the determinants of micro and small ... - ir.bdu.edu.et
TRANSCRIPT
DSpace Institution
DSpace Repository http://dspace.org
Economics Thesis and Dissertations
2021-08-09
THE DETERMINANTS OF MICRO AND
SMALL ENTERPRISES
PERFORMANCE IN ENARJ ENAWGA
WOREDA, EAST GOJJAM ZONE,
AMHARA REGIONAL STATE, ETHIOPIA
YIGREM WAGANEH
http://ir.bdu.edu.et/handle/123456789/12331
Downloaded from DSpace Repository, DSpace Institution's institutional repository
BAHIRDAR UNIVRERSITY
COLLEGE OF BUSINESS AND ECONOMICS
DEPARTMENT OF ECONOMICS (MSc)
THE DETERMINANTS OF MICRO AND SMALL ENTERPRISES
PERFORMANCE IN ENARJ ENAWGA WOREDA, EAST GOJJAM
ZONE, AMHARA REGIONAL STATE, ETHIOPIA
BY
YIGREM WAGANEH ABEBE
A THESIS SUBMITTED TO THE DEPARTMENT OF ECONOMICS IN PARTIAL
FULFILLMENT OF THE REQUIREMENTS OF THE DEGREE OF MASTER OF
SCIENCE IN DEVELOPMENT ECONOMICS BAHIRDAR UNIVERSITY
JUNE/2021
BAHIRDAR
i
BAHIR DAR UNIVERSITY
COLLEGE OF BUSINESS AND ECONOMICS
DEPARTMENT OF ECONOMICS
THE DETERMINANTS OF MICRO AND SMALL ENTERPRISES PERFORMANCE
IN ENARJ ENAWGA WOREDA, EAST GOJJAM ZONE, AMHARA REGIONAL
STATE, ETHIOPIA
BY
YIGREM WAGANEH ABEBE
ADVISOR: DAREGOT BERIHUN (PhD)
A THESIS SUBMITTED TO THE DEPARTMENT OF ECONOMICS,
COLLEGE OF BUSINESS AND ECONOMICS,
BAHIR DAR UNIVERSITY
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE IN ECONOMICS (DEVELOPMENT ECONOMICS)
JUNE, 2021
BAHIR DAR
ii
BAHIR DAR UNIVERSITY
COLLEGE OF BUSINESS AND ECONOMICS
DEPARTMENT OF ECONOMICS
“THE DETERMINANTS OF MICRO AND SMALL ENTERPRISES
PERFORMANCE IN ENARJ ENAWGA WOREDA, EAST GOJJAM
ZONE, AMHARA REGIONAL STATE, ETHIOPIA”
By
Yigrem Waganeh Abebe
Approved by the Board of Examiners:
______________________________ _______________ _________________
Advisor Name Signature Date
______________________________ _______________ _________________
Internal Examiner Signature Date
_____________________________ _____________ _________________
External Examiner Signature Date
iii
DECLARATION
I, the undersigned, declare that this study entitled “Determinants of Micro and Small Enterprises
Performance in Enarj Enawga Woreda of Amhara Regional State” is my own work. I have
undertaken the research work independently with the guidance and support of the research
advisor. This study has not been submitted for any degree or diploma program in this or any
other institutions and that all sources of materials used for the thesis have been duly
acknowledged.
Name: Yigrem Waganeh
Signature: ____________
Place: Bahir-dar University
Date of Submission: June, 2021
iv
ACKNOWLEDGMENTS
Firstly, I would like to thank The Almighty God with his mother for making this master‟s degree
a reality. My special gratitude goes to my advisor Dr. Daregot Berihun, whose work ethic is
worth modeling, for his commitment on the thesis at each stage and for making invaluable
comments and suggestions. I would like to thank my employer land administration and
environmental protection of Enarj Enawga woreda for giving me time and budget. I also want to
extend my gratitude and sincere appreciation to my family, relatives and real friends for their
encouragement and inspiration, which made the study a success. Finally I would like to thank to
operators of MSEs in Enarj Enawga Woreda for their cooperative and willingness participation
during data collection.
v
Table of Contents ACKNOWLEDGMENTS .............................................................................................................. iv
List of Figures ............................................................................................................................... viii
List of Annexes ............................................................................................................................. viii
List of tables.................................................................................................................................... ix
ABBREVIATIONS ......................................................................................................................... x
Abstract ........................................................................................................................................... xi
CHAPTER ONE ............................................................................................................................. 1
1. INTRODUCTION ...................................................................................................................... 1
1.1 BACKGROUND ................................................................................................................... 1
1.2 Statement of the Problem ...................................................................................................... 4
1.3 Research Questions ............................................................................................................... 6
1.4 Objectives of the study .......................................................................................................... 6
1.4.1 General objective ............................................................................................................ 6
1.4.2 Specific objectives .......................................................................................................... 6
1.5 Significance of the Study ...................................................................................................... 7
CHAPTER TWO ............................................................................................................................ 9
2. Literature Review........................................................................................................................ 9
2.1 Theoretical Review ............................................................................................................... 9
2.1.1 Definitions of MSEs ....................................................................................................... 9
2.1.2 The Concept of Business Performance and measurement? .......................................... 11
2.1.3 Micro and Small Enterprises Development strategy in Ethiopia ................................. 14
2.1.4 The role of MSEs in the economy ................................................................................ 16
2.1.5 The Role of Micro and Small Enterprises in Poverty Reduction ................................. 17
2.1.6 Characteristics of MSEs ............................................................................................... 19
2.1.7 Constraints and Factors affecting the performance of MSEs ....................................... 19
2.1.7.1 Internal factors of MSEs ............................................................................................ 21
2.1.7.2 External Factors of MSEs .......................................................................................... 22
2.2 Empirical Studies on MSE‟s performance .......................................................................... 24
2.4 Conceptual Framework ....................................................................................................... 29
vi
CHAPTER THREE ...................................................................................................................... 31
3. RESEARCH METHODOLOGY.............................................................................................. 31
3.1 Description of Study Area ................................................................................................... 31
3.2 Research design ................................................................................................................... 33
3.3 Research methods ................................................................................................................ 33
3.3.1 Data sources, collection techniques and procedures .................................................... 33
3.3.1.1 Primary source ........................................................................................................... 33
3.3.1.2 Secondary sources ..................................................................................................... 34
3.3.2 Sampling techniques and size ....................................................................................... 34
3.4 Data Analysis ...................................................................................................................... 35
3.4.2 Descriptive analysis ...................................................................................................... 36
3.4.3 Econometrics Analysis ................................................................................................. 36
3.4.3.2 The logit model .......................................................................................................... 36
3.4.4 Definition of Variables and Working Hypothesis ........................................................ 40
3.4.4.1 Definition of dependent variable ............................................................................... 40
3.4.4.2 Definition of independent variables .......................................................................... 41
CHAPTER FOUR ......................................................................................................................... 45
4. RESULTS AND DISCUSSION ............................................................................................... 45
4.1. Demographic Characteristics of Respondents.................................................................... 45
4.1.1 Age of the respondents and age of the enterprise ...................................................... 45
4.1.2 Sex of the MSEs owners ............................................................................................... 46
4.1.3 Education level of respondents ..................................................................................... 47
4.1.4 Marital Status of Operators ........................................................................................... 47
4.2. Characteristics of Micro and Small Enterprises and their Operators ................................. 47
4.2.1 Startup capital of enterprises ........................................................................................ 48
4.2.2 Source of startup finance .............................................................................................. 48
4.2.3 Annual Sales Revenue and Total Costs of MSEs ......................................................... 49
4.2.4 Type of Enterprises ....................................................................................................... 49
4.2.5 Number of Employees at Startup and at Current .......................................................... 50
vii
4.2.6 Term of Employment.................................................................................................... 51
4.2.7 Challenges of MSEs. ........................................................................................................ 51
4.3. External/ Business Environment Factors Related to MSEs ............................................... 52
4.3.1. Working Spaces of MSEs ............................................................................................ 52
4.3.2. Access to Raw Materials ............................................................................................. 53
4.3.3 Factors Related to Government Policies and Regulation ............................................. 54
4.3.4 Factors Related to Market Competition........................................................................ 55
4.3.5 Factors Related to Access to Training .......................................................................... 56
4.3.6 Use of Modern Technological Related Factors ............................................................ 57
4.3.7 Access to infrastructural problems ............................................................................... 58
4.3.8 Access to credit ............................................................................................................. 59
4.4.1 The role of MSEs on Household Food Consumption................................................... 60
4.4.2 The role of MSEs on Education Expenditure of the Households ................................. 61
4.4.3 The role of MSEs on Health Condition of the Households .......................................... 61
4.4.4 The Capacity of MSEs in Poverty Reduction based on Owners perception ................ 62
4.4.5 MSE‟s role in Generating Income based on Owners perception .................................. 62
4.5.1 Testing and Examining the Goodness of Fit of the Model .............................................. 63
4.5.2. Determinants of MSE‟s performance in terms of profit .............................................. 64
4.5.3 Interpretation of Econometric Results .......................................................................... 65
4.5.5. Interpretation of the Results ........................................................................................ 70
CHAPTER FIVE .......................................................................................................................... 72
5. CONCLUSIONS AND RECOMMENDATIONS ................................................................... 72
5.1 Conclusions ......................................................................................................................... 72
5.2 Recommendations ............................................................................................................... 74
References ..................................................................................................................................... 76
Annexes......................................................................................................................................... 84
viii
List of Figures
Figure2. 1 Conceptual Framework .............................................................................................. 30
Figure3. 1: location map of the study area. ................................................................................... 32
List of Annexes Annex1: variance inflation factor for continuous variables .......................................................... 84
Annex2: pairwise correlation for categorical variables ................................................................ 84
Annexes 3: Model misspecification Test for profit and employment ........................................... 84
Annex 4: heteroskedasticity test for in terms of profit and employment ...................................... 84
Annex 5 stata logistic regressions output for performance of MSEs in terms of profit ............... 85
Annex 6 marginal effect of stata logistic regressions output for performance of MSEs (profit) . 85
Annex 7 stata logistic regressions output for performance of MSEs in terms of employment .... 86
Annex 8 marginal effect stata output for performance of MSEs in terms of employment ........... 86
Annex9: Questionnaire ................................................................................................................. 87
ix
List of tables Table3. 1 Type and Number of Micro and Small Enterprises in sample kebelles of Enarj Enawga
Woreda .......................................................................................................................................... 35
Table3. 2; sample size selection in each sector............................................................................. 35
Table3. 3; sample size from each kebelle and each sub-sector, systematic and proportional ...... 35
Table.3.4: Description of the variables, measurement, and expected hypothesized ..................... 44
Table 4.1 Age of the respondents and age of the enterprise ......................................................... 45
Table 4.2 Sex of the MSEs owners ............................................................................................... 46
Table 4.3 education level of respondents ...................................................................................... 47
Table 4.4 Marital Status of Operators of MSEs ............................................................................ 47
Table 4.5 Startup capital ............................................................................................................... 48
Table 4.6: Source of startup finance ............................................................................................. 48
Table 4.7: Annual sales revenue and total costs ........................................................................... 49
Table 4.8: type of enterprise ......................................................................................................... 49
Table 4.9 Number of Employees at Startup and at Current .......................................................... 50
Table 4.10: Term of employment ................................................................................................. 51
Table 4:11 working space ............................................................................................................. 52
Table 4.12 access to raw materials ............................................................................................... 53
Table 4.13: Impact of government policies and regulation .......................................................... 54
Table 4.14: level of market competition ....................................................................................... 55
Table 4.15: Operators of MSEs and their access to training ......................................................... 56
Table 4.16: Access to technological related factors ...................................................................... 57
Table 4.17 Access to infrastructural problems ............................................................................. 58
Table 4.18 Access to credit ........................................................................................................... 59
Table 4.19 the role of MSEs on household health, education and diet ......................................... 60
Table 4.20: MSEs‟ capacity in poverty reduction ........................................................................ 62
Table 4.21: MSEs and Income generating capacity...................................................................... 62
Table 4.22 Stata Output of the logistic regression model (profit) ................................................ 65
Table 4.23 Output of the model (employment) ............................................................................ 69
x
ABBREVIATIONS
ACSI Amhara Credit and Saving Institution
BDS Business Development Service
CED Committee for Economic Development
CSA Central Statistical Agency
FeMSEDA Federal Micro and Small Enterprise Development Agency
GDP Gross Domestic Product
GEM Global Entrepreneurship Monitor
GTP Growth Transformation Plan
IFC International Finance Corporation
ILO International Labor Organization
MFIs Micro-Finance Institutions
MoTI Ministry of Trade and Industry
MSEs Micro and Small enterprises
NGOs Non-Governmental Organizations
STATA Statistics and Data
UNIDO United Nations Industrial Development Organization
xi
Abstract It is generally accepted that Micro and Small Enterprises (MSEs) have significant contributions to job
creating, generating income, and poverty alleviation. However, the performance of MSEs in Enarj
Enawga Woreda is low. So, this study was done to investigate the determinants of MSE’s performance in
the woreda using descriptive statistics and an econometric model of logistic regressions. The main
objective of the study was to find out the determinants of Micro and Small Enterprise’s performance in
terms of employment and profit. More primary and some background secondary data were employed in
getting the necessary information for the analysis of the study. A total of 181 sample respondents were
identified using the multiple-stage sampling technique. The result of the study shows that the majority of
the MSEs have been a recent establishment and faced challenges of inadequate startup capital, limited
access to credit, government policies and regulation related factors, unfair competition, limited
infrastructure facility, lack of training, and lack of know-how and skills to use technology. Furthermore,
the study also noted that most of the operators were found to be young labor force of male operators with
educational qualification of high school or less. The study used profit and employment as performance or
growth indicators. Profit calculated as the total sales revenue minus annual total costs and employment
measured as the natural logarithm of current employment minus the natural logarithm of initial
employment and dividing by age of the enterprise. After calculating, Micro and Small Enterprises were
grouped into two categories good and low performance. Micro and Small Enterprises which had growth
rate ≤ 0 categorized into low performance and MSEs which had growth rate > 0 are good performance.
The findings of the study show that out of the total sample 40.88% of MSEs have low performance and
59.12% of MSEs have good performance in terms of profit and 49.17% of MSEs have low performance
and 50.83% of MSEs have good performance in terms of employment. The result of logistic regression
analysis shows that out of 13 determinant variables 10 variables; age of the operator, age of the
enterprise, amount of initial capital, access to raw material, market competition problems, government
policies and regulation problems, access to training, education level of the operator, access to modern
technology, and access to market factors revealed statically significant to affect performance of MSEs in
terms of profit. Whereas out of 12 explanatory variables 6 variables namely government policies and
regulation problems, access to modern technology, infrastructural related problems, prior experience of
the owners, ownership of work premises and access to credit factors revealed statically significant to
affect the performance of MSEs in terms of employment. Therefore, attention should be given by the
policy makers and other concerned bodies to develop supportive programs and corrective measures to
ease the constraints and difficulties facing performance of MSE.
Keywords: Performance of MSEs, Determinants, logistic regression model, Enarj Enawga Woreda
1
CHAPTER ONE
1. INTRODUCTION
1.1 BACKGROUND
Some years ago micro and small enterprises were considered as unproductive and have no
contribution for the growth and development of nations (Thurik et al., 2002 and Goshu, 2016).
But now a days MSEs play an important role in contributing to the overall economic performance
of countries (Thurik et al., 2002 and Senzu & Ndebugri, 2018) and serving as income generation,
reduction of unemployment, and increase and motivate innovation(Tekele, 2020). Micro and
small enterprises have contributions for socio-economic development as a means for generating
sustainable employment and incomes throughout the world (ILO, 2003). MSEs are an engine of
economic growth and sustainable development (Wasihun & Paul, 2010). Suleiman et al., (2016),
indicated that MSE gives high employment opportunity with less startup capital as compared to
large-scale sectors. They are major drivers of both employment and economic growth
contributing to more than 50 % to GDP and 60 % to employment in developed economies
(Batisa, 2019). Bowale & Ilesanmi, (2013), have shown considering MSEs as fighter of poverty,
creating jobs, mobilize local resource, and reduce migration from rural to urban and generating
income.
Existing evidence to date indicates that micro and small enterprises' (MSEs) ' performance is a
critical component of sustainable development in developing economies (Youtang & Yesuf,
2021). Business performance is the accomplishment of a given task measured against preset
known standards of accuracy, completeness, cost, and speed. In a contract, performance is
deemed to be the fulfillment of an obligation, in a manner that releases the performer from all
liabilities under the contract. Performance of a MSEs is defined as a firm‟s ability to create action
and acceptable results (Affecting et al., 2017). There exists are variety of business performance
indicators or measurements which are broadly categorized as financial and non-financial
performance measurements. Traditionally, performance measurement has been assessed on
purely financial criteria (L. M. Mbugua et al., 1999). Their main advantage of the financial
performance measurement is that they are easily figured out and provide a quantitative output.
2
In Ethiopia, next to agriculture MSEs are the second largest employment generating sector
(Central Statistical Agency, 2007). Sampieri, (2010); Geleta, (2013); Tarfasa et al., (2016), and
Batisa, (2019), indicated that MSEs have vital roles in the economy as a whole in terms of
employment opportunity generation, powerful instrument in economic growth, source of income,
quick production response, their adaptation to poor infrastructure and use of local available raw
materials and resources. MSEs also have great value in Ethiopian socio-economic growth as it
requires small or less startup capitals, increased domestic saving and investment and also they
help for balanced development provision of goods and services which are better adapted to local
needs, access to improve quality of work and working conditions which may contribute to a
better quality, increased economic participation of disadvantaged and marginalized groups in
society, access for training and development of human resources stimulating innovation,
entrepreneurship, technology development, research, and eventually alleviation of poverty. MSEs
have also an advantage in stimulating other sectors such as trade, construction, services and
agriculture (Amha, 2015 and Leza et al., 2016).
Oppong et al., (2014), have showed that sustainable local economic development and poverty
reduction through creation of job opportunity have been realized when governments of
developing countries designed MSE‟s based policies. Therefore, in recognition for MSEs
contribution, government intervention and massive support to the sector can facilitate economic
growth, creating long term jobs, and income generating thereby poverty reduction. For this
reason, studies aiming at investigating determinants of MSE‟s performance have become
important (James et al, 2014).
The Ethiopia government, by knowing the great role of the micro and small enterprises, designs
development strategy to enhance the micro and small enterprises development by the issuance of
National Micro and Small Enterprises Strategy in 1997/2011 and establish Federal Micro and
Small Enterprises Development Agency to eradicate poverty and unemployment. The promotion
of this sector is justified on the grounds of enhancing growth with equity, creating long-term
jobs, providing the basis for medium and large enterprises and promoting exports. The strategy
puts a means to support the MSEs through the provision of infrastructure, technology, training
and working space, financial facilities, supply of raw materials, and access to market because
3
MSEs are commonly accepted as the right solution to reduce urban unemployment and poverty
(Assefa et al., 2014; Gebremariam, 2017 and Wami, 2019).
Even though MSEs have several contributions, there are many challenges that hinder the
performance. According to Abera, (2012), micro and small enterprises in Ethiopia faced several
factors that determine their performance. The major factors include financial problems, lack of
skilled employees, lack of proper financial records, marketing problems and lack of work
premises, etc. Other environmental factors which affect the business include social, economic,
cultural, political, legal and technological factors. In addition to the environmental or external
factors there are also internal factors that affect the performance of MSEs related to the person‟s
individual attitude, training and technical know-how.
In Amhara region in general and Enarj Enawga Woreda in particular, despite there is expansion
and establishment of MSEs activities to create job opportunities, generating income, and poverty
alleviation, studies on their performance in this critical sector (MSEs) is extremely limited though
some other studies on the performance of MSEs were carried out in different parts of the country.
Thus, the assessment of the performances of MSEs and the factors affecting the potential
performance of MSEs is therefore essential. In this regard, no previous studies were available in
Enarj Enawga Woreda. Hence, this study is carried out to assess determinants of MSE‟s
performance and their contribution.
4
1.2 Statement of the Problem MSEs are effective creators of employment and job opportunity, innovation for new thing, and
income generation and they can transform the economy as a result they have significant role in
reducing and eradicating poverty both in developing and developed countries (Politis &
Gabrielsson, 2009; Gebrehiwot, 2015; Gidey, 2017; Length, 2018). Most developing countries
like Ethiopia have been formulated and implemented different Micro and Small Enterprise
development strategy to support the development of the sector, thereby transforming economies
and generate employment opportunities. In the study area of Enarg Enawga Woreda also micro
and small enterprises have been established and a lot of jobs are created by the sector as income
generation and transform the economy encountering many problems.
There are many problems and constraints that face MSE's performance. Some of the major
constraints affecting the performance of MSEs are Cumbersome rules/regulations related
problems such as high tax level, uncertainty about tax policy, high collateral requirement, lack of/
inadequate working premise, lack of business support service and inadequate access to credit,
inadequate access to finance, lack of infrastructure related to interruption of electric power,
unavailability of adequate transport service and unavailability and unreliability of water supply,
weak supporting institutional quality, access to raw material, access to training, marketing and
competition. Bureaucratic requirements, penalties, weak legal enforcement, entry regulations, and
inability to use the institutional enforcement mechanism were also among the major problems of
MSEs (Mezgebe, 2012). Apart from its business opportunity and reduction of poverty and
unemployment rate of youths, MSE hampered their performance through several variables such
as lack of finance, lack of qualified employees, marketing problems, etc. Besides, environmental
factor affects the business which includes social, economic, cultural, political, legal and
technological factors as well as personal attitudes related to the person‟s attitude, training and
technical know-how problems (CSA, 2007). The works of Assefa et al., (2014); Bouazza et al.,
(2015); Senzu & Ndebugri, (2018); Tesgera, (2019); Zone & Mengesha, (2019), in common
showed and argues that the performance of MSEs depends on several determinant factors which
include; sufficient amount of finance, access to training, entrepreneurial skills, access to security,
access to promotion, cost of input or raw materials, education level of the owner or the manager,
gender of the owner or the manager, access to market, access to credit, access to business
information, access to appropriate technology, access to quality of infrastructure and others have
5
been identified as major determinants affecting the performance of MSEs implies that the
performance of MSEs depends on internal and external factors that require investigation in
addressing the challenges facing MSEs performances.
Understanding why some firms performed well and others not is crucial to the stability and
health of the economy. Despite this fact, however, which factors are the most important factor
affecting the performance of the MSE Sector in Ethiopia in general, and in the study area of Enarj
Enawga specifically has not been adequately studied empirically. In this regard the study was
assessed several academic researches to the reduced area of similarity, accordingly, there was a
study that took place by Giday, (2017); Abera, (2012); Yikeber, (2019); Mulugeta et al., (2010);
Mehari W / Aregay, (2016); and Abebe, (2011), have looked at the performance of MSEs and
their determinants in Ethiopia in their respective specific areas and covered specific objectives
using more of the internal factors and about MSEs owned by women or youths only excluding
some sectors of MSEs and external factors as well as not studied well using both descriptive and
econometric model and the performance of MSEs was measured only by profit that have
knowledge gap and inconclusive results. These may be happen due to inappropriate
methodology.
Hence, in this study attempts was made to assess all types of MSE‟s Sectors and tried to identify
factors affecting the performance of the sectors using descriptive and logistic regression
econometric model analysis to establish the relationship between dependent and independent
variables. The majority of the study carried out on the performance of MSEs which was biased
and researches have not done yet in East Gojjam Zone, Enarj Enawga Woreda even in the
Amhara region more concerning determinants and the performance level of the MSEs both in
terms of profit and employment, as well as their contributions despite the establishment of MSEs
increases, in number from time to time which they have been creating employment opportunity,
generating income, and alleviating poverty.
Even though the establishment of MSEs increases in number from time to time with the aim to
provide job opportunities, generating income, and alleviating poverty, their performance status is
low as it was indicated by Enarj Enawga Woreda MSEs Development Agency Statistical Report
(2012 E.C) high rate of failure has emerged as a thoughtful concern to conduct a research to
identify and investigate factors affecting the performance of MSEs. If attention is not given to
6
find out factors affecting the performance of MSEs, the expected performance of MSEs, and their
contribution to income-generating, employment opportunity thereby poverty reduction cannot be
addressed in the study area of Enarj Enawga Woreda. Moreover, studies that have dealt with
determinants of MSEs‟ performance in the Amhara region particularly in the study area reported
in this thesis were limited.
Hence, in this study, the performance of MSEs in Enarj Enawga Woreda was measured by profits
as measured by annual total sales minus annual total costs including the total variable cost and
total fixed cost and by employment as measured by the natural logarithm of current employment
minus initial employment and by dividing by age of the enterprise.
1.3 Research Questions The key questions that are answered by this study are:
• What is the performance status of MSEs in generating profits and creating employment?
• What are the major internal and external factors affecting performance MSEs?
• What are the changes in terms of saving, and the status of a standard of living /health,
education, and diet/, poverty reduction, and income generation after MSE‟s managers or
owners engage in the activities?
1.4 Objectives of the study
1.4.1 General objective
The general objective of the study is to investigate the performance level and, determinants of the
performance of MSEs in Enarj Enawga woreda, East Gojam zone, Amhara regional state,
Ethiopia.
1.4.2 Specific objectives
• To investigate the MSE‟s performance in terms of employment and profit.
• To assess and identify the major external and internal determinants of performance of
MSEs in Enarj Enawga woreda.
• To assesses the role and contributions of MSEs in Enarj Enawga woreda.
7
1.5 Significance of the Study This study has some significance and value in the development affairs in employment creation,
poverty reduction, and economic growth. This study is significant in that it examines and
describes factors affecting the performance of MSEs to assist the government policymakers,
donors, and other interested agencies as it recommends that practical measures to overcome the
constraints and challenges facing the performance of MSEs. In addition, the study gives
significant information and evidence to owners/operators and managers of MSEs themselves.
Moreover, it is hoped that the findings of this study is an important addition to existing
knowledge and conducting further research for academicians and consultants who may be
focusing on similar topics and issues as a base, particularly in identifying factors affecting the
performance of MSEs, their contribution.
1.6 Scope of the Study
The inclusion of the region or the zone as a whole in the study is found to be unmanageable for
the study because of shortage of finance, time, and materials that is why the study was limited to
Enarj Enawga Woreda. The study is concerned only with micro and small enterprises established
by government intervention and privately established which were actually registered by Trade
and Industry Development Office in Enarj Enawga Woreda. However, there are a number of self-
initiated and unregistered informal micro-enterprises that employ a large proportion of the poor
but were not be included in the study because they didn‟t have a fixed working place.
1.7 Limitation of the Study
This study was limited to only Enarj Enawga Woreda in five kebeles only. Other limitations are
difficulty in getting a proper response and their limited number of households, memory, and
reluctance of respondents to participate due to the fear of disclosing information that may lead to
a negative effect on their business and the methodology itself. Furthermore, the quality and
accuracy of data gathered through structured questionnaires may also have its own limitation due
to respondents‟ differences in truthfulness, understanding, and interpretation. Some data were
highly dependent on the memory of the respondents e.g. revenue and cost. Accordingly, some
data particularly the quantitative data might have some inaccuracies. The study was limited to a
manageable sample size because of time and resource constraints. Besides, some secondary data
8
found at the woreda level were not clear and well documented. Data analysis was limited to
descriptive and econometric regressions made use of tables and logistic regression respectively.
To overcome the limitations the researcher tried to convince the respondents to give an honest
and genuine response and the questionnaire helps to meet only the objective of this study as well
as the information they provide was used for academic purposes only and kept confidential.
1.8 Organization of the Thesis
The thesis is organized into five chapters. The first chapter deals with the introduction. Chapter
two is concerned with the review of related literature including theoretical and empirical
literature. Chapter three contains the methodology followed by description of the study area,
research design, sample size and sampling procedure, data sources and method of data collection,
methods of data analysis, and econometric model specification. Chapter four presents the results
and discussion and the last chapter contains the summary, conclusion, and recommendations of
the study.
9
CHAPTER TWO
2. Literature Review
2.1 Theoretical Review
2.1.1 Definitions of MSEs
There is no single and universally accepted definition of micro and small enterprises. Different
countries have defined enterprises by different conditions based on the number of paid employees
by the sector, the amount of paid-up capital, total assets, the volume of sales, and value-added or
net worth (Tadesse, 2010; Abera, 2012, and Munira 2012). Thus it makes problematic to speak
or define MSEs in universally accepted way. A definition of MSEs in the industrialized world
would differ from how MSEs are defined in the emerging economies. An enterprise categorized
as micro enterprise in USA may be treated as medium enterprise in Africa or somewhere in Asia
for the fact that the definition of MSE is relative to economic development. The annual turnover
figures also differ from country to country, depending among other factors on population size and
stage of economic development. From this we can learn that there is no common definition of
MSEs and that the definitions vary from country to country depending largely on the size of the
economy, the levels of development, culture and population size of a country involved (Gidey,
2017). In support to the view of Gidey, Berihu (2006) has also pointed out that lack of consistent
to define MSEs has evidently led to the confusion and failure to distinguish between one segment
and another and this can have significant implication on the structure of intervention and
promotional support that could be provided to the sector.
In the United States of America, a small business is a business that is independently owned and
operated and not dominant in its field of operation. The act also further stated that, number of
employees and sales volume as a guideline in defining small business (Clark, 2010). In United
States of America, a committee for economic development (CED) has defined small businesses
are businesses that are characterized by at least two of the key features: management which is the
managers or owners is independent, the place of the premise capital is supplied and an individual
or small group holds ownership and the area of operation is mainly local that implies the workers
and the owners of the enterprises are in one home country.
10
In Kenya, the definition of MSEs is based on three criteria: the number of workers, the turnover,
and the assets of the enterprises. Therefore, Micro Enterprises are defined as a firm, trade,
service, industry, or a business activities whose annual turnover does not exceed 500,000 Kenya
Shillings and having less than 10 people total employees whereas Small Enterprises are those
firms, trade, service, industry, or business activities that post an annual turnover of between 500,
000 - 5 million Kenya Shillings and have 10 - 50 employees (Micro et al., 2018) and (Tekele,
2019).
Whereas in Ghana Micro enterprises are enterprises that have 1-4 employees, Small enterprise
having 5-29 employees and Medium enterprise are enterprises that have 30-140 employees.
In Indonesia, the definition is also different that Micro enterprises are those having less than 20
employees, Small enterprise are those having 20-99 employees and Medium enterprise are those
having 100-499 employees (Tadesse, 2010 and Garmarodi, 2014).
In Ethiopia, Micro and Small scale enterprises by their improved and current definition are
categorized into the industrial sector and service sector based on the number of employees that
the enterprises hire and the size of the capital they own. Under the industry sector
(manufacturing, construction, and mining) micro enterprise is an enterprise that processes or
involves 5 people including the owner and/or its asset is not exceeding Birr 100,000 (one hundred
thousand Ethiopian birrs); whereas under service sector (retailer, maintenance service, transport,
hotel and Tourism, ICT) enterprises which have up to 5 persons with the owner of the enterprise
and/or the values of total asset is not exceeding Birr 50,000 (fifty thousand Ethiopian birrs). A
small enterprise in industrial sectors is an enterprise which is doing with 6-30 persons and/or with
a paid-up capital of total asset Birr 100,000 - Birr 1.5 million. In Service sector, small enterprise
is an enterprise that operates with 6-30 persons or/and total asset, or a paid-up capital is Birr
50,001 - Birr 500,000 (NPC, 2015). When ambiguity is encountered between manpower and total
assets as explained above, total asset is taken as primary yardstick (Federal Micro and Small
Enterprise development agency, 2011). These imply that no universally acceptable definition of
MSEs. Different countries define MSEs differently based on the level of development of the
country under review using their own parameters and government policies. There are different
MSEs, which have different technological advancement or know how, the nature of the raw
11
materials use and the market they have for their product. Thus it makes problematic to speak or
define MSEs in universally accepted way (Tadesse, 2010).
Thus, as there is no uniform definition of MSEs, an operational definition was used for the
purposes of this study the one offered by the Micro & Small Enterprises Development Strategy of
Ethiopia (FeMSED) published in 2011.
2.1.2 The Concept of Business Performance and measurement?
Performance is defined in terms of output terms which are measured in numbers /quantified/
objectives or profitability of the MSEs. Performance is related to the quantity of output, quality
of output, timeliness of output, presence/ attendance on the job, efficiency of the work completed
and effectiveness of work completed” (Thao & Hwang, 2010). Consistency with Rami and
Ahmed, (2007:6-13), define good performance as an increment of monetary assets with
sustainable profits and also defined the job satisfaction of the enterprises by accomplishing the
proposed plans or goals. Global Entrepreneurship Monitor (GEM) defined Performance as the act
of performing; of executing something in effective and efficient way and successfully; using the
knowledge that one has an entrepreneurial skill(GEM, 2018).
The foremost commonly adopted definition of good performance is financial growth with
adequate profits (Mutairi et al., 2017). The owner or managers of the enterprise formulates and
monitors a variety of goals, concerning the survival and stability of the firm (Jarvis et al., 1996).
There are also other goals like growth, customer satisfaction, quality of products they produced,
the efficiency of the MSEs, market share, liquidity, size, leverage and influences, contribution to
community development and job created for their own families (Murphy et al., 1996). Business
performance is defined as the business success assessed by using financial and/or non-financial
performance. Financial performance is the economic success, while non-financial performance
means the operational goal of the business (Mozumdar et al., 2020). Financial performances are
usually considered to be the most appropriate measure of business success, yet many small
business owners are motivated to start a business on the basis of lifestyle or personal factors.
Non-financial goals could lead to alternative measures of success, particularly in the small
business sector (Walker & Brown, 2004).
12
Bourne & Neely, (2003), defined Performance measurement as the process of quantifying the
efficiency and effectiveness of action and it is a metric used to quantify the efficiency and/or
effectiveness of action.
It has been recognized for many years that financial management in small enterprises plays a
critical role in their success and survival (Mutairi et al., 2017). A business may discontinue for
several reasons; could primary unprofitability, or maybe run without capital and bureaucracy
(GEM, 2018). Enterprises fail within the first few years of their start-up; some grow faster, while
others grow slowly because of several reasons related to success or performance affecting MSEs,
and knowing these causes is very important for the new entrants of the sector for their future in
the business. The MSEs should have a strategic planning process that allows them to evaluate and
monitor internal strengths and weaknesses and its external opportunities and threats and also
should have a clear vision and mission to become a success (Dinh, 2011 and Mutairi et al., 2017).
Assessment of performance in MSE ranges of goals involves and measured and calculated by
both financial and non-financial profits. The performance of firms owned by individual in the
MSEs examined by the amount that the MSEs add values to the economy since the interest of
studies in the sector is derived from the roles of development of the economy (Mutairi et al.,
2017).
In MSEs, entrepreneurial and independent firms, the measurement of performance is difficult to
quantify and measure than financial success (Abebe, 2011). Non-financial measurement of
performances are belongs to an individuals which are subjective and personally defined that
complex to quantify non-financial performances (Walker & Brown, 2004). A business enterprise
could measure its performance using financial and non- financial measures. The financial
measures of performance include profit before tax and turnover which means benefits over
expenses while the non-financial measures of performance focus on issues belonging to
customer's satisfaction and customer's referral rates, waiting time and employee's turnover,
delivery time (Chong, 2008 and Abebe, 2011).
Business performance is usually measured in terms of economic performance. As (Walker &
Brown, 2004), small business performance can be measured by financial and nonfinancial criteria
although the former has been given most attention in the literature. Measures of business success
13
have been based on employee numbers or financial performance, such as profit, turn over or
return on investment. Implicit in these measures is an assumption of growth that pre-supposes all
small business owners want or need to grow their businesses. For businesses to be deemed, well
perform or not these financial measurements should be increased in profit or turn-over and/or
increased numbers of employees. The most obvious measures of performance are profitability
and growth of employment. In economic terms, this is seen as profit maximization. Economic
measures of performance have generally been popular due to the ease with which they can be
administered and applied since they are very much hard measures.
Furthermore, Walker & Brown, (2004), suggested, „all businesses must be financially viable on
some level in order to continue to exist. However, given that some businesses have no interest in
growth, thereby implying that financial gain is not their primary or only motivation, then there
must therefore be other non-financial criteria that these small business owners use to measure
their business performance. In smaller, entrepreneurial and independent firms, measures of
performance may have more complex dimensions than just financial performance (Mohan-Neill,
2009). Non-financial measures of performance used by business owners, such as autonomy, job
satisfaction or the ability to balance work and family responsibilities are subjective and
personally defined and are consequently more difficult to quantify. The hard measures previously
mentioned therefore, are easier to understand and can be used in a comparative way against
existing data and as benchmarks for future measures. Non-financial measures are based on
criteria that are personally determined by the individual business owner although commonalities
within the partners of small business owners occur. These non-financial measures presume that
there is a given level of financial security already established; it may be that this is within the
business, or that the small business owner does not require the business to be the primary source
of income (Walker & Brown, 2004). The selection of performance measures that reflect the true
situation of small businesses with some degree of certainty and reliability is indeed a crucial
process. The lack of universally accepted standard performance measures left the door open to
business organizations to decide and choose their own performance measure that might not truly
reflect their performance (Alasadi and Abdelrahim, 2007) and (Mutairi et al., 2017).
The performance of MSEs can be also measured by using employment size. In measuring
employment growth, although theoretically alternative measurement tools such as growth rate of
14
sales or profits could give precise results, in practice they are not as credible as the employment
growth measure because of entrepreneurs‟ hesitation to report the true values of their sales and
profits. This hesitation, which leads to measurement errors, makes the employment based
measure preferable in studies considering enterprise growth. Moreover, in a relatively high
inflationary economy, avoiding data in value terms is preferable, so using the employment
growth rate as the measurement tool is beneficial. In addition, taking employment as a measure of
enterprises growth needs to be consistent with the goal set for the sector. In this study, therefore,
the growth rate of the number of persons engaged is used as growth measure. Therefore,
employment is the most preferred measure of enterprise performance. Applying the two
approaches became important to determine the performance of MSEs (Adem et al., 2014) and
(Alemayehu & Gecho, 2016).
Hence, measuring performance of MSEs may depend up on the interest and objective of the
researcher in including both financial and non-financial or using either of them. Equally in this
research performance of MSEs was measured by both in terms of profit calculated as annual total
sales minus annual total cost and employment calculated as natural logarithm of current
employment minus natural logarithm of initial employment and by dividing age of the enterprise
(Evans, 1987).
2.1.3 Micro and Small Enterprises Development strategy in Ethiopia
Ethiopia gives attention and recognition to micro and small enterprises as they are a motor for
poverty alleviation and creating employment opportunities. In 1997, Ethiopia has adapted and
formulated an MSE development strategy to enhance the sector to create a suitable environment
for the sector (MOTI, 1997 and Engida et al., 2017) that targets reducing poverty mainly in urban
areas and putting the foundation for industrial development. This strategy was revised in 2010/11
with renewed interests and targets but having similar objectives of creating job opportunities to
reduce unemployment problem, alleviate poverty and promote industrial development by
considering the MSEs as a base as well as to give special attention for the graduates from
technical and vocational institutions, colleges and universities (Joshi & Mihreteab, 2013). The
revised MSE strategy has two development stages; firstly the transition of an enterprise from
Micro to small and from Small to Medium Enterprise level and second maintaining MSEs from
failure and reinforces the competitiveness among them. The strategy facilitates support from the
15
government in the form of access to credit, auditing and financial management support, finding a
market, giving efficient and appropriate technology, industry extension support and training, and
working space (Federal Democratic Republic of Ethiopia, 2016). The strategy also identifies
main challenges which are related to working place, credit, technology, market, rent-seeking, and
other challenges like perceiving enterprises themselves as a reflection of poverty and
backwardness, waiting for the government for the job rather than being innovative, failure in
developing the culture of saving are some of the indicators of failure in improving productivity
and being competent in the market (Federal Democratic Republic of Ethiopia MSEDA, 2011).
Assefa et al., (2014), and Seyoum et al., (2014), stated that Policy support for MSE development
in Ethiopia by the new or revised strategy depends on stages of development in which MSEs are
categorized into three which are Start-ups which are at the establishment or beginning stage
having problems and needs support; the second is Growing-middle which are competent in the
market and are profitable in their business and the third is Maturity stage, when they are reaching
fully profitable and engaged in further expansion and investments in the sector and the
government transfer knowledge of international standards and better production technology to
Enterprises.
To create conducive environment for MSEs and facilitate the support from the government for
better performance the Ethiopian government also prepares Growth and Transformation Plan one
GTP I in 2010/11 – 2014/15 which aiming at promoting the development of MSEs and to transit
or transfer into medium-scale enterprises by giving training and credit access; and GTP Two that
were formulated in 2014/15 – 2019/20 that support MSEs for their transition to medium and
large-scale enterprises, encourages the MSEs to invest in the manufacturing sector for economic
structural transformation National Planning Commission (NPC, 2015).
Ethiopia has reorganized the MSE development agency at the federal, region, district, and town
levels to improve its service delivery to the sector in a better way in terms of resource, leadership,
manpower, etc. but not actively accomplishing their duties as per the strategy set to improve the
performance of micro and small enterprises. Currently in the study area of Enarj Enawga Woreda
the competent agency concerning MSEs is technical, vocational, and enterprises development
office and in two towns, namely, Debre-work and Felege-birhan having one-stop service offices
16
to perform the activities but it has not been doing as the name given as one-stop which is
indicated and set in the strategy.
2.1.4 The role of MSEs in the economy
Generally, MSEs have two important roles first accelerate economic growth through the growth
of their output contributions to gross domestic product (GDP), and second to reduce poverty
through employment creation and income generation effects of their generated output growth
(Tambunan, 2019). In relation to this Drbie & Kassahun, (2013), showed that MSEs are bringing
economic transition by using the skill and the talent of people without requiring high-level
training, much capital and sophisticated technology. Micro and small enterprises the main creator
of job opportunity or they are the source of employment, sources of income, the source of skill
development, the source of goods and services delivery, etc. in developing countries (Reeg,
2015; Aktar, 2016) and Cherkos et al., 2018).
UNDP, (2015), in Kenya on MSEs contributed for the GDP which was increased from 13.8% in
1993 to about 40% in 2008 through employment creation, income generation, and improved food
security is widely recognized. Additionally, Micro, small and medium enterprises are over 90%
of all enterprises in all countries. They are an important source of output and employment (ILO,
2009 and 2013). According to Caroline, (2016), micro and small enterprises are the base or
foundation for industrialization.
The role of MSEs to economic development is also for developed countries like the European and
American countries; Netherland, for example accounts 95% total of business establishment
(Tambunan, 2011); and around 97% of firms in Mexico and Thailand are MSEs. MSEs generate
high share of employment and output and also the income by maintaining the existing employee
not by creating new employment and income for the poor (Mead, 2004), and (Nichter &
Goldmark, 2009). However, in Ethiopia despite its importance, the size of the Ethiopian MSE
sector is less known. Though promoting MSE‟s performance is a key target; Berihu et al (2014),
during their consultation with key MSE‟s implementers including FeMSEDA its current size or
performance in terms of its contribution to GDP, employment and export and total manufacturing
output was largely unknown. Moreover, given the importance attached to the MSE sector and
massive support extended, results were also less known.
17
Generally, by increasing the performance of MSEs strengths their considerable role-playing and
the potential contribution to improvements of the income distribution, employment creation,
poverty reduction, and industrial development, export of growth, achieving economic and social
objectives government intervention and supporting extensively through different programs is
very important.
2.1.5 The Role of Micro and Small Enterprises in Poverty Reduction
The vital role of MSEs in developing countries‟ economies is increasingly being recognized.
MSEs are key players in these economies in providing significant benefits and employment
opportunities to poor societies and also providing essential goods and services to the poorer
communities (Birhanu, 2017). Moreover, apart from Birhanu, the importance of MSEs has also
been confirmed in the work of (Gebreeyesus et al., 2018) and argued that the role of MSEs to the
country‟s development is significant in terms of employment-generating capacity, quick
production response, their adoption to weak infrastructure and the use of local resources and as a
means of developing indigenous entrepreneurial and managerial skills for sustained
industrialization development.
In United Nations Industrial Development Organization Unido et al., (2002), MSEs as a tool to
fight poverty in the long run implies MSEs are recognized to play a significant role in providing
self-employment to the poor people and the self-employment opportunities, make the economy
more flexible, generate new skills, service and products and add to the nation productive
capacity, generate more equitable income distribution to activate competition, find markets
access, enhance productivity and technical change, and through all of these stimulate economic
development. One of the important roles of MSEs in this context includes poverty alleviation
through job creation and fighting against unemployment has been one of the key challenges
facing the African continent. Hence, MSEs have been recognized as a tool of poverty reduction
by governments at various levels to promote the development of MSEs in order to reduce
poverty; stimulate employment; mobilize local resources; reduce migration from rural to urban
area and disperse industrial enterprise more evenly across the country (Bereket, 2010). Regarding
the role of MSEs in poverty alleviation and employment creation, Todaro, (2000), has also
justified MSE‟s contributions to employment creation and income generation on global based
researches and the informal sector was found to be a major provider of urban jobs in many Asian
18
countries. Among individual countries for which statistics available, the figure reaches 50 percent
in India, 45 percent in Indonesia, 35 percent in Malaysia and 60 percent in Pakistan. In the case
of Latin American countries 61 percent in Bolivia, 55 percent in Argentina, 56 percent in Brazil,
and 69 percent in Paraguay. Poverty reduction is simply not going to happen by government fiat
but only through private sector dynamism. Supporting MSE development as a world shows have
a benefit for poverty reduction by investing in private sector-driven strategies.
MSE sector reduces the level of poverty by two dimensions mainly. First, if there are more
MSEs, more employment can be created for the poor people erased from unemployment and
reduce underemployment to some of the poor. Second, when MSEs are grown can increase
wages for employees who are already hired in those companies and lift them up above the
poverty line but also may induce the MSEs to hire more workers (Dobenecker, 2010).
Poverty is very vast in Ethiopia and it is the main problem for sustainable development of the
country and stability and peace. The role played by MSEs, through the various socio-economic
benefits are great in the overall development effort and by generating larger volumes of
employment as well as higher levels of income, the MSEs not only for poverty reduction but also
increases the welfare and standard of living of the society. MSEs, in Ethiopia background, are a
particular core of the government issue, which is a sector, acting vital function by generating
income chances and eradicating poverty and also removing the problem of income inequality,
then government sketched first micro and small enterprises development strategy and organize
agencies up to kebele level to help the sector to play its role (Gebreeyesus et al., 2018).
Therefore, the government of Ethiopia has recognized and gave great attention to the expansion
and development of MSEs since they are the motor for reducing the unemployment problem,
economic growth and equity in Ethiopia, and reducing poverty. Poverty means the lack of access
to clean water, sanitation, basic health care, and education, and the poor are under-serviced that
governments are unable to cover or give adequately these basic services that show poverty
reduction can be correlated have a direct relationship with job creation. Well, preparation of MSE
development can create many job and employment opportunities for the unemployed and hence
consequently reduce the poverty level of the poor (Assefa et al., 2014).
Generally, studies have been carried out about the performance of MSEs and the government of
Ethiopia makes efforts to enhance and encourage the development of MSEs however the planned
19
and desired result yet not come. As a result, an empirical examination concerning the
determinants of MSEs, performance, and their contribution in Enarj Enawga woreda was
conducted as MSEs are tools and engine for generating income, employment opportunity, and
eradication & reduce of poverty.
2.1.6 Characteristics of MSEs
The main characteristics of MSEs are very multidimensional activities, which creates for many
peoples of the country /, for example, labor force engaged in informal sector activities and small-
scale manufacturing industries is more than eight- times that of the medium- and large-scale
manufacturing industries/, they did not need very large capital which is covered by personal and
family resources, they are easy to enter, it requires low starting capital, reduce income equality,
they can be done everywhere in any area, it uses labor-intensive techniques, and they can be done
by the local skill that the gab will be filled with on job training (Zewde & Associates October,
2002).
2.1.7 Constraints and Factors affecting the performance of MSEs
MSEs face serious challenges and different impacts that hinder their growth and effective
operation. This sector is often referred to as small businesses with big problems(Alene, 2020).
Many problems face the MSEs both at the operation and start-up level and even they cannot
identify the problems they face on their own. Factors that affect the performance of MSEs could
vary from one country to another due to the economic, geographical, and cultural differences but
the common constraints faced by MSEs are lack of capital or finance, lack of availability of raw
materials, lack of business information communication, difficulties in marketing and distribution;
low technological capabilities, high transportation costs; bureaucratic procedures in getting
licenses and credit; and policies and regulations that cause market imperfection (Tambllnan,
2009).
These problems are also the same in Ethiopia, (Tekele, 2020). The main factors that affect the
performance of MSEs are access to markets, as well as lack of information and lack of finance
and institutional support (Tekalign Lemma Woldesilassie & Venkata Mallikhajuna Kishan
Ivatury, 2020). Kurnia, (2017), findings in Ethiopia showed that lack of capital and finance were
the major problem, which leads to failures of micro-enterprises. Among the respondents
investigated, 80% of them complained that lack of capital was contributing to the low
20
performance of their business. Other causes that failure of micro-enterprises are land and
premises 80%, taxation 70%, poor market and market information 68%, business support service
64%, poor record-keeping wrong pricing 64%, negative cash flow 60%, management problems
58%, and conflict among partners of 50% respondents that claimed the cause as a contributor to
failure. Limited access to financial resources remains a fundamental cause of failure in MSEs in
both the developed and developing economies since other vital resources are partly dependent on
financial resources and he concluded that over 40% fail during their first year of start-up, 60%
during the second year, whereas 90% fail within their first 10 years of the establishment because
of lack of finance (El-hagrassey, 2002, Meressa, 2020).
Bowen et al. (2009), findings showed that, disciplined financial management their business,
availability of market /customers, location/, accessibility of the business in terms of
infrastructure, clear vision and smart plan the business of what is required, skilled labor and
employee, good business networking and communication, competitive and affordable pricing
/low cost, selling variety of products/services availability of capital, availability of credit from the
banks, fair competition, reading business books and magazines, attending workshops /seminars
and focusing on niche market respective of their sequence are factors that contribute to business
success and good performance (El-hagrassey, 2002). On the other hand, increased competition,
lack of credit, insecurity, debt collection, power interruptions, political uncertainty, cost of
materials (inputs), low demand, unfavorable business laws, high transportation costs, few
customers/low demand, high rent charges, lack of water, and cost of production, cheap imports,
and technological constraints are challenges and market failure in facing micro and small
businesses (Ginbite, 2017).
The main factors/problems that limit a small firm‟s success/growth or the performance of MSEs
into two groups; the first is the factors that arise the firm itself that are internal factors and the
second group is factors from outside the firm which is external to the firm. Accordingly, this part
assesses the most serious challenges that constrain and affects /internal and external/ the
development of Micro and Small Enterprises (Abera, 2012; Joshi & Mihreteab, 2013; Seyoum et
al., 2014; Gobaw, 2016; Ginbite, 2017; Batisa, 2019, Of et al., 2019; Weldeslassie et al., 2019).
21
2.1.7.1 Internal factors of MSEs
There are many internal factors that limit the performance of MSEs. The main internal factors are
discussed below shortly.
Prior working experience in the business and industry sector: Experience of the owner or
manager affects access to finance both from the demand and supply side (Mersha, 2017).
Relevant experience helps to become a successful business owner and to survive. Usually, Most
studies show that firm performance is positively influenced and affected by the owner‟s level of
education and, prior work and managerial experience (Abebe, 2011 and KS & DB, 2016).
Workers who have worked in factories have experience and knowledge and perform better by
utilizing their accumulated experience and knowledge to lead their own business to be profitable
and (Politis & Gabrielsson, 2009; Mezgebe, 2012 and Hagos et al., 2014).
Family business background: The family encourages their youth to be economically
independent and they have to take the first place to motivate their children to stand alone
considering the age, especially in creating their own jobs. Some families, even if they have a
better place to undertake business activities, push their youth to wait for the support of the
government and get some jobs (Kesehatan, 2019). Those family members who have worked in
family business have been succeeded more than those who have not (Assefa et al., 2014).
Skilled manager and entrepreneurial ability: if the owner or the manager is skilled enough,
visionary, have the entrepreneurial ability, the performance of MSEs would be high because the
manager or the owner can apply the skill, ability and knowledge that he have (Abraham, 2013);
(Assefa et al., 2014) and (Assessment Of et al., 2019).
Age: Peter & Munyithya, (2015), showed that the skill of a person improve with age. According
to Yikeber, (2019), there are positive and significant linkages between age and business
performance of entrepreneurs in the MSE sector of developing countries. Young entrepreneurs
are likely to be more willing to assume risks and grow their business compared to older
individuals (Tassew et al, 2015). Peter & Munyithya, (2015), shows that there is a negative
relationship between owner‟s age and business performance means that old owner less performs
than young owners. Byishimo, (2018), argued that the relationship of the business owner‟s age
22
and its effect on the performance of the enterprises and stated that younger have a greater
probability to fail than older people starting a business.
Number of owners /group in one business: there are large number of business associations and
cooperatives organized to do business by pooling their resources and skills. Assefa et al., (2014),
noted in their study; a group with small number of people tends to do well in business than a
group consisting of large number of people. This is as the group member of the business
increases conflict and disagreement may arise that leads the low performance of MSEs.
Education: Education that is related to knowledge and skills, motivation, self-confidence,
problem solving ability, commitment, and discipline may be formal or informal has positive
impact on the performance of the business (Byishimo, 2018 and Meressa, 2020). Higher
education is expected to increase the ability to cope with problems and seize opportunities
(Yikeber, 2019 and Tefera, 2019). There is a positive relationship between formal education and
business performance in general and survival of the business (Carton, 2004; Cherkos et al., 2018;
Byishimo, 2018 and Yikeber, 2019).
Marketing skills: Berkenesh-Negi, (2013); Tom et al., (2015) and Bouazza et al., (2015), stated
that the owner who has marketing skills increases the performance and expansion of MSEs and
survival. Marketing activities such as product/service marketing, marketing research, and
information and promotion impact negatively the performance of MSEs due to the lack of
marketing skills by MSEs owners (Collis & Jarvis, 2002).
2.1.7.2 External Factors of MSEs
The main selected external factors are;
Access to Finance: it is a vital problem that hinders the development and survival of MSEs.
Related to this, the problems are twofold. First, the supply of credit is much smaller than the
demand that lending institutions have only met about 50% of the demand for finance; Second,
prices of goods and services have been increasing, the real value of the loan is so small and does
not provide MSEs much leverage (Abdissa & Fitwi, 2016 and Byishimo, 2018). Availability of
finance ensures the profitability of firms as it injects working capital (Alene, 2020). Banks in
Ethiopia do not provide credit in the form of loan to MSE„s due to collateral issue. MSEs get a
loan from micro finance institutions to start-up their business with long process to secure the
23
credit, high collateral requirement and high interest rate and they are forced to use the informal
institutions to get credit. Paper, (2014) and Goshu & Mba, (2016), studied that the credit given
for micro and small enterprises is not sufficient to start business or enterprise that they want to
perform, run and expand their enterprise to transform to medium and large enterprise that they
want to purchase equipment and machinery to modernize the firm, to expand their market out-
side to their localities and to cover working capital shortages necessary for the firm.
Access to market: MSEs in Ethiopia are encountering a lack of access to a sufficient and
sustainable market, lack of suitable working and selling premises, and inadequate market
competition and knowledge. Often, planning of marketing activities is limited to planning for
“selling” within a narrow industry perspective rather than broad (Assessments Of et al., 2019).
El-hamidi & Baslevent, (2011); Studies, (2015); City & Wereda, (2020); Alene, (2020),
identified that marketing is one of the severe problems that hinder development and growth of
micro and small enterprises impeding their transition to the next step.
Business area/operation location: Assefa et al., (2014); Mulugeta, (2014); Gebrehiwot, (2015);
Gebreeyesus et al., (2018); Weldeslassie et al., (2019), and Alene, (2020), confirmed and argued
as: “Location has a significant role for the performance of MSEs” indicates that MSEs located in
good areas where it is highly accessible to customers have a high probability of survival and
success of MSEs than poorly located enterprises.
Access to Infrastructure: low infrastructure is among the causes of low levels of investment and
unsatisfactory and low performance of Micro and Small Enterprises (KS & DB, 2016);(Batisa,
2019); (Alene, 2020). (W/gebriel, 2012; (Mulugeta, 2014); (Cherkos et al., 2018) and (Tekele,
2020) in Ethiopia reported that; good infrastructure facilities have a positive effect in reducing
the cost of operation. Access to adequate water supply, a reliable power supply that means
electrification, transportation facilities like road, and access to information technology on
business opportunities have a vital role in the performance of MSEs. The absence of
infrastructure facilities may impede and hinder the performance of MSEs implies that
infrastructural facility is a determinant factor influencing the performance of MSEs (Degefu,
2018).
24
Access to raw material: raw material problems have a high cost for enterprises. Lack of
standardization, raw material storage, and poor quality of raw materials are also major problems.
Strong forward and backward linkages between sectors of the economy in the supply of raw
materials facilitate a market for the output goods and services (Cherkos et al., 2018).
Government training and support: The government is expected to create a conducive
environment as well as provision of entrepreneurial training to MSEs operators by identifying the
gaps for the survival and good performance of their firm (Yikeber, 2019).
Technological Capacities: Bouazza et al., (2015), in Algeria the main problem facing the MSEs
was the huge lack of technological capabilities, which is the key to developing the competency
and performance of MSEs owners and managers. Among the MSE operators studied by CSA,
(2007), in Ethiopia 29% reported machinery failure was the major reason for their inability to be
operational.
2.2 Empirical Studies on MSE’s performance
MSEs are considered by many policymakers and researchers as important for economic
development, employment generation and poverty reduction both in developed and developing
countries. Researchers have been conducting research and get findings of MSE‟s performances,
determinants, and contributions in Ethiopia and other countries.
Kongolo, (2010), from the period 2001 to 2003, small businesses with less than 20 employees
improved employment by 853, 074 which account for 99.7% of the firms, generating more than
half of the private sub-sectors GDP, including 60% to 80% of the employment in the economy. In
2004 there were about 5 million medium, small and micro enterprises in the US create jobs above
6 million people implies a high contribution of MSEs in reducing unemployment and increasing
economic growth in the global economy. In New Zealand, MSEs accounted for 39% of total
value-added output in 2004, upwards of 2% points from the previous year and accounts
for 96% of all enterprises.
Chowdhury, (2017), Findings revealed that the size and age of the firms, education, and skills of
the owners, and unfavorable credit terms such as high-interest rates, lack of collateral security,
corruption by bank officials, etc. are some of the challenges in Bangladesh that face SMEs to get
a loan. The study showed that only 4% borrowed from banks others not from banks because
25
repayment is short the pre-conditions that 3-year statement of financial report, business plan,
collateral of assets should be attached to a bank which is difficult for MSEs.
Ranjith & Banda, (2014), on the determinants of success of the small business: in Kuliyapitiya
Division Secretariat of Srilanka studied the variable factors of the family background of
managers or owners, vocational training to the owners, decision-making ability of entrepreneur or
owners and managers, entrepreneur‟s knowledge about trade, the amount of invested capital in
the business, and management and leadership skill of the entrepreneur was significant factors
having positive relationship except vocational training of entrepreneur and entrepreneur‟s
knowledge of trade that was not found statistically significant.
Obi & Ph, (2015), in Nigeria conducted a research that consists of 50 MSEs by using simple
random sampling from Lagos and Anambra States. Even if in Nigeria micro and small enterprises
have significant role like employment creation and poverty reduction, there are many factors
affecting the performance such as poor managerial skills; negligence of financial records;
government regulations and policies; challenges of infrastructure, lack of capital and credit are
the main once.
Kinyua, (2014), studied on factors affecting the performance of MSEs in Kenya Limura Town
market. The factors were business information service, access to finance, management
experience, access to infrastructure, and government policy and regulation. They have used
descriptive research and linear regression design in making data analysis and their findings
indicated that all variables have positive and significant to affect the performance of MSEs with
the exception to infrastructure.
Abeka, (2011), also studied in Kenya used multi-stage cluster sampling method and revealed that
network relations are important and vital for MSEs that have not resources such as raw
materials, capital, machinery, etc. and making networking is necessary to gain resources
information, technology, finance and credit as well as networks are key factor affecting
performance of MSEs. Logistic Regression Analysis was used 20.8 percent having growth and
33.5 percent represented a less growth decline with a higher percentage of 45.8 stayed as it is
stagnating at a neutral state.
26
Tarfasa et al., (2016), conducted research in Addis Ababa, Ethiopia to assess the determinants of
growth of Micro and Small Enterprises which are 300 MSEs by using a random sampling
method. The findings of the study showed that factors affecting MSEs arise from internal
problems like weak human resources and other assets and of external factors including lack of
access to credit, limited market facilities, policy and regulation imposed by the government. For
small enterprises, access to credit is a big challenge that restricts from a bank loan and also
showed that among manager„s or owner„s characteristics, age, marital status and education level
of the owner were important factors affecting performance of MSEs. A weak business
environment influences the growth of firms that means frequent power interruptions, lack of
access to credit, and shortage of water is inversely correlated with the growth of Micro and Small
Enterprises.
Gebreeyesus, (2007), conducted a study in six towns of Ethiopia and revealed that firm‟s initial
size and age are inversely related with growth or performance implies larger and older firms grow
less small and younger firms Entrepreneurs with some business experience before starting this
firm grows faster and become profitable. The same is true for high school graduate owners of the
firm shows faster growth related to not graduate high school. Firms which are located at
traditional market engaged in manufacturing and service sectors and male-headed grow faster
than not located in traditional markets and those who are women-headed firms. Firms having
license grow rapidly than not having.
Alene, (2020), studied on influence women entrepreneur‟s performance in micro and small
enterprises in Gondar city, Northwest Ethiopia, and finds that educational level, a previous
entrepreneurial experience that the owner has, access to business training related to the market
and business management, access to credit or finance to survive or expand the firm, access to
business information that has an advantage for profitability and productivity, government
support, land ownership for operation and sale, and tax are significant factors of women
entrepreneur‟s performance and age, marital status, access to market, and access to physical
infrastructure are insignificant variables.
Abera, (2012), studied on factors affecting the performance of MSEs in Arada and lideta, Addis
Ababa using a linear regression analysis and identifies eight major challenges that affect
performance of MSEs inadequate finance or credit, lack of working premises or operation area,
27
marketing problems, inadequate infrastructures means road or electric or water etc., poor
management practices, technological, entrepreneurial and politico-legal problems including
bureaucratic bottlenecks system. The study shows that there is linear and positive significant
ranging from substantial to strong relationship was found between independent variables and
dependent variable.
Cherkos et al., (2018), studied significant factors in Micro and Small Enterprises performance in
8 cities of Amhara region and investigates that the most critical factors faced by MSEs are access
to finance, infrastructure, work premises, business and entrepreneurship managerial problems.
The impact is high and 50% out of work or the drop-out rate in 2014/2015. More than 25% work
time was losses due to electric power interruption daily and around 65% work premises or
operation area problems challenged MSEs. The study also shows that even though working areas
are built, they are not functional due to lack of infrastructure problems.
KS & DB, (2016), revealed that overall; eleven factors were identified as influencing the business
performance or success in Ethiopia such as Gender, Education status, Age of owner/manager,
Work experience of owner/manager, family background, and business characteristics like
Industrial sector, Age of business, Legal status and formality and other characteristics influences
the business success. But contrary to the expectation only the Age of business, having a recording
system, and borrowing from the external source were seen as significant in predicting business
success.
Orkaido & Mitiku, (2020), studied on factors affecting the performance of MSEs in konso Karat
town. Uses Simple random sampling technique and Ordered logistic regression model was used
to analysis the data. The findings showed that age entrepreneur, access to finance, marketing
skill, industry experience of the owner in years, corruption, access to market and government
policy and regulation were the determinants of MSEs Performance.
A study done by Mehari W/Aregay, (2016), on factors affecting the performance of MSEs.in
Kirkos Sub City of Addis Ababa, Ethiopia and he has used a methodology of descriptive research
in making data analysis that he did not use statistical inferences and regression analysis to show
the strength and direction of the association between the variable factors access to credit, working
premises, marketing issues, record-keeping practice and access to bank account affecting the
28
performance of MSEs. He also found that 78% of MSEs have not received any credit service in
the sub-city from lending institutions. For the operation, many MSEs did not receive the required
land implies only around 28% received land.
Abebe, (2011), was researching on analysis of success factors of MSEs in Addis Ababa with the
objective to investigate the role of age of the operator, educational level of the owner,
management experience, industry experience, marketing skill, plan, record keeping, and financial
control, and forms of ownership variables on the performance of MSEs in the study area. He has
used descriptive and multiple linear regression in making data analysis. The findings indicate
there is no significance variation on the performance of MSEs in relation to the variations to each
of the eight independent variables of the study. But the descriptive statistics result in shows
better performance for enterprises owned by individuals with better education level those
having more than 10+3 education level have better performance, have prior management and
industry experience having greater than 5-year management experience have better performance.
Moreover, MSEs using planning and record-keeping also show better performance.
Mezgebe, (2012), studied on problems of Micro and Small Enterprises in Addis Ababa: The Case
of Kirkos, Kolfe and Yeka sub-city using the variables of the age of the firm, favorability of
business environment, competition level, and institutional quality, access to raw materials, access
to training, management, access to finance, and government rules regulation. Qualitative and
quantitative research methods were used with the stratified sampling method. The finding of
Mezgebe shows that competition level, access to raw material, and marketing found to be
negative and significant. However, the favorability of business environment, institutions quality,
and government rule and regulation was found to be positive and significant.
Generally, many researches have been carried out locally and internationally concerning small
and micro enterprise's performance in the cities and urban centers. The researchers are biased on
their study areas based on their own objectives. There is a scarcity of literature touching that is
used for references in the study area of Enarj Enawga Woreda. Therefore, this study is built on
the local literature study area gap on factors that affect the performance of MSEs in Enarj
Enawga Woreda and tries to bridge the gap through studying in different sub-sectors in the Trade,
Construction, Manufacturing, Urban agriculture, and Service.
29
2.4 Conceptual Framework
The performance of MSEs in Enarj Enawga Woreda depends on internal and external factors.
The internal factors include prior work experience in business or industry, age of the enterprise,
age of the operator, education of the operator, marketing skill, skilled manager, and
entrepreneurial ability, and amount of initial capital. The external factors that influence the
performance of MSEs consist of access to government training and support, access to market,
access to infrastructure, access to finance, access to raw material, and use of modern technology
as shown below the figure.
The government or agencies should develop strategies to provide and address different supports
and create linkage with stakeholders and other non-governmental support agencies like giving
business development Services, access to markets, working premises, credit to encourage and
enhance MSEs. As a result, the local people especially the poor will generate their own livelihood
and have income, save, and will pay the necessary taxes for the government consequently their
standard of life can be improved.
30
Source: Modified from Munira, (2012) and Berihu, (2017)
Figure2. 1 Conceptual Framework (Own model; 2020)
Internal factors External factors
prior work
experience
age of the
enterprise
age of the
operator
Education of the
operator
marketing skill
skilled manager
entrepreneurial
ability
Initial capital.
access to
government
training and
support
access to market
access to
infrastructure
access to finance
access to raw
material
use of modern
technology
Performance
of the MSEs
Government
support
Other NGO
support
Develop appropriate
strategy
Good
performanc
e of MSEs
Increase
Employment,
income
reduce
poverty
Better food
Better health
Better
education
Access to
other facility
Working area
Credit
Training
Infrastructure
31
CHAPTER THREE
3. RESEARCH METHODOLOGY
3.1 Description of Study Area
This study was conducted in Enarj Enawga Woreda in Amhara region focusing on determinants
of the performance of MSEs. According to the woreda administration plan commission data
Enarj Enawga Woreda has a total population of 214866 (104197 male and 110669 female). The
majority of the populations above 80% lives in rural areas which are depend on agriculture. The
largest ethnic group of Enarj Enawga Woreda is Amhara (99.96% the rest very few are oromo
which is 0.04%. The total area of the woreda is 96095 hectare. The agro climatic zone is divided
in to 30% dega, 50% woyna dega, and 20% kolla. The Woreda has 27 rural and 4 urban
administration kebeles and a town administration center is Debre-Work.
Enarj Enawga worada is bordered on the south by Enemay, on the southwest by Debay Telatgen,
on the west by Hulet Ej Enese, on the north by Goncha Siso Enese, on the northeast by Enbise
Sar Midir, on the east by the Abbay River which separates it from the south Wollo Zone, and on
the southeast by Shebel Berenta woreda. Geographically, the woreda is located between 49'
59.99" latitude North and Longitude: 38° 04' 60.00" East in the north of Ethiopia.
Enarj Enawga wereda has an altitude of 1100-3200 meters above sea level which means the
lowest is found at the border of Abay River and the highest is near the Chockie Mountain. The
mean annual rainfall ranges from 700mm to 2000mm. It is located at a distance of 295 km in the
north from capital city of Ethiopia, Addis Ababa and 198 km from the capital city of the Amhara
region, Bahir Dar and 116 km far from the capital city of east gojjam zone Debre markos.
The economic activity of Enarj Enawga wereda consists of crop production which is productive
in different crops namely teff, bean, wheat, burly, soya bean, maize, etc., are some staple crops
and beekeeping, livestock production (which is rich in livestock production like cattle, goats,
sheep, and chickens, mules, and horse), a trade which is highly dominated by the micro and small
enterprises activities and very few in medium enterprises.
32
Ethiopia Amhara region
Source; Enarj Enawga woreda land administration and use office.
Figure3. 1: location map of the study area.
East Gojjam Zone
33
3.2 Research design
Descriptive and explanatory research designs were used in this study. Research design is
essentially a statement of the object of the inquiry and the strategies for collecting the evidences,
analyzing the evidences and reporting the findings or it is systematic plan to study a scientific
problem (Mohajan, 2017). Descriptive research is theory based that was used for describing the
state of affairs as it exists at present and also describes the nature of micro and small enterprises
and the challenges that hinder the performance of MSEs in Enarj Enawga Woreda and their
contributions. This study describes and investigates the determinants of the performance of MSEs
in Enarj Enawga woreda.
Explanatory research design helps to explain how MSEs are performing and why they are
performing in such a way and to determine the cause and effect relationships. It also used to
explain why events are occurred and to build or test theories (Sampieri, 2004).
3.3 Research methods Research methods are specific procedures for sampling, collecting and analyzing data
(Kecerdasan & Ikep,kothari, 2004).
3.3.1 Data sources, collection techniques and procedures
To achieve the objectives of this study the research used both primary and secondary sources of
data in which more priority is given to primary data. Some of the specific data that were collected
for this study are the sex, age, marital, educational, family size status of the respondents or the
owner and/or the manager of the enterprise. The data of income condition, saving status, health
and diet status, as well as employment status of the enterprise. Sales of the firm and expenses of
the enterprise were collected. The main challenges that are internal and external to the enterprise
related to Politico-legal factors, Working premises, Technological factors, Infrastructural factors,
Marketing factors, Financial factors, Entrepreneurial factors, etc. were collected.
3.3.1.1 Primary source
The primary sources that were used are questionnaire and interview. Face-to-face interview that
have high response rate using questions were asked and conducted with the MSE‟s Operators or
owners and others from related sectors. The Questionnaires which are closed and open ended
were prepared and distributed to the respondents with a request to answer the questions on their
own language and return back to the researcher. The questionnaires were designed in English
34
language and translated in to the respondent‟s mother tongue language, Amharic for simplicity.
And an in-depth interview with purposively selected key informants (from ACSI, trade,
technical, vocational and enterprise offices, kebelle administrators, and selected MSEs
managers/owners) was carried out.
3.3.1.2 Secondary sources
Official statistical Reports, Files, office manuals, policy and strategy papers were referred and
used to collect secondary data for further information. Books published and/or unpublished
papers, government documents, website /internet/ and other sources were used to enrich the study
with secondary data.
3.3.2 Sampling techniques and size The study used combined or multi-stage probability sampling techniques. In the first stage all
MSEs which are formal enterprises until 2012 E.C were listed remained in operation at least for
one year for sample size selection with Woreda‟s Trade and Industry Development and technical,
vocational and enterprise Office. From the study area of Enarj Enawga Woreda Debre-work town
having three kebelles, felege-birhan sub municipal town and Gedeb kebelle having small town
were selected purposively where the majority of MSEs are found.
Secondly, all enterprises were stratified in to five groups of trade, service, urban agriculture,
construction and manufacturing. After stratification was made thirdly, simple random sampling
technique was applied to obtain representative samples.
Based on Yemane (1996) sample size determination formula, it is possible to determine the
sample size, at 93 % confidence level and 0.07 precision levels.
n=
Where: n is number of respondents
N = population size =1579
e = sampling error/level of precision = 0.07
Therefore by using the above formula we can get the value of sample size (n) which is 181.
35
Table3. 1 Type and Number of Micro and Small Enterprises in sample kebelles of Enarj Enawga Woreda
MSEs by Sector Total number
Trade 998
Service 413
Manufacturing 76
Construction 75
Urban-agriculture 17
Total 1579 Source: Enarj Enawga Woreda Trade and market Development Office:
Representative sample (ni) = npi where pi = Ni/N, Ni is total population of the strata example
trade, whereas N is total population of the whole strata.
Table3. 2; sample size selection in each sector
MSEs by Sector Sample (ni) Percentage (%)
Trade 114 63
Service 47 26
Manufacturing 9 5
Construction 9 5
Urban-agriculture 2 1
Total 181 100
The size of the samples from the different strata was proportional to the size of the strata by using
proportional allocation methods.
Table3. 3; sample size from each kebelle and each sub-sector, systematic and proportional
Place or kebelle
Tra
de
Ser
vic
e
Man
ufa
ctu
ring
Const
ruct
ion
Urb
an-
agri
cult
ure
Tota
l
Sample taken from Each Sub- sector
Tra
de
Ser
vic
e
Man
ufa
ct
uri
ng
Const
ruct
i
on
Urb
an-
agri
cult
ure
Tota
l
Debre-work- 01 219 143 24 30 6 422 25 16 3 3 1 48
Debre-work- 02 352 76 23 31 9 491 40 9 3 4 1 57
Debre-work- 03 69 63 17 3 1 153 8 7 2 1 - 18
Feleg-birhan 280 112 11 11 1 415 32 13 1 1 - 47
Gedeb 78 19 1 - - 98 9 2 - - - 11
Total 998 413 76 75 17 1579 114 47 9 9 2 181
3.4 Data Analysis This study used descriptive statistics and econometric regression model after completing the data
collection. The descriptive analysis was made use of tools such as mean, standard deviation,
percentage, and frequency distribution. The econometric regression model applied for analyzing
the data was estimated by using logistic regression model with the help of STATA software. In
36
this case the value of dependent variable (performance of MSEs) is measured by profit and
employment.
3.4.2 Descriptive analysis
The descriptive analysis was made use of tools such as mean, standard deviation, percentage, and
frequency distribution. Qualitative and quantitative analysis methods were used for this study;
Qualitative analysis method determines qualities of phenomena that were studied which are not
measured by in numbers. Whereas quantitative method determines the data that are measured and
recorded in numeric form in STATA software for computing and analyzing data by calculating
frequency, mean, percentage and making tables.
3.4.3 Econometrics Analysis
Econometric analysis using logistic regression model were used for this study. For the analysis
of quantitative data, coding of data was done by converting raw data collected from respondents
into numerical symbols using STATA software. Inferential analysis was applied in order to
assess the factors affecting the performance of MSEs in terms of profit and employment.
3.4.3.2 The logit model
Logistic regression model could be used as the performance measure used as the dependent
variable takes a discrete (categorical) measure (Mozumdar et al., 2020), (Alene, 2020) and
(Welsh et al., 2018). Accordingly, this study was used binary logistic regression (logit) model
since MSE‟s performance is considered as a discrete (categorical) variable. The contributions of
MSEs to the development of local communities are immense. Identifying factors which
contributes for the good performance of MSEs have a positive impact on the sustainability of
those contributions. The logistic distribution (logit) is more preferable than others in the analysis
of dichotomous outcome variable, it is extremely flexible and easily use model from
mathematical point of view and the results will be meaningful interpretation and also solve
heteroscedastic. Logit model is preferred than probit model in this study primarily because of its
mathematical convenience, simplicity and resolve the problem of heteroscedastic. Following
Green, (2003), and Gujarati, (2006), the logit model for extension participation determinant
specified as follows:
……………………3
37
For ease of the expression this can be written as follows
…………………4
Where: P (Yi=1/X) is the probability that MSEs performance being improved or not, Zi= the
function of a vector of n- explanatory variables, e- represents the base of natural logarithms and
equation 4 is the cumulative logistic distribution function. If P (Yi=1) is the probability of MSEs
performance being good, then 1- P (Yi=0) represents the probability of MSEs performance being
not good or improved i.e. constant or declining and is expressed as:
…………………..5
…………………………6
Equation (6) simply is the odds ratio, the ratio of the probability that enterprises income being
increased to enterprises income being not improved either constant or declining. Taking the
natural logarithm of equation (6), we can get:
…………………...7
Where Li, is log of the odds ratio, which is not only linear in Xi but also linear in the parameters.
Finally, by introducing the stochastic disturbance term ( i) we get the logit model that is given
as:
∑ ……………………………….8
38
The empirical model for MSE performance improvement or not is specified as follows:
Yi = β0 + β1(AGE) + β2(GEN) + + β3(EDL) + β4(AOE) + β5(AIC) + β6 (audit) + β7(ARM) +
β8(AT) + β9(ATY) + β10(AM) + β11 (MKTC) + β12(GPR) + β13(FP) +
ε…………………………………………………………..……9
Where, Yi = 1 if income or profit improved and Yi = 0 if not
0 = the intercept term- constant which would be equal to the mean if all slope
coefficients are 0.
Coefficients indicating the degree of association between each independent
variable and the outcome
Xi = All independent variables expected to affect the dependent variable.
The error term
In measuring the performance of MSEs in terms of employment, although theoretically
alternative measurement tools like rate of growth of sales or profits could give precise results, in
practice they're not as credible because the employment growth measure due to entrepreneurs‟
hesitation to report truth values of their sales and profits. This hesitation, which results in
measurement errors, makes the utilization based measure preferable in studies considering
enterprise performance. Moreover, during a relatively high inflationary economy, avoiding data
in value terms is preferable, so using the utilization rate of growth because the measurement tool
is useful. Additionally, taking employment as measure of enterprises performance must be
according to the goal set for the world. During this study, therefore, the expansion rate of the
amount of persons engaged is employed as growth measure or performance. Therefore,
employment is that the most preferred measure of enterprise performance. In order that during
this a part of analysis additionally to profit, performance in terms employment is applied.
Consistent with Evans (1987), enterprise growth equation are often specified as:
……………………………10
39
Where: gr is the growth rate of the enterprises, ln Stʹ is natural logarithm of current employment
size, ln St is natural logarithm of initial employment size and 𝐴 is age of the MSEs. MSEs are
assumed to be either growing or survival (not growing). Hence, the logistic regression model that
assumes dichotomous dependent variable which takes either 1 or 0 value depending on Y* is
used. In this case, a value if 1 is given for those enterprises those who are growing or having
good performance and 0 for those who are not.
Thus, in a qualitative response model, the probability that
Y=1 is given by the sign of the latent variable that is the probability that the latent variable
becomes positive.
Thus, the logit model becomes:
…………………………11
Where: 0 is the intercept, i are the parameters of interest to be estimated, Xi is a vector of
variables expected to affect the dependent variable and i is the error term that has a logistic
distribution with mean 0 and variance 1.
The general logistic regression model is then specified as:
Entgrowth = β0+ β1 (AGE) + + β2 (GEN) + β3 (EDL) + β4 (AOE) + β5 (AC) + β6 (ATY) + β7
(AIP) + β8 (GPR) + β9 (experience) + β10 (workspace) + β11 (FS) + β12 (FP) +
Ɛ………………………………12
Where β0, β1 … β12 are parameters to be estimated, while „Ɛ‟ the error term and β0 is constant.
The term Entgrowth is enterprise performance of MSEs in terms of employment, the dependent
variable. Taking the calculated growth in employment, MSEs are classified in to two categories
i.e., good performance (if gr > 0) and low performance (if gr ≤ 0) represented in the model by 1
for the good performance and 0 for low performance MSEs.
40
3.4.4 Definition of Variables and Working Hypothesis
3.4.4.1 Definition of dependent variable
Performance of MSEs is a dependent variable measured by profit and employment.
Profit: describes the financial benefit realized when revenue generated from a business activity
exceeds the expenses, costs, and taxes involved in sustaining the activity in question and was
calculated and measured as annual total sales revenue minus annual total costs (Abraham, 2013;
Ranjith & Banda, 2014, and Giday, 2017).
Total sales/ Revenue: It is the total amount of money that a firm received during a given period
of time as a result of rendering services or selling commodities to its customers. In this case the
annual total sale received by the operators/owners of MSEs was taken.
Total cost: it is the total amount of money incurred in a given period of time in the process of
earning revenue. In this case the total amount of money incurred in the process of earning
revenue by the operators/owners of MSEs was taken.
Employment: the number of persons engaged and hired in the business in order to perform the
activities of the firm. Employment was calculated as natural logarithm of current employment
minus natural logarithm of initial employment and dividing by age of the enterprise (Evans,
1987).
Therefore if the total sales revenue is greater than total cost, performance of MSEs is good and
the enterprise is operating at profit otherwise their performance is low and is operating at a loss.
In measuring performance of MSEs Xheneti & Bartlett, (2012), argued that the basic objective of
the firm is to maximize profit as a result performance of the firm should be largely measured
based on profit. Equally, Abraham (2013), Ranjith & Banda, (2014), and Giday, (2017), have
also used profit in measuring performance of MSEs in their respective study area.
As well as taking the calculated growth in employment, MSEs are classified in to two categories
i.e., good performance (if Yi> 0) and low performance (if gr ≤ 0) represented in the model by 1
for the good performance and 0 for low performance MSEs according to Evans (1987).
41
3.4.4.2 Definition of independent variables
Education level of the operator (EDL): The level of education attained by the operators of the
enterprises is the attainment level of formal education. Most studies reveal that formal education
has a positive impact on the performance of MSEs. The level of education attained is likely to
affect the levels of skills using which one may survive in the business (Solomon, 2004);
(Abraham, 2013); (Tassew el al, 2015).
Amount of initial capital (AIC): is amount of start-up capital obtained from different sources to
start a business (Abraham, 2013; Ranjith et al, 2014).
Age of the enterprise (AOE): The longer a firm has been in the market the more knowledge it
has about its own abilities and the probability of survival is positively related to firm age (Francis
and Dedan 2015; Abraham, 2013). In contrast Atsede et al, (2008), Kayode & Afred, (2014),
Mesfin, (2015), and in their findings a negative relationship existed between age of the enterprise
and performance of MSEs.
Age of the operator (AGE): empirical studies conducted by various researchers (Solomon,
(2004); Mohammed et el, (2013); Tassew et al, (2015), reveals that a negative relationship exists
between age of the operator and performance of MSEs. However, the finding of Abraham (2013)
shows a positive relationship. In some cases the finding of research carried out by (Abebe, 2011)
age of the enterprise has found to be insignificant in influencing performance of MSEs.
Access to market (AM): Access to market refers to the availability of market demand for the
particular commodity or service. Enterprises create different market access for their products and
services insure the existence of market alternatives for their product. According to the findings of
Mahmud (2011), (Abera, 2012), as cited in Abraham, and Mohammed et al, (2013), the higher
level of market access results the greater level of enterprises performance. This is measured as a
dummy variable taking a value of two if the enterprise has access to market and one otherwise.
Level of market competition (MKTC): the degree of market competition can influence the
performance of MSEs. A research carried out by Dietsch, (2010), W/gebriel, (2012), Kukov and
Ying Xie, (2012), and Tejvan (2016), in their empirical study noted that negative and significant
relationship exists between the level of market competition and performance of MSEs..
Therefore, based on the above researchers if the number of firms in the market is low then the
42
degree of competition will be little and the demand will be more inelastic. This enables a firm to
increase profits by increasing the price. However, if the market is very competitive this leads to
price reduction thereby a decline of profit in particular and the performance of MSEs in general.
This is measured as a dummy variable taking a value of one if the competition level is high or
unfavorable and zero otherwise.
Access to training (AT): Access to training for enterprises refers to the facilitation of different
trainings which assists the operators of the enterprises to perform in a suitable way. Capacity
building trainings would better prepare enterprises to perform in the business they engaged
(Solomon, 2004; Benjamin and Bonno, 2007 cited in Abraham, 2013; Ranjith, 2014; and
(Bouazza et al., 2015). Therefore, training for MSEs operators allows them to develop the
substantial skills to ensure the survival and performance of their enterprises. This is measured as
a dummy variable taking a value of one if the operators have get trained with skill needed since
starting a business or before and two otherwise.
Gender of the operator (GEN): Accordingly the finding of research carried out by Solomon,
(2004), Mulu (2009), Mesfin, (2015), Tassew et al, (2015), female-owned enterprises; their
performance level was found to be less as compared to male-owned enterprises. In other words,
negative and significant relationship between female-owned enterprise and performance of MSEs
was found. On the other hand, Menzies, (2004), as cited in Tassew et al, (2015), found that
hardly any differences between male and female owned enterprise on their performance level.
This is measured as a dummy variable taking a value of one if the enterprise is male owned and
zero female.
Access to credit (AC): the findings of the research carried out by Berihu et al, (2014), reveals
that financial constraints were found to be one of the critical bottlenecks for the performance of
MSEs. Enterprises that have access to formal credit are expected to grow faster than those that
have not (Solomon, 2004). This is measured as a dummy variable taking a value of one if the
enterprise has provided with financing from any formal financial institutions since establishment
and two otherwise.
Government policy and regulation (GPR): government policies and regulation related factors
such as bureaucratic procedures in lending terms, business licensing and registration, high tax
43
rate, and lack of government incentives have negatively influenced the performance of MSEs.
Government regulation about wages, taxation, licensing and others are among the important
reasons affecting performance of MSEs. Without careful attention, government policies could
crush the small business sector in any economy (Kinyua, 2014). Government policies should aim
to encourage and promote the development of local technologies. Emphasis should be on the
promotion of the local tool industry to reduce reliance on imports (Assefa et al., 2014); and
(Mbugua et al, 2014). This is measured as a dummy variable taking a value of one if the
government policy and regulation is found to be unfavorable to the enterprises and two
otherwise.
Use of modern technology (ATY): machineries, tools and others related to service delivery
practices that improve efficiency, greater production, higher profit, lower cost, and broaden
market share locally and globally. This is measured as a dummy variable taking a value of one if
the enterprise adopts a technological capacity and two otherwise.
Access to raw materials/ARM/: the availability of raw materials in their locality with
reasonable price for the production also it is a dummy variable takes a value of 2 having better
access in their locality and 1 if not.
Lack of infrastructure (AIP): these are infrastructural facilities related to adequate water
supply, reliable power supply (electrification, transportation, etc.) measured as a dummy variable
taking a value of one if there infrastructure problem and two otherwise.
Work space: ownership of the work premises that the MSEs operates their business measured as
dummy variable taking a value of one if owns or bought the work space and two rents.
Prior work experience : the status of prior experience of the owner or manager of the MSEs
before this business and it is a dummy variable taking a value of one if have experience and two
otherwise
Financial statement (FS): This variable is defined as a dummy variable which is assigned a
value of 1 if the enterprise has financial record and 2, if otherwise. This variable is used because
it indicates the firm‟s transparency.
44
Table.3.4: Description of the variables, measurement, and expected hypothesized
Notation Variable description Measurement Expect
ed sign
EDL Education level of the operator Ordinal (1=<grade 8; 2=grade9-12;
3=>grade 12)
+
AIC Amount of initial capital at startup Continuous: In Birr +
AOE Age of the enterprise since its
establishment
Continuous: Full years +/-
AGE Age of the operator Continuous: Full years +/-
AM Access to market Dummy: Access=2 Otherwise = 1 +
GEN Gender of the operator Dummy: male owned = 1, female= 0 +/-
AT Access to training before and after
starting a business
Dummy: Access = 1; Otherwise = 2 +
MKTC Level of market competition Dummy: yes/have comp = 1; Otherwise
= 2
-
AC Access to credit since starting a
business
Dummy: Access= 1; Otherwise = 2 +
GPR Government policies and regulation Dummy unfavorable = 1 Otherwise = 2 +/-
ATY Access to modern technology Dummy: Access = 1; Otherwise = 2 +
AIP Access to infrastructural problems Dummy AIP yes=1; otherwise=2 -
experience Owners experience before this
business
Dummy experience1 = having,
2=otherwise
+/-
Marital status Marital status of the operator Dummy married=1; 0= otherwise +/-
Work space Ownership of the work space Dummy own(bought)=1; otherwise=2 +/-
ARM Access to raw material Dummy access =2; 1= otherwise +
FS Financial statement Dummy FS 1=have; 2=otherwise +
FP Future plan categorical 1=expand the business;
2=open branch; 3= change field
+/-
Source: Own survey, (2021)
45
CHAPTER FOUR
4. RESULTS AND DISCUSSION
In this section, the results of descriptive analyses are presented first, followed by econometric
results. Generally, this section is organized in the following manner: First, the background of
respondents and internal factors related to MSEs are analyzed and presented. Second, external/
business environment factors related to MSEs are presented and analyzed in the form of tables,
and finally, the econometric results of determinants of MSEs performance are presented and
analyzed.
4.1. Demographic Characteristics of Respondents
4.1.1 Age of the respondents and age of the enterprise Table 4.1 Age of the respondents and age of the enterprise
Variable Performance of MSEs (profit)
Obs. Mean Std.error Std.dev. t-test (p-value)
Age Low performance 74 29.95946 0.669989 5.763464 0.0000
Good performance 107 35.84112 0.6528628 6.753266
Total 181 33.43646 0.5189035 6.981133
Performance of MSEs (employment)
Low performance 89 32.22472 .8102309 7.643703 0.0212
Good performance 92 34.6087 .6348225 6.089004
Total 181 33.43646 .5189035 6.981133
Age of
enterprise Performance of MSEs (profit)
Low performance 74 6.72973 0.2770349 2.383145 0.0000
Good performance 107 10.83178 0.3310441 3.424347
Total 181 9.154696 0.2710544 3.646664
Performance of MSEs (employment)
Low performance 89 7.898876 .3665136 3.457682 0.0000
Good performance 92 10.36957 .3567376 3.421706
Total 181
Source: Own survey (2021)
Before presenting the result, 181 questionnaires were distributed across the Woreda and all 181
were completed and retrieved successfully, representing a 100% response rate. Out of the 181
questionnaires administered 114, 47, 8, 10, and 2 were distributed to trade, service,
46
manufacturing, construction, and urban agriculture enterprises respectively. As it can be seen
from table 4.1, of the total MSE operators in Enarj Enawga Woreda, the average age of the
respondents is 33.43 indicates that most of the MSEs were owned and run by a young and
productive labor force and older respondents have relatively good performance than the
Youngers. The t-test result indicates that age of the operators was found to be significant between
good performance and low performance at a 1% level of significance
The average age of the enterprises since their establishment is about 9 years. Older enterprises are
more likely to have attained the ability to operate more efficiently than recently established ones
both in terms of profit and employment as shown from table 4.1 average age of the enterprise for
good performance in terms of profit is 10.83 whereas the average age of the enterprise for low
performance is 6.73 years at t-test p-value 0.00 which is significant at 1%.
4.1.2 Sex of the MSEs owners Table 4.2 Sex of the MSEs owners
Performance of MSEs (profit)
Gender of the enterprise
Female % Male % Total % P-value
Low performance 18 9.94 56 30.94 74 40.88 0.7681
Good performance 24 13.26 83 45.86 107 59.12
Total 42 23.20 139 76.80 181 100.00
Performance of MSEs (employment)
Low performance 26 14.36 63 34.81 89 49.17 0.601
Good performance 16 8.84 76 41.99 92 50.83
Total 42 23.20 139 76.80 181 100.00
Source: Own survey (2021)
In order to determine the proportion of gender distribution of the operators/owners of MSEs,
respondents were asked to indicate their gender. As a result, as it can be seen from table 4.2, of
the total MSE operators, 42 (23.2%) and 139 (76.8%) of the respondents were found to be female
and male operators respectively. This shows that the participation of women in business activity
is very low as compared to male operators. This indicates that male operators are dominant in
MSEs operations because the gender distribution reflects a wide variation of gap. The chi-
squared shows that there is a weak relationship between the variable gender and performance of
MSEs and it is insignificant.
47
4.1.3 Education level of respondents Table 4.3 education level of respondents
Performance of MSEs in terms of
profit
Education level total Chi-squared
(P-value) Up to grade 8 Grade 9-12 >12
Low performance 37 22 15 74 0.001
Good performance 25 53 29 107
Total 62 75 44 181
Performance of MSEs in terms of
employment
Low performance 36 38 15 89 0.049
Good performance 26 37 29 92
Total 62 75 44 181
Source own survey (2021)
As shown in Table 4.3 above, the education qualification of the operators of MSEs in Enarj
Enawga Woreda was assessed. Accordingly, the finding reveals that the majority of the
operators/owners were high school and elementary qualification and the remaining were qualified
above grade 12 (TVET and college diploma as well as degree and above). With regard to the
education level of the respondents 62(34.25%), 75(41.44%), and 44(24.31%) were grade 8 and
less, grade 9-12, and above grade 12 respectively according to the performance in terms of profit
shows more educated enterprises have good performance in terms of profit but not in terms of
employment except the degrees. There is a strong relationship between the performance of MSEs
in terms of profit and education level, with a P-value of 0.001 as shown in table 4.3 above.
4.1.4 Marital Status of Operators Table 4.4 Marital Status of Operators of MSEs
Marital status Frequency Percent
Married 144 79.56
Single 37 20.44
Total 181 100
Source Own survey (2021)
With this regard, Table 4.4 demonstrates that the majority 144 (79.56%) of the respondents were
married while 37(20.44%) of them were unmarried. This finding suggests that married operators
were most engaged in MSEs activities.
4.2. Characteristics of Micro and Small Enterprises and their Operators
The study identified the following basic characteristics of MSEs along with their operators that
exhibited and related to the operation of their business.
48
4.2.1 Startup capital of enterprises Table 4.5 Startup capital
Performance of MSEs in
terms of profit
Amount of initial capital t-test
(P–value) Obs. Mean Std.error Std.dev.
Low performance 74 5154.054 183.079 1574.905 0.0000
Good performance 107 6557.57 215.6459 2230.659
Total 181 5983.757 156.1779 2101.158
Performance of MSEs in terms
of employment
Low performance 89 5720.225 188.4238 1777.586 0.1
Good performance 92 6238.696 245.4682 2354.448
Total 181 5983.757 156.1779 2101.158
Source own survey (2021)
As shown in table 4.6, MSEs started their business with the amount of average capital, 5983 ETH
birr. The above table also shows that the growth rate both in terms of profit and employment
increases with increase in amount of initial capital. The higher the amounts of start-up capital
have the better performance level. The t-test p-value also indicates significant relationship
between performance of MSEs in terms of profit and amount of initial capital but there is no
statistical mean difference between performance in terms of employment and startup capital.
4.2.2 Source of startup finance Table 4.6: Source of startup finance
Source of startup capital Frequency Percent
Own saving and from family support 120 66.3
Loan from MFI and bank 61 33.7
Total 181 100
Source own survey (2021)
In order to identify the main source of financing for startup a business, operators were asked
about the source of finance. In this regard as table 4.5 depicts 120(66.3%) of the respondents
reported that to start their own business the required capital came from own saving and family
support, 61(33.7%) of the respondents were granted capital from micro finances institution and
bank. The findings of this study show that the vast majority of operators have started their
business by their source of finance.
49
4.2.3 Annual Sales Revenue and Total Costs of MSEs Table 4.7: Annual sales revenue and total costs
Annual Sales revenue in
Birr/per year
Frequency
Percent Annual total cost in
birr/per year
Frequency
Percent
5000-20000 29 16.02 5000-10000 4 2.21
20001-50000 115 63.54 10001-25000 46 25.41
50001-100000 32 17.68 25001-50000 97 53.59
100001-300000 3 1.66 50001-75000 25 13.81
300001-500000 1 0.55 Above 75000 9 4.97
Above 500000 1 0.55 - - -
Total 181 100 Total 181 100
Source own survey (2021)
To evaluate the capacity of MSEs in generating revenue, respondents were asked issues related to
their annual sales revenue or gross profit. In light of this as table 4.7 depicts, 29(16.02%) of the
respondents reported that their annual sales were 5000- 20000, the majority 115(63.54%) of the
respondents said 20001-50000, 32(17.68%) of the respondents conformed that their annual sales
were 50001 – 100000, 3(1.66%) of the respondents reported that their annual sales were 100001-
300000, 1(0.55%) of the respondents reported that their annual sales were 300001- 500000 and
1(0.55%) of the respondents reported that the annual sales were above 500000.
Again on the same of sample respondents 4(2.21%) reported that their annual costs was within
the range 5000-10000, 46(25.41%) of the respondents were in the range of 10001– 25000,
97(53.59%) of the respondents were in the range of 25001– 50000, 25(13.81%) of the
respondents were in the range of 50001– 75000, and the remaining balance 9(4.97%) had annual
costs above 75000 birr. In sum, many efforts have to be made by the operators in maximization
of the sales revenue or profit and minimizing costs in the realization of MSEs the performance.
4.2.4 Type of Enterprises
Table 4.8: type of enterprise
Performance of MSEs in terms of
profit
Types of the enterprise Total
trade service Manufacturing Construction Urban agriculture
Low performance 53 19 2 0 0 74
Good performance 61 28 6 10 2 107
Total 114 47 8 10 2 181
Performance of MSEs in terms of
employment
Low performance 61 27 1 0 0 89
Good performance 53 20 7 10 2 92
Total 114 47 8 10 2 181
Source own survey (2021)
50
Regarding the type of enterprises as table 4.8 indicates of the total 181 enterprises, 114(63%) of
them were retail trade activities, 47(26%) were service rendering enterprises, 8(4.4%) were
manufacturing enterprises, 10(5.5%) were construction enterprises and 2(1.1%) were urban
agriculture enterprises. Thus given the outcome findings one can infer that the capacity of MSEs
in creating job opportunity is limited because the number of manufacturing, construction, and
urban agriculture enterprises were less in number as they are a source of employment and income
generation much better than other enterprises according to the respondents and discussion made
with the key informants on the issue of which sector creates more job opportunity and gives
profitability.
4.2.5 Number of Employees at Startup and at Current Table 4.9 Number of Employees at Startup and at Current
Performance of MSEs
in terms of profit
Amount of initial employment size p-
value
Amount of current employment size
p-
value
Obs Mean std.error Std.dev. Obs Mean std.error Std.dev.
Low performance 74 1.0945 0.04374 0.37631 0.0012 74 1.5540 0.09837 0.84629 0.0004
Good performance 107 1.5233 0.1040 1.0758 107 2.4018 0.18393 1.90262
Total 181 1.3480 0.06580 0.88528 181 2.0552 0.11978 1.61149
Performance of MSEs in
terms of employment
Low performance 89 1.0337 0.01923 0.18149 0.0000 89 1.0337 0.01923 0.18149 0.000
Good performance 92 1.6521 0.12015 1.15249 92 3.0434 0.18347 1.75982
Total 181 1.3480 0.06580 .885286 181 2.0552 0.11978 1.61149
Source: Own survey (2021)
Initial employment size is another variable in relation to firm‟s related or internal factors. Table
4.9 shows that from 89 MSEs on having low performance in terms of employment, all MSEs
started their business with on an average of 1.03 employees. But from 92 having good
performance in terms of employment MSEs was started their business with an average of 1.65
employees. This indicates that most of the MSEs started their business with a relatively large
number of employees were had good performance in terms of employment and profit.
Current employment size is another variable in relation to internal factor. As we understand from
Table 4.9, from 89 surveyed MSEs in low performance in terms of employment, all enterprises
were activating their business with an average of 1.03 employees. From 92 MSEs having good
performance in terms of employment, enterprises were operating their business with an average
of 3.04 employees.
51
4.2.6 Term of Employment Table 4.10: Term of employment
Term of employment Current Initial
N % N %
Permanent 264 71 231 94.67
Temporary 36 9.67 7 2.87
Unpaid and family members 72 19.33 6 2.46
Total 372 100 244 100
Source: Own survey (2021)
Table 4.10 compares the term of employment opportunity provided by MSEs. Accordingly,
enterprises that provide employment opportunities at full time, temporary/contract, and unpaid
and family members are: 264(71%), 36 (9.67%), and 72 (19.33%) respectively. The total
employment absorbed in the sample rose from 244 at the starting time to 372
employees/individuals currently with an average annual growth rate of 4.6 percent. This result is
small when compared to previous studies in Ethiopia such as (Gebreeyesus, 2007) found 9 %
growth rate, (Kefale m., 2012) found 6.5 percent and (Hagos et al., 2014) who found 5.3 %
growth rate. In light of with this, the manufacturing, urban agriculture, and construction sectors
were better in terms of creating employment opportunities by creating 4-10 job opportunities in
one enterprise according to the respondents and discussion made with key informants but they are
insignificant in number. Hence other sectors (service & trade) were in their infant stages but large
in number.
4.2.7 Challenges of MSEs.
It is generally accepted that MSEs are becoming increasingly important in terms of employment,
wealth creation, and the development of innovation (F. Mbugua & Moronge, 2016). However,
many problems were encountered. Moreover, it is generally known and accepted that MSEs can
fail within a short period of time. Therefore, it would be crucial to study to identify constraints
facing performance of MSEs. All 181 (100%) respondents reported that challenges were facing
their enterprise performance. This indicates that enterprises have faced with challenges that
hinder their performance in generating profits in particular and in creating employment
opportunities, generating income and poverty alleviation in general in Enarj Enawga Woreda.
Some of the challenges that are mentioned by the respondents are lack of workspace, lack of
infrastructure, lack of raw material, lack of access to market, lack of access to credit, lack of
52
access to technology, lack of access to training, government policy and regulation problems,
market competition problems.
Key informants were also interviewed to mention the major challenges impeding performance of
MSEs after they have agreed with their existence. With this regard, key informants identified the
major problems as internal ones such as lack of education, lack of marketing skills, lack of
experience, lack of proper record-keeping, limited entrepreneurial skills, etc. and the external
factors such as limited market-linkage, lack of working premises, limited range of government
support, lack of using technology, lack of credit access, market competition problems,
infrastructural problem, pandemic, etc. were reported as major constraints of MSEs in Enarj
Enawga Woreda.
4.3. External/ Business Environment Factors Related to MSEs
The Performance of MSEs can be affected by a number of business environmental related factors.
This part of the study examines the most external determinants affecting performance of MSEs.
In addition to data obtained through a questionnaire, an interview has also made with the key
informants group with the aim of contribute to a better understanding of how certain external
related factors determine the performance of MSEs such as work-space of the enterprise, access
to raw material, government policy and regulation, access to credit, level of competition, use of
modern technology, access to training, and market access.
4.3.1. Working Spaces of MSEs
Table 4:11 working space
Performance of MSEs
profit
work space Total Chi squared
(p-value) Owen or bought Rent from private/others
low performance 14 60 74 0.000
Good performance 72 35 107
Total 86 95 181
Performance of MSEs
employment
low performance 21 68 89 0.000
Good performance 65 27 92
Total 86 95 181
Source: Own survey (2021)
Working space is one of the essentials for MSE‟s growth and development and its lack therefore
will challenge the ability of MSE‟s performance. Table 4.11 above shows, among 86 owners or
managers who own or bought their work premises 72 and 65 enterprises have good performance
53
in terms of profit and employment respectively. However, from 95 MSEs which done their
business by renting working space, the majority 60 and 68 have low performance in terms of
profit and employment respectively. The chi squared also shows that there is strong relationship
between the variable and performance of MSEs at p-value of 0.000 (1% sig. level).
4.3.2. Access to Raw Materials
Table 4.12 access to raw materials
Performance of MSEs (profit) Access to raw material Total Chi squared
(p-value) Yes No
low performance 7 67 74 0.000
Good performance 71 36 107
Total 78 103 181
Performance of MSEs
(employment)
low performance 35 54 89 -
Good performance 43 49 92
Total 78 103 181
Source: Own survey (2021)
Table 4.12 above displays the level of access of raw materials on MSE's performance and
development. Among 78 MSEs that have access to raw material 71(91%) and 43(55.13%)
enterprises have good performance in terms of profit and employment respectively. However,
from 103 MSEs that have not accessed raw materials, the majority 67(65%) and 54(52.43%) have
low performance in terms of profit and employment respectively. The chi-squared test also shows
that there is a strong relationship and revealed that there is a difference between the variable and
performance of MSEs especially for performance in terms of profit at a p-value of 0.000 (1% sig.
level).
54
4.3.3 Factors Related to Government Policies and Regulation
Table 4.13: Impact of government policies and regulation
Performance of MSEs (profit) government policies and regulations adverse effects
Yes No Total χ2 (p-value)
low performance 69 20 74 0.000
Good performance 27 65 107
Total 96 85 181
Performance of MSEs in terms of
employment
low performance 60 14 89 0.001
Good performance 36 71 92
Total 96 85 181
government policies and regulations effects
Freq. Rank
registration and licensing 19 3
Lack of incentive and support 12 4
Lack of working premises 22 2
Lack of training 6 5
Lack of infrastructure 32 1
High tax imposition 1 6
Total 96
Source: Own survey (2021)
Government policies and regulations have an impact on the performance of MSEs. For example a
study done by International Finance Corporation (IFC, 2013) based on responses of more than
45,000 firms in developing countries found that the top obstacles to their operations were
government(legal & regulatory) related factors(IFC,2013) cited in (Bouazza et al., 2015).
Moreover, (Abera, 2012), in his study has also noted that lack of government support was one of
the problems that affect the performance of MSEs. Therefore, having said this, findings of this
research Table 4.13 above reveals that 96 of the respondents were generally reported that
government policies and regulation was not found to be favorable to their business operations and
they didn‟t get any incentive support from the government of which 69(72%) and 60(64.6%)
have low performance both in terms of profit and employment respectively whereas 85 were not
affected by government policies and regulation related factors in operating their business
activities of which 65(76.5%) and 71(83.53%) have good performance in terms of profit and
employment respectively. In relation to this respondents who had reported challenges and
constraints related to government policies and regulations were asked to list briefly the main
55
factors that have hurt their business operations. The responses are summarized and presented.
Given the outcome of the findings indicates that the first and second-ranked found to be lack of
infrastructure, and lack of working premises respectively were the major government policies and
regulations related factors affecting the performance of MSEs in the study area of Enarj Enawga
Woreda. The chi-squared indicates that there is a strong relationship revealed that there is a
difference between the variable and performance of MSEs at a p-value of 0.000.
4.3.4 Factors Related to Market Competition Table 4.14: level of market competition
performance of MSEs
profit
market competition effects
Yes No Total Pearson chi2
(p value)
Low performance 61 13 74 0.000
Good performance 37 70 107
Total 98 83 181
Source own survey (2021)
The performance of MSEs can be affected by the number of firms that exist in the market that
produces and sell similar products. With regard to market competition (table 4.14), the study
observed that about 98 respondents confirmed the level of competition has created an adverse
effect on the performance of their enterprise in generating adequate profits. However, 83
respondents didn‟t agree with the competition level and its negative impact on the performance of
MSEs and the majorities 70(84.33%) have good performance. This indicates that when the
number of firms increases from time to time, and the existence of unfair competition may have its
own impact on the survival and performance of MSEs in generating adequate profits thereby
creating employment opportunity and poverty reduction. The chi-squared indicates that there is a
strong relationship between the variable and performance of MSEs at a p-value of (0.000) 1% sig.
level.
Key informants have also been interviewed regarding the level of market competition and its
fairness. With this regard, they agreed that the competition level among MSEs is high because of
engagement in similar trade activities and their number is ever increasing. They also added that
the competition is somewhat unfair in terms of selling commodities at lower price and possession
of some products illegally which are highly demanded by consumers (e.g. sugar & edible oil) due
to creating a special relationship with some government officials.
56
4.3.5 Factors Related to Access to Training
Table 4.15: Operators of MSEs and their access to training
performance of MSEs
profit
vocational/technical training
Yes No Total Pearson chi2
(p-value)
Low performance 14 60 74 0.000
Good performance 97 10 107
Total 111 70 181
performance of MSEs
(employment)
Low performance 52 37 74
Good performance 59 33 107
Total 111 70 181
Source own survey (2021)
Adequate provision of overall training may place the operator in a better position to make sound
business-related decisions and forecast the future of business conditions of uncertainty that will
have an impact on the performance of MSEs (Mesfin, 2015). Additionally, Tassew et al, (2015),
in their study found that access to training was an important factor determining the survival and
performance of MSEs. Table 4.15 depicts the availability of training business related skills given
to operators of MSEs. With this regard, 70(38.58%) respondents have not provided with any
form of training since and before they started their business of which only 10 respondents have
good performance the rest 60(85.71%) have low performance but the remaining 111(61.32%)
have taken training and 97(87.4) respondents have good performance in terms of profit
especially. The chi-squared test indicates that there is strong relationship between the variable
and performance of MSEs at a p-value of 0.000.
Furthermore, the interview made with the key informants provided similar responses on the issue
of access to training is limited because of lack of linkage and educated trainer man power that
provides training to MSEs operators. Thus given the findings the majority of operators of MSEs
in the study area have access to training but it lacks quality.
57
4.3.6 Use of Modern Technological Related Factors
Table 4.16: Access to technological related factors
Item Alternative Freq. %
use of modern business technology Yes 100 55.24
No 81 44.76
Total 181 100
reasons not using technology Freq. Rank
Lack of skills and knowledge 27 2
Lack of money 39 1
Unable to select proper technology 15 3
Performance(profit)
Low Performance 46 28 74
Good Performance 54 53 107
Total 100 81 181
Performance(employment)
Low Performance 75 14 89
Good Performance 25 67 92
Total 100 81 181
Pearson chi2(1) = 59.6457 Pr = 0.000
Source own survey (2021)
Table 4.16 is concerned with the adoption of technological capacity. The study confirms that 100
respondents adopted technological facility in their business activities, as a result, they have high
probability of good performance on the other hand, and 81 respondents did not adopt
technological facilities in operating their business. Further, the respondents who never used
technological capacities in their business operation were asked to explain their main reasons for
not using a technology that applies to their respective business activity. As a result, the findings
of Table 4.16 indicate that the 1st reason was lack of money, the 2
nd lack of skills and knowledge
and the third is unable to choosing the right and appropriate proper technology to their business.
The chi-squared test indicates that there is strong relationship between the variable technology
and performance of MSEs in terms of employment at a p-value of 0.000 1% sig. levels.
58
4.3.7 Access to infrastructural problems
Table 4.17 Access to infrastructural problems
Item Alternative Freq. %
Infrastructural problems Yes 93 51.38
No 88 48.62
Total 181 100
List of Infrastructural problems Freq. Rank
1
3
2
Insufficient and interruption of electric power 88
Insufficient and interruption of water supply 2
Insufficient and inconvenient road 3
Performance(profit) Yes % No % total %
Low Performance 55 30.39 19 10.50 74 40.88
Good Performance 38 20.99 69 38.12 107 59.12
Total 93 51.38 88 48.62 181 100
Performance(employment)
Low Performance 70 38.67 19 10.50 89 49.17
Good Performance 23 12.71 69 38.12 92 50.83
Total 93 51.38 88 48.62 181 100
Chi-squared p-value- 0.00
Source own survey (2021)
From 181 MSEs surveyed in the study area, 93 MSEs are affected by infrastructural problems.
From these 55 have low performance and 38 MSEs are good performance in terms of profit.
Whereas in terms of employment from 93 MSEs affected by infrastructural problems 70 have
low performance and 23 have good performance. However, 88 MSEs are not affected by
infrastructural problems as a result the majorities have good performance. With respect to
infrastructure factors, insufficient and interruption of power is the main problem followed by the
problems of insufficient and inconvenient road and insufficient and interruption of water supply.
There is also a strong relationship between the variable and performance especially in terms of
employment at a p-value of 0.00.
59
4.3.8 Access to credit Table 4.18 Access to credit
Item Alternative Freq. %
Access to credit Yes 75 41.43
No 106 58.57
Total 181 100
Reasons for no access to credit
Lack of collateral 62 58.49
Interest rate is high 5 4.72
Complex loan procedure 7 6.6
Most MFIs are reluctant 23 21.70
I don‟t need credit 8 7.55
Loan insufficient 1 0.94
Total 106 100
Saving institutions (accounts opened)
MFIs(ACSI) 64 39.51
BANK 78 48.15
IQUB 20 12.34
Total 162 100
Performance(profit) Yes No Total
Low Performance 19 55 74
Good Performance 56 51 107
Total 75 106 181
Performance(employment)
Low Performance 14 75 86
Good Performance 61 31 89
Total 75 106 181
Source own survey (2021)
In light of this respondents were asked if they have access to credit since starting their business,
with this regard as it can be revealed above (table 4.18) 75 of the MSE owners in the sample
survey accessed loan from formal sources, while the remaining 106 (58.57%) didn't borrow from
any source. From the 181 respondent‟s 64, 78, 20, of the respondents said that they have an
account and save in Amhara saving and credit institution (ACSI), banks, and equb respectively.
With regard to access to credit, respondents that have never accessed credit were asked about
their inability to access credit. Accordingly, the finding obtained 72 respondents Lack collateral,
5 respondents high interest rate, 7 respondents with complex loan procedures, 23 respondents
most lenders are reluctant to give credit to MSEs, 8 respondents don‟t need credit, and 1
respondent with the reason of insufficient loan. The majority of MSEs who have access to credit
score good performance compared to those that don‟t have credit access.
60
In light of the above findings, an interview has also made with the key informants on the issue of
credit facilities given to MSEs. They agreed that the loan is given to MSEs‟ is not adequate and
all enterprises did not have equal access to credit due to lack of collateral requirements, policy-
related requirements and procedures of the financial institutions, repayment problems, and capital
shortage by the lending institution particularly Amahara Credit and Saving institution.
4.4 The role of MSEs
Table 4.19 the role of MSEs on household health, education and diet
Item Strongly agree Agree Medium Disagree Strongly
disagree Total
Freq. % Freq. % Freq % Freq. % Fr % Freq
.
%
Diet improvement 23 12.71 125 69.06 25 13.81 8 4.4
2
- - 181 100
Performance(profit)
low Performance 8 33.33 57 44.7 7 28 2 25 74
good Performance 15 66.66 68 55.3 18 72 6 75 107
Total 23 100 125 100 25 100 8 100 181 100
Children’s
education status
25 13.81 114 62.98 36 19.89 6 3.31 - - 181
100
low Performance 10 5.52 46 25.41 17 9.39 1 0.55 74 40.88
good Performance 15 8.29 68 37.57 19 10.5 5 2.76 107 59.12
health improvement 24 116 32 9 - - 181 100
Performance(profit)
low Performance 11 6.08 51 28.18 10 5.52 2 1.10 74 40.88
good Performance 13 7.13 65 35.91 22 12.15 7 3.87 107 59.12
Total 24 13.26 116 64.09 32 17.68 9 4.97 181 100
Source own survey (2021)
4.4.1 The role of MSEs on Household Food Consumption
MSEs Managers or owners were asked about changes that occurred in their food habits after
engaged in this business. Accordingly, 23(12.71%), 125(69.06%), 25(13.81%), and 8(4.42%)
managers or owners responded strongly agree, agree, medium, and disagree respectively with
respect to improvement in their household‟s diet (food consumption). Enterprises having good
performance have also better diet improvement as shown from the above table compared to
having a low performance. Income and profit stand first for the improvement of the food
consumption pattern of the household, whereas income from other sources being other factors.
Therefore, from the above findings, one could conclude that the contribution of income by MSEs
contributes to meeting the food needs of the poor.
61
4.4.2 The role of MSEs on Education Expenditure of the Households
Recent theoretical models routinely portray human capital investment (e.g. education) as a
primary engine of economic growth. It is assumed that households with higher incomes have
greater choices and opportunities for access to education. It is true that currently in Ethiopia basic
education (primary and secondary education) is free of charge but this does not mean it is without
cost for poor families. The family spends money for school uniforms, stationery, and teaching
materials…etc. In the survey, an attempt has been made to investigate the impact of the MSEs
sub-sector in improving household‟s access to education. Therefore, as indicated in table 4.18
above 25(13.81%), 114(62.98%), 36(19.89%) and 6(3.31%) respondents of the MSEs sub-sector
reported that improvement in household schooling after engagement in MSEs Strongly agree,
agree, medium and disagree respectively. Therefore, we can conclude that MSEs can improve the
education expenditure of the households that decrease illiteracy after engaging in this business. It
is also possible to conclude that MSEs have relatively improved education expenditure of the
households if their performance is good compared to having low performance as shown above
table in 4.19.
4.4.3 The role of MSEs on Health Condition of the Households
One of the manifestations of poverty is high malnutrition and a general lack of care. Better
health can also be a complementary strategy in poverty reduction. Better health improves
people‟s productivity, thereby adding significant value to include generalization. People work
harder when healthy; avoid the expense by health programs are valuable complementary
strategies (Ozcan & Tone, 2014). Regarding health conditions, respondents were asked about
improvement in their household‟s health condition. As a result, from this finding 24(13.26%),
116(64.09%), 32(17.68%) and 9(4.97%) respondents responded that they strongly agree, agree,
medium, and disagree respectively in the improvement of their household health condition.
Therefore, it is possible to conclude that MSEs have relatively improved health situations if their
performance is good compared to having low performance as shown above in Table 4.19.
62
4.4.4 The Capacity of MSEs in Poverty Reduction based on Owners perception Table 4.20: MSEs‟ capacity in poverty reduction
poverty reduction evaluation Frequency Percent
High 39 21.54
Medium 126 69.61
No change 16 8.85
Total 181 100
Source: Own survey (2021)
The study also assessed to what extent MSEs do alleviate poverty by providing employment
opportunities. The finding in table 4.20 shows that 39 (21.54%) of the respondents reported that
their enterprise's contribution to poverty reduction is high, and 126(69.61%) of the respondents
reported medium. On the other hand, 16 (8.85%) of the respondents highlighted that capacity of
MSEs in fighting against poverty is low. Hence, based on the findings it is possible to infer that
MSEs are well-acknowledged and can be considered as tools for poverty reduction through
providing jobs.
4.4.5 MSE’s role in Generating Income based on Owners perception Table 4.21: MSEs and Income generating capacity
performance of MSEs (profit) Income level after starting a business
Increased % Decreased % No change % total
Low performance 26 14.36 42 23.20 6 3.31 74
Good performance 83 45.86 16 8.84 8 4.42 107
Total 109 60.22 58 32.04 14 7.73 181
performance of MSEs (employment)
Low performance 45 24.86 36 19.89 8 4.42 89
Good performance 64 35.36 22 12.15 6 3.31 92
Total 109 60.22 58 32.04 14 7.73 181
Source: Own survey (2021)
The study has also assessed the income-generating capacity of MSEs, for this reason respondents
were asked if their income level is increased or not after owning a business. Accordingly, as it is
indicated in table 4.21, 109 of the respondents reported that their income level was increased, 58
respondents reported decreased, and 14 of the respondents confirmed that there was no a
significant differences in their income level before and after starting a business. In closing, it can
be said that MSE‟s capacity in generating income good but not sufficient enough to alleviate
poverty hence, government, operators, and other concerned body should have to work together to
enhance capacity of MSEs in generating adequate income in Enarj Enawga Woreda.
63
4.5 Determinants of MSE’s Performance: An Econometric View
In this section, the selected explanatory variables were used to estimate the logistic regression
model to analyze the determinants of MSE‟s performance. This study uses the performance of
MSEs as the dependent variable (Y). Performance of MSEs was measured by profits as total sales
minus total cost and employment as the natural logarithm of current employment minus the
natural logarithm of start-up employment and by dividing by the age of the enterprise.
4.5.1 Testing and Examining the Goodness of Fit of the Model
Multi co-linearity test: The term Multi co-linearity indicates the existence of an association
between two or more of explanatory variables, this association level might be nil that can be
ignored or high that significantly affects the estimation of the parameters. If Multi-co-linearity is
perfect, the regression coefficients of the independent variables are undetermined and their
standard errors are immeasurable. If Multi-co-linearity is less than perfect, the regression
coefficients, although determinate, possess large standard errors, which mean the coefficients
cannot be estimated with great precision or accuracy (Gujarati 2003). A serious problem for
Multi-co-linearity is occurs if the correlation is about 0.8 or larger (Gujarati 2003). The Multi-co-
linearity of the explanatory variables are below 0.40 and it can be confident to say there is no
significant Multi co-linearity since any of them are not above the conventional 80 percent (Annex
2). The variance inflation factor (VIF) also was used to test the degree of multi co-linearity
among the independent variables for continuous variables. All the tested variable were found to
be VIF of less than 10 indicating no multi co-linearity problem because according to the rule of
thumb a VIF greater than 10 indicates trouble (annex 1).
Pseudo-R-squared (R2): The conventional measure of goodness of fit, R
2, is not particularly
meaningful in binary logistic regression models. But measures similar to R2, called pseudo R
2 are
available Gujarati, D. N., (2004). The higher the pseudo-R-squared (R2) statistics, the better the
model fits our data. The pseudo-R-squared (R2) statistics for MSE‟s performance in terms of
profit and employment is high enough 0.8729(87.29%) and 0.7637 (76.37%) respectively.
The Hosmer-Lemeshow statistic: measures the goodness-of-fit by creating 10 ordered groups of
subjects and then compares the number actually in each group (observed) to the number predicted
by the logistic regression model (predicted). Thus, the test statistic is a chi-square statistic with a
64
desirable outcome of non-significance, indicating that the model prediction does not significantly
differ from the observed. The p-value of the Hosmer-Lemeshow test is 0.9(Chi-square = 0.45 and
df = 8) for performance of MSEs in terms of profit and the p-value of the Hosmer-Lemeshow test
is 0.79(Chi-square = 4.64 and df = 8) for in terms of employment showing insignificance p-value
that we fail to reject the null hypothesis that there is no difference between observed and
predicted values, implying that the model adequately fits the data.
Model misspecification Test: it basically checks whether more variables are needed in the model
by looking the significance of hatsq. The null hypothesis is that there is no specification error. If
the p-value of _hatsq is not significant (P=0.393) for performance in terms of profit and
(p=0.153) for performance in terms of employment then the null is accepted and conclude that
the model is correctly specified (See annex 3).
Homoscedasticity: One way to detect heteroscedasticity is the Breusch-Pagan test. The null
hypothesis is that residuals are homoscedastic. According to the STATA 15 result (P= 0.2725)
for in terms of profit and (p=0.50) for in terms of employment the null was accepted at 95% and
concluded that residuals are homogeneous (See annex 4).
4.5.2. Determinants of MSE’s performance in terms of profit
Among 13 explanatory variables, 10 variables were found to be significant in determining
probability of MSE‟s performance in terms of profit (Table 21). These variables include age of
the operator (AGE), Education level of MSE owners (EDL), amount of initial capital (AIC), age
of the enterprise establishment (AOE), access to technology (ATY), access to market (AM),
access to training (AT), market competition problem (MKTC), government policy and regulation
problems (GPR) and access to raw materials (ARM).
65
Table 4.22 Stata Output of the logistic regression model (profit)
Variable Coef. Std. Err. Sig. Odd ratio Marginal effect
AGE 0.201 0.102 0.049** 1.223 0.005
GEN 0.452 1.203 0.707 1.571 0.011
EDL(2) 3.294 1.552 0.034** 26.939 0.102
EDL(3) 3.640 2.161 0.092* 38.060 0.113
AOE 0.612 0.265 0.021** 1.843 0.015
AIC 0.008 0.031 0.01*** 1.079 0.002
Auditing 1.892 1.840 0.304 6.634 0.038
ARM (yes) 3.693 1.364 0.007*** 40.159 0.131
AT (no) -4.722 1.787 0.008*** 0.0089 -0.186
ATY (yes) 2.003 1.154 0.083* 7.391 0.053
AM (yes) 2.331 1.146 0.042** 10.294 0.077
MKTC (no) 3.331 1.503 0.027** 27.960 0.118
GPR (no) 2.945 1.565 0.06** 19.013 0.092
FP -0.396 4.760 0.934 0.673 -0.001
_cons -22.731 9.180 0.013 1.34e-10
Number of obs = 181
LR chi2(14) = 213.74
Prob > chi2 = 0.0000
Log likelihood = -15.566
Pseudo R2 = 0.873
Source: Survey result, 2021. *, ** & *** indicates significant at 10%, 5% & 1%.
4.5.3 Interpretation of Econometric Results
Education level of MSE owners (EDL): Education was found to positively and significantly
influence the probability of MSE‟s performance in terms of profit at less than 5% significance
level. The marginal effect for the variable education EDL (2) is 0.102 which indicates that
keeping the influence of other factors constant; the probability of MSEs performance in terms of
profit for MSEs those owners or managers have an education level of grade 9-12 would increases
by 10.2% higher than those MSEs owners which have an education level of primary or less than
grade 8. Whereas the marginal effect for the variable education EDL (3) is 0.113 which indicates
that keeping the influence of other factors constant; the probability of MSEs performance in
terms of profit for MSEs which owners or managers have an education level of above grade 12
would increases by 11.26% higher than those MSEs owners which have an education level of
primary. Since the education level of owners influences the performance of MSEs, we accept the
66
null hypothesis and consistent with (Solomon, (2004); Abraham, (2013); Woldehanna, (2017),
and Giday, (2017).
Age of the operator (AGE): there is a statistically positive and significant relationship between
the age of the operator and the performance of MSEs at less than 5% significance level. The
marginal effect of the variable age is 0.005 which indicates a one-year increase in the age of the
operator, the probability of performance of MSEs in terms of profit increases by 0.5%, other
factors kept constant. The finding of the econometric result implies that older operators tend to be
more successful than younger ones. The fact that older operators are more likely to have a long
business related skill history which is vital for performing their business effectively and more
likely to have assets that can be a guarantee for insufficient startup capital for starting a new
business. Additionally, older operators can have management experience in managing their
business effectively. This finding is supported by the findings of the research carried out by
Abraham (2013) but inconsistence with (Solomon, (2004); Mohammed et el, (2013); Tassew et
al, (2015), and (Araya, 2018)).
Age of the enterprise (AOE): is one of the determinant internal factors that affect an enterprise's
performance. The marginal effect of this variable has a positive sign and is statistically significant
at less than a 5% level of significance. The coefficient implies a one-year increase in the age of
the enterprise increases the log of the odds that the enterprise is growing by 1.5%, keeping the
other variables constant. The fact is as the firm remains in the market there is a possibility to
learn its own abilities, benefits, and opportunities that help him to develop experiences in
productivity, management, marketing skill, and others than newly established enterprises which
fail to have the mentioned benefits and abilities. The hypothesized statement that states a
significant relationship exists between the age of the firm and the performance of MSEs is
accepted with positive significance. Abraham, (2013), Gebreeyesus, (2014), and Francis and
Dedan, (2015), have also found similar findings.
Amount of initial capital (AIC): the amount of initial capital at startup of the enterprise shows
statistically positive and significant at a 1% significant level. The marginal effect for AIC is
0.002 indicates a 1 Birr increase in initial capital; the probability of performance of MSEs as
measured by profits increases by 0.2%. Sufficiency of initial capital in the time of starting a
business was found to be a relevant factor to affect the performance of MSEs in Enarj Enawga
67
Woreda. The null hypothesis which states that there is a positive and significant relationship
between adequate amount of initial capital and performance of MSEs is accepted. This finding is
similar to Mohammed, (2013) and Abraham, (2013).
Access to training of MSE owners (AT): Econometric result of this study shows that there is
statistically significant relationship between the performances of MSEs in terms of profit and
access to training at a 1% significance level. The marginal effect of this variable is -0.1864
indicating that the probability of performance of MSEs in terms of profit that didn‟t perceived the
acquired training decreases by 18.64 percent as compared to MSEs that had get training access.
Therefore we accept the null hypothesis and this finding is consistence with (Dayavanda(2014),
2014), and (Town, 2016), and inconsistence with Andualem, (2013).
Access to market: table 4.21 shows a statistical significance of positive relationship between
access to market and performance of MSEs in terms of profit at less than 1% significance level.
The marginal effect of (0.077) shows the probability of performance of MSEs in terms of profit
increases by 7.7 percent for those MSEs which had market access as compared to those MSEs
that did not have market access, all other factors kept constant. This indicates the availability of
market access and market linkage positively affects performance of MSEs. When MSEs have the
opportunity of market accessibility for their products, there is a chance to grow and perform
better which can help them to transform in to small and medium ones successfully. The stated
hypothesis of positive relationship exists between access to market and performance of MSEs is
accepted. This result is found to be similar to the findings of Abraham (2013) and (Giday, 2017).
Access to raw material (ARM): there is statistical significance of positive relationship between
access to market and performance of MSEs in terms of profit at less than 1% significance level.
The marginal effect of (0.131) shows the probability of performance of MSEs in terms of profit
increases by 13.1 percent for those MSEs which have access to raw material than that did not
have access to raw material, all other factors kept constant. The null hypothesis is accepted and
consistent with the finding of (Mezgebe, 2012) and Fetene, (2017).
Market competition problems (MKTC): is found to be positively and significantly affecting
the performance of MSEs at less than 5% significance level. The marginal effect of (0.118)
shows the probability of performance of MSEs in terms of profit increases by 11.8 percent for
68
those MSEs which have not encounter market competition problems than for those MSEs which
have market competition problems, all other factors kept constant. The fact that is as the number
of firms increases from time to time the competition among them becomes intense hence the
price and demand declines as buyers have more choices and this leads to a decline in profit. This
finding is similar to the finding of (Mezgebe, 2012).
Access to technology (ATY): there is statistical significance of positive relationship between
access to technology and performance of MSEs in terms of profit at less than 10% significance
level. The use of modern technology can help enterprises to improve their performance of
efficiency through greater production and is a source of profits for MSEs by reducing their costs
and broaden market share (Asma Benzazoua, 2015). The marginal effect of (0.053) shows the
probability of performance of MSEs in terms of profit increases by 5.3 percent for those MSEs
which have access to technology than that did not have access to technology, all other factors
kept constant. Performance level of MSEs in generating profits can be improved through the
application of modern technology and help them to be competitive in terms of quality and prices
against their rivals in the study area in particular. The null hypothesis which was stated as
positive and significant relationship exists between use of modern technology and performance of
MSEs in terms of profit is accepted. The study is consistence with the finding of (Mezgebe,
2012) and (Araya, 2018).
Government policy regulation problems (GPR): The econometric result table 4.21 indicates
that there is a statistically significant relationship between the performance of MSEs and
government support policy to MSE‟s related rules and regulations at less than 10% significance
level. The marginal effect (0.092) shows the probability of performance of MSEs in terms of
profit increases by 9.2 percent for those MSEs which have not encounter government policy and
regulation problems than that have government policy and regulation related problems. These
problems are related to credit, training, removal of trade barriers, lowering tax rate at startup, and
creating conducive environment for promoting MSE‟s performance. An increase in government
support and subsidies given to MSEs leads to an increase in the performance of MSEs. This
finding is similar to (Abera, 2012), Mohammed et al (2013), and Mbugua, (2014).
69
4.5.4 Determinants of MSE’s performance in terms of Employment
Table 4.23 Output of the model (employment)
Variable Coef. Std. Err. Sig. Odd ratio Marginal effect
AGE -.0372 0.059 0.49 0.963 -0.002
GEN 1.307 0.960 0.173 3.067 0.072
EDL (2) -0.684 0.831 0.411 0.505 -0.041
EDL (3) 1.324 0.958 0.167 3.757 0.072
AOE -0.086 0.102 0.399 0.917 -0.005
AC (yes) 3.479 0.860 0.00*** 32.442 0.238
ATY (yes) -4.215 0.947 0.00*** 0.015 -0.300
AIP (no) 2.625 0.821 0.001*** 13.798 0.183
GPR (No) 1.672 0.829 0.044** 5.321 0.104
experience (have) 3.237 0.894 0.00*** 25.457 0.200
Workspace (rented) -1.595 0.870 0.067* 0.203 -0.096
FS -0.311 0.805 0.699 0.732 -0.017
FP 0.207 1.981 0.917 1.230 0.011
_cons 0.142 3.417 0.967 1.153
Number of obs = 181
LR chi2(14) = 191.59
Prob > chi2 = 0.0000
Log likelihood = -29.64
Pseudo R2 = 0.7637
Source: Survey result, 2021. *, ** & *** indicates significant at 10% 5% & 1%.
The result of logistic regression of employment growth shows that among 12 explanatory
variables 6 variables access to technology (ATY), government policy and regulation problems
(GPR), workspace, access to infrastructural problems (AIP), the experience of the owner or
manager before this business and access to credit (AC) were found significant in determining the
probability of MSEs employment growth with less than 5% of significance level as table 4.21
above shows.
70
4.5.5. Interpretation of the Results
Access to technology (ATY): machine or technology can be either a substitute or a complement
to human labor. A machine can substitute for human labor when it has the ability to produce
more than the worker for the same cost (such as his or her wages), or as much as the worker for a
fraction of the price. Technology has always fueled economic growth, improved standards of
living, and opened up avenues to new and better kinds of work but with less and very qualified
persons which reduce employment rate (Saunders, 2017). The marginal effect of (0.3) shows the
probability of performance of MSEs in terms of employment decreases by 30 percent for those
MSEs which have using modern technology than for those MSEs which have not used
technology, all other factors kept constant. This is consistent with the founding of (Tumidado,
2019).
Government policy and regulations (GPR): It is found to be positively and significantly
affecting the performance of MSEs at less than 5% significance level. Government policy which
encourages expansion of MSEs and provision of support in the form of credit provision, technical
training, removal of trade barriers, lowering tax rate at startup, and creating conducive
environment for promoting MSE‟s performance can contribute to employment opportunities and
poverty reduction. The marginal effect indicates that the probability of performance of MSEs in
terms of employment increases by 10.05 percent for those MSEs which have not faced this
problem than for those MSEs which have government policy and regulations problems, all other
factors kept constant. The government regulation factors are high tax levied on enterprises, low
governmental support; like training, working premises and information deliverance and high
bureaucracy on registration and licensing.
Workspace: is found to be negatively and significantly affecting the performance of MSEs at
less than 10% significance level. The marginal effect of (0.096) shows the probability of
performance of MSEs in terms of employment decreases by 9.6 percent for those MSEs which
have not to own or rent their workspace than for those MSEs which have own their work
premises, all other factors kept constant. This study is consistent with the founding of (Mezgebe,
2012). The hull hypothesis is accepted.
Experience: is also affects positively and significantly affecting the probability of performance
of MSEs at 1% significance level. The marginal effect of the variable prior experience is 0.200
71
and implies that the probability of performance of MSEs in terms of employment increases by
20% MSEs with managers who have prior experience than those MSE‟s managers who do not
have prior experience. Therefore, we accept the null hypothesis the prior experience of MSE‟s
managers increase the performance of enterprises. This is consistent with the founding of Tariku,
(2017) and (Giday, 2017).
Access to Infrastructural problem (AIP): is found positively and significantly affecting the
probability of performance of MSEs at 1% significance level. The marginal effect of the variable
is 0.183 implies that the probability of performance of MSEs in terms of employment increases
by 18.3 percent for those MSEs which not have infrastructural problems access when compared
to MESs that have infrastructural problems. These infrastructural problems are insufficient and
interruption of power, insufficient and interruption of water supply, insufficient and interruption
of communication service, lack of sufficient and quick transportation (road). This study is
consistent with the founding‟s of Abraham, (2013) and Tariku, (2019).
Access to credit (AC): is found positively and significantly affecting the probability of
performance of MSEs at a 1% significance level. The marginal effect implies that the probability
of performance of MSEs in terms of employment increases by 23.8 percent for those MSEs who
have to get access to credit than MESs who have not to get credit access. This is true that credit
requirements at startup, growth, and maturity stages is significant factor in determining
performance of MSEs that calls for intervention of government and other concerned body in the
area. Therefore, having a better access to credit enables the firm greater production which
increases performance of MSEs in terms of employment and can create a significant difference
between those who have credit access and those who do not have. Thus, the null hypothesis
which was stated existence of positive and significant relationship between access to credit and
performance of MSEs was accepted. This finding is similar to the research findings carried out by
(Mbugua, 2014), and (Afande, 2015).
72
CHAPTER FIVE
5. CONCLUSIONS AND RECOMMENDATIONS
5.1 Conclusions
In light of this study findings on determinants of MSEs performance in Enarj Enawga Woreda
are based on internal (age of the operator, age of the enterprise, family size, education level,
gender, and amount of initial capital) and external (access to market, access to credit, access to
training, level of market competition, government policies and regulation, workspace, access to
raw material, and use of modern technology) factors to investigate performance and major
determinants of MSEs.
The descriptive result of the study shows that the majority of the operators of MSEs were male in
the age group of 25-54 ages indicating the productive unit of the labor force. Most of the
operators have been attained their educational qualifications of high school and elementary. This
implies that the majority of the operators are less in higher academic qualification hence they are
generally less educated lacking the skills and knowledge that comes from higher formal
education that is significant in managing and enhancing their business performance effectively. It
was also observed that most of the enterprises have been in operation for about the last 9 years
and when they have been started their initial capital was Birr 12000 and less on average. This
implies that MSEs activities and establishments are a recent one and given the limited startup
capital achieving the operator's plan and capacity in generating profits and creating employment
would be difficult as they were faced the problem of insufficient startup capital.
The problem of poverty and unemployment can be addressed in the long run if the performance
of MSEs both in terms of profit and employment are improved in the study area since they are the
source of employment and tools of poverty alleviation. Power interruption, unfair tax assessment,
limited government support, limited access to credit, high cost of rent, shortage of capital, etc.
were found to be the top constraints impeding the performance of MSEs not to generate adequate
profit, create job opportunity, and poverty alleviation to the majority of the enterprises. The study
also confirmed that the majority of MSEs performance in terms of profit and employment was
59.12% and 50.83% respectively was found to be good because total sales exceed total costs
implying operating at profits and the natural logarithm of current capital minus natural logarithm
73
of initial capital divided by age of the enterprise exceeds zero at good performance in terms of
employment. With regard to government policies and regulations, the operators reported that they
have been adversely affected by a high tax burden, lack of working premises, limited access to
training, and other related factors.
The finding also indicates that most of MSEs have been affected by high and unfair market
competition. The existence of high and unfair competition may have its own impact on firms who
produce and sell similar products on their profits and survival. Another finding worthy of
attention is the issue of access to training. Accordingly, the majority of the operators have been
trained in the skills needed to manage their business effectively before and after started a business
even if there is a lack of quality training. Furthermore with regard to the use of modern
technological capacity, almost above half of the operators of MSEs use technology-related
equipment and machinery as well as service deliveries in their business operation because of
shortage of money and lack of skills to choose and how to use appropriate technology-related
constraints. The study also confirmed that many MSEs did not have access to market and market
linkage with other institutions for their product.
In general, the econometric result shows that variables include Education level of MSE owners
(EDL), amount of initial capital (AIC), age of the enterprise establishment (AOE), age of the
owner or managers (AGE), access to market (AM), access to training (AT), market competition
problem (MKTC), government policy and regulation problems (GPR), access to technology
(ATY) and access to raw materials (ARM) were found significant in determining the probability
of performance of MSEs in terms of profit. However, the gender of the operator and audited
financial statement was not found to be statistically significant to affect the performance of MSEs
in terms of profit in Enarj Enawga Woreda. The result of logistic regression of employment
growth shows that among 12 explanatory variables 6 variables access to technology (ATY),
government policy and regulation problems (GPR), workspace, the experience of the operator,
access to infrastructural problems (AIP), access to credit (AC) were found significant in
determining the probability of performance of MSEs in terms of employment but age of the
operator, age of the enterprise, future plan, and education level were not found to be statistically
significant to affect the performance of MSEs in terms of employment.
74
5.2 Recommendations
Having identified the major determinants of MSE‟s performance in Enarj Enawga Woreda, it is
possible to forward some policy implications that the government, NGOs, MSE operators, and
other concerned bodies are responsible for further improvement of MSEs in the study area.
It has been found that MSEs have been faced problems with a lack of infrastructural
facilities. Therefore, the study recommends that improved provision and expansion of the
necessary infrastructural facility such as uninterrupted power supply, water, and the road
should need special attention.
The amount of initial capital at startup was found to be insufficient. The finding also
supports that the adequacy of initial capital affects the performance of MSEs positively.
Therefore, government, financial institutions, donors, and other interested parties should
have to make efforts of making a conducive climate to provide loans at startup to achieve
the expected performance and survival of MSEs.
The study noted that government policy and regulation were not found in favor of MSEs.
Moreover, it was found to be statistically significant to affect the performance of SMEs.
As the legal and regulatory framework plays a significant role in improving the smooth
operation of MSEs, the government should encourage and simplify the government policy
and regulation-related factors impeding the performance of MSEs. Therefore, the study
recommends that policymakers should strengthen the government policies and regulatory
framework in favor of MSEs to create a conducive climate, special priority assistance, and
MSEs based policies should be designed to promote the performance of MSEs in the
study area.
Access to training was found to be a key factor to influence the performance of SMEs.
Therefore, the operators, government, and other concerned bodies should make efforts to
provide as packages in any TVET programs and short-term quality training basis to
upgrade their entrepreneurial skills whenever operators of MSEs seek supports.
The finding reveals that most of the operators of MSEs have used personal savings and
from relatives when they faced the problem of insufficient startup capital in the time of
starting their business due to lack of collateral, loan insufficiency, complex loan
procedure, high-interest rate. The study, therefore, recommends banks, microfinance
75
institutions, governments, and other donor bodies should work together hand in hand and
improve their approach.
Access to the market is found to be a significant factor related to the performance of
MSEs. Hence, the government, operators of MSEs, and other concerned bodies should
have to facilitate the creation of sustainable market linkage and access to local and
regional markets to increase MSEs‟ competitiveness in terms of price, quality, and supply
to achieve the performance of MSEs in the study area. The trade and market development
has to office organizes and facilitates marketing promotional programs like trade fairs and
bazaars this will give them an opportunity to display their respective products so as to
expand their market share, exchange experiences, knowledge transfer as to how to utilize
marketing instruments, and so on.
Concerning the use of modern technological capacity is found statistically significant to
influence the performance of MSE. The respondents have also reported that their inability
to use technology-related equipment in their business was due to a lack of skills and
shortage of money. Therefore the study recommends that creating a conducive climate for
improvement of skills and education, and facilitating credit provision would be important
to address the technological-related factor.
Government in general and the MSE development agency in particular need to solve the
credit, infrastructure, supply, and market access problems in collaboration with MFI,
banks, Ethiopian Electric Power Corporation, suppliers, and other organizations.
76
References Abdissa, G., & Fitwi, T. (2016). Factors Affecting Performance of Micro and Small Enterprises in South
West Ethiopia: The Case of Bench Maji, Sheka, and Kefa Zones. 16(10).
Abebe, T. (2011). Analysis of the Success Factors of Micro and Small Business Enterprises in Addis
Ababa. 69.
Abeka, E. (2011). Networks, Micro Small Enterprises (MSE‟S) and Performance: the Case of Kenya.
African Research Review, 5(6), 172–187. https://doi.org/10.4314/afrrev.v5i6.15
Abera, A. (2012). Factors Affecting the Performance of Micro and Small Enterprises in Arada and Lideta
Sub-Cities , Addis Ababa. Journal of Accounting and Finance, 2(4), 15–27.
Adem, M., Ma, H. W., & Beyene, D. (2014). Constraints and Growth Potentials of Micro and Small
Enterprises : Case from Mekelle City. 4(12), 1–7.
Afande, F. O. (2015). Factors Influencing Growth of Small and Microenterprises in Nairobi Central
Business District. Journal of Poverty, Investment and Development, 9(0), 104–137.
https://www.iiste.org/Journals/index.php/JPID/article/view/20383/0
Affecting, F., Performance, B., & Enterprise, M. S. (2017). St. mary’s university.
Affecting, F., Performance, T. H. E., Women, O. F., Enterprises, E., Case, T. H. E., & Dessie, O. F.
(2010). No Title.
Aktar, S. (2016). Factors Influencing Entrepreneurs‟ Business Performance in Small and Medium
Businesses: Comparative Analysis Between Demographic and Environmental Factors in Bangladesh.
Journal of Business and Technology (Dhaka), 10(2), 1–17. https://doi.org/10.3329/jbt.v10i2.29464
Alemayehu, D., & Gecho, Y. (2016). Determinants of Micro and Small Enterprises Growth : The Case of
Durame Town , Kembata Tembaro Zone , Southern Nations and Nationalities and Peoples Region ,.
5(5), 161–175. https://doi.org/10.11648/j.ijber.20160505.15
Alene, E. T. (2020). Determinants that influence the performance of women entrepreneurs in micro and
small enterprises in Ethiopia. Journal of Innovation and Entrepreneurship, 9(1).
https://doi.org/10.1186/s13731-020-00132-6
Amha, W. (2015). Growth of Youth-owned MSEs in Ethiopia : Ethiopian Journal of Economics
Araya, S. T. (2018). Growth Determinants of Micro and Small Enterprises: Empirical Evidence from
Eastern Zone, Tigray Regional State of Ethiopia. International Journal of Science and Research
(IJSR), 7(7), 286–292. https://doi.org/10.21275/ART20183609
Assefa, B., Zerfu, A., & Tekle, B. (2014). IDENTIFYING KEY SUCCESS FACTORS AND
CONSTRAINTS IN ETHIOPIA ’ S MSE DEVELOPMENT : AN EXPLORATORY RESEARCH By
Berihu Assefa , Abebaw Zerfu , and Addis Ababa , Ethiopia October 2014 (Issue October).
https://doi.org/10.13140/RG.2.1.1572.4963
Batisa, S. (2019). Determinants of Youth Based Micro and Small Enterprises Growth in Dawro Zone A
Case of Mareka Wereda. 6(12), 27–37.
Berkenesh-Negi. (2013). Determinants of Micro and Small Eterminants of Micro and Small Scale
Enterprises’ Performance Scale Enterprises’ Performance in Burayu Town.
77
Bouazza, A. B., Ardjouman, D., & Abada, O. (2015). Establishing the Factors Affecting the Growth of
Small and Medium-sized Enterprises in Algeria. American International Journal of Social Science,
4(2), 101–115.
Bowale, K. E., & Ilesanmi, A. O. (2013). Determinants of Factors Influencing Capacity of Small and
Medium Enterprises (SMEs) in Employment Creation in Lagos State, Nigeria. International Journal
of Financial Research, 5(2). https://doi.org/10.5430/ijfr.v5n2p133
Byishimo, P. (2018). ASSESSMENT OF CLIMATE CHANGE IMPACTS ON CROP YIELDS AND
FARMERS‟ ADAPTATION MEASURES: a case of Rwanda Patrick. Journal of Chemical
Information and Modeling, 53(9), 1689–1699.
Caroline, I. (2016). FACTORS AFFECTING THE GROWTH OF YOUTH MICRO ENTERPRISES.
Carton, R. . (2004). MEASURING ORGANIZATIONAL PERFORMANCE: AN EXPLORATORY STUDY
by ROBERT B. CARTON (Under the Direction of Charles W. Hofer). 11(2), 2–4.
Cherkos, T., Zegeye, M., Tilahun, S., & Avvari, M. (2018). Examining significant factors in micro and
small enterprises performance: case study in Amhara region, Ethiopia. Journal of Industrial
Engineering International, 14(2), 227–239. https://doi.org/10.1007/s40092-017-0221-y
Chong, G. H. (2008). Measuring performance of small-and-medium sized enterprises: the grounded theory
approach. Journal of Business and Public Affairs, 2(1), 1–10.
Chowdhury, M. (2017). FACTORS AFFECTING ACCESS TO FINANCE OF SMALL AND MEDIUM
ENTERPRISES (SMEs) OF BANGLADESH. The USV Annals of Economics and Public
Administration, 17(2(26)), 55–68.
City, A. A., & Wereda, S. (2020). working paper Determinants of Financial Inclusion for Youth
Entrepreneurs : Evidence from Addis Ababa City and Shirka Wereda , Ethiopia Degife Ketema Abel
Tewolde. April.
College of Business & Economics Department of public Administration & Development Management
Performance of Micro and Small Enterprises and their Role in Enhancing Local Economic
Development : A Case Study in Gullele Sub City of Addis Ababa College of Bus. (2012).
Collis, J., & Jarvis, R. (2002). Financial information and the management of small private companies.
Journal of Small Business and Enterprise Development, 9(2), 100–110.
https://doi.org/10.1108/14626000210427357
CSA. (2007). Compilation of Economic Statistics in Ethiopia. July, 1–10.
Degefu, D. G. (2018). Factors That Determine the Growth of Micro and Small Enterprises : In the Case of
Hawassa City , Ethiopia. 185–200. https://doi.org/10.4236/ib.2018.104011
Dinh, C. N. (2011). P ERFORMANCE OF S MALL AND M EDIUM E NTERPRISES AND THE I MPACT
OF E NVIRONMENTAL V ARIABLES : January, 1–259.
Dobenecker, G. (2010). Business development. EMPA Activities, 2010, 50–51.
https://doi.org/10.1201/9781351216623-5
Drbie, M., & Kassahun, T. (2013). Deterrents to the Success of Micro and Small Enterprises in Akaki-
Kality Sub-City. Journal of Business and Administrative Studies, 5(2), 1–33.
78
El-hagrassey, G. M. (2002). E T & P and New Venture Performance : 1–28.
El-hamidi, F., & Baslevent, C. (2003). MICRO AND SMALL ENTERPRISES ( MSES ) IN URBAN
ECONOMIES : A COMPARATIVE STUDY OF EGYPT AND TURKEY AT THE PROVINCE
LEVEL.
Engida, E., Dereje, M., Worku, I., Yifredew, S., & Yimer, F. (2017). The role of micro and small
enterprises in reducing unemployment and poverty in Ethiopia. Pep Plicy Brief.
Evans, D. S. (2012). The Relationship Between Firm Growth , Size , and Age : Estimates for 100
Manufacturing Industries Author ( s ): David S . Evans Reviewed work ( s ): Source : The Journal of
Industrial Economics , Vol . 35 , No . 4 , The Empirical Renaissance in Industria. 35(4), 567–581.
Federal Democratic Republic of Ethiopia. (2016). Micro and small enterprise development strategy Addis
Ababa. April.
Garmarodi, G. (2014). A Research Proposal for Partial Fulfillment of MSc Degree in Health Education
and Promotion Submitted to Tehran University of Medical Sciences , International Campus ( TUMS-
IC ) office of the Deputy for Research Affairs Psychosocial and Behavioural Determ.
Gebreeyesus, M. (2007a). Growth of Micro-Enterprises: Empirical evidence from Ethiopia. Ethiopian
Development Research Institute (EDRI), March 2007, 1–21.
http://www.csae.ox.ac.uk/conferences/2007-EDiA-LaWBiDC/papers/275-Gebreeyesus.pdf
Gebreeyesus, M. (2007b). Growth of Micro-Enterprises: Empirical evidence from Ethiopia. Ethiopian
Development Research Institute (EDRI), May, 1–21. http://www.csae.ox.ac.uk/conferences/2007-
EDiA-LaWBiDC/papers/275-Gebreeyesus.pdf
Gebreeyesus, M. (2014). Research Paper No . 2009 / 51 Innovation and Microenterprises Growth in
Ethiopia Mulu Gebreeyesus *. May.
Gebreeyesus, M., Ambachew, A., Getahun, T., Assefa, B., Abebe, G., Hassen, S., & Medhin, H. (2018).
Main Features of Micro and Small Manufacturing Enterprises in Ethiopia: Baseline Survey Report
(Issue August).
Gebrehiwot, B. A. (2015). IDENTIFYING KEY SUCCESS FACTORS AND CONSTRAINTS IN
ETHIOPIA ’ S MSE IDENTIFYING KEY SUCCESS FACTORS AND CONSTRAINTS IN ETHIOPIA
’ S MSE DEVELOPMENT : AN EXPLORATORY RESEARCH By Berihu Assefa , Abebaw Zerfu ,
and Biruk Tekle Addis Ababa , Ethiopia October (Issue May).
https://doi.org/10.13140/RG.2.1.1572.4963
Gebremariam, F. M. (2017). Factors Affecting the Growth of Women-Operated Micro and Small
Enterprises (MSEs) in Ethiopia. Üniversitepark Bülten, 6(1), 56–66.
https://doi.org/10.22521/unibulletin.2017.61.5
Geleta, D. (2013). Socio-economic Contributions of Micro and Small Enterprises: The Case of Jimma
City. Science, Technology and Arts Research Journal, 2(2), 123.
https://doi.org/10.4314/star.v2i2.98898
GEM. (2018). Global Entrepreneurship Monitor 2017/18. In Global Entrepreneurship Monitor.
http://www.gemconsortium.org/report/50012
Giday, B. (2017). DETERMINANTS PERFORMANCE OF IN AND OF ENTERPRISES WOREDA
REGIONAL STATE , ETHIOPIA.
79
Ginbite, A. H. (2017). FACTORS DETERMINING THE FINANCIAL PERFORMANCE OF MICRO AND
SMALL ENTERPRISEs IN ASELLA TOWN ADMINISTRATION. June.
Goshu, F., & Mba, F. (2016). Determinants of Access to Credit and Credit Source Choice by Micro ,
Small and Medium Enterprises in Nekemte , Ethiopia. 28, 11–27.
Hagos, H., Gebremichael, A., & Getie, A. (2014). Determinants of Micro and Small Enterprises Growth in
Rural Area : Evidence from Feresmay Town. Journal of Economics and Sustainable Development,
5(19), 68–79.
ILO. (2003). Statistics on the employment situation of people with disabilities : A Compendium of
national methodologies. In Labour.
ILO. (2013). Measurement of the Informal Economy. The Informal Economy and Decent Work: A Policy
Resource Guide, 8.
Jarvis, R., Kitching, J., Curran, J., & Lightfoot, G. (1996). The Financial Management of Small Firms: An
Alternative Perspective. In ACCA research report (Vol. 49, pp. 1–47).
Joshi, V., & Mihreteab, G. (2013). Critical Success Factors of Micro & Small Enterprises in Ethiopia: A
Review. International Journal of Science and Research, 5(10), 2319–7064.
https://doi.org/10.21275/ART20162223
Kinyua, A. N. (2014). Factors Affecting the Performance of Small and Medium Enterprises in the Jua Kali
Sector In Nakuru Town, Kenya. IOSR Journal of Business and Management, 16(1), 80–93.
https://doi.org/10.9790/487x-16148093
Kongolo, M. (2010). Job creation versus job shedding and the role of SMEs in economic development.
4(11), 2288–2295.
Kotey, B., & Meredith, G. G. (1997). Relationships among owner/manager personal values, business
strategies, and enterprise performance. Journal of Small Business Management, 35(2), 37–64.
KS, A., & DB, D. (2016). Determinants of Micro and Small Enterprises Success: The Case of Ambo
Town, Ethiopia. Journal of Tourism & Hospitality, 5(3). https://doi.org/10.4172/2167-0269.1000214
Kurnia, D. (2017). Опыт аудита обеспечения качества и безопасности медицинской деятельности в
медицинской организации по разделу «Эпидемиологическая безопасностьNo Title. Вестник
Росздравнадзора, 4, 9–15.
Length, F. (2018). Assessment of the factors affecting the performance of micro and small scale
enterprise : The case of Wolkite. 10(June), 192–199. https://doi.org/10.5897/JDAE2017.0899
Leza, T., Rajaan, S., & Kuma, B. (2016). Determinant of Technical Efficiencies of Micro and Small
Enterprises in Wolaita Zone, Ethiopia. Journal of Economics and Sustainable Development
Www.Iiste.Org ISSN, 7(21), 70–81. www.iiste.org
Mbugua, F., & Moronge, M. (2016). Effects of County Government Policies on the Performance of Small
and Medium Enterprise in Kenya : a Case of Kiambu County Effects of County Government Policies
on the Performance of Small and Medium Enterprise in Kenya : a Case of Kiambu County. Strategic
Journal of Business & Change Management, 3(2), 756–785.
80
Mbugua, L. M., Harris, P., Holt, G. D., & Olomolaiye, P. O. (1999). A Framework for Determining
Critical Success Factors Influencing Construction Business Performance. Proceedings of the 15th
Annual ARCOM Conference, 1(OCTOBER 1999), 255–264.
Meressa, H. A. (2020). Growth of micro and small scale enterprises and its driving factors: empirical
evidence from entrepreneurs in emerging region of Ethiopia. Journal of Innovation and
Entrepreneurship, 9(1). https://doi.org/10.1186/s13731-020-00121-9
Mersha, D. (2017). Determinants of Access to Formal Financial Sources of Micro and Small Enterprises
(MSEs) in West Oromia Region, Ethiopia. International Journal of Business and Economics
Research, 6(5), 100. https://doi.org/10.11648/j.ijber.20170605.13
Mezgebe, W. (2012). Problems of Micro and Small Enterprises i n Addis Ababa : The Case of Kirkos ,
Kolfe , a nd Yeka Sub Cities. May.
Micro, C. O. F., In, S. E., Town, W., Zone, S., Snnp, O. F., & State, R. (2018). School of Graduate Studies
College of Development Studies Challenges of Micro and Small Enterprises in.
Mohajan, H. K. (2017). Aspects of mathematical economics, social choice and game theory: Jamal Nazrul
Islam Research Centre for Mathematical Research. Journal of Economic Development, Environment
and People, March, 2009–2010.
Mozumdar, L., Hagelaar, G., van der Velde, G., & Omta, S. W. F. (2020). Determinants of the Business
Performance of Women Entrepreneurs in the Developing World Context. J — Multidisciplinary
Scientific Journal, 3(2), 215–235. https://doi.org/10.3390/j3020017
Mulugeta, H. (2014). the Performance of Micro and Small Scale Enterprises the Case of Yeka Sub-
City,Addis Ababa.
Mutairi, A. AL, Naser, K., & Fayez, F. (2017). Factors Determine Small Businesses (SBS) Success in
Kuwait. Asian Economic and Financial Review, 7(9), 929–942.
https://doi.org/10.18488/journal.aefr.2017.79.929.942
Nichter, S., & Goldmark, L. (2009). Small Firm Growth in Developing Countries. World Development,
37(9), 1453–1464. https://doi.org/10.1016/j.worlddev.2009.01.013
NPC. (2015). Growth and transformation Plan II- (2016/17-2019/20), Volume II. Ministry of Finance and
Economic Development, I(GTP II), 236.
Of, Assessment, Practices, M., Ethio-telecom, T. H. E. C. O. F., Office, H., Andarge, T., Submitted, T.,
Fulfillment, P., & Management, G. (2019). School of Graduate Studies School of Graduate Studies.
August, 1–29.
Of, Assessments, Challenges, M., & Micro, O. O. F. (2019). COLLEGE OF DEVELOPMENT STUDIES
CENTER FOR REGIONAL & LOCAL DEVELOPMENT STUDIES By : Hiyaw Hizkel ADVISOR :
Andualem Goshu ( PhD ).
Oppong, M., Owiredu, A., & Churchill, R. Q. (2014). Micro and Small Scale Enterprises Development in
Ghana. European Journal of Accounting Auditing and Finance Research, 2(6), 84–97.
Orkaido, K., & Mitiku, S. (2020). Determinants of Micro and Small Enterprises Performance in Karat
town , Determinants the of Micro and Small Enterprises Performance in Karat town , Konso ,
Ethiopa. June, 0–10.
81
Ozcan, Y. A., & Tone, K. (2014). Health Care Benchmarking and Performance Evaluation An Assessment
using Data Envelopment Analysis (DEA). In International Series in Operations Research &
Management Science.
Peter, P. W., & Munyithya, H. M. (2015). The Gender Factor Influence on Entrepreneurial Success in
Kitui County, Kenya. International Journal of Education and Research, 3(7), 13–32.
Politis, D., & Gabrielsson, J. (2009). Entrepreneurs‟ attitudes towards failure: An experiential learning
approach. International Journal of Entrepreneurial Behaviour and Research, 15(4), 364–383.
https://doi.org/10.1108/13552550910967921
Ranjith, J. G. S., & Banda, O. G. D. (2014). Determinants of Success of Small Business: A Survey-Based
Study in Kuliyapitiya Divisional Secretariat of Sri Lanka. International Journal of Business and
Social Research, 4(6), 38–50. https://doi.org/10.18533/ijbsr.v4i6.410
Reeg, C. (2015). Micro and Small Enterprises as Drivers for Job Creation and Decent Work.
Saunders, A. (2012). Technology’s impact on growth and employment.
School of Management Studies A Thesis on Factors Affecting the Performance of Micro and Small
Enterprises in kirkos Sub- city , Addis Ababa , Ethiopia Project work submitted to the Indira Gandhi
National Open University in By : Mehari W / Aregay. (2016).
Senzu, E. T., & Ndebugri, H. (2018). Examining business performance of micro, small and medium scale
enterprise through accounting records keeping; case study in Ghana. Researchgate.Net, 84022.
https://www.researchgate.net/profile/Emmanuel_Senzu/publication/322660987_Examining_business
_performance_of_micro_small_and_medium_scale_enterprise_through_accounting_records_keepin
g_case_study_in_Ghana/links/5a6730d90f7e9b76ea8d6b61/Examining-business-pe
Seyoum, A., Aragie, M., & Tadesse, D. (2014). Growth of Micro and Small Enterprises in Addis Ababa
City Administration: A Study on Selected Micro and Small Enterprise in Bole Sub City.
International Journal of Scientific and Research Publications, 6(1), 581–2250. www.ijsrp.org
Studies, D. C. (2015). Determinants of Growth and Diversification of Micro and Small Enterprises : The
Case of Dire Dawa , Ethiopia. 5(1), 61–76.
Suleiman, M. S., Neshamba, F., & Valero-Silva, N. (2016). Small Businesses as Vehicles for Job Creation
in North-West Nigeria. International Journal of Business and Economics Engineering, 10(6), 1997–
2001. schlar.waset.org/1307-6892/10004688
Tadesse, B. (2010). The Role of Micro and Small Enterprises in Employment Creation and Income
Generation A Survey Study of Mekelle City , Tigray Region , Ethiopia. June, 1–76.
Tambunan, T. (2011). MICRO , SMALL AND MEDIUM ENTERPRISES AND ECONOMIC GROWTH
Tulus Tambunan WORKING PAPER SERIES NO . 14 CENTER FOR INDUSTRY AND SME
STUDIES FACULTY OF ECONOMICS , UNIVERSITY OF TRISAKTI October 2006. 14, 1–25.
Tambunan, T. (2019). Recent evidence of the development of micro, small and medium enterprises in
Indonesia. Journal of Global Entrepreneurship Research, 9(1). https://doi.org/10.1186/s40497-018-
0140-4
Tarfasa, S., Ferede, T., Kebede, S., & Behailu, D. (2016). Determinants of growth of micro and small
enterprises (MSEs): Empirical evidence from Ethiopia. World Trade Institute, 1–29.
82
Tefera, K. T. (2019). Determinants of Access to Finance for Micro and Small Scale Enterprises in
Nekemte Town. Research Journal of Finance and Accounting, 10(19), 34–46.
https://doi.org/10.7176/rjfa/10-19-04
Tekalign Lemma Woldesilassie, & Venkata Mallikhajuna Kishan Ivatury. (2020). Key Determinant
Factors Affecting the Performance of Small and Medium Scale Manufacturing Enterprise: A Case
Study on West Shoa Zone, Oromia National Regional State, Ethiopia. International Journal of
Engineering Research And, V9(02), 555–562. https://doi.org/10.17577/ijertv9is020295
Tekele, A. A. (2019). Factors Affecting the Performance of Micro and Small Enterprises in Wolita Sodo
Town. International Journal of Research in Business Studies and Management, 6(June), 18.
http://www.ustr.gov/
Tekele, A. A. (2020). Factors Affecting the Performance of Micro and Small Enterprises in Diredawa City
Administration. Research Journal of Finance and Accounting, 6(12), 18–26.
https://doi.org/10.7176/rjfa/11-11-03
Tesgera, W. D. (2019). The Role of Micro and Small Enterprises in Creating Employment and Poverty
Reduction in Jima Genet Woreda. International Journal of Finance and Banking Research, 4(6), 91–
100. https://doi.org/10.11648/j.ijfbr.20180406.11
Thao, T. L. T., & Hwang, C. J. (2010). Factors Affecting Employee Performance – Evidence From
Petrovietnam Engineering. Journal of Management Research, 21(3), 21–33.
Thurik, R., Wennekers, S., Uhlaner, L. M., & Zoetermeer. (2002). Entrepreneurship and economic
performance: A macro perspective. International Journal of Entrepreneurship Education, 1(2), 157–
179. https://www.researchgate.net/publication/5012657
Tom, E., Alfred, J., & Akwaya, P. (2015). Marketing Problems and the Performance of Selected Small
and Medium Enterprises ( SMEs ) in Southern Senatorial District of Cross River State , Nigeria
University of Calabar Department of Vocational Education. American International Journal of
Contemporary Research, 5(5), 70–76.
Town, A. (2016). Success Factors of Micro and Small Enterprises at Gamo Gofa Zone of International
Journal of Education and Science Research Review. 1, 25–27.
https://doi.org/10.13140/RG.2.2.18470.24640
Tumidado, T. (2019). Determinants of Micro and Small Scale Enterprises Growth in terms of Employment
in Hosanna Town , SNNPR , Ethiopia JIMMA UNIVERSITY COLLEGE OF BUSINESS &
ECONOMICS MASTERS OF DEVELOPMENT ECONOMICS PROGRAM Determinants of Micro
and Small Scale Enterprises .
UNDP. (2015). Micro, Small and Medium-size Enterprises (MSMES) as suppliers to the extractive
industry. January, 1–72. https://www.undp.org/content/dam/kenya/docs/Poverty Reduction/Supply
Chain Analysis.pdf
Unido, M., Unido, M., Unido, E., Woods, B., Protocol, M., Report, I. D., Partnership, N., & Ii, V. (2013).
Chapter I : Contributing To the Development Debate Chapter Ii : Programme Development and
Technical Cooperation
Walker, E., & Brown, A. (2004). What success factors are important to small business owners?
International Small Business Journal, 22(6), 577–594. https://doi.org/10.1177/0266242604047411
83
Wami, T. T. (2019). The Contribution of Group- Based Micro and Small Enterprises in Employment
Creation and Income Generation: Evidence from Woreda Fourteen of Kolfe Keranio Sub-City,
Addis Ababa, Ethiopia. International Journal of African and Asian Studies, 55, 8–19.
https://doi.org/10.7176/jaas/55-02
Wasihun, R., & Paul, I. (2010). Growth determinants of women-operated micro and small enterprises in
Addis Ababa. Journal of Sustainable Development in Africa, 12(6), 1–14.
Weldeslassie, H. A., Vermaack, C., Kristos, K., Minwuyelet, L., Tsegay, M., Tekola, N. H., & Gidey, Y.
(2019). Contributions of Micro, Small and Medium Enterprises (MSMEs) to Income Generation,
Employment and GDP: Case Study Ethiopia. In Journal of Sustainable Development (Vol. 12, Issue
3). https://doi.org/10.5539/jsd.v12n3p46
Welsh, D. H. B., Kaciak, E., Memili, E., & Minialai, C. (2018). Business-family interface and the
performance of women entrepreneurs: The moderating effect of economic development.
International Journal of Emerging Markets, 13(2), 330–349. https://doi.org/10.1108/IJoEM-03-
2017-0095
Woldehanna, T. (2017). Financial Access to Micro and Small Enterprise Operators: the Case of Youth-
Owned Firms in Ethiopia. Ethiopian Journal of Economics, 26(1), 67–100.
Xheneti, M., & Bartlett, W. (2012). Institutional constraints and SME growth in post-communist Albania.
Journal of Small Business and Enterprise Development, 19(4), 607–626.
https://doi.org/10.1108/14626001211277424
Yikeber, Y. (2019). The growth determinants of micro and small enterprises and its linkages with food
security: The case of Mecha district, Amhara region, Ethiopia. African Journal of Business
Management, 13(4), 138–146. https://doi.org/10.5897/ajbm2018.8697
Youtang, Z., & Yesuf, A. E. (2021). Driving Model of Determinant Factors Affecting the Performance of
Small and Micro Enterprises : Empirical Evidence from Amhara Region , Ethiopia. March.
https://doi.org/10.20944/preprints202103.0106.v1
Zewde & Associates October. (2002). Women entrepreneurs in Ethiopia. October, 1–69.
Zone, G., & Mengesha, B. T. (2019). Determinants of Micro and Small Business Enterprises Growth :
The Case of Three Selected. June. https://doi.org/10.21275/ART20196905
84
Annexes Annex1: variance inflation factor for continuous variables
Annex2: pairwise correlation for categorical variables
Annexes 3: Model misspecification Test for profit and employment
Annex 4: heteroskedasticity test for in terms of profit and employment
Mean VIF 1.11
AIC 1.04 0.959754
AOE 1.15 0.873177
AGE 1.15 0.868590
Variable VIF 1/VIF
FP -0.1413 -0.0540 1.0000
GPR 0.2227 1.0000
MKTC 1.0000
MKTC GPR FP
FP 0.0303 -0.0276 -0.1116 0.0121 0.1779 -0.0501 -0.0996
GPR -0.0859 0.1087 -0.1640 0.2989 -0.2926 -0.2440 0.2133
MKTC 0.0068 -0.0694 -0.1831 0.1843 -0.4121 -0.0191 0.2162
AM -0.1203 0.0740 -0.0809 0.3476 -0.3897 -0.0403 1.0000
ATY -0.1525 -0.0008 0.1348 -0.0470 0.0759 1.0000
AT 0.0334 -0.1052 0.1929 -0.4391 1.0000
ARM 0.0819 0.0405 -0.0960 1.0000
auditing -0.0964 -0.1558 1.0000
EDL 0.0659 1.0000
GEN 1.0000
GEN EDL auditing ARM AT ATY AM
_cons -.1854102 .531589 -0.35 0.727 -1.227306 .8564851
_hatsq .0434886 .0509315 0.85 0.393 -.0563353 .1433125
_hat 1.068166 .2646395 4.04 0.000 .5494819 1.58685
performance Coef. Std. Err. z P>|z| [95% Conf. Interval]
_cons -.236232 .3707251 -0.64 0.524 -.9628398 .4903758
_hatsq .0626598 .0438789 1.43 0.153 -.0233413 .1486609
_hat 1.087159 .1990207 5.46 0.000 .6970857 1.477233
Entgrowthemployment Coef. Std. Err. z P>|z| [95% Conf. Interval]
Prob > chi2 = 0.2725
chi2(1) = 1.20
Variables: fitted values of performance
Ho: Constant variance
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
. estat hettest
Prob > chi2 = 0.5068
chi2(1) = 0.44
Variables: fitted values of Entgrowthemployment
Ho: Constant variance
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
85
Annex 5 stata logistic regressions output for performance of MSEs in terms of profit
Annex 6 marginal effect of stata logistic regressions output for performance of MSEs (profit)
_cons -23.12695 7.265807 -3.18 0.001 -37.36767 -8.886225
open branch -.3963821 4.760413 -0.08 0.934 -9.72662 8.933856
FP
no 2.945121 1.565128 1.88 0.060 -.1224728 6.012715
GPR
no 3.330789 1.502873 2.22 0.027 .3852112 6.276366
MKTC
yes 2.331554 1.145561 2.04 0.042 .0862966 4.576812
AM
yes 2.000292 1.15464 1.73 0.083 -.2627606 4.263345
ATY
no -4.722493 1.787007 -2.64 0.008 -8.224962 -1.220024
AT
yes 3.692854 1.364196 2.71 0.007 1.019078 6.366629
ARM
no 1.892248 1.839826 1.03 0.304 -1.713744 5.498241
auditing
AIC .0007897 .0003079 2.56 0.010 .0001863 .0013932
AOE .6116368 .2649456 2.31 0.021 .0923529 1.130921
> grade 12 3.639183 2.161329 1.68 0.092 -.5969443 7.87531
grade 9-12 3.293575 1.551683 2.12 0.034 .2523316 6.334818
EDL
male .4521267 1.202682 0.38 0.707 -1.905087 2.80934
GEN
AGE .201234 .1024122 1.96 0.049 .0005098 .4019583
performanceprofit Coef. Std. Err. z P>|z| [95% Conf. Interval]
open branch -.0098777 .1191214 -0.08 0.934 -.2433514 .223596
FP
no .0924167 .0534925 1.73 0.084 -.0124267 .1972601
GPR
no .1181462 .0561738 2.10 0.035 .0080477 .2282448
MKTC
yes .0771085 .0400945 1.92 0.054 -.0014752 .1556923
AM
yes .0532452 .0279929 1.90 0.057 -.0016199 .1081104
ATY
no -.1864423 .0581527 -3.21 0.001 -.3004195 -.0724651
AT
yes .1308212 .0505264 2.59 0.010 .0317914 .229851
ARM
no .0385996 .0295703 1.31 0.192 -.0193571 .0965562
auditing
AIC .0000196 6.36e-06 3.09 0.002 7.17e-06 .0000321
AOE .0152066 .0061932 2.46 0.014 .003068 .0273451
> grade 12 .1126853 .0667277 1.69 0.091 -.0180986 .2434692
grade 9-12 .1019594 .0444504 2.29 0.022 .0148381 .1890806
EDL
male .011078 .0291932 0.38 0.704 -.0461396 .0682956
GEN
AGE .0050031 .0024238 2.06 0.039 .0002525 .0097537
dy/dx Std. Err. z P>|z| [95% Conf. Interval]
Delta-method
86
Annex 7 stata logistic regressions output for performance of MSEs in terms of employment
Annex 8 marginal effect stata output for performance of MSEs in terms of employment
_cons .0379726 2.430853 0.02 0.988 -4.726411 4.802356
open branch .2073745 1.981187 0.10 0.917 -3.67568 4.090429
FP
no -.3114954 .8050682 -0.39 0.699 -1.8894 1.266409
FS
rented -1.594929 .8703723 -1.83 0.067 -3.300827 .1109695
workspace
have 3.237003 .894098 3.62 0.000 1.484603 4.989403
experience
no 1.671777 .8292418 2.02 0.044 .0464932 3.297061
GPR
no 2.624539 .8213343 3.20 0.001 1.014753 4.234325
AIP
yes -4.214649 .946721 -4.45 0.000 -6.070188 -2.359109
ATY
yes 3.47947 .8600831 4.05 0.000 1.793738 5.165202
ACyes
AOE -.0863858 .1024937 -0.84 0.399 -.2872697 .1144981
> grade 12 1.323605 .958012 1.38 0.167 -.5540643 3.201274
grade 9-12 -.6836339 .8313945 -0.82 0.411 -2.313137 .9458695
EDL
male 1.307307 .9601794 1.36 0.173 -.5746097 3.189225
GEN
AGE -.040905 .0592721 -0.69 0.490 -.1570761 .0752661
Entgrowthemployment Coef. Std. Err. z P>|z| [95% Conf. Interval]
open branch .0113715 .1076702 0.11 0.916 -.1996581 .2224012
FP
no -.0172823 .0446786 -0.39 0.699 -.1048508 .0702863
FS
rented -.09606 .0525391 -1.83 0.067 -.1990347 .0069147
workspace
have .1994466 .0479038 4.16 0.000 .1055568 .2933363
experience
no .1043817 .0531803 1.96 0.050 .0001502 .2086131
GPR
no .1830002 .0572786 3.19 0.001 .0707362 .2952643
AIP
yes -.2992931 .0526345 -5.69 0.000 -.4024547 -.1961314
ATY
yes .2378393 .0507533 4.69 0.000 .1383646 .3373141
ACyes
AOE -.0047805 .0055976 -0.85 0.393 -.0157515 .0061906
> grade 12 .0720478 .0526394 1.37 0.171 -.0311236 .1752192
grade 9-12 -.0406896 .0477443 -0.85 0.394 -.1342668 .0528875
EDL
male .0734303 .05311 1.38 0.167 -.0306634 .177524
GEN
AGE -.0022636 .0032549 -0.70 0.487 -.0086431 .0041159
dy/dx Std. Err. z P>|z| [95% Conf. Interval]
Delta-method
87
Annex9: Questionnaire
Dear respondent, my name is Yigrem Waganeh and I am doing a research as part of the MA program in
Bahir dar University. The topic of my research is “THE DETERMINANTS OF MICRO AND SMALL
ENTERPRISES PERFORMANCE in Enarg Enawga Woreda.”
Therefore, your honest and genuine response to the items in this questionnaire helps to meet the objective
of this study. The information you provide will be used for academic purpose only and it will be kept
confidential. Hence, I would like to thank you in advance for giving me your valuable time.
To be filled by the MSEs Owners/Managers
General Instruction
Don‟t write your name;
Put a tick or circle;
For multiple choice items, you can use more than one answer, if you believe two or more
alternatives are important;
Write your answer briefly for open ended questions.
Part 1: Socio-economic and demographic profile of the Operator
kebelle: …………………..
Age: ……………………
Family size: ……………………………...
4. Sex: 1. Male 2. Female
5. Marital Status: 1. Married 2. Divorced 3. Widowed 4. Single
6. What is your level of schooling? ………………………..
7. When did your business established? ……………………
8. Business/Enterprise type: 1. trade 2. service 3. manufacturing 4. construction 5. urban
agriculture
9. Ownerships of the enterprise: 1. Sole proprietorship 2. Partnership 3. Cooperatives
Part 2: Information on employment, income, saving and credit
1. What were you doing before you engage in the current job? 1. Unemployed 2. Student 3. Private
employee 4. Government employee 5.Other (specify) __________________
2. How many employees did the enterprise have when starting the business including the owner? ………
A. Permanent ……….. B. Temporary……… C. Family member and/or Unpaid worker
………
3. How many employees does the enterprise have now currently including the owner?
88
A. Permanent ……….. B. Temporary……… C. Family member and/or Unpaid worker
……… 4. What was the total amount of revenue in birr of your enterprise in the year 2012 EC: ---
------------
5. What was the amount of total cost (TVC + TFC) in birr of your enterprise in 2012 EC: ------------
6. How is your income or profitability after you start the business? 1. Increased 2. Decreased 3. No
change
7. Do you save from what you earn per day/week/month? 1. Yes 2. No
7.1. If yes how much in birr? …………………
8. Does your saving increased after your engagement in this business? 1. Agree 2. Disagree
9. Where does your household save? 1. MFI 2. Iqub 3. Formal bank 4. MFI and formal bank 5. All
10. Does your business have access to formal credit facility since you started your business? A. Yes B. no
11. If your answer is “No” circle the possible reasons from the following your inability to access credit
A. Lack of collateral B. Interest rate is high C. Complex loan procedure D. Most MFIs are reluctant to
provide credit to MSEs E. Amount of the loan is insufficient F. I don‟t need credit H. Already have
easy access from other sources I. shorter loan repayment periods K. Others (specify) -------------------
12. If yes show loan amount in birr?...........................
Part 3: Information on improvement of living standard and capital of HH
1. Do you have the following assets after you own this business? A. House B. Working tools C. Bed
D. Sofa E. Table, Chairs, Shelf F. Refrigerator G. Television, Tape Recorder, Radio H.
Jewelry I. Others/specify
2. Is there an improvement in your and your family diet after an increase in your income because of this
business? 1. strongly agree 2. Agree 3. medium 4. Disagree 5. strongly disagree
3. Are you able to send your families/ or relatives to school after an increase in your income because of
this business? 1. strongly agree 2. Agree 3. medium 4. Disagree 5. strongly disagree
4. Does your and your family health status improve after an increase in your income because of this
business? 1. strongly agree 2. Agree 3. medium 4. Disagree 5. strongly disagree
5. Do you believe that the income that you get from this job allow you to finance all costs of the house
hold? 1. Yes, most of the time 2. Sometimes 3. No
6. How do you evaluate your enterprise‟s role in poverty reduction? A. high B. medium C. low
Part 4: Information on Capital:
1. How much was the initial capital of your enterprise? _________ETB
2. How much is your current Capital? ______________________ETB
89
3. What was the major source of your initial capital? 1. Own saving 2. Loan from MFI 3. Loan from
Bank 4. Loan from MFI & own saving 5. Loan from friends 6. others/
specify_______________
Part 5: Sales, income and expenditure
1. How much was your average monthly sales for the past year (In ETB)? ________
2. How was the trend of your sales for the past year? 1. Increasing 2.No change 3. Decreasing
3. How is the trend of your income during 2012 E.C.? 1. Increasing 2. No change 3. Decreasing
4 How much is your monthly average expense for the last year? ______________
5. How was the trend of your expense during the last year? 1. Increasing 2. No change 3. Decreasing
6. Do you have financial statement or bookkeeping? A. Yes B. No
7. Does your enterprise was audited ever by others who are legal? A. yes B. No
Part 6: Challenges facing small and micro enterprises
1. Do you face challenges? A. Yes B. No
2. If you face challenges, what is the impact of the types of challenges indicated below on your business in
the scale provided? Tick an (X) mark depending upon your relative answer in the table provided below.
Types of impacts Impact or degree of severity
Low middle High
Lack of working spaces
Lack of sufficient Finance
Limited access to market
Limited access to credit facilities
High price of raw material
Lack of raw materials
Heavy government tax
Pressure from government rules and regulations
Lack of technical skill
Lack of information
Unfair competition from other enterprises
Lack of infrastructure
Other/specify……………..
Part 7: Information on Inputs and external factors
1. Do you frequently face shortage of raw material? A. Yes B. No
2. What type of raw materials do you use for the production process? 1. Local produced material
2. Imported materials 3. Both 4. others (specify) ____________________________
3. Who is your primary inputs source? 1. Large producers 2. Wholesalers and large retailers 3. Smaller
and the same size retailer 4. Government project 5. Farmers 6. other (specify) ____________
90
4. Who are your primary customers? 1. Private users 2. Wholesalers/ large retailer 3. Smaller and the
same size retailer 4. Large producers 5. Government projects
5. Where is your products‟ market destination?
1. Local market 2. External market inside the country 3. External market outside the country
6. Have you received any vocational/technical training in relation to your business? 1. Yes 2. No
7. If yes, does/do the training/s you obtained above improved your knowledge? 1. Agree 2. Disagree
8. Does your enterprise use a modern business technology? A. Yes B. No
9. If your answer is “No” indicate your reason for not using a technology? A. Lack of skills and
knowledge B. Lack of money C. Unable to select proper technology D. Others (specify) ---------------
10. Does your business situated in good business location which is accessible to customers and is situated
in town center? A. Yes B. no C. no idea
11. How do you acquire the working space on which you operate your business? 1. Bought 2. Rented
from private owners 3. Leased 4. Given by the government 5. Others, specify-----------------
12. Does your business product/service have market demand and market linkage with other enterprises
enough to sell it easily? A. yes B. no
13. Does the current state of market competition affect negatively your business capacity in generating
adequate profits? A. yes B. no C. I cannot decide
14. Do the current government policies and regulations affect adversely your business performance?
A. Yes B. no C. no idea
15. If your answer is “Yes” what are those? A. registration and licensing B. lack of infrastructure
C. Lack of incentive and support D. Lack of working premises E. Lack of training
F. Lack of loan provision H. High tax imposition
16. Does your enterprise have infrastructural related factors limiting the performance? A. Yes B. No
17. If yes what are? A. electric power B. water C. transport/road D. others specify……………………..
18. Do you invest more in this business when your revenues are higher?
A. Yes, most of the times B. Sometimes C. Rarely D. No
19. What is your future plan? 1.To expand the business in the same line 2. To open a branch in other
location 3. To expand the business in other field 4. Other (specify) _________________
THANK YOU FOR YOUR CO-OPERATION
91
Key informant interview
Key informant interview guidance questionnaire for government office and other relevant organizations
concerning determinants of MSEs‟ performance.
Thank you for your cooperation to the interview
Date of interview ____________________________
Name of the Organization _________________________________________
Name of interviewee _____________________________________________
Position in the institution _________________________________________
Time of interview: Started at __________________ Ended at __________________
1. Can you mention the type of government support and incentives given to MSEs?
2. How is the current performance status of MSEs and their capacity in generating adequate profits and
employment in Enarj Enawga Woreda?
3. How do you assess the current working premises and operating location of all MSEs?
4. What are the infrastructural related factors limiting performance of MSEs?
5. How do you describe MSEs their access to credit?
6. How do you describe the level of market competition and its fairness among MSEs in the Woreda?
7. How do you evaluate MSEs access to market and their market linkage?
8. From which government bodies do MSEs get support principally?
7. How do you monitor the activities of MSEs in your woreda?
8. What mechanisms does your office use to measure the performance of MSEs working in the woreda?
9. What are the roles of MSEs in reducing poverty in the woreda?
10. Could you mention some major internal and external challenges facing performance of MSEs?
11. Which of the problems are solved and not? Explain how and why?
12. How do you view MSEs activities towards ensuring sustainability?
13. How important would you consider the relevance of MSEs in enhancing the local economy?
14. Who are the major sources of fund for the services your office provide?
15. What should be done for MSEs to continue successfully in their respective business operation?
Thank you again for your cooperation