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.

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.

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

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No part or all of this thesis maybe excerpted without inclusion

and mentioning the sources.

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Excerption only for research and education use, writing for

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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

Agreed

Examination Date :

Submission Date :

Dr. Ir. Nunung Kusnadi, MS Chairman

Dr. Stefan Schwarze Member

Dr. Ir. Suharno, M. Adev Member

Prof. Dr. Matin Qaim Member

2. Coordinator of Major Agribusiness

Prof. Dr. Rita Nurmalina, MS

3. Dean of Graduate School

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

3.1Theoritical Framework 11

3.2Operational Framework 16

IV.Methodology

4.1Data Source 18

4.2Data Modifying Method 18

4.3Model 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

6.2Business 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

List of Tables

Table Page

1 Criteria of Micro, Small, and Medium Enterprises in Indonesia 7 2 Comparison Data Share of Micro, Small, Medium, and Big

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 38

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

Credit 39

6 The Marginal Effects of Random Effects Logit Model

for Determinant of Access to Formal Credit of MSMEs 41 7 Summary of Variables Used to Analyze the Role of

Access to Credit to the Success of MSMEs 44 8 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 45

9 The Coefficients of Random Effects OLS model for

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

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I.

INTRODUCTION

1.1Background

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

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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.

1.2Statement 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

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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?

1.3Objectives 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.

1.4Limitation 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.

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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.

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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)

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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 female-owned firms are more likely to have access to credit than male-female-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.

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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

(without land and building)

≤ Rp 50 Million

>Rp 50 million - ≤ Rp 500 million

>Rp 500 million

≤ Rp 10 billion

Net Annual Sales

≤ Rp 300 million

>Rp 300 millon

≤ Rp 2,5 billion

>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.

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

Type of Enterprise

Number of Business

Unit Employment GDP

Number (Units) Percentage (%) Number (People) Percentage (%) Amount (Rp) Percentage (%)

Micro 52,176,795 98.88 90,012,694 91.03 1,751,644.60 33.08

Small 546,675 1.04 3,521,073 3.56 528,244.20 9.98

Medium 41,133 0.08 2,677,565 2.71 713,262.90 13.47

Big 4,677 0.01 2,674,671 2.7 2,301,709.20 43.47

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.

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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

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

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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

12

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 whichis 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

13 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.}

14 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.

15

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

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.

Figure . Unoptimal Level of Input Use

(Source : Varian, 2003 (modified))

Output Value ($)

Input Use

Output Value ($)

Input Use

C

16

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.

3.2Operational 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

17 Figure 3. Flow Chart of Research Process

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

Linear Panel Model : 1. Pooled OLS

2. Fixed Effects OLS 3. Random Effects OLS 4. Population average OLS

Choosing the best Model : Hausman Specification Test

Policy Implication 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

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

18

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.

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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.

20

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) and

y = 1 if y* > 0 and y = 0 if y ≤ 0 (4.3) 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 access to formal credit of MSMEs.

Access to formal credit of the firms will also be estimated by other panel logit models i.e. random effect (RE) logit model, fixed effect (FE) logit model and pooled logit estimator (pa) model. The logit individual-effects model is specified as follow

Pr (yit = 1 | xit, β, αi) = Ʌ (αi + x‟itβ) (4.4) Where αi could be FE or RE.

21

The RE logit specifies that that αi ~ N (0 , σ2α), then the joint density for

the ith observation after integrating out αi is

F (yit,.., yiT) = ʃ[ΠT Ʌ(αi + x‟itβ)yit {1- Ʌ (αi + x‟itβ)}1-yit ] g (αi | σ2) d α (4.5) Where g (αi | σ2) is the N (0 , σ2α) density. RE model parameters are not comparable to those pooled logit and pa logit, since after αi is integrated out, Pr(yit

= 1 | xit, β) ≠ Ʌ (x‟itβ). Based on equation (4.4), the probability of RE model is depends on αi , but it is unknown, so consistent estimation of β does not predict

the individual (Cameron and Trivedi, 2009).

In the FE logit model, the αi maybe correlated with the dependent variables

in the model. Parameter estimation is difficult to predict with linear approach, so method of conditional maximum likelihood (MLE) is used. This method based on log density of for the ith individual, the total number of outcomes equal to 1 for a

given individual over time. MLE is consistent estimation which eliminates the αi

from the estimation equation. In population averaged (pa) model, we weight the

first order condition of the estimator to account for correlation over time for a

given individual. Pa model sets αi = α. This estimation is consistent since the conditional mean is correctly specified as E (yit | xit) = g(αi + x‟itβ) for the

specified function.

After estimating access to formal credit of MSMEs with the four models, we will compare those models and chose the best model. If FE model is appropriate, the FE estimator must be used. The RE model has a different conditional mean, so it cannot be compared with pooled and pa model. But, in linear panel data model, pooled estimation lead to the inconsistent parameter if the RE model is appropriate.

4.3.2 Linear Panel Data Model

Pooled Ordinarily Least Square (OLS) model is used with assumption that there is a linear relationship between dependent and independent variables. General specification of pooled OLS is explained as follow

22

yit= α + β‟ Xit+ ρ Wi + µ Zt + uit (4.6)

yit is the dependent variable where in this estimation is LPROFIT, which

describes profit of the firms over time. While Xit is a vector of independent

variables used to examine the determinant of business‟s profit. In this model, we use formal credit limit (FORCRE), informal credit limit (INCRE), experience (EXP), age (AGE), training (TRA), main profit from business (MAIN), whether the owner has more than one business (OTHER) as the dependent variables, and they are assumed vary over individuals and over time. Wi is vector of independent

variables which only vary over individuals and Zt is vector of independent

variables which only vary over time. uit is the composite standard error which

contains individual specific error (µ) or time in-invariant error, time specific error

(λ) or individual-invariant error, and error which vary over individual and over time (v) or idiosyncratic error (Hsiao, 1986). Therefore we assumed that uit = µ + λ + v. While α, β, and ρ are vector of coefficients which are assumed to be constant over individuals and over time. Based on the general specification, pooled OLS model for this research is specified as follow

LPROFITit = α+1*FORCRE1it+2*INCREit+3*EXPit+4*AGEit+ *TRAit +

*MAIN it + 7*OTHERit + uit

The model above is consistent if the composite error uit is uncorrelated with the independent variables (xit). But, the error uit is likely to be correlated over time for a given individual. To solve this problem we use cluster-robust standard error which clusters on the individual.

Pooled OLS model above will be modified by fixed effect model. In the fixed effect (FE) model, time-invariant component of the error (µ) in () is allowed to be correlated with the independent variables Xit, but Xit is not

correlated with idiosyncratic error (v). It is different with pooled OLS which assume endogeneity. So, in this model we assumed that if the independent variables are correlated to unobserved variables, they are only correlated with time-invariant component of profit. The advantage of using FE model is that we

23

can obtain a consistent estimate of the marginal effect of the independent variable on E (yit| αi , xit ) and allow xj, it is time varying even the independent variables

are endogenous (Cameron and Trivedi, 2010).

We will also estimate the role of access to credit and other explanatory

variables on firm‟s profit using random effect (RE) model. In this model, it is assumed that time-invariant component of the error (µ ) in (4.6) is purely random, that means µ is uncorrelated to the independent variables, so we have an efficient model. Advantages of using RE model are that it can estimate all coefficient of independent variables even those which time-invariant, and that E (yit | xit) can be

estimated. However, RE model is inconsistent if FE model is appropriate. We will test the consistency of RE using the Hausman test.

Another panel model used in this research is pooled OLS estimator. This model is also called population-average (pa) estimator. Pooled estimator regress yit on an intercept and independent variables (xit) using both between (cross

section) and within (time series) variation in the data. Any time-specific effects are assumed to be fixed and already included as time dummies in the independent variables. Consistency of OLS is achieved if the standard error is uncorrelated with any independent variables. Thus pooled OLS estimator in consistent in the RE model but is inconsistent in the FE model because the time-invariant component of the standard error will be correlated with the independent variables.

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V.

DESCRIPTION OF RESEARCH AREA

5.1 Description of STORMA and Research Area

Stability of Rain Forest Margin (STORMA) is joinly research program between scientists from the Universities of Göttingen and Kassel in Germany and the Institut Pertanian Bogor and Universitas Tadulako in Indonesia. The research is funded by the German Scientific Foundation (DFG), and supported by a number of other organizations in Indonesia and Germany. The forest margins of the Lore Lindu National Park were selected as the research area. In this area exists a great variation in ecology, agriculture and socio-economic conditions. The park is home for some of the world‟s unique plant and animal species. However, the area is confronted by many complicated problems. One of major problem is changing of land use systems for farming activities (Zeller et al, 2002).

The vicinity of the Lore Lindu National Park is located Central Sulawesi Province. Central Sulawesi is the largest province on the Sulawesi Island which covers 68,059.71 km2. There are eight districts (kabupaten), one municipality (kotamadya), 81 sub-districts and 1440 villages in this province. The research covers two districts in Central Sulawesi, the first is Poso and the second is Donggala. Poso is the capital of Central Sulawesi. Figure 4 shows the map of location of the research. In 2003, Poso has 283,378 inhabitants who reside in 242 villages. The population density of in 2004 was 16 per km2. There are 70,484 households and 80.8 % of them are engaged in agriculture and make it as their main profit resource (Nuryartono, 2005). In this district, the percentage of poor households is relatively high which reaches 46.1 percent. Donggala is the largest district in the Central Sulawesi province. Total population in this district is 421,912 people and they were living in 265 villages. The population density of this district in 2004 was 40 per km2. The total number of households is 102,285 and 83 percent of them are involved in agriculture. The share of poor households in Donggala is 41.3 percent which is slightly lower than Poso.

The STORMA program divided into four sub-programs focusing on different disciplinary and interdisciplinary aspects of land use change and deforestation and its underlying causes as well as impacts on socio-economic

25

development, nature conservation and ecology. The four sub-programs are social and economics dynamics, water and nutrient turnover, biodiversity, land use system.

Figure 4. Map of Lore Lindu National Park and the Research Area (Source : Webber, 2000 in Nuryartono, 2005)

The overall goal of the STORMA program is to identify processes of destabilization and to determine factors of stabilization in forest margin areas. However, there are also some long-term objectives of STORMA i.e. 1) Identify ecological and socio-economic indicators of instability; 2) Develop principles and procedures of resource utilization that contribute to the stabilization of forest margins; 3) Develop procedures of resource utilization that contribute to the stabilization of the forest margins; 4) Promote interdisciplinary and intercultural research.

26 5.2 Formal Credit Market in Research Area

There are two sources of credit in rural area i.e. formal and informal lenders. Formal lenders are institutions which have legality to give loan to consumer with some requirements depend on their regulation. Formal credit market provides mediation between borrowers and lenders and charge relatively low rates of interest that are usually government subsidized (Hoff and Stiglitz, 1993 in Nuryartono, 2005). Example for formal lenders is bank, microfinance institution (MFI), and government microfinance program. Informal lenders are individual or institution which gives loan to consumer without any legality and formal organization which manage the loan. Trader, friends, relative, etc. are example for informal credit lenders. In this chapter we just explain about formal credit market in the research area since our analysis focus on the access to formal credit. Generally, there are three formal credit market institutions in the research area i.e. Bank Rakyat Indonesia Unit Desa (BRI-UD), microfinance institution, and government credit program.

5.2.1 Bank Rakyat Indonesia Unit Desa (BRI-UD)

Bank Rakyat Indonesia Unit Desa (BRI-UD) is one of bank which has already established in rural area of Indonesia. BRI-UD has many branches which are spread in almost every sub-district in Indonesia. Establishing branches near to the potential consumer is one of marketing and risk management strategies of BRI. In the research area, each sub-district has a BRI-UD with a different year of construction. Branch of BRI-UD in the sub-districts Kulawi and Sigi-Biromaru have been established for a long time. Meanwhile BRI-UD in the sub-district Lore Utara is established in 2003, and BRI-UD in the sub-district Palolo established in 2001.

Product of BRI-UD is Kredit Umum Pedesaan (KUPEDES) which aims to develop and improve small businesses. The amount of KUPEDES loans varies from Rp 25,000 to 50 million rupiah, with a fix monthly interest rate of 1.5 percent. KUPEDES provides two types of loans i.e. working capital and investment loans. Working capital loans are addressed to small businesses and to individuals who have a permanent monthly profit. The objective of this loan is

27

providing additional capital to fulfill and accomplish the client‟s needs of working capital or finance consumption such as buying transportation vehicles. Meanwhile investment loans are provided to small-scale businesses to finance their businesses infrastructure or production equipment such as buying a new machine. These loans are also offered to the permanent profit individuals to finance their need for consumption (such as buying transportation vehicles or production activities). Both types of credits finance business sector of agriculture, industry (manufacture), trade, services, and individuals with a permanent profit (Nuryartono, 2005).

The development of KUPEDES considers some key principles i.e.: simplicity, transparency, accessibility, not being subsidized, cost recovery, profitability, and sustainability (BRI 2001, in Nuryartono 2005). BRI-UD requires collateral from the borrowers. Types of collateral demanded by BRI-UD can be classified into two types: (a) moveable materials such as transportation vehicles complete with the legal aspects of an official letter, machinery, equipment, or jewellery; and (b) immobile materials such as legally titled land or buildings.

5.2.2 Government Credit Programs and Microfinance Institution (MFI)

Other actor of formal credit market in the research area is microfinance institution (MFI) and government credit program. There are some microfinance institutions (MFI) which exist in the research area such as Bank Perkreditan Rakyat (BPR) and cooperatives. Some government credit programs have ever applied in the research area. The Programs are mainly assigned to the agricultural sector. One of government credit programs implemented in the research area is Kredit Usaha Tani (KUT). The objective of this program is to improve level of food security of farmers household by giving credit schemes to increase the available working capital and then enhance their agricultural activities. Farmers do not require collateral to access credit from KUT. KUT is also subsidized by the government and is accompanied by extension service.

KUT is not given to the individual but to the farmers group based on their capacity. The farmers group should apply the amount of working capital they need to the BRI as channel institution which distributes KUT. The program

28 indeed does not require physical collateral from farmers but it uses „social collateral‟ based on joint liability through group lending Farmers‟ groups. Nuryartono (2005) explained that some problems hampering KUT implementation lead to misuse of the loans and result the low rates of repayment. One of problems is the lengthy procedure which makes credit is not distributed on time, thus farmers do not use the loans as they proposed.

29

VI.

DESCRIPTIVE ANALYSIS OF ACCESS TO CREDIT

AND BUSINESS ACTIVITIES

6.1 Access to Formal Credit of Non-Agricultural Firms

Access to formal credit of MSMEs in research area is described by the formal credit-limit. A positive credit-limit means that the firms have access to formal credit market, while a zero credit-limit means that the firms do not have access to formal credit. The highest amount of credit limit is IDR Rp 50 million and the lower amount is IDR Rp 0. In this research, we use dummy variable

“Access to formal credit” as the dependent variable to analyze factors influencing access to formal credit of the firms. Firms which have a positive formal credit limit are coded by 1 and 0 for otherwise.

The minimum credit limit over time is always zero which means there are always households which have no access to formal credit in 2001-2007. The maximum credit limit varies over time namely IDR Rp 20 million in 2001, IDR Rp 50 million in 2004, and IDR Rp 35 million in 2007. As showed in figure 5, the formal credit limit has a positive trend in 2001 until 2004, reaches the peak in 2004 and then the trend decreases in 2007.

Figure 5. Maximum and Minimum Formal Credit Limit Over Time

Percentage of firms having access to formal credit in research area is also varied year by year. Following the credit limit, the percentage of the firms having

30

access to formal credit increased in 2001-2004, reached the peak in 2004, and decreased in 2004-2007, as described in figure 5 below.

Figure 6. Percentage of Firms Having Access to Credit 2001-2007

Based on figure 6, there are 29.27 percent of firms having access to credit market in 2001, 49.1 percent in 2004 and 37.03 percent in 2007. On average, 39.33 percent of the firms have at least once access to formal credit in 2001-2007. It shows that most of the firms are still credit constrained. If we compare to Nuryartono‟s research (2005) which said that only 21.5 percent of agricultural household in research area having access to formal credit, then we could conclude that the agricultural firms are relatively more credit constrained than the non-agricultural firms.

6.2Business Characteristics of MSMEs

As we saw in some researches about determinant of access to credit reviewed in the chapter 2, the credit access and the success of MSMEs can be determined with various variables. In this research, we use business related and socioeconomic related variables of the firms to analyze the determinant of access to credit and its role to the success of MSMEs in Central Sulawesi. Business characteristics of the firms are described as follow:

31 1) Business Location

The firms are spread equally in research area. Roughly 20.67 percent of the firms are located in Kulawi, 22 percent of them are located in Lore Utara, 17.33 percent of them are established in Paolo, and 40 percent of them are located in Sigibir. Distance to road could describe the variable of business location. The longer the distance to road the more isolated the business location. Distance to road of the firms varies and changes over time. The distance to road could be longer or shorter over time. The shortest distance to road is zero minutes, which means that the firm is located near to the road and then could be easily accessed by lenders or consumers. The farthest distance to road is 720 minutes which means the firm is located far from the road and then relatively harder to be accessed. Generally the firms are located near to the road since 62 percent of them have 0 minute distance to road.

2) Type of Business

There are 150 firms reported that they have business activities. In this research, business type is categorized into three classifications based on their main activities. About 28.67 percent of them are classified as

„manufacturer‟, they are including firms which produce something, such as craft, clothes, fibers, etc. Roughly 12 percent of them are classified as

„service‟, including firms that provide services, like rice milling, tractor leasing, transportation, etc. and About 59.33 percent of them are „trader‟, including firms that sell something, for example agricultural input trader, cocoa trader, small shop, kiosk, etc.

Majority or about 76.67 percent of the firm owners make their business as the main profit resource for them and their family. There are 23,33 percent of the owners who do not rely on their profit to their business because they have other resources of profit, like working as government employee, salaried worker, etc. So, business activities are not their first occupation. Some samples also reported that they have another business besides his main business. About 68 percent of them has no

32

another business. That means, most of them are concerned on their main business.

3) Business’s Profit

Profit is obtained by subtracting the revenue and the cost of

business‟s activity. About 147 firms reported that they have positive profit from business in 2001-2007. The average Profit from business is increasing year by year. In 2001 the average profit from business is IDR Rp 4,671,621.95 , while in 2004 is IDR Rp 6,677,239.09 and in 2007 is IDR Rp 10,622,694.44. as described in the following figure.

Figure 7. The Average Profit from Business over Time

4) Experience of Firm’s owner

Experience describes how long the firm‟s owners are engaged in their businesses. It also describes the firm‟s age. About 64.67 percent of the firm‟s owners have less than 10 years experiences in their business,

and only 10 percent of them who have business experiences more than 20 years. That means majority of the firms are founded before 1997. The oldest firm is established in 1970.

33 5) Firm’s Asset and Land Ownership

Asset and landownership are often considered as significance factor of access to credit. About 74.37 percent of the firms have a positive value of asset. Generally, the average value of asset of the firms increases over time. The average value of asset in 2001 is IDR Rp 3,232,317.07, while in 2004 is IDR Rp 3,822,418.18. The average value of asset in 2007 is Rp 5,821,814.81, which increases almost twice of it in 2004. This data is described by the following figure 8.

Figure 8. Average Value of Asset over Time

The highest value of asset is Rp 58.722.500 while the lowest is 0. We could classify size of business whether they are categorized as

“micro”, “small”, or “medium” enterprises by counting the value of asset. Based on Indonesian Government‟s law number 8 in 2008,

microenterprises are firms which have asset less than Rp 50 million, while small enterprises are firms which have asset between Rp 50 million and Rp 500 million, and medium enterprises are firms which have asset between Rp 500 million and Rp 10 billion. According to this criterion, about 99.87 percent of the samples are microenterprise and only 0.13 percent of them are small enterprises.

34

Landownership is a proxy variable of collateral which is usually being an important consideration factor for bank in lending credit. The widest land ownership is 3.578 are and the narrowest is 0. Generally, the average landownership from 2001-2007 of the firms is 242.2 are. About 65.07 percent of the firms have landownership below the average. The average landownership varies over time. The average landownership in 2001 is 225.38 are, while in 2004 is 325.03 are and in 2007 is 170.60.

Therefore, the average landownership‟s trend increases in 2001-2004, reaches the peak in 2004 and then decreases in 2004-2007 as showed in figure below.

Figure 9: The Average Land Ownership over Time

6.3 Characteristics of the Owners

We also use socioeconomic characteristics of the owners to analyze the determinant of access to formal credit of the firms. The characteristics of the owners are described as follow:

1. Age

Most of the firm‟s owners are in their productive period. About 66.68 percent of them are under 50 years old and 37.5 percent of them are above 50 years old. The youngest owner is 20 years old, and the oldest is 81 years old.

51

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55 Appendices

Table 1. Estimation Results of Pooled Logit, Pa Logit, Fixed effects Logit, and Random Effects Logit of Determinants of Access to Formal Credit of MSMEs

Variable Pooled Pa RE FE

accesstofo~t

Educ 1.2022499*** 1.3888157** 1.8931037** (omitted) (0.47192449) (0.47751251) (0.83061034)

Lasset 0.07954714* 0.05633561 0.07153981* -0.03425401 (0.04055078) (0.03546516) (0.04297105) (0.06389412) Male 1.1036686 1.2247607 2.1125352 (omitted)

(1.2667637) (1.3577142) (1.6526315)

Age 0.03017798 0.03406235* 0.05203285* -0.17408904 (0.01847036) (0.0180687) (0.02994616) (0.31955774) landowners~p 0.00059453 0.00064684 0.0009939 1.4633953

(0.00062926) (0.00054557) (0.00129749) (1.7666972) distanceto~d -0.01424549** -0.01380706** -0.0216353 -0.0340725

(0.00694457) (0.00662236) (0.01610569) (0.03510907) Org 0.06043205 0.07647959 0.11398069 0.78589595

(0.07436977) (0.06946288) (0.13626192) (1.4551842) _cons -7.2384062*** -7.8440215*** -11.332234***

(2.2521056) (2.2851421) (3.8742065) lnsig2u

_cons 1.2444894

0.74724718 Statistics

N 150 150 150 32

Ll -82.455223 -78.720123 -5.4406001

Chi2 (7) 18.80*** 24.11*** 13.54***

LR (5) 12.24**

Rho 0.51340726

R2 0.1798

*, **,*** significant at 10%, 5%, and 1% respectively ; Standard error in parenthese

56 Table 2. Estimation Result of Pooled OLS, Pa OLS, Fixed Effects OLS, and Random Effects OLS of the Role of Credit in MSMEs Success

Variable POOLED PA FIXED RANDOM

Formalcreditlimit 0.084639*** 0.108538*** 0.022534 0.084639*** (0.019296) (0.027591) (0.080223) (0.032011) Informalcreditlimit 0.078877 0.054915 0.065455 0.078877

(0.053837) (0.107235) (0.218425) (0.119139) Experience 0.006434 -0.01254 0.304588 0.006434

(0.017456) (0.021623) (0.90009) (0.034259)

Age 0.049843*** 0.071388** -0.15916 0.049843**

(0.023715) (0.027818) (0.903572) (0.021118)

Training 0.02175 0.103379 0.468617 0.02175

(0.452612) (0.583499) (1.708077) (0.722936) mainprofit~s 2.185129** 3.461909*** -0.00535 2.185129***

(0.924437) (1.193894) (1.801446) (0.625789) hasanother~s -0.07633 0.009757 0.130531 -0.07633

(0.657917) (0.616682) (1.355741) (0.563288) _cons 10.25661*** 8.252535*** 19.12172 10.25661***

(1.965244) (2.332677) (34.30705) (1.216709)

Statistics

N 150 150 150 150

Ll -370.809 -338.677

chi2 (6) 23.15257*** 27.19559***

F test (8, 88) 4.71*** 0.14

r2 0.160735 0.015364

Rho 0.42003 0

Hausman Test (P-Value) 0.9070

*, **,*** significant at 10%, 5%, and 1% respectively ; Standard error in parenthese