The Analysis Factors of Micro Small and Medium Enterprises
Income in Indonesia
Inda Arfa Syera
1
, Muhammad Yusuf
2
and Fitrawaty
2
1
Post Graduate of Economics, Universitas Negeri Medan, Medan, Indonesia
2
Faculty of Economics, Universitas Negeri Medan, Medan, Indonesia
Keywords: Micro Small and Medium Enterprises Income, Bank Credit Distribution, Micro Small and Medium
Enterprises Labor, and Interest Rate
Abstract: This study aims to analyze the factors that effect Micro Small and Medium Enterprises income in Indonesia
by using the Ordinary Least Square (OLS) method. The source of data were Bank Indonesia and The
Cooperative Ministry in 2010:1 – 2017:4. The results of data analysis indicates that: (1) Bank credit
distribution has a positive and not significant effect on Micro Small and Medium Enterprises income in
Indonesia, (2) Micro Small and Medium Enterprises Labor has a positive and significant effect on Micro
Small and Medium Enterprises income in Indonesia, (3) Interest rate has a negative and significant effect on
on Micro Small and Medium Enterprises income in Indonesia.
1 INTRODUCTION
The era of reform, the economy was built on the
basis of a populist economic system. The main
components of the people's economic system are
human resources as consumers, as workers, and as
entrepreneurs. Thus a populist economic system is
an economic order that provides the widest
opportunity for employment and effort for the
community to achieve an even and equitable welfare
improvement. Concretely, efforts to improve the
community's economy must be carried out in various
programs, including the development of Micro,
Small and Medium Enterprises (MSMEs).
MSMEs are the key to economic growth because
they can help the recovery of the economy with
income earned (Brașoveanu and Bălu, 2014).
According to Law Number 20 of 2008 Definition of
Micro, Small and Medium Enterprises is a business
carried out by individuals or groups of people on a
small scale. The law also emphasizes that micro-
enterprises are one form of productive business
owned by individuals and / or individual business
entities that conform to the criteria of micro-
enterprises.
Furthermore, it is also explained about small
businesses, namely a form of independent business
that is carried out by people per group or group of
people or business entities that are not subsidiaries
or branches of companies owned, controlled or
become a part, either directly or indirectly from
medium-sized businesses or large businesses that
meet the criteria of small businesses. Whereas the
definition of a medium business is a productive
economic enterprise that is independent, carried out
by individuals or business entities that are not
subsidiaries or branches of the company owned,
controlled, or become the amount of net assets or
proceeds of sales as stipulated in Law Number 20 In
2008.
Based on data from the Ministry of Cooperatives
and Small and Medium Enterprises of the Republic
of Indonesia in 2011-2017, MSME revenue growth
has fluctuated from year to year. The highest income
growth occurred in 2016 amounting to 67.98% and
the lowest growth occurred in 2017 at 4.68%.
According to the 2016 World Business Activity
Survey conducted by Bank Indonesia, MSME's
rapid revenue growth in 2016 was due to the easier
access to bank loans supported by lower interest
rates, increased demand for goods and services, and
an increase in the number of workers in several
important sectors.
Syera, I., Yusuf, M. and Fitrawaty, .
The Analysis Factors of Micro Small and Medium Enterprises Income in Indonesia.
DOI: 10.5220/0009501704470454
In Proceedings of the 1st Unimed International Conference on Economics Education and Social Science (UNICEES 2018), pages 447-454
ISBN: 978-989-758-432-9
Copyright
c
 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
447

Whereas in 2017, there was a decline in MSMEs
revenue growth in Indonesia due to a slowdown in
business activities mainly due to the decline in
business activities in the agriculture, plantation,
livestock, forestry and fisheries deposits due to
seasonal factors and unfavorable weather conditions,
decreased activity industrial sector business, and the
decline in the number of workers in the fourth
quarter of 2017.
Figure 1: MSME Income Growth in Indonesia in 2011-
2017
Based on the production theory, the factors that
influence the increase in production associated with
increased income are capital and labor (Sukirno,
2014). Since the 1970s, the Indonesian government
has facilitated the distribution of funds to the
MSMEs sector which began with two credit schemes
from Bank Indonesia, namely Permanent Working
Capital Credit (PWCC) and Small Investment Credit
(SIC).
In addition, Bank Indonesia has issued Bank
Indonesia Regulation (BIR) Number 3/2/PBI/20011
which requires banks to provide 20 percent of their
total loans to small businesses. The regulation was
issued to encourage banks to increase the
distribution of funds to the MSMEs sector which is
used as capital.
The following are data on bank credit
dictribution, MSMEs labor, interest rate and
MSMEs income in Indonesia in 2014: 1-2017: 4:
Table 1: Bank Credit Distribution, MSMEs Labor, Interest
Rate and MSMEs Income in Indonesia 2014:1-2017:4
Years
MSME
Income
(Billion)
Bank
Credit
Distributi
on
(Billion)
MSME
Labor
(Million)
Interest
Rate
(%)
2014-1 374.92 637.52
27.82
7.5
2014-2 380.78 669.28
28.27
7.5
2014-3 387.15 683.02
28.75
7.5
2014-4 394.04 694.97
29.28
7.67
2015-1 270.04 702.85
30.71
7.58
2015-2 330.53 735.37
30.98
7.5
2015-3 444.10 752.36
30.93
7.5
2015-4 610.74 797.84
30.59
7.5
2016-1 1,090.56 815.33
28.62
7
2016-2 1,259.33 851.98
28.19
6.67
2016-3 1,377.15 868.25
27.99
5.58
2016-4 1,444.00 898.04
28.01
4.75
2017-1 1,459.90 888.37
28.25
4.75
2017-2 1,424.84 992.47
28.71
4.75
2017-3 1,338.82 942.69
29.39
4.5
2017-4 1,201.84 976.40
30.30
4.25
Source: Bank Indonesia, Ministry of Cooperatives and Small and
Medium Enterprises of the Republic of Indonesia
Based on table 1 above, in 2014: 4, when interest
rates rose to 7.67%, bank lending also rose to 694.97
Billion and MSMEs income continued to increase.
In fact, when interest rates should rise, bank lending
falls which results in a decline in MSMEs income in
Indonesia.
In 2016: 1-2016: 3, when the number of workers
fell, MSMEs income continued to increase. In fact,
the decline in the number of workers affects the
production process. Then, in 2017: 1, when interest
rates fell to 4.5%, bank lending fell to 942.69 Billion
and MSMEs income continued to increase. In fact,
when interest rates have dropped, bank lending has
risen which has resulted in increased MSMEs
income in Indonesia.
Because of the phenomena that contradict the
theory, it is interesting to do further research on the
factors that influence the income of MSMEs in
Indonesia. The purpose of this study is to find out
what factors influence the income of MSMEs in
Indonesia in 2010: 1-2017: 4.
2 THEORETICAL FRAMEWORK
According to Sukirno (2014), production theory in
economics distinguishes its analysis from two
approaches, namely:
1. Production Theory with One Factor Changed
Production theory with one factor changes explains
the relationship between the level of production of
goods produced based on the amount of labor used.
In the analysis of production theory with one factor
5.45
6.33
5.655.56
7.15
67.98
4.68
0
100
2011 2012 2013 2014 2015 2016 2017
MicroSmalland
MediumEnterprises
Income Income
Micro
Smalland
Medium
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448

changes only the amount of labor can be changed in
number.
Then the production function can be expressed as
follows:
Q = f (L) (1)
Where:
L = The Amount of Labor;
Q = The Amount of Production Produced.
2. Production Theory with Two Factors Change
According to Pracoyo and Pracoyo (2006), the
theoretical concept of long-term production is if all
the production factors used in the production process
are variable. The concept of long-term production
theory uses 2 variable inputs. According to Akhmad
(2014), the production theory with two changing
factors is a combination of labor and capital change.
In this case, how changes in producer behavior
choose the combination of labor and capital to
produce the same output is explained by the isoquant
and isocost curves.
a. Isoquant
According to Akhmad (2014), isoquant is a curve
that describes various combinations of the use of
two types of variable inputs efficiently with a certain
level of technology to produce the same level of
production. So production analysis with two factors
(all factors) input is a variable, both capital and
labor.
Then the production function can be expressed as
follows:
Q = f (K, L) (2)
Where:
K = The Amount of Kapital;
L = The Amount of Labor;
Q = The Amount of Production Produced.
The isoquant curve can be described as follows:
Figure 2: The Isoquant Curve
Caption: Isokuan shows a combination of 2 inputs
namely capital and labor which can be used to
produce the same level of output. Each point on the
isoquant curve shows various combinations of the
same input which can produce the same output. The
farther from the origin (upwards), the more output
will be generated, because the use of input increases.
b. Isocost
In carrying out production activities, producers have
problems regarding limited funds to allocate a
number of inputs. The limitations of these funds are
shown in a curve called isocos. According to
Pracoyo and Pracoyo (2006), Isokos is a curve that
describes the combination of two inputs that require
the same cost.
If it is assumed that producers only use two
inputs in their production, namely labor and capital,
the production costs that must be spent are:
TC = rK + wL (3)
Dimana:
r = Rent;
K = The Amount of Kapital;
w = Wage;
L = The Amount of Labor.
The isocost curve can be described as follows:
Figure 3: The Isocost Curve
Caption: The isocos curve shows various
combinations of 2 inputs, namely capital and labor
used to produce output at the same cost. If the
producers' funds change, while the price of the two
inputs is fixed, the isocos will shift parallel to the
previous one, because it has the same slope. If the
price of one or both inputs changes, while the funds
held are fixed, the slope of the isocos will change.
In this study, the amount of production is
considered as income earned by MSMEs from
capital (bank credit), labor and interest rates that
have been empirically proven by other researchers.
According to Kasmir (2014), the more loans
channeled, the better, especially in terms of
increasing income. Thus, it can be said that credit
distribution has a positive effect on income.
According to Sindani's (2018) research on the
effect of trade accounts receivable financing on the
growth of SMEs in Kakamega District, Kenya with
OLS estimation (Ordinary Least Square), the result
is trade receivables financing positively and
significantly affects SME growth in Kakamega
The Analysis Factors of Micro Small and Medium Enterprises Income in Indonesia
449

District, Kenya individually without include other
factors.
Whereas according to research conducted by
Nwosa and Oseni (2013) about the impact of bank
loans on SMEs in the manufacturing sector in
Nigeria with estimation of ECM (Error Correction
Mode), the result is bank loans have no significant
effect both in the short and long term for SMEs in
the manufacturing sector in Nigeria.
In addition to lending, labor is an important
factor in production, because labor is the driving
force of other input factors, without the presence of
labor, other production factors will not stop.
According to Todaro (2000) labor force growth is
traditionally regarded as one of the positive factors
that spur economic growth, a greater number of
labor means that it will increase the level of
production.
According to Maryati (2014) about the role of
Sharia Community Financing Banks in the
development of MSMEs and rural agribusiness in
West Sumatra, the result is large productive
financing and business assets that have a significant
and positive effect on the value of business
production, while labor has a significant and
negative effect on business production.
Meanwhile, according to research conducted by
Ulrich and Cyrille (2016), we examine the effect of
commercial bank credit on SME income in
Cameroon: Empirical evidence from 1980-2014 with
OLS (Ordinary Least Square) estimates. The result
is that the stock of capital and labor has a positive
impact on the income of SMEs in Cameroon. Also
revealed that commercial bank loans and real
interest rates have a negative and significant impact
on the income of SMEs in Cameroon.
According to Mishkin (2008) the stability of
interest rates is highly expected, because the stability
of interest rates also encourages financial market
stability so that the ability of financial markets to
channel funds from people who have the opportunity
to produce investment can run smoothly and
economic activity also remains stable. When interest
rates are low, the more funds flow, resulting in
increased economic growth and vice versa (Sundjaja
and Berlian, 2003).
3 RESEARCH METHOD
This type of research is quantitative research using
secondary data from 2010: 1-2017: 4. Data on bank
creding distribution in billion rupiah units is
obtained from Bank Indonesia and interest rates in
percent units are obtained from Bank Indoensia. The
MSMEs labor in units of millions per person was
obtained from the Ministry of Cooperatives and
Small and Medium Enterprises of the Republic of
Indonesia and MSMEs income in billion units was
obtained from the Ministry of Cooperatives and
Small and Medium Enterprises of the Republic of
Indonesia
The analytical method used in this study is
Multiple Regression Analysis where regression
analysis is known as Ordinary Least Square (OLS)
analysis with classic assumption tests, namely
normality test, mutlikollinearitas test and
heteroscedasticity test. The hypothesis test
conducted is t test, F test and R
2
test.
The following is a multiple linear regression
equation:
Y = β
0
+ β
l
X
1
+ β
2
X
2
+ β
3
X
3
+ e
i
(4)
Where:
Y = MSMEs Income;
X
1
= Bank Credit Distribution;
X
2
= Labor;
X
3
= Interest Rate;
β
0
= Parameter Constants;
β
1
= Bank Credit Distribution Regression
Coefficient;
β2 = Labor Regression Coefficient;
Β3 = Interest Rate Regression Coefficient;
ei = Disturbance Error.
4 ANALYSIS
4.1 Classic Asumption Test
4.1.1 Normality Test
The normality test is used to test whether in the
regression model, the independent variable and the
dependent variable are normally distributed or not.
A good regression model is if the data distribution is
normal or near normal. Tests are carried out using
the Jarque Bera Test or J-B Test. The following are
the results of the normality test:
Data is processed with eviews 9
Figure 4: Result of Normality Test
0
1
2
3
4
5
6
7
8
-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
Series: Residuals
Sample 2010Q1 2017Q4
Observations 32
Mean 2.21e-15
Median 0.018619
Maximum 0.907312
Minimum -0.591216
Std. Dev. 0.351064
Skewness 0.727603
Kurtosis 3.577200
Jarque-Bera 3.267716
Probability 0.195175
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450

Based on the data above, Jarque-Bara value is
3.267716 with p value of 0.195175 > 0.05, it can be
concluded that the data used is normally distributed.
4.1.2 Uji Multikolinearitas
Multicollinearity is the condition of a linear
relationship between independent variables.
Multicollinearity testing uses a variance inflation
factor (VIF). If the VIF value of a variable is not
more than 10, then the variable does not multiply
with other variables in the model (Gujarati, 2003).
The following are the results of the
multicollinearity test:
Table 2: Result of Multicollinearity Test
Variance Inflation Factors
Date: 12/03/18 Time: 01:49
Sample: 2010Q1 2017Q4
Included observations: 32
Coefficient Uncentered Centered
Variable Variance VIF VIF
C 203.6926 47769.19 NA
LNBCD 0.006453 274.5416 1.201890
L
NSMEs_
L 0.737744 50684.12 1.206438
IR 0.004360 42.48433 1.015031
Data is processed with eviews 9
Based on the data above, it shows that the VIF
values of all variables are less than 10. This means
that all variables in this study are not
multicolinearity with other variables in the model.
4.1.3 Heteroscedasticity Test
Heteroscedasticity aims to test whether in the
regression model there is an inequality of variance
from the residual one another observation. A good
regression model is homoschedasticity or
heteroscedasticity does not occur.
Table 3: Result of Heteroscedasticity Test
Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic 0.805684 Prob. F(3,28) 0.5013
Obs* R-squared 2.542840
P
rob. Chi-Square(3) 0.4676
S
caled explained
SS 2.508726
P
rob. Chi-Square(3) 0.4737
Data is processed with eviews 9
Based on the data processing above, where the
value of p value is indicated by the value of the
Prob. chi square (3) in Obs * R-Squared which is
equal to 0.4676. Because the p value is 0.4676>
0.05, it can be concluded that there is no problem of
heteroscedasticity.
4.2 Ordinary Least Square (OLS) Test
This study uses multiple linear regression with an
estimation model of Ordinary Least Square (OLS).
The following are the results of Ordinary Least
Square (OLS) calculations:
Table 4: Result of Ordinary Least Square (OLS) Test
Dependent Variable: LNSMEs_Income
Method: Least Squares
Date: 12/03/18 Time: 01:35
Sample: 2010Q1 2017Q4
Included observations: 32
Variable Coefficient Std. Error t-Statistic Prob.
C -49.50241 14.27209 -3.468477 0.0017
LNBCD 0.053353 0.080329 0.664183 0.5120
LNSMEs_L 3.759710 0.858920 4.377251 0.0002
IR -0.387454 0.066027 -5.868097 0.0000
R-squared 0.660139 Mean dependent var 13.09665
Adjusted R-
squared
0.623725 S.D. dependent var 0.602193
S.E. of
regression
0.369393 Akaike info criterion 0.962557
Sum squared
resid
3.820632 Schwarz criterion 1.145774
Log
likelihood
-11.40091 Hannan-Quinn criter. 1.023288
F-statistic 18.12885 Durbin-Watson stat 0.443552
Prob(F-
statistic)
0.000001
Based on the data in table 2 above, the Ordinary
Least Square (OLS) equation is obtained:
Y = β
0
+β
1
X
1t
+β
2
X
2t
+β
3
X
3t
+e
t
Y = -49.50241 + 0.053353 X
1
+ 3.759710 X
2
–
0.387454 X
3
This means that bank lending has a positive
effect on MSME income in Indonesia, labor has a
positive effect on MSME income in Indonesia and
interest rates have a negative effect on MSME
income in Indonesia.
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451

4.3 Hypothesis Test
4.3.1 F-Test
This test aims to see whether there is a significant
influence between independent variables on the
dependent variable simultaneously or together. In
the context of this study, this simultaneous testing
wanted to see whether the variables of Banking
Credit Distribution, MSMEs Labor and Interest Rate
had an effect on MSMEs Income or not. To see
whether or not the influence of the independent
variables on the dependent variable is seen from the
significance value. If the significance value is <
alpha, then there is a significant effect between the
independent variables on the dependent variable.
And vice versa, if the value of sig. > alpha, then
there is no significant effect between the
independent variables on the dependent variable.
After testing, it can be seen from Table 4. above,
the results of the significance value are 0.000001
<0.05, which means that independent variables
(Bank Credit Distribution, MSMEs Labor and
Interest Rate) have a significant effect on MSMEs
Income or jointly influence revenue MSMEs, so that
changes in MSMEs income can be explained by the
independent variables tested.
4.3.2 t-Test
The t test statistic shows how far the influence of
one free varaibel individually in explaining the
variation of the dependent variable. To do the t test
by Quick Look, is if the prob value < alpha then
there is a significant effect between the independent
variables on the dependent variable, and vice versa.
a. The Bank Credit Distribution
After testing using the eviews 9.0 application, it can
be seen from Table 4. above, that the probability
value for the bank lending variable is 0.5120 > 0.05.
This shows that the variable of bank credit
distribution does not have a significant effect on
MSMEs income in Indonesia. The direction of the
regression coefficient for the bank credit distribution
variable is positive, the positive value has the
meaning that the higher bank credit distribution will
be followed by an increase in MSMEs income in
Indonesia.
The coefficient value of 0.053353 means that the
value that will be obtained if bank credit distribution
rises by 1 billion, it will be followed by an increase
in MSMEs income of 0.053353 billion. Likewise, on
the contrary, if there is a decrease in bank credit
distribution of 1 billion, it will be followed by a
decrease in MSMEs income of the same value,
namely 0.053353 billion, cateris paribus.
b. The MSMEs Labor
Based on the results of the study, it shows that the
probability value for the labor variable is 0.0002 <
0.05. This shows that labor variables have a
significant effect on MSMEs income in Indonesia.
The direction of the regression coefficient for the
labor variable is positive, the positive value has the
meaning that the higher the number of workers it
will be followed by an increase in MSMEs income
in Indonesia.
The coefficient value of 3.759710 means that the
value that will be obtained if the amount of labor
increases by 1 million people will be followed by an
increase in MSMEs income of 3.759710 billion.
Likewise, on the contrary, if there is a decrease in
the amount of labor of 1 million people, it will be
followed by a decrease in MSMEs income of the
same value, namely 3.759710 billion, cateris
paribus.
c. The Interest Rate
Based on the results of the study, it shows that the
probability value for the interest rate variable is
0.0000 < 0.05. This shows that the interest rate
variable has a significant effect on MSMEs income
in Indonesia. The direction of the regression
coefficient for the interest rate variable is negative,
the negative value means that the higher the interest
rate will be followed by a decrease in MSMEs
income in Indonesia.
The coefficient value of -0.387454 means that
the value to be obtained if the interest rate rises by 1
percent will be followed by a decrease in MSMEs
income of 0.387454 billion. Likewise with the
opposite, if there is a decrease in the interest rate of
1 percent, it will be followed by an increase in
MSMEs income of the same value, which is
0.387454 billion, cateris paribus.
4.3.3 Determination Test (R
2
)
Based on Table 4. above, it is known that the results
of the data show that the value of R² obtained from
the estimation results is 0.660139. This means that
66.01 percent of the variation in MSME income is
explained by the variable bank lending, MSME
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452

labor and interest rates. While 33.99 percent is
explained by other variables outside the model.
5 RESULTS
5.1 Effect of Bank Credit Distribution
on MSMEs Income in Indonesia
Based on the results of the study, it was shown that
positive credit distribution for MSMEs income in
Indonesia, but not significant. The effect of
insignificant credit disbursement on MSMEs income
in Indonesia with research conducted by Nwosa and
Oseni (2013) on the effect of bank loans on SMEs in
the manufacturing sector in Nigeria with estimation
of ECM (Error Correction Mode), which is not
significant bank loans in the long run short and long
for the SME sector in Nigeria.
5.2 Effect of Labor on MSMEs Income
in Indonesia
Based on the results of the study showed that labor
has a positive and significant effect on MSMEs
income in Indonesia. This is in accordance with the
production theory which states that labor is a factor
that affects production. If the number of workers
increases, it will affect the amount of production that
increases the income of MSMEs in Indonesia.
5.3 Effect of Interest Rate on MSMEs
Income di Indonesia
Based on the results of the study indicate that
interest rates have a negative and significant effect
on MSMEs income in Indonesia. This is in line with
the statement of Sundjaja and Berlian (2003) when
interest rates are low, so more funds flow so that
economic growth also increases and vice versa.
Thus, Bank Indonesia must maintain the stability of
interest rates, because it encourages financial market
stability so that the ability of financial markets to
channel funds from people who have the opportunity
to produce investment can run smoothly and
economic activity also remains stable.
6 CONCLUSIONS
MSMEs are the key to economic growth that can
help the Indonesian economy from income earned
by MSMEs. Based on the production theory, the
main factor that helps increase income is capital and
labor. The business capital obtained by MSMEs
comes from bank lending.
Based on the research that has been done, the
results obtained are that bank lending has a positive
and not significant effect on MSMEs income in
Indonesia. MSMEs labor has a positive and
significant effect on MSMEs income in Indonesia
and interest rates have a negative and significant
effect on MSME income in Indonesia.
REFERENCES
Akhmad. (2014). Ekonomi Mikro Teori dan Aplikasi
di Dunia Usaha.Yogyakarta: ANDI.
Brașoveanu, Iulian Viorel dan Bălu, Petronela –
Evelina. (2014). The Influence of the
Business Environment on Small and Medium
Enterprises. Journal of Knowledge
Management, Economics and Information
Technology. Vol. IV, Issue 2. Pp 1-13.
Gujarati, Damodar, (2003), Ekonometri Dasar.
Terjemahan: Sumarno Zain, Jakarta:
Erlangga.
Kasmir. (2014). Dasar-Dasar Perbankan Edisi
Revisi. Jakarta: PT Raja Grafindo Persada.
Maryati, Sri. (2014). Peran Bank Pembiayaan
Rakyat
Syariah dalam Pengembangan UMKM dan
Agribisnis Pedesaan di Sumatera Barat.
Journal of Economic Education. Vol 3. No. 1.
Pp 1 17.
Nwosa, Philip Ifeakachukwu and Oseni, Isiaq
Olasunkanmi. (2013). The Impact of Banks
Loan to SMEs on Manufacturing Output in
Nigeria. Journal of Social and Development
Sciences. Vol. 4, No. 5. Pp 212-217.
Pracoyo, Tri Kunawangsih dan Pracoyo, Antyo.
(2006). Aspek Dasar Ekonomi Mikro.
PTGramedia Widiasarana Indonesia: Jakarta
Sindani, Mary Nelima Lyani. (2018). Effects of
Accounts Receivable FinancingPractices
onGrowth of SMEs in Kakamega County,
Kenya.Expert Journal of Finance. Vol. 6. Pp
1-11.
Sukirno, Sadono. (2014). Mikroekonomi Teori
Pengantar. Edisi Ketiga. Jakarta: PTRaja
Grafindo Persada.
Sundjaja, Ridwan S dan Inge Berlian.
(2003).Manajemen Keuangan 2. Edisi
Keempat. Yogyakarta: Literata Lintas Media.
Todaro, Michael P. 2000. Pembangunan Ekonomi di
Dunia Ketiga. Erlangga. Jakarta
The Analysis Factors of Micro Small and Medium Enterprises Income in Indonesia
453

Ulrich, D’Pola Kamdem dan Cyrille, Tour Moise
Alexis. (2016). The Effect of Commercial
Bank Credit on The Output of Small and
Medium Enterprises in Cameroon: Empirical
Evidence From 1980-2014. Maghreb
Review of Economic and Management. Vol,
03, No. 02. Pp 43-53.
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