Factors Affecting Growth of Assets in Ethiopian Micro-Businesses

Determinants of Total Asset Growth in Micro and Small-Scale Enterprise in Gondar City, Ethiopia
AJAERD
Determinants of Total Asset Growth in Micro and Small-
Scale Enterprise in Gondar City, Ethiopia
Adem Endiris1, *Gebre-Egziabher Fentahun2
1,2Lecturer, Department of Agricultural Economics, Assosa University, P.O. Box 18, Assosa, Ethiopia
This study assessed the determinants of total asset growth of micro and small-scale enterprises
in Gondar city of Amhara regional state, and more specifically; to find out the major constraints
which affect the total asset growth of the sector. In this study, both primary and secondary
sources were employed to gather the data. The primary data were collected by questionnaire.
These methods were helpful in collecting information from operators of micro and small-scale
enterprise in the organization of the sector. The secondary data had been collected from
published and unpublished documents and also collected from bureau of MSE in Gondar city. For
this study, simple random sampling technique was employed to select kebeles administrations
in the study area. In addition, stratification sampling technique was employed based on types of
micro and small-scale enterprises (construction, trade, associations, service and manufacturing).
In this study the data was analyzed by using descriptive statistics and econometric model
(multiple linear regression models). The result this study education, interest and working premise
are most significant factors that affect the total asset growth of MSEs in the study area. Finally,
the study suggesting that any interventions designed to increase the total asset growth of micro
and small-scale enterprises in the study area.
Keywords: Growth, Determinant, Micro and Small Enterprises (MSEs), Multiple Linear Regressions
INTRODUCTION
The micro and small scale enterprises (MSEs) are long
recognized as important vehicles of economic
diversification, income generation and distribution, and
accelerating the economy growth of a country. They can
also help to achieve a more equitable distribution of the
benefits of economic growth and thereby help alleviate
some of the problems associated with uneven income
distribution and poverty (Zemenu and Mohammed, 2014).
And also MSEs are recognized as vehicles for economic
growth and reduce poverty and unemployment by virtue of
their size, capital investment and their capacity to generate
greater employment (ILO, 2008). Despite the large
potential contribution of MSEs, the sector face problems in
both at their start up and after operation phase (World
Bank, 2004).
MSEs are the second largest employment generating
sector next to agriculture in Ethiopia and more than 1.3
million people in the country are engaged in this sector. A
National survey conducted by Central Statistics Agency
(CSA) in 2007 indicates that more than 1.3 million people
1
Ministry of Trade and Industry
in the country are engaged in MSEs sector. It contributes
3.4% of GDP, 33% of the industrial sector and 52% of the
manufacturing sector (CSA, 2005). However, this sector
had faced lack of access to credit, insufficient loan size,
time delay and collateral (Gebrehiwot and Wolday, 2006;
Mulu,2007; MoTI1, 2005), and faced lack of access to
markets, finance, working premises, supply of raw
material, lack of sufficient capital, business information,
business premises, the acquisition of skills and managerial
expertise and access to modern technology (MoTI2, 2005).
Similarly, most MSEs have no growth and remain at their
initial level due to different internal (owner’s/operator’s
related and/or firm’s related) and external factors
(Admasu, 2012).
*Corresponding Author: Gebre-Egziabher Fentahun,
Department of Agricultural Economics, Assosa University,
P.O.Box 18, Assosa, Ethiopia.
E-mail: gebreegzi22@gmail.com
Co-Author Email: aendris76@gmail.com
2
Ministry of Trade and Industry
Research Article
Vol. 6(1), pp. 689-696, January, 2020. © www.premierpublishers.org, ISSN: 2167-0477
Journal of Agricultural Economics and Rural Development

Endiris and Fentahun 690
In order to alleviate such problems, the federal
government of Ethiopia has introduced its first MSEs
development strategy which states that a special focus of
the government, given that they comprise the largest share
of total enterprises and employment in the non-agricultural
sectors. In recognition of the important role MSEs have to
play in creating income and employment opportunities and
in reducing poverty, the government drafted its first Micro
and Small Enterprise Development Strategy (MoTI,
2005).” Accordingly, the region based on the countries
policy and strategy has design to implement the activities
of MSEs such services, construction, manufacture,
trading, association and retailer and others. This designed
strategy recognizes the contribution of MSEs in the
employment generation, increasing individual’s income
and total growth asset; in general, improving the quality of
life of citizens.
In this regard, Gondar city, which is found in the Amhara
regional state of North Gondar zone, has been faced with
economic, social and political constraints towards its
residents. Like any other cities in the country as a whole
and in the region explicitly, Gondar city has acute shortage
of capital, working place, training, and lack of accessibility
to market to their products and others by the small scale
operators during their start-up and in the process of their
operation.
In this study, to collect the basic information and special
attention to the concerned bodies about the determinants
of growth total asset in micro and small enterprises. Having
the factors in mind, this study can be used to show the
major problems on MSEs in the study area and to assist
poverty reduction actors to improve the total asset or
standard of living through providing some possible
solutions in the major constraint areas which base the
major findings. In general, this study addressed to find out
the major factors that affect the total asset growth in micro
and small scale enterprises in the study area.
METHODOLOGY
Description of the Study Area
Gondar is located in North Gondar zone of the Amhara
Regional state, and it is designed as a capital centre for
North Gondar Zone in the Amhara Regional state. The city
is 748 kms way from Addis Ababa and 182 Kms away from
the capital city of Amhara Regional State (Bahir Dar).
Gondar is also 220 kms away from boarder of Sudan.
Figure 1: Location map of the study area

J. Agric. Econ. Rural Devel. 691
Sample size Determination
In this study, the sample size was determined by using
Yamane formula (Yamane, 1967).
This formula expressed as: 2
)(1 eN
N
n
+
=
Where: n = statistically acceptable sample size
N = Total size of target population (298)
e = level of precision 10% (0.1) and at 90%
confidence level (0.1).
75
)1.0(2981
298
2
=
+
= nn
From the above calculation, we can find 75 enterprises
acceptable sample size from the given enterprises in study
area with maintaining level of precision (0.1).
Sampling Technique
There are two general types of sampling methods. These
are probability and non-probability methods of sampling. In
this study two stage sampling technique were employed.
In the first stage, simple random sampling technique was
used to select the three kebeles administrations because
of each element in the population has a chance of being
chosen., which are particularly kebele1, 3 &13 kebele
administrations. In the second stage, stratification
sampling method were used to categorized micro and
small scale enterprises in to five groups (trade
associations, service, manufacturing, construction and
private traders) from the selected kebeles administrations
in Gondar city. Finally, simple random sampling technique
was employed to select all respondent owner of the
enterprise in sample kebeles. This implies that (75)
sampled owner of enterprises were selected randomly
from a total population of (298) in three sampled kebele
administrations (Table 1).
Table 1: Target population and stratification sampling
technique
No Strata
enterprise
Population
Size
Proportional
Sample Size
Sample
size
1 Construction 35 12 10
2 Private trade 155 52 37
3 Association 24 8 6
4 Services 48 16 12
5 Manufacture 36 12 10
Total 298 100 75
Source: Our Survey Design, 2015
Sources of Data and Method of Data Collection
In this study, both primary and secondary sources of data
were employed. To achieve the purpose of this study, the
primary data was conducted to collect the cross-sectional
data on pertaining to socio-economic, demographic, and
institutional variables that influence the total asset growth
of micro and small scale enterprise by using structure
questionnaire (open and closed ended questions). This
questionnaire was first prepared in English language and
then translated into local language (Amharic) to make
questions clear for the enumerator and to facilitate data
collection during the survey data. Secondary sources of
data was gathered from different published and
unpublished documents, books, magazines, direct from
bureau of MSE in Gondar city.
Definition of variables
The dependent variable of the model
There are various arguments in the existing literature on
how to measure growth, and scholars have used a variety
of different measures. These measures include, for
example, growth of sales, employees, assets, profit, equity
(Davidsson and Wiklund, 2004). Growth in total Asset is
any liability owed by the enterprise and the enterprises
capital that currently have. Total asset components can be
different from company to company but the main elements
are the same. It can be classified as current asset and long
term asset. But in this study to measure the dependent
variable (growth in total asset of MSE) by the amount of
initial enterprise capital and the current capital of MSE in
Birr with the firm age. Therefore, the dependent variable of
this study is the total asset growth of MSE in the Gondar
city as expressed by this formula.
Growth in total Asset =Current total asset−Initial total asset
MSE age
The independent variables and hypothesis
See table 2
Methods of Data Analysis
The raw quantitative and qualitative data were collected
from the survey enterprise owner and then edited, coded,
entered, cleaned and analyze the data by using STATA-
14 software. Descriptive statistics and econometric model
were used for this study.
Descriptive statistics analysis
Descriptive statistics is important to have clear picture of
the characteristics of the sample units. By applying
descriptive statistics, one can compare and contrast
different categories of the sample units with respect to the
desired characteristics. The descriptive statistics used in
this study include mean, percentages and frequency of
occurrence.
Econometric model specification
We used the multiple linear regression model of OLS
(Ordinary Least Square) estimation. Because of the

Table 2: Measurements of the independent variables and working hypothesis
Name Variable Type Code Measurement in Value Expected Sign
Total Asset Growth Continuous TAG (current total asset−initial total asset)/ firm age
Independent Variable
Gender Dummy GEND ( 1) male and (0 ) female +
Working premise Categorical WPRM Working premise in which MSEs operate: 1 (Own
premise), 2 (Family), 3(Rented), 4 (Government building)
+
Credit Dummy ACRED 1 is if MSEs access to credit, 0 otherwise +
Interest rate Dummy INTRT 1 If MSE interest rate high ,0 otherwise -
Infrastructure Dummy INFR 1 if access to infrastructure and 0 otherwise. +
Education level Categorical EDU Level of education in which MSE operator 1 illiterate, 2
primary 3 secondary 4 certificate & above
+
Firm age Categorical FIAG The age of MSE in year (1) if the age <1 year, (2 )
between 1-5 year and (3) above five year
+
Source: Own Survey, 2015
dependent variable is a continuous variable and the
independent variable either continuous variable or dummy
variables. It was an essential method of econometric
analysis to recognize and realize patterns of the
influencing factors. It should be noted that in the course of
analyzing the factors affecting MSE in Gondar city, which
is the main objective of this study, it was important to first
identify factors underlying the MSE decision and examine
the magnitude and significance of these factors.
The equation of regressions on this study is generally built
around two sets of variables, namely dependent variable
(growth in total asset of MSEs) and independent variables
(education level, Access to infrastructures, access to
institutional credit, interest rate, access to working
premise/access to working place and MSE age). The basic
objective of using regression equation on this study was to
make the study more effective at describing,
understanding and predicting the stated variables.
Regress Growth of Total Asset in MSEs on Selected
Variables:
Yi = β0+ β1X1 + β2X2 + β3 X3 + β4X4 + β5X5 + β6X6 +
β7X7 + iu
Where: Yi= Growth in total asset of MSEs = [(current total
asset−initial total asset)/ firm age]
X1- gender,
X2- education level,
X3 - access to infrastructures,
X4 – access to credit,
X5 – firm age,
X6, - interest rate,
X7- access to working premise,
iu -error term
β0- intercept and β1, β2, β3, β4, β5, β6, and β7 are the
coefficients of each independent variable.
RESULTS AND DISCUSSIONS
This chapter analyses the determinants of total asset
growth of micro and small enterprises by using descriptive
statistics and econometrics model (multiple linear
regression).
Descriptive Result and Discussion
The survey has collected a wide range of information
which is essential to the interpretation of the findings and
the understanding of the results of the study in MSE
growth. Since development is impossible without female’s
participation, the government should play a great role to
change negative attitude of the society towards women
and to improve their participation in the growth of MSE.
This helps to increase the growth of total asset of micro
and small enterprise on improving household. The table
below shows women and men participants in micro and
small scale enterprise in the study area.
Table 3: Gender of respondents
Gender Frequency Percent Mean of total asset growth
Female 35 46.7 5234.22
Male 40 53.3 17305.47
Total 75 100 22539.69
Source: own Survey result, 2015
As survey result, 53.3% the respondents are men who
engaged in micro and small-scale enterprise of in three
sampled kebeles administration of Gondar city. The
remaining 46.7%sample respondents are women
participants in the sector. From this we understand that
men participation and chance to run the MSE business is
more than women participation in Gondar city.
Table 4: Working premise of MSEs operator
Working premise Frequency Percentage
1-own premise 27 36
2- family/friends 21 28
3- rented 12 16
4-government building 15 20
Total 75 100
As a result, the study, 64% of respondents do not own
premise and only 36% of them have own premise. This

shows that a large majority of MSE operators are exposed
to rent from private owner, family, government building or
others and this increases vulnerability of MSE owners. As
a result, MSEs that were operating at own working premise
grow faster than those that operates at rented, at family
working premise and government building. Besides, those
MSEs that were operating their business at rented working
premise shown lower growth rate than those MSEs that
were operating their business at their family working
premise because the growth rate gap between the MSEs
operating at own and family working premise is narrow
than the MSEs operating at rented working premise and
govement building.
Table 5: Educational status of the respondents
Educational status Frequency Percent
Illiterate 15 20
Primary 20 26.7
Secondary 24 32
Certificate and above 16 21.3
Total 75 100
As a result of the study, 32% of requested operators of
micro and small-scale enterprise had attended secondary
school. This shows that most of the operators had
secondary school level. This is so, because of MSEs
requires knowledge and the government has given great
attention to support educated young unemployed by
providing credit through group formation and training. The
others 26.7%of the respondents have primary education
level. From the sample respondents 20 % of MSE
operators are illiterate and the others sample respondents
have certificate and above which accounts 21.3%. This
group is also more encouraged by the government to
participate in MSE due to government strategy to improve
the total asset growth of MSEs.
Table 6: Firm/MSE Age
MSE age Frequency Percent
<1 year 19 25.3
1-5 year 38 50.6
>5 year 18 24.1
Total 75 100
Source: Survey result, 2015.
The survey result indicated that majority 38(50.6%) of
MSE age existed between 1-5 years. Next to this
19(25.3%) of MSE age is < 1 year and 18(24.1%) of the
MSE age existed >5 years. This shows that as the age of
micro and small enterprise increases, their effectiveness
in growth of total asset also increases.
Table 7: Access to credit
Access to
credit
Frequency Percent Mean of total asset
growth
No 35 46.7 5193.04
Yes 40 53.3 17022.48
Total 75 100 22215.52
Source: Survey result, 2015
A survey result indicated that, 53.3% the respondents
have access to credit who engaged in micro and small
scale enterprise of in the three sampled kebele
administrations. The remaining 46.7%sample respondents
have not access to credit in the MSE sector in the three
sampled kebele administrations. This shows that a
majority of MSE operators have access to credit.
Table 8: Access to infrastructure
Access to
infrastructure
Frequency Percent Mean of total
asset growth
No 34 45.3 5494.83
Yes 41 54.7 16479.27
Total 75 100 21974.1
The survey result of the study revealed that about 54.7%
of the respondents have access to infrastructure who
engaged in micro and small-scale enterprise of in sampled
kebeles of Gondar city. The remaining 45.3%sample
respondents have not access to infrastructure in the
sector. This shows that a large majority of MSE operators
have access to infrastructure.
Table 9: Interest rate
Frequency Percent Mean of total
asset growth
Low interest rate 23 30.7 4777.00
High interest rate 52 69.3 14545.19
Total 75 100 19322.19
The survey result of the study revealed that 69.3% of the
respondents are exposed to high interest rate from
financial institutions who engaged in micro and small-scale
enterprise of in sampled kebeles of Gondar city. The
remaining 30.7%sample respondents are getting low
interest rate from financial institutions in the sector. This
shows that a large majority of MSE operators are largely
vulnerable to high interest rate arising from both formal and
informal financial institutions.
Econometric Results and Discussion
In order to identify the most affecting variables on growth
total asset in MSE by using multiple linear regression
model. Before estimating the chance of the event using
multiple regression model, goodness of fit of the model and
multi-co linearity diagnoses were made.
Test for Multi-collinearity: The test for multi-collinearity
tests whether there are no perfect linear relationships
among the explanatory variables. However, multi-
collinearity problem is the existence of a “perfect,” linear
relationship among some or all explanatory variables of a
regression model (Gujarati, 2004). In order to test the
existence of multi-collinearity problem among continuous
variable, VIF (Variance Inflation Factor) is utilized. As a

rule of thumb for multi-collinearity, test of the model states
a variable whose values are greater than 10 or whose
1/VIF value is less than 0.1 indicates possible problem of
multi-collinearity. Thus, in this study there is no value
greater than 10, all value of the Variance Inflation Factors
are less than 3.49. Therefore, in this study there is no
multi-collinearity problem among continuous variable.
Correlation matrix illustrates the relationship between two
independent and/or independent dependent variables.
Correlation matrix examines the extent or direction of
relationship among two variables and how one variable is
related to another. Correlation problem exists when the
correlation result is above 0.80 and below -0.80 (Gujarati,
2004). But, in this study, the correlation coefficients are
under 0.7582 and over -0.0340. Therefore, the correlation
matrix tests revealed that there is no serious correlation
problem among continuous variable.
From the result of the model, R Square value of 0.8274
and adjusted R square value of 0.8079, it may be realized
that 82.74% of the variation in total asset growth can be
explained by the independent variables. The remaining
17.26%% of the variance is explained by other variables
not included in this study and the F test shows the model
is goodness of fitness because the prob> F is 0.000.
The multiple regression model was used to identify the
major determinant factors for MSEs growth in terms of total
asset by using the compound annual total asset growth
rate as an objective measure. After the regression of the
model we can find the access to working premise, access
to credit, education level and firm age are found significant
in determining total asset growth of MSEs in the study
area. The results of the multiple linear regression models
are summarized in Table 3.8 below.
Table 10: Determinants of total asset growth in MSE in
study area
Independent variable Coef. Std. Err. P> (t)
Firm age 2554.312 1106.233 0.026**
Gender of
respondent
415.6216 1847.87 0.823
Educational status 3381.553 825.6866 0.000***
Working promise 5936.324 883.4229 0.000***
Access to credit -912.1649 1658.954 0.584
Access to
infrastructure
-558.1207 1536.616 0.718
Interest Rate -4763.776 1754.62 0.009***
Cons -11076.21 1824.616 0.000***
R-squired 0.8274 No
observation
75
Adjusted R squired 0.8079 Prob>F 0.0000
Source: Survey result 2015
Note that: ***significance at 1% and **significance at 5%.
Growth in total asset = -11076+3381.55 (education)
+5936.32(working premise) -4763.78(interest rate)
+2524.31(firm age)
From the result of the study, all the explanatory variables
included education, working premise and interest rate in
this study can significantly affect the dependent variable.
The standardized beta coefficient column shows the
contribution that an individual variable makes to the model.
The beta weight is the average amount the dependent
variable increases when the independent variable
(education, working premise and MSE age) increases by
one standard deviation (all other independent variables
are held constant). But, the standardized beta coefficient
in the dependent variable decrease when interest rate
increase at 1% (all other independent variables are held
constant).
Education Level: The survey result of the study revealed
that the education level is one of the most significance
factors that affect the growth in total asset of MSE. The
education level of owner/operator was positively significant
factor affecting the total asset growth of MSEs at 1% level
of significance. The education level of owner/operator one
grade increase, the total asset growth of MSE increased
by 3381.55 Birr at 1% level of significance by assuming all
other variables were constant. This finding is consistent
with other empirical studies (Mulu, 2007; Habtamu, et al.,
2013; Mbugua et al., 2013). The possible explanations
given by previous studies with regard the education
improves the ability of efficiently allocating resources to
more productive lines of business and to select profit
maximizing inputs/materials. Therefore, we can therefore
say that level of education is a major factor that affects the
total asset growth in MSEs in the study area.
Work promise: Work promise has significant and
positively affects the growth in total asset of MSE at 1%
significant level. So, the own premise increase in the
operator of MSEs in 1%, their growth in total asset
increase by 5936.32 Birr at 1% significant level by
assuming other variables constant. More availability of
access of working premise would mean more capital that
enhances the operation of the MSE sector. Therefore, the
hypothesis which states “MSEs that have own working
premise are more likely to grow faster as compared to
others” is accepted.
Firm age: The MSE’s age has positive and significant
effect on the total asset growth of MSE at 5% significant
level. This means that in the age of MSEs increased by
one year, their growth in total asset increased by 2524.31
Birr, ceteris paribus. This finding is consistent with the
earlier finding (Mateev & Anastasov, 2010).This implies
that as the age of an MSE increases, so does his business
experience, practical, wisdom and his income generating
capacity.
Interest rate: interest rate has negative and significant
effect on total asset growth of MSEs at 1 % level of
significance. This shows that at 1% increase in interest
rate of financial institutions, total asset growth of MSEs
deceased by 4763.778 Birr by assuming the other
variables constant.

CONCLUSION AND RECOMMENDATION
Conclusions
Based on the findings the study concluded that in three
sampled kebeles administration of Gondar city (namely
kebele1, 3, 13,) factors that affect growth in total asset of
the micro and small enterprises. It comprise construction,
private trade, services association and manufacture of
economic activities. In terms of their sector classification,
most of the micro and small enterprises were involved in
trading activities. Creating new job opportunities for
unemployed citizens, providing trainings which are related
with vocational and commercial administrative issues,
creating favorable condition to become beneficiary of the
existed loan, create a market linkage, making an effort to
be users of the new technology, create a favorable
condition to be users of production and selling space are
some of the major roles of MSEs.The determinant of
growth total asset in the MSEs were tested by using the
linear regression model. As an economical results, access
to working premise,education level,firm age and interset
rate have econometrically significant effect on the total
asset growth of the MSEs.
MSEs that were operating at own working premise grow
faster than those that operates at rented, at family working
premise and government building. Besides, those MSEs
that were operating their business at rented working
premise shown lower growth rate than those MSEs that
were operating their business at their family working
premise because the growth rate gap between the MSEs
operating at own and family working premise is narrow
than the MSEs operating at rented working premise and
govement building. The education level owner/operator
and firm age have a positive and significant effect on
MSEs growth in total asset. The interest rate has negative
and significant effect on their growth in total asset of MSE.
Since those MSEs that have low interest rate from formal
financial sources grow faster than their counterpart.
Recommendations
The role of MSEs is consistently recognized in total asset
and income generating and has become a major playing
with dual objective of enhancing total asset growth and
identifying the factors. However, MSE growth is
multidimensional phenomenon and there is substantial
heterogeneity in a number of factors. Support program
need to consider the heterogeneous nature of the MSEs.
The finding result shows the factor that affects the growth
in total asset of MSEs are interest rate, the firm age,
education level and access to working premise.
❖ Access to working place: In the study area the main
problem of micro and small-scale enterprise is difficult
to get working premise. Working premise is found to
have significant positive impact on MSEs growth in total
asset. Therefore, the MSEs development office in
collaboration with the municipality should strive for the
MSEs to have own working premise or construct
shades and avail them at fair rent.
❖ Interest rate: In order to initiate small enterprise at the
initial stage the government should decrease the
interest rate of the small enterprise operator and reduce
their collateral to run and facilitate their businesses
effectively .Because small enterprise are infant at the
initial stage and this is an alternative means of
moralizing and initiating the small enterprise .
❖ Education; The educated entrepreneurs have the skills
to manage the other functions of the business such as
finance, marketing, human resources and these skills
results to high performance of the business which helps
those firms accumulate the total asset without any
difficulty. Therefore, government could give favorable
condition to participate more educated operator as well
as illiterate operators by providing training on the MSE
in order to accumulate the growth in total asset.
❖ In general the responsible bodies associated with micro
and small scale enterprise and the government could
provide facilities, expand opportunities in order to
develop their capital and establish suitable policies and
strategies appropriate with MSE.
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Accepted 23 December 2019
Citation: Endiris A, Fentahun G (2020). Determinants of
Total Asset Growth in Micro and Small-Scale Enterprise in
Gondar City, Ethiopia. Journal of Agricultural Economics
and Rural Development, 6(1): 689-696.
Copyright: © 2020: Endiris and Fentahun. This is an
open-access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are cited.

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