Determinants of Credit Rationing of Small and Micro ...

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Wageningen University: Department of Social Science

Development Economics Group

Determinants of Credit Rationing of

Small and Micro Enterprises:

Case of Mekelle City, North Ethiopia

MSc Thesis Development Economics (DEC-80433)

Tsehaye Gebrekiros

Supervisor

Marrit van Den Berg (PHD)

Wageningen, The Netherlands

July, 2013

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Small and Micro Enterprises in the city of Mekelle, North Ethiopia

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Wageningen University: Department of Social Science

Development Economics Group

Determinants of Credit Rationing of Small and Micro

Enterprises: Case of Mekelle City, North Ethiopia

MSc Thesis Development Economics (DEC-80433)

Tsehaye Gebrekiros

Supervisor

Marrit Van Den Berg (PHD)

Wageningen, The Netherlands,

July, 2013

|

Table of Contents

Page

LIST OF TABLES ...................................................................................................................... v

LIST OF FIGURES .................................................................................................................... v

ABBREVIATIONS .................................................................................................................... vi

ACKNOWLEDGEMENT .......................................................................................................... vii

ABSTRACT .............................................................................................................................. viii

1. Introduction............................................................................................................................ 1

1.1Statement of the Problem ................................................................................................... 2

1.2Objectives of the Study ............................................................................................................... 3

1.3Significant of the Study ............................................................................................................... 3

1.4Organization of the Paper ............................................................................................................ 4

2. Background of the Study ......................................................................................................... 5

2.1 Small and Micro Enterprises in Ethiopia ................................................................................... 5

2.2 Credit Market in Ethiopia ........................................................................................................... 7

2.2. Types of Financial Market ........................................................................................................ 7

2.2.1 Formal Financial Market .................................................................................................... 8

2.2.2 Informal Financial Market ................................................................................................. 9

3. Theoretical Framework ................................................................................................................... 10

3.1 Concept and Definition of Credit Market ................................................................................. 10

3.2 Adverse Selection and Moral Hazard ....................................................................................... 10

3.3 Credit Constraints ..................................................................................................................... 11

3.4 Collateral .................................................................................................................................. 12

3.5 Social Capital ........................................................................................................................... 13

3.6 Entrepreneur Characteristics .................................................................................................... 13

3.7 Firms Characteristics ................................................................................................................ 14

3.8 Empirical Framework ............................................................................................................... 14

3.9 Direct Elicitation Method (DEM) ............................................................................................ 15

4. Research Methodology .................................................................................................................... 19

4.1 Study Area ................................................................................................................................ 19

4.2 Data Source, Sampling Procedure and the Survey ................................................................... 21

4.2.1 Measurement (Description) of Variables ......................................................................... 22

4.3 Data Analysis Method .............................................................................................................. 23

Determinants of Credit Rationing of Small and Micro Enterprises 2013

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4.3.1 Descriptive Statistics ........................................................................................................ 23

4.3.2 Econometric Specification ............................................................................................... 23

4.4 Multicollinearity Test ............................................................................................................... 25

4.5 Test of Independent Irrelevant Alternatives (IIA) .................................................................... 25

5. Empirical Results of the Study .............................................................................................. 27

5. 1 Descriptive Statistics ............................................................................................................. 27

5.1.1 Entrepreneur Socioeconomic Characteristics ........................................................................ 27

5.1.2 Application for Credit ........................................................................................................... 28

5.1.3 Distribution of Credit Constraints ......................................................................................... 30

5.1.4 Reason for not Applied from Formal Financial Institutions.................................................. 32

5.2 Econometric Results ..................................................................................................... 33

6. Conclusion and Recommendations ........................................................................................ 38

References ...................................................................................................................................... 40

APPENDICES ................................................................................................................................ 43

Annex-A: Survey Questionnaire .................................................................................................... 43

Annex-B: Multicollinearity test: .................................................................................................... 51

Annex-C: Test of Independent Irrelevant Alternatives (IIA) ......................................................... 52

Annex-D: Multinomial logit ........................................................................................................... 53


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LIST OF TABLES

Table2.1 Definitions of Ethiopian MSMEs by the EMTI and ECSA......................... 6

Table3.1 Credit rationing category’s using DEM..................................................... 17

Table 4.1 Description of variables............................................................................ 22

Table5.1 Entrepreneurs socioeconomic characteristics............................................. 28

Table5.2 Entrepreneurs socioeconomic characteristics discrete variable………….. 28

Table 5.3 Firms applied for loan............................................................................. 28

Table 5.4 Firms applied and received..................................................................... 29

Table 5.5 Purpose of the loan............................................................................... 29

Table 5.6 Source of finance.................................................................................. 30

Tble5.7 Distribution of credit constrained............................................................. 31

Table 5.8 Cross tabulation between sector and credit constraints........................... 31

Table 5.9 Cross tabulation between credit constraints with experience................... 32

Table 5.10 Cross tabulation between sector and credit constraints......................... 33

Table5.11 Marginal effect estimation after multinomial logit regression................... 34

LIST OF FIGURES

1. Figure1 Empirical framework of the study............................................................ 15

2. Overview of the study area of Mekelle (Orange paint)........................................... 18

3. The city of Mekelle................................................................................................. 19


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ABBREVIATIONS

CSA Central Statistics Agency

DECSI Dedebit Credit and Saving Institution

DEM Direct Elicitation Method

ECSA Ethiopian Central Statistics Authority

EMTI Ethiopian Ministry of Trade and Industry

GDP Gross Domestic Product

ME Marginal Effect

MFI Microfinance Institutions

MSMEs Medium, Small and Micro Enterprises

SMEs Small and Micro Enterprises

TOL Tolerance

VIF Variance Inflation Factor


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ACKNOWLEDGEMENT

I would like to express my sincere gratitude to all individuals in one way or another who

have supported me in all my life.

Above all I would like to thank the Almighty of God, who give me the strength and

capability to finish my study successfully.

I am highly thankful to my Supervisor Dr. Marrit van Den Berg for her excellent guidance

in constructively shaping the thesis. She was very great starting from focusing on the

subject area till the end of the thesis. She has been also fully cooperatives, friendly to

share her experience and to make my thesis more professional. I thank you Marrit.

I am also highly thankful to the Netherlands Organization for International Corporation in

Higher Education(NUFFIC) for give me the chance to purse my master’s study in

Wageningen University, in The Netherlands and covered all my expenses. I also thank you

Mekelle University for gave me permission for the study.

My heartfelt gratitude also goes to all my family, specially my mother(Esheie) and my

little sisters, Grmush and Frey I have no words to express my appreciation what you did

for me.........................you were great for the moral you gave me and for praying for me.

Brother as well as friend Tewodros(PHD) many many thanks for everything you did for

me in my academics journey. Without your guidance and inspiration I would not arrived at

this stage. I proud of you because you are my brother and friend.

My appreciations also goes to Zenebe( brother) ,Haftermariam, Fitsum and Fantaye for

conducting data collection as much clean as possible.

Last but not least, I am very grateful to my friends Tafesse, Thedros Abebe, Alex,

Lissane(Dutch) and Frederike (Dutch) for the enjoyable time in abroad, which made life

easier at the Wageningen.

Tsehaye, 2013


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ABSTRACT

Small and micro enterprises (SMEs) greatly contribute in promoting economic growth and

poverty alleviation in both developed and less developed countries. SMEs contribute

immensely to Gross Domestic Product (GDP) and it has a sizeable influence in growth of

economy. However SMEs are constrained in their access to formal credit, Commercial

banks and other financial institutions, fail to provide credit for the needs of firms due to

information asymmetry and SMEs do not meet the required collateral. This study

investigates the determinants of credit rationing of SMEs in the city of Mekelle. A sample

of 200 firms was selected and analysed using descriptive statistics and multinomial logit.

The result suggests that majority (89.15%) of the firms obtained loan form microfinance

institutions (MFI). The firms that obtained their loan form bank is 10.85%. In the study

credit rationing was categorized in four rationings. 46% of them were unconstrained non-

borrowers, 26% unconstrained borrowers, 17% quantity rationed and 17% risk rationed

borrowers. Econometrics result shows that gender, education, firm age and collateral does

not have any impact on credit rationing. Age of the owner of the firm, household size,

initial investment and social capital have impact on credit rationing.

Key words: Credit rationing, MSEs, MFI, bank, multinomial logit, Mekelle


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1. Introduction

In most African countries, the share of Small and Micro Enterprises (SMEs) in economic

activities has been significantly increasing (Aga and Reilly, 2011). MSEs greatly

contribute in promoting economic growth and poverty alleviation in both developed and

less developed countries (Katundu et al., 2012). SMEs contribute immensely to Gross

Domestic Product (GDP) and it has a sizeable influence in growth of economy

(Okpukpara, 2009). For example the importance of SMEs has increased for employment

generation, income and poverty reduction in Ethiopia (Bekele and Worku, 2008b). A

research for 76 developed and developing countries shows that on average SMEs account

for about 60% of manufacturing employment. Likewise in Ethiopia a survey conducted by

the country’s Central Statistics Agency (CSA) in 2002 showed that 974, 679 micro

enterprises, generating a means of livelihood for about 1.3 million people. A study

conducted by the same institution in 2003, 1863 SMEs employing 97,782 individuals (Aga

and Reilly, 2011).

However many SMEs are constrained access to credit. Economic theory suggests that

credit constraint may have significant negative impacts on income and welfare especially

for small firms (Boucher et al., 2006). SMEs are constrained in their access to formal

credit, commercial banks and other financial institutions, fail to provide credit for the

needs of firms due to the rules and regulation created, information asymmetry and SMEs

do not meet the required collateral (Atieno, 2001). Credit is constrained when the demand

for credit exceeds the supply of credit (Boucher et al., 2006). In case of credit constraint,

some firms able to obtain credit while others with identical characteristics who are wanting

to borrow at exactly the same term do not or firms are either received lower amount than

demanded or rejected.

Information is one of the most important factors in the decision making of financial

institutions. Banks face challenges to get information about their borrowers however

borrowers have more information than the lender about the project. Banks are also

uninterested to allow credit to SMEs due to the vast problem of information asymmetry,

screening, and monitoring and enforcement problems. In this case when there is an

information asymmetry financial institution, are uncertain about the repayment of the loan.

In addition SMEs are unable to provide reliable financial information and business plan;

this will be leading to banks to incur higher cost in dealing with the SMEs as a result banks


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unable to assess the creditworthiness of individual SMEs and this will lead banks either

grant small loan or reject.

In line with theme of the thesis, determinants of credit rationing were widely discussed in

many developed and developing countries. For example in Brazil using logit model find

that banks faces difficulties in expanding the supply of credit to MSEs mainly due to

transaction cost, collateral and asymmetric information (Zambaldi et al., 2011). Study in

South Africa the constraint of credit access by new SMEs from commercial bank showed

that collateral, business information, managerial competencies and networking are major

determinants of credit constraints (Fatoki and Odeyemi, 2010). Using enterprise survey

data from Kosova showed that commercial banks made decision to grant loan to firms

primary on the basis of collateral but they did not consider firm profitability as a sufficient

condition to get credit (Krasniqi, 2010). Study carried in South- East Europe to investigate

the impact of firms characteristics on SMEs of credit constraints, small firms are more

likely refused a loan and face problems in accessing both short-term and long-term form

banks (Hashi and Toçi, 2011). Study in UK investigate impact of business and

entrepreneur characteristics on severity of financial problem faces in access to credit by

entrepreneurs, showed that characteristics of entrepreneur, such as education, experience,

wealth and business characteristics such as size and credit card have strong effect on the

dangers of financial problems faced by SMEs (Han, 2008).

1.1 Statement of the Problem

Provision of credit has been considered as a crucial instrument for raising income by

mobilizing resources to the most productive uses and it can help borrower to take

entrepreneur activities (Atieno, 2001). Credit programmes have been given due attention

by donors and governments (Bigsten et al., 2003). This is due to the fact credit markets are

not functioning well in many developing countries and resulted to low economic activity

and growth in most Africa countries. Microcredit has been a popular tool in poverty

alleviation strategy in developing countries. However the poor, which are most of the time

engaged in small enterprises in developing countries have limited access to formal

financial services due to lack of collateral and relatively high transaction cost for small

loans (Doan et al., 2010). Yet, majority of SMEs in developing countries are considered

unworthy by formal financial institutions. Therefore improving the availability of credit

facility is crucial for the development of SMEs in developing countries thereby realizing

the potential contribution to the economy.


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Credit constraint by formal financial institutions stifles growth of SMEs. To fill the gap in

some developing countries informal financial institutions have become successful in

meeting the credit demand by SMEs, however due to their limited resources they are

restricted from effectively satisfying the credit need of SMEs (Atieno, 2001). This is due to

SMEs are increasing in number and size, and the loan they demand have become beyond

the reach informal financial institutions. Despite financing is a major factor for potential

growth of SMEs, several researchers and consultancy reports showed that SMEs face

credit constraint. During credit constraints SMEs may not be able to invest, despite their

willingness to invest unless they have enough internal source of finance available. As a

result the economy will losing some of the potential benefits of promising projects due to

the constraint of credits and credit constrained firms may hinder their contribution to the

employment creation and poverty alleviation. Therefore understanding the major factors

that responsible for credit constrained of SMEs is very important so this study will

examine the determinants of credit constraints.

1.2 Objectives of the Study

The objective of the study is to investigate the determinants of credit rationing of SMEs. In

doing so, it is also aimed at investigating the characteristics of SMEs and the major source

of credit for SMEs and provides policy implications to enhance access to credit by SMEs.

To achieve the above objectives the study was answer the following research questions;

What are characteristics of SMEs in the study area?

What are the major sources of credit for SMEs?

What factors influence credit rationing of SMEs?

1.3 Significant of the Study

SMEs in both developed and developing countries greatly contributes in creating of

employment opportunities, income generation. They also used as source of livelihood and

fighting poverty. SMEs have been contributing a higher share for GDP to Ethiopian

economy as well. However in most developing countries SMEs have been facing problems

to access to credit due to imperfection of credit market. The imperfection in the credit

market and the problem of asymmetric information has been also leading to credit

constraint. Therefore this study will try to investigate the major determinant of credit

constraints that exist on SMEs in Mekelle.


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1.4 Organization of the Paper

The paper is organized into five chapters. Chapter one includes introduction, statement of

the problem, objectives and research questions, chapter two deals with background of the

study, chapter three deals with theoretical framework, relevant literature and empirical

framework of the study. Chapter four deals with the methodology and study area. Chapter

five covered the results and discussions and the final chapter deals with conclusions and

recommendations of the study.


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2. Background of the Study

2.1 Small and Micro Enterprises in Ethiopia

Although Small and Micro Enterprises (SME) contribute significantly to the national

economy by alleviating poverty and creating jobs, SME sector has been given little

attention and support from the Ethiopian government in terms of technical and managerial

support, provision of credit and other basic facilities. Only large-scale firms and state

owned institutions have enjoyed supreme support in terms of policy and institutional

support from successive governments. Historically, SMEs in Ethiopia have done relatively

well during Emperor Hailesilassie’s regime before 1974. Following regime (19974 -1990)

Mengistu Hailemariam came to power and the sector has performed poorly. In comparison

with previous governments the current government seemed well in delivered a national

development strategy for the development of SME though the success achieved so far has

not been as expected(Gebeyehu and Assefa, 2004). Lack of access to finance is the most

crucial factor hindering for the growth and development of SME in developing countries in

general in Ethiopia in particular(Bekele and Worku, 2008a). The performance of SME is

poor even today in comparison with similar sectors in other Sub-Saharan African

countries. SME in Ethiopia are generally characterized by an acute shortage of finance,

lack of technical skills, poor management, and lack of training opportunities, shortage of

raw materials, poor infrastructure and over-tax (Ibid).

Though the current government of Ethiopia has a great interest in helping and creating

conducive environment for the growth and development of SMEs, the macro-economic

environment (monetary and fiscal policy) in many developing countries including Ethiopia

is not appropriate for the growth and development of medium, small and micro enterprises

(MSMEs). For example the IMF recently agreed with government of Ethiopia to strict

monetary and fiscal policies, such as reduction of public expenditure on investments,

increase commercial bank reserve requirements and deflating while there is inflation in the

country. Therefore though macroeconomic tightening is a cruel medicine for short term

but it devastating long term consequences(Hailu, 2009). In addition several development

economists have called for intervention in order to alleviate the acute shortage of finance

experienced by the MSME sector, no meaningful institutional support has so far been

given to the struggling sector(Ageba and Amha, 2004). SMEs have a greater credit demand

both at the start-up and expansion phase in comparison with well-established firms

however due to the rules and regulation by formal financial institutions as a result many of


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the SMEs stand at their very low level in terms of number of employment creation and

capital(Aryeetey et al., 1997).

While you are coming to the definition of SMEs, there is no single or universally accepted

definition of SMEs. SMEs varies from country to country depending on factors such as

the country’s state of economic development, the strength of the industrial and business

sectors, the size of SMEs and the particular problems experienced by SMEs. Hence, there

is no definition of SMEs is suitable for all countries of the world, for example in Ethiopia,

parameters such as the level of capital investment, the number of workers employed and

the level of automation are used for the classification of SMEs. Based on this, two types of

working definitions are used by the Ethiopian Ministry of Trade and Industry (EMTI) and

the Ethiopian Central Statistics Authority (ECSA). According to the EMTI (1997), the

definition of MSMEs is based on the level of capital investment of the firm, while the

ECSA classifies enterprises into different categories based on the number of workers

employed in the firm and the level of automation of the firm(Bekele and Worku, 2008a).

Table2.1Definitions of Ethiopian MSMEs by the EMTI and ECSA

Name Capital investment( EMTI) Number of employees and level of

automation (ECSA)

Micro enterprises Up to 2,250 US$ excluding high-tech

consultancy firms & establishments

Up to 10 employees and using non-power

driven machines for operation

Small enterprises 2,250-56,000 US$ excluding high tech

consultancy firms and establishments

Less than 10 employees using motor-operated

equipment

Medium & large enterprises Above 56,000 US$ Above 50 employees

Source: adopted from Eshetu and Zeleke (2008)

In many developing countries including Ethiopia the majority of MSMEs operate at under

capacity(Bekele and Worku, 2008a). This was due to factors such as lack of credit and

over regulations. The problem has been more exacerbated by demanding collateral by

commercial banks for the approval of loan applications. This was wittiness by the

Ethiopian Central Statistical Authority report in 2003 only 0.2% of small scale enterprises

was given loans by the Commercial Bank of Ethiopia at the their start-up stage while 45%

of them were supported by their own savings, 24% were supported by friends and 20%


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were supported by their relatives and only 0.8% of the small scale enterprise operators

raised their finance from micro finance institutions(Bekele and Worku, 2008a).

2.2 Credit Market in Ethiopia

Credit markets in Africa indicates that a large proportion of financial transactions occur

outside the formal financial system due to limitations in the formal financial system. The

majority of small businesses in Ethiopia raise finance from informal money lenders such as

from family and relatives and equib1 schemes. This is because it is very difficult for them

to meet the demand for collateral as well as the high interest rates of the banks. Though

formal financial institutions owned by government and private investors, such as

commercial banks and micro finance institutions are growing in number, informal financial

institution in general, equib systems in particular are very popular and widely operational

in all parts of Ethiopia. Equib systems function on the basis of mutual trust and it operate

on cyclic basis, most of the time it undertaken weekly and of course same times also

monthly. The equib system at one drew it satisfying the demand of only one member. Next

one member from the rest the member satisfy and works the same for the rest member

again and again but make sure the other members must wait their turn. Finally the last

member receives a lump sum only at the very end of the cycle. However the lengthy

waiting period in equib cycles often results in the loss of investment opportunity, loss of

valuable time, loss of resources and money, etc. Equib systems can be large or small

depend on the contribution of group members. Small equib systems, most of the time they

are located in small towns and rural communities and have smaller lump sums. Large

equib systems are often located in major towns and generate a lump sum of about half a

million Ethiopian Birr (60,000 US$) per month(Bekele and Worku, 2008a). Therefore

there is a need to improve the capacity of equib systems in Ethiopia so that they can lend

more money to more small businesses at the same time.

2.2. Types of Financial Market

Like in many countries financial market in Ethiopia can also be classified in to formal

financial institution and informal financial institution. Formal financial institutions are

those financial institutions which are licensed and supervised by central bank. These

institutions are included public commercial banks, private commercial banks, development

___________________________________

1Equib in Ethiopia is similar with other countries the so called Rotating Saving and Credit Cooperative (ROSCA)


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banks, microfinance institutions, construction and business banks, development banks and

saving and credit cooperatives. Informal financial institutions are those institutions which

are not licensed and regulated by anybody. These informal financial institutions are

included, money lenders, equib, family and relatives and equib

2.2.1 Formal Financial Market

The banking system in Ethiopia appear unique from East Africa countries and many

developing countries in that it has not yet opened its banking sector to foreign

participant’s. And the Ethiopian banking sector remain unaffected by globalization due to

the fact that the Ethiopian policy maker understand the potential importance of financial

liberalization for their country may result in loss of control over the economy and may not

be economically beneficial. The benefit of financial liberalization for Ethiopia has not been

yet studied but studies carried in many developing countries showed that financial

liberalization has been bring positive effect for the given country’s economic

growth(Kiyota et al., 2007). The Ethiopian economy has been state controlled through

series of industrial development plans since the Imperial Government of Haile Selassie. It

was followed as a Soviet-style centrally planned economy under a socialist government

from 1976-1991. After the current government came in to power in 1991the country led

transition to a more market-oriented system and subsequently the government has

introduced further reforms. Right after the reform the state control has been reduced and

domestic and foreign (private) investment promoted and of course state still plays a

dominant role in the economy’s today(Kiyota et al., 2007).

Currently in Ethiopia the banking system is public-private enterprises. Until recently the

industry was dominated by the public owned Commercial Bank of Ethiopia and

Development Bank of Ethiopia. The sector was opened for private investors after 1991s

and immediately many private banks has been opened and now around 18 private banks

working in the market and they have been a significant engine for the country’s economic

growing. Currently around 19 commercial banks and 28 microfinance institutions as of

2008 are engaging in the banking sector in all round of the country. Their main objective is

to mobilizing resources and channelling to users based on agreement between financial

institutions and borrowers. Prior to entering into lending contracts banks need to

understand to whom they giving credit. Banks want to be family with borrower and be

confident that they are dealing with an individual or company or institution of repute and

creditworthiness. However to conduct an effective credit granting programs banks shall


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receive sufficient information and need to consider the following factors and documented

during the loan application process;

Purpose of the credit and source of repayment

Borrower’s business expertise and managerial capacity

Adequate collateral

Borrower’s repayment history

Terms and conditions of the credit

Current risk profile

In addition to the above factors, banks must have a clear established process in place for

approving new credit or renewal and refinancing of existing credits then approvals should

be made in accordance with the bank’s written guidelines and after visiting the prospective

firm, evaluate the business plan and decides whether to extend the loan or not. However

like in many developing countries in Ethiopia the credit market is also characterized by

market imperfection which will be resulted to information asymmetry thereby to adverse

selection and moral hazards.

2.2.2 Informal Financial Market

Informal financial institutions are those institutions which are not licensed and regulated

by central banks. These informal financial institutions are included money lenders, family

and relatives and equibs. Informal financial institutions obtain credit from formal financial

institution then the credit will lend to farmers, household and traders(Moll, 1989). Traders

can obtain loan directly from formal financial institutions, but sometimes they prefer to use

informal financial institutions due to the reasons related financing advantages, transaction

cost and flexibility of informal financial institutions. Informal financial institutions have a

common characteristics they perform active monitoring. This means that they try to keep

their agents project to not to fail and to reduce the possibility that the projects cash flow

may be diverted to purpose other than meeting promised repayment(Reyes Duarte, 2011).


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3. Theoretical Framework

3.1 Concept and Definition of Credit Market

The theoretical model of equilibrium of credit rationing is based on credit market

imperfection due to asymmetric information. Asymmetric information, makes it costly and

difficult for banks to obtain correct information of borrowers and to monitor the action of

the borrowers (Stiglitz and Weiss, 1981). When there is asymmetric information in the

credit market the interest rate will not clear the excess demand for credit in the credit

market. The interest rate charged by banks are consider a dual purposes, of sorting

potential borrowers that can repay its debt and affecting the action of borrowers. Raising

interest rates or collateral in the case of excess demand for credit is not always profitable.

Therefore banks try to use non-interest screening devices based on firm and entrepreneur

characteristics. This will result to credit rationing in the credit market, which refers to

situations, among the loan applicants who are seemingly identical, some received in full

amount, some received lower than demanded and other do not or rejected(Hashi and Toçi,

2011).

3.2 Adverse Selection and Moral Hazard

Credit rationing exists due to adverse selection, moral hazards and contract enforcement

problems. Adverse selection arises when there is information asymmetry between lender

and borrower and when lenders would like to identify the borrowers most likely to repay

their loans. This is because banks expected high return depends on the probability of

repayment. In try to identify borrowers with high probability of repayment, banks use

interest rate as a screening device. However borrowers that willing to pay high interest rate

may on average are those risky borrowers and this will in turn lead to less likely of the

repayment of the loan. In this case the availability of information in decision to lend is an

important because it helps for banks to evaluate the risk-return profile of borrowers. Full

information to obtain from borrowers is not always possible for banks. During information

asymmetry, the high interest rate charged by banks fail to equate the supply and the

demand for credit(Stiglitz and Weiss, 1981).This is because borrowers have their own

information about their type and nature of the project they want financed and can obtained

substantial profit from the project but lenders do not have any information about its

borrowers. Therefore lender face difficulties in distinguishing between good and bad credit

risks and lender they simply increase the price of credit to all borrowers and this will lead


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to adverse selection which is instead of driving out the potential defaulter from market,

they will stay in the credit market and willing to pay high interest rate.

Moral hazard is also arises when lenders are unable to controlled borrowers action while

borrowers are engaged in risky projects. In this case it is very difficult and costly for the

banks to control the action of borrowers and banks enforced to unwillingly to increase

interest rate to clear the excess demand (Stiglitz and Weiss, 1981). When the interest rate

is increased by banks the behaviour of borrowers become changing since higher interest

rate attracts the attention of risky projects for which the success of the project is less likely.

Therefore high interest rate may lead borrowers to take action to contrary to the incentive

of lenders. As a result bank rationed credit instead of increasing the interest rate while

there is excess demand.

Given credit rationing exist due to adverse selection and moral hazards; enforcement

problem is also vast in credit market in developing countries. In many developing

countries the enforcement problem is very poor since there is no a well-functioning legal

system in the credit market. In addition the major reason for the contract enforcement

problem is due to the poor development of property right among small firms. Therefore

when borrowers have not collateral, they will not borrow any money from formal financial

institutions at the prevailing interest rate rather they will borrow at higher interest rate to

cover monitoring and enforcement costs(Bigsten et al., 2003)

3.3 Credit Constraints

Credit rationing can investigate at two stages, the first stage is loan quantity rationing,

when credit is granted to a group of individuals who are selected as creditworthy

borrowers, while others rejected as they are unworthy. The second stage is loan size

rationing, when borrowers get smaller loan than their desired amount (Baydas et al., 1994).

In credit market there are five categories of borrowers (Boucher et al., 2006); Price

rationed borrowers (unconstrained borrowers), price rationed non- borrowers

(unconstrained non-borrowers), quantity rationed, risk rationed and transaction cost

rationed. Unconstrained borrowers are those who are not affected by credit limit from

financial institutions. Unconstrained non-borrowers are those who are unaffected by credit

limit but do not borrowed from financial institutions. Quantity rationed, risk rationed and

transactional rationed are called non-price rationed (Boucher et al., 2006). Quantity

rationed borrowers are those borrowers who applied for loan but either obtained lower

amount than their demanded or rejected totally. Risk and transaction cost rationed


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borrowers or firms are those who voluntarily withdrawn from the credit market because the

risk associated with collateral and transaction cost associated with loan application is too

high, respectively. All three form of non-price rationed arises because of information

asymmetric and enforcement problems in relation to credit and inhibit borrowers from

achieved profitable project. Any firms that face any of these three forms of non-price

rationed are considered as credit constrained. It is particularly important to account for

credit constraints deriving from risk and transaction rationing because the types of policies

that can alleviate them may be quite different from those designed to alleviate quantity

rationed.

3.4 Collateral

The value of Collateral offered by borrower can affect the credit rationing behaviour of

lenders. The availability of collateral can reduce the asymmetric information between

borrowers and banks (Chan and Kanatas, 1985). Collateral can also solve the problems that

arise due to the cost of monitoring and super visioning of borrowers behaviour. When

SMEs provided collateral, financial institutions allow credit even if uncertainty characterize

the firm. Therefore when banks do not have information about its borrower’s type of

riskiness, the collateral provided by firms can serve as a screen device to differentiate

between good and bad borrowers and of course to overcome the adverse selection problem

as well. Collateral is help to alleviate moral hazards problem because it forces the

arrangement of lenders and borrowers interest by reducing the motives to change from safe

project to risky project (Aghion and Bolton, 1992). Collateral requirements also serve as an

incentive mechanism that higher collateral enforces a selection of less risky projects

(Katundu et al., 2012). This is because a lower risky borrower has greater interest to pledge

collateral than a risky borrowers because the lower risky borrower knows that his lower

probability of failure and loss of collateral. In addition collateral can also serve as

protection for lender against a borrowers default or it serves as the last resort recovery of

the loan in case of default, where the bank can sell the collateral to recovery some of the

loan. A high value of collateral could increase the return for bank and reduce risk. Stiglitz

and Weiss (1981) concluded on their model collateral has a positive effect on moral

hazards, this causes to increase profit for banks and a negative adverse selection effect

since an increase demand for high value collateral by banks cause the average and

marginal borrower to become more risky.


13

3.5 Social Capital

Social capital is a broad concept, defined differently by many scholars. Social capital

measures in terms of cultural value, that is by considering the degree of altruism in society,

as connection among individuals, social networks and the norms of mutuality and

trustworthiness that arise from them and as the norms and networks enabling people to

share resources and work together (Fukuyama, 1995). However according most definition

social capital is strongly related to trust, refers to the set of rules, norms and value that

allow people to work with each other and trust each other. Social capital is important in

developing countries since most of the time the credit market is characterized by

information asymmetry. Given that information asymmetry problem, social capital may

help to overcome information asymmetry (Berger and Udell, 1998). Social capital can

solve the information asymmetry and thereby credit rationing by producing and analysing

information. Social capital such as the form of network may facilitate screening and

monitoring of borrowers and hence improve access to credit. In addition since in

developing countries in the credit market to obtain necessary information is very difficult,

the development of social capital may help to improve information sharing between

lenders and borrowers. Therefore in this study social capital is related to bank-firm

relationship, connection among business partners and suppliers, networks in business and

related issues and the trust they have among business partners.

Empirical study on social capital on the relationship between bank and borrower showed

that borrowers that pay a high rate and pledge collateral at the early stage of relationship,

and then pay a lower rate and do not pledge collateral later in the relationship after they

have revealed some project success(Boot and Thakor, 1994). Study on the relationship of

lending among small firms showed that the longer the relationships, number of finance

obtained from bank increases and enhances availability of fund(Petersen and Rajan, 2012).

Other study on the relationship of lending on small business showed that banks are more

likely to extend credit to firms with which they have long-time relationship as a source of

fund, but they found that long-time relationship is not an important factor(Cole, 1998)

3.6 Entrepreneur Characteristics

Entrepreneur characteristics such as age, gender and education have an impact on credit

constraints. Education can help for the entrepreneur to enhance stock of skill, improve

communication skill with finance suppliers and prepare a good business plan. Therefore an

educated entrepreneur has low level of credit constraints. Study in Indonesia showed that


14

women entrepreneur in small firms is relatively low this is due to factors mainly low level

of education, lack of training opportunities, heavily household responsibilities that hinder

women’s participation in the credit market(Tambunan, 2011). Other study carried in

Nigeria showed that female entrepreneur is constrained credit due to their weak financial

base and lack of collateral. Many of the entrepreneurs that face challenges are more linked

to the inferior status of women in many Africans, tribal and cultural norm and gender bias

in practice in dealing financial with female entrepreneur(Adesua-Lincoln, 2011).

3.7 Firms Characteristics

Firm’s characteristics, such as firm age and size are the main variables on the

determination of credit rationing. Size and age of the firm provide as an indicator

concerning credit risk. Firm age is usually consider as an indicator of firms quality since

those firms that stayed long by itself is an indication of survival ability, quality

management and accumulation of reputation (Diamond, 1991). Information asymmetry

between financial institutions and young firms are likely more because banks have not had

enough time to monitor and supervise such firms. In addition the young firms have not had

enough time and opportunity to build good long term relationship with suppliers of

finance. Empirical study showed that young firms due to lack of reputation, they

constrained credit as information asymmetry growing(Dunkelberg, 1998). There are many

research studied about young firms disadvantages in credit market for example, their fixed

cost requirement for credit application, relatively high probability of failure, relatively high

monitoring cost and lower collateral values of small firms (Boocock and Woods, 1997).

3.8 Empirical Framework

In this section the main concept will be explained with the help of framework as shown

below in figure1. The framework shows the position of firms, how some firm are

unconstrained in their access to credit from financial institution and how some other firms

are constrained while they are borrowing from financial institution in the study area, city of

Mekelle. In short the framework shows what determines credit rationing of SMEs in the

city of Mekelle.

Firms that exist in the study area, some of them were apply for loan and some of them did

not apply for loan depends on their specific characteristics. Those firms that were applied

for loan, some of them they received the amount they wanted, some of they received less

that the amount they wanted and some of the totally rejected. On the other side there are

firms did not apply for loan due to different socioeconomic factors .Those who were not


15

applied for loan were categorize either due to fear of losing their collateral or enough

money. After we reviewed of different literatures we used the Direct Elicitation Method

(DEM, see below) to identifying the determinants of credit rationing to small and micro

enterprises in the city of Mekelle.

Figure1 Empirical framework of the study

3.9 Direct Elicitation Method (DEM)

The analytical model distinguishes four categories of borrowers; price rationed borrowers

(unconstrained borrowers), price rationed non-borrowers (unconstrained non-borrowers),

quantity rationed and risk rationed and transaction cost rationed. But in other studies they

identified five categories. In our study there is no transaction cost rationed therefore in this

particular study the model will be focused on four mutually exclusive borrowers’

categories. In our study we used the Direct Elicit Method (DEM), first we identify firms

that are applied for loan and did not applied. Next we defined firms that are constrained

and unconstrained borrowers based up on firms characteristics toward to credit

market(Boucher et al., 2006). Constrained firm can be either due to from supply side or

demand side constrained. Supply side constrained or quantity rationed happened when

firms face a binding credit limit by financial institutions. Demand side constrained is mean


16

when firms did not face a binding credit limit by financial institutions. Unconstrained

borrower is mean when firms did not affected by credit limit even while there was

asymmetry information in the credit market. To elaborate more and to identifying the

supply side constrained we operationalize as follows: If a given firm applied for loan and

received less than the amount desired of credit we called it supply side constrained or

quantity rationed. In this case we identified the supply side constrained or quantity

rationed in to three groups: firms applied for loan and received less that the amount

desired, firms that are rejected their application and firms did not applied for loan due to

their past experience their application would be rejected. The demand side constrained

firms can be further grouped in to constrained borrower and unconstrained non borrowers.

Here the main objective was to identifying unconstrained non borrower firms. The

unconstrained non borrowers can be again grouped in to two categories. First firms did not

apply because they have enough money these types of firms are classified under

unconstrained non borrower. The second one was firms did not apply for loan due to the

risk associated with collateral, fearful for loan are classified under risk rationed borrowers.

The main objective of this DEM was to get additional information on the credit market

perceptions of non-borrowers and to determine constraint status requires knowledge why

some firms chosen not to borrow even though they believe they can qualify for a

loan(Boucher et al., 2006).

The DEM helped us to identifying borrowers that did not apply by asking qualitative

questions(Boucher et al., 2006). Based up on their responses we classified in to four credit

rationing category. Table3.1 shows detail of the response of borrowers and their

corresponding category’s.


17

Table3.1 Credit rationing category’s using DEM

Response constrained category

I have enough money

I do not have feasible project that repaid the

loan

Unconstrained non-borrowers

I have received the amount I desired from

formal financial institutions

Unconstrained borrowers

I received loan from formal financial

institutions, but less than I desired

I applied for loan from forma financial

institutions, but my application was rejected

Quantity rationed borrowers

I did not want to risk my collateral

I did not apply because I was afraid

Formal financial institutions are strict

Risk rationed borrowers

In short to explain the determinants of credit rationing, in our study the dependent variable

is credit rationing, it has four categories, the unconstrained non-borrowers, unconstrained

borrowers, quantity rationed borrowers and risk rationed borrowers. Credit is rationing to

SMEs due to entrepreneur characteristics, firms characteristics and institutional factors.

Financial institutions credit rationing behaviour theoretically is influenced by different

factors such as age, gender, wealth, experience and credit history, interest rate, firma age,

collateral, loan maturity, social capital and amount of loan (Okurut et al., 2012).

Entrepreneur characteristics include variables, age of the entrepreneur, gender, family size,

dependency ratio, education, collateral and social capital. Firm characteristics are includes

firm age, initial investment and working place. Rules and regulation of financial institution

such as interest rate of the entrepreneur are expecting to affect credit rationing behaviour

of financial institutions. Below are explained the major variables that expecting to

influence credit rationing in our study.

Age of entrepreneur: As the age of the owner of the firm is increase the probability to

constrained credit will be increase. This is as the age of the owner getting older and older

the possibility of the firm to make profit will be decrease as a result financial institutions

decided to decrease the amount of loan they extended in order to reduce their risk.


18

Gender: Male owned firms are less like to constrained in the credit market this is due to

financial institutions believed male can make profit than female.

Education: Educated firm owners are less likely to credit constrained since educated

people can present plausible case for loan to financial institutions during their application

for loan and convincing to financial institutions during the client interview.

Family size: Family size is expecting more credit constrained. Large family size meanS

large demand for credit and larger also consumptions. As a result financial institutions

tempt to reduce credit to large family size.

Age of the firm: Firm age is also expecting to affect credit decision of financial institutions.

As the firm age is increase there is less likely to constrain in the cred t market since as the

age of the firm is increase there is high chance of well-established good business record

and develop accounting system as a result financial institutions are less likely to credit

constrained to those old firms.

Initial investment: Firms that have higher initial investment are expecting less likely to

credit constrained.

Collateral: Firm that have collateral are expecting to less likely to credit constrained. This

is because if in case firms decline to repaid back their loan financial institution will sell the

collateral and covered at least some of the loan.

Social capital: In many research social capital is related to bank-borrower relationship. In

our study it is indexed of bank-borrower relationship, relationship among business partner

and input suppliers, trust among banks, business and suppliers. So we expecting the higher

is the social capital the less likely is credit constrained.


19

4. Research Methodology

4.1 Study Area

Mekelle city is the capital of Tigray Regional State and is located in the Northern part of

Ethiopia found at 783 Km away from the national capital, Addis Ababa at a latitude of

13°32’ north and longitude of 39°28’ east in which case the city is accessible by highway

and air transport. The city was founded by emperor Yohannes IV 150 years back as the

political center of Ethiopia; it is a city experiencing one of the fastest growing urban areas

in the country. In 1984, the city of Mekelle had a built up area of 16 square Kilometers.

The spatial expansion of the city of Mekelle is so amazing that by the year 2004, it had an

area of more than 100 square kilometers. Mekelle city is the capital of the national state of

Tigray region, which is also the political and commercial center of the region(Tadesse,

2006)

1. Overview of the study area of Mekelle2 (Orange paint)

___________________________________

(2 http://www.nationmaster.com/encyclopedia/Mek'ele as cited in Tadesse 2006)

http://www.nationmaster.com/encyclopedia/Mek'ele


20

Mekelle is one of the largest city in the region and being the political, cultural, and

commercial center of the Tigray regionl, Mekelle has a current population of about 257,

290,including two other small towns Aynalem and Quiha that had their own administration

before and annual growth rate of 5.4 percent and an average family size of 5 people. Its

population has been rapidly growing through migration and high birth rates (FDRE and

Commission, 2008). In the city an approximate 91.3 percent of the city’s population

accounts for Orthodox Christians and Muslims constitute 7.7 percent, the remaining share

being other religions. The male-to-female ratio is 89:100, and the dependency ratio is

estimated to be 79.9 percent. The city is experiences a high population density of 6125

people per square kilometers(Tadesse, 2006)

2. The city of Mekelle3

____________________

(3 http://www.nationmaster.com/encyclopedia/Mek'ele)

http://www.nationmaster.com/encyclopedia/Mek'ele


21

4.2 Data Source, Sampling Procedure and the Survey

In trying to answer the research question posed by the study, different methodological

tools were used in the analysis. Primary and secondary data collected from the study area.

A primary data, sample of 200 SMEs was selected for the study. The secondary sources

include published and unpublished materials about credit from commercial bank of

Ethiopia and Dedebit Credit and Saving Institution (DECSI). The data collected by

employing structural questionnaire that administered by enumerators in association with

the researcher on various socio-economic characteristics of the SMEs and credit rationing

of SMEs.

In order to meet the objective of the study, a total sample of 200 SMEs from the city of

Mekelle randomly were selected. The SMEs were sampled from different sub-sectors such

as, service, urban agriculture, construction, manufacturing and trade. Mekelle has 8

administrative tabias4. In order to obtain representative sample, a stratified and clustered

random sampling procedure were employed. More specifically, the city’s 8 tabias can be

considered as clusters, with further stratification within each tabias using SMEs key

characteristics were assessed in the field. The criteria considered when selecting the area of

the study were firm’s economic status, which are high, medium and low income, location

and size of the firm. In stratifying the firms based on income, the convenient procedure

used was to select firms based on traditional measurement of wealth, such as place of work

(housing and know how whether a specific firm is rich, middle income and poor). The

questionnaire-interview was administered from a total of 200 SMEs sampled from the city

of Mekelle and the fieldwork was carried out in the period between March, 14 to 21, 2013

of course before the final version of the survey a pre-test survey was conducted. A

respondent older than the age of 18 and who is the owner of the enterprise was chosen for

the interview. The interview on average took 15 minutes per interviewee. The

questionnaire consists of five sections. Section one covers general information about

entrepreneur characteristics, section two includes questions dealing with firm

characteristics, section 3 includes questions dealing with firms source of finance, such as

either they get loan from formal financial institutions or informal institutions and section

four includes question like social capital such as the networks that firms they have with

financial institutions and business partners and the level of trust they have with any of their

____________________

4 lowest administrative in the city


22

business partners. The last section was about general question, it deals about the

difficulties they faced during loan application process.

4.2.1 Measurement (Description) of Variables

Table4.1 shows the different variables how they coded and measured in our study. These

variables are among the variable that are expected to influence the outcome of the study. In

this study social capital is indexed of different aspect of social capital (see table 4.1 in the

last section). The index is calculated by weighing all of the social capital aspect variables

expecting to influence the determinants of credit ration to SMEs in the city of Mekelle.

Table 4.1 Description of variables

Variable name Measurement unit

1. 1.Entrepreneur characteristics

Age of entrepreneur Age of the entrepreneur

Gender of entrepreneur 1 if male, 0 if female

Marital status 1 if married, 0 if nor married

Education of entrepreneur Number of year of schooling

Head of household 1 if yes, 0 if otherwise

Family size Number of household member

Place live in 1 if own, 0 if otherwise

2. Firms characteristics

Age of firm Age of the firm

Initial investment Initial investment of the firm

Applied for loan from formal financial

institutions

1 if yes, 0 if otherwise

Applied and received from formal financial

institutions


Received the desired amount from formal

financial institutions


Social capital indexing

Participation in any social group

Extent the trust you have on the above group you belong

Extent you rate the relationship that you have with the above group you belong


23

In the event of financial shortage, your partner will provide some or full of credit

In the event of financial shortage, your relatives/family will provide some or full of credit

In the event of material shortage, your partner will provide some or full of credit

If you have relation with financial institutions how do you rate the relationship that you

have?

4.3 Data Analysis Method

In trying to answer the research question posed by the study and analysed the data, we used

descriptive statistics and multinomial logit model.

4.3.1 Descriptive Statistics

Descriptive statistical tools mean were used to study SMEs characteristics and their major

source of their finance. The descriptive analysis includes calculation and comparison of

SMEs Characteristics. The descriptive analysis is intended to provide some insight about

the importance of various characteristics and socioeconomic factors related to credit vis-

à-vis SMEs performance and growth.

4.3.2 Econometric Specification

Multinomial logit

Multinomial logit model will be used to examine the different factors that influence credit

rationing of SMES or it examines the determinants of credit rationing of SMEs. In our

study there are four mutually exclusive categories of credit rationings, price rationed

borrowers (unconstrained borrowers), price rationed non-borrowers (unconstrained non-

borrowers), quantity rationed, risk rationed and transaction cost rationed. Therefore the

dependent variable y is a categorical variable that takes values 0, 1, . . . , J and that

represents the observed credit market rationing outcome of firm i. In this case the

dependent variable can be unconstrained non borrower, unconstrained borrower, quantity

rationed borrower or risk rationed borrower. In this particular study the objective is to

examine the determinants credit rationing of firms therefore we can use latent variable, *

iy .

Supposed*

iy is a latent variable (unobserved variable) for bank’s decision whether to grant

the loan or not. This will be given as follows;

...................1


24

Where iy* is the unobserved credit rationed of a firm which is a function of the row

vector of various firms socio-economic factors (χ), parameters corresponding to each

independent variable β and ɛi is random error component of the i firm in the j category. To

explained more the χ’s are age of the entrepreneur, gender, education level of the owner of

the firm, marital status, family size, collateral, age of the firm, initial investment, annual

sales of the firm, main activity of the firm and social capital. The probability that firm i is

in the jth rationing category (in this case, unconstrained non borrowers, unconstrained

borrowers, quantity rationed borrower or risk rationed borrowers) is thus

The above equation shows that the relationship between the observed iy

and the

unobserved credit rationed (yi*). Firm i is credit rationed when the observed variable iy

is greater than the unobserved variable (yi*). Having declared both the observed and

unobserved of firm’s behaviour of credit rationing, the multinomial logit can be illustrated

as

3........................................,......3,2,1,

)exp(1

)exp()(

1

'

'

mJjyprm

j

íj

ij

i

Where χ׳ij represents the row vector of firm’s characteristics such as age of the owner of the

firm, gender, education, age of the firm, collateral, social capital etc. and j is the firm’s

category of credit rationing. Then the model is estimated using econometrics software,

STATA. By doing so, the determinants of credit rationing of SMEs will be answered.

Having said about multinomial logit, the parameter coefficients of multinomial logit model

are not directly interpreting. The result obtained from multinomial logit is not

straightforward and depends on whether the categories are ordered or unordered. In our

study since the model has unordered outcomes, there is no single conditional mean of

dependent variables instead there are j alternatives, and we model the probabilities of these

alternatives. Therefore we do not interpret the multinomial logit rather we interpret the

marginal effect of each repressor on the probability of the mean firm being observed in

2..................*

iii yyprjypr


25

each rationing category of course after first we regressed the multinomial logit (Greene,

1997). The marginal effect (MEs) is also estimated using STATA and it measures the

impact of observing each of several outcomes instead of the impact on the single

conditional mean. The marginal effects (MEs) can be shown to be:

4..........................................

ii

ij

i yyx

y

Where = represents the coefficient of explanatory variable corresponding to credit

rationing category j. It measures the probability of being credit rationing when one of the

explanatory variables changes.

4.4 Multicollinearity Test

Before running a model, in our case the multinomial logit, explanatory variables were

checked for multicollinearity (Verbeek, 2008). Multicollinearity is a problem when the

explanatory variables in multiples regression model are highly correlated and provide

redundancy information about the response. The existence of multicollinearity in the

model may cause large variance, large T-value and misleading results. Two popular

method to detect the presence of multicollinearity are Variance Inflation Factor(VIF) and

Tolerance(TOL).

21

1

i

iR

VIF

, 21 iRTOL

A common rule of thumb is that if VIF is 10 or greater than 10 and a TOL of 0.10 0r less

may indicate the presence of multicollinearity. So in our result there is no problem of

multicollinearity (See Appendix-B )

4.5 Test of Independent Irrelevant Alternatives (IIA)

Multinomial logit models are valid when the independent irrelevant alternative (IIA) is

validated. The IIA assumptions states that characteristics the choice of one from the other

alternatives do not impact the relative probability of choosing other

alternatives(Vijverberg, 2011).

A stringent assumption of multinomial logit and conditional logit model is that

outcome categories for the model have the property of independent irrelevant

alternatives (IIA). Stated simply, this assumption requires that the inclusion or


26

exclusion of categories does not affect the relative risk associated with the

repressors in the remaining categories. One classic example of a situation in

which this assumption would be involves the choice of transportation model. For

simplicity postulate a transportation model with the four possible outcomes; rides a

train to work, take a bus to work, drives the Ford to work and drives the Chevrolet

to work. Clearly, drives the Ford is a close substitutes to drives the Chevrolet than

it is to ride a train (at least for most people). This means that excluding drives the

Ford from the model could be expected to affect the relative risks of the remaining

options and that the model would not obey the IIA assumption(McFadden, 1974).

Therefore in our case the IIA is validated, the choice of one credit rationing category do

not impact on the relevant of the choice of the other credit rationing category (See-

Appendix-C).


27

5. Empirical Results of the Study

This section has two parts, descriptive statistics and econometric, multinomial logit model

analysis. Discussion of the theoretical framework and methodology has laid foundation for

the discussion of descriptive statistics and empirical analyses. The descriptive statistics

presents the characteristics of SMEs and major source of their financing. The descriptive

statistics includes such as mean, standard deviation, minimum and maximum values were

used to compare SMEs. The multinomial logit were used to examine the determinants of

credit rationing of SMEs.

5. 1 Descriptive Statistics

5.1.1 Entrepreneur Socioeconomic Characteristics

Table5.1 shows difference in mean between firms applied for loan and non-applied from

formal financial institutions. It also shows entrepreneurs socioeconomic characteristics.

The variables age, education, household size, and dependency are not significant to apply

for loan from formal financial institutions. In this case there is no difference between those

entrepreneurs that applied and did not apply for loan. In our study the sampled firm

comprises various age groups ranging from 20 to 61 years and the average age of

entrepreneur is 35 year old. The average education level of the entrepreneur is grade 10.

The average household size is 4 and the dependency ration is very low, 0.3. As you can see

in table5.1.2 of the discrete variables, out the total firms applied to loan form formal

financial institutions most of them are male. There is no significant difference in gender to

apply or not. Most of the applicants are head of household. Being head household is a

significant effect to apply for loan. Most of the firm owners are married.


28

Table5.1 Entrepreneurs socioeconomic characteristics

Applied,

N=83

Not-Applied,

N=117

Total, N=200

Charac. mean Std.

deviation

mean Std.

deviation

T-Value mean Std.

deviation

Age 35.87952 8.1546 34.76068 11.08327 0.7823 35.225 9.965487

Education 10.31325 4.242433 10.55556 3.972848 0.3400 10.455 4.078128

HH size 4.60241 2.224803 3.641026 2.465142 0.9974 4.0400 2.409862

Dependent 0.3614458 0.5962957 0.2478632 0.5858384 0.9093 0.2950 0.5913743

Source: own survey, 2013

Table5.2 Entrepreneurs socioeconomic characteristics of discrete variables

Characteristics Applied for loan

N=83

Not applied for loan

N=117

2

Gender(1=male, 0=female) 58 72 1.5

HH head(1=yes, 0= no) 76 95 4.2**

Married(1= yes, 0=no) 50 57 2.6


5.1.2 Application for Credit

A total of 200 SMEs were successfully interviewed form the city of Mekelle. As table 5.2

below shows, out of the total 200 SMEs, 41.6% applied for loan from formal financial

institutions within the last three years and 58.5% did not apply for loan. This implies

majority of the firms did not apply for loan due to different reasons for different firms,

some of the firms did not want loan either they have enough money or they feared to lose

their collateral.

Table 5.3 Firms applied for loan

Applied for loan Freq. Percent Cum.

No 117 58.5 58.5

Yes 83 41.6 100.0

Total 200 100.0



29

Majority (89.2%) of the SMEs applied for loan from microfinance institutions and few

SMEs are applied for loan from banks (10.8%).

As table 5.4 below shows out of the total 83 firms applied for loan from formal financial

institutions, almost all of them were get credit. Firms that applied and rejected are rare.

This implies majority of the firms that were applied for loan from formal financial

institutions in this case either from bank or microfinance institutions got loan. Having said

this out of the total 81 firms applied and received, above average of the firms were get in

full amount and smaller share of the firms were quantity rationed. This implies the highest

share of firms were unconstrained borrowers, they were not bind by credit limit formal

financial institutions.

Table 5.4 Firms applied and received

Loan received formal Freq. Percent Cum.

No 2 2.4 2.4

Yes 81 97.6 100

Total 83 100


As above mentioned out of the total 83 firms applied for loan, most of them received credit

form formal financial institution. As table5.5 shows the higher share of firms that applied

for loan for the purpose of their business expansion of course few firms were also applied

for the purpose of start new business. Therefore this implies that the loan was mainly

targeting for income generation activities’.

Table 5.5 Purpose of the loan

purpose of the loan Freq. Percent Cum.

Expansion 71 87.7 87.7

Start business 10 12.3 100

Total 81 100


Most firms financed their business from MFI, own savings and friends/family. Few firms

are also financed their business form equib and banks (see table5.6). This implies the

major source of finance for MSEs are microfinance institutions due to many of the small

firms do not have collateral that can provide for banks and also they do not meet the


30

requirements that are set by banks. In short the major source of finance for SMEs in

Mekelle was from formal financial institution mainly microfinance institution. The number

of firm that were finance form their own saving was also enormous. The share of informal

financial institutions in this case family/friends, money lenders and equib that financed for

SMEs was also huge. This implies that informal financial institutions are also greatly

contributing for the development of SMEs and creating of employment opportunities.

Table 5.6 Source of finance

Major finance Freq. Percent Cum.

Bank 8 4 4

MFI 78 39 43

Money lender 2 1 44

Own saving 60 30 74

Friends/family 38 19 93

Equib 11 5.5 98.5

Sales of house 2 1 99.5

Lottery 1 0.5 100

Total 200 100


5.1.3 Distribution of Credit Constraints

Table5.7 presents credit rationing status for sampled of 200 SMEs form the city of

Mekelle. Out of the total 200 SMEs, 40% of the SMEs were unconstrained non-borrowers,

26% of them unconstrained borrowers, 17% of them quantity rationed and 17% them risk

rationed borrowers. In this case higher share of the sample are unconstrained non-

borrowers. In other word the majority of the firms did not apply for loan, either they have

enough money to run their business or the firm they have is not as such promising or the

firm did not have enough market that can pay back the loan. The unconstrained borrowers

mean those firms they applied and received the amount they desired. The share of quantity

rationing and risk rationing is 17% each. Quantity rationed firms were those firms applied

for loan and got less than they desired. The risk rationed borrowers were those firms which

did not apply for loan simply they voluntary withdrew from credit market due to the risk

associated with collateral.


31

Tble5.7 Distribution of credit constrained

Credit rationed category Freq. Percent Cum.

Unconstrained borrowers 52 26 26

Unconstrained non-borrowers 80 40 66

Quantity rationing 34 17 83

Risk rationing 34 17 100

Total 200 100


As table5.8 below shows service is the highest share and they engaged mainly in cafe and

restaurant, beauty salon and internet cafe. Out of the total firms that engaged in service,

most of them were unconstrained non-borrowers. Trade is the second higher shared in our

sample. The firms classified as trade were local whole sales, retailers and input suppliers.

Here also most of the firms were unconstrained non-borrowers. Manufacturing is the third

highest share in our sample. The firms that are operating in manufacturing are wood work,

metal work, handicrafts and gold smith, textile and agro processing. Still higher share of

the trade are unconstrained non-borrowers. Sectors such as urban agriculture and

construction shared are small in comparison with the other sectors. We sampled only 9

firms of urban agriculture and 7 firms of construction since these sectors are not yet

expanded and of course they might categorizes as medium and large scales since they

demand huge capital to start. Of the total urban agriculture most of them were

unconstrained borrowers.

Table 5.8 Cross tabulation between sector and credit constraints

Rationed Category Service Urban

Agriculture

Construc. Manufactu

ring

Trade Total

Unconstrained borrowers 25 5 3 10 9 52

Unconstrained-Non-

borrowers

40 2 0 14 24 80

Quantity rationed 11 2 2 11 8 34

Risk Rationed 14 0 2 11 7 34

Total 90 9 7 46 48 200



32

In our study we also assessed credit constrained with firms that had applied for loans in

previous years. As a result 98 firms they had applied for loan and 102 firms they had not

applied for loan in previous years. As table5.9 below shows out of the total firms that had

worked with formal financial institutions, most of them are unconstrained borrowers. This

implies that firms that had applied and repaid their loan helped them to create good

relationship as a result they did not constrained by credit limit. The number of firms that

had applied in previous years is now quantity rationed is also high. Few firms were

unconstrained non-borrowers. From the total firms that had not been experiences in

previous years most of them are unconstrained non-borrowers. The share of risk rationed

borrower also quite high number.

Table 5.9 Cross tabulation between credit constraints with experience

Ration category Experience (firms applied in previous years)

No Yes Total

Unconstrained borrower 3 49 52

Unconstrained non-borrower 65 15 80

Quantity rationed 3 31 34

Risk rationed 31 3 34

Total 102 98 200


5.1.4 Reason for not Applied from Formal Financial Institutions

As above mentioned of the total firms in our study, 83 of them applied and 117 did not

apply due to different reason. Majority of the firm’s did not apply for loan form formal

financial because the loan was not needed. Some of the firms also did not apply because

they have enough money that can run for their business and others did not apply because of

lack of collateral that can pledge to financial institutions. Few firms were also suggested

due to formal financial institutions are strict and do not have any feasible project (see

table5.10).


33

Table 5.10 Cross tabulation between sector and credit constraints

Why not apply formal Freq. Percent Cum.

Loan was not needed 55 47 47

Have enough money 25 21.4 68.4

Do not want risk collateral 16 13.7 82.1

Formal institution too strict 4 3.4 85.5

Interest is high 2 1.7 87.2

No feasible project 4 3.4 90.6

Fear of repayment 7 6 96.6

No collateral 2 1.7 98.3

Firm is small 2 1.7 100

Total 117 100


5.2 Econometric Results

The econometric software STATA is used to estimate the parameter coefficients and

predicted marginal effect. The direct interpretation of the coefficient estimates from

multinomial logit model is misleading. Therefore, the marginal effect is used to describe

the determinants of variables on credit rationing. The interpretation of the parameter

estimates of a multinomial logit are explained with respect to the baseline scenario

specified, output of four different categories can be outlined (See Appendix-D). This

means that each of the credit rationing categories can act a base case and allow

interpretation of the coefficients in terms of the base case. The dependent variable, credit

rationing has four categories: 1 = unconstrained non-borrowers, 2 = unconstrained

borrowers, 3 = quantity rationed borrowers and 4 = risk rationed borrowers. The result of

marginal effect is shown in table5.11


34

Table5.11 Marginal effect estimation after multinomial logit regression

variables Unconstrained

Non-borrower

Unconstrained

borrower

Quantity rationed

borrower

Risk rationed

borrower

Age .0034695

(.005730)

.0134789**

(.006560)

-.0037375

(.00385)

-.0132109***

(.00481)

Gender -.0069529

(.077890)

-.1047242

(.085810)

.0760798

(.04680)

.0355973

(.05175)

Married .0478860

(.087290)

-.1754076*

(.09819)

.0891015

(.05771)

.038420

(.062000)

HH size .0402433**

(.0402433)

-.039686**

(.01987)

.0054133

(.010780)

-.0059706

(.012640)

Education .0006902

(.008810)

-.0019146

(.01007)

.0059764

(.006090)

-.004752

(.006810)

Firm age -.0182891

(.011240)

.0054971

(.012070)

.0026701

(.00663)

.0101219

(.00814)

Initial-

investment

5.20e-07*

(.00000)

9.17e-07**

(.00000)

-9.48e-07**

(.00000)

-4.89e-07

(.00000)

Social-capital .0330533

(.027820)

.0011222

(.029610)

-.0363944**

(.016020)

.0022189

(.018540)

House owner .1363862*

(.069940)

-.2193881***

(.075630)

.1101561**

(.049450)

-.0271543

(.050590) Notes:

Standard error is in parenthesis/

*** 1% significant level, ** 5% significant level, * 10% significant level

Pseudo R2 = 0.1035

Gender is a dummy variable, 1 if male, 0 if female

Married is a dummy variable, 1 if yes, 0 if no

House owner is dummy variable, 1 if yes, 0 if no

Number of observation, N= 200


The main objective of this study is to see the determinants of credit rationing in small and

micro enterprise in the city of Mekelle. To begin with, age has a positive significant impact

on being unconstrained borrower and a negative impact on being risk rationed borrower.

As the age of the firm owner is increase the probability of being unconstrained borrower is

also increase. This implies financial institutions like to extend loan to middle aged group

than to elderly. As our data shows the average age of the sample is 35 years old, this is

believed to be the most economically active and expecting to make profit and repaid their

loan. In case of risk rationing, as the age of the owner of the firm is increase the probability

of being risk rationing is decrease. This implies when age of the firm owner increases


35

he/she become risk averse. It is obvious older people do not want to take any risk since

they are not sure to make profit and repaid their loan when they become elderly.

Being married is negatively correlated with unconstrained borrowers. This implies when

firm owner is getting married the probability of being unconstrained borrower is decrease.

Possible reason can be married one have more consumptions so financial institution are not

interested to extend loan as the married demanded rather they rationed in order to

minimize risk.

Household size has a positive and significant impact on being unconstrained non-

borrowers. As the household family size increase the probability of being unconstrained

non-borrower is also increases. Possible reason can be on average those who have more

family member will have high consumption. The income they get from their firm may also

allocated for consumption, through time the firm will be deteriorated and finally they will

not applied for loan because they are not sure to make profits. Household size is also

negatively associated with being unconstrained borrower. As the household size is increase

the probability of being unconstrained borrower is decrease. Possible reason can be higher

family size mean high consumptions in turn higher demand for loan, during this time the

probability to repaid the loan will be low so financial institution decide to limit the credit.

This is assured large family size are more likely to apply for loan because large family

implies large credit needs and consumptions(Chivakul and Chen, 2008).

Initial investment is one of the most significant variables that affect credit rationing of

SMEs. Initial investment is positive and significant impact on being unconstrained non-

borrower and unconstrained borrowers. As the firm’s initial capital increases the

probability of being unconstrained non-borrower as well unconstrained borrower is also

increase. This implies firms that have enough capital to start their business do not need to

apply for loan form formal financial institutions. The same firms with higher initial

investment are expecting higher return thereby a higher probability to repay back their

loan. Therefore financial institution will be interested to extend loan to firms with higher

initial investment without credit limit. Initial investment is also negatively associated with

quantity rationed. Those firms that have high initial investment are less likely to rationed in

quantity from formal financial institutions. This implies that though financial institutions

can not identified good borrower from the poor borrower by their initial

investment(Berglöf and Roland, 1997) but they expecting those with high initial

investment more likely more profit. So firms with higher investment are not rationed in


36

quantity in their access for loan. In other words financial institutions are more likely to

reimburse their loan when firm’s initial investment is high.

Social capital is negatively correlated with quantity rationed. As the social capital is

increase the probability of being quantity rationed will be decrease. This is because the

longer the firms have relationship with banks, financial institution, business partner and

suppliers the less likely firms to be constrained by quantity. This is consistent with other

researches. For example in many research social capital grouped in to, cluster (the number

of relationship between farmers/firms and farmer cooperatives) and the length of

farmer/firm- bank relationship. This implies that the higher is the firm –bank relationship

the less likely firms will be quantity rationed (Reyes Duarte, 2011b). In addition small

firms with less established repayment history and poor credit rating are the most

beneficiary form the relationship(Diamond and Rajan, 2001). At the same time firms that

maintained long relationship with financial institutions the cost of borrowing is smaller and

collateral is less frequently required(Cole, 1998).

In our study we used house owner as proxy for collateral. The result shows collateral is

positive and significant impact on being unconstrained non-borrower and quantity rationed

and negative and significant impact on being unconstrained borrower. Possible reason for

the case of unconstrained non-borrower is that firms with collateral but did not apply for

loan was due to they have enough money and they can run their business by their own

money. It is also obvious people that have their own house are the medium and higher

class family so it is easy for them to owned small business and finance by their own

money. For the case of unconstrained borrower, it is strange while borrower with

collateral constrained by credit in the credit market. It is contrary to other research’s, for

example in Peru firm with collateral are unconstrained borrowers, as far as they pledge

their collateral to financial institutions(Boucher et al., 2006). The same in Chile firms that

have collateral are not constrained since collateral can solved the problem that rose from

information asymmetry, uncertainty about the profitability of the project and the riskiness

of the borrower (Reyes Duarte, 2011b). But one thing we need to consider, in our study,

city of Mekelle majority of the small firms took loan form Dedebit Credit and Saving

Institution (DECSI). To borrow from DECSI it is not a must for them to provide collateral.

Rather if they have someone who works in government institution as permanent employee

and whose monthly salary is above 2000 Ethiopian Birr can bail them. So that they can

take loan without collateral. To explain more the one who is bailed should be a


37

government employee, he/she has required to get letter from his employer that specifies his

detail including his/her monthly salary. Then the letter will be send to DECSI so that

he/she is going to bail to the borrower and reached an agreement. In case if the borrower is

declined to repaid the loan the employee will be enforced to repaid the loan by deducting

from his/her salary on behalf of the defaulter. Therefore having collateral does not have

any impact on credit rationing to small firms in the city of Mekelle. The same for quantity

rationed, firms with collateral are more likely to be quantity rationed. Our result is contrary

to other researches. Collateral consider as a means of solving problems of information

asymmetry and banks uses collateral as a sorting out risky borrower and reducing risk of

default. For example research in Bhutan shows that firms that have collateral are less likely

being credit constrained from formal financial institutions(Gyeltshen, 2012). Other study

shows that firm with collateral are less quantity rationed since collateral can help to

overcome adverse selection and moral hazards problem(Reyes Duarte, 2011).


38

6. Conclusion and Recommendations

This paper examines the determinants of credit rationing of SMEs in the city of Mekelle.

A field survey was conducted and a total of 200 SMEs were randomly selected from the

city and interviewed with structural questionnaire. To answered the research questions

posted by the researcher both descriptive and econometrics method of analysis was used.

Here are below the main research questions answered by the researcher:

What are the characteristics of SMEs in the city of Mekelle? The average age of firm

owner is 35 years of old, 65% of the firms are owned by male and 35% of them by female

entrepreneurs, 54% of the firm’s owners are married, the dependency ratio is 0.36 and

average school level is grade10.

What is the major source of finance? The major source of finance for SMEs is

microfinance institutions (39%), 30% from their own savings, 19% from family/relatives,

5.5% form equib and 4% form banks. This shows the majority of the SMEs were financed

form formal financial institutions of course the share of informal financial institutions is

also high.

The third and most important questioned was, the determinants of credit rationing of SMEs

in the city of Mekelle. Out of the sampled 200 SMEs, 83 of them applied for loan and 117

did not apply for loan from formal financial institutions. Descriptive statistics was used to

examine the credit rationing category’s firms. Of the 83 applied for loan 81 received and 2

of the rejected their application. Out of the 81 firms 51 of them received in full amount and

31 of the firms received less that the amount requested. Using Direct Elicitation Method

(DEM) we also categorizes firms bases their response to qualitative question. So based

DEM 46% of the firms were unconstrained non-borrowers, 26% unconstrained borrowers,

17% quantity rationed borrowers and 17 risk rationed borrowers. After DEM we employed

multinomial logit regression to see the determinants of credit ration of SMEs. The result

shows that gender, education, firm age and collateral does not have any impact on credit

rationing. Age of the owner of the firm, household size, initial investment and social

capital have impact on credit rationing.

From the discussion of our research we raised issues in terms of policy recommendations

from the descriptive results are:

Formal financial institutions should reduce interest rate.


39

Banks should reduce the rigid rules and regulations.

As in the discussion part explained microfinance institutions extend loan to SMEs

without collateral up to 10,000 Ethiopian Birr. So MFI should increase the loan

amount.


40

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43

APPENDICES

Annex-A: Survey Questionnaire

Determinants of Credit Rationing of Small and Micro Enterprises:

A Survey Questionnaire for Mekelle

(January 2013)

Objective of the Survey

The data collected are going to be used only for the purpose of (MSc) study. It

focuses prominently on the determinants of credit rationing of small and micro

enterprises and characteristics of small and micro enterprises.

Respondents’ right and obligations

All the information you provide remains totally confidential. Hereby, you are

requested to give us your genuine feeling about the questions we ask.

Thank you in advance for your collaboration!

1. Entrepreneur characteristics:

1.1 Age______

1.2 Gender 1. Male 0. Female

1.3 Marital status?

1. Single 2.Married 3. Divorced 4. Widowed

1.4 What is the level of education of the owner? _____________

1.5 Are you head of the household?

1. Yes 0.No

1.6 What is the size of your family?

Children (<=15) Adult Elderly (>=64) Total

Male Female Male Female Male Female Male Female

2. Firm characteristics

2.1 What is the age of your firm? _______

2.2 How much is your initial investment? __________

2.3 How many employees do you have? Temporary_________ Permanent_________


44

2. 4 what is the main activity of the firm?

Services Urban Agriculture Construction Manufacturing Trade

1. In ternate cafe 21. Urban vegetables

31.Contracting 41.Wood work 51.Local whole sale

2. Café & restaurant 22. Urban irrigation

32. Mineral stones 42. Metal work 52. Local retailer

3.Beauty salon 23.Animal forage 33.Coble stone 43. Handicraft & Gold

smith

53. Input supplier

4.Tourism 34.Sub-contracting

44. Agro-processing

5.Sanitation 45.Textile

6. Electric & software

service

46.Leather & leather

products

7. Decoration

8. Small transport

9. Storage

10. Packing

2.5 The place where you working in is?

1. Own 2. Rented 3. Family (rented) 4. Family (free)

5. Other___________________

2.6 The place where you living in is?

1. Own 2. Rented 3. Family (rented) 4. Family (free)

5. Other___________________

3. Source of finance

3.1 Did you work with any financial institution?

1. Yes 0. No (If No, skip to question 3.3)

3.2 For how many years had you worked with the financial institution? _______

3.3What is your major source of finance? (Multiple answers is possible)

1. Banks 2. MFI 3. Money lenders 4. Own savings

5. Friends/family 6. Equib 7. Other_____________

3.4 Did you apply for loan from formal financial institutions within the last 3 years?

1. Yes 0. No (if No, skip to question3.9)

3.5 If Q3.4 is yes, which formal financial institution did you apply?


45

1. Banks 2. MFI 3. Other_______________

3.6 Did you receive any loans from formal financial institutions within the last 3 years?

1. Yes (, please fill out the questions below) 0. No (if No, skip to Q 3.10)

Name of the

institution

Value of the

loan

Value of

interest

rate

Repayment

system ( daily,

monthly,

annually)

For how

long will

stay the

loan

Purpose of

the loan

1.Bank

2.MFI

3.Other

3.7 Did you receive the amount you wanted to borrow (in full) from the formal financial

institution?

1. Yes (if yes skip to Q 3.13) 0. No (if no fill table below)

3.8 If Q 3.7 is no, how much did you want to borrow? ____________Birr (Skip to Q 3.13)

3.9 Why the firm did not apply for loan? (Multiple answers is possible)

1. The loan was not needed

2. I have enough money

3. The firm did not want to risk its collateral (house, any asset)

Which financial institution did not give you the amount you wanted

Reason for not

give you the

amount you

wanted (multiple

answer is

possible)

Bank MFI Other______________

1.Lack of collateral

2. Lack of sound financial

statement

3. Poor repayment history

4. Sector bias

5. Risky venture

6. Others (list all the possible

reasons___________________



statement


4. Sector bias

5. Risky venture


reasons________________



statement


4. Sector bias

5. Risky venture


reasons_____________


46

4. Formal institution are too strict (not flexible like informal lenders)

5. The interest rate is high

6. Application cost is high (too much paper work)

7. I have no feasible project that repaid the loan

8. Fear of repayment the loan

9. Other________________

3.10 If the firm had applied, would the formal financial institution have accepted the application?

1. Yes (if yes skip to Q to 3.13) 0. No

3.11 Why wouldn’t formal financial institutions have accepted the loan application? (Multiple

answers is possible) 1. Lack of collateral

2. Lack of sound financial statements

3. Lack of revenue

4. The firm is small

5. The business is risky

6. Other___________________

3.12 If you had been certain that financial institutions would approve your application, would you

apply? 1. Yes 0. No

3.13 Did you worked with any informal financial institutions?

1. Yes 0. No (if No skip to Q 3.15)

3.14 If Q 3.13 is yes for how many years had you worked with the informal financial institutions?

________

3.15 Did you apply for loan from informal financial institutions within the last 3 years?

1. Yes 0. No (, please skip to question3.20)

3.16 If Q3.15 is yes, which informal financial institution did you apply?

1. Equb 2. Family/Friend’s 3.Money lenders

4. Others__________________

3.17 Did you receive any loans from informal financial institutions within the last 3 years?


47

1. Yes (, please fill out the questions below) 0. No (if No skip to Q 3.21)

Name of the

institution

Value of the

loan

Value of

interest rate

Repayment

system

For how long

will stay the

loan

Purpose of

the loan

1.Equib

2.Family/Friend

3. _______

3.18 Did you receive the amount you wanted to borrow from the informal financial institutions?

1. Yes (If yes, skip to Q 3.23) 0. No

3.19 If Q3.18 is no why informal financial institution did not give you the amount you wanted?

(Skip to Q 3.23)

3.20 Why the firm did not apply for loan? (Multiple answers is possible)

1. The loan was not needed

2. I have enough money

3. The firm did not want to risk its collateral (house, any asset)

4. Informal money lenders are too strict

5. The interest rate is high

6. Application cost is high (too much paper work)

Which informal financial institution did not give you the amount you wanted

Reason for not

give you the

amount you

wanted (multiple

answer is

possible)

Money lenders Family/friends Other______________



statement


4. Sector bias

5. Risky venture


reasons___________________



statement


4. Sector bias

5. Risky venture


reasons________________



statement


4. Sector bias

5. Risky venture


reasons_____________


48

7 I have no feasible project that repaid the loan

8. Fear of repayment of loan

9. Other_________________

3.21 Why wouldn’t informal money lenders have accepted the loan application? (Multiple answers is

possible) 1. Lack of collateral

2. Lack of sound financial statements

3. Lack of revenue

4. The firm is small

5. The business is risky

6. Other_________________

3.22 If you had been certain that informal money lenders would approve its application, would you

apply? 1. Yes 0. No

3.23 Which of the aspect would you like to improve by financial institutions so that the firm will apply

for loan? (Multiple answers is possible)

1. Collateral requirements

2. Interest rate

3. Duration of the loan

4. Repayment systems (Daily, monthly, annually...)

5. Application process

6. Other____________________

4. Social capital

4.1 Do you participate in any social groups?

1. Yes 0. No ( Skip to Q 4.4)

4.2 If Q 4.1 is yes, in which groups did you participate in?( Multiple answer is possible)

1. Civil associations

2. Edir

3. Banks

4. Equib

5. Saving and credit cooperatives


49

6. Cooperatives

7. Other____________________

4.3 To what extent is the trust you have on the above group you belong?

4.4 In the event of finance shortage, your business partners will provide you some or full of the credit

you need? 1. Strongly disagree

2. Disagree

3. Neither nor

4. Agree

5. Strongly agree

4.5 In the event of finance shortage, your family/ relatives will provide you some or full of the credit

you need?

1. Strongly disagree

2. Disagree

3. Neither nor

4. Agree

5. Strongly agree

4.6 In the event of input (material) shortage, your family/ relatives will provide you some or full of

the input/material you need? 1. Strongly disagree

2. Disagree

3. Neither nor

4. Agree

Civil ASS. Edir Bank Saving &

C.C

Cooperative Equib

Low (poor) 1 1 1 1 1 1

Small 2 2 2 2 2 2

Average 3 3 3 3 3 3

Good 4 4 4 4 4 4

Very good 5 5 5 5 5 5


50

5. Strongly agree

4.7 Whenever you want to withdraw large amount of money from your savings account, you can

easily do that? 1. Strongly disagree

2. Disagree

3. Neither nor

4. Agree

5. Strongly agree

4.8 If you have relationship, to what extent do you rate the relationship that you have with one or

more of the following partners?

4.9

Wh

at is

your

ann

ual sale? __________ Birr

5. General questions

5.1 If you face any difficulties and challenges during the loan application process, please

mention?

_________________________________________________________________________

_________________________________________________________________________

Thank you!

Bank MFI Business Partner Input supplier

Very bad quality 1

Bad quality 2

Nether bad nor good quality 3

Good quality 4

Very quality 5


51

Annex-B: Multicollinearity test:

. collin ageowner genderowner married hhsize eduowner firmage invest_begin

index_SCP houseowner3

Collinearity Diagnostics

SQRT R-

Variable VIF VIF Tolerance Squared

----------------------------------------------------

ageowner 2.79 1.67 0.3583 0.6417

genderowner 1.16 1.08 0.8617 0.1383

married 1.67 1.29 0.5997 0.4003

hhsize 1.40 1.18 0.7163 0.2837

eduowner 1.17 1.08 0.8576 0.1424

firmage 1.97 1.40 0.5079 0.4921

invest_begin 1.04 1.02 0.9638 0.0362

index_SCP 1.09 1.04 0.9191 0.0809

houseowner3 1.06 1.03 0.9440 0.0560

----------------------------------------------------

Mean VIF 1.48

Cond

Eigenval Index

---------------------------------

1 7.1392 1.0000

2 0.9632 2.7225

3 0.6194 3.3950

4 0.4075 4.1854

5 0.2874 4.9843

6 0.2586 5.2539

7 0.1716 6.4505

8 0.0887 8.9723

9 0.0507 11.8656

10 0.0137 22.8473

---------------------------------

Condition Number 22.8473


52

Eigenvalues & Cond Index computed from scaled raw sscp (w/ intercept)

Det(correlation matrix) 0.1700

Correlations:

orr ageowner genderowner married hhsize eduowner firmage invest_begin index_SCP

houseowner3

(obs=200)

| ageowner gender~r married hhsize eduowner firmage invest~n index_~P

houseo~3

-------------+-----------------------------------------------------------------------------

----

ageowner | 1.0000

genderowner | 0.3414 1.0000

married | 0.5969 0.3037 1.0000

hhsize | 0.4646 0.1997 0.4075 1.0000

eduowner | -0.3377 -0.0983 -0.2629 -0.2023 1.0000

firmage | 0.6713 0.2482 0.3935 0.3487 -0.2700 1.0000

invest_begin | 0.0296 0.0433 0.0673 -0.0230 0.0917 0.0892 1.0000

index_SCP | -0.0179 -0.0462 0.0134 0.1641 0.0380 0.1354 0.0386 1.0000

houseowner3 | 0.0747 0.0199 -0.0380 -0.0744 -0.0544 -0.0619 -0.0812 -0.0524

1.0000

Annex-C: Test of Independent Irrelevant Alternatives (IIA)

mlogtest, iia base

**** Hausman tests of IIA assumption (N=200)

Ho: Odds(Outcome-J vs Outcome-K) are independent of other alternatives.

Omitted | chi2 df P>chi2 evidence

---------+------------------------------------

uncontra | -12.614 18 --- ---

quantity | 0.433 18 1.000 for Ho

risk_rat | 0.141 18 1.000 for Ho

uncontra | -2.553 18 --- ---

----------------------------------------------

Note: If chi2<0, the estimated model does not


53

meet asymptotic assumptions of the test.

**** suest-based Hausman tests of IIA assumption (N=200)


Omitted | chi2 df P>chi2 evidence

---------+------------------------------------

uncontra | 14.052 20 0.828 for Ho

quantity | 7.325 20 0.995 for Ho

risk_rat | 8.660 20 0.987 for Ho

uncontra | 14.304 20 0.815 for Ho

----------------------------------------------

**** Small-Hsiao tests of IIA assumption (N=200)


Omitted | lnL(full) lnL(omit) chi2 df P>chi2 evidence

---------+---------------------------------------------------------

uncontra | -53.401 -45.570 15.662 20 0.737 for Ho

quantity | -88.082 -69.560 37.044 20 0.012 against Ho

risk_rat | -78.757 -62.147 33.220 20 0.032 against Ho

uncontra | -48.014 -36.184 23.660 20 0.258 for Ho

-------------------------------------------------------------------

Annex-D: Multinomial logit:

Case1: when the unconstrained non-borrower is the base case

mlogit rationing_categ ageowner genderowner married hhsize eduowner firmage

invest_begin index_SCP houseo wner3

Multinomial logistic regression Number of obs = 200

LR chi2(27) = 54.61

Prob > chi2 = 0.0013

Log likelihood = -236.54069 Pseudo R2 = 0.1035

------------------------------------------------------------------------------

rationing_~g | Coef. Std. Err. z P>|z| [95% Conf. Interval]

-------------+----------------------------------------------------------------

uncontrain~r |

ageowner | -.0174736 .0315474 -0.55 0.580 -.0793054 .0443582


54

genderowner | .2014775 .4236758 0.48 0.634 -.6289119 1.031867

married | .565736 .4923955 1.15 0.251 -.3993413 1.530813

hhsize | .2328368 .0941206 2.47 0.013 .0483638 .4173098

eduowner | .0067347 .0482936 0.14 0.889 -.087919 .1013883

firmage | -.0779586 .061765 -1.26 0.207 -.1990157 .0430985

invest_begin | -1.69e-07 7.16e-07 -0.24 0.813 -1.57e-06 1.23e-06

index_SCP | .116327 .1535817 0.76 0.449 -.1846876 .4173415

houseowner3 | 1.002521 .3931124 2.55 0.011 .2320347 1.773007

_cons | -1.854213 1.333694 -1.39 0.164 -4.468206 .7597796

-------------+----------------------------------------------------------------

quantity_r~g |

ageowner | -.060576 .0386503 -1.57 0.117 -.1363292 .0151772

genderowner | .9109035 .5437764 1.68 0.094 -.1548786 1.976686

married | 1.135998 .6056036 1.88 0.061 -.0509628 2.32296

hhsize | .1325332 .1129587 1.17 0.241 -.0888618 .3539282

eduowner | .0532317 .0628889 0.85 0.397 -.0700283 .1764917

firmage | .0096693 .0674476 0.14 0.886 -.1225256 .1418641

invest_begin | -9.81e-06 5.37e-06 -1.83 0.068 -.0000203 7.09e-07

index_SCP | -.3007549 .1562292 -1.93 0.054 -.6069584 .0054487

houseowner3 | 1.396468 .4730875 2.95 0.003 .4692337 2.323703

_cons | -.2389124 1.45258 -0.16 0.869 -3.085916 2.608091

-------------+----------------------------------------------------------------

risk_ratio~g |

ageowner | -.1182 .0416674 -2.84 0.005 -.1998666 -.0365334

genderowner | .4752603 .469648 1.01 0.312 -.4452329 1.395754

married | .6526971 .5521448 1.18 0.237 -.4294868 1.734881

hhsize | .048284 .1104591 0.44 0.662 -.1682119 .2647799

eduowner | -.0274916 .0578249 -0.48 0.634 -.1408264 .0858431

firmage | .0554054 .0679114 0.82 0.415 -.0776985 .1885092

invest_begin | -5.30e-06 4.00e-06 -1.33 0.185 -.0000131 2.54e-06

index_SCP | .0123301 .1582711 0.08 0.938 -.2978755 .3225357

houseowner3 | .3153029 .4492754 0.70 0.483 -.5652608 1.195867

_cons | 2.342362 1.458715 1.61 0.108 -.5166667 5.20139


55

------------------------------------------------------------------------------

(rationing_categ==uncontrained_non-borrower is the base outcome)

. mfx compute, predict(outcome(2))

Marginal effects after mlogit

y = Pr(rationing_categ==2) (predict, outcome(2))

= .45010789

------------------------------------------------------------------------------

variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X

---------+--------------------------------------------------------------------

ageowner | .0134789 .00656 2.05 0.040 .000617 .02634 35.225

gender~r*| -.1047242 .08581 -1.22 0.222 -.272901 .063452 .65

married*| -.1754076 .09819 -1.79 0.074 -.367858 .017042 .535

hhsize | -.039686 .01987 -2.00 0.046 -.078626 -.000746 4.04

eduowner | -.0019146 .01007 -0.19 0.849 -.021647 .017818 10.455

firmage | .0054971 .01207 0.46 0.649 -.01816 .029155 5.53112

invest~n | 9.17e-07 .00000 2.29 0.022 1.3e-07 1.7e-06 67492

index_~P | .0011222 .02961 0.04 0.970 -.056918 .059162 4.3

houseo~3*| -.2193881 .07563 -2.90 0.004 -.367616 -.07116 .485

------------------------------------------------------------------------------

(*) dy/dx is for discrete change of dummy variable from 0 to 1




= .12202178

------------------------------------------------------------------------------


---------+--------------------------------------------------------------------

ageowner | -.0037375 .00385 -0.97 0.332 -.011285 .00381 35.225

gender~r*| .0760798 .0468 1.63 0.104 -.015651 .167811 .65

married*| .0891015 .05771 1.54 0.123 -.024016 .202219 .535

hhsize | .0054133 .01078 0.50 0.616 -.015717 .026543 4.04


56

eduowner | .0059764 .00609 0.98 0.326 -.005954 .017907 10.455

firmage | .0026701 .00663 0.40 0.687 -.010331 .015671 5.53112

invest~n | -9.48e-07 .00000 -2.09 0.036 -1.8e-06 -6.0e-08 67492

index_~P | -.0363944 .01602 -2.27 0.023 -.067796 -.004993 4.3

houseo~3*| .1101561 .04945 2.23 0.026 .013228 .207085 .485

------------------------------------------------------------------------------





= .14969108

------------------------------------------------------------------------------


---------+--------------------------------------------------------------------

ageowner | -.0132109 .00481 -2.75 0.006 -.022631 -.00379 35.225

gender~r*| .0355973 .05175 0.69 0.492 -.065829 .137024 .65

married*| .03842 .062 0.62 0.535 -.083095 .159935 .535

hhsize | -.0059706 .01264 -0.47 0.637 -.030742 .0188 4.04

eduowner | -.004752 .00681 -0.70 0.485 -.018101 .008597 10.455

firmage | .0101219 .00814 1.24 0.213 -.005824 .026067 5.53112

invest~n | -4.89e-07 .00000 -1.04 0.297 -1.4e-06 4.3e-07 67492

index_~P | .0022189 .01854 0.12 0.905 -.034118 .038556 4.3

houseo~3*| -.0271543 .05059 -0.54 0.591 -.12631 .072002 .485

------------------------------------------------------------------------------


Case2: when the unconstrained borrower is the base case


invest_begin index_SCP houseowner3, baseoutcome(1)


LR chi2(27) = 54.61


57

Prob > chi2 = 0.0013


------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

uncontrain~r |

ageowner | .0174736 .0315474 0.55 0.580 -.0443582 .0793054

genderowner | -.2014775 .4236758 -0.48 0.634 -1.031867 .6289119

married | -.565736 .4923955 -1.15 0.251 -1.530813 .3993413

hhsize | -.2328368 .0941206 -2.47 0.013 -.4173098 -.0483638

eduowner | -.0067347 .0482936 -0.14 0.889 -.1013883 .087919

firmage | .0779586 .061765 1.26 0.207 -.0430985 .1990157

invest_begin | 1.69e-07 7.16e-07 0.24 0.813 -1.23e-06 1.57e-06

index_SCP | -.116327 .1535817 -0.76 0.449 -.4173415 .1846876

houseowner3 | -1.002521 .3931124 -2.55 0.011 -1.773007 -.2320347

_cons | 1.854213 1.333694 1.39 0.164 -.7597796 4.468206

-------------+----------------------------------------------------------------

quantity_r~g |

ageowner | -.0431024 .0398985 -1.08 0.280 -.121302 .0350972

genderowner | .7094261 .5736198 1.24 0.216 -.4148481 1.8337

married | .5702624 .6322322 0.90 0.367 -.6688899 1.809415

hhsize | -.1003036 .1122943 -0.89 0.372 -.3203963 .1197891

eduowner | .046497 .0655788 0.71 0.478 -.0820351 .1750292

firmage | .0876279 .0729662 1.20 0.230 -.0553832 .230639

invest_begin | -9.64e-06 5.37e-06 -1.79 0.073 -.0000202 8.95e-07

index_SCP | -.4170818 .1726651 -2.42 0.016 -.7554992 -.0786644

houseowner3 | .3939474 .4983797 0.79 0.429 -.5828589 1.370754

_cons | 1.615301 1.601802 1.01 0.313 -1.524174 4.754775

-------------+----------------------------------------------------------------

risk_ratio~g |

ageowner | -.1007264 .0442519 -2.28 0.023 -.1874585 -.0139943

genderowner | .2737829 .5171822 0.53 0.597 -.7398756 1.287441


58

married | .086961 .5942229 0.15 0.884 -1.077694 1.251617

hhsize | -.1845528 .1134062 -1.63 0.104 -.4068249 .0377194

eduowner | -.0342263 .0613 -0.56 0.577 -.1543721 .0859195

firmage | .1333639 .0772713 1.73 0.084 -.018085 .2848129

invest_begin | -5.13e-06 4.02e-06 -1.28 0.201 -.000013 2.74e-06

index_SCP | -.1039968 .1810448 -0.57 0.566 -.4588382 .2508446

houseowner3 | -.6872179 .4848881 -1.42 0.156 -1.637581 .2631453

_cons | 4.196575 1.636961 2.56 0.010 .9881903 7.404959

------------------------------------------------------------------------------

(rationing_categ==uncontrained_borrower is the base outcome)




= .27817926

------------------------------------------------------------------------------


---------+--------------------------------------------------------------------

ageowner | .0034695 .00573 0.61 0.545 -.007758 .014697 35.225

gender~r*| -.0069529 .07789 -0.09 0.929 -.159612 .145706 .65

married*| .047886 .08729 0.55 0.583 -.123195 .218967 .535

hhsize | .0402433 .01642 2.45 0.014 .008064 .072422 4.04

eduowner | .0006902 .00881 0.08 0.938 -.016586 .017966 10.455

firmage | -.0182891 .01124 -1.63 0.104 -.040314 .003736 5.53112

invest~n | 5.20e-07 .00000 1.93 0.053 -7.6e-09 1.0e-06 67492

index_~P | .0330533 .02782 1.19 0.235 -.021463 .08757 4.3

houseo~3*| .1363862 .06994 1.95 0.051 -.00069 .273462 .485

------------------------------------------------------------------------------






59

= .12202178

------------------------------------------------------------------------------


---------+--------------------------------------------------------------------

ageowner | -.0037375 .00385 -0.97 0.332 -.011285 .00381 35.225

gender~r*| .0760798 .0468 1.63 0.104 -.015651 .167811 .65

married*| .0891015 .05771 1.54 0.123 -.024016 .202219 .535

hhsize | .0054133 .01078 0.50 0.616 -.015717 .026543 4.04

eduowner | .0059764 .00609 0.98 0.326 -.005954 .017907 10.455

firmage | .0026701 .00663 0.40 0.687 -.010331 .015671 5.53112

invest~n | -9.48e-07 .00000 -2.09 0.036 -1.8e-06 -6.0e-08 67492

index_~P | -.0363944 .01602 -2.27 0.023 -.067796 -.004993 4.3

houseo~3*| .1101561 .04945 2.23 0.026 .013228 .207085 .485

------------------------------------------------------------------------------





= .14969108

------------------------------------------------------------------------------


---------+--------------------------------------------------------------------

ageowner | -.0132109 .00481 -2.75 0.006 -.022631 -.00379 35.225

gender~r*| .0355973 .05175 0.69 0.492 -.065829 .137024 .65

married*| .03842 .062 0.62 0.535 -.083095 .159935 .535

hhsize | -.0059706 .01264 -0.47 0.637 -.030741 .0188 4.04

eduowner | -.004752 .00681 -0.70 0.485 -.018101 .008597 10.455

firmage | .0101219 .00814 1.24 0.213 -.005824 .026067 5.53112

invest~n | -4.89e-07 .00000 -1.04 0.297 -1.4e-06 4.3e-07 67492

index_~P | .0022189 .01854 0.12 0.905 -.034118 .038556 4.3

houseo~3*| -.0271543 .05059 -0.54 0.591 -.12631 .072002 .485

------------------------------------------------------------------------------


60


Case3: when quantity rationed is the base case




LR chi2(27) = 54.61

Prob > chi2 = 0.0013


------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

uncontrain~r |

ageowner | .0431024 .0398985 1.08 0.280 -.0350972 .121302

genderowner | -.7094261 .5736198 -1.24 0.216 -1.8337 .4148481

married | -.5702624 .6322322 -0.90 0.367 -1.809415 .6688899

hhsize | .1003036 .1122943 0.89 0.372 -.1197891 .3203963

eduowner | -.046497 .0655788 -0.71 0.478 -.1750292 .0820351

firmage | -.0876279 .0729662 -1.20 0.230 -.230639 .0553832

invest_begin | 9.64e-06 5.37e-06 1.79 0.073 -8.95e-07 .0000202

index_SCP | .4170818 .1726651 2.42 0.016 .0786644 .7554992

houseowner3 | -.3939474 .4983797 -0.79 0.429 -1.370754 .5828589

_cons | -1.615301 1.601802 -1.01 0.313 -4.754775 1.524174

-------------+----------------------------------------------------------------

uncontrain~r |

ageowner | .060576 .0386503 1.57 0.117 -.0151772 .1363292

genderowner | -.9109035 .5437764 -1.68 0.094 -1.976686 .1548786

married | -1.135998 .6056036 -1.88 0.061 -2.32296 .0509628

hhsize | -.1325332 .1129587 -1.17 0.241 -.3539282 .0888618

eduowner | -.0532317 .0628889 -0.85 0.397 -.1764917 .0700283

firmage | -.0096693 .0674476 -0.14 0.886 -.1418641 .1225256


61

invest_begin | 9.81e-06 5.37e-06 1.83 0.068 -7.09e-07 .0000203

index_SCP | .3007549 .1562292 1.93 0.054 -.0054487 .6069584

houseowner3 | -1.396468 .4730875 -2.95 0.003 -2.323703 -.4692337

_cons | .2389124 1.45258 0.16 0.869 -2.608091 3.085916

-------------+----------------------------------------------------------------

risk_ratio~g |

ageowner | -.057624 .0483307 -1.19 0.233 -.1523503 .0371023

genderowner | -.4356432 .6090922 -0.72 0.474 -1.629442 .7581556

married | -.4833014 .6729096 -0.72 0.473 -1.80218 .8355773

hhsize | -.0842492 .1272775 -0.66 0.508 -.3337085 .1652102

eduowner | -.0807233 .0714993 -1.13 0.259 -.2208593 .0594126

firmage | .0457361 .0797691 0.57 0.566 -.1106084 .2020805

invest_begin | 4.51e-06 6.32e-06 0.71 0.476 -7.88e-06 .0000169

index_SCP | .313085 .179629 1.74 0.081 -.0389813 .6651513

houseowner3 | -1.081165 .5412009 -2.00 0.046 -2.141899 -.0204311

_cons | 2.581274 1.676895 1.54 0.124 -.705379 5.867927

------------------------------------------------------------------------------

(rationing_categ==quantity_rationing is the base outcome)




= .27817926

------------------------------------------------------------------------------


---------+--------------------------------------------------------------------

ageowner | .0034695 .00573 0.61 0.545 -.007758 .014697 35.225

gender~r*| -.0069529 .07789 -0.09 0.929 -.159612 .145706 .65

married*| .047886 .08729 0.55 0.583 -.123195 .218967 .535

hhsize | .0402433 .01642 2.45 0.014 .008064 .072422 4.04

eduowner | .0006902 .00881 0.08 0.938 -.016586 .017966 10.455

firmage | -.0182891 .01124 -1.63 0.104 -.040314 .003736 5.53112

invest~n | 5.20e-07 .00000 1.93 0.053 -7.6e-09 1.0e-06 67492


62

index_~P | .0330533 .02782 1.19 0.235 -.021463 .08757 4.3

houseo~3*| .1363862 .06994 1.95 0.051 -.00069 .273462 .485

------------------------------------------------------------------------------





= .45010789

------------------------------------------------------------------------------


---------+--------------------------------------------------------------------

ageowner | .0134789 .00656 2.05 0.040 .000617 .02634 35.225

gender~r*| -.1047242 .08581 -1.22 0.222 -.272901 .063453 .65

married*| -.1754076 .09819 -1.79 0.074 -.367858 .017042 .535

hhsize | -.039686 .01987 -2.00 0.046 -.078626 -.000746 4.04

eduowner | -.0019146 .01007 -0.19 0.849 -.021647 .017818 10.455

firmage | .0054971 .01207 0.46 0.649 -.01816 .029155 5.53112

invest~n | 9.17e-07 .00000 2.29 0.022 1.3e-07 1.7e-06 67492

index_~P | .0011222 .02961 0.04 0.970 -.056918 .059162 4.3

houseo~3*| -.2193881 .07563 -2.90 0.004 -.367617 -.07116 .485

------------------------------------------------------------------------------





= .14969108

------------------------------------------------------------------------------


---------+--------------------------------------------------------------------

ageowner | -.0132109 .00481 -2.75 0.006 -.022631 -.00379 35.225

gender~r*| .0355973 .05175 0.69 0.492 -.065829 .137024 .65

married*| .03842 .062 0.62 0.535 -.083095 .159935 .535


63

hhsize | -.0059706 .01264 -0.47 0.637 -.030742 .0188 4.04

eduowner | -.004752 .00681 -0.70 0.485 -.018101 .008597 10.455

firmage | .0101219 .00814 1.24 0.213 -.005824 .026067 5.53112

invest~n | -4.89e-07 .00000 -1.04 0.297 -1.4e-06 4.3e-07 67492

index_~P | .0022189 .01854 0.12 0.905 -.034118 .038556 4.3

houseo~3*| -.0271543 .05059 -0.54 0.591 -.12631 .072002 .485

------------------------------------------------------------------------------


Case4: when risk rationed borrower is the base case




LR chi2(27) = 54.61

Prob > chi2 = 0.0013


------------------------------------------------------------------------------


-------------+----------------------------------------------------------------

uncontrain~r |

ageowner | .1007264 .0442519 2.28 0.023 .0139943 .1874585

genderowner | -.2737829 .5171822 -0.53 0.597 -1.287441 .7398756

married | -.086961 .5942229 -0.15 0.884 -1.251617 1.077694

hhsize | .1845528 .1134062 1.63 0.104 -.0377194 .4068249

eduowner | .0342263 .0613 0.56 0.577 -.0859195 .1543721

firmage | -.1333639 .0772713 -1.73 0.084 -.2848129 .018085

invest_begin | 5.13e-06 4.02e-06 1.28 0.201 -2.74e-06 .000013

index_SCP | .1039968 .1810448 0.57 0.566 -.2508446 .4588382

houseowner3 | .6872179 .4848881 1.42 0.156 -.2631453 1.637581

_cons | -4.196575 1.636961 -2.56 0.010 -7.404959 -.9881903

-------------+----------------------------------------------------------------


64

uncontrain~r |

ageowner | .1182 .0416674 2.84 0.005 .0365334 .1998666

genderowner | -.4752603 .469648 -1.01 0.312 -1.395754 .4452329

married | -.6526971 .5521448 -1.18 0.237 -1.734881 .4294868

hhsize | -.048284 .1104591 -0.44 0.662 -.2647799 .1682119

eduowner | .0274916 .0578249 0.48 0.634 -.0858431 .1408264

firmage | -.0554054 .0679114 -0.82 0.415 -.1885092 .0776985

invest_begin | 5.30e-06 4.00e-06 1.33 0.185 -2.54e-06 .0000131

index_SCP | -.0123301 .1582711 -0.08 0.938 -.3225357 .2978755

houseowner3 | -.3153029 .4492754 -0.70 0.483 -1.195867 .5652608

_cons | -2.342362 1.458715 -1.61 0.108 -5.20139 .5166667

-------------+----------------------------------------------------------------

quantity_r~g |

ageowner | .057624 .0483307 1.19 0.233 -.0371023 .1523503

genderowner | .4356432 .6090922 0.72 0.474 -.7581556 1.629442

married | .4833014 .6729096 0.72 0.473 -.8355773 1.80218

hhsize | .0842492 .1272775 0.66 0.508 -.1652102 .3337085

eduowner | .0807233 .0714993 1.13 0.259 -.0594126 .2208593

firmage | -.0457361 .0797691 -0.57 0.566 -.2020805 .1106084

invest_begin | -4.51e-06 6.32e-06 -0.71 0.476 -.0000169 7.88e-06

index_SCP | -.313085 .179629 -1.74 0.081 -.6651513 .0389813

houseowner3 | 1.081165 .5412009 2.00 0.046 .0204311 2.141899

_cons | -2.581274 1.676895 -1.54 0.124 -5.867927 .705379

------------------------------------------------------------------------------

(rationing_categ==risk_rationing is the base outcome)




= .27817926

------------------------------------------------------------------------------


---------+--------------------------------------------------------------------


65

ageowner | .0034695 .00573 0.61 0.545 -.007758 .014697 35.225

gender~r*| -.0069529 .07789 -0.09 0.929 -.159612 .145706 .65

married*| .047886 .08729 0.55 0.583 -.123195 .218967 .535

hhsize | .0402433 .01642 2.45 0.014 .008064 .072422 4.04

eduowner | .0006902 .00881 0.08 0.938 -.016586 .017966 10.455

firmage | -.0182891 .01124 -1.63 0.104 -.040314 .003736 5.53112

invest~n | 5.20e-07 .00000 1.93 0.053 -7.6e-09 1.0e-06 67492

index_~P | .0330533 .02782 1.19 0.235 -.021463 .08757 4.3

houseo~3*| .1363862 .06994 1.95 0.051 -.00069 .273462 .485

------------------------------------------------------------------------------





= .45010789

------------------------------------------------------------------------------


---------+--------------------------------------------------------------------

ageowner | .0134789 .00656 2.05 0.040 .000617 .02634 35.225

gender~r*| -.1047242 .08581 -1.22 0.222 -.272901 .063452 .65

married*| -.1754076 .09819 -1.79 0.074 -.367858 .017042 .535

hhsize | -.039686 .01987 -2.00 0.046 -.078626 -.000746 4.04

eduowner | -.0019146 .01007 -0.19 0.849 -.021647 .017818 10.455

firmage | .0054971 .01207 0.46 0.649 -.01816 .029155 5.53112

invest~n | 9.17e-07 .00000 2.29 0.022 1.3e-07 1.7e-06 67492

index_~P | .0011222 .02961 0.04 0.970 -.056918 .059162 4.3

houseo~3*| -.2193881 .07563 -2.90 0.004 -.367616 -.07116 .485

------------------------------------------------------------------------------





66


= .12202178

------------------------------------------------------------------------------


---------+--------------------------------------------------------------------

ageowner | -.0037375 .00385 -0.97 0.332 -.011285 .00381 35.225

gender~r*| .0760798 .0468 1.63 0.104 -.015651 .167811 .65

married*| .0891015 .05771 1.54 0.123 -.024016 .202219 .535

hhsize | .0054133 .01078 0.50 0

.616 -.015717 .026543 4.04

eduowner | .0059764 .00609 0.98 0.326 -.005954 .017907 10.455

firmage | .0026701 .00663 0.40 0.687 -.010331 .015671 5.53112

invest~n | -9.48e-07 .00000 -2.09 0.036 -1.8e-06 -6.0e-08 67492

index_~P | -.0363944 .01602 -2.27 0.023 -.067796 -.004993 4.3

houseo~3*| .1101561 .04945 2.23 0.026 .013228 .207085 .485

------------------------------------------------------------------------------


Determinants of Credit Rationing of Small and Micro ...

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