essential to control any kind of crisis in the industry. Internal controls

have a major role in the credit risk management and influence the decisions of the

management.

70

Management information system is another parameter which helps in credit risk assessment.

MIS determines the quality of the reporting tools which can be used for credit decision and

hence the credit risk management.

The asset liability management and credit risk management are negatively correlated , asset

liability management is dependent on the leverage, exposure to foreign currency and the

liquidity. Higher leverage lowers that credit risk management in the MFI since the repayment

capability of the MFI is dependent on the credit risk management. Also higher exposure to

foreign currency leads to poor credit risk management.

From the descriptive table one can observe that the credit risk grading of the NBFI present in

Hyderabad and Bangalore lies between, 2.94 and 4.4, with a standard deviation of 0.0967

which is very low, suggesting that the credit risk management in the NBFIs present in

Hyderabad and Bangalore are of similar kind, probably because of the fact that loan

portfolio, growth, asset liability management are of similar kind ( suggested by the low

standard deviations). This is due to the fact that all the NBFIs are working in the similar

markets such as Andhra Pradesh, Karnataka, Tamil Nadu, Maharashtra, and Madhya

Pradesh. One can observe that trident microfinance has the highest credit grading, even with

mediocre loan portfolio, and asset liability management they have maintaining high

profitability , management quality , MIS and strong market share which has given them the

edge over the other NBFIs. Ujjivan has strong financials which suggests its strategy to

diversify with respect to loan portfolio through strong management quality. The NBFIs have

differentiated with respect to internal process controls and system reporting ( standard

deviation of .87) which suggests that they have tried to improve on their profitability through

lowering their operational expenses. Which also suggest that they have strategized on the

latest regulation of 26% cap on interest rate and tried to offset it with improving operations.

Growth is of almost the same nature, unless they look into other areas of India such as north

and north west parts of India they are likely to compete in the same market.

71

5.3 ESTIMATION METHODOLOGY:

The estimation methodology is used to form a random effect model with the given data of

microfinance industry. This also helps in concluding the hypotheses which are explained in

the chapter 3.

5.3.1 The Estimation methodology

In this a random effect model has been built through the estimation methodology where the

dependent variable is portfolio at risk ( >30) and the independent variables are write offs, log

normal of gross loan portfolio, operational self-sufficiency, productivity, Return on assets,

non-banking dummy, NGO dummy, credit union dummy. The following table given the

descriptive of the data used to build the model. The sample size taken was 439.

72

source: computed with spss on the data collected from mixmarket.com

Table 5.9: Descriptive Statistics of the dependent and independent variables taken for estimation model.

N Minimum Maximum Mean Std.

Deviation

Variance Skewness

par30 439 .000000 .999500 .05227813 .153390179 .024 4.763 .117

writeoff 439 .000000 .460500 .00764260 .027840084 .001 11.340 .117

loggrossloan 439 11.486 20.683300 15.91522 1.706926 2.914 .234 .117

oss 439 -.122400 3.356500 1.12136720 .314076655 .099 .631 .117

ROA 439 -1.0126 .308200 .00386264 .098490110 .010 -6.323 .117

OE 439 .008500 2.748500 .14810820 .190873 .036 8.434 .117

Produ 439 32.000 15677.00 579.14127 1060.6313 1124938.5 9.367 .117

loglev 439 5.4931 20.310000 15.58982 1.844966 3.404 -.271 .117

liquidity 439 .0000 2.5234 .2140 .2429389 .059 4.522 .117

CU 439 .000000 1.00 .0592251 .236315 .056 3.747 .117

nonbankingduy 439 .000000 1.000000 .51480638 .500350924 .250 -.059 .117

NGO 439 .000000 1.000000 .40774487 .491975951 .242 .377 .117

73

Table 5.10 : Correlation Coefficient Matrix For Estimation Model

PAR

30

Write-

off ratio

log of

gross

loan

portfolio

Operational

self

sufficiency

Return

on assets

Operating

expense/

loan

portfolio

Borrowers

per loan

officer

log of

leverage liquidity

cooperative

union

dummy

non

banking

Dummy

NGO

dummy

PAR30 1 0.407327 0.152774 -0.20627 -0.12922 -0.07862 -0.098866 0.144689 0.9645 0.101339 0.006779 -0.05387

Write-off ratio 1 0.113657 -0.17069 -0.29374 0.04174 0.001693 0.131071 -0.02463 -0.02177 0.114464 -0.10164

log of gross

loan portfolio 1 0.27575 0.214061 -0.30611 0.018086 0.928303 -0.02442 -0.09783 0.327801 -0.2947

Operational

self

sufficiency 1 0.709287 -0.45979 0.091162 0.19929 -0.17622 0.00487 -0.01627 0.018861

Return on

assets 1 -0.81794 0.074271 0.177751 -0.13939 0.024756 -0.09484 0.083658

Operating

expense/ loan

portfolio 1 -0.12694 -0.28373 0.155844 -0.09549 0.142305 -0.10108

Borrowers per

loan officer 1 0.016183 -0.01529 0.176452 -0.13075 0.058598

74

Source: Correlation matrix computed with the data from mixmarket.com, N=439

log of

leverage 1 -0.02949 -0.15234 0.285799 -0.21395

liquidity 1 0.092182 0.052495 -0.11892

cooperative

union dummy 1 -0.26442 -0.8621

non-banking

Dummy 1 -0.8621

NGO dummy 1

75

5.3.2 ANALYSIS OF THE CORRELATION MATRIX

From the above table one can infer that, PAR30 and write offs are positively correlated,

while PAR30 and operational self-sufficiency are negatively correlated. Positive correlation

between PAR30 and liquidity has been observed. PAR30 is negatively correlated with

operational sustainability, calculated as (operational expenses/loan portfolio)and

productivity.

Hence one can infer that as there is an increase in MFI‟s productive efficiency and financial

performance there is a reduction of credit risk and hence portfolio at risk is reducing.

There is a negative correlation between PAR30 and gross loan portfolio , hence one imply

that incremental growth in the gross loan portfolio would lead to stability and also future

growth . Positive correlation between PAR30 and leverage shows that , higher leverage

would lead to an increase in credit risk. Since higher the leverage, higher the credit

forwarding ability, which indirectly will give away higher credit risk. Return on asset and

operational sufficiency depict the same phenomenon of a MFI being able to generate revenue

out of the operation and since the correlation between ROA and PAR30 is lower than the

correlation between Operational efficiency and PAR30 , Operational efficiency is considered

as the variable which has to be used for revenue generating ability of the MFI.

5.3.3 THE RANDOM EFFECT MODEL :

In order to investigate the dependency of MFI‟s credit risk on operational efficiency,

sustainability, liquidity, gross loans and write offs the following random effect model is used,

the definitions of the independent variables have been given In chapter 3

Yit = β0+β1*(writeoffs)it+β2*(log gross loan portfolio)it+β3*(operational self-

sufficiency)it+β4(operational efficiency)it+β5( productivity)it +β6(liquidity)it+β7(credit union

dummy)it+ +β8(NBFI dummy)it+β9(NGO dummy)it+ β10(rural dummy)it

Here Yit is the dependent variable PAR30 of MFIi at time t.

76

Random effect model considers the effect of variances within the group and in between the

group in order to estimate the component of variances and also helps in building models

which come with in one group such as same time period or same company. So that we can

project the future credit risk applied to one particular MFI at a particular year.

A data set of 439 was collected from mixmarket.com and they were subjected to the

estimation methodology in order to build the random effect model and table 5.11 is the

estimation table for the same.

From the table of estimation of fixed effects the following hypothesis can be concluded.

5.3.3.1 Hypothesis:

Hypothesis 1:

H0: write offs have no effect on the credit risk (PAR30) of the MFI

H1: write offs have effect on the credit risk

From the table one can the observe that the p value is at .000 which is less than the

significance level of .05 hence one can reject the null hypothesis here, meaning one accept

the alternate hypothesis , which says there is an effect of write offs on credit risk from the

estimates we can see that there is a positive relationship between write offs and credit risk.

Hence concluding higher the write offs higher is the credit risk

Hypothesis 2:

H0: gross loan portfolio have no effect on the credit risk ( PAR30)

H1: gross loan portfolio have effect on the credit risk (PAR30)

From the table one can observe that p value is at 0.002 which is way less than 0.05, meaning

one can reject the null hypothesis, accepting the alternate hypothesis. Also one can observe

that there is a positive relationship between credit risk and gross loan portfolio.

Hypothesis 3:

H0: operating sustainability have no effect on the credit risk(PAR30)

77

H1: operating sustainability have effect on the credit risk(PAR30)

From the table one can observe that the p value is at 0.000 which is way less than .05 hence

one can interpret that the null hypothesis is rejected, accepting the alternate hypothesis.

Hence there is negative relationship between operating sustainability and credit risk

Hypothesis 4:

H0: operating efficiency have no effect on credit risk (PAR30)

H1: operating efficiency has effect on credit risk (PAR30)

From the table one can infer that the p value is at .005, which is less than .05 hence the null

hypothesis is rejected, accepting the alternate hypothesis. Concluding that operating

efficiency has an effect on credit risk

Hypothesis 5:

H0: productivity has no effect on credit risk (PAR30)

H1: productivity has an effect credit risk (PAR30)

From the table one can see that the value of P is .047 which is less than 0.05 hence e reject

the null hypothesis, meaning the alternate hypothesis is accepted.

Hypothesis 6:

H0: liquidity has no effect on the credit risk of the MFI

H1: liquidity has an effect on the credit risk of the MFI

From the table one can infer that the p value(0.01) is less than .05 hence the null hypothesis

is rejected while the alternate hypothesis is accepted.

Hypothesis 7:

H0: the type of the MFI has no effect on the credit risk it faces.

H1: the type of MFI has an effect on the credit risk it faces.

78

This hypothesis is tested with respect to the dummies taken up with respect to different

MFIs, ie. NGOs, credit union, NBFI and rural banking dummy and it has been seen that all

the three variables have significance more than .05 which means that there is no relation

between credit risk and the type of MFI.

79

Table 5.11:Estimates of Fixed Effectsa

Parameter Estimate Std. Error df t p 95% Confidence Interval

Lower Bound Upper Bound

Intercept -.024311 .148518 428.000 -.164 .0070 -.316227 .267605

WRITEOFF 1.882204 .236432 427.023 7.961 .000 1.417488 2.346919

log gross loan

portfolio .013215 .004321 427.613 3.058 .002 .004722 .021708

Operational self

sufficiency -.135697 .023939 427.825 -5.669 .000 -.182749 -.088645

Operational efficiency -.110980 .038974 427.930 -2.848 .005 -.187584 -.034376

Productivity 1.171849E-005 6.134454E-006 426.399 1.910 .047 -3.390411E-007 2.377602E-005

Liquidity -.070454 .027058 427.363 -2.604 .010 -.123636 -.017272

Credit union dummy .079569 .135656 425.834 .587 .558 -.187070 .346207

NBFI dummy .020385 .133428 425.448 .153 .879 -.241875 .282645

NGO dummy .026004 .133301 425.605 .195 .845 -.236007 .288014

RURAL dummy .039522 .142342 425.597 .278 .781 -.240259 .319304

a. Dependent Variable: PAR, N=439,

Source: based on the calculation done on SPSS from the data obtained from Mixmarket.com

82

Table 5.12: F value and significance of fixed effects for random effect model

Type III Tests of Fixed Effectsa

Source Numerator df Denominator

df

F Sig.

Intercept 1 428.000 .027 .870

WRITEOFF 1 427.023 63.375 .000

LOGGROSSLOA

N

1 427.613 9.354 .002

OSS 1 427.825 32.132 .000

OE 1 427.930 8.108 .005

PRO 1 426.399 3.649 .047

LIQ 1 427.363 6.780 .010

CUDUM 1 425.834 .344 .558

NBFIDUM 1 425.448 .023 .879

NGODUM 1 425.605 .038 .845

RURALDUM 1 425.597 .077 .781

a. Dependent Variable: PAR.

Source: computed on spss , data source: mixmarket.com

Table 5.13: Goodness Of Fit

Information Criteriaa

-2 Restricted Log Likelihood 60.211

Akaike's Information Criterion

(AIC)

56.211

hHurvich and Tsai's Criterion

(AICC)

56.182

Bozdogan's Criterion (CAIC) 46.092

83

Schwarz's Bayesian Criterion

(BIC)

Person chi-square

48.092

2.762

The information criteria are displayed in

smaller-is-better forms.

a. Dependent Variable: PAR.

Source: computed by SPSS on the secondary data taken from mixmarket.com

Table 5.14: Covariance Parameters

Estimates of Covariance Parametersa

Parameter Estimat

e

Std.

Error

Repeated Measures Varianc

e

.017538 .001206

Intercept [subject =

NUM]

Varianc

e

.0339 .0327

a. Dependent Variable: PAR.

Source: computed by spss on the secondary data taken from mixmarket.com

5.3.3.2 DATA ANALYSIS:

One can see from the above results that there is a considerable relationship between the credit

risk and the write offs, gross loan portfolio, operational self-sufficiency, operational

efficiency, productivity, liquidity. But there is no statistical significance between the credit

risk and the type of MFI. Even though there is no statistical significance, we are going to use

the dummy variables from the model.

The goodness of fit is determined by the table information criteria, i.e.

84

Person chi-square is about 2.76 which means that predicted values when compared the actual

values are about 2.762 times. According to the standards, the lower the better is the fit, since

random effect model is nested models, the data can be altered until the person chi-square is

minimized and the estimates are accordingly taken

The -2 residual log likelihood is about 60.211 which signifies the log likelihood of the final

model and the lower this value is the higher the fit, hence usually the data is checked for

hetroskedacity to minimize this hetroskedacity, which is beyond this research due to data

constraints of the MFI industry

From the covariance parameter table 5.13 , the intercept variances is estimated as .0339 and

the standard deviation is .0327 , hence from this one can find out that, the intercept which is

-0.024 will have individual intercepts that are about .0327 higher or lower than the group

average about 65.95% times.

5.3.3.3 THE RANDOM EFFECT MODEL BUILD BY THE

ESTIMATION METHOD:

Yit = -0.024311+ 1.882204 *(writeoffs)it+0.013215*(log gross loan portfolio)it-

0.135697*(operational self-sufficiency)it-0.110980*(operational efficiency)it+1.171849E-

005*( productivity)it -0.070454 *(liquidity)it+0.079569 (credit union dummy)it+

0.020385* (NBFI dummy)it+0 .026004 *(NGO dummy)it+ 0.039522 *(rural dummy)it

Here Yit is the dependent variable PAR30 of MFIi at time t.

The random effect model is used to estimate random variance components for groups i.e. the

MFI the following model considers the constant as a part of errors.

CHAPTER VI

FINDINGS, CONCLUSIONS AND SUGGESTIONS

85

6.1 Introduction:

This chapter the findings of the study are presented. This findings are based on chapter 5,

which talks about analysis and interpretation of data. Also this chapter gives conclusions

which have been observed during the research.

6.2 Discussion of the Findings:

a) From the following research one can decipher that the Morgan Stanley credit risk

assessment model is establishes the relationship between credit risk management and

loan portfolio, profitability/sustainability /operating efficiency, asset-liability

management , management quality, internal controls, growth, system and reporting

b) Also from the model one can see that NBFIs present in Bangalore and Hyderabad have a

credit grading in between 2.93 and 4.4.

c) From hypothesis testing one can find that there is a relationship between the credit risk (

PAR30) and write offs, gross loan portfolio, operational self-sufficiency, operational

efficiency, productivity, liquidity but the relationship is not statistically significant with

credit union dummy, NBFI dummy, rural dummy.

6.3 Conclusions:

From Morgan Stanley credit assessment model, I conclude that NBFI present in Bangalore

and Hyderabad have similar loan portfolios this could be due to the concentration of their

loans in sectors such as farm and farm related activity and urban poor, micro creditors. NBFI

such as trident microfinance, grameen financial services, Ujjivan microfinance which have

exceptionally high credit risk management in place, this is probably because of the

operational efficiency and management quality. Trident has shown high credit grading with

all the factors combined, due to the fact that is has a business model which is presence in one

place until saturation. This would not only reduce the operational expenses but also better

hold on the market with respect to customer reliability. Ujjivan microfinance, quickly

diversified its operations in various areas as soon there was a crisis in Andhra Pradesh.

Grameen financial services has its presence in agricultural and farm related activities of

86

Karnataka. These companies not only react quickly to market changes but also have strong

customer hold

On the other hand share microfinance , is one of the most respected NBFI which has its

presence in 19 states and has been established in 1989. Its credit risk grade is about 3.88

which is well above the average. Share microfinance has gone through organic growth over

the years and has established itself.

From the estimation model, I conclude that the credit risk of MFI is dependent on operational

efficiency, gross loan portfolio, operational self-sufficiency , liquidity , but has little

statistical significance with the type of MFI i.e. NBFI, NGO, rural bank and credit union.

This is partly because of the fact that all the MFIs have similar business models and face the

same kind of risk. The business models hence could be either brick and mortar i.e. branches,

or Self-help group or business correspondence model.

6.4 Suggestions:

From the above analysis, I think the microfinances which have grading below 3.64 have to

work on achieving economies of scale, improve their management quality, and work on

corporate governance. The operational efficiency can be improved through improving their

MIS. MIS plays and important role to identify the customers with respect to their credit

history. Growth of microfinance institutions is mostly based on loan disbursement and the

liquidity for this loan disbursement is majorly coming from equity or debts. Hence

microfinances should work not only on improving the quality of loans disbursed but also on

the returns to the investors.

6.5 Scope for Further Research:

The scope for further research would be to try this model on various other areas and compare

between the different types of microfinance. Also analyses the credit risk based on the

business model rather than the legal structure of the Microfinance.

BIBLIOGRAPHY

87

BIBLIOGRAPHY

(2008). managing microfinance risks. asian development bank.

Abiola, B. (2011). Impact Analysis of Microfinance in Nigeria. International Journal of

Economics and Finance, 217-225.

Arch, G. (2005). Microfinance and development: Risk and return from a policy outcome

perspective. Journal of Banking Regulation, 227–245.

Arvelo, M. (2008). Morgan Stanley‟s Approach to Assessing Credit Risks in the

Microfinance Industry. Journal ofAPPLIED CORPORATE FINANCE, 125-134.

Ayayi, A. G. (2012). Credit risk assessment in the microfinance industry. Economics of

Transition, 37-72.

Barman, D. (2009). Role of Microfinance Interventions in Financial Inclusion: A

Comparative Study of Microfinance Models. The Journal of Business Perspective,

51-59.

Barone, L. R. (2011). EXPLORING HOUSEHOLD MICROFINANCE DECISIONS: AN

ECONOMETRIC ASSESSMENT FOR THE . Georgetown University.

Bruett, T. (2004). Four Risks That Must be Managed by Microfinance Institutions.

Alternative Credit Technologies LLC .

CHIUMYA, C. (2006). THE REGULATION OF MICROFINANCE INSTITUTIONS:. The

University of Manchester.

crabb, p. (2007). foreign exchange risk management practices of microfinance institutions.

Journal of Microfinance, 52-63.

EDARURAL. (n.d.). MFI models & practice. Retrieved from

http://www.edarural.com/impact/chap2a.pdf.

88

Eversole, R. (2003). Help, risk and deceit: microentrepreneurs talk about microfinance.

Journal of International Development , 179-188.

GUTHRIE, P. M. (2010). DETERMINANTS OF CREDIT RATINGS OF MICROFINANCE

INSTITUTIONS IN. THE UNIVERSITY OF TEXAS AT SAN ANTONIO.

I.B., B. (2007). Performance of microfinance providers in karnataka. Kurnool: University of

Agricultural Sciences.

Jiwani, J. (2007). Sustainable microfinance : the impact of pay for performance on key

performance indicators. university of hong kong.

Karlan, D. S. (2008). Credit Elasticities in Less-Developed Economies:implications for

microfinance. American Economic Review, 1048-1068.

Kero, A. (2011). Macroprudential Regulations and the Basel III. Universitat Pompeu Fabra.

Khan, S. (2012). Cost Control in Microfinance: Lessons from ASA. Journal of Cost

Management, 5-22.

Kundu, A. (2011). Savings, Lending Rate. The IUP Journal of Managerial Economics, 33-

51.

Kundu, A. (2012). Savings, Lending Rate and Skill Improvement in Microfinance Operating

through Public-Private Cooperation. The IUP Journal of Managerial Economics, 33-

51.

Muriu, P. (2011). What Explains the Low Profitability of Microfinance Institutions in

Africa? African Journal Of Social Sciences, 850115.

OGUNTOYINBO, M. (2011). Credit risk assessment of the microfinance industry in

Nigeria:An application to Accion Microfinance Bank Limited (AMFB). University of

Stellenbosch.

Pearlman, S. (2007). ESSAYS ON VULNERABILITY, MICROFINANCE AND

ENTREPRENEURSHIP. University of Maryland.

89

Rahman, W. (2011). The Development Perspective of Finance and Microfinance Sector in

china: how far is microfinance regulation? International Journal of Economics and

Finance, 160-170.

Saad, S. T. (2009). Essays on Microfinance: financial and social impacts in bangladesh.

University of North Carolina.

Saltzman, S. B. (1998). The ACCION Camel. ACCION International.

Stackel, K. (2010). Reducing defaults in microfinance: A case study of fundacion integral

campesino (FINCA) Costa Rica. DUQUESNE UNIVERSITY.

Venkataraman, S. (2006). Integrated Risk Management Framework & Basel-II. Misys

Banking.

vurren, F. j. (2011). Risk Management For Microfinance Institutions In South Africa.

University of Pretoria.

Davis, S. (2006). Taking Stock of the microcredit summit campaign. microcredit summit. microcredit

summit.

khan, s. (n.d.). enterprise wide risk management in microfinancing institutions.

Vijender, A. (2012). Micro Finance and Risk Management for Poor in India. Research Journal of

Recent Sciences, 104-107.

Appendix

90

APPENDIX 1:

QUESTIONNAIRE

Morgan Stanley Credit risk assessment

This a part of my research where i have to assess the credit risk of a microfinance, kindly fill

the following sheet so that i can get a better perspective through my research

1) what is the composition of the board

2) kindly tell us the frequency of broad meetings

3) how would you rate the education background of the board members

1 -very highly qualified and have very good experience in MFI industry

2-highly qualified and have good experience in MFI industry

3-moderately qualified and have reasonable experience in MFI industry

4- Have low qualification and also the experience low

5-hav e very low qualification and also have very low experience in MFI industry

4) do you have a formal business plan

Yes no

91

5) do you prepare budget annually

6) where is the current growth/financial projection

7) how many competitors do you have

8) what is the market share of you company

9) what is your market

10) how many branches do you have

92

11) what is your expansion plan

12) who are your shareholders

13) what is the current proportion of shareholders

14) what is the takeoff authorized and paid up capital

93

15) who are the donors to the investment fund

16) do you have a HR policy

17) what is the objective of the HR policy

18) what is the total number of personnel

19) what is your training policy

94

20) how are the staff motivated

21) how do the salary of those working for MFI and other banks

22) what is the staff percentage turnover

23) how do you grade your MIS(very low to very high)

1 2 3 4 5

24) do you have a core banking system

YES

NO

25) if yes kindly grade the core banking system (very low to very high)

95

1 2 3 4 5

26) how would you grade the IT link between the HQ and branches (very low to very high)

1 2 3 4 5

27) how are the branches linked

28) what accounting software do you use

29) how would you grade your accounting software on a scale of 1 to 5(very low to very

high)

1 2 3 4 5

30) at what intervals do you make a consolidated report

daily

weekly

monthly

quarterly

semi annually

annually

31) do you have formal operational procedures

yes

no

96

32) how often do you review your procedures

33) do you have a problem with the senior managers to comply

yes

no

34) how many staff do you have for internal audit .

35) how do you control the processes involved

36) what is the relationship with internal auditor and external auditor

97

37) when was the MFI incorporated

38) when did the bank actually commence business has the MFI been licensed by the RBI

39) How would you grade the regulatory control of RBI on a scale of 1 to 5(very low to very

high)

1 2 3 4 5

40) what is the frequency of RBI inspections

41) what are the number of savings customers

98

42) what is the area of coverage of bank operations

43) what is the size of potential customers

44) Kindly fill in the name of the microfinance

99

APPENDIX 2: RESPONSES TO THE QUESTIONNAIRE 1 2 3 4 5 6

what is the composition of the board

how would you rate the education background of the board members

kindly tell us the frequency of broad meetings

do you have a formal business plan

do you prepare budget annually

where is the current growth/financial projection

Board Comprises of 8 Members -3 Independent -3 Nominee Directors -2 Promoter Directors

2-highly qualified and have good experience in MFI industry

-Every Quarterly a board meeting is conducted. -Annual General Meeting is conducted once in a year. All the meeting are conducted as stipulated by law

Yes Yes For the current FY our growth /financial projections for some parameters are on track and for few parameters we have already exceeded the plan.

board comprises of chairman and managing director( both being the same), it is a family owned NBFC with Blue orchid microfinance investments on board as minority shareholder

3-moderately qualified and have reasonable experience in MFI industry

the board meetings are often as the NBFI is family owned business

Yes Yes the current FY , we have reduced the interest rates in order to meet targets at certain places

100

a Governing Board of five members ,Governing Board of 11 members and Vijay is the Chairman of the Board

2-highly qualified and have good experience in MFI industry

Every quarter the board meeting is conducted.AGM is conducted once in a year, also board meeting is also conducted when ever any investor/donor is interested to invest

Yes Yes the projections for current financial year has been on track and we are trying to push new products for the benefit of the rural sector

the board of directors comprises of 1- managing director,3-executive directors,3-independent directors,1-additional directors,2-directors

2-highly qualified and have good experience in MFI industry

twice in a quarter the board meets to review on any kind of revisions that has to be made in order to meet the quaterly targets

Yes Yes

3 independent directors,1 cofounder,1 nominee director, 1 founder

1-very highly qualified and have very good experience in MFI industry

once in a quarter the broard of directors meet

Yes Yes there is a very focused effort to provide to the rural population rather than looking at financial projection also we have over achieved our recovery quota for the year

101

1 chairman, 1 executive vice chairman, 1CEO-MD, 8 directors

1-very highly qualified and have very good experience in MFI industry

once in a quarter the boards meet

Yes Yes the prjection of the current year has been competitive, and we are looking at volumes rather than profits

1 managing director, 3 independent directors, 1 chairman

3-moderately qualified and have reasonable experience in MFI industry

once in a quarter Yes Yes the current financial projection is to improve on the credit quality and look at the spaces where there is rapid grwoth

1 managing director, 3 independent directors, 1 chairman

3-moderately qualified and have reasonable experience in MFI industry

once in a quarter Yes Yes the current financial projection is to improve on the credit quality and look at the spaces where there is rapid grwoth

1 chairman.3 directors 2-highly qualified and have good experience in MFI industry

once in a quarter Yes Yes For the current FY our growth /financial projections for some parameters are on track and for few parameters we have already exceeded the plan.

1-managing director, 3 directors

3-moderately qualified and have reasonable experience in MFI industry

once in a quarter Yes Yes there is a very focused effort to provide to the rural population rather than looking at financial projection

102

also we have over achieved our recovery quota for the year

1-managing director, 4-directors,2-nominee directors

1-very highly qualified and have very good experience in MFI industry

twice in a quarter and when ever there is a need for policy change

Yes Yes the financial projection for this year is already met but we r looking are incremental repayment rates

1-MD,4-directors 2-highly qualified and have good experience in MFI industry

once in 2 quarters

Yes Yes the financial projection for this year is already met hence we r looking into next year's plans

1-MD, 6 directors 3-moderately qualified and have reasonable experience in MFI industry

half yearly Yes Yes

1-MD, 4 directors 2-highly qualified and have good experience in MFI industry

twice in a quarter Yes Yes the financial projections for this year are on track and have come to the key end

103

1-non executive chairman, 1-CEO,1-nominee director,

1-very highly qualified and have very good experience in MFI industry

more than twice a quarter

Yes Yes

104

7 8 9 10 11 12

how many competitors do you have

what is the market share of you company

what is your market strategy

how many branches do you have

what is your expansion plan

D2 who are your shareholders

We operate in Multiple areas/states where we compete with different number of competitors. We have competition from Local players(region specific) as well as national players (operating in multiple regions). on an average we compete with more than 8-10 players in any area.

We operate in Multiple areas/states where we have different market share

We target the economically active poor in rural and urban area and provide them with financial services catering to all major life cycle needs.

160 as on 31 January 2013

For the current year we will consolidate our operations and will not expand to new area.

4 Our shareholders comprises of 1.) Promoters 2.) Investors 3.) Employees

105

we operate in multiple states and areas, hence the competitors are on basis of competitive interest rates

the microfinance industry unorganised to calculate the marketshare, but we hold varied market share at varied places

we target rural areas of adhra pradesh, tamil nadu, maharastra and orissa ( 18 states) by reduced interest rates

485 as on july 2009

as of now, we have been concentrating on the existing markets

3 the shareholders are, promoters, blue orchid microfinanceinvestors ltd

basix has different competitors at different areas and it is able to differentiate itself through its scale up plans by securing additional commercial equity of US$10 million and US$26 million

market share is different in different places, but as said earlier, BASIX focuses on market share through external commercial equity

yes our market strategy to provide not only the finance to rural farmers but also provide technical expertise in non farm spaces and also farm spaces, so that the farmers/micro entreprenuers are able to improve their incomes, reduce the operational cost and also improve the yield

921 as on july 2009

as of now we are trying to expand through the equity capital obtained through commericial equity , to diary industry since it has 4% growth YoY, and 10 million by 2014

2 the promoters, SIDBI, external commercial equity

106

in karnataka, which is our major focus we have multiple competitors how ever our products are competitive and also the market size is high

the market share of BSS is based out of karnataka , which is significant considering our focus,we have covered upto 43 villages considering the target being 75 villages

the market strategy of BSS is to talk to the village leaders ,so that the SHGs are formed with the recommendations by them and also the whole village is services by BSS

we have branches in the villages that are exposed to BSS so its approx. 291 branches as on 2010

our expansion plans are to provide mobile banking facilities to villages, also apply Business correspondent model so as to help reduce the operationa expenses and hence reduce the interest rate

3

we have multiple competitors but the areas we operate are unexplored and we have customised products for a particular individual according to his requirement

the market share is not of essence right now , its mostly about the servicing and being able to improve on the recovery rate

the market strategy is to provide basic computer education to the children of the rural areas, also provide with expertise to do well in the areas of agricuture and diary , so that the farmers can improve on their operational efficiency

we have about 20 branches as on 2012

the expansion plans are related to villages which have rich productivity and are in dire need of funds, also help them get the government schemes so that they can utilise resources available

4

we have different competitors at different areas hence we

we have strong increments on the market share we are concentrating on

we are looking at urban markets with poor polulation, hence our strategy is to

we have about 66 branches across the country

our expansion plan is to target alll the main cities which have high rural

2 the promoters of the company are major shareholders of the company

107

operate in SBU structure

markets present in and around karnataka, since they have promising retur

make sure we have microcreditors

population and give them credit in order to help them build their business

the market share is not the main focus of the company

the market strategy is to focus on the areas which are interfaced with the SEZ and hence help the empowerment of the poor people also help them improve their livelihood

20 branches our expansion plans are to earmark all the urban and semi urbar areas which are potential SEZs

4 the promoters are the major share holders

the market share is not the main focus of the company

the market strategy is to focus on the areas which are interfaced with the SEZ and hence help the empowerment of the poor people also help them improve their livelihood

20 branches our expansion plans are to earmark all the urban and semi urbar areas which are potential SEZs

3 the promoters are the major share holders

we operate in multiple states and areas, hence the competitors are on basis of

market share is different in various places

the market strategy is to concentrate on diary production, microcredit to

39 branches expansion plans remains towards diary production

4 the promoters are the shareholders of nano microfinace

108

competitive interest rates

farmer

we operate in multiple states and areas, hence the competitors are on basis of competitive interest rates

the market share is dependent on the southern and western sides of the india and the rual and urban poor population

the market strategy to be present at the western and southern part of india

29 branches the expansion plans are give community development to as many villages as possible ,Samasta will be operational in Maharashtra, Gujarat and Madhya Pradesh. By 2013, we plan to reach 1.8 million people in India

5 the promoters

we operate in Andhra Pradesh, Chhattisgarh, Delhi, Karnataka, Maharashtra, Madhya Pradesh, Uttar Pradesh, Rajasthan, Bihar, Uttarakhand, Gujarat, Haryana, Himachal Pradesh, Tamil Nadu, West Bengal,

since this is mostly unorganised space, we cannot pinpoint the market share but we are targeting at higher revenue through higher market share

the market strategy is to concentrate on women entreprenuers and also give higher empowerment at bother rural and urban poor population

914 branches the expansion plan is to to reach other parts of india

3 the promoters, Legatum Ventures Limited,Aavishkaar Goodwell,poor wome

109

Jharkhand, Orissa ,Kerala and Assam.

we operate in andhra pradesh and karnataka

17% market share from andhra pradesh according to the latest daya

the market strategy is to work towards obtaining higher market share over the years

1674 the expansion plan is to look into more districts and introduce mobile banking, or business correspondent model

4 the promoters,JM Financial India Fund,Valiant Capital Management,Helion Venture Partners,SIDBI

3

Trident is currently working in two states of Andhra Pradesh and Maharasthra covering seven districts

we r growing in terms of market share adding customers rapidly in order to make economies of scale

strategy is to introduce micro loans for education, insurance, and innovative ideas

31 branches expand to adjoining states of Madhya Pradesh, Chhattisgarh and Northern Karnataka in 3-5 years time. Key growth strategy will be market saturation rather than spatial expansion

4 the promoters, Caspian Advisors,Bellwether Microfinance Fund,India Financial Inclusion Fund

110

Karnataka, Bengal, Tamil Nadu and Jharkhand States

we are focusing on expanding in northern and eastern areas

we are looking into expanding into 6 major cities

5 the promoters, The Michael & Susan Dell Foundation,Bellwether Microfinance Fund Private Limited,India Financial Inclusion Fund,Sequoia Capital ,The Lok Capital Group,Elevar Equity,

111

13 14 15 16 17 18 19

what is the current proportion of shareholders

what is the take off authorized and paid up capital

who are the donors to the investment fund

do you have a HR policy

what is the objective of the HR policy

what is the total number of personnel

what is your training policy

-Promoters & Management (including directors and their relatives, friends, associates and affiliates) -24.62% -Investors - 53.04% -Trust- 18.11% -Employees 4.23%

Aurthorized capital : Rs 35 Cr Paid up capital : Rs 24.84 Cr

Right now we do not have any donors.

Yes To provide various types of Benefit and welfare to employees

1191 (as of 31 Jan 2013)

To orient the employees in a manner which enables them to contribute towards organizational goal

2.9% blue orchid microfinance ltd, the 87.5% by promoters

paid up capital 182.828 million

there are no donors since we have obtained the investments at the share of equity

yes To empower the employes to not only improve on their knowledge but also service the borrowers.

2359 as of mach 2012

To enable employees to make decisions on behalf of the organisation.

112

Bharatiya Samruddhi Investments and Consulting Services has about 41.9% and IFC has about 17.6% , the other investors are Lok capital LLC, Aavishkar goodwell and SIDBI as the largest shareholders.

700 crores of paid up capital

donors such as ford foundation, the swiss agency for development and cooperation and canadian international development agency

yes to improve the confidance of the employees and help them improve their knowledge on microentreprenuers, also show them clear growth paths from the prositions of field executives and customer service associate to higher positions

10,000 as on 2012

give regular internal and external training to the employees so as to improve their knowledge

238million of authorised capital and 202 million of paid up capital

Yes have to not only pay competitively to the employees to attract their and retain them but also train them in different areas so as to help them build on their expertise

to help the employees get exposure with all the tools used on the microfinance space so that they can help build a competitive organisation

100 million authorised capital , 80.8 million paid up capital

no Yes to train and employee them so that there is collective growth of both organisation and the employee

120 as of 2011 to train the employees in different areas of rural or microcredit space so that they only able

113

to take decisions for the company but also convence the customers about good investments

300 million of authorise capital and 230 million of paid up capital

no Yes to help the employees grow and also build trust among themselves for the betterment of the organisation

1004 as of 2011

our training policy is to help our employees not ony have domain expertise but also to be able to analyse the business models, industry analysis and take decisions

35million of authorized capital , 28million paid up

no Yes the HR policy of the company is to provide fair of education and employability of the potential candidates

230 as on 2010

35million of authorized capital , 28million paid up

no Yes the HR policy of the company is to provide fair of education and employability of the potential candidates

230 as on 2010

paid up capital 3.92 crores and

no yes To empower the employes to not

the training policy is to give

114

authorized capital is 4 crorers

only improve on their knowledge but also service the borrowers.

an industry overview before the credit is forwarded

66.4 million of paid up capital

no Yes the HR policy is to empower the employees with relevent knowledge and hence growth

198 as on 2011 the trainig policy is to give a full view of the organisation and also enhance the knowledge of the employees on only domain specific but also others

60 million of paid up,

no Yes the objective of HR policy is to give the employes an exposure to cross functional fields so that they make good impact for the company

4320 as on 2012

the training policy is to give an expose to the employee for cross fuctional activity

100 million authorise capital, 98.2 million paid up capital

no yes the objective of the HR policy is to provide proper work culture to the employees and also train them in cross functional activities so that they r empowered

8321 as on 2011

we have a clear training policy with respect to technology, and with respect to cross functional, it states that all employees should be trained In every aspect with

115

respect to IT and also be familiar with all the processes

86.5 million,

100 million authorized capital

no Yes individually or through team that contribute to the overall objectives of the organization. The aim of spotlight and Talent Development program is to identify and reward the best talent and performance within the organisation.

To enhance the performance, competencies and skills of the Associates, through constant training & development programs to achieve individual career and organizational goals. • Build capacity by enabling employees to reach highest level of productivity and efficiency

116

400 million authorised capital, 383 million paid up capital

no Yes Provide employees the skills and confidence required to execute their current role in a timely and professional manner and prepare them for future roles

2354 as on 2012

• Provide career development opportunities for existing staff to continuously upgrade their skills and knowledge so that they are at the cutting edge level of the industry

20 21 22 23 24 25 26

how are the staff motivated

how do the salary of those working for MFI and other banks comparatively

what is the staff percentage turnover

how do you grade your MIS

do you have a core banking system

if yes kindly grade the core banking system

how would you grade the IT link between the HQ and branches

By way of Regular trainings (soft skills), clearly defined career path, regular feedback, quick grievance redressal.

Very Competitive Salaries

Around 30% for the company

4 no 4

117

the employees are motivated through regular trainings and also take part in regular meetings

around 14-20 % 3 no 3

the motivations levels are based on the external and internal training, this is also supported with ESOPS hence the retention rate is high

the salaries are pretty competitive also the employees enjoy microentreprenurial ventures

25-40% 4 no 4

we motivate the staff by paying them competitively , training them at regular intervals also take them on field trips so that they get a real fell of what they are upto

the salaries are competitive since we believe in the retaining the employee with a good incentive

30-35% 4 no 4

118

the staff are self motivated since they are volunteering,also they get trained in various fields apart from customisng the products for poor, and meeting targets

most of our employees are volunteers, hence but on roll employees are paid according to the work genre

20-40% 2 no 3

the staff are motivated through constant increments, also help them have a sense of achievement through projecting their effort's final meaning

the salaries are competitive and are based on the expertise they have

30-40% 4 no

20% 3 no 3

20% 3 no 3

the motivations levels are based on the external and internal training, this is also supported with ESOPS hence the retention rate is high

salaries are pretty competitive and is based on experience

30-50% 3 no 3

119

they are usually motivated by the training, compensation and also through field knowledge

the salaries are according to the efficiency and industry specifications

30-40% 3 no 3

they are usually motivated by the training, compensation and also through field knowledge

the salaries are competitive as compared to banks

60-70% 5 no 4

the high motivation of employees can be seen with respect to their training and also since the compensation is competitive

the salaries paid are competitive and are based on performance

50-70% 4 no 4

Rewards and Recognition Policy in Trident is designed to encourage employees particularly field staff whose performance is outstanding either.

the salaries paid r competitive

60-70% 4 no 4

120

the employees get motivated through Medical insurance for self and family • Group life and accident insurance • Employee Stock Option Scheme • Free health checks • Vehicle loans • Employee referral program • Cafeteria • Sabbatical

salaries are as per industry standards

70-80% 5 no 5

121

27 28 29 30 31 32 33

how are the branches linked

what accounting software do you use

how would you grade your accounting software on a scale of 1 to 5

at what intervals do you make a consolidated report

E3 do you have formal operational procedures

how often do you review your procedures

do you have a problem with the senior managers to comply

automatically linked through high speed internet at the branches

Microfin at Branches & Tally in Head office

3 daily yes Different procedures are reviewed at different frequencies

no

they are linked to the HQs, the branches are not interlinked

manual accounting at the branches and tally at the HQ

3 weekly yes according to the requirement we change them

no

BASIX Information Infrastructure Services Ltd provides the network infrastructure to BASIX hence it helps in transaction processing also

tally at the HQ,while microfin at the branches

4 quaterly yes we review the procedures according to the employee's recommendations

no

122

the branches are connected to the HQ and they are routed back to branch

BSS has no inter connectivity between the branches, since the branches are linked to a village and one village is independent of the next, hence all branches are linked to HQ but not linked to branches

tally at the head office microfin at the branches

4 weekly Yes the SOPs are reviewed every quaterly to see if it can be optimised

no

the branches are not links they are all linked to the head office

we use tally at the head office and manual accounting at branch levels

3 weekly Yes the procedures are set up but are under continous observation

no

123

the branches are linked through a VAN based network,and also they are linked with each other through a webmail

we use tally at head as well as at the branch offices

4 dialy Yes we review the procedures as an when there is a compaint either from the employee or from the customer

no

branches are manually linked

we do manual acounting

2 quaterly No once in a year no

branches are manually linked

we do manual acounting

2 quaterly Yes once in a year no

manually tally 3 monthly Yes once in a year we look for feedbacks

no

manually tally 3 monthly Yes once in a year no

they connected through VAN and software which gives them speed in operations

tally 4 dialy Yes once in a quarter to make the operations competitive

no

they are linked through the head office

tally 4 dialy Yes once in a year no

they r linked through VAN connections

tally 4 dialy Yes once in a quarter no

124

they are linked through microfin

tally 4 dialy Yes every quarter no

125

34 35 36 37 38 39

how many staff do you have for internal audit

how do you control the processes involved

what is the relationship with internal auditor and external auditor

F2 when was the MFI incorporated

when did the bank actually commence business

has the MFI been licensed by the RBI

16 We have got Standard Operating Procedures (SOP) defined for all the processes. Employees have to stick to the SOP while conducting there work. The Conrol mechanism ensures that the work done by a Junior is checked by the Senior. In addition to that we have Audit Department which checks the compliance level of various process across the company. The services of external auditors

Internal Auditors and External Auditors work in Sync and they report directly to board of directors

1991 We are not Bank, We are MFI. As an MFI we commenced our Business in 1999

Yes

126

are also utilized from time to time.

4 we have a SOP which is defined similarly for all the branches and they are followed

the internal and external audit work under the board of directors

2001 2001 yes

13 we have the SOP which can be bipassed according to the wish of the reporting manager

the internal audit works under the board of directors, the external audit works indepedently and reports directly to the Chairman

1996 1996 yes

127

6 the control of processes are done by continous feed back from the employees who can give their expertise

both internal and external audit directly report to the board of directors

1st april 2008 2008 yes

4 we control processes through real time updation from the volunteers also we have feedback system from the branch manager who help us controlt he operation

the internal audit and the external audit report independently to board of directors

2004 2007 yes

4 the processes are overlooked by the individual reporting manager and hence the hierarchy is maintaining

the internall and external audit reports to the board of directors at each time of reporting

2006 2006 yes

5 the control is delegated to the maangers of the branch

the internal and external report to board of directors

1995 1995 yes

5 the control is the internal and 1995 1995 yes

128

delegated to the maangers of the branch

external report to board of directors

7 its based on reporting manager's choice of process

they both report to the board of directors

1996 1996 yes

3 based on the employee feedback once in a year we change them

the relationship between the internal audit and external audit is minimal, both report to the BoD at different time

2008 2008 yes

12 the control of the operations are done on hierarchical basis

the relationship between internal and external audit is minimal to get 2 types of perspective

1989 1989 yes

16 the processes are usually controlled using the SOPs given to the managers who intern report in hierarichal method

both independently report to BOD

1998 2000 yes

5 the processed

are controled through constant

both are independnt to each other

2008 2008 yes

129

monitoring and feed back system

12 they are controlled through continous monitoring

independent to each other

2006 2006 yes

130

40 41 42 43 44 45

how would you grade the regulatory control of RBI

what is the frequency of RBI inspections

what are the number of savings customers

what is the area of coverage of bank operations

what is the size of potential customers

Kindly fill in the name of the microfinance

5 Yearly NIL We are not Bank. We are MFI. Our MFI operations are in 3 states ( 41 districts)

All economically active poor in rural and urban area are our customers.

Grameen Financial Services Pvt Ltd

5 yearly NIL 18 states all small traders/farmers/farm labours are our customers

Asmitha microfinance

4 yearly Basix India

131

4 yearly no saving customers

we cover only the southern part of karnataka so that our effectiveness is higher

BSS microfinance

4 yearly no services for savings

we cover the areas Jagalur in Davangere District, Khanahosahalli and Kottur in Bellary District, Nayakanahatti and Holalkere in Chitradurga District, Bailhongal in Belgaum District, Hirevankulakunte in Koppal District of Karnataka.

the potential size of the customer is based on the villages who have tied up for our serives

chaitanya microfinance

4 yearly about 45000 we cover jaipur, bangalore, chennai, hyderabad

janalakshmi microfinance

4 yearly no customers in savings

we have covered the regions around karnataka and

KCIPL

132

we are looking at andhra pradesh

4 yearly no customers in savings

we have covered the regions around karnataka and we are looking at andhra pradesh

KOPSA

4 Yearly no savings customers

we have our base in andhra pradesh and karnataka

nano

5 Yearly no customers in savings

Chennai, Kancheepuram, Vellore, Krishnagiri, Coimbatore and Nilgiri districts in Tamil Nadu, and Bangalore in Karnataka.

1.8 million Samasta

5 Yearly no customers in savings

19 states share microfinance

4 Yearly no depositors 185 districts 6 million spandana spoorti

tbf

5 yearly no saving schemes

31 branches 21 million trident microfinance

133

5 yearly no schemes for savings

299 branches 106 million ujjivan microfinance

Source: collected from various microfinances by administering the questionnaire

134

APPENDIX 3: Secondary Data For Morgan Stanley Credit Assessment Model

year

loan portfolio

A1 A2 A3

A4

PAR>30 days

write off(%)

size of portfolio

loan loss

outstanding loans

resheduled.restructured loans

total gross loan portfolio

total write offs over last 12 months

average loan portfolio

gross loan portfolio

loan loss reserves

PAR>30 days

AML(ashmita microfin ltd

2004

ANN

1 0.00%

0 0 605.644

1 0 0 605.644

1 605.644

6 0 0.00%

AML 2005

ANN

1 0.15%

266.77695

0 1778.513

3 4.38%

7789.88694

1778.513

1 1778.513

1 8452.667

12.679

0.15%

AML 2006

AN

1 2.39%

4722.8551

0 1976.09

1 0.42%

829.9578

1976.09

1 1976.09

1 194.979

4.66 2.39%

135

N 1

AML 2007

ANN

1 0.63%

2115.85374

0 3358.498

1 0.79%

2653.21342

3358.498

1 3358.498

1 610.4762

3.846

0.63%

AML 2008

ANN

1 0.34%

2404.16074

0 7071.061

1 0.04%

282.84244

7071.061

1 7071.061

1 102.3529

0.348

0.34%

AML 2009

ANN

1 0.33%

4681.15791

0 14185.327

1 0.56%

7943.78312

14185.327

1 14185.327

1 1007.576

3.325

0.33%

AML 2010

ANN

5 48.29%

639733.8958

0 13247.751

5 9.46%

125323.724

13247.751

1 13247.751

3 74.29696

35.878

48.29%

AML 2011

ANN

5 55.78%

669041.4404

0 11994.289

1 0 0 11994.289

1 11994.289

1 1219.944

680.485

55.78%

AML 2 1.75

1 1.875

BASIX 1996

ANN

1 0 0 0.349

1 0 0 0.349

6 0.349

6 0 0 0

BASIX 1997

ANN

4 13.64%

236.313

0 17.325

1 0 0 17.325

5 17.325

6 0.095308

0.013

13.64%

136

BASIX 1998

ANN

1 0 0 58.646

1 0 0 58.646

4 58.646

6 0 0.754

0

BASIX 1999

ANN

5 19.34%

2152.69672

0 111.308

1 0.49%

54.54092

111.308

3 111.308

6 8.159255

1.578

19.34%

BASIX 2000

ANN

5 15.56%

2382.01816

0 153.086

1 1.65%

252.5919

153.086

3 153.086

6 16.37532

2.548

15.56%

BASIX 2001

ANN

4 13.00%

2890.225

0 222.325

2 2.46%

546.9195

222.325

3 222.325

6 14.66923

1.907

13.00%

BASIX 2002

ANN

3 7.97%

2446.36759

0 306.947

2 3.30%

1012.9251

306.947

2 306.947

6 36.90088

2.941

7.97%

BASIX 2003

ANN

2 4.80%

1846.5072

0 384.689

2 2.40%

923.2536

384.689

1 384.689

5 57.6875

2.769

4.80%

BASIX 2004

ANN

1 1.79%

1017.90319

0 569 1 1.58%

898.48438

569 1 569 1 473.4078

8.474

1.79%

BASIX 2005

ANN

1 2.11%

2115.72232

0 1002.712

1 1.09%

1092.95608

1002.712

1 1002.712

1 774.1706

16.335

2.11%

BASIX 2006

ANN

1 1.37%

1909.93755

0 1394.115

1 0.73%

1017.70395

1394.115

1 1394.115

1 1022.044

14.002

1.37%

BASIX 2007

ANN

1 1.25%

2479.225

0 1983.38

1 0.67%

1328.8646

1983.38

1 1983.38

1 276.32

3.454

1.25%

BASIX 2008

ANN

1 2.51%

11599.30487

0 4621.237

1 0.00%

0 4621.237

1 4621.237

1 1335.618

33.524

2.51%

BASIX 20 A 5 37. 29304 0 7756 1 0.4 3490. 7756 1 7756 4 62.7 23.7 37.

137

09 NN

78%

6.1614

.648 5% 4916 .648 .648 5013

07 78%

BASIX 2010

ANN

5 62.31%

778139.6797

0 12488.199

3 4.20%

52450.4358

12488.199

1 12488.199

1 677.957

422.435

62.31%

BASIX 2011

ANN

5 60.67%

177089.5417

0 2918.898

6 46.05%

134415.253

2918.898

1 2918.898

1 928.7852

563.494

60.67%

BASIX 2.813

1.625

2.188

3.625

BSS 2003

ANN

1 0.00%

0 0 26.383

1 0 26.383

5 26.383

6 0 0.791

0.00%

BSS 2004

ANN

1 0.11%

5.89952

0 53.632

1 0.00%

0 53.632

4 53.632

6 0 1.609

0.11%

BSS 2005

ANN

1 0.00%

0 0 103.522

1 0.00%

0 103.522

3 103.522

6 0 5.176

0.00%

BSS 2006

ANN

1 0.00%

0 0 388.676

1 0.00%

0 388.676

1 388.676

6 0 19.434

0.00%

BSS 2007

ANN

1 1.80%

1465.4574

0 814.143

1 0.00%

0 814.143

1 814.143

6 0 0 1.80%

BSS 2008

ANN

1 1.84%

2012.17984

0 1093.576

1 0.00%

0 1093.576

1 1093.576

1 121.3587

2.233

1.84%

BSS 2009

ANN

1 0.00%

0 0 1447.745

3 4.70%

6804.4015

1447.745

1 1447.745

6 0 2.233

0.00%

BSS 2010

AN

1 0 0 1151.745

1 1.40%

1612.443

1151.745

1 1151.745

6 0 2.914

138

N

BSS 2011

ANN

1 0.00%

0 0 1252.914

1 0 1252.914

1 1252.914

6 0 1.349

0.00%

BSS 1 1.222

2 5.444

Chaitanya

2009

ANN

1 0.00%

0 0 10.658

1 0.00%

0 10.658

5 10.658

6 0 0 0.00%

Chaitanya

2010

ANN

1 0.02%

1.86248

0 93.124

1 0.02%

1.86248

93.124

4 93.124

1 1155

0.231

0.02%

Chaitanya

2011

ANN

1 0 0 0 168.712

1 0.27%

45.55224

168.712

3 168.712

1 1820

0.364

0.02%

Chaitanya

1 1 4 2.667

GFSPL( grameen koota)

2000

ANN

1 0.00%

0 0 1.227

1 0 1.227

6 1.227

6 20.4 0.0204

0.10%

GFSPL 2001

ANN

1 0.00%

0 0 2.777

1 0.00%

0 2.777

6 2.777

6 52.1 0.0521

0.10%

GFSPL 2002

ANN

1 0.00%

0 0 7.932

1 0.00%

0 7.932

6 7.932

6 0 0 0.10%

GFSPL 2003

ANN

1 0.00%

0 0 23.713

1 0.00%

0 23.713

5 23.713

1 443 0.443

0.10%

GFSPL 2004

AN

1 0.00%

0 0 63.723

1 0.00%

0 63.723

4 63.723

1 1174

1.174

0.10%

139

N

GFSPL 2005

ANN

1 0.00%

0 0 221.663

1 0.00%

0 221.663

3 221.663

1 4433

4.433

0.10%

GFSPL 2006

ANN

1 0.00%

0 0 459.791

1 0.00%

0 459.791

1 459.791

1 9195

9.195

0.10%

GFSPL 2007

ANN

1 0.13%

107.43993

0 826.461

1 0.00%

0 826.461

1 826.461

6 0 0 0.13%

GFSPL 2008

ANN

1 1.47%

2665.18791

0 1813.053

1 0.33%

598.30749

1813.053

1 1813.053

6 0 0 1.47%

GFSPL 2009

ANN

1 1.42%

4688.43672

0 3301.716

1 0.62%

2047.06392

3301.716

1 3301.716

1 1995.563

28.337

1.42%

GFSPL 2010

ANN

1 1.22%

3056.16466

0 2505.053

1 1.51%

3782.63003

2505.053

1 2505.053

1 2091.148

25.512

1.22%

GFSPL 2011

ANN

1 1.40%

5337.6736

0 3812.624

1 0.00%

0 3812.624

1 3812.624

1 787.8571

11.03

1.40%

GFSPL 1 1 3 3.083

Janalakshmi Financial Services Pvt. Ltd.

2004

ANN

3 6.31%

292.80924

0 46.404

1 0 46.404

5 46.404

6 0 0 6.31%

Janalakshmi

2005

AN

3 6.49%

567.99831

0 87.519

1 0.00%

0 87.519

4 87.519

6 0 0 6.49%

140

Financial Services Pvt. Ltd.

N

Janalakshmi Financial Services Pvt. Ltd.

2008

ANN

1 0.57%

174.22962

0 305.666

1 0 305.666

2 305.666

6 0 0 0.57%

Janalakshmi Financial Services Pvt. Ltd.

2009

ANN

1 1.63%

1092.84654

0 670.458

1 0.00%

0 670.458

1 670.458

1 322.2699

5.253

1.63%

Janalakshmi Financial Services Pvt. Ltd.

2010

ANN

1 1.03%

1867.2149

0 1812.83

2 2.64%

4785.8712

1812.83

1 1812.83

6 0 0 1.03%

Janalakshmi Financial Services Pvt. Ltd.

2011

ANN

1 0.23%

806.71005

0 3507.435

1 0.67%

2349.98145

3507.435

1 3507.435

6 0 0 0.23%

Janala 1. 1. 2.33 5.

141

kshmi Financial Services Pvt. Ltd.

667

167

3 167

KCIPL 2009

ANN

1 0.66%

73.09038

0 110.743

1 0 110.743

3 110.743

6 0 0 0.66%

KCIPL 2010

ANN

1 1.94%

362.67718

0 186.947

1 0.00%

0 186.947

3 186.947

6 24.58763

0.477

1.94%

KCIPL 2011

ANN

1 0 0 112.189

1 0.00%

0 112.189

3 112.189

6 17.68473

0.359

2.03%

KCIPL 1 1 3 6

KOPSA 2007

ANN

5 43.15%

6476.68555

0 150.097

1 0 150.097

3 150.097

6 0 0 43.15%

KOPSA 2008

ANN

5 99.96%

2571.471

0 25.725

1 0.00%

0 25.725

5 25.725

6 3.903561

3.902

99.96%

KOPSA 2009

ANN

1 0 0 9.264

1 0.00%

0 9.264

6 9.264

6 1.697628

0.859

50.60%

KOPSA 3.667

1 4.667

6

Nano 2008

ANN

1 0.01%

0 0 0 1 0 0 6 0 6 0 0 0.01%

Nano 2009

AN

1 0 0 167.271

1 0.00%

0 167.271

3 167.271

1 3010

0.602

0.02%

142

N

Nano 2010

ANN

1 0.00%

0 0 133.306

1 0 133.306

3 133.306

1 6020

0.602

0.01%

Nano 1 1 4 2.667

Samasta

2008

ANN

1 0.00%

0 0 23.987

1 0 23.987

5 23.987

6 0 0 0.01%

Samasta

2009

ANN

1 1.31%

345.61468

0 263.828

1 0.00%

0 263.828

3 263.828

6 0 0 1.31%

Samasta

2010

ANN

1 1.09%

310.59441

0 284.949

1 0.02%

5.69898

284.949

3 284.949

6 26.05505

0.284

1.09%

Samasta

2011

ANN

1 0.01%

3.42072

0 342.072

1 0.00%

0 342.072

2 342.072

1 14460

1.446

0.01%

Samasta

1 1 3.25 4.75

SHARE 2003

ANN

1 0.19%

155.6917

0 819.43

1 0.00%

0 819.43

1 819.43

6 0 0 0.19%

SHARE 2004

ANN

4 13.48%

23696.99076

0 1757.937

1 0.00%

0 1757.937

1 1757.937

6 0 0 13.48%

SHARE 2005

ANN

4 9.71%

35559.69983

0 3662.173

2 2.22%

8130.02406

3662.173

1 3662.173

6 0 0 9.71%

SHARE 2006

ANN

2 3.73%

14906.83683

0 3996.471

1 0.00%

0 3996.471

1 3996.471

6 0 0 3.73%

143

SHARE 2007

ANN

1 0.23%

1400.54981

0 6089.347

1 1.79%

10899.9311

6089.347

1 6089.347

1 39316.09

90.427

0.23%

SHARE 2008

ANN

1 0.16%

1947.09312

0 12169.332

1 0.25%

3042.333

12169.332

1 12169.332

6 0 0 0.16%

SHARE 2009

ANN

5 52.10%

882334.4963

0 16935.403

1 0.57%

9653.17971

16935.403

1 16935.403

6 0 0 52.10%

SHARE 2010

ANN

5 52.18%

1077462.994

0 20648.965

6 10.24%

211445.402

20648.965

1 20648.965

6 0 0 52.18%

SHARE 2011

ANN

5 53.80%

1135301.696

0 21102.262

1 0.25%

5275.5655

21102.262

1 21102.262

6 0 0 53.80%

SHARE 3.111

1.667

1 5.444

Spandana

1998

ANN

1 1.48%

1.79228

0 1.211

1 0 1.211

6 1.211

6 0 0 1.48%

Spandana

1999

ANN

1 0.50%

2.27995

0 4.5599

1 0.00%

0 4.5599

6 4.5599

6 0 0 0.50%

Spandana

2000

ANN

1 0.14%

1.88328

0 13.452

1 0.00%

0 13.452

5 13.452

6 0 0 0.14%

Spandana

2001

ANN

1 0.19%

8.87072

0 46.688

1 0.00%

0 46.688

5 46.688

6 0 0 0.19%

Spandana

2002

ANN

1 0.06%

9.14334

0 152.389

1 0.00%

0 152.389

3 152.389

6 0 0 0.06%

Spand 20 A 1 0.0 4.408 0 440. 1 0.0 0 440. 1 440. 6 0 0 0.0

144

ana 03 NN

1% 98 898 0% 898 898 1%

Spandana

2005

ANN

3 8.17%

23199.34409

0 2839.577

4 6.93%

19678.2686

2839.577

1 2839.577

6 0 0 8.17%

Spandana

2006

ANN

2 4.43%

17349.43936

0 3916.352

2 2.56%

10025.8611

3916.352

1 3916.352

6 0 0 4.43%

Spandana

2007

ANN

1 0.07%

511934.64

0 7,313,352

1 0.09%

658201.68

7,313,352

1 7,313,352

6 0 0 0.07%

Spandana

2008

ANN

1 0.13%

2428.80859

0 18683.143

1 0.59%

11023.0544

18683.143

1 18683.143

1 143563.8

186.633

0.13%

Spandana

2009

ANN

5 47.75%

1690592.188

0 35405.072

1 0.66%

23367.3475

35405.072

1 35405.072

1 754.9885

360.507

47.75%

Spandana

2010

ANN

5 52.47%

1814507.151

0 34581.802

3 3.66%

126569.395

34581.802

1 34581.802

1 510.6823

267.955

52.47%

Spandana

2011

ANN

5 50.68%

1376078.919

0 27152.307

1 0.49%

13304.6304

27152.307

1 27152.307

6 0 0 50.68%

Spandana

2.154

1.462

2.538

4.846

TBF( opportunity micofinance)

2004

ANN

1 1.40%

32.8468

0 23.462

1 0 23.462

5 23.462

6 0 0 1.40%

TBF 2005

ANN

1 1.24%

31.2728

0 25.22

1 0.00%

0 25.22

5 25.22

6 0 0 1.24%

145

TBF 2006

ANN

1 0.69%

24.89658

0 36.082

1 0.00%

0 36.082

5 36.082

6 0 0 0.69%

TBF 2007

ANN

5 28.57%

1226.08155

0 42.915

1 0.00%

0 42.915

5 42.915

6 0 0 28.57%

TBF 2 1 5 6

Trident Microfinance

2007

ANN

1 0.00%

0 0 47.148

1 0 47.148

5 47.148

6 0 0 0.01%

Trident Microfinance

2008

ANN

1 0.18%

76.0527

0 422.515

1 0.00%

0 422.515

1 422.515

6 37.77778

0.068

0.18%

Trident Microfinance

2009

ANN

5 63.85%

82660.78465

0 1294.609

1 0.00%

0 1294.609

1 1294.609

6 0.216132

0.138

63.85%

Trident Microfinance

2010

ANN

5 99.95%

168749.3831

0 1688.338

1 0.00%

0 1688.338

1 1688.338

6 4.278139

4.276

99.95%

Trident Microfinance

2011

ANN

1 0 0 1287.968

6 20.05%

25823.7584

1287.968

1 1287.968

1 658.7

6.587

0.01

Trident Microfinance

2.6

2 1.8 5

Ujjivan 2005

AN

1 0.21%

17.70174

0 84.294

1 0.00%

0 84.294

4 84.294

6 0 0 0.21%

146

N

Ujjivan 2006

ANN

1 0.20%

72.9336

0 364.668

1 0.07%

25.52676

364.668

1 364.668

6 0 0 0.20%

Ujjivan 2007

ANN

1 0.22%

371.33448

0 1687.884

1 0.10%

168.7884

1687.884

1 1687.884

6 0 0 0.22%

Ujjivan 2008

ANN

1 0.46%

1705.51762

0 3707.647

1 0.11%

407.84117

3707.647

1 3707.647

1 179.5652

0.826

0.46%

Ujjivan 2009

ANN

1 1.03%

6438.98732

0 6251.444

1 0.51%

3188.23644

6251.444

1 6251.444

1 1620.583

16.692

1.03%

Ujjivan 2010

ANN

1 1.20%

8441.1

0 7034.25

1 0.15%

1055.1375

7034.25

1 7034.25

1 3781.167

45.374

1.20%

Ujjivan 2011

ANN

1 0.69%

5713.44978

0 8280.362

1 0.36%

2980.93032

8280.362

1 8280.362

1 8351.304

57.624

0.69%

Ujjivan 1 1 1.429

3.143

147

profitability, sustainablity, operating efficiency, productivity

B1 B2 B3 B4

sustainability

ROAA

operating efficiency

productivity

operating income

financial expenses

loan loss provision

operating expense

write off

financial expenses, loan loss provision, write offs

Net income

total assets

total operating expenses

aveage gross loan portfolio

number of borrowers

total headcount of staff

average gross loan portfolio

148

, operating expenses, staff expenses

6 68% 23.449

33.203

0 1.481

0 34.684

2 2.201775

0.335

15.215

1 0.244533092

1.481

605.644

4 152.019048

127,696

840

605.644

6 61% 66.734

92.211

12.679

3.677

52.027

108.567

3 1.927803

0.957

49.642

1 0.206745748

3.677

1778.513

1 352.31692

393,538

1,117

1778.513

6 40% 49.353

115.931

4.66

4.251

8.015

124.842

4 0.893829

0.468

52.359

1 0.215121781

4.251

1976.09

1 377.220814

416,829

1,105

1976.09

6 22% 66.564

288.284

3.846

6.913

20.392

299.043

3 1.067819

1.119

104.793

1 0.205836061

6.913

3358.498

1 323.872925

565,806

1,747

3358.498

6 54% 336.642

607.441

0.348

10.85

2.122

618.639

1 4.35294

7.4 170

1 0.15344231

10.85

7071.06

1 362.864358

890,832

2,455

7071.06

149

1 9 1 1

6 74% 899.019

1195.186

3.325

14.397

59.94

1212.908

1 3.200797

12.46

389.278

1 0.101492197

14.397

14185.327

1 378.505507

1,340,288

3,541

14185.33

6 18% 327.173

1811.145

35.878

20.761

1320.616

1867.784

3 1.431687

4.608

321.858

1 0.156713392

20.761

13247.751

1 385.606209

1,341,524

3,479

13247.75

6 -77%

-1514.848

1264.879

680.485

15.35

0 1960.714

6 -13.6558

-31.824

233.043

1 0.127977573

15.35

11994.289

1 415.410809

1,099,177

2,646

11994.29

6 2.875

1 1.375

6 6100%

0.305

0 0.005

0 0.005

3 1.172115

0.004876

0.416

1 1.432664756

0.005

0.349

6 37.5666667

1,127

30 0.349

6 106%

0.983

0.013

0.913

0 0.926

4 0.588847

0.119

20.209

1 5.26984127

0.913

17.325

1 210.325581

9,044

43 17.325

6 -13%

-0.952

0.754

6.550

0.00

7.304

3 1.01533

0.748

73.67

1 11.1687071

6.550

58.646

4 159.822785

12,626

79 58.646

150

9 6

6 1% 0.101

1.578

11.795

0.42

13.373

4 0.793487

1.04

131.067

1 10.59672261

11.795

111.308

6 97.1258741

13,889

143

111.308

6 13% 2.331

2.548

15.37

2.203

17.918

4 0.64408

1.276

198.112

1 10.04010817

15.37

153.086

6 125.613208

26,630

212

153.086

6 16% 4.436

1.907

25.306

4.602

27.213

4 0.570505

2.326

407.709

1 11.38243562

25.306

222.325

5 135.956229

40,379

297

222.325

6 15% 6.894

2.941

42.701

8.771

45.642

3 1.010458

4.314

426.935

1 13.91152218

42.701

306.947

6 0 414

306.947

6 6% 3.721

2.769

64.634

8.315

67.403

4 0.60334

3.14

520.436

1 16.80162417

64.634

384.689

4 145.319871

89,953

619

384.689

6 21% 21.076

8.474

90.667

7.747

99.141

4 0.395456

2.522

637.745

1 15.94394551

90.667

569

3 178.052174

143,332

805

569

6 26% 42.048

16.335

145.703

8.527

162.038

3 1.252725

15.042

1200.742

1 14.53089222

145.703

1002.712

4 155.637363

198,282

1,274

1002.712

6 27% 62.366

14.002

217.395

9.002

231.397

3 1.813968

32.1

1769.601

1 15.59376379

217.395

1394.115

4 157.037532

305,438

1,945

1394.115

6 38% 125. 3.4 323 10. 327. 3 1.6 39. 24 1 16.3 32 19 4 149. 498, 3,3 19

151

666 54 .899

888

353 45683

894

24.16

3065777

3.899

83.38

754054

681 30 83.38

6 76% 458.538

33.524

572.419

0 605.943

3 1.53668

86.81

5649.193

1 12.38670512

572.419

4621.237

3 176.116941

1,114,468

6,328

4621.237

6 18% 177.725

23.707

986.494

27.685

1010.201

2 2.276837

309.839

13608.31

1 12.71804522

986.494

7756.648

4 163.399358

1,526,150

9,340

7756.648

6 -261%

-5772.838

422.435

1789.439

525.053

2211.874

4 0.651675

101.966

15646.75

1 14.32903976

1789.439

12488.199

6 99.4803836

570,520

5,735

12488.2

6 0% 563.494

1401.21

3787.766

1964.704

5 -0.16213

-5.84

3602.022

5 48.0047607

1401.21

2918.898

4 163.946349

406,423

2,479

2918.898

6 3.5 1.25

4.375

6 0% 0 0 0.791

0 0 0.791

5 0 0 34.091

1 0 0 26.383

1 208.371429

7,293

35 26.383

6 33% 3.026

0 1.609

7.572

0 9.181

1 4.801806

3.02

62.893

1 14.11843675

7.572

53.632

1 229.574074

12,397

54 53.632

6 70% 11.605

0 5.176

11.373

0 16.549

1 8.548145

10.605

124.062

1 10.98607059

11.373

103.522

1 237.572917

22,807

96 103.522

6 26% 11.988

0 19.43

26.575

0 46.009

2 2.633

10.98

417.1

1 6.83731

26.57

388.6

1 305.869

63,315

207

388.6

152

4 799

8 92 437 5 76 565 76

6 116%

68.044

0 0 58.675

0 58.675

1 7.494608

68.044

907.906

1 7.20696487

58.675

814.143

1 366.209239

134,765

368

814.143

6 50% 107.041

102.476

2.233

109.643

0 214.352

1 5.482902

69.014

1258.713

1 10.02609787

109.643

1093.576

1 354.684211

168,475

475

1093.576

6 7% 15.91

91.89

2.233

138.889

59.586

233.012

4 0.733311

12.38

1688.234

1 9.593471226

138.889

1447.745

1 236.802073

228,514

965

1447.745

6 11% 42.707

157.79

2.914

218.119

19.264

378.823

3 1.762625

28.305

1605.844

1 18.93813301

218.119

1151.745

1 232.79476

213,240

916

1151.745

6 1% 4.080

110.476

1.349

208.725

0.004323

320.55

4 0.146305

2.627

1795.558

1 16.65916416

208.725

1252.914

1 204.898491

149,371

729

1252.914

6 2.444

1 1

6 -49%

-1.329

0 0 2.724

0 2.724

6 -5.83611

-1.329

22.772

3 25.55826609

2.724

10.658

6 76.3181818

1,679

22 10.658

6 11% 1.519

1.305

0.231

11.996

0.011614

13.532

3 1.131555

1.519

134.24

1 12.88174907

11.996

93.124

2 196.274194

12,169

62 93.124

6 27% 6.971

2.679

0.364

23.085

0.343

26.128

1 39.51

6.971

17.64

1 13.6830

23.08

168.7

3 183.02

18,302

100

168.7

153

814

8123

5 12 12

6 3.333

1.667

3.667

6 -70%

-0.528

0.034

0.0204

0.698

0.7524

6 -30.2579

-0.528

1.745

6 56.88671557

0.698

1.227

6 29.6666667

445 15 1.227

6 -63%

-1.161

0.234

0.0521

1.563

1.8491

6 -14.535

-0.658

4.527

6 56.28375945

1.563

2.777

6 28.8181818

951 33 2.777

6 -49%

-2.002

0.685

0 3.394

4.079

6 -11.5656

-1.377

11.906

5 42.78870398

3.394

7.932

6 59.0869565

2,718

46 7.932

6 -33%

-3.187

2.579

0.443

6.76

9.782

1 3.887552

1.47

37.813

3 28.50756969

6.76

23.713

6 94.6666667

8,236

87 23.713

6 -8% -1.45

5.129

1.174

12.543

18.846

2 2.89042

2.375

82.168

1 19.68363071

12.543

63.723

6 122.038168

15,987

131

63.723

6 1% 0.416

13.766

4.433

26 44.199

1 4.931682

15.199

308.191

1 11.72951733

26 221.663

5 144.575972

40,915

283

221.663

6 26% 24.478

34.796

9.195

48.782

92.773

1 6.373059

35.103

550.803

1 10.60960306

48.782

459.791

1 204.361386

82,562

404

459.791

6 9% 18.719

93.7

0 109.89

203.591

3 1.837

19.88

1082.

1 13.2965

109.8

826.4

1 245.097

117,647

480

826.4

154

1 001

5 471

7419

91 61 917 61

6 2% 6.414

168.092

0 159.183

327.275

4 0.321666

4.854

1509.018

1 8.779831588

159.183

1813.053

1 275.113134

211,562

769

1813.053

6 4% 18.698

228.206

28.337

245.564

502.107

4 0.308598

9.456

3064.183

1 7.437465851

245.564

3301.716

1 255.542029

352,648

1,380

3301.716

6 5% 37.921

336.847

25.512

399.378

761.737

3 1.213422

35.101

2892.728

1 15.94289622

399.378

2505.053

3 183.730549

321,161

1,748

2505.053

6 -3% -17.074

306.832

11.03

346.318

664.18

5 -0.91097

-29.026

3186.262

1 9.083455384

346.318

3812.624

1 247.521705

313,610

1,267

3812.624

6 3.5 2.333

3.583

6 -31%

-2.92

2.13

0 7.413

0 9.543

5 0.209

51.713

1 15.97491596

7.413

46.404

1 1040.69231

13,529

13 46.404

6 29% 3.121

4.448

0 6.211

0 10.659

5 0.749

94.475

1 7.096744707

6.211

87.519

1 1277.33333

19,160

15 87.519

6 -32%

-53.114

57.187

0 106.698

0 163.885

5 -54.1

387.687

4 34.90672826

106.698

305.666

1 399.601852

43,157

108

305.666

6 -13%

-23.8

58.77

5.253

121.75

0 185.781

5 -22.

1148.

1 18.1596

121.7

670.4

1 273.87

82,161

300

670.4

155

81 51 3 1 928

479

7592

53 58 58

6 -4% -21.773

140.186

33.519

357.518

33.459

531.223

5 -21.773

1941.588

1 19.72154035

357.518

1812.83

1 210.026115

193,014

919

1812.83

6 2% 12.217

191.893

41.552

465.037

17.604

698.482

5 12.217

4186.425

1 13.25860636

465.037

3507.435

1 299.648406

300,847

1,004

3507.435

6 5 1.5 1

6 -72%

-9.744

2.372

0 11.082

0 13.454

5 -9.744

120.487

1 10.00695304

11.082

110.743

6 0 110.743

6 7% 3.509

22.039

0.477

28.517

0 51.033

5 2.935

261.456

1 15.25405596

28.517

186.947

1 291.185185

31,448

108

186.947

6 -1% -0.324

19.996

0.359

26.526

0 46.881

5 -0.266

169.977

2 23.64402927

26.526

112.189

1 339.471698

17,992

53 112.189

6 5 1.333

2.667

6 9% 1.637

8.845

0 9.105

1.775

17.95

5 1.303

181.673

1 6.06607727

9.105

150.097

1 276.035294

23,463

85 150.097

6 -26%

-5.208

7.56

3.902

8.819

0 20.281

5 -5.208

51.766

4 34.28182702

8.819

25.725

1 450.461538

11,712

26 25.725

6 - - 1.9 0.8 4.8 0 7.61 5 - 17. 6 51.8 4.8 9.2 6 0 1,28 0 9.2

156

168%

12.829

6 59 9 12.828

646

134715

64 4 64

6 5 3.667

2.667

6 9% 0.00699

0 0 0.081

0 0.081

5 0.00378

3.302

1 0.144642857

0.081

56 6 0 0 0

6 16% 3.796

11.963

0.602

11.407

0 23.972

5 2.633

76.683

1 6.819472592

11.407

167.271

6 71.8556701

6,970

97 167.271

6 -2% -0.607

10.761

0.602

16.778

0 28.141

5 -0.62

127.446

1 12.58608015

16.778

133.306

6 78.0555556

8430

108

133.306

6 5 1 6

6 3% 0.335

0.492

0 11.38

0 11.872

5 0.31

3434.411

5 47.44236461

11.38

23.987

3 175.045455

19,255

110

23.987

6 -12%

-3.909

7.366

0 24.875

0 32.241

5 0.874

314.237

1 9.42849129

24.875

263.828

3 183.182648

40,117

219

263.828

6 1% 1.097

29.334

0.284

51.663

0.068

81.281

5 1.106

335.873

1 18.13061285

51.663

284.949

3 183.586873

47,549

259

284.949

6 -2% -1.531

28.036

1.446

39.234

0 68.716

5 0.804

409.858

1 11.46951519

39.234

342.072

1 222.691919

44,093

198

342.072

157

6 5 2 2.333

6 18% 37.967

86.164

0 122.91

0 209.074

5 24.288

1009.836

1 14.99945084

122.91

819.43

2 196.934263

197,722

1,004

819.43

6 20% 73.886

157.491

0 211.453

0 368.944

5 46.849

1956.538

1 12.02847429

211.453

1757.937

3 183.946162

368,996

2,006

1757.937

6 23% 144.151

206.106

0 410.156

60.101

616.262

5 70.662

4331.434

1 11.19979859

410.156

3662.173

1 331.496743

814,156

2,456

3662.173

6 9% 56.865

201.183

0 415.585

0 616.768

5 53.017

4416.903

1 10.39879934

415.585

3996.471

1 349.774439

826,517

2,363

3996.471

6 10% 106.874

414.168

90.427

521.629

87.567

1026.224

5 62.915

7601.423

1 8.566255134

521.629

6089.347

1 327.478822

989,641

3,022

6089.347

6 51% 862.828

837.494

0 851.746

22.838

1689.24

5 558.775

12463.48

1 6.999118768

851.746

12169.332

1 352.76309

1,502,418

4,259

12169.33

6 56% 1664.184

1779.879

0 1191.471

82.365

2971.35

5 1087.226

25943.85

1 7.035386167

1191.471

16935.403

1 435.920118

2,357,456

5,408

16935.4

6 3% 225.896

5324.453

0 1526.542

2291.562

6850.995

5 91.044

24560.53

1 7.392825742

1526.542

20648.965

1 503.56773

2,840,122

5,640

20648.97

158

6 -76%

-2533.355

2125.133

0 1193.5

48.53

3318.633

5 -2566.28

20763.82

1 5.655791782

1193.5

21102.262

1 496.580653

2,161,119

4,352

21102.26

6 5 1 1.333

6 -51%

-0.107

0.014

0 0.194

0 0.208

5 0.042

1602.552

1 16.01981833

0.194

1.211

6 52 520 10 1.211

6 14% 0.106

0.173

0 0.583

0 0.756

5 0.349

4865.166

1 12.7853681

0.583

4.5599

6 94.1666667

1,695

18 4.5599

6 62% 1.007

0.664

0 0.969

0 1.633

5 2.578

15.249

1 7.203389831

0.969

13.452

1 217.9

4,358

20 13.452

6 79% 3.81 2.862

0 1.947

0 4.809

5 2.02

51.185

1 4.170236463

1.947

46.688

1 287.086957

13,206

46 46.688

6 120%

12.105

2.862

0 7.186

0 10.048

5 14.238

163.483

1 4.715563459

7.186

152.389

1 347.908163

34,095

98 152.389

6 159%

39.968

9.518

0 15.578

0 25.096

5 45.137

496.131

1 3.533243517

15.578

440.898

1 607.79558

110,011

181

440.898

6 92% 219.545

86.869

0 152.064

181.108

238.933

5 143.271

3280.781

1 5.355163815

152.064

2839.577

1 479.801197

721,621

1,504

2839.577

159

6 14% 42.273

114.309

0 210.489

88.719

299.208

5 28.4

4423.893

1 5.374619033

210.489

3916.352

1 479.466771

916,261

1,911

3916.352

6 106%

458.17

154.491

0 316.648

5.119

430.957

5 270.639

8385.833

1 0.004329725

316.648

7,313,352

1 393.141865

1,188,861

3,024

7,313,352

6 127%

1419.268

424.813

186.633

779.282

74.891

1120.406

5 903.147

18286.43

1 4.171043384

779.282

18683.143

1 381.609917

2,432,000

6,373

18683.14

6 139%

3110.905

1181.633

360.507

1459.627

180.392

2244.947

5 2035.135

29196.86

1 4.122649433

1459.627

35405.072

1 351.251055

3,662,846

10,428

35405.07

6 0% 3.8 2201.342

267.955

2166.651

1305.211

3616.239

5 -92.359

31027.05

1 6.265292364

2166.651

34581.802

1 358.09652

4,188,655

11,697

34581.8

6 -66%

-2698.353

5339.646

0 1905.83

145.922

4107.172

5 -2698.35

27906

1 7.019035252

1905.83

27152.307

1 413.602666

3,444,483

8,328

27152.31

6 5 1 1.769

6 -10%

-0.39

0.072

0 3.882

0 3.954

5 0.451

65.423

1 16.54590402

3.882

23.462

1 410.68

10,267

25 23.462

6 14% 0.715

0.057

0 4.918

0 4.975

5 4.215

70.049

1 19.50039651

4.918

25.22

1 486.166667

11,668

24 25.22

160

6 12% 0.687

0.074

0 5.471

0 5.545

5 1.504

71.528

1 15.162685

5.471

36.082

1 591.454545

13,012

22 36.082

6 21% 0.478

0 0 2.225

0 2.225

5 0.478

73.364

1 5.184667366

2.225

42.915

1 799 16,779

21 42.915

6 5 1 1

6 52% 0.034

0.008805

0 0.056

0 0.064805

5 0.022

11.331

1 0 47.148

1 206.25

8,250

40 47.148

6 14% 0.208

0.649

0.068

0.751

0 1.468

5 0.107

10.197

1 0 422.515

1 357.013216

81,042

227

422.515

6 35% 1.504

2.397

0.138

1.796

0 4.331

5 0.955

38.884

1 0 1294.609

1 411.465882

174,873

425

1294.609

6 -16%

-1.906

4.597

4.276

3.106

0 11.979

5 -1.269

41.541

1 0 1688.338

1 409.568873

228,949

559

1688.338

6 -16%

-1.597

1.894

6.587

1.662

6.012

10.143

5 -1.818

31.468

1 0 1287.968

1 765.156398

161,448

211

1287.968

6 5 1 1

6 -87%

-6.031

0 0 6.923

0 6.923

5 -6.031

28.444

1 8.212921442

6.923

84.294

6 7.35 441 60 84.294

6 - - 0 0 25. 0.0 25.6 5 - 11 1 7.02 25. 36 6 98.8 19,4 19 36

161

60% 15.258

615 304

15 15.258

4.342

4197352

615

4.668

527919

74 7 4.668

6 -43%

-29.684

0 0 68.663

0.215

68.663

5 -29.687

409.962

1 4.067992824

68.663

1687.884

6 106.435572

58,646

551

1687.884

6 -3% -6.678

0 0.826

225.876

1.027

226.702

5 -6.678

1950.799

1 6.092165732

225.876

3707.647

4 154.933767

261,993

1,691

3707.647

6 17% 118.93

178.803

16.692

517.97

13.922

713.465

5 96.392

4069.751

1 8.285605694

517.97

6251.444

1 200.328269

566,929

2,830

6251.444

6 13% 177.279

458.067

45.374

835.389

7.458

1338.83

5 114.092

7060.198

1 11.8760209

835.389

7034.25

1 211.442005

847,671

4,009

7034.25

6 1% 21.93

553.997

57.624

895.472

22.125

1507.093

5 17.148

8883.207

1 10.81440642

895.472

8280.362

1 237.576109

819,400

3,449

8280.362

6 5 1 3.571

t and liablity management

C1 C2 C3

162

leverage

exposure to foreign exchange

liquidity

million

total liablity

networth

subordinated debt

financial debt in non hedged forex

total financial debts

cash short term investment

gross loan portfolio

6 9.623656

15.215 1.581 0 1 0 0 7.578 5 4.606831

0.638 0 13.849

6 23.18636

49.642 2.141 0 1 0 0 20.79 1 20.57503

8.201 0 39.859

1 0.507586

52.359 3.153 100 1 0 0 31.745 3 10.60596

4.808 0 45.333

1 1.081544

104.793

9.392 87.5 1 0 0 75.487 1 22.11171

18.496 0 83.648

1 3.119381

170 16.998 37.5 1 0 0 139.92 1 44.18503

61.43 0 139.029

6 9.016491

389.278

43.174 0 1 0 0 314.694

1 29.46275

92.937 0 315.439

3 6.6440 321.85 48.443 0 1 0 0 269.01 4 7.1038 21.196 0 298.37

163

56 8 7 84 2

1 2.959464

233.043

78.745 0 1 0 0 151.285

5 3.493891

8.238 0 235.783

3.125

1 2.625

1 1.012165

0.416 0.411 0 1 0 0 0 6 0 0 0 0.349

1 2.393014

20.209 8.445 0 1 0 0 0 6 0 0 0 17.325

1 1.43391

73.67 41.377 10 1 0 0 0 6 0 0 0 58.646

1 2.067075

131.067

42.407 21 1 0 0 0 6 0 0 0 111.308

1 1.773608

198.112

81 30.7 1 0 0 0 6 0 0 0 153.086

1 1.471369

407.709

211.516

65.579 1 0 0 0 6 0 0 0 222.325

1 1.338065

426.935

214.769

104.3 1 0 0 168.593

6 0 0 0 306.947

1 1.727732

520.436

229.835

71.39 1 0 0 199.583

1 29.23218

82.815 29.638 384.689

1 2.002327

673.745

235.356

101.125 1 0 0 345.64 1 15.13837

46.099 39.987 568.661

1 3.7214 1200.7 251.56 71.09 1 0 0 785.53 1 17.713 127.56 50.053 1002.7

164

31 42 6 1 76 5 12

2 5.670726

1769.601

257.74 54.319 1 0 0 1056.158

1 25.33973

267.171

86.094 1394.115

4 7.454389

2424.16

285.625

39.574 1 0 0 1711.027

1 19.58772

252.865

135.634 1983.38

5 8.425758

5649.193

641.577

28.89 1 0 0 4046.035

1 39.78805

1578.452

260.248 4621.237

3 6.97769

13608.31

1925.76

24.5 1 0 0 9614.886

1 73.34996

5280.079

409.419 7756.648

4 7.378526

15646.75

2102.579

18 1 0 0 12.306 1 28.33425

2345.922

1192.516

12488.199

1 -0.96442

3602.022

-3734.93

0 1 0 0 6816.413

1 30.82711

655.974

243.838 2918.898

1.813

1 3.188

6 11.40167

34.091 2.99 0 1 0 0 26.984 6 0 0 0 26.383

6 9.76751

62.893 6.439 0 1 0 0 41.313 1 17.18004

7.681 1.533 53.632

1 4.038608

124.062

30.719 0 1 0 0 69.276 1 16.25838

13.209 3.622 103.522

4 7.882553

417.192

52.926 0 1 0 0 319.978

3 9.800708

33.047 5.046 388.676

1 3.772693

907.906

240.652

0 1 0 0 659.909

4 8.206298

46.342 20.469 814.143

1 4.795481

1258.713

262.479

0 1 0 0 804.922

3 10.07566

93.171 17.014 1093.576

2 5.141287

1688.234

328.368

0 1 0 0 1172.295

3 11.00263

102.655

56.635 1447.745

1 4.088125

1605.844

392.807

0 1 0 0 944.161

1 31.02753

312.146

45.212 1151.745

1 4.411452

1795.558

407.022

0 1 0 0 1349.905

1 35.31424

388.759

53.698 1252.914

165

2.556

1 2.556

1 0.026914

0.562 20.881 0 1 0 0 0.0109 1 92.5502

9.864 0 10.658

1 0.18764

21.239 113.19 0 1 0 0 19.488 1 23.46119

21.848 0 93.124

1 0.195444

43.866 224.443

0 1 0 0 37.331 6 2.801223

4.726 0 168.712

1 1 2.667

1 -3.4893

2.446 -0.701 0 1 0 6 0 1.227

1 -4.33186

5.887 -1.359 0 1 0 6 0 2.777

1 -5.35038

14.644 -2.737 0 1 11.638 6 0 7.932

1 -4.69724

39.081 -8.32 0 1 31.173 6 0 23.713

1 -10.96 81.06 -7.396 0 1 66.706 6 0 63.723

6 17.89931

291.884

16.307 0 1 161.096

6 0 221.663

6 11.52718

499.392

43.323 0 1 464.613

6 0 459.791

1 4.955246

914.342

184.52 0 1 843.212

6 0 826.461

2 5.827633

1291.044

221.5383

0 1 1233.246

6 0 1813.053

2 5.024336

2561.753

509.869

0 1 2461.376

6 0 3301.716

166

1 4.271483

2355.377

551.419

0 1 1910.788\

6 0 2505.053

1 4.866366

2677.937

550.295

0 1 2354.142

6 0 3812.624

2 1 6

3 6.568125

44.88 6.833 0 1 0 40.044 6 0 0 0 46.404

6 11.08046

86.893 7.842 0 1 0 71.518 2 12.57064

10.136 0.8657 87.519

6 15.78764

311.569

19.735 0 1 0 243.624

1 22.29034

27.692 40.442 305.666

1 1.857668

695.028

374.14 0 1 0 582.254

1 19.53948

83.96 47.044 670.458

1 4.567264

1509.91

330.594

0 1 0 1152.225

1 21.47686

272.64 116.699 1812.83

1 3.261125

3229.74

865.376

125 1 0 2865.331

1 33.53873

1078 98.349 3507.435

3 1 2

2 5.777354

95.517 14.858 1.675 1 0 94.75 4 6.628861

6.54 0.801 110.743

1 3.023601

192.552

61.811 1.872 1 0 189.157

1 34.72909

46.701 18.224 186.947

1 0.865509

76.337 86.279 1.92 1 0 74.574 1 44.73166

23.026 27.158 112.189

1.333

1 2

1 2.849436

146.045

51.254 0 1 0 145.072

1 22.29158

32.192 1.267 150.097

1 0.522699

21.346 40.838 0 1 0 20.604 4 7.533528

0.856 1.082 25.725

1 0.003623

0.055 15.181 0 1 0 0 1 183.9162

0.175 16.863 9.264

167

1 2

1 0.010623

0.0409 3.85 0 1 0 0 6 3.302 0 0

1 0.5844 28.891 49.437 0 1 0 26.25 5 3.08063

0.979 4.174 167.271

1 1.700789

80.83 47.525 0 1 0 78.801 1 16.66392

10.226 11.988 133.306

1 1 4

1 0.216188

6.194 28.651 0 1 0 4.638 1 16.21712

0.839 3.051 23.987

1 2.662709

229.198

86.077 0 1 0 206.447

2 14.96733

36.14 3.348 263.828

1 2.800817

248.987

88.898 0 1 0 220.765

2 12.49487

25.65 9.954 284.949

1 3.579068

322.27 90.043 0 1 0 317.991

2 14.23385

40.033 8.657 342.072

1 1 1.75

2 5.677211

857.713

151.08 0 1 589.867

1 18.19557

119.607

29.493 819.43

3 6.100515

1679.417

275.291

0 1 1186.984

4 7.316417

67.859 60.759 1757.937

6 11.68411

4007.791

343.012

0 1 2044.781

1 18.3186

512.372

158.487 3662.173

6 10.16123

4044.545

398.037

0 1 2,386,489

3 9.972723

213.164

185.393 3996.471

2 5.210214

6415.858

1231.4 0 1 5162737

1 24.57255

1384.03

112.278 6089.347

1 3.446347

10683.21

3099.866

0 1 9713.76

1 22.1721

2650.577

47.62 12169.332

1 4.630828

23021.82

3971.426

1000 1 20983 1 52.69 8574.524

348.74 16935.403

168

2 5.211583

21546.66

4134.38

0 1 1 19.38587

2959.109

1043.872

20648.965

1 2.386187

13613.07

5704.945

0 1 5 3.889597

703.555

117.238 21102.262

2.667

1 2

1 1.604878

0.987 0.615 0 1 0 6 0 0 0 1.211

1 2.435028

3.448 1.416 0 1 0 6 0 0 0 4.5599

1 2.621853

11.038 4.21 0 1 0 6 0 0 0 13.452

1 4.087873

41.124 10.06 0 1 0 6 0 0 0 46.688

2 5.544032

138.501

24.982 0 1 102.162

6 0 0 0 152.389

2 5.256268

446.725

84.989 0 1 371.032

5 3.25835

4.589 9.777 440.898

6 26.34364

3163.897

120.101

0 1 1158.512

2 14.75086

314.541

104.321 2839.577

6 25.37889

4279.87

168.639

0 1 2945.801

2 12.18632

448.016

29.243 3916.352

3 6.866876

7508.421

1093.426

0 1 4877.672

6 0.014904

1012.615

77.357 7,313,352

1 3.971248

15508.29

3720.142

185 1 14761.186

1 15.27543

2723.711

130.22 18683.143

1 3.269766

24255.8

7418.205

0 1 21943.771

1 22.4558

7765.733

184.758 35405.072

1 3.64105

26283.24

7218.587

0 1 22253.091

4 6.141646

1950.664

173.228 34581.802

1 1.299477

15644.23

12038.87

0 1 12845.627

1 19.89643

5171.322

231.018 27152.307

2.077

1 4

169

1 0.021979

1.407 64.015 0 1 0 6 0 0 0 23.462

1 0.026645

1.818 68.231 0 1 0 6 0 0 0 25.22

1 0.025697

1.792 69.735 0 1 0 6 0 0 0 36.082

1 0.026487

1.893 71.47 0 1 0 6 0 0 0 42.915

1 1 6

1 1.697769

0.837 0.493 0 1 0 31.613 3 10.33978

2.776 2.099 47.148

1 1.492424

6.107 4.092 0 1 0 296.963

2 14.43594

58.873 2.121 422.515

2 5.107505

33.209 6.502 0 1 0 1421.552

1 33.24656

422.686

7.727 1294.609

4 7.290121

36.159 4.96 0 1 0 1476.73

3 10.13748

142.756

28.399 1688.338

1 0.971705

14.767 15.197 0 1 0 738.731

4 8.81171

65.281 48.211 1287.968

1.8 1 2.6

1 1.3773 28.444 20.652 0 1 0 0 1 30.19195

25.162 0.288 84.294

6 10.6434

114.342

10.743 0 1 0 61.565 6 0.213893

0 0.78 364.668

6 9.518284

409.962

43.071 0 1 0 245.8 6 1.759185

23.464 6.229 1687.884

1 2.060689

1950.799

946.673

0 1 0 721.739

5 5.96087

203.022

17.986 3707.647

1 3.819566

4069.751

1065.501

0 1 0 2369.583

5 4.623908

225.854

63.207 6251.444

2 5.62714

7060.198

1254.669

0 1 0 4721.31

3 10.51133

606.725

132.668 7034.25

170

1 3.506635

8883.207

2533.257

0 1 0 6172.44

1 23.17355

1689.275

229.579 8280.362

2.571

1 3.857

Ssource: data taken from mixmarket.com

171

APPENDIX 4: data for random effect model

MFI name Fiscal Year Period Diamonds

Portfolio

at risk

&gt; 30

days

Write-

off

ratio

log of

gross

loan

portfolio

Operational

self

sufficiency

Return

on

assets

ABCRDM 2005.0000 7.0000 ANN 3.0000 0.0200 0.0000 13.4449 1.0205 0.0038

ABCRDM 2006.0000 6.0000 ANN 3.0000 0.0020 0.0000 15.1669 1.0088 0.0017

ABCRDM 2007.0000 5.0000 ANN 3.0000 0.0111 0.0000 14.8424 1.0243 0.0062

ABCRDM 2008.0000 4.0000 ANN 3.0000 0.0004 0.0000 14.7371 1.0037 0.0009

Adhikar 2006.0000 6.0000 ANN 4.0000 0.0580 0.0000 14.8399 1.3761 0.0631

Adhikar 2007.0000 5.0000 ANN 4.0000 0.0042 0.0059 15.3418 1.2758 0.0643

Adhikar 2008.0000 4.0000 ANN 4.0000 0.0027 0.0078 15.6124 1.1291 0.0307

Adhikar 2009.0000 3.0000 ANN 4.0000 0.0077 0.0035 16.0173 1.1541 0.0266

Adhikar 2010.0000 2.0000 ANN 4.0000 0.0212 0.0112 15.8560 1.0104 0.0017

Ajiwika 2010.0000 2.0000 ANN 4.0000 0.0259 0.0000 14.4063 1.0222 0.0050

Ajiwika 2011.0000 1.0000 ANN 4.0000 0.0199 0.0000 13.8252 1.0124 0.0025

AML 2005.0000 7.0000 ANN 4.0000 0.0015 0.0438 17.5009 1.2089 0.0295

AML 2006.0000 6.0000 ANN 4.0000 0.0239 0.0042 17.6296 1.1650 0.0167

AML 2007.0000 5.0000 ANN 4.0000 0.0063 0.0079 18.2421 1.1153 0.0143

AML 2008.0000 4.0000 ANN 4.0000 0.0034 0.0004 18.7502 1.3104 0.0533

AML 2009.0000 3.0000 ANN 4.0000 0.0033 0.0056 19.5695 1.4666 0.0431

AML 2010.0000 2.0000 ANN 4.0000 0.4829 0.0946 19.5139 1.0799 0.0130

AMMACTS 2005.0000 7.0000 ANN 4.0000 0.0477 0.0000 15.5803 1.3259 0.0481

AMMACTS 2006.0000 6.0000 ANN 4.0000 0.0000 0.0394 15.5151 1.0394 0.0056

AMMACTS 2007.0000 5.0000 ANN 4.0000 0.0000 0.0000 16.8602 1.4457 0.0376

172

AMMACTS 2010.0000 2.0000 ANN 4.0000 0.9881 0.0000 14.8203 0.9690

-

0.0032

AMPL 2011.0000 1.0000 ANN 4.0000 0.0166 0.0000 12.9813 1.0030 0.0013

Arohan 2007.0000 5.0000 ANN 4.0000 0.0063 0.0000 14.9884 1.0106 0.0024

Arohan 2008.0000 4.0000 ANN 4.0000 0.0025 0.0028 15.9244 1.2037 0.0353

Arohan 2009.0000 3.0000 ANN 4.0000 0.0079 0.0022 16.8950 1.1485 0.0202

Arohan 2010.0000 2.0000 ANN 4.0000 0.0359 0.0030 16.8229 1.0179 0.0032

Arohan 2011.0000 1.0000 ANN 4.0000 0.0072 0.0607 16.1766 0.5411

-

0.1626

Arth 2011.0000 1.0000 ANN 4.0000 0.0155 0.0000 14.4177 1.0629 0.0081

ASA India 2009.0000 3.0000 ANN 4.0000 0.0124 0.0000 16.8019 1.7658 0.0545

ASA India 2010.0000 2.0000 ANN 4.0000 0.0189 0.0000 17.3888 1.1656 0.0229

ASA India 2011.0000 1.0000 ANN 4.0000 0.0542 0.0259 16.8281 1.0448 0.0087

Asirvad 2009.0000 3.0000 ANN 4.0000 0.0002 0.0000 16.4463 1.5698 0.0740

Asirvad 2010.0000 2.0000 ANN 4.0000 0.0063 0.0004 16.9409 1.2289 0.0422

Asirvad 2011.0000 1.0000 ANN 4.0000 0.0001 0.0198 16.5622 1.0880 0.0146

Asomi 2006.0000 6.0000 ANN 4.0000 0.0063 0.0000 15.0480 1.0275 0.0024

Asomi 2007.0000 5.0000 ANN 4.0000 0.1176 0.0000 15.2650 1.3044 0.0397

Asomi 2008.0000 4.0000 ANN 4.0000 0.0000 0.0000 13.7329 1.3681 0.0016

Asomi 2009.0000 3.0000 ANN 4.0000 0.0000 0.0000 15.3702 0.9421

-

0.0165

Asomi 2010.0000 2.0000 ANN 4.0000 0.0229 0.0074 15.8157 1.1458 0.0172

Asomi 2011.0000 1.0000 ANN 4.0000 0.0134 0.0012 15.6518 1.1455 0.0174

ASP 2005.0000 7.0000 ANN 1.0000 0.0915 0.0000 14.4235 0.3031

-

0.2184

ASSIST 2005.0000 7.0000 ANN 1.0000 0.0099 0.0000 12.1591 0.6323

-

0.0518

AWS 2007.0000 5.0000 ANN 1.0000 0.0016 0.0000 15.7517 1.1860 0.0297

AWS 2008.0000 4.0000 ANN 1.0000 0.0007 0.0000 15.4442 1.1941 0.0287

Bandhan 2004.0000 8.0000 ANN 5.0000 0.0000 0.0000 14.4896 0.8657 -

173

0.0320

Bandhan 2005.0000 7.0000 ANN 5.0000 0.0000 0.0000 15.9338 1.0487 0.0102

Bandhan 2006.0000 6.0000 ANN 5.0000 0.0009 0.0000 17.2163 1.5160 0.0876

Bandhan 2007.0000 5.0000 ANN 5.0000 0.0013 0.0005 18.2275 1.3314 0.0505

Bandhan 2008.0000 4.0000 ANN 5.0000 0.0009 0.0000 18.6476 1.7423 0.0866

Bandhan 2009.0000 3.0000 ANN 5.0000 0.0013 0.0000 19.6220 1.5830 0.0352

Bandhan 2010.0000 2.0000 ANN 5.0000 0.0057 0.0000 20.1518 1.5652 0.0532

Bandhan 2011.0000 1.0000 ANN 5.0000 0.0016 0.0060 20.4130 1.6268 0.0644

BASIX 2004.0000 8.0000 ANN 4.0000 0.0480 0.0158 16.3808 1.0315 0.0004

BASIX 2005.0000 7.0000 ANN 4.0000 0.0179 0.0109 16.9278 1.0988 0.0087

BASIX 2006.0000 6.0000 ANN 4.0000 0.0211 0.0073 17.2807 1.1394 0.0156

BASIX 2007.0000 5.0000 ANN 4.0000 0.0137 0.0067 17.7154 1.1089 0.0177

BASIX 2008.0000 4.0000 ANN 4.0000 0.0125 0.0000 18.3249 1.1412 0.0180

BASIX 2009.0000 3.0000 ANN 4.0000 0.0251 0.0045 18.9658 1.2632 0.0312

BASIX 2010.0000 2.0000 ANN 4.0000 0.3778 0.0420 19.4548 1.0431 0.0066

BASIX 2011.0000 1.0000 ANN 4.0000 0.6231 0.4605 17.8652 0.1462

-

0.6213

BISWA 2005.0000 7.0000 ANN 5.0000 0.0031 0.0000 16.3412 2.0812 0.0351

BISWA 2006.0000 6.0000 ANN 5.0000 0.0079 0.0000 16.8889 1.2654 0.0228

BISWA 2007.0000 5.0000 ANN 5.0000 0.0053 0.0000 17.1965 3.3565 0.3082

BISWA 2008.0000 4.0000 ANN 5.0000 0.0030 0.0000 17.4391 2.2122 0.1065

BISWA 2009.0000 3.0000 ANN 5.0000 0.0011 0.0000 17.8926 1.4110 0.0558

BISWA 2010.0000 2.0000 ANN 5.0000 0.7994 0.0000 18.0517 1.4208 0.0621

BISWA 2011.0000 1.0000 ANN 5.0000 0.7770 0.0000 17.9307 1.1689 0.0339

BJS 2007.0000 5.0000 ANN 4.0000 0.0000 0.0000 12.2468 1.1547 0.0481

BJS 2008.0000 4.0000 ANN 4.0000 0.0046 0.0000 12.3299 1.1067 0.0289

BJS 2009.0000 3.0000 ANN 4.0000 0.0024 0.0000 13.2211 1.0562 0.0156

BJS 2010.0000 2.0000 ANN 4.0000 0.0012 0.0010 14.1290 1.1213 0.0273

BJS 2011.0000 1.0000 ANN 4.0000 0.0016 0.0027 13.8828 1.2410 0.0550

174

BSS 2004.0000 8.0000 ANN 4.0000 0.0000 0.0000 14.0196 1.2457 0.0606

BSS 2005.0000 7.0000 ANN 4.0000 0.0011 0.0000 14.6571 1.5231 0.1244

BSS 2006.0000 6.0000 ANN 4.0000 0.0000 0.0000 16.0034 1.2044 0.0430

BSS 2007.0000 5.0000 ANN 4.0000 0.0000 0.0000 16.8250 1.5592 0.1052

BSS 2008.0000 4.0000 ANN 4.0000 0.0180 0.0000 16.8836 1.4954 0.0634

BSS 2009.0000 3.0000 ANN 4.0000 0.0184 0.0470 17.2873 1.0561 0.0078

BSS 2010.0000 2.0000 ANN 4.0000 0.0000 0.0140 17.0713 1.1103 0.0165

BSS 2011.0000 1.0000 ANN 4.0000 0.0000 0.0000 17.0195 1.0130 0.0018

BWDA Finance 2005.0000 7.0000 ANN 4.0000 0.0067 0.0000 15.9523 1.1278 0.0038

BWDA Finance 2006.0000 6.0000 ANN 4.0000 0.0229 0.0000 16.6223 1.1871 0.0118

BWDA Finance 2007.0000 5.0000 ANN 4.0000 0.0479 0.0000 16.8698 1.2445 0.0220

BWDA Finance 2008.0000 4.0000 ANN 4.0000 0.0165 0.0008 16.8138 1.1724 0.0103

BWDA Finance 2009.0000 3.0000 ANN 4.0000 0.0351 0.0000 17.0674 1.1128 0.0097

BWDA Finance 2010.0000 2.0000 ANN 4.0000 0.0599 0.0000 16.9458 1.1306 0.0112

BWDA Finance 2011.0000 1.0000 ANN 4.0000 0.0734 0.0008 16.5546 1.0788 0.0084

BWDC 2009.0000 3.0000 ANN 4.0000 0.0039 0.0000 14.0097 1.1624 0.0310

BWDC 2010.0000 2.0000 ANN 4.0000 0.0044 0.0000 14.0888 1.3074 0.0585

Cashpor MC 2004.0000 8.0000 ANN 4.0000 0.0575 0.0000 15.6368 0.6039

-

0.1228

Cashpor MC 2005.0000 7.0000 ANN 4.0000 0.0297 0.0001 16.2521 0.6302

-

0.1074

Cashpor MC 2006.0000 6.0000 ANN 4.0000 0.0259 0.0007 16.8086 0.8628

-

0.0301

Cashpor MC 2008.0000 4.0000 ANN 4.0000 0.0044 0.0074 17.3879 1.0154 0.0037

Cashpor MC 2009.0000 3.0000 ANN 4.0000 0.0006 0.0021 17.9008 1.2064 0.0399

Cashpor MC 2010.0000 2.0000 ANN 4.0000 0.0025 0.0019 17.7977 1.1126 0.0257

Cashpor MC 2011.0000 1.0000 ANN 4.0000 0.0011 0.0005 17.9667 1.1194 0.0250

CCFID 2010.0000 2.0000 ANN 4.0000 0.0054 0.0000 13.7632 1.0755 0.0168

CDOT 2010.0000 2.0000 ANN 4.0000 0.0000 0.0000 14.1462 1.1865 0.0383

175

CDOT 2011.0000 1.0000 ANN 4.0000 0.0022 0.0000 14.1375 1.1414 0.0282

Chaitanya 2009.0000 3.0000 ANN 5.0000 0.0000 0.0000 12.3759 0.4933

-

0.1113

Chaitanya 2010.0000 2.0000 ANN 5.0000 0.0000 0.0002 14.5562 1.1316 0.0189

Chaitanya 2011.0000 1.0000 ANN 5.0000 0.0002 0.0027 15.0144 1.3278 0.0419

CMML 2011.0000 1.0000 ANN 4.0000 0.0545 0.0000 13.4590 0.9665

-

0.0055

CReSA 2005.0000 7.0000 ANN 3.0000 0.0000 0.0000 13.9156 1.2094 0.0457

CReSA 2006.0000 6.0000 ANN 3.0000 0.0000 0.0000 14.6048 1.0524 0.0103

CReSA 2007.0000 5.0000 ANN 3.0000 0.0000 0.0000 15.0550 1.0402 0.0069

CReSA 2008.0000 4.0000 ANN 3.0000 0.0000 0.0090 15.3183 1.1385 0.0340

CReSA 2009.0000 3.0000 ANN 3.0000 0.0000 0.0000 15.5255 1.0924 0.0154

Disha 2009.0000 3.0000 ANN 4.0000 0.0051 0.0000 13.4838 0.8822

-

0.0327

Disha Microfin 2010.0000 2.0000 ANN 4.0000 0.0006 0.0000 15.5039 1.1787 0.0317

Disha Microfin 2011.0000 1.0000 ANN 4.0000 0.0017 0.0004 15.8940 1.1453 0.0210

Equitas 2008.0000 4.0000 ANN 5.0000 0.0003 0.0000 17.8520 1.0893 0.0152

Equitas 2009.0000 3.0000 ANN 5.0000 0.0011 0.0000 18.7178 1.4496 0.0450

Equitas 2010.0000 2.0000 ANN 5.0000 0.0053 0.0335 19.0017 1.2650 0.0363

Equitas 2011.0000 1.0000 ANN 5.0000 0.0097 0.0008 18.7736 1.1723 0.0210

ESAF 2005.0000 7.0000 ANN 4.0000 0.0097 0.0003 15.1336 1.0381 0.0057

ESAF 2006.0000 6.0000 ANN 4.0000 0.0473 0.0000 16.3711 1.0687 0.0136

ESAF 2007.0000 5.0000 ANN 4.0000 0.0235 0.0000 16.7990 1.0312 0.0071

ESAF 2008.0000 4.0000 ANN 4.0000 0.0207 0.0000 16.5155 1.0507 0.0077

ESAF 2009.0000 3.0000 ANN 4.0000 0.0093 0.0000 17.3597 1.0301 0.0025

ESAF 2010.0000 2.0000 ANN 4.0000 0.0183 0.0000 17.6656 1.0376 0.0057

ESAF 2011.0000 1.0000 ANN 4.0000 0.0123 0.0008 17.8284 1.1226 0.0203

FFSL 2009.0000 3.0000 ANN 4.0000 0.0002 0.0133 17.8106 1.5243 0.0704

FFSL 2010.0000 2.0000 ANN 4.0000 0.9940 0.0703 17.7664 1.1944 0.0252

176

FFSL 2011.0000 1.0000 ANN 4.0000 0.2137 0.0087 17.3712 0.6763

-

0.0896

Fusion Microfinance 2011.0000 1.0000 ANN 4.0000 0.0000 0.0095 15.8063 1.0201 0.0057

GFSPL 2003.0000 9.0000 ANN 4.0000 0.0000 0.0000 13.2122 0.6742

-

0.1254

GFSPL 2004.0000 8.0000 ANN 4.0000 0.0000 0.0000 14.1920 0.9212

-

0.0235

GFSPL 2005.0000 7.0000 ANN 4.0000 0.0000 0.0000 15.4185 1.0097 0.0021

GFSPL 2006.0000 6.0000 ANN 4.0000 0.0000 0.0000 16.1715 1.2771 0.0555

GFSPL 2007.0000 5.0000 ANN 4.0000 0.0000 0.0000 16.8400 1.0951 0.0214

GFSPL 2008.0000 4.0000 ANN 4.0000 0.0013 0.0033 17.3892 1.0194 0.0017

GFSPL 2009.0000 3.0000 ANN 4.0000 0.0147 0.0062 18.1117 1.0361 0.0040

GFSPL 2010.0000 2.0000 ANN 4.0000 0.0142 0.0151 17.8483 1.0487 0.0100

GFSPL 2011.0000 1.0000 ANN 4.0000 0.0122 0.0000 18.1323 0.9731

-

0.0102

GLOW 2010.0000 2.0000 ANN 4.0000 0.0100 0.0000 13.6805 1.0055 0.0012

GLOW 2011.0000 1.0000 ANN 4.0000 0.0039 0.0000 13.4367 1.0180 0.0032

GMSSS 2011.0000 1.0000 ANN 4.0000 0.0614 0.0000 13.7596 1.3553 0.0210

GOF 2007.0000 5.0000 ANN 4.0000 0.0090 0.0006 14.1398 0.9705

-

0.0090

GOF 2008.0000 4.0000 ANN 4.0000 0.0133 0.0061 15.2334 1.0863 0.0205

GOF 2009.0000 3.0000 ANN 4.0000 0.0980 0.0244 15.6115 1.0394 0.0071

GOF 2010.0000 2.0000 ANN 4.0000 0.0280 0.0255 15.6773 1.0830 0.0136

Grama Siri 2005.0000 7.0000 ANN 1.0000 0.0250 0.0000 13.9756 1.0564 0.0061

Grama Siri 2006.0000 6.0000 ANN 1.0000 0.0000 0.0000 14.2701 0.9982

-

0.0002

Grama Vidiyal Microfinance Ltd. 2003.0000 9.0000 ANN 4.0000 0.0215 0.0199 15.1071 0.9360

-

0.0166

Grama Vidiyal Microfinance Ltd. 2004.0000 8.0000 ANN 4.0000 0.0183 0.0201 15.1329 1.0269 0.0059

Grama Vidiyal Microfinance Ltd. 2005.0000 7.0000 ANN 4.0000 0.0086 0.0142 15.5537 1.0072 0.0012

177

Grama Vidiyal Microfinance Ltd. 2008.0000 4.0000 ANN 4.0000 0.0001 0.0040 17.2787 1.2561 0.0413

Grama Vidiyal Microfinance Ltd. 2009.0000 3.0000 ANN 4.0000 0.0000 0.0001 18.7176 1.2536 0.0365

Grama Vidiyal Microfinance Ltd. 2010.0000 2.0000 ANN 4.0000 0.0031 0.0004 18.5787 1.1485 0.0304

Grama Vidiyal Microfinance Ltd. 2011.0000 1.0000 ANN 4.0000 0.0014 0.0021 18.4427 1.0016 0.0005

Grameen Sahara 2011.0000 1.0000 ANN 4.0000 0.0041 0.0000 14.8217 1.0100 0.0017

GSGSK 2009.0000 3.0000 ANN 1.0000 0.0474 0.0000 15.2311 0.9630

-

0.0044

GTFS 2009.0000 3.0000 ANN 1.0000 0.0000 0.0000 12.5737 1.0560 0.0044

GU 2005.0000 7.0000 ANN 4.0000 0.0052 0.0000 14.4036 0.9114

-

0.0095

GU 2006.0000 6.0000 ANN 4.0000 0.0184 0.0000 15.5163 0.9426

-

0.0077

GU 2007.0000 5.0000 ANN 4.0000 0.0193 0.0000 16.0461 1.1131 0.0160

GU 2008.0000 4.0000 ANN 4.0000 0.0039 0.0000 15.9003 1.0716 0.0118

GU 2009.0000 3.0000 ANN 4.0000 0.0192 0.0000 16.0168 1.0112 0.0016

GU 2010.0000 2.0000 ANN 4.0000 0.0040 0.0000 16.0826 1.0422 0.0068

GU 2011.0000 1.0000 ANN 4.0000 0.0091 0.0000 15.8195 0.7122

-

0.0626

GUARDIAN 2010.0000 2.0000 ANN 5.0000 0.0009 0.0000 13.9894 1.1579 0.0391

GUARDIAN 2011.0000 1.0000 ANN 5.0000 0.0020 0.0000 14.0431 1.0788 0.0103

HiH 2007.0000 5.0000 ANN 4.0000 0.0159 0.0056 15.5373 0.3041

-

0.1565

HiH 2008.0000 4.0000 ANN 4.0000 0.0656 0.0000 15.3565 0.8669

-

0.0153

HiH 2009.0000 3.0000 ANN 4.0000 0.0108 0.0000 16.0149 0.2502

-

0.3372

HiH 2011.0000 1.0000 ANN 4.0000 0.0107 0.0068 15.8287 0.2511

-

0.2463

Hope Microcredit 2010.0000 2.0000 ANN 4.0000 0.0000 0.0000 15.5342 1.2447 0.0438

IASC 2005.0000 7.0000 ANN 1.0000 0.2532 0.0158 15.2315 1.0323 0.0004

178

IASC 2006.0000 6.0000 ANN 1.0000 0.2093 0.0278 15.2804 1.0698 0.0011

ICNW 2009.0000 3.0000 ANN 4.0000 0.1935 0.0000 14.6440 1.2345 0.0243

ICNW 2010.0000 2.0000 ANN 4.0000 0.2483 0.0145 15.1336 1.2132 0.0255

ICNW 2011.0000 1.0000 ANN 4.0000 0.2226 0.0348 14.8233 1.2254 0.0276

IDF Financial Services 2008.0000 4.0000 ANN 4.0000 0.0132 0.0000 15.7701 1.0216 0.0030

IDF Financial Services 2009.0000 3.0000 ANN 4.0000 0.0333 0.0068 16.3759 1.2525 0.0293

IDF Financial Services 2010.0000 2.0000 ANN 4.0000 0.0305 0.0105 16.6037 1.0424 0.0044

IDF Financial Services 2011.0000 1.0000 ANN 4.0000 0.0647 0.0216 16.1848 1.0664 0.0076

India's Capital Trust Ltd 2009.0000 3.0000 ANN 4.0000 0.9607 0.1599 14.8684 1.0700 0.0184

India's Capital Trust Ltd 2010.0000 2.0000 ANN 4.0000 0.0016 0.0000 15.1571 1.0887 0.0278

Indur MACS 2008.0000 4.0000 ANN 4.0000 0.0000 0.0000 14.9668 1.1313 0.0189

Indur MACS 2009.0000 3.0000 ANN 4.0000 0.0000 0.0000 15.3047 0.9463

-

0.0076

Indur MACS 2010.0000 2.0000 ANN 4.0000 0.0827 0.0000 14.8768 0.9549

-

0.0089

IRCED 2011.0000 1.0000 ANN 4.0000 0.0000 0.0000 12.6433 0.9087

-

0.0130

Janalakshmi Financial Services Pvt.

Ltd. 2005.0000 7.0000 ANN 4.0000 0.0631 0.0000 14.4892 1.2433 0.0428

Janalakshmi Financial Services Pvt.

Ltd. 2009.0000 3.0000 ANN 4.0000 0.0057 0.0000 16.5175 0.8646

-

0.0305

Janalakshmi Financial Services Pvt.

Ltd. 2010.0000 2.0000 ANN 4.0000 0.0163 0.0264 17.5249 0.9563

-

0.0138

Janalakshmi Financial Services Pvt.

Ltd. 2011.0000 1.0000 ANN 4.0000 0.0103 0.0067 18.0489 1.0175 0.0041

Janodaya 2008.0000 4.0000 ANN 3.0000 0.0069 0.0185 14.7616 1.2348 0.0441

Janodaya 2009.0000 3.0000 ANN 3.0000 0.2501 0.0000 14.2575 1.0387 0.0087

JFSL 2005.0000 7.0000 ANN 1.0000 0.0458 0.0000 16.6065 1.2372 0.0171

JFSL 2006.0000 6.0000 ANN 1.0000 0.0729 0.0000 17.1389 1.2779 0.0210

JFSL 2007.0000 5.0000 ANN 1.0000 0.0911 0.0041 16.8565 1.0192 0.0034

179

JFSL 2009.0000 3.0000 ANN 1.0000 0.0105 0.0000 15.8686 1.0576 0.0056

KBSLAB 2005.0000 7.0000 ANN 4.0000 0.0901 0.0000 15.2475 1.0796 0.0078

KBSLAB 2006.0000 6.0000 ANN 4.0000 0.0684 0.0063 15.7119 1.0791 0.0076

KBSLAB 2007.0000 5.0000 ANN 4.0000 0.0590 0.0000 16.3408 1.0737 0.0074

KBSLAB 2008.0000 4.0000 ANN 4.0000 0.0001 0.0000 16.3441 1.0985 0.0100

KBSLAB 2009.0000 3.0000 ANN 4.0000 0.0493 0.0018 16.6702 1.0954 0.0112

KBSLAB 2010.0000 2.0000 ANN 4.0000 0.0413 0.0089 16.8058 1.1299 0.0133

KBSLAB 2011.0000 1.0000 ANN 4.0000 0.0620 0.0102 16.6158 1.0815 0.0089

KCIPL 2010.0000 2.0000 ANN 4.0000 0.0066 0.0000 15.2531 1.0688 0.0117

KCIPL 2011.0000 1.0000 ANN 4.0000 0.0194 0.0000 14.6064 0.9930

-

0.0012

KOPSA 2008.0000 4.0000 ANN 1.0000 0.4315 0.0000 13.1339 0.7432

-

0.0409

Kotalipara 2006.0000 6.0000 ANN 4.0000 0.0042 0.0026 15.5148 1.0999 0.0154

Kotalipara 2009.0000 3.0000 ANN 4.0000 0.0149 0.0030 15.4499 1.3984 0.0941

Kotalipara 2010.0000 2.0000 ANN 4.0000 0.0195 0.0035 15.5184 1.0433 0.0099

Kotalipara 2011.0000 1.0000 ANN 4.0000 0.0186 0.0108 15.5223 1.0953 0.0231

KRUSHI 2005.0000 7.0000 ANN 1.0000 0.0000 0.0000 14.8001 1.1426 0.0089

KRUSHI 2006.0000 6.0000 ANN 1.0000 0.0000 0.0000 15.7623 1.3651 0.0278

KRUSHI 2007.0000 5.0000 ANN 1.0000 0.0000 0.0000 15.5985 1.1272 0.0159

KRUSHI 2008.0000 4.0000 ANN 1.0000 0.0000 0.0000 15.4033 1.2818 0.0369

LBT 2011.0000 1.0000 ANN 4.0000 0.0000 0.0017 13.3243 1.1394 0.0351

Mahasemam 2005.0000 7.0000 ANN 4.0000 0.0212 0.0000 14.9173 1.0536 0.0254

Mahasemam 2008.0000 4.0000 ANN 4.0000 0.0020 0.0000 15.7147 1.0554 0.0236

Mahasemam 2009.0000 3.0000 ANN 4.0000 0.0012 0.0039 16.1639 1.0202 0.0078

Mahasemam 2010.0000 2.0000 ANN 4.0000 0.0004 0.0005 16.3873 1.0412 0.0149

Mahashakti 2009.0000 3.0000 ANN 4.0000 0.0048 0.0000 14.9729 1.0179 0.0030

Mahashakti 2010.0000 2.0000 ANN 4.0000 0.0078 0.0000 14.6712 0.9915

-

0.0018

180

Mahashakti 2011.0000 1.0000 ANN 4.0000 0.0144 0.0000 13.6207 0.9339

-

0.0136

Mimo Finance 2007.0000 5.0000 ANN 4.0000 0.0035 0.0000 14.1712 0.5946

-

0.1287

Mimo Finance 2008.0000 4.0000 ANN 4.0000 0.0063 0.0016 15.4466 1.0148 0.0031

Mimo Finance 2009.0000 3.0000 ANN 4.0000 0.0162 0.0145 15.8524 1.0913 0.0136

Mimo Finance 2010.0000 2.0000 ANN 4.0000 0.0047 0.0171 16.2431 1.0564 0.0082

Mimo Finance 2011.0000 1.0000 ANN 4.0000 0.0442 0.0000 15.2829 0.9302

-

0.0188

MMFL 2006.0000 6.0000 ANN 4.0000 0.0347 0.0000 17.7403 2.5091 0.0420

MMFL 2007.0000 5.0000 ANN 4.0000 0.0029 0.0000 16.8436 1.3239 0.0147

MMFL 2008.0000 4.0000 ANN 4.0000 0.0049 0.0030 16.8624 1.3257 0.0542

MMFL 2009.0000 3.0000 ANN 4.0000 0.0088 0.0108 17.3163 1.6220 0.0441

MMFL 2010.0000 2.0000 ANN 4.0000 0.0209 0.0130 17.5088 1.3857 0.0429

MMFL 2011.0000 1.0000 ANN 4.0000 0.0245 0.0439 16.8511 1.1210 0.0183

Muthoot 2011.0000 1.0000 ANN 4.0000 0.0037 0.0000 17.3757 1.5244 0.0916

Nano 2009.0000 3.0000 ANN 4.0000 0.0001 0.0000 15.1291 1.1625 0.0631

NBJK 2005.0000 7.0000 ANN 4.0000 0.0140 0.0022 13.5655 1.2300 0.0296

NBJK 2007.0000 5.0000 ANN 4.0000 0.0298 0.0000 14.0256 1.3494 0.0462

NBJK 2008.0000 4.0000 ANN 4.0000 0.0131 0.0000 13.8280 1.4637 0.0595

NBJK 2009.0000 3.0000 ANN 4.0000 0.0082 0.0021 14.0604 1.5712 0.0777

NBJK 2010.0000 2.0000 ANN 4.0000 0.0000 0.0012 14.1846 1.7866 0.1153

NBJK 2011.0000 1.0000 ANN 4.0000 0.0161 0.0000 14.1757 2.0363 0.1520

NCS 2007.0000 5.0000 ANN 4.0000 0.0000 0.0024 12.8524 0.9938

-

0.0018

NCS 2008.0000 4.0000 ANN 4.0000 0.0000 0.0025 13.3394 0.9962

-

0.0011

NCS 2009.0000 3.0000 ANN 4.0000 0.0024 0.0000 14.0376 1.0724 0.0178

NCS 2010.0000 2.0000 ANN 4.0000 0.0054 0.0000 13.8388 1.0082 0.0025

NCS 2011.0000 1.0000 ANN 4.0000 0.0066 0.0049 13.0554 0.9866 -

181

0.0035

NDFS 2005.0000 7.0000 ANN 1.0000 0.0000 0.0000 13.3428 1.0765 0.0108

NDFS 2007.0000 5.0000 ANN 1.0000 0.0000 0.0014 14.6630 1.0525 0.0063

NEED 2007.0000 5.0000 ANN 4.0000 0.0000 0.0000 14.4616 1.4749 0.0562

NEED 2008.0000 4.0000 ANN 4.0000 0.0114 0.0123 15.0395 1.1275 0.0284

NEED 2009.0000 3.0000 ANN 4.0000 0.0196 0.0109 15.3146 1.1274 0.0247

NEED 2010.0000 2.0000 ANN 4.0000 0.0045 0.0115 15.5852 1.1164 0.0228

NEED 2011.0000 1.0000 ANN 4.0000 0.0025 0.0197 15.0550 1.1748 0.0300

Nidan 2005.0000 7.0000 ANN 1.0000 0.0263 0.0000 11.7059 1.4566 0.0873

Nidan 2007.0000 5.0000 ANN 1.0000 0.0261 0.0000 12.8793 1.1322 0.0127

Nidan 2008.0000 4.0000 ANN 1.0000 0.0000 0.0000 12.2057 0.4870

-

0.1267

Nidan 2009.0000 3.0000 ANN 1.0000 0.0000 0.0000 12.2918 0.4216

-

0.0140

Nirmaan Bharati 2007.0000 5.0000 ANN 1.0000 0.0003 0.0031 15.6695 1.2188 0.0485

PRAYAS 2011.0000 1.0000 ANN 5.0000 0.0061 0.0000 14.1623 1.4674 0.0919

Pustikar 2008.0000 4.0000 ANN 4.0000 0.0428 0.0000 16.2796 1.2594 0.0321

Pustikar 2009.0000 3.0000 ANN 4.0000 0.0080 0.0000 16.6325 1.4158 0.0439

Pustikar 2010.0000 2.0000 ANN 4.0000 0.0762 0.0000 16.8128 1.3937 0.0358

Pustikar 2011.0000 1.0000 ANN 4.0000 0.0800 0.0000 16.7933 1.3566 0.0329

PWMACS 2005.0000 7.0000 ANN 4.0000 0.0046 0.0067 13.4965 1.0029 0.0004

PWMACS 2007.0000 5.0000 ANN 4.0000 0.0141 0.0000 15.2132 1.0251 0.0034

PWMACS 2008.0000 4.0000 ANN 4.0000 0.0001 0.0036 15.4785 1.0817 0.0114

PWMACS 2009.0000 3.0000 ANN 4.0000 0.0000 0.0011 15.7625 1.0794 0.0117

PWMACS 2010.0000 2.0000 ANN 4.0000 0.4084 0.0192 15.4164 1.0071 0.0008

PWMACS 2011.0000 1.0000 ANN 4.0000 0.0907 0.0008 15.1488 0.7860

-

0.0373

RASS 2005.0000 7.0000 ANN 4.0000 0.0237 0.0473 14.0954 1.1272 0.0153

RASS 2006.0000 6.0000 ANN 4.0000 0.0455 0.0000 15.0109 1.4603 0.0516

182

RASS 2007.0000 5.0000 ANN 4.0000 0.0021 0.0004 15.9500 1.2798 0.0307

RASS 2008.0000 4.0000 ANN 4.0000 0.0042 0.0000 16.1344 1.3891 0.0439

RASS 2009.0000 3.0000 ANN 4.0000 0.0026 0.0044 16.5244 1.4462 0.0443

RGVN 2005.0000 7.0000 ANN 4.0000 0.0582 0.0050 13.9880 0.7139

-

0.0644

RGVN 2006.0000 6.0000 ANN 4.0000 0.0516 0.0631 14.8661 1.1086 0.0148

RGVN 2007.0000 5.0000 ANN 4.0000 0.0720 0.0001 15.6687 1.2347 0.0346

RGVN 2008.0000 4.0000 ANN 4.0000 0.0589 0.0000 15.7748 1.3024 0.0466

RGVN 2009.0000 3.0000 ANN 4.0000 0.0644 0.0000 16.3381 1.2109 0.0325

RGVN 2010.0000 2.0000 ANN 4.0000 0.0358 0.0000 16.6626 1.1843 0.0115

RGVN 2011.0000 1.0000 ANN 4.0000 0.0066 0.0209 16.8161 1.2632 0.0349

RISE 2009.0000 3.0000 ANN 4.0000 0.0033 0.0000 13.0113 0.6396

-

0.1045

RISE 2010.0000 2.0000 ANN 4.0000 0.0098 0.0008 12.9962 0.8689

-

0.0476

RISE 2011.0000 1.0000 ANN 4.0000 0.0137 0.0007 12.3761 0.9081

-

0.0268

RORES 2009.0000 3.0000 ANN 4.0000 0.0055 0.0301 15.0328 1.3565 0.0823

RORS 2010.0000 2.0000 ANN 4.0000 0.0016 0.0264 14.5954 1.5441 0.1355

RORS 2011.0000 1.0000 ANN 4.0000 0.0025 0.0185 14.1703 1.0884 0.0110

Saadhana 2005.0000 7.0000 ANN 3.0000 0.0000 0.0000 15.1202 1.0705 0.0149

Saadhana 2006.0000 6.0000 ANN 3.0000 0.0000 0.0000 15.7469 1.3895 0.0682

Saadhana 2007.0000 5.0000 ANN 3.0000 0.0000 0.0000 15.8661 1.2919 0.0525

Saadhana 2008.0000 4.0000 ANN 3.0000 0.0000 0.0000 16.1785 1.2292 0.0432

Saadhana 2009.0000 3.0000 ANN 3.0000 0.0000 0.0000 16.5153 1.2482 0.0487

Sahara Utsarga 2009.0000 3.0000 ANN 4.0000 0.0111 0.0064 16.3578 1.3570 0.0586

Sahara Utsarga 2010.0000 2.0000 ANN 4.0000 0.0254 0.0000 16.5612 1.2671 0.0506

Sahara Utsarga 2011.0000 1.0000 ANN 4.0000 0.0459 0.0075 16.1851 1.1582 0.0351

Sahayata 2009.0000 3.0000 ANN 4.0000 0.0035 0.0082 16.8219 1.3880 0.0634

183

Saija 2010.0000 2.0000 ANN 4.0000 0.0018 0.0000 14.5735 0.7931

-

0.0703

Saija 2011.0000 1.0000 ANN 4.0000 0.0015 0.0021 13.0548 0.4892

-

0.1841

Samasta 2009.0000 3.0000 ANN 4.0000 0.0000 0.0000 15.5848 0.8797

-

0.0238

Samasta 2010.0000 2.0000 ANN 4.0000 0.0131 0.0002 15.6746 1.0135 0.0033

Samasta 2011.0000 1.0000 ANN 4.0000 0.0109 0.0000 15.7213 0.9776

-

0.0035

Sanchetna 2010.0000 2.0000 ANN 4.0000 0.0052 0.0000 13.8979 1.0462 0.0145

Sanchetna 2011.0000 1.0000 ANN 4.0000 0.0262 0.0000 13.0557 0.8716

-

0.0376

Sangamam 2007.0000 5.0000 ANN 1.0000 0.0039 0.0000 13.9513 1.3203 0.0675

Sanghamithra 2005.0000 7.0000 ANN 4.0000 0.0993 0.0102 15.4574 1.0425 0.0057

Sanghamithra 2006.0000 6.0000 ANN 4.0000 0.0282 0.0104 15.8312 1.0149 0.0020

Sanghamithra 2007.0000 5.0000 ANN 4.0000 0.0520 0.0029 16.3110 1.0128 0.0019

Sanghamithra 2008.0000 4.0000 ANN 4.0000 0.0926 0.0033 16.2468 1.1163 0.0169

Sanghamithra 2009.0000 3.0000 ANN 4.0000 0.0482 0.0428 16.5498 1.1913 0.0247

Sanghamithra 2010.0000 2.0000 ANN 4.0000 0.0800 0.0313 16.7012 1.2304 0.0322

Sanghamithra 2011.0000 1.0000 ANN 4.0000 0.0178 0.0176 16.7132 1.2169 0.0293

Sarala 2007.0000 5.0000 ANN 5.0000 0.0002 0.0000 14.0620 1.4008 0.0516

Sarala 2008.0000 4.0000 ANN 5.0000 0.0002 0.0000 15.0983 1.2701 0.0430

Sarala 2009.0000 3.0000 ANN 5.0000 0.0025 0.0000 15.8533 1.8262 0.0842

Sarala 2010.0000 2.0000 ANN 5.0000 0.0313 0.0006 16.1737 1.5357 0.0566

Sarala 2011.0000 1.0000 ANN 5.0000 0.0475 0.0079 15.8784 1.5175 0.0578

Sarvodaya Nano Finance 2005.0000 7.0000 ANN 4.0000 0.0386 0.0000 15.9631 0.9430

-

0.0101

Sarvodaya Nano Finance 2006.0000 6.0000 ANN 4.0000 0.0769 0.0000 16.5061 1.0111

-

0.0025

Sarvodaya Nano Finance 2007.0000 5.0000 ANN 4.0000 0.0872 0.0022 16.9088 1.0703 0.0047

184

Sarvodaya Nano Finance 2008.0000 4.0000 ANN 4.0000 0.0666 0.0000 16.7265 0.9966

-

0.0016

Sarvodaya Nano Finance 2009.0000 3.0000 ANN 4.0000 0.0971 0.0029 16.7619 1.0472 0.0018

Sarvodaya Nano Finance 2010.0000 2.0000 ANN 4.0000 0.0863 0.0156 16.1980 1.0540 0.0049

Sarvodaya Nano Finance 2011.0000 1.0000 ANN 4.0000 0.0772 0.0501 15.6308 0.7610

-

0.0426

SCDS 2011.0000 1.0000 ANN 4.0000 0.0127 0.0000 15.3834 0.9370

-

0.0137

SCNL 2005.0000 7.0000 ANN 4.0000 0.1375 0.0000 14.9764 1.0913 0.0083

SCNL 2007.0000 5.0000 ANN 4.0000 0.1093 0.0068 16.1057 1.0742 0.0098

SCNL 2008.0000 4.0000 ANN 4.0000 0.0595 0.0054 16.5202 1.0718 0.0091

SCNL 2009.0000 3.0000 ANN 4.0000 0.0245 0.0051 17.1626 1.1414 0.0182

SCNL 2010.0000 2.0000 ANN 4.0000 0.0135 0.0065 17.7613 1.0613 0.0086

SCNL 2011.0000 1.0000 ANN 4.0000 0.0086 0.0041 17.9576 1.0390 0.0052

SDF 2011.0000 1.0000 ANN 4.0000 0.0268 0.0000 14.7296 1.0700 0.0162

SEIL 2009.0000 3.0000 ANN 4.0000 0.0466 0.0066 18.1706 1.5793 0.0565

SEIL 2010.0000 2.0000 ANN 4.0000 0.0572 0.0002 19.0461 1.9028 0.0677

SEIL 2011.0000 1.0000 ANN 4.0000 0.1428 0.0120 18.9696 1.9168 0.0620

SEWA MACTS 2009.0000 3.0000 ANN 1.0000 0.0321 0.0000 14.7215 0.6910

-

0.0557

SHARE 2003.0000 9.0000 ANN 4.0000 0.0000 0.0000 16.7548 1.1816 0.0304

SHARE 2004.0000 8.0000 ANN 4.0000 0.0019 0.0000 17.5094 1.2003 0.0307

SHARE 2005.0000 7.0000 ANN 4.0000 0.1348 0.0222 18.2231 1.1951 0.0225

SHARE 2006.0000 6.0000 ANN 4.0000 0.0971 0.0000 18.3338 1.1009 0.0118

SHARE 2007.0000 5.0000 ANN 4.0000 0.0373 0.0179 18.8372 1.1063 0.0110

SHARE 2008.0000 4.0000 ANN 4.0000 0.0023 0.0025 19.2931 1.5172 0.0553

SHARE 2009.0000 3.0000 ANN 4.0000 0.0016 0.0057 19.7467 1.5494 0.0550

SHARE 2010.0000 2.0000 ANN 4.0000 0.5210 0.1024 19.9577 1.0327 0.0033

SHARE 2011.0000 1.0000 ANN 4.0000 0.5218 0.0025 19.8434 0.4339

-

0.1163

185

SKDRDP 2005.0000 7.0000 ANN 4.0000 0.0002 0.0000 17.0083 0.8639

-

0.0169

SKDRDP 2006.0000 6.0000 ANN 4.0000 0.0022 0.0000 17.7709 0.9733

-

0.0029

SKDRDP 2007.0000 5.0000 ANN 4.0000 0.0015 0.0000 18.2600 0.9830

-

0.0023

SKDRDP 2008.0000 4.0000 ANN 4.0000 0.0059 0.0000 18.3868 1.0134 0.0015

SKDRDP 2009.0000 3.0000 ANN 4.0000 0.0031 0.0000 18.7335 1.1270 0.0129

SKDRDP 2010.0000 2.0000 ANN 4.0000 0.0031 0.0001 19.1894 1.1159 0.0103

SKDRDP 2011.0000 1.0000 ANN 4.0000 0.3306 0.0000 19.5900 0.9485

-

0.0094

SKS 2003.0000 9.0000 ANN 4.0000 0.0000 0.0082 14.8098 0.9698

-

0.0067

SKS 2004.0000 8.0000 ANN 4.0000 0.0518 0.0000 15.8443 0.9979

-

0.0006

SKS 2005.0000 7.0000 ANN 4.0000 0.0152 0.0100 16.8405 1.2074 0.0283

SKS 2006.0000 6.0000 ANN 4.0000 0.0012 0.0061 17.9626 1.1029 0.0076

SKS 2007.0000 5.0000 ANN 4.0000 0.0015 0.0029 19.3827 1.1975 0.0199

SKS 2008.0000 4.0000 ANN 4.0000 0.0019 0.0060 19.9955 1.2853 0.0368

SKS 2009.0000 3.0000 ANN 4.0000 0.0022 0.0086 20.6833 1.3888 0.0496

SMILE 2008.0000 4.0000 ANN 4.0000 0.0074 0.0000 16.4475 1.1488 0.0165

SMILE 2009.0000 3.0000 ANN 4.0000 0.0010 0.0014 17.2705 1.1937 0.0151

SMILE 2010.0000 2.0000 ANN 4.0000 0.0056 0.0040 17.4978 1.3834 0.0473

SMILE 2011.0000 1.0000 ANN 4.0000 0.0006 0.0056 17.5729 1.1943 0.0294

SMS 2005.0000 7.0000 ANN 1.0000 0.0346 0.0000 14.4650 1.0548 0.0072

SMS 2006.0000 6.0000 ANN 1.0000 0.0045 0.0000 15.1380 1.0186 0.0027

SMSS 2005.0000 7.0000 ANN 4.0000 0.0000 0.0000 14.4601 0.9307

-

0.0085

SMSS 2006.0000 6.0000 ANN 4.0000 0.0000 0.0000 14.8798 1.1477 0.0341

SMSS 2007.0000 5.0000 ANN 4.0000 0.0000 0.0000 15.0417 1.2612 0.0638

186

SMSS 2008.0000 4.0000 ANN 4.0000 0.0000 0.0000 15.2684 1.2551 0.0535

SMSS 2009.0000 3.0000 ANN 4.0000 0.0000 0.0696 15.3753 1.0991 0.0246

SMSS 2010.0000 2.0000 ANN 4.0000 0.9852 0.0000 15.3020 1.1361 0.0206

SMSS 2011.0000 1.0000 ANN 4.0000 0.9954 0.0000 15.1211 0.0628

-

0.1424

Sonata 2006.0000 6.0000 ANN 4.0000 0.0000 0.0000 13.9493 0.5310

-

0.0967

Sonata 2007.0000 5.0000 ANN 4.0000 0.0006 0.0001 15.5660 1.0151

-

0.0121

Sonata 2008.0000 4.0000 ANN 4.0000 0.0078 0.0000 15.9953 1.4442 0.0736

Sonata 2009.0000 3.0000 ANN 4.0000 0.0122 0.0000 16.3467 1.0834 0.0112

Sonata 2010.0000 2.0000 ANN 4.0000 0.0133 0.0046 16.7402 1.3816 0.0494

Sonata 2011.0000 1.0000 ANN 4.0000 0.0054 0.0051 16.8134 1.2820 0.0330

Spandana 2003.0000 9.0000 ANN 4.0000 0.0006 0.0000 16.1350 1.7606 0.1186

Spandana 2004.0000 8.0000 ANN 4.0000 0.0001 0.0000 17.8155 1.9255 0.0813

Spandana 2005.0000 7.0000 ANN 4.0000 0.0000 0.0693 17.9687 1.4906 0.0472

Spandana 2006.0000 6.0000 ANN 4.0000 0.0817 0.0256 18.3135 1.0976 0.0072

Spandana 2007.0000 5.0000 ANN 4.0000 0.0443 0.0009 19.0203 1.5907 0.0434

Spandana 2008.0000 4.0000 ANN 4.0000 0.0007 0.0059 19.7218 1.6629 0.0689

Spandana 2009.0000 3.0000 ANN 4.0000 0.0013 0.0066 20.4841 1.8004 0.0899

Spandana 2010.0000 2.0000 ANN 4.0000 0.4775 0.0366 20.4734 1.0005

-

0.0030

Spandana 2011.0000 1.0000 ANN 4.0000 0.5247 0.0049 20.0955 0.5636

-

0.1004

SSD 2011.0000 1.0000 ANN 4.0000 0.0091 0.0035 13.3605 0.5782

-

0.1203

SU 2007.0000 5.0000 ANN 4.0000 0.0171 0.0133 15.0431 1.4332 0.0743

SU 2008.0000 4.0000 ANN 4.0000 0.0206 0.0000 15.4639 1.1765 0.0311

SU 2009.0000 3.0000 ANN 4.0000 0.0220 0.0141 15.6843 1.2934 0.0592

SU 2010.0000 2.0000 ANN 4.0000 0.0563 0.0000 15.5938 1.1060 0.0256

187

SU 2011.0000 1.0000 ANN 4.0000 0.0766 0.0446 15.0281 0.9595

-

0.0098

Suryoday 2009.0000 3.0000 ANN 4.0000 0.0001 0.0000 15.0609 0.8105

-

0.0539

Suryoday 2010.0000 2.0000 ANN 4.0000 0.0502 0.0333 16.2011 1.0870 0.0257

Suryoday 2011.0000 1.0000 ANN 4.0000 0.0143 0.0151 16.7279 1.0368 0.0073

SVCL 2009.0000 3.0000 ANN 4.0000 0.0000 0.0000 14.5074 0.0734

-

0.6068

SVCL 2010.0000 2.0000 ANN 4.0000 0.0067 0.0000 15.9000 0.6916

-

0.1404

SVCL 2011.0000 1.0000 ANN 4.0000 0.0092 0.0012 16.2209 1.0273 0.0078

SVSDF 2009.0000 3.0000 ANN 4.0000 0.0000 0.0000 14.1619 1.0543 0.0056

SVSDF 2010.0000 2.0000 ANN 4.0000 0.0000 0.0000 14.7826 1.0248 0.0070

Swadhaar 2006.0000 6.0000 ANN 4.0000 0.0000 0.0304 11.4860 0.1626

-

1.0126

Swadhaar 2007.0000 5.0000 ANN 4.0000 0.0166 0.0374 12.5194 0.1788

-

0.9721

Swadhaar 2008.0000 4.0000 ANN 4.0000 0.0108 0.0000 13.9231 0.3118

-

0.4491

Swadhaar 2009.0000 3.0000 ANN 4.0000 0.0091 0.0185 15.2858 0.4924

-

0.2075

Swadhaar 2010.0000 2.0000 ANN 4.0000 0.0109 0.0156 16.2449 0.7937

-

0.0589

Swadhaar 2011.0000 1.0000 ANN 4.0000 0.0204 0.0146 16.5532 1.0179 0.0091

SWAWS 2005.0000 7.0000 ANN 4.0000 0.0032 0.0009 15.5254 1.2301 0.0235

SWAWS 2006.0000 6.0000 ANN 4.0000 0.0115 0.0001 15.9878 1.1598 0.0211

SWAWS 2007.0000 5.0000 ANN 4.0000 0.0006 0.0000 16.1789 1.0967 0.0178

SWAWS 2008.0000 4.0000 ANN 4.0000 0.0033 0.0000 16.2718 1.2426 0.0328

SWAWS 2009.0000 3.0000 ANN 4.0000 0.0046 0.0000 16.8019 1.6598 0.0745

SWAWS 2011.0000 1.0000 ANN 4.0000 0.9691 0.0000 16.5864 -0.1224

-

0.2581

188

Swayamshree Micro Credit

Services 2010.0000 2.0000 ANN 4.0000 0.0488 0.0081 15.7451 1.1184 0.0182

Swayamshree Micro Credit

Services 2011.0000 1.0000 ANN 4.0000 0.0499 0.0291 15.4094 1.1342 0.0225

Trident Microfinance 2008.0000 4.0000 ANN 4.0000 0.0000 0.0000 15.9327 1.1417 0.0186

Trident Microfinance 2009.0000 3.0000 ANN 4.0000 0.0018 0.0000 17.1755 1.3473 0.0390

Trident Microfinance 2010.0000 2.0000 ANN 4.0000 0.6385 0.0000 17.4538 0.8408

-

0.0316

Trident Microfinance 2011.0000 1.0000 ANN 4.0000 0.9995 0.2005 17.0471 0.8425

-

0.0506

UFSPL 2009.0000 3.0000 ANN 5.0000 0.0137 0.0000 14.1441 1.2429 0.0384

UFSPL 2010.0000 2.0000 ANN 5.0000 0.0000 0.0000 14.5351 1.1016 0.0135

UFSPL 2011.0000 1.0000 ANN 5.0000 0.0350 0.0184 14.1521 1.0322 0.0045

Ujjivan 2006.0000 6.0000 ANN 5.0000 0.0021 0.0007 14.4751 0.4447

-

0.2073

Ujjivan 2007.0000 5.0000 ANN 5.0000 0.0020 0.0010 16.0248 0.6422

-

0.1151

Ujjivan 2008.0000 4.0000 ANN 5.0000 0.0022 0.0011 17.3188 0.9767

-

0.0060

Ujjivan 2010.0000 2.0000 ANN 5.0000 0.0103 0.0015 18.7628 1.1301 0.0201

Ujjivan 2011.0000 1.0000 ANN 5.0000 0.0120 0.0036 18.7448 1.0142 0.0025

Utkarsh 2011.0000 1.0000 ANN 5.0000 0.0000 0.0000 16.5104 1.2005 0.0225

VFPL 2010.0000 2.0000 ANN 4.0000 0.0016 0.0025 13.9261 1.2298 0.0619

VFS 2005.0000 7.0000 ANN 4.0000 0.0241 0.0109 15.1374 1.3629 0.0844

VFS 2006.0000 6.0000 ANN 4.0000 0.0220 0.0004 15.0676 1.4258 0.0762

VFS 2007.0000 5.0000 ANN 4.0000 0.0047 0.0000 15.2691 1.1625 0.0109

VFS 2008.0000 4.0000 ANN 4.0000 0.0053 0.0000 15.7002 1.1608 0.0174

VFS 2009.0000 3.0000 ANN 4.0000 0.0056 0.0005 16.9790 1.1026 0.0110

VFS 2010.0000 2.0000 ANN 4.0000 0.0088 0.0066 17.0477 1.4020 0.0574

VFS 2011.0000 1.0000 ANN 4.0000 0.0126 0.0094 16.8462 1.1606 0.0212

189

VSS 2005.0000 7.0000 ANN 1.0000 0.0000 0.0000 12.2903 1.0881 0.0138

VSSU 2009.0000 3.0000 ANN 1.0000 0.1236 0.0000 13.9681 0.7991

-

0.0281

WSE 2009.0000 3.0000 ANN 4.0000 0.0034 0.0014 14.9104 1.2477 0.0330

WSE 2010.0000 2.0000 ANN 4.0000 0.0020 0.0000 15.3255 1.3222 0.0352

WSE 2011.0000 1.0000 ANN 4.0000 0.0006 0.0000 15.4323 1.1198 0.0124

YFS 2011.0000 1.0000 ANN 4.0000 0.0204 0.0000 13.0599 1.2135 0.0274

YVU 2011.0000 1.0000 ANN 4.0000 0.0065 0.0000 14.4254 1.0771 0.0119

204

rating expense/ loan portfolio

Borrowers per loan officer

log of leverage

liquidity cooperative union dummy

non banking Dummy

NGO dummy

rural banking dummy

Current legal status

0.1021 317.0000 13.0276 0.0888 0.0000 0.0000 1.0000 0.0000 NGO

0.1008 278.0000 14.6083 0.2039 0.0000 0.0000 1.0000 0.0000 NGO

0.1849 390.0000 14.9888 0.4699 0.0000 0.0000 1.0000 0.0000 NGO

0.2192 360.0000 14.7578 0.2779 0.0000 0.0000 1.0000 0.0000 NGO

0.0868 306.0000 13.7234 0.1173 0.0000 1.0000 0.0000 0.0000 NBFI

0.1138 258.0000 15.3416 0.1082 0.0000 1.0000 0.0000 0.0000 NBFI

0.1346 437.0000 15.6574 0.1354 0.0000 1.0000 0.0000 0.0000 NBFI

0.0959 409.0000 16.0014 0.3479 0.0000 1.0000 0.0000 0.0000 NBFI

0.1181 412.0000 15.9407 0.3058 0.0000 1.0000 0.0000 0.0000 NBFI

0.1039 318.0000 14.3972 0.1837 0.0000 1.0000 0.0000 0.0000 NBFI

0.1105 267.0000 14.0571 0.2630 0.0000 1.0000 0.0000 0.0000 NBFI

0.1369 371.0000 16.8500 0.2058 0.0000 1.0000 0.0000 0.0000 NBFI

0.0998 399.0000 17.2733 0.1060 0.0000 1.0000 0.0000 0.0000 NBFI

0.1072 420.0000 18.1395 0.2211 0.0000 1.0000 0.0000 0.0000 NBFI

0.0975 517.0000 18.7566 0.4419 0.0000 1.0000 0.0000 0.0000 NBFI

0.0634 518.0000 19.5671 0.2946 0.0000 1.0000 0.0000 0.0000 NBFI

0.0676 553.0000 19.4103 0.0711 0.0000 1.0000 0.0000 0.0000 NBFI

0.0586 337.0000 15.4649 0.3336 1.0000 0.0000 0.0000 0.0000 CU

0.0708 423.0000 16.0181 0.4393 1.0000 0.0000 0.0000 0.0000 CU

0.0777 560.0000 16.5886 0.1047 1.0000 0.0000 0.0000 0.0000 CU

0.0675 333.0000 14.9676 0.1261 1.0000 0.0000 0.0000 0.0000 CU

0.1400 81.0000 13.1986 2.5234 0.0000 1.0000 0.0000 0.0000 NBFI

0.1874 311.0000 14.8257 0.2250 0.0000 1.0000 0.0000 0.0000 NBFI

0.1442 334.0000 15.7280 0.0298 0.0000 1.0000 0.0000 0.0000 NBFI

0.1225 325.0000 16.8457 0.1382 0.0000 1.0000 0.0000 0.0000 NBFI

0.1635 314.0000 16.6163 0.1682 0.0000 1.0000 0.0000 0.0000 NBFI

0.2332 253.0000 15.6969 0.2363 0.0000 1.0000 0.0000 0.0000 NBFI

0.1246 428.0000 13.7401 0.0444 0.0000 1.0000 0.0000 0.0000 NBFI

0.0914 266.0000 16.3925 0.2061 0.0000 1.0000 0.0000 0.0000 NBFI

0.1439 319.0000 17.0704 0.0990 0.0000 1.0000 0.0000 0.0000 NBFI

0.1347 245.0000 16.3978 0.0636 0.0000 1.0000 0.0000 0.0000 NBFI

0.1159 538.0000 16.1838 0.1229 0.0000 1.0000 0.0000 0.0000 NBFI

0.1242 600.0000 16.6242 0.2099 0.0000 1.0000 0.0000 0.0000 NBFI

0.1330 651.0000 16.1087 0.2626 0.0000 1.0000 0.0000 0.0000 NBFI

205

0.0707 139.0000 14.4504 0.2008 0.0000 0.0000 1.0000 0.0000 NGO

0.0805 192.0000 15.1799 0.0817 0.0000 0.0000 1.0000 0.0000 NGO

0.0106 84.0000 9.8864 1.4260 0.0000 0.0000 1.0000 0.0000 NGO

0.2696 382.0000 14.6614 0.2417 0.0000 0.0000 1.0000 0.0000 NGO

0.1286 537.0000 15.3387 0.2804 0.0000 0.0000 1.0000 0.0000 NGO

0.1388 577.0000 15.3841 0.2661 0.0000 0.0000 1.0000 0.0000 NGO

0.3478 477.0000 14.0932 0.4325 1.0000 0.0000 0.0000 0.0000 CU

0.1496 107.0000 12.4148 0.5880 0.0000 0.0000 1.0000 0.0000 NGO

0.0342 604.0000 15.7342 0.0218 0.0000 0.0000 1.0000 0.0000 NGO

0.0321 759.0000 15.3983 0.0725 0.0000 0.0000 1.0000 0.0000 NGO

0.2148 255.0000 14.3653 0.1078 0.0000 1.0000 0.0000 0.0000 NBFI

0.1181 227.0000 15.6753 0.0553 0.0000 1.0000 0.0000 0.0000 NBFI

0.0876 360.0000 16.9814 0.0618 0.0000 1.0000 0.0000 0.0000 NBFI

0.1044 431.0000 18.1473 0.1946 0.0000 1.0000 0.0000 0.0000 NBFI

0.0878 530.0000 18.7212 0.4884 0.0000 1.0000 0.0000 0.0000 NBFI

0.0543 522.0000 19.5109 0.4021 0.0000 1.0000 0.0000 0.0000 NBFI

0.0612 521.0000 19.8465 0.2220 0.0000 1.0000 0.0000 0.0000 NBFI

0.0588 504.0000 20.3100 0.3087 0.0000 1.0000 0.0000 0.0000 NBFI

0.1847 237.0000 15.8829 0.0810 0.0000 1.0000 0.0000 0.0000 NBFI

0.1858 297.0000 16.6837 0.1272 0.0000 1.0000 0.0000 0.0000 NBFI

0.1768 277.0000 17.0031 0.1917 0.0000 1.0000 0.0000 0.0000 NBFI

0.1981 279.0000 17.5677 0.1275 0.0000 1.0000 0.0000 0.0000 NBFI

0.1776 252.0000 18.1919 0.3416 0.0000 1.0000 0.0000 0.0000 NBFI

0.1588 219.0000 19.1806 0.6807 0.0000 1.0000 0.0000 0.0000 NBFI

0.1431 350.0000 19.4402 0.1878 0.0000 1.0000 0.0000 0.0000 NBFI

0.1703 246.0000 18.7133 0.2247 0.0000 1.0000 0.0000 0.0000 NBFI

0.0145 1056.0000 14.5973 0.0133 0.0000 0.0000 1.0000 0.0000 NGO

0.0517 277.0000 16.2779 0.0111 0.0000 0.0000 1.0000 0.0000 NGO

0.0648 190.0000 16.7889 0.0359 0.0000 0.0000 1.0000 0.0000 NGO

0.0169 2202.0000 17.4119 0.2443 0.0000 0.0000 1.0000 0.0000 NGO

0.0519 1757.0000 17.7308 0.1045 0.0000 0.0000 1.0000 0.0000 NGO

0.0770 2208.0000 17.7995 0.0894 0.0000 0.0000 1.0000 0.0000 NGO

0.1192 1579.0000 17.5367 0.0241 0.0000 0.0000 1.0000 0.0000 NGO

0.1805 288.0000 12.1969 0.0105 0.0000 0.0000 1.0000 0.0000 NGO

0.1886 234.0000 12.3702 0.1145 0.0000 0.0000 1.0000 0.0000 NGO

0.1739 318.0000 13.1756 0.0262 0.0000 0.0000 1.0000 0.0000 NGO

0.1362 389.0000 14.0234 0.0966 0.0000 0.0000 1.0000 0.0000 NGO

0.1101 388.0000 13.7960 0.0564 0.0000 0.0000 1.0000 0.0000 NGO

0.1839 295.0000 13.7587 0.1432 0.0000 1.0000 0.0000 0.0000 NBFI

206

0.1450 296.0000 14.2554 0.1276 0.0000 1.0000 0.0000 0.0000 NBFI

0.1048 391.0000 15.8089 0.0850 0.0000 1.0000 0.0000 0.0000 NBFI

0.1000 429.0000 16.6150 0.0569 0.0000 1.0000 0.0000 0.0000 NBFI

0.1142 411.0000 16.5772 0.0852 0.0000 1.0000 0.0000 0.0000 NBFI

0.1096 277.0000 17.0762 0.0709 0.0000 1.0000 0.0000 0.0000 NBFI

0.1581 261.0000 16.8726 0.2711 0.0000 1.0000 0.0000 0.0000 NBFI

0.1931 325.0000 17.0940 0.3103 0.0000 1.0000 0.0000 0.0000 NBFI

0.0701 1128.0000 15.9561 0.1157 0.0000 1.0000 0.0000 0.0000 NBFI

0.0669 3230.0000 16.6009 0.1092 0.0000 1.0000 0.0000 0.0000 NBFI

0.0579 772.0000 16.8197 0.0608 0.0000 1.0000 0.0000 0.0000 NBFI

0.0598 504.0000 16.9046 0.1718 0.0000 1.0000 0.0000 0.0000 NBFI

0.0548 496.0000 17.1839 0.2101 0.0000 1.0000 0.0000 0.0000 NBFI

0.0541 635.0000 16.9827 0.1569 0.0000 1.0000 0.0000 0.0000 NBFI

0.0845 787.0000 16.3659 0.1314 0.0000 1.0000 0.0000 0.0000 NBFI

0.1220 330.0000 13.7996 0.1133 0.0000 0.0000 1.0000 0.0000 NGO

0.0991 318.0000 13.8286 0.0877 0.0000 0.0000 1.0000 0.0000 NGO

0.2900 163.0000 15.5509 0.3472 0.0000 0.0000 1.0000 0.0000 NGO

0.2368 197.0000 15.7667 0.1859 0.0000 0.0000 1.0000 0.0000 NGO

0.1788 218.0000 16.6592 0.1673 0.0000 0.0000 1.0000 0.0000 NGO

0.1300 298.0000 16.9715 0.1105 0.0000 0.0000 1.0000 0.0000 NGO

0.1143 371.0000 17.7580 0.2638 0.0000 0.0000 1.0000 0.0000 NGO

0.1188 403.0000 17.7439 0.2904 0.0000 0.0000 1.0000 0.0000 NGO

0.0941 532.0000 17.6411 0.2505 0.0000 0.0000 1.0000 0.0000 NGO

0.1854 406.0000 13.9539 0.2995 0.0000 0.0000 1.0000 0.0000 NGO

0.1223 342.0000 14.3094 0.1923 0.0000 0.0000 1.0000 0.0000 NGO

0.1158 348.0000 14.2446 0.2802 0.0000 0.0000 1.0000 0.0000 NGO

0.4873 168.0000 5.4931 0.9256 0.0000 1.0000 0.0000 0.0000 NBFI

0.2258 393.0000 12.9921 0.2347 0.0000 1.0000 0.0000 0.0000 NBFI

0.1798 374.0000 13.5061 0.0280 0.0000 1.0000 0.0000 0.0000 NBFI

0.0772 354.0000 12.8288 0.0464 1.0000 0.0000 0.0000 0.0000 CU

0.1545 256.0000 13.9058 0.2281 0.0000 1.0000 0.0000 0.0000 NBFI

0.1124 312.0000 14.5486 0.1475 0.0000 1.0000 0.0000 0.0000 NBFI

0.0937 402.0000 15.1264 0.1309 0.0000 1.0000 0.0000 0.0000 NBFI

0.1394 551.0000 15.3695 0.1319 0.0000 1.0000 0.0000 0.0000 NBFI

0.1261 413.0000 15.4557 0.0722 0.0000 1.0000 0.0000 0.0000 NBFI

0.2393 299.0000 13.7024 0.2873 0.0000 0.0000 1.0000 0.0000 NGO

0.2181 354.0000 15.0041 0.1917 0.0000 1.0000 0.0000 0.0000 NBFI

0.1426 563.0000 14.7394 0.1755 0.0000 1.0000 0.0000 0.0000 NBFI

0.1223 945.0000 17.4023 0.2962 0.0000 1.0000 0.0000 0.0000 NBFI

207

0.0807 959.0000 18.3801 0.4112 0.0000 1.0000 0.0000 0.0000 NBFI

0.1030 888.0000 18.7081 0.3414 0.0000 1.0000 0.0000 0.0000 NBFI

0.1161 1011.0000 18.3562 0.2912 0.0000 1.0000 0.0000 0.0000 NBFI

0.1321 207.0000 15.4234 0.6742 0.0000 0.0000 1.0000 0.0000 NGO

0.1313 211.0000 16.1379 0.1080 0.0000 0.0000 1.0000 0.0000 NGO

0.1318 225.0000 16.6111 0.0917 0.0000 0.0000 1.0000 0.0000 NGO

0.1804 227.0000 16.0825 0.2421 0.0000 0.0000 1.0000 0.0000 NGO

0.1374 328.0000 17.2900 0.2725 0.0000 0.0000 1.0000 0.0000 NGO

0.1369 322.0000 17.4670 0.1165 0.0000 0.0000 1.0000 0.0000 NGO

0.1382 391.0000 17.5109 0.1409 0.0000 0.0000 1.0000 0.0000 NGO

0.0491 723.0000 17.6769 0.2087 0.0000 1.0000 0.0000 0.0000 NBFI

0.0537 864.0000 17.1782 0.0796 0.0000 1.0000 0.0000 0.0000 NBFI

0.0787 862.0000 16.8911 0.0452 0.0000 1.0000 0.0000 0.0000 NBFI

0.1853 587.0000 15.1482 0.1995 0.0000 1.0000 0.0000 0.0000 NBFI

0.4183 121.0000 13.4858 0.4513 0.0000 1.0000 0.0000 0.0000 NBFI

0.2789 170.0000 14.2378 0.1847 0.0000 1.0000 0.0000 0.0000 NBFI

0.1830 266.0000 15.0993 0.3541 0.0000 1.0000 0.0000 0.0000 NBFI

0.1393 370.0000 16.1819 0.1673 0.0000 1.0000 0.0000 0.0000 NBFI

0.1757 310.0000 16.8601 0.2187 0.0000 1.0000 0.0000 0.0000 NBFI

0.1232 481.0000 17.0038 0.1315 0.0000 1.0000 0.0000 0.0000 NBFI

0.0954 527.0000 17.8180 0.1857 0.0000 1.0000 0.0000 0.0000 NBFI

0.1333 243.0000 17.5775 0.2431 0.0000 1.0000 0.0000 0.0000 NBFI

0.1397 355.0000 17.6502 0.0907 0.0000 1.0000 0.0000 0.0000 NBFI

0.1051 755.0000 13.6959 0.0869 0.0000 0.0000 1.0000 0.0000 NGO

0.0946 430.0000 13.4383 0.0729 0.0000 0.0000 1.0000 0.0000 NGO

0.0693 119.0000 13.3072 0.0544 0.0000 1.0000 0.0000 0.0000 NBFI

0.5236 153.0000 13.8506 0.4511 0.0000 1.0000 0.0000 0.0000 NBFI

0.2606 314.0000 15.0592 0.2211 0.0000 1.0000 0.0000 0.0000 NBFI

0.2192 280.0000 15.6656 0.6262 0.0000 1.0000 0.0000 0.0000 NBFI

0.2050 273.0000 15.4088 0.3104 0.0000 1.0000 0.0000 0.0000 NBFI

0.0369 458.0000 13.8920 0.0932 0.0000 0.0000 1.0000 0.0000 NGO

0.0488 628.0000 14.0404 0.0859 0.0000 0.0000 1.0000 0.0000 NGO

0.2188 563.0000 15.0101 0.2449 0.0000 1.0000 0.0000 0.0000 NBFI

0.1975 237.0000 15.3251 0.5324 0.0000 1.0000 0.0000 0.0000 NBFI

0.2132 238.0000 16.2760 1.2501 0.0000 1.0000 0.0000 0.0000 NBFI

0.1708 401.0000 17.2791 0.2216 0.0000 1.0000 0.0000 0.0000 NBFI

0.1179 576.0000 18.5043 0.2803 0.0000 1.0000 0.0000 0.0000 NBFI

0.1547 429.0000 18.1745 0.3808 0.0000 1.0000 0.0000 0.0000 NBFI

0.1401 476.0000 17.9971 0.4701 0.0000 1.0000 0.0000 0.0000 NBFI

208

0.0913 362.0000 15.0672 0.2936 0.0000 0.0000 1.0000 0.0000 NGO

0.0288 2322.0000 15.2817 0.0161 0.0000 0.0000 1.0000 0.0000 NGO

0.1456 304.0000 12.1496 0.0071 0.0000 1.0000 0.0000 0.0000 NBFI

0.0745 301.0000 14.7313 0.7188 0.0000 0.0000 1.0000 0.0000 NGO

0.0572 331.0000 15.4438 0.2008 0.0000 0.0000 1.0000 0.0000 NGO

0.0473 345.0000 15.8922 0.1743 0.0000 0.0000 1.0000 0.0000 NGO

0.0534 363.0000 15.5710 0.1259 0.0000 0.0000 1.0000 0.0000 NGO

0.0595 378.0000 15.6188 0.1474 0.0000 0.0000 1.0000 0.0000 NGO

0.0711 413.0000 15.6575 0.1266 0.0000 0.0000 1.0000 0.0000 NGO

0.1529 302.0000 15.2248 0.1390 0.0000 0.0000 1.0000 0.0000 NGO

0.1260 899.0000 13.9164 0.0714 0.0000 0.0000 1.0000 0.0000 NGO

0.1466 543.0000 14.0419 0.1384 0.0000 0.0000 1.0000 0.0000 NGO

0.6104 96.0000 15.6190 1.1689 0.0000 0.0000 1.0000 0.0000 NGO

0.0892 55.0000 15.4500 0.3117 0.0000 0.0000 1.0000 0.0000 NGO

0.7468 125.0000 16.1859 1.2044 0.0000 0.0000 1.0000 0.0000 NGO

0.7024 153.0000 15.7393 1.3569 0.0000 0.0000 1.0000 0.0000 NGO

0.1574 250.0000 13.0294 0.0742 0.0000 1.0000 0.0000 0.0000 NBFI

0.0612 396.0000 15.3488 0.2321 0.0000 0.0000 1.0000 0.0000 NGO

0.0628 463.0000 15.3225 0.1992 0.0000 0.0000 1.0000 0.0000 NGO

0.0994 15677.0000

13.6736 0.6128 1.0000 0.0000 0.0000 0.0000 CU

0.0937 3971.0000 14.0223 0.2093 1.0000 0.0000 0.0000 0.0000 CU

0.0976 2357.0000 13.8462 0.3749 1.0000 0.0000 0.0000 0.0000 CU

0.0706 1555.0000 15.7858 0.1413 0.0000 0.0000 1.0000 0.0000 NGO

0.0658 1994.0000 16.1832 0.0565 0.0000 0.0000 1.0000 0.0000 NGO

0.0735 1747.0000 16.4980 0.1424 0.0000 0.0000 1.0000 0.0000 NGO

0.0840 1510.0000 16.0279 0.1638 0.0000 0.0000 1.0000 0.0000 NGO

0.3319 152.0000 14.0158 0.0866 0.0000 1.0000 0.0000 0.0000 NBFI

0.3353 184.0000 14.7642 0.2186 0.0000 1.0000 0.0000 0.0000 NBFI

0.0651 314.0000 15.0203 0.0767 1.0000 0.0000 0.0000 0.0000 CU

0.0694 398.0000 15.3296 0.1097 1.0000 0.0000 0.0000 0.0000 CU

0.0762 367.0000 14.5158 0.1267 1.0000 0.0000 0.0000 0.0000 CU

0.1007 282.0000 13.0431 0.4505 0.0000 0.0000 1.0000 0.0000 NGO

0.0929 2129.0000 14.2873 0.1158 0.0000 1.0000 0.0000 0.0000 NBFI

0.2467 480.0000 16.3764 0.1252 0.0000 1.0000 0.0000 0.0000 NBFI

0.2819 324.0000 17.0717 0.1504 0.0000 1.0000 0.0000 0.0000 NBFI

0.1780 490.0000 17.8467 0.3075 0.0000 1.0000 0.0000 0.0000 NBFI

0.0987 578.0000 14.8544 0.1914 0.0000 0.0000 1.0000 0.0000 NGO

0.1629 333.0000 14.3090 0.2621 0.0000 0.0000 1.0000 0.0000 NGO

0.0695 306.0000 12.2074 0.0772 0.0000 0.0000 1.0000 0.0000 NGO

209

0.0506 290.0000 12.1753 0.0446 0.0000 0.0000 1.0000 0.0000 NGO

0.0717 269.0000 16.8938 0.0593 0.0000 0.0000 1.0000 0.0000 NGO

0.1735 245.0000 15.8001 0.1645 0.0000 0.0000 1.0000 0.0000 NGO

0.1776 296.0000 14.6728 0.4529 0.0000 0.0000 0.0000 1.0000 Rural Bank

0.1712 157.0000 15.0443 0.1960 0.0000 0.0000 0.0000 1.0000 Rural Bank

0.1534 200.0000 15.7252 0.1896 0.0000 0.0000 0.0000 1.0000 Rural Bank

0.1384 1149.0000 15.6397 0.1256 0.0000 0.0000 0.0000 1.0000 Rural Bank

0.1377 349.0000 15.7172 0.4655 0.0000 0.0000 0.0000 1.0000 Rural Bank

0.1366 342.0000 15.6020 0.4884 0.0000 0.0000 0.0000 1.0000 Rural Bank

0.1411 287.0000 14.8588 0.5339 0.0000 0.0000 0.0000 1.0000 Rural Bank

0.1878 491.0000 15.2649 0.2498 0.0000 1.0000 0.0000 0.0000 NBFI

0.1776 692.0000 14.1980 0.2053 0.0000 1.0000 0.0000 0.0000 NBFI

0.0904 689.0000 12.9119 0.0332 0.0000 1.0000 0.0000 0.0000 NBFI

0.1446 159.0000 14.7339 0.2011 0.0000 0.0000 1.0000 0.0000 NGO

0.1721 227.0000 14.9888 0.0732 0.0000 0.0000 1.0000 0.0000 NGO

0.1771 243.0000 14.9792 0.0728 0.0000 0.0000 1.0000 0.0000 NGO

0.1710 213.0000 15.3523 0.0557 0.0000 0.0000 1.0000 0.0000 NGO

0.0465 545.0000 13.5682 1.1972 0.0000 0.0000 1.0000 0.0000 NGO

0.0346 754.0000 14.8007 0.2265 0.0000 0.0000 1.0000 0.0000 NGO

0.0441 584.0000 15.5042 0.1408 0.0000 0.0000 1.0000 0.0000 NGO

0.0490 390.0000 15.2955 0.0281 0.0000 0.0000 1.0000 0.0000 NGO

0.1363 567.0000 13.1398 0.0525 0.0000 0.0000 1.0000 0.0000 NGO

0.4629 238.0000 14.8144 0.3506 0.0000 0.0000 1.0000 0.0000 NGO

0.3992 201.0000 15.4814 0.1338 0.0000 0.0000 1.0000 0.0000 NGO

0.4108 552.0000 16.1452 0.2928 0.0000 0.0000 1.0000 0.0000 NGO

0.3640 531.0000 15.8691 0.1898 0.0000 0.0000 1.0000 0.0000 NGO

0.0771 460.0000 14.9802 0.0041 0.0000 0.0000 1.0000 0.0000 NGO

0.0939 386.0000 14.7709 0.1614 0.0000 0.0000 1.0000 0.0000 NGO

0.1044 269.0000 13.6603 0.1879 0.0000 0.0000 1.0000 0.0000 NGO

0.3521 194.0000 13.8714 0.0132 0.0000 1.0000 0.0000 0.0000 NBFI

0.1402 340.0000 15.3575 0.0647 0.0000 1.0000 0.0000 0.0000 NBFI

0.1676 255.0000 15.9149 0.2824 0.0000 1.0000 0.0000 0.0000 NBFI

0.2235 374.0000 16.0870 0.1273 0.0000 1.0000 0.0000 0.0000 NBFI

0.2032 265.0000 14.8417 0.2649 0.0000 1.0000 0.0000 0.0000 NBFI

0.0294 320.0000 15.3142 0.0184 0.0000 1.0000 0.0000 0.0000 NBFI

210

0.0085 146.0000 16.7328 0.0896 0.0000 1.0000 0.0000 0.0000 NBFI

0.0504 1195.0000 16.6765 0.1049 0.0000 1.0000 0.0000 0.0000 NBFI

0.0253 1940.0000 16.9747 0.1015 0.0000 1.0000 0.0000 0.0000 NBFI

0.0417 1806.0000 17.2226 0.0671 0.0000 1.0000 0.0000 0.0000 NBFI

0.0847 1716.0000 16.5999 0.3252 0.0000 1.0000 0.0000 0.0000 NBFI

0.0765 438.0000 17.2790 0.0012 0.0000 1.0000 0.0000 0.0000 NBFI

0.1300 139.0000 13.2772 0.0059 0.0000 1.0000 0.0000 0.0000 NBFI

0.1231 212.0000 12.0831 0.0885 0.0000 0.0000 1.0000 0.0000 NGO

0.1076 266.0000 12.8576 0.1134 0.0000 0.0000 1.0000 0.0000 NGO

0.1060 252.0000 12.9680 0.0887 0.0000 0.0000 1.0000 0.0000 NGO

0.1087 261.0000 13.0293 0.0841 0.0000 0.0000 1.0000 0.0000 NGO

0.1133 327.0000 12.8854 0.0644 0.0000 0.0000 1.0000 0.0000 NGO

0.1027 246.0000 12.6238 0.0655 0.0000 0.0000 1.0000 0.0000 NGO

0.1666 409.0000 12.9623 0.0867 0.0000 0.0000 1.0000 0.0000 NGO

0.1914 574.0000 13.5423 0.2121 0.0000 0.0000 1.0000 0.0000 NGO

0.1555 307.0000 14.1427 0.0741 0.0000 0.0000 1.0000 0.0000 NGO

0.2193 382.0000 14.0341 0.1428 0.0000 0.0000 1.0000 0.0000 NGO

0.2045 784.0000 13.5330 0.2976 0.0000 0.0000 1.0000 0.0000 NGO

0.0701 375.0000 13.4648 0.2817 0.0000 0.0000 1.0000 0.0000 NGO

0.0464 591.0000 14.6588 0.1370 0.0000 0.0000 1.0000 0.0000 NGO

0.0430 210.0000 14.1551 0.0725 0.0000 0.0000 1.0000 0.0000 NGO

0.1079 262.0000 14.8144 0.0969 0.0000 0.0000 1.0000 0.0000 NGO

0.0992 292.0000 15.1158 0.1592 0.0000 0.0000 1.0000 0.0000 NGO

0.0897 299.0000 15.3241 0.0988 0.0000 0.0000 1.0000 0.0000 NGO

0.0864 304.0000 14.9530 0.3058 0.0000 0.0000 1.0000 0.0000 NGO

0.1282 106.0000 11.3900 0.0311 0.0000 1.0000 0.0000 0.0000 NBFI

0.0349 124.0000 12.9212 0.2270 0.0000 1.0000 0.0000 0.0000 NBFI

0.1988 223.0000 12.0473 0.3055 0.0000 1.0000 0.0000 0.0000 NBFI

0.0116 119.0000 13.4187 2.1500 0.0000 1.0000 0.0000 0.0000 NBFI

0.1642 387.0000 15.7698 0.1506 0.0000 0.0000 1.0000 0.0000 NGO

0.0756 439.0000 13.8925 0.0238 0.0000 0.0000 1.0000 0.0000 NGO

0.0150 761.0000 14.1771 0.2373 1.0000 0.0000 0.0000 0.0000 CU

0.0153 428.0000 13.7313 0.2772 1.0000 0.0000 0.0000 0.0000 CU

0.0142 486.0000 13.0188 0.3315 1.0000 0.0000 0.0000 0.0000 CU

0.0153 465.0000 11.9657 0.3765 1.0000 0.0000 0.0000 0.0000 CU

0.0954 150.0000 13.3153 0.2158 1.0000 0.0000 0.0000 0.0000 CU

0.0967 1007.0000 15.4084 0.5990 1.0000 0.0000 0.0000 0.0000 CU

0.0847 421.0000 15.4805 0.4584 1.0000 0.0000 0.0000 0.0000 CU

0.0882 389.0000 15.6272 0.2587 1.0000 0.0000 0.0000 0.0000 CU

211

0.1104 315.0000 15.4667 0.5923 1.0000 0.0000 0.0000 0.0000 CU

0.1517 354.0000 15.3717 0.4410 1.0000 0.0000 0.0000 0.0000 CU

0.0577 1501.0000 13.9003 0.1856 0.0000 0.0000 1.0000 0.0000 NGO

0.0441 1561.0000 14.8097 0.0257 0.0000 0.0000 1.0000 0.0000 NGO

0.0232 509.0000 15.8997 0.0972 0.0000 0.0000 1.0000 0.0000 NGO

0.0202 558.0000 16.0494 0.0643 0.0000 0.0000 1.0000 0.0000 NGO

0.0201 569.0000 16.5008 0.1369 0.0000 0.0000 1.0000 0.0000 NGO

0.1663 227.0000 14.0688 0.1604 0.0000 0.0000 1.0000 0.0000 NGO

0.1150 165.0000 15.1497 0.4688 0.0000 0.0000 1.0000 0.0000 NGO

0.0921 289.0000 15.6481 0.1731 0.0000 0.0000 1.0000 0.0000 NGO

0.0871 359.0000 15.8357 0.0257 0.0000 0.0000 1.0000 0.0000 NGO

0.0782 430.0000 16.2659 0.0767 0.0000 0.0000 1.0000 0.0000 NGO

0.0538 486.0000 16.3918 0.1202 0.0000 0.0000 1.0000 0.0000 NGO

0.0961 516.0000 16.7267 0.1738 0.0000 0.0000 1.0000 0.0000 NGO

0.2464 140.0000 13.0394 0.1913 0.0000 0.0000 1.0000 0.0000 NGO

0.2537 338.0000 12.9977 0.0424 0.0000 0.0000 1.0000 0.0000 NGO

0.1894 279.0000 12.5605 0.1540 0.0000 0.0000 1.0000 0.0000 NGO

0.1427 610.0000 14.8363 0.0056 0.0000 0.0000 1.0000 0.0000 NGO

0.4149 452.0000 13.7691 0.0468 0.0000 1.0000 0.0000 0.0000 NBFI

0.1976 253.0000 12.6307 0.0059 0.0000 1.0000 0.0000 0.0000 NBFI

0.1299 354.0000 15.2373 0.1652 0.0000 0.0000 1.0000 0.0000 NGO

0.0985 741.0000 15.7893 0.1201 0.0000 0.0000 1.0000 0.0000 NGO

0.1088 506.0000 16.1005 0.2141 0.0000 0.0000 1.0000 0.0000 NGO

0.1051 537.0000 16.2573 0.1219 0.0000 0.0000 1.0000 0.0000 NGO

0.1027 798.0000 16.5034 0.0243 0.0000 0.0000 1.0000 0.0000 NGO

0.1165 191.0000 16.3573 0.3303 0.0000 0.0000 1.0000 0.0000 NGO

0.1148 224.0000 16.2245 0.1046 0.0000 0.0000 1.0000 0.0000 NGO

0.1473 169.0000 16.0294 0.2237 0.0000 0.0000 1.0000 0.0000 NGO

0.1942 445.0000 16.6918 0.2663 0.0000 1.0000 0.0000 0.0000 NBFI

0.3394 262.0000 14.6296 0.1552 0.0000 1.0000 0.0000 0.0000 NBFI

0.3877 124.0000 13.7016 0.6563 0.0000 1.0000 0.0000 0.0000 NBFI

0.1663 311.0000 15.3396 0.1370 0.0000 1.0000 0.0000 0.0000 NBFI

0.1848 330.0000 15.4194 0.0901 0.0000 1.0000 0.0000 0.0000 NBFI

0.1254 401.0000 15.6483 0.1171 0.0000 1.0000 0.0000 0.0000 NBFI

0.2494 388.0000 13.6907 0.0174 0.0000 1.0000 0.0000 0.0000 NBFI

0.2335 258.0000 12.7735 0.0592 0.0000 1.0000 0.0000 0.0000 NBFI

0.2029 122.0000 13.1807 0.1295 1.0000 0.0000 0.0000 0.0000 CU

0.0452 2425.0000 15.2513 0.0510 0.0000 0.0000 1.0000 0.0000 NGO

0.0477 1773.0000 15.6422 0.0389 0.0000 0.0000 1.0000 0.0000 NGO

212

0.0354 2183.0000 16.1542 0.0442 0.0000 0.0000 1.0000 0.0000 NGO

0.0400 2074.0000 16.0776 0.0496 0.0000 0.0000 1.0000 0.0000 NGO

0.0390 1306.0000 16.3941 0.0270 0.0000 0.0000 1.0000 0.0000 NGO

0.0392 1604.0000 16.5389 0.0239 0.0000 0.0000 1.0000 0.0000 NGO

0.0399 1120.0000 16.5803 0.0358 0.0000 0.0000 1.0000 0.0000 NGO

0.0818 250.0000 13.8181 0.0091 0.0000 0.0000 1.0000 0.0000 NGO

0.0755 459.0000 14.8182 0.0017 0.0000 0.0000 1.0000 0.0000 NGO

0.0723 486.0000 15.5579 0.0239 0.0000 0.0000 1.0000 0.0000 NGO

0.0861 418.0000 15.9002 0.0855 0.0000 0.0000 1.0000 0.0000 NGO

0.0922 402.0000 15.3698 0.0488 0.0000 0.0000 1.0000 0.0000 NGO

0.0520 133.0000 15.7849 0.1577 0.0000 1.0000 0.0000 0.0000 NBFI

0.0336 127.0000 16.4190 0.1361 0.0000 1.0000 0.0000 0.0000 NBFI

0.0243 158.0000 16.8833 0.1565 0.0000 1.0000 0.0000 0.0000 NBFI

0.0250 160.0000 16.6725 0.1442 0.0000 1.0000 0.0000 0.0000 NBFI

0.0241 1731.0000 16.6397 0.1352 0.0000 1.0000 0.0000 0.0000 NBFI

0.0326 130.0000 15.7629 0.1103 0.0000 1.0000 0.0000 0.0000 NBFI

0.0510 98.0000 14.8221 0.1803 0.0000 1.0000 0.0000 0.0000 NBFI

0.1443 395.0000 15.2024 0.0248 0.0000 0.0000 1.0000 0.0000 NGO

0.1405 90.0000 15.0216 0.4419 0.0000 1.0000 0.0000 0.0000 NBFI

0.1617 57.0000 16.3892 0.5853 0.0000 1.0000 0.0000 0.0000 NBFI

0.1359 1007.0000 16.5655 0.3107 0.0000 1.0000 0.0000 0.0000 NBFI

0.1441 194.0000 17.5280 0.6488 0.0000 1.0000 0.0000 0.0000 NBFI

0.1349 1540.0000 17.5924 0.4346 0.0000 1.0000 0.0000 0.0000 NBFI

0.1126 345.0000 17.4238 0.3347 0.0000 1.0000 0.0000 0.0000 NBFI

0.1700 331.0000 14.3072 0.0208 0.0000 1.0000 0.0000 0.0000 NBFI

0.1707 480.0000 17.5593 0.6154 0.0000 1.0000 0.0000 0.0000 NBFI

0.1482 582.0000 18.3894 0.1425 0.0000 1.0000 0.0000 0.0000 NBFI

0.0317 320.0000 18.2171 0.1507 0.0000 1.0000 0.0000 0.0000 NBFI

0.1452 494.0000 13.9783 0.0712 1.0000 0.0000 0.0000 0.0000 CU

0.1854 298.0000 16.4261 0.1459 0.0000 1.0000 0.0000 0.0000 NBFI

0.1594 193.0000 17.1166 0.0386 0.0000 1.0000 0.0000 0.0000 NBFI

0.1517 351.0000 17.6404 0.1399 0.0000 1.0000 0.0000 0.0000 NBFI

0.1059 376.0000 17.8183 0.0534 0.0000 1.0000 0.0000 0.0000 NBFI

0.1067 760.0000 18.6721 0.2273 0.0000 1.0000 0.0000 0.0000 NBFI

0.0948 503.0000 19.0677 0.2178 0.0000 1.0000 0.0000 0.0000 NBFI

0.0820 607.0000 19.9318 0.5063 0.0000 1.0000 0.0000 0.0000 NBFI

0.0682 727.0000 19.9738 0.1433 0.0000 1.0000 0.0000 0.0000 NBFI

0.0618 760.0000 19.3631 0.0334 0.0000 1.0000 0.0000 0.0000 NBFI

0.0391 242.0000 17.0466 0.1201 0.0000 0.0000 1.0000 0.0000 NGO

213

0.0330 416.0000 17.9225 0.2378 0.0000 0.0000 1.0000 0.0000 NGO

0.0886 394.0000 18.4464 0.3123 0.0000 0.0000 1.0000 0.0000 NGO

0.0416 415.0000 18.4734 0.2138 0.0000 0.0000 1.0000 0.0000 NGO

0.0478 510.0000 18.6773 0.1994 0.0000 0.0000 1.0000 0.0000 NGO

0.0412 647.0000 19.0947 0.1490 0.0000 0.0000 1.0000 0.0000 NGO

0.1100 389.0000 19.4870 0.1264 0.0000 0.0000 1.0000 0.0000 NGO

0.1794 205.0000 15.2046 0.4542 0.0000 1.0000 0.0000 0.0000 NBFI

0.1507 281.0000 15.5890 0.2004 0.0000 1.0000 0.0000 0.0000 NBFI

0.1047 235.0000 16.5729 0.2403 0.0000 1.0000 0.0000 0.0000 NBFI

0.1322 386.0000 17.8608 0.2048 0.0000 1.0000 0.0000 0.0000 NBFI

0.1232 436.0000 19.0973 0.2619 0.0000 1.0000 0.0000 0.0000 NBFI

0.1331 443.0000 19.7581 0.6321 0.0000 1.0000 0.0000 0.0000 NBFI

0.1014 488.0000 20.2111 0.2253 0.0000 1.0000 0.0000 0.0000 NBFI

0.0482 315.0000 16.1381 0.1581 0.0000 1.0000 0.0000 0.0000 NBFI

0.0566 462.0000 17.1477 0.1778 0.0000 1.0000 0.0000 0.0000 NBFI

0.0967 502.0000 17.0416 0.1405 0.0000 1.0000 0.0000 0.0000 NBFI

0.1556 618.0000 16.9641 0.2436 0.0000 1.0000 0.0000 0.0000 NBFI

0.0555 417.0000 14.3144 0.0383 1.0000 0.0000 0.0000 0.0000 CU

0.0608 350.0000 15.1057 0.0956 1.0000 0.0000 0.0000 0.0000 CU

0.0533 231.0000 14.5973 0.0082 0.0000 0.0000 1.0000 0.0000 NGO

0.1117 393.0000 14.2529 0.2584 0.0000 0.0000 1.0000 0.0000 NGO

0.1474 324.0000 15.0417 0.0000 0.0000 0.0000 1.0000 0.0000 NGO

0.1078 559.0000 15.2648 0.0755 0.0000 0.0000 1.0000 0.0000 NGO

0.0938 595.0000 15.3139 0.0769 0.0000 0.0000 1.0000 0.0000 NGO

0.0930 560.0000 15.2036 0.0761 0.0000 0.0000 1.0000 0.0000 NGO

0.0554 813.0000 14.9855 0.0010 0.0000 0.0000 1.0000 0.0000 NGO

0.3628 158.0000 14.1417 0.4070 0.0000 1.0000 0.0000 0.0000 NBFI

0.1647 260.0000 15.4565 0.1040 0.0000 1.0000 0.0000 0.0000 NBFI

0.1226 241.0000 15.6150 0.0800 0.0000 1.0000 0.0000 0.0000 NBFI

0.1568 205.0000 16.0979 0.4182 0.0000 1.0000 0.0000 0.0000 NBFI

0.1391 298.0000 16.5535 0.3403 0.0000 1.0000 0.0000 0.0000 NBFI

0.1282 390.0000 16.4764 0.6051 0.0000 1.0000 0.0000 0.0000 NBFI

0.0514 710.0000 15.9625 0.0105 0.0000 1.0000 0.0000 0.0000 NBFI

0.0411 695.0000 16.6440 0.0550 0.0000 1.0000 0.0000 0.0000 NBFI

0.0582 659.0000 17.0722 0.1108 0.0000 1.0000 0.0000 0.0000 NBFI

0.0608 645.0000 18.0288 0.1144 0.0000 1.0000 0.0000 0.0000 NBFI

0.0579 535.0000 18.6153 0.1385 0.0000 1.0000 0.0000 0.0000 NBFI

0.0617 534.0000 19.4862 0.1457 0.0000 1.0000 0.0000 0.0000 NBFI

0.0536 503.0000 20.0058 0.2193 0.0000 1.0000 0.0000 0.0000 NBFI

214

0.0608 514.0000 20.0325 0.0564 0.0000 1.0000 0.0000 0.0000 NBFI

0.0642 631.0000 19.3470 0.1904 0.0000 1.0000 0.0000 0.0000 NBFI

0.2386 239.0000 13.3427 0.0811 0.0000 0.0000 1.0000 0.0000 NGO

0.1109 373.0000 15.2353 0.4878 0.0000 0.0000 1.0000 0.0000 NGO

0.1165 345.0000 15.5304 0.2931 0.0000 0.0000 1.0000 0.0000 NGO

0.1177 243.0000 15.4245 0.0858 0.0000 0.0000 1.0000 0.0000 NGO

0.1542 229.0000 15.5064 0.3221 0.0000 0.0000 1.0000 0.0000 NGO

0.1680 181.0000 15.0267 0.2055 0.0000 0.0000 1.0000 0.0000 NGO

0.3374 308.0000 14.9453 0.2952 0.0000 1.0000 0.0000 0.0000 NBFI

0.2527 433.0000 15.7320 0.1613 0.0000 1.0000 0.0000 0.0000 NBFI

0.1705 752.0000 15.7569 0.3693 0.0000 1.0000 0.0000 0.0000 NBFI

1.1543 49.0000 13.4107 0.5373 0.0000 1.0000 0.0000 0.0000 NBFI

0.3863 404.0000 15.6806 0.3578 0.0000 1.0000 0.0000 0.0000 NBFI

0.1939 378.0000 15.7264 0.2352 0.0000 1.0000 0.0000 0.0000 NBFI

0.1045 232.0000 14.1562 0.1094 0.0000 0.0000 1.0000 0.0000 NGO

0.1597 475.0000 14.6781 0.1332 0.0000 0.0000 1.0000 0.0000 NGO

2.7485 125.0000 11.8324 0.2422 0.0000 1.0000 0.0000 0.0000 NBFI

1.8708 57.0000 12.8129 0.0958 0.0000 1.0000 0.0000 0.0000 NBFI

1.1579 158.0000 10.8027 0.0432 0.0000 1.0000 0.0000 0.0000 NBFI

0.5583 190.0000 14.9076 0.2937 0.0000 1.0000 0.0000 0.0000 NBFI

0.3646 223.0000 15.6771 0.2341 0.0000 1.0000 0.0000 0.0000 NBFI

0.2207 318.0000 15.7301 0.1475 0.0000 1.0000 0.0000 0.0000 NBFI

0.0818 419.0000 13.9887 0.1640 0.0000 1.0000 0.0000 0.0000 NBFI

0.0875 422.0000 15.4791 0.0951 0.0000 1.0000 0.0000 0.0000 NBFI

0.1109 288.0000 16.2227 0.1241 0.0000 1.0000 0.0000 0.0000 NBFI

0.0871 743.0000 15.2159 0.0332 0.0000 1.0000 0.0000 0.0000 NBFI

0.0914 475.0000 16.1592 0.1554 0.0000 1.0000 0.0000 0.0000 NBFI

0.0897 925.0000 16.6968 0.1066 0.0000 1.0000 0.0000 0.0000 NBFI

0.0406 962.0000 15.4983 0.0670 0.0000 0.0000 1.0000 0.0000 NGO

0.0474 32.0000 15.2960 0.0633 0.0000 0.0000 1.0000 0.0000 NGO

0.1586 523.0000 15.5800 0.1393 0.0000 1.0000 0.0000 0.0000 NBFI

0.0969 653.0000 17.2690 0.3265 0.0000 1.0000 0.0000 0.0000 NBFI

0.0930 845.0000 17.3199 0.0846 0.0000 1.0000 0.0000 0.0000 NBFI

0.0554 1755.0000 16.4912 0.0507 0.0000 1.0000 0.0000 0.0000 NBFI

0.1855 363.0000 13.9215 0.1595 0.0000 1.0000 0.0000 0.0000 NBFI

0.1646 316.0000 14.2690 0.2010 0.0000 1.0000 0.0000 0.0000 NBFI

0.1534 273.0000 13.3016 0.1389 0.0000 1.0000 0.0000 0.0000 NBFI

0.5729 160.0000 14.1608 0.0000 0.0000 1.0000 0.0000 0.0000 NBFI

0.3094 144.0000 15.6274 0.0641 0.0000 1.0000 0.0000 0.0000 NBFI

215

0.2322 249.0000 16.4681 0.1202 0.0000 1.0000 0.0000 0.0000 NBFI

0.1644 350.0000 18.4821 0.0970 0.0000 1.0000 0.0000 0.0000 NBFI

0.1456 438.0000 18.6141 0.2402 0.0000 1.0000 0.0000 0.0000 NBFI

0.1337 492.0000 16.1573 0.7352 0.0000 1.0000 0.0000 0.0000 NBFI

0.3610 297.0000 13.9314 0.1796 0.0000 1.0000 0.0000 0.0000 NBFI

0.1645 620.0000 15.0230 0.2413 0.0000 1.0000 0.0000 0.0000 NBFI

0.1534 484.0000 15.4028 0.7392 0.0000 1.0000 0.0000 0.0000 NBFI

0.1026 682.0000 15.2436 0.2076 0.0000 1.0000 0.0000 0.0000 NBFI

0.1146 598.0000 15.7274 0.2057 0.0000 1.0000 0.0000 0.0000 NBFI

0.1313 575.0000 17.0579 0.2363 0.0000 1.0000 0.0000 0.0000 NBFI

0.1063 452.0000 16.7391 0.0692 0.0000 1.0000 0.0000 0.0000 NBFI

0.1360 397.0000 16.6034 0.1624 0.0000 1.0000 0.0000 0.0000 NBFI

0.1236 369.0000 12.5285 0.0396 0.0000 0.0000 1.0000 0.0000 NGO

0.1160 150.0000 13.9562 0.0642 0.0000 0.0000 1.0000 0.0000 NGO

0.0646 7551.0000 14.7796 0.0722 0.0000 0.0000 1.0000 0.0000 NGO

0.0508 7979.0000 15.3330 0.1706 0.0000 0.0000 1.0000 0.0000 NGO

0.0468 8905.0000 15.5105 0.2093 0.0000 0.0000 1.0000 0.0000 NGO

0.2001 359.0000 9.2824 0.0524 0.0000 1.0000 0.0000 0.0000 NBFI

0.1613 229.0000 14.2419 0.1345 0.0000 0.0000 0.0000 0.0000 Other

Source: data from mixmarket.com