Access to formal credit and the success of micro, small, and medium enterprises in Central Sulawesi, Indonesia

ACCESS TO FORMAL CREDIT AND THE SUCCESS OF
MICRO, SMALL, AND MEDIUM ENTERPRISES IN
CENTRAL SULAWESI, INDONESIA

RATNA SOGIAN SIWANG

GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
2012

DECLARATION

I, Ratna Sogian Siwang, hereby declare that the thesis entitled:

ACCESS TO FORMAL CREDIT AND THE SUCCESS OF
MICRO,

SMALL,

AND

MEDIUM

ENTERPRISES

IN

CENTRAL SULAWESI, INDONESIA

Submitted to fulfill a requirement for the award of Master of Science
in Agribusiness from Bogor Agricultural University Indonesia and
Georg August University of Goettingen Germany in the framework of
international joint degree program between both universities is my
own work through the guidance of my academic advisors and to the
best of my knowledge it has not been submitted for the award of any
degree in any other academic institutions. This thesis does not contain
any pieces of work of other person, except those are acknowledged
and referenced herein.

Bogor, November 2012

RATNA SOGIAN SIWANG
NRP H451100131

ABSTRACT

RATNA SOGIAN SIWANG, Access to formal Credit and The Success of
Micro, Small, and Medium Enterprises in Central Sulawesi, Indonesia (NUNUNG
KUSNADI as a Chairman and SUHARNO , STEFAN SCHWARZE, and
MATIN QAIM as Member of Advisory Committee)
Micro, small, and medium enterprises (MSMEs) have an important role in
Indonesia economy with the share in the numbers of the firms reaches 99.99
percent. Central Sulawesi is one of province in Indonesia which also has many
MSMEs engaged in agricultural and non-agricultural sectors. Access to credit is
one of major problems hampering MSMEs development in Central Sulawesi since
only 39.33 percent of MSMEs in the research area which had access to credit in
2001-2007. This research is aimed to analyze the determinant of access to formal
credit of non-agricultural MSMEs in Central Sulawesi and to determine the role of
access to credit to the success of MSMEs. We use panel data from household’s
survey of STORMA project in 2001, 2004, and 2007. There are two models are
used in this research namely logit model (non-linear panel data model) and OLS
model (linear panel data model). Each models are extended by panel data models
i.e. pooled, population average (Pa), fixed effects (FE), and random effects (RE)
model. Based on the evaluation of the four models and the result of the Hausman
specification test for the FE and RE model, we decided to use random effects
model for both analysis. Econometrics results from random effects show that the
significance factors influencing access to credit are education, value of asset, and
age of the owners. Whilst the result from random effects OLS model proves that
formal credit limit has positive roles in the success of MSMEs in Sulawesi
Tengah. On the other hand, informal credit limit is not statistically significance
influencing the profit because of the inability of the informal lenders to provide
amounts of credit as needed by the firms. Besides the formal credit limit, other
variables which also significantly influence MSMEs’s profit are age of the owners
and main Income from business.

Key words: MSMEs, Access to Credit, Success

ABSTRAK
RATNA SOGIAN SIWANG, Akses kepada Kredit Formal dan Kesuksesan
Usaha Mikro, Kecil, dan Menengah di Sulawesi Tengah, Indonesia (NUNUNG
KUSNADI sebagai ketua SUHARNO, STEFAN SCHWARZE, dan MATIN
QAIM sebagai anggota komisi pembimbing)

Usaha Mikro, Kecil, dan Menengah (UMKM) memiliki peran yang
penting dalam perekonomian Indonesia dengan kontribusi pada total jumlah
perusahaan mencapai 99.99 persen. Sulawesi Tengah adalah salah satu propinsi di
Indonesia yang juga memiliki banyak UMKM yang bergerak di bidang pertanian
dan non pertanian. Kurangnya akses kepada kredit merupakan salah satu masalah
utama yang menghalangi perkembangan UMKM di Sulawesi Tengah. Hanya
sekiitar 39.33 persen UMKM di wilayah penelitian yang mendapatkan akses
kepada kredit pada tahun 2001-2007. Penelitian ini bertujuan untuk menentukan
faktor-faktor yang mempengaruhi akses kredit UMKM di Sulawesi Tengah dan
untuk menganalisis peran kredit dalam kesuksesan UMKM di Sulawesi Tengah.
Penelitian ini menggunakan data panel dari proyek STORMA pada tahun 2001,
2004, dan 2007. Dalam penelitian ini digunakan dua model panel data untuk
menjawab permasalahan, yaitu model regresi logistik (untuk analisis non-linier)
dan model OLS (untuk analisis linier). Setiap model akan dikembangkan dengan
menggunakan metode panel yaitu model pooled, population average (Pa), fixed
effects (FE), dan random effects (RE). Berdasarkan evaluasi menggunakan tes
Hausman pada kedua model, penelitian ini menggunakan model random effects
(RE) untuk kedua permasalahan penelitian. Analisis ekonometrik menunjukkan
bahwa factor-faktor yang mempengaruhi akses UMKM terhadap kredit dari
lembaga formal adalah pendidikan, nilai asset, dan usia pemilik perusahaan.
Analisis random effects pada model OLS menunjukkan bahwa kredit dari
lembaga formal memiliki dampak positif dan signifikan terhadap kesuksesan
UMKM, sedangkan kredit dari lembaga non-fomal tidak signifikan secara statistik
mempengaruhi kesuksesan UMKM. Selain kredit dari lembaga formal, variable
yang juga mempengaruhi kesuksesan UMKM adalah usia pemilik perusahaan dan
jika usaha yang dijalankan adalah sumber penghasilan utama.

Kata Kunci: UMKM, Akses kredit, Kesuksesan

SUMMARY
RATNA SOGIAN SIWANG, Access to formal Credit and The Success of
Micro, Small, and Medium Enterprises in Central Sulawesi, Indonesia (NUNUNG
KUSNADI as a Chairman and SUHARNO , STEFAN SCHWARZE, and
MATIN QAIM as Member of Advisory Committee)

Micro, small, and medium enterprises (MSMEs) have a big role in global
economy because they are often considered as the most dominant sector in nation
economy, especially in the developing countries. In Indonesia, they are also
important with the share in the numbers of the firms reaches 99.99 percent.
MSMEs development in Indonesia face some problems hampering their growth.
Lack of access to credit, especially access to formal credit is one of the major
problem of MSME’s development in Indonesia. Central Sulawesi is one of
province in Indonesia which also has many MSMEs engaged in agricultural and
non-agricultural sectors. Access to credit is one of major problems hampering
MSMEs development in Central Sulawesi since only 39.33 percent of MSMEs in
the research area which had access to credit in 2001-2007.
This research is aimed to analyze the determinant of access to formal
credit of non-agricultural MSMEs in Central Sulawesi and to determine the role of
access to credit to the success of MSMEs. We use panel data from household’s
survey of STORMA project in 2001, 2004, and 2007. There are two models are
used in this research namely logit model (non-linear panel data model) and OLS
model (linear panel data model). Each models are extended by panel data models
i.e. pooled, population average (Pa), fixed effects (FE), and random effects (RE)
model. Based on the evaluation of the four models and the result of the Hausman
specification test for the FE and RE model, we decided to use random effects
model for both analysis.
The econometrics analysis using random effects logit model shows that the
significant factors influencing access to credit are education, value of asset, and
age of the owners. Those variables have the positive marginal effects which mean
that they have positive influence to the probability of MSMEs having access to
formal credit. Hence, the formal lenders tend to choose firms whose owner with a
higher education, a higher value of assets, and the older one. Other variables in
the model are not statistically significant influencing the probability of access to
formal credit of MSMEs since they have p-value bigger than 10 percent. It means
that there is no enough evidence to prove that the means of distance to road, male,
landownership, and organization membership are significantly different from
zero. The results are quite surprising since these variables are often significant in
others research, such as land which is often used as collateral.
Econometrics analysis using random effects OLS model shows that formal
credit limit has positive roles in the success of MSMEs in Central Sulawesi. This
variable is statistically significant at 1 percent level. Therefore, the bigger formal
credit limit the higher profit for MSMEs. On the other hand, informal credit limit
is not statistically significant influencing the profit because of the inability of the
informal lenders to provide amounts of credit as needed by the firms. Besides the

formal credit limit, other variables which also significantly influence MSMEs’s
profit are age and main income from business, while variable of experience,
training, and has another business do not statistically significant. Variable of age
has a positive coefficient which indicates that older firm’s owners the higher the
profit. But, there is a limit when the firm’s owners become too old, the profit will
decrease. Variable of experience also has a positive coefficient but it is not
statistically significance. Firm’s owners who focus on their main business have a
better Profit than who those have another business.

Copyright© 2012. Bogor Agricultural University All Right
Reserved
1. No part or all of this thesis maybe excerpted without inclusion
and mentioning the sources.
a. Excerption only for research and education use, writing for
scientific papers, reporting, critical writing or reviewing of
a problem.
b. Excerption does not inflict a financial loss in the proper
interest of Bogor Agricultural University
2. No part of or entire of this thesis maybe translated and
reproduced in any form or by any means without written
permission from Bogor Agricultural University

ACCESS TO FORMAL CREDIT AND THE SUCCESS OF
MICRO, SMALL, AND MEDIUM ENTERPRISES IN
CENTRAL SULAWESI, INDONESIA

RATNA SOGIAN SIWANG

A thesis
Submitted to the Graduate School in Partial Fulfillment of the Requirement for
Master of Science
Degree in
Agribusiness

GRADUATE SCHOOL
BOGOR AGRICULTURAL UNIVERSITY
2012

1. External Thesis Examiner

: Dr. Ir. Heny K. Daryanto, MEc

2. Study Program Representative : Dr. Amzul Rifin, SP. MA

Thesis Tittle

: Access to Formal Credit and the Success of Micro,
Small,

and

Medium

Enterprises

in

Central

Sulawesi, Indonesia
Name

: Ratna Sogian Siwang

Registration Number : H451100131

Approved
1.

Advisory Committee

Dr. Ir. Nunung Kusnadi, MS

Dr. Stefan Schwarze

Chairman

Member

Dr. Ir. Suharno, M. Adev

Prof. Dr. Matin Qaim

Member

Member

Agreed
2. Coordinator of Major

3. Dean of Graduate School

Agribusiness

Prof. Dr. Rita Nurmalina, MS

Examination Date :

Dr. Ir. Dahrul Syah, MSc, Agric

Submission Date :

Acknowledgement
This research would have been impossible without the support from many
people. I would like to appreciate everything they have given to me. First of all,
all praise to God, who the most precious and the most merciful for His blessing
from the first until the last step of the research process. I would like to
acknowledge the support of the National Education Ministry of Indonesia for
funding my study in Germany.
I would acknowledge my supervisors in Indonesia, Dr. Ir. Nunung
Kusnadi, MS and, Dr. Ir. Suharno, M. Adev from Bogor Agricultural University
Indonesia, for their support and their insight of my research and my study. I also
thank to Dr. Amzul Rifin SP, MA and Dr. Ir. Heni Kuswanti, Mec as examiners in
my final examination for their contructive critism and comments. I am indebted
to my supervisors in Germany, Dr. Stefan Schwarze and Prof. Matin Qaim from
Goettingen University, who supports me academically and mentally in thesis
writing from the beginning until the last step. I would like also to thank for their
insight and his constructive criticism of my thesis.
My Special thank to Anisa Dwi Utami who helps me to proofreading this
thesis. My sincere thank further to all my friends and family in SIA program and
in Goettingen Indonesian Student Community, especially for the ‘Roko Jaya
Family’ for providing me a friendly and warm environment during my study in
Goettingen.
Finally I would like to thank my family and my husband for their love and
their support for me. I dedicate this work to my beloved mother who always gave
me her love and taught me the values of life.

Bogor, November 2012
Ratna Sogian Siwang

Autobiography

Ratna Sogian Siwang, the author of this thesis, was born in Palembang, on
4th of July 1986. She completed her primary education in 1999 at SD Adhyaksha I
Jambi. She did her Junior high school at SLTP As-Syafiiyah Sukabumi in 2002
and completed her senior high school at SMAN 3 Sukabumi in 2005. She spent
her bachelor degree in Bogor Agricultural University with major Agribusiness.
She got her B.A in 2009. During the bachelor degree she got scholarship from
Tanoto Foundation. She got married to Nazrul Anwar in September 2012.
Ratna has ever worked as a field officer at a Non-Government
Organization (NGO) which engaged in Small and Medium Enterprises and
Community Development. She continued her study to the master’s degree with
joint-degree program between Department of agribusiness of Bogor Agricultural
University, Indonesia and Department of International Agribusiness and Rural
Development of Goettingen University, Germany. She spent her first year in
Bogor and her second year in Goettingen. She got scholarship from National
Education Ministry of Indonesia.

Table of Content
Table of Content

xiii

List of Tables

xv

List of Figures

xvi

List of Abbreviation

xvii

I. Introduction
1.1 Background

1

1.2 Statement of Problem

2

1.3 Objective of the Study

3

1.4 Limitation of Study

3

1.5 Significance of Study

4

II. Literature Review
2.1 Determinants of Access to Credit

5

2.2 MSMES and the Role of Access to Credit in Their Success

7

III. Framework
3.1 Theoritical Framework

11

3.2 Operational Framework

16

IV. Methodology
4.1 Data Source

18

4.2 Data Modifying Method

18

4.3 Model Estimation

19

V. Description of Research Area
5.1 Description of STORMA and Research Area

25

5.2 Formal Credit Market in the Research Area

27

VI. Descriptive Analysis of Access to Credit and Business Activities
6.1 Access to Formal Credit of Non-Agricultural Firms

30

6.2 Business Characteristics of the Firms

31

6.3 Characteristics of the Owners

35

VII.

Econometrics Results

7.1 Determinants of Access to Formal Credit of MSMEs

37

7.2 The Role of Access to Credit in MSMEs Success

43

VIII. Conclusion and Policy Implication
8.1 Conclusion

49

8.2 Policy Implications

50

References

53

Appendices

57

List of Tables
Table

Page

1

Criteria of Micro, Small, and Medium Enterprises in Indonesia

2

Comparison Data Share of Micro, Small, Medium, and Big

7

Enterprises in Number, Employment, and GDP (2009)

8

3

Importance of Profit Making in Theories of Entrepreneur

14

4

Summary of Variables Used to Analyze the Determinant
of Access to Credit

5

38

The Result of the Hausman Specification Test for RE
logit and FE logit of The Determinant of Access to formal
Credit

6

The Marginal Effects of Random Effects Logit Model
for Determinant of Access to Formal Credit of MSMEs

7

41

Summary of Variables Used to Analyze the Role of
Access to Credit to the Success of MSMEs

8

39

44

The Result of the Hausman Specification Test for RE OLS
and FE OLS of the Role of Access to Credit in the Success
of MSMEs

9

45

The Coefficients of Random Effects OLS model for
the Role of Access to Credit in the Success of MSMEs

46

List of Figures
Figure

Page

1

Optimal Level of Input Use

15

2

Unoptimal Level Input Use

15

3

Flow Chart of Research Process

17

4

Map of the Lore Lindu National Park and the Research Area

26

5

Maximum and Minimum Formal Credit Limit over Time

30

6

Percentage of Firms Having Access to Formal Credit
in 2001-2007

31

7

The Average Profit From Business over Time

33

8

The Average Value of Asset Over Time

34

9

Average Landownership Over Iime

35

10 Line Graph Age and LPROFIT

47

List of Abbreviation

MSMEs

Micro, Small, and Medium Enterprises

FE

Fixed Effects

RE

Random Effects

Pa

Population Average

ME

Marginal Effects

I. INTRODUCTION
1.1 Background
Micro, small, and medium enterprises (MSMEs) have strategic roles in
global economy especially in the developing country. MSMEs are the dominant
sector in many countries, like in China (99,9 percent), Russia (98 percent), and
Portugal (98 percent). The huge number of MSMEs in each countries enable them
to have big contribution in nation‟s GDP, employment, export, etc. World Bank
(2003) listed the importance of MSMEs globally i.e. 1) They are engine of
growth, 2) They create a competitive and efficient market, and 3) They are
important in poverty alleviation.
Nevertheless there are some problems hampering MSMEs development.
The problems are related to their characteristics; small size, widely dispersed, and
has limited resource (World Bank, 2003). Small size makes them become lack of
economies of scale and facing a high cost of information. Widely dispersed causes
them become lack of collective voice and bargaining power. Limited capital
background renders them to have limited management capability.
As a consequence, lack of access to credit is one of the results of these
problems. Credit rationing among MSMEs happens because of asymmetric
information and imperfect competition in credit market which lead to the market
failure. Market failure in credit market makes MSMEs cannot get credit access
especially credit from formal lenders. This condition can be filled by informal
credit market, but amount of credit from informal lenders is often too small to
fulfill MSME‟s needs and the informal lenders often impose them a high interest
rate (Bebczuk, 2001). MSMEs have a limited capital background therefore access
to credit is one of important thing in MSMEs success. Lack of access to credit
causes MSMEs could not expand their production and their market and then
inhibit their growth.
MSMEs are also important sector in Indonesia. They are the back bone of
Indonesian economy with the share in enterprise‟s number reaches 99.99 percent.
MSMEs also contribute 97 percent of employment and 56.53 percent of GDP
(Indonesian Cooperative and SMEs Ministry, 2009). MSMEs, especially micro
1

and small enterprise are mostly engaged in agricultural sector therefore they have
strategic roles in agricultural and rural development, and also in reduction of
unemployment and poverty.

1.2 Statement of Problem
MSMEs development in Indonesia also faces lack of access to credit,
especially access to formal credit. In West Java, only 25 percent of MSMEs get
credit from banks. Other problems facing by MSMEs in Indonesia are marketing
problem and lack of knowledge and technology extension (BPS, 2006). These
problems lead to a lower competitiveness of MSMEs compared to the big
enterprise. As evidence, MSMEs‟s contribution to Indonesian export was very
low only 17.02 percent. Especially for micro and small enterprises which are the
biggest part of MSMES, their export contribution was only 1.51 percent and 3.57
percent (BPS, 2009).
Central Sulawesi is one of agricultural based province in Indonesia. In
2010, there are 213 thousand MSMEs in Central Sulawesi, and mainly they are
involved in Agricultural sector. Agricultural sector‟s share in GDP reaches 41.56
percent (BPS of Central Sulawesi, 2010). The main agricultural products of
Central Sulawesi are cocoa, coconut, rice, maize, and rattan. MSMEs in Central
Sulawesi sell their product not only as fresh product, but also in processed product
such as brown sugar and handy craft. A survey conducted by Bank of Indonesia in
2011 showed that lack of credit access is one of the main problems faced by
MSMEs in Central Sulawesi. Bank of Indonesia suggested the government of
Central Sulawesi to enhance credit access of MSMEs by implementing micro
finance program like

Micro Credit without Collateral for Microenterprise

(KUMLTA = Kredit Usaha Mikro Layak Tanpa Agunan).
Nuryartono (2005) has studied about credit constrains among farmer
household in rural area of the forest margin of the Lore Lindu National Park,
Central Sulawesi, Indonesia. His research reported that is only 21.5% of the
households have access to formal credits, and only 18.1% of the households are
not credit constrained. Most households are credit constrained because of lack of
collateral and self-selection problem. This thesis will continue his study with the
2

emphasis to non-agricultural enterprises, such as manufacturer, trader, and service
and to credit from formal lenders. Agricultural and non agricultural sectors are
linked as one chain in agribusiness system. The non agricultural firms are
important

to

support

the

agricultural

sector

especially

in

terms

of

commercialization process of agricultural sector.
According to the background, there are several research questions
addressed in this research:
1. What are the determinants of access to credit of MSMEs in Central Sulawesi,
Indonesia?
2. How does the role of access to credit in the success of MSMEs?

1.3 Objectives of Study
Based on the research questions, the objectives of this research are:
1. To determine the determinant of access to credit of MSMEs in Central
Sulawesi, Indonesia.
2. To analyze the role of credit access in the success of MSMEs in Central
Sulawesi, Indonesia.

1.4 Limitation of Study
This study has some limitations which restrict the analysis, i.e.:
1. Just concerning to the non agricultural firms in research area.
2. Just capturing the condition of sample and research area in 2001, 2004,
and 2007.
3. Just analyzing access of formal credit.

1.5 Significance of Study
The significances of this research are:
1. For the researcher and academician, this study will be an additional
reference for others research about access to credit of MSMEs and its
implication to their success.

3

2. For the government, this study will give information and policy
recommendation about how to increase access to credit of MSMEs and
how to enhance their profit.

4

II. LITERATURE REVIEW

In this chapter we will review some previous studies about access to credit
and its determinant and the role of access to credit in the success of MSMEs. The
review of determinant of access to credit will focus on determinants of access to
formal credit of MSMEs in different cases and different countries. The review
will give us a description about variables which often used in the previous
research to determine access to credit and its relationship to the success of
MSMEs.

2.1 Determinants of Access to Credit
The previous researches about determinant of access to credit of SMEs
around the world have shown various results. Many variables are used to find the
main factors influencing the access to credit. The variables are usually related to
socioeconomic condition of the firm, business characteristic and activities, and
human capital. Socioeconomic characteristics that usually affect the access to
credit of MSMEs is education of the firm‟s owner (Bebczuk, 2004 ; Kedir, 2000;
Pandula, 2011). Pandula (2011) said that education is important in access to
formal credit because more educated entrepreneurs have a better capability to
show positive financial information and business plans and to establish
relationship with financial institutions. Hence, from the lender‟s point of view,
more educated firm‟s owners are likely to have better managerial skills, so the
lenders will rate them higher in the credit assessment.
Another variable is firm‟s location (Kedir, 2000; Fatoki and Odeyemi,
2010; Aga and Reily, 2011). This variable reflects population density of the
potential borrowers. Banks usually have more information about potential
borrowers in less densely populated area, so firms which are located in densely
populated area of potential borrowers are more likely to be credit constrained
(Aga and Reily, 2011). Location in terms of infrastructure development also
influences access to credit. Fatoki and Odeyemi (2010) find that firms which are
located in urban area are less credit constrained than those which are located in
rural area. Age of the owners also affects firm‟s access to credit. Mwangi (2010)
5

analyzed that access to credit of MSMEs in Kenya increases as the age of the
firm‟s owner increase, but the probability decreases when the age of firm‟s owner
is closely to retirement. Other variable is gender, some research show that femaleowned firms are more likely to have access to credit than male-owned firms
(Yehuala, 2008; Aga and Reily, 2011).
Business related variables that influence access to credit of firms in some
literature are membership to business association (Pandula, 2011; Fatoki and
Odeyemi, 2010; Aga and Reily, 2011; Yehuala, 2008). This variable is significant
because being a member of business association gives many advantages to
MSMEs such as association will facilitates member firms to vocational training or
extension services, which also significantly affect access to credit (Pandula,
2011). Association can also provide the members with information related to their
business development including information about credit.
Variable of collateral is also found as determinant of access to credit
(Yehuala, 2008; Kedir, 2000) because lenders usually need collateral as a
requirement in credit assessment. Collateral is important because of some reasons
i.e. 1) it increases the expected return and reduces the variance of return for the
lender; 2) it partly shifts the risks of loss of the principle from the lender to the
borrowers; 3) it provides additional incentives for the borrowers to repay the loan;
and 4) it has a screening effect on the applicant pool, discriminating against poor
but often credit-worthy loan applicants with little or no suitable collateral
(Binswanger, Mc Intire, and Udry (1989) in Nuryartono (2005)).
Land ownership with a legal land certificate is one of the ideal collaterals
which is demanded by a lender because of its immobility and virtual
indestructibility, therefore it ownership rights can be easily transferable. Access to
credit and amount of credit are usually correlated with land ownership, especially
in underdeveloped formal financial systems. Therefore, inequalities in land
ownership are often the cause and the effect of credit market inequalities (Meyer
(1990) in Nuryartono (2005)). Another variable is value of asset (Kedir, 2000;
Diagne, 1999). Same as collateral, value of asset are often used as consideration in
credit assessment by lenders.

6

Some human capital related variables also usually influence the
probability of firms get access to formal credit e.g. managerial competency and
business planning (Fatoki and Odeyemi, 2010). These skills are important because
banks usually use business plan to evaluate the firms when they decide to give
credit to the firms. Managerial and business planning competencies are the result
of education, vocational training, or participation in extension service (Fatoki and
Odeyemi, 2010).

2.2 MSMEs and the Role of Access to Credit in Their Success
In Indonesia, micro, small, and medium enterprises are distinguished
by their net asset and net annual sales. According to the Indonesian law number
20 in 2008, the criteria of micro, small, and medium enterprise in Indonesia is
showed on the table below.

Table 1. Criteria of Micro, Small, and Medium Enterprise in Indonesia
Criteria

Micro

Small

Medium

Net Asset

≤ Rp 50

>Rp 50 million

>Rp 500 million

(without land

Million

- ≤ Rp 500

≤ Rp 10 billion

and building)

million

Net Annual

≤ Rp 300

>Rp 300 millon

>Rp 2,5 billion -

Sales

million

≤ Rp 2,5 billion

≤ Rp 50 billion

Source: Indonesian Law Number 20, 2008
While the BPS and the Industrial Ministry of Indonesia classify micro,
small, and medium enterprises based on the number of labor. Micro-enterprise
is a business that has 1-4 employees, while small enterprise has 5-19
employees, medium enterprise has 20-99 employees, and large enterprise has
more than 100 employees. MSMEs are the most dominant sector in
Indonesia‟s economy. Their contribution compared with big enterprises is
described in the following table 2.

7

Table. 2 Comparison Data of Micro, Small, Medium, and Big Enterprises in
Number, Employment, and GDP (2009)
Number of Business
Unit

Type of
Enterprise Number
(Units)

Employment

GDP

Percentage Number

Percentage Amount

Percentage

(%)

(%)

(%)

(People)

Micro

52,176,795

Small

546,675

1.04

3,521,073

3.56

528,244.20

9.98

41,133

0.08

2,677,565

2.71

713,262.90

13.47

4,677

0.01

2,674,671

2.7 2,301,709.20

43.47

Medium
Big

98.88 90,012,694

(Rp)
91.03 1,751,644.60

33.08

Source: Indonesian Cooperative and SMEs Ministry

MSMEs in Indonesia have special characteristics. Tambunan (2008)
explains the characteristics of MSMEs in Indonesia are ; 1) Easily accessed
because they do not require large capital; 2) Rely on local resources; 3) Family
ownership; 4) Small-scale business‟s activities; 5) Traditional production
technology; 6) Low quality and low productivity of products; 6) Labor intensive;
7) Less Educated; 8) Low and not stable Profit; 9) Competitive market with no
special regulation; 10) Local level marketing.
Success of MSMEs is influenced by some variables. Access to credit is
one of the key factors of the success of MSMEs. Availability and accessibility of
credit have a positive impact to the firm‟s performance in terms of purchasing
input, increasing product‟s quantity and quality, marketing process, and soon.
Those activities will lead to a higher profit from business. Using logit model,
several researchers have analyzed that the main factors influencing the success of
MSMEs in Indonesia are financial access (Indonesian Cooperative and SMEs
Ministry, 2005; Bowo, 2003; Prasetyo, 2010; Darroch and Clover, 2005). Jasra
et al (2011) also found that the most important factor influencing the success of
small firms in Pakistan is financial resources, which is equivalent with access to
credit. Access to credit also significantly influence the success of MSMEs in
Tanzania (Kuzilwa, 2005). In line with results which describe the positive impact
of access to credit in business success, lack of credit has the negative impact to
business performance.
8

Besides access to credit, variable of socioeconomic characteristics of the
firm‟s owner, business characteristics, and human capital variables are also used
by several researchers to analyze factors influencing MSMEs success. One of
Socioeconomic variables which is statistically significant to influence MSMEs‟s
success is experience of the firm‟s owners (Bosma, 2000 ; Roy 2004; Saleem
2012). Experience in business activity describes how long the entrepreneur has
involved in his business. A longer period of experience shows that the firm‟s
owner has managed the firm well so the firm can survive facing the obstacles and
gain profit. Other variables is education (Bosma 2000; Rob and Fairly, 2008),
because education level of the firm‟s owners describes their level of knowledge,
so the owners use their knowledge to make a good strategy and appropriate
decision for their business. Bosma (2000) find that variable of age also influence
the MSMEs success, he said that a younger entrepreneur tend to make more
profit than the older.
Business related variables which significantly influence the success of
MSMEs is business type (Saleem, 2012), which shows the nature of goods. Other
variable are training and extension service (Kuzilwa, 2005), because these
activities increase the firm‟s owners knowledge and skill so they can apply it in
their business. Kuzilwa (2005) find that MSMEs which receive training and
extension service perform better than those who do not. Bosma (2000) analyze
that other profit resource also a significant determinant of MSMEs success but in
negative relationship, it means that other profit resource decrease the firm‟s
profit.
Human capital related variables are relatively difficult to define because
they are basically qualitative variables which need to be quantified in regression
model. Human capital related which are significantly affect the business success
is marketing strategies (Jasra et al, 2011), because market development is
important to enhance MSMEs growth and success. But, most of MSMEs do less
marketing and sell their product without market orientation (Jasra et al, 2011).
Another human capital related variable is entrepreneurial skill (Kuzilwa (2005),
Jasra et al (2011)) e.g. ability to create a business plan. This skill is important

9

because well planned business activities will make the firms become more
efficient and more profitable.

10

III.

FRAMEWORK

3.1 Theoretical Framework
3.1. 1 Concept of Access to Credit and Credit Limit
Based on Diagne et al (2000) access to credit is defined as a condition
where the credit limit for a type of credit is positive. While credit limit is the
maximum amount a lender willing to lend. One is classified as lack of access to
credit if the credit limit is zero. Access to credit is not the same concept with
participation in credit programs. Indeed, the two concepts are often used
interchangeably in many credit studies. The main difference between the two
concepts is the fact that participation in a credit program is something that
households choose to do, while access to a credit program is a limiting constraint
put upon them (e.g., availability and eligibility criteria of credit programs). It
means that participation tend to a demand-side issue related to the potential
borrower‟s choice of the optimal loan size, while access to credit tends to a
supply-side issue related to the potential lender‟s choice of the credit limit.
If a household does not participate in credit program, it does not mean the
household has lack of access to credit. The nonparticipant households can be
divided into two subgroups, the first subgroup consists of the nonparticipants who
were constrained based on the eligibility criteria, while the second subgroup
consists of nonparticipants who chose not to participate because their optimal
demands for formal credit were zero which means they decide not to participate
because of some reasons, like interest rate and risk averse behavior (Bebczuk,
2001). If assumed that the credit program participants are always able to borrow,
so, for the participants, the second subgroup of eligible nonparticipants have
access to credit, while the first subgroup have no access to credit (Diagne et al,
2000).
Diagne and Zeller (2001) said that credit limit is one of the central
concepts for quantifying the extent of access to credit and its impact. The credit
limit concept starts with the lender chooses the pair (bmax, Rl(.)) where bmax is the
maximum amount he is willing to lend and Rl is a repayment function Rl : [0,
bmax], which specifies how much, when, under what condition he wants to be
11

repaid for any given loan size b ϵ [0, bmax]. Afterwards, the lender gives
opportunity to potential borrower to choose the optimal amount of loan (b* ϵ [0,
bmax]) he wants to borrow. In other words, the lender offers contract (bmax, Rl(.)) to
the borrower, and the borrower accepts or rejects the contract by his choice of b* ϵ
[0, bmax]. the contract is accepted if the b* is positive and rejected if the b* is 0.
The credit limit (bmax) from lender is constrained by the ba , which is the
maximum amount he is able to lend.
In credit market, there is a risk of possibility of credit default and lack of
effective contract enforcement, thus lender has incentives to restrict the supply of
credit although he has more than enough money to accomplish a given demand of
credit and borrowers which are willing to pay a high enough interest rate.
Therefore, from the lender point of view, the relevant limit of supply is not the
maximum amount the lender able to lend (ba), but the maximum amount the
lender willing to lend (credit limit (bmax)) (Diagne and Zeller, 2001). The credit
limit bmax which is interpreted as the supply of credit, is a function of maximum
amount the lender able to lend (ba), lender‟s subjective assessment of the
likelihood of default, and the borrower‟s characteristics. This concept is slightly
different with the traditional supply function of credit which explains the schedule
of amount of credit supplied based on the interest rate.
Access to credit is related to credit rationing and credit constraint. Credit
rationing is a condition where there is a wedge between what a lender is willing
and is able to lend. Stiglitz and Weiss (1981) in Diagne et al (2000) said that the
wedge is resulted from the exclusive choice of the lender. In the other words,
credit rationing is a condition where the lender constraint the supply of additional
credit to borrowers who demand funds below the amount he is actually able to
lend. While credit constraint is defined as a condition where the borrowers cannot
reach the maximal amount credit they need from lenders because of some
constraints e.g. eligibility of borrowers with the lender‟s requirements and the
availability of credit.

12

3.1.2 Concept of Success of Small Business
There are many indicators used to measure the business success. In
general, business success is achieved when the entrepreneur meets his personal
objectives for his business1. Nevertheless, the range of business objectives was so
broad. Therefore, there are many different definitions of success. Business
success‟s indicator can be classified to financial and non financial aspects. Bosma
(2000) explained that there are three general indicators of success for small
business, they are:

1. Profit
Profit is financial aspect of indicator of business success. The association between
the successful entrepreneur and profit making is proved by many researchers.
Every firm‟s owner wants to get profit from his business activities. The bigger the
profit the more his opportunity to stay in his business, increase his production, and
hire more labor. Vann Dijk (1996) in Bosma (2000) summarized the importance
of profit in different entrepreneurship theories which shows by following table
Table 3. Importance of Profit Making in Theories of Entrepreneur
Theory
Importance of Profit
Cantilion (1931)
Profit making is considered to be an
important result of entrepreneurial act
Say (1845)
Profit making is associated with the
entrepreneur
Marshall (1961)
Profit making is considered to be an
important result of entrepreneurial act
Menger (1950)
Profit making is considered to be an
important result of entrepreneurial act
Knight (1921)
Profit making is the central issue of the
theory
Schumpeter (1943)
Profit making is considered to be an
important result of entrepreneurial act
Kirzner (1981)
Profit making is the central issue of the
theory
Source : Vann Dijk (1996) in Bosma (2000)

1

[Anonimus]. 2008. Defining Small Business Success.
th
http://www.smallbizlabs.com/2008/08/how-many-types.html. [last visited : October 12 2012)

13

2. Generating Job Opprtunities
Profit is not the one indicator considered as parameters of small
business success. A successful entrepreneur not only gives benefit to him
self but also to his environment and society. One of contribution which
could the firm‟s owner gives is generating new job opportunities. Bosma
(2000) said that generating employment is the parameter of success which
is related to social aspect of the entrepreneur.
3. Survival Time
Another parameter of small business success is survival time.
Survival time of business describes how long the business can stay
running the business activities, facing the obstacles, and gaining profit
(Bosma, 2000). Cressy (2006) said that small firms are less likely to
survive and tend to grow faster than large firm.

In this research, profit will be used as the proxy of business success. We
use profit due to the limitation of data available and because profit is the most
recommended parameter of business success by some economist and it is
approved theoriticalyl. We get the business profit by subtracting business total
revenue and total cost.
3.1.3 Role of Credit in MSMEs’s Profit
MSMEs have limited resource background to expand their business. This
condition restricts them to use the optimal input to maximize their profit.
Theoritically, profit is maximum when the Marginal Revenue (MR) of firm equal
to the Marginal Cost (MC) (MR=MC). Using derivatives of MR and MC, Varian
(2003) said that under profit maximization assumption, the optimal level of input
use which maximizes profit is happened when the Marginal Value of Product
(MVPx) is equal to the input price (Px). Moreover, Varian (2003) also said that
MVP can also be seen as the slope of Total Value of Product (TVP) curve, while
input price can also be seen as the slope of Total Factor Cost (TFC) curve.
14

Intersection of these two curves will show the profit. Graphically, this condition
can be seen in the following figure 1.

Output
Value ($)

Input Use

Figure 1. Optimal Level of Input Use
(Source : Varian, 2003)

Figure 1 shows that AB is the maximum profit of the firm and the level of input
used at those position is the optimal input use. As we discussed before, MSMEs
can not use the optimal level of input because of their source limitation, for
example they have not enough money for buying their input of production. As the
consequence, they can not reach the maximum profit as described in the figure 2
below.

C

Output
Value ($)
D

Input Use

Figure . Unoptimal Level of Input Use
(Source : Varian, 2003 (modified))
15

As we see in figure 2, if the firm has limitation in using optimal input, they can
not reach the maximum profit. Credit can help MSMEs to solve this problem. If
MSMEs have access to credit they could use more input which means their
production would increase and they could gain more profit. They could maximize
their profit with using the optimal level of input. Conversely, if they have lack of
access to credit or they are credit constrained, they could not use the optimal level
of input and maximize their profit.

3.2 Operational Framework

This research is starting by the problem of lack of access to formal credit
of MSMEs in Central Sulawesi and then we want to know how to increase access
to formal credit of MSMEs and how the access to credit influence the success of
MSMEs in the research area.

Afterwards, we determine variables which

influence access to credit of MSMEs in Central Sulawesi and variables which
influence business‟s profit of MSMEs. The variables are chosen according to
variables which commonly used by researchers reviewed in the literature review.
We continue to analyze the determinant of access to formal credit by
using four types of logit panel model i.e. pooled logit, fixed effects logit, random
effects logit, and population average logit. We use hausman specification test to
choose the best model, and the chosen model will be interpreted. We also analyze
the role of access to formal credit in the success of MSMEs using four types of
panel OLS model i.e. pooled OLS, fixed effects OLS, random effects OLS, and
population average OLS. We also use hausman specification test to choose the
best model, and the chosen model will be interpreted. The result of both analysis
will be used to construct a policy implication of this research. The operational
framework of this research is showed by the graph below

16

Problem of lack of access
to credit of MSMEs in
Central Sulawesi

1. What are the determinants of access to
formal credit of MSMEs in Central
Sulawesi, Indonesia?
2. How does the role of access to formal
credit in the success of MSMEs?

Variables which influence access
to formal credit :
1. Asset
2. Education
3. Gender
4. Age
5. Distance to road
6. Landownership
7. Organization Membership

Variables which influence
Business‟s profit :
1. Formal credit limit
2. Informal credit limit
3. Experience
4. Age
5. Training
6. Main Income from Business
7. Has another business

Non Linear Panel Model :
1. Pooled logit
2. Fixed Effects logit
3. Random Effects logit
4. Population average logit
Choosing the best Model :
Hausman Specification Test

1.
2.
3.
4.

Linear Panel Model :
Pooled OLS
Fixed Effects OLS
Random Effects OLS
Population average OLS

Choosing the best Model :
Hausman Specification Test

Interpretation

Interpretation

Policy Implication

Figure 3. Flow Chart of Research Process

17

IV.

METHODOLOGY

4.1 Data Source
This research uses panel data from household surveys that was collected in
the framework of The Collaborative Research Center of Stability of Rain Forest
Margin in Indonesia (STORMA) of Georg August University of Gottingen and
Kassel University Germany, and Bogor Agricultural University, and Tadulako
University, Indonesia. This study uses particular data of that program, especially
data which are related to socioeconomic condition and business operation of
MSMEs and their access to credit. The analysis will focus on the non agricultural
firms.
Panel data or longitudinal data are repeated measurements at different
points in time but on the same individual unit (Cameron and Travedi, 2009). In
this research, household is used as individual unit. Regression with panel data can
capture both variations over individual and over time. Data used in this research
are categorized as unbalanced panel because there are some data missing in some
years, for example not all samples are available in all years and not all samples
have variables needed, such as variable of profit, business type, and experience
which only available for particular individuals. Data used in this research is also
categorized as short panel since the data have view time periods and many
individuals.

4.2 Data Modifying Method
The STORMA data are aggregate data. They include data of agricultural
and non agricultural households in the research area. We just using data which are
related to the non agricultural households for this research, therefore the data need
to be modified first before we used them in this research. We modify the data both
for the econometrics analysis and descriptive analysis. All data modification is
done by using stata 11 software package.
The data are modified by :
1. Choosing the non agricultural households which have small business
activities.
18

2. Choosing variables needed in the estimation.
3. Converting some variables into dummy variables or categorical variables
e.g. formal credit limit, gender, education, main profit from business, and
has another business.
4. Subtracting yearly total profit and total cost to get business profit.
5. Converting variables of profit and asset into log form.
6. Pooling the data based on the identity number of sample and the year.

7. Measuring of central tendency which consist of minimum, maximum, and
average values of observations, and then making tabulation of variables
which will be the explained in the descriptive analysis.

4.3 Model Estimation
This research uses two panel data models to answer the research questions
i.e. the linear panel data models and the non-linear panel data models. The linear
panel data models are used to investigate determinant of access to credit of those
MSMEs, while the non-linear panel data models are used analyze effect of access
to credit to the success of MSMEs. We use logistic (logit) model in the non-linear
panel analysis and ordinary least square (OLS) regression model in the linear
panel analysis. For both regressions, we use four types of panel data models
namely pooled model, population average (PA) model, fixed effects (FE) model,
and random effects (RE) model. We will compare the result of these models and
then the best model will be explained.

4.3.1 Non-Linear Panel Data Models
Logistic regression (logit) is a special form of regression analysis with the
dependent variables are dummy variable, while the independent variables are
metric, dummy or a combination of both of them (Firdaus, 2009). We use this
model because our dependent variable is a dummy variable which is coded by 1 if
a household had access to formal credit and 0 otherwise. Logistic equation does
not produce a single value on the dependent variable, but generating a probability
of the dependent variable. The value of this probability is used to classify the
observations.
19

The pooled logit model is specify as follow
Pr (yit = 1 | xit) = Ʌ (xitβ)

(4.1)

Where Ʌ (z)= ez/(1+ez).
Hsiao (1986) assumed that the continuous random variable (y*) is a linear
function of x
y* = β‟ x + v

(4.2)

y = 1 if y* > 0 and y = 0 if y ≤ 0

(4.3)

and

Then, logit model correspond to the cumulative distribution of v being logistic
distributed. In this research, a cluster-robust estimate for the variance-covariance
matrix of the estimator (VCE) is used to correct for error correlation over time for
a given individual. Based on equation (3.2) the specification logit model used in
this research is formulated as follows
y (x) = ACCFORit = 0 + 1*LASSETit + 2*EDUC it + 3*MALEit + 4AGEit +
*DISit + LANDit + RGit + v

With the dependent variable (y) is access to formal credit, and the independent
variables are log of value of asset (LASSET), education (EDUC), gender
(MALE), age (AGE), distance to road (DIS), landownership (LAND), and
organization membership (ORG). This analysis will results the value of
coefficient in each variables. The value of coefficient is used to measure the
probability of the dependent variables, whether the firm will have access to credit
or not. This model will also show variable which significantly influence acce

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Access to formal credit and the success of micro, small, and medium enterprises in Central Sulawesi, Indonesia