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
|
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
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Determinants of Credit Rationing of Small and Micro Enterprises 2013
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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Determinants of Credit Rationing of Small and Micro Enterprises 2013
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.................................................................................................
v
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Determinants of Credit Rationing of Small and Micro Enterprises 2013
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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Determinants of Credit Rationing of Small and Micro Enterprises 2013
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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Determinants of Credit Rationing of Small and Micro Enterprises 2013
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 nonborrowers, 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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Determinants of Credit Rationing of Small and Micro Enterprises 2013
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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Determinants of Credit Rationing of Small and Micro Enterprises 2013
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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Determinants of Credit Rationing of Small and Micro Enterprises 2013
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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Determinants of Credit Rationing of Small and Micro Enterprises 2013
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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Determinants of Credit Rationing of Small and Micro Enterprises 2013
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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Determinants of Credit Rationing of Small and Micro Enterprises 2013
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 Up to 10 employees and using non-power
consultancy firms & establishments
Small enterprises
driven machines for operation
2,250-56,000 US$ excluding high tech Less than 10 employees using motor-operated
Medium & large enterprises
consultancy firms and establishments
equipment
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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Determinants of Credit Rationing of Small and Micro Enterprises 2013
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
___________________________________
1
Equib in Ethiopia is similar with other countries the so called Rotating Saving and Credit Cooperative (ROSCA)
7
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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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Determinants of Credit Rationing of Small and Micro Enterprises 2013
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).
9
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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
10
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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
11
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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.
12
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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
13
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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
14
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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
15
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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.
16
Determinants of Credit Rationing of Small and Micro Enterprises 2013
Table3.1 Credit rationing category’s using DEM
Response
I have enough money
constrained category
Unconstrained non-borrowers
I do not have feasible project that repaid the
loan
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
Quantity rationed borrowers
I applied for loan from forma financial
institutions, but my application was rejected
I did not want to risk my collateral
Risk rationed borrowers
I did not apply because I was afraid
Formal financial institutions are strict
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.
17
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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.
18
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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)
19
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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)
20
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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
21
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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
1.
Measurement unit
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
1 if yes, 0 if otherwise
institutions
Applied and received from formal financial
1 if yes, 0 if otherwise
institutions
Received the desired amount from formal
1 if yes, 0 if otherwise
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
22
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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 socio- economic factors related to credit visà-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 nonborrowers), 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, y i* .
Supposed y i* is a latent variable (unobserved variable) for bank’s decision whether to grant
the loan or not. This will be given as follows;
...................1
23
Determinants of Credit Rationing of Small and Micro Enterprises 2013
Where y * i 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


pr  yi  j   pr yi  yi* ..................2
The above equation shows that the relationship between the observed
 yi 
and the
unobserved credit rationed (yi*). Firm i is credit rationed when the observed variable  yi 
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
pr ( yi  j ) 
exp(  ' ij  )
m
1   exp(  íj  )
, J  1,2,3,......m........................................3
'
j 1
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
24
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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:
yi
 yi    yi  ..........................................4
xij
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).
VIFi 
1
, TOL  1  Ri2
2
1  Ri
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
25
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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 (SeeAppendix-C).
26
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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.
27
Determinants of Credit Rationing of Small and Micro Enterprises 2013
Table5.1 Entrepreneurs socioeconomic characteristics
Applied,
Not-Applied,
N=83
Total, N=200
N=117
Charac.
mean
Std.
deviation
Age
35.87952
8.1546
Education
10.31325
HH size
Dependent
mean
Std.
deviation
T-Value
mean
Std.
deviation
34.76068
11.08327
0.7823
35.225
9.965487
4.242433
10.55556
3.972848
0.3400
10.455
4.078128
4.60241
2.224803
3.641026
2.465142
0.9974
4.0400
2.409862
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
2
Applied for loan
Not applied for loan
N=83
N=117
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
Source: own survey, 2013
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
Source: own survey, 2013
28
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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.
No
2
Yes
Total
Percent
Cum.
2.4
2.4
81
97.6
100
83
100
Source: own survey, 2013
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
Source: own survey, 2013
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
29
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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
200
100
Total
Source: own survey, 2013
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 nonborrowers. 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.
30
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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
Source: own survey, 2013
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
Construc.
Agriculture
Manufactu
Trade
Total
ring
Unconstrained borrowers
25
5
3
10
9
52
Unconstrained-Non-
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
borrowers
Source: own survey, 2013
31
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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
Source: own survey, 2013
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).
32
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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
117
100
Total
Source: own survey, 2013
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
33
Determinants of Credit Rationing of Small and Micro Enterprises 2013
Table5.11 Marginal effect estimation after multinomial logit regression
variables
Age
Gender
Married
HH size
Education
Firm age
Initialinvestment
Social-capital
House owner
Unconstrained
Non-borrower
Unconstrained
borrower
.0034695
(.005730)
-.0069529
(.077890)
.0478860
(.087290)
.0402433**
(.0402433)
.0006902
(.008810)
-.0182891
(.011240)
5.20e-07*
(.00000)
.0330533
(.027820)
.1363862*
(.069940)
.0134789**
(.006560)
-.1047242
(.085810)
-.1754076*
(.09819)
-.039686**
(.01987)
-.0019146
(.01007)
.0054971
(.012070)
9.17e-07**
(.00000)
.0011222
(.029610)
-.2193881***
(.075630)
Quantity rationed
borrower
-.0037375
(.00385)
.0760798
(.04680)
.0891015
(.05771)
.0054133
(.010780)
.0059764
(.006090)
.0026701
(.00663)
-9.48e-07**
(.00000)
-.0363944**
(.016020)
.1101561**
(.049450)
Risk rationed
borrower
-.0132109***
(.00481)
.0355973
(.05175)
.038420
(.062000)
-.0059706
(.012640)
-.004752
(.006810)
.0101219
(.00814)
-4.89e-07
(.00000)
.0022189
(.018540)
-.0271543
(.050590)
Notes:
Standard error is in parenthesis/
*** 1% significant level,
** 5% significant level,
* 10% significant level
2
Pseudo R = 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
Source: own survey, 2013
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
34
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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 nonborrowers. 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 nonborrower 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
35
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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
36
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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).
37
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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.
38
Determinants of Credit Rationing of Small and Micro Enterprises 2013

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.
39
Determinants of Credit Rationing of Small and Micro Enterprises 2013
References
ADESUA-LINCOLN, A. 2011. Assessing Nigerian female entrepreneur‟ s access to finance for
business start-up and growth. African Journal of Business Management, 5, 5348-5355.
AGA, G. A. & REILLY, B. 2011. Access to credit and informality among micro and small enterprises
in Ethiopia. International Review of Applied Economics, 25, 313-329.
AGEBA, G. & AMHA, W. Policy Impact on Regulatory Challenges of the Micro and Small
Enterprises (MSE) in Ethiopia. second International Conference on the Ethiopian Economy,
2004. 3-8.
AGHION, P. & BOLTON, P. 1992. An incomplete contracts approach to financial contracting. The
review of economic Studies, 59, 473-494.
ARYEETEY, E., HETTIGE, H., NISSANKE, M. & STEEL, W. 1997. Financial market fragmentation and
reforms in Ghana, Malawi, Nigeria, and Tanzania. The World Bank Economic Review, 11,
195-218.
ATIENO, R. 2001. Formal and informal institutions' lending policies and access to credit by smallscale enterprises in Kenya: An empirical assessment, African Economic Research
Consortium.
BAYDAS, M. M., MEYER, R. L. & AGUILERA‐ALFRED, N. 1994. Credit rationing in small‐scale
enterprises: Special microenterprise programmes in ecuador. The Journal of Development
Studies, 31, 279-288.
BEKELE, E. & WORKU, Z. 2008a. FACTORS THAT AFFECT THE LONG‐TERM SURVIVAL OF MICRO,
SMALL AND MEDIUM ENTERPRISES IN ETHIOPIA. South African Journal of Economics, 76,
548-568.
BEKELE, E. & WORKU, Z. 2008b. Women entrepreneurship in micro, small and medium
enterprises: The case of Ethiopia. Journal of International Women’s Studies, 10, 3-19.
BERGER, A. & UDELL, G. 1998. Lines of credit and relationship lending in small firm finance.
Jerome Levy Economics Institute Working Paper.
BERGLÖF, E. & ROLAND, G. 1997. Soft budget constraints and credit crunches in financial
transition. European Economic Review, 41, 807-817.
BIGSTEN, A., COLLIER, P., DERCON, S., FAFCHAMPS, M., GAUTHIER, B., GUNNING, J. W., ODURO,
A., OOSTENDORP, R., PATILLO, C. & SÖDERBOM, M. 2003. Credit constraints in
manufacturing enterprises in Africa. Journal of African Economies, 12, 104-125.
BOOCOCK, G. & WOODS, M. 1997. The evaluation criteria used by venture capitalists: Evidence
from a UK venture fund. International Small Business Journal, 16, 36-57.
BOOT, A. W. A. & THAKOR, A. V. 1994. Moral hazard and secured lending in an infinitely repeated
credit market game. International Economic Review, 899-920.
40
Determinants of Credit Rationing of Small and Micro Enterprises 2013
BOUCHER, S., GUIRKINGER, C. & TRIVELLI, C. 2006. Direct elicitation of credit constraints:
Conceptual and practical issues with an empirical application to Peruvian agriculture.
CHAN, Y. S. & KANATAS, G. 1985. Asymmetric valuations and the role of collateral in loan
agreements. Journal of Money, Credit and Banking, 17, 84-95.
CHIVAKUL, M. & CHEN, K. 2008. What Drives Household Borrowing and Credit Constraints?
Evidence from Bosnia & Herzegovina. IMF Working Papers, 1-34.
COLE, R. A. 1998. The importance of relationships to the availability of credit. Journal of Banking &
Finance, 22, 959-977.
DIAMOND, D. W. 1991. Monitoring and reputation: The choice between bank loans and directly
placed debt. Journal of political Economy, 689-721.
DIAMOND, D. W. & RAJAN, R. G. 2001. Banks, short-term debt and financial crises: theory, policy
implications and applications. Carnegie-Rochester Conference Series on Public Policy, 54,
37-71.
DOAN, T., GIBSON, J. & HOLMES, M. 2010. What determines credit participation and credit
constraints of the poor in peri-urban areas, Vietnam?
DUNKELBERG, W. C. 1998. Credit, banks and small business in America. Journal of Banking &
Finance, 22, 1085-1088.
FATOKI, O. & ODEYEMI, A. 2010. The determinants of access to trade credit by new SMEs in South
Africa. Afr. J. Bus. Manage, 4, 2763-2770.
FDRE, P. & COMMISSION, F. D. R. O. E. P. C. 2008. Summary and Statistical Report of the 2007
Population and Housing Census: Population Size by Age and Sex. Federal Democratic
Republic of Ethiopia Population Census Commission, Addis Ababa, Ethiopia.
FUKUYAMA, F. 1995. Trust: The social virtues and the creation of prosperity Free Press. New York.
GEBEYEHU, W. & ASSEFA, D. 2004. The Role of Micro and Small Enterprises in the Economic
Development of Ethiopia. Addis Ababa: The Ethiopian Federal Micro and Small Enterprises
Agency.
GREENE, W. H. 1997. Frontier production functions. Handbook of applied econometrics, 2, 81166.
GYELTSHEN, T. 2012. Formal and informal credit markets in rural Bhutan, [S.l., s.n.].
HAILU, D. 2009. The Rich Expand, the Poor Contract. The Paradox of Macroeconomic Policy in
Ethiopia. International Policy Centre for Inclusive Growth.
HAN, L. 2008. Bricks vs clicks: entrepreneurial online banking behaviour and relationship banking.
International Journal of Entrepreneurial Behaviour & Research, 14, 47-60.
41
Determinants of Credit Rationing of Small and Micro Enterprises 2013
HASHI, I. & TOÇI, V. Z. 2011. Financing constraints, credit rationing and financing obstacles:
evidence from firm-level data in South-Eastern Europe. Economic and Business Review, 12,
29-60.
KATUNDU, M. A., MHINA, M. L., MBEIYERERWA, A. G. & KUMBURU, N. P. 2012. 17 th ANNUAL
RESEARCH WORKSHOP.
KIYOTA, K., PEITSCH, B. & STERN, R. 2007. Research Seminar in International Economics, the Case
for Financial Sector Liberalization in Ethiopia, the University of Michigan. Ann Arbor,
Michigan. Discussion Paper.
KRASNIQI, B. A. 2010. Are small firms really credit constrained? Empirical evidence from Kosova.
International Entrepreneurship and Management Journal, 6, 459-479.
MCFADDEN, D. 1974. The measurement of urban travel demand. Journal of public economics, 3,
303-328.
MOLL, H. 1989. Farmers and finance: Experience with institutional savings and credit in West Java,
Wageningen Agricultural University Wageningen.
OKPUKPARA, B. 2009. Strategies for effective loan delivery to small–scale enterprises in rural
Nigeria. Journal of Development and Agricultural Economics, 1, 041-048.
OKURUT, F., OLALEKAN, Y. & MANGADI, K. 2012. Credit rationing and SME development in
Botswana: Implications for economic diversification. Botswana Journal of Economics, 8, 6285.
PETERSEN, M. A. & RAJAN, R. G. 2012. The benefits of lending relationships: Evidence from small
business data. The journal of finance, 49, 3-37.
REYES DUARTE, A. 2011. Credit constraints in rural financial markets in Chile: determinants and
consequences, [Sl: sn].
STIGLITZ, J. E. & WEISS, A. 1981. Credit rationing in markets with imperfect information. The
American Economic Review, 71, 393-410.
TADESSE, T. 2006. Household behavior and solid waste management: survey evidence from
Mekelle.
TAMBUNAN, T. T. H. 2011. Development of small and medium enterprises in a developing
country: The Indonesian case. Journal of Enterprising Communities: People and Places in
the Global Economy, 5, 68-82.
VERBEEK, M. 2008. A guide to modern econometrics, Wiley.
VIJVERBERG, W. 2011. Testing for IIA with the Hausman-McFadden test.
ZAMBALDI, F., ARANHA, F., LOPES, H. & POLITI, R. 2011. Credit granting to small firms: A Brazilian
case. Journal of Business Research, 64, 309-315.
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Determinants of Credit Rationing of Small and Micro Enterprises 2013
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
Male
Male
Female
Female
Elderly (>=64)
Total
Male
Male
Female
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_________
43
Permanent_________
Determinants of Credit Rationing of Small and Micro Enterprises 2013
2. 4 what is the main activity of the firm?
Services
1. In ternate cafe 
Urban Agriculture
21. Urban vegetables
Construction
31.Contracting 
Manufacturing
41.Wood work 
Trade
51.Local whole sale
32. Mineral stones  42. Metal work 
52. Local retailer 

2. Café & restaurant 

22. Urban irrigation

3.Beauty salon 
23.Animal forage  33.Coble stone 
4.Tourism 
34.Sub-contracting
43. Handicraft & Gold 53. Input supplier 
smith 
44. Agro-processing 

5.Sanitation 
6. Electric & software
service 
7. Decoration
8. Small transport 
9. Storage 
10. Packing 
45.Textile 
46.Leather & leather
products 
2.5 The place where you working in is?
1. Own 
2. Rented 
3. Family (rented) 
4. Family (free) 
3. Family (rented) 
4. Family (free) 
5. Other___________________
2.6 The place where you living in is?
1. Own 
2. Rented 
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 
5. Friends/family 
6. Equib 
3. Money lenders 
4. Own savings 
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?
44
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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
Value of the
Value of
Repayment
For how
Purpose of
institution
loan
interest
system ( daily,
long will
the loan
rate
monthly,
stay the
annually)
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)
Which financial institution did not give you the amount you wanted
MFI
Bank
Reason for not
Other______________
1.Lack of collateral 
1.Lack of collateral 
1.Lack of collateral 
2. Lack of sound financial
2. Lack of sound financial
2. Lack of sound financial
statement 
statement 
statement 
give you the
3. Poor repayment history 
3. Poor repayment history 
3. Poor repayment history 
amount you
4. Sector bias 
4. Sector bias 
4. Sector bias 
wanted (multiple
5. Risky venture 
5. Risky venture 
5. Risky venture 
6. Others (list all the possible
reasons___________________
6. Others (list all the possible
reasons________________
6. Others (list all the possible
reasons_____________
answer is
possible)
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) 
45
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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
1. Lack of collateral
answers is possible)
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
1. Yes 
apply?
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?
46
Determinants of Credit Rationing of Small and Micro Enterprises 2013
1. Yes  (, please fill out the questions below)
0. No (if No skip to Q 3.21)
Name of the
Value of the
Value of
Repayment
For how long
Purpose of
institution
loan
interest rate
system
will stay the
the loan
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)
Which informal financial institution did not give you the amount you wanted
Family/friends
Money lenders
Reason for not
Other______________
1.Lack of collateral 
1.Lack of collateral 
1.Lack of collateral 
2. Lack of sound financial
2. Lack of sound financial
2. Lack of sound financial
statement 
statement 
statement 
give you the
3. Poor repayment history 
3. Poor repayment history 
3. Poor repayment history 
amount you
4. Sector bias 
4. Sector bias 
4. Sector bias 
wanted (multiple
5. Risky venture 
5. Risky venture 
5. Risky venture 
6. Others (list all the possible
reasons___________________
6. Others (list all the possible
reasons________________
6. Others (list all the possible
reasons_____________
answer is
possible)
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) 
47
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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
1. Lack of collateral 
possible)
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
1. Yes 
apply?
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 
48
Determinants of Credit Rationing of Small and Micro Enterprises 2013
6. Cooperatives 
7. Other____________________
4.3 To what extent is the trust you have on the above group you belong?
Civil ASS.
Edir
Bank
Saving &
Cooperative Equib
C.C
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
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 
49
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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?
Bank
MFI
Business Partner
Input supplier
Very bad quality
1




4.9
Bad quality
2




Wh
Nether bad nor good quality
3




at is
Good quality
4




your
Very quality
5




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!
50
Determinants of Credit Rationing of Small and Micro Enterprises 2013
Annex-B: Multicollinearity test:
. collin ageowner genderowner married hhsize eduowner firmage invest_begin
index_SCP houseowner3
Collinearity Diagnostics
SQRT
Variable
VIF
VIF
RTolerance
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
51
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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 |
1.0000
0.0747
0.0199
-0.0380
-0.0744
-0.0544
-0.0619
-0.0812
-0.0524
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
52
Determinants of Credit Rationing of Small and Micro Enterprises 2013
meet asymptotic assumptions of the test.
**** suest-based 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 |
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)
Ho: Odds(Outcome-J vs Outcome-K) are independent of other alternatives.
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
Log likelihood = -236.54069
Number of obs
=
200
LR chi2(27)
=
54.61
Prob > chi2
=
0.0013
Pseudo R2
=
0.1035
-----------------------------------------------------------------------------rationing_~g |
Coef.
Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------uncontrain~r |
ageowner |
-.0174736
.0315474
-0.55
53
0.580
-.0793054
.0443582
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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
54
Determinants of Credit Rationing of Small and Micro Enterprises 2013
-----------------------------------------------------------------------------(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
. mfx compute, predict(outcome(3))
Marginal effects after mlogit
y
= Pr(rationing_categ==3) (predict, outcome(3))
=
.12202178
-----------------------------------------------------------------------------variable |
dy/dx
Std. Err.
z
P>|z|
[
95% C.I.
]
X
---------+-------------------------------------------------------------------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
55
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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
-----------------------------------------------------------------------------(*) dy/dx is for discrete change of dummy variable from 0 to 1
. mfx compute, predict(outcome(4))
Marginal effects after mlogit
y
= Pr(rationing_categ==4) (predict, outcome(4))
=
.14969108
-----------------------------------------------------------------------------variable |
dy/dx
Std. Err.
z
P>|z|
[
95% C.I.
]
X
---------+-------------------------------------------------------------------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
-----------------------------------------------------------------------------(*) dy/dx is for discrete change of dummy variable from 0 to 1
Case2: when the unconstrained borrower is the base case
mlogit rationing_categ ageowner genderowner married hhsize eduowner firmage
invest_begin index_SCP houseowner3, baseoutcome(1)
Multinomial logistic regression
56
Number of obs
=
200
LR chi2(27)
=
54.61
Determinants of Credit Rationing of Small and Micro Enterprises 2013
Log likelihood = -236.54069
Prob > chi2
=
0.0013
Pseudo R2
=
0.1035
-----------------------------------------------------------------------------rationing_~g |
Coef.
Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------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
genderowner |
.2737829
.5171822
-2.28
0.023
-.1874585
-.0139943
0.53
0.597
-.7398756
1.287441
57
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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)
. mfx compute, predict(outcome(1))
Marginal effects after mlogit
y
= Pr(rationing_categ==1) (predict, outcome(1))
=
.27817926
-----------------------------------------------------------------------------variable |
dy/dx
Std. Err.
z
P>|z|
[
95% C.I.
]
X
---------+-------------------------------------------------------------------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
-----------------------------------------------------------------------------(*) dy/dx is for discrete change of dummy variable from 0 to 1
. mfx compute, predict(outcome(3))
Marginal effects after mlogit
y
= Pr(rationing_categ==3) (predict, outcome(3))
58
Determinants of Credit Rationing of Small and Micro Enterprises 2013
=
.12202178
-----------------------------------------------------------------------------variable |
dy/dx
Std. Err.
z
P>|z|
[
95% C.I.
]
X
---------+-------------------------------------------------------------------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
-----------------------------------------------------------------------------(*) dy/dx is for discrete change of dummy variable from 0 to 1
. mfx compute, predict(outcome(4))
Marginal effects after mlogit
y
= Pr(rationing_categ==4) (predict, outcome(4))
=
.14969108
-----------------------------------------------------------------------------variable |
dy/dx
Std. Err.
z
P>|z|
[
95% C.I.
]
X
---------+-------------------------------------------------------------------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
------------------------------------------------------------------------------
59
Determinants of Credit Rationing of Small and Micro Enterprises 2013
(*) dy/dx is for discrete change of dummy variable from 0 to 1
Case3: when quantity rationed is the base case
mlogit rationing_categ ageowner genderowner married hhsize eduowner firmage
invest_begin index_SCP houseowner3, baseoutcome(3)
Multinomial logistic regression
Log likelihood = -236.54069
Number of obs
=
200
LR chi2(27)
=
54.61
Prob > chi2
=
0.0013
Pseudo R2
=
0.1035
-----------------------------------------------------------------------------rationing_~g |
Coef.
Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------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
60
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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)
. mfx compute, predict(outcome(1))
Marginal effects after mlogit
y
= Pr(rationing_categ==1) (predict, outcome(1))
=
.27817926
-----------------------------------------------------------------------------variable |
dy/dx
Std. Err.
z
P>|z|
[
95% C.I.
]
X
---------+-------------------------------------------------------------------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
61
Determinants of Credit Rationing of Small and Micro Enterprises 2013
index_~P |
.0330533
.02782
1.19
0.235
-.021463
.08757
4.3
houseo~3*|
.1363862
.06994
1.95
0.051
-.00069
.273462
.485
-----------------------------------------------------------------------------(*) dy/dx is for discrete change of dummy variable from 0 to 1
. 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
.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
-----------------------------------------------------------------------------(*) dy/dx is for discrete change of dummy variable from 0 to 1
. mfx compute, predict(outcome(4))
Marginal effects after mlogit
y
= Pr(rationing_categ==4) (predict, outcome(4))
=
.14969108
-----------------------------------------------------------------------------variable |
dy/dx
Std. Err.
z
P>|z|
[
95% C.I.
]
X
---------+-------------------------------------------------------------------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
62
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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
-----------------------------------------------------------------------------(*) dy/dx is for discrete change of dummy variable from 0 to 1
Case4: when risk rationed borrower is the base case
mlogit rationing_categ ageowner genderowner married hhsize eduowner firmage
invest_begin index_SCP houseowner3, baseoutcome(4)
Multinomial logistic regression
Log likelihood = -236.54069
Number of obs
=
200
LR chi2(27)
=
54.61
Prob > chi2
=
0.0013
Pseudo R2
=
0.1035
-----------------------------------------------------------------------------rationing_~g |
Coef.
Std. Err.
z
P>|z|
[95% Conf. Interval]
-------------+---------------------------------------------------------------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
-------------+----------------------------------------------------------------
63
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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)
. mfx compute, predict(outcome(1))
Marginal effects after mlogit
y
= Pr(rationing_categ==1) (predict, outcome(1))
=
.27817926
-----------------------------------------------------------------------------variable |
dy/dx
Std. Err.
z
P>|z|
[
95% C.I.
]
X
---------+--------------------------------------------------------------------
64
Determinants of Credit Rationing of Small and Micro Enterprises 2013
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
-----------------------------------------------------------------------------(*) dy/dx is for discrete change of dummy variable from 0 to 1
. 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
. mfx compute, predict(outcome(3))
Marginal effects after mlogit
65
Determinants of Credit Rationing of Small and Micro Enterprises 2013
y
= Pr(rationing_categ==3) (predict, outcome(3))
=
.12202178
-----------------------------------------------------------------------------variable |
dy/dx
Std. Err.
z
P>|z|
[
95% C.I.
]
X
---------+-------------------------------------------------------------------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
-----------------------------------------------------------------------------(*) dy/dx is for discrete change of dummy variable from 0 to 1
66