International Journal of Economic Perspectives, 2015

International Journal of Economic Perspectives, 2015, Volume 9, Issue 2, 57-70.

Micro Enterprises' Access to People Business Credit Program in Indonesia: Credit Rationed or Non-Credit Rationed? Farida FARIDA* Faculty of Economics, Persada YAI University, Jl. Diponegoro 74 Jakarta, Indonesia Phone +62-21-3904858. Email: [email protected].

Hermanto SIREGAR Department of Economics, Faculty of Economic and Management, Bogor Agricultural University, Kampus Dramaga, Bogor, Indonesia Phone +62-251-626602. Email: [email protected].

Nunung NURYARTONO Department of Economics, Faculty of Economic and Management, Bogor Agricultural University, Kampus Dramaga, Bogor, Indonesia Phone +62-251-626602. Email: [email protected].

Eka INTAN K. P. Department of Resource and Enviromental Economics, Faculty of Economic and Management, Bogor Agricultural University, Kampus Dramaga, Bogor, Indonesia Phone +62-251-626602. Email: [email protected].

ABSTRACT Access to People business credit program (KUR) for micro-enterprise households are in line with the financial inclusion goals in Indonesia. This study aims to determine the factors which affect access of formal microcredits. The data collected randomly have been assigned to 30 villages to either the treatment group (Non-credit rationed) or the control group (credit rationed and non-rationed). The data are analyzed with the use of descriptive statistics and logistic regression model. Socio-economic characteristics are identified, including constraints faced by micro-enterprises. The data showed that there are some factors which significantly may affect the access of people business credit program. These include gender, length of business, business barriers, bank account, the spousal working and other loan sources. Based on the odds ratio results conclude that males have the possibility of 6.56 fold more likely to credit access opportunities than females. People who have a bank account have 3.66 times greater chance of getting credit. New ventures which faced capital barriers and people whose the spousal are working would tend to access of KUR . From the total of observations, there were about 52.4 percent of fully non-credit rationed group, 6.3 percent of partially non-credit rationed group, and 41.3 percent of credit rationed group in this research study. JEL Classification: B26; C39; D14; G21. Keyword: Credit Rationed; Logistic Regression; Odds Ratio; Household Saving; Microfinance. *Corresponding author. 1.

INTRODUCTION

Loan is critical to encourage the growth of micro-scale enterprises; however, not many of them have the access to formal financial institutions (Mwangi and Sichei 2010; Togba 2012). According to Togba (2012), 85.06% of households having the access to loan applied to informal sources. Further data from Central Bureau Statistics of Indonesia (BPS) recorded 55.25 million units SMEs in Indonesia scattered throughout Indonesia. Only 39% or 21.5 million units receive loan from banks, while the remaining proportion are still out of reach. Out of 55.25 units of SMEs, 99% of 54.55 units are categorized as micro-scale enterprises, comprising household businesses, street merchants and other types of informal business with assets of maximum IDR 50 million and total revenues maximum IDR 300 million per year. In addition, enterprises at this level absorb the highest proportion of work International Journal of Economic Perspectives ISSN 1307-1637 © International Economic Society http://www.econ-society.org

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International Journal of Economic Perspectives, 2015, Volume 9, Issue 2, 57-70. force, at the figures of 94.9 million people or 90.77% of SME workforce. Micro enterprises contribute to 57.94% of national GDP, 16.44% of total exports and 50% investments in Indonesia. SMEs have proven their contribution to the economic accelerator (Amaizo 2009; Nguyen 2013). Micro enterprises with low revenues are normally rejected by formal financial institutions, therefore, take loans from informal financial institutions. Formal financial institutions or banks do not have sufficient information to distinguish applicants with low risk and high risks or whether the use of funds is on the right target. On the other hand, informal financial institutions are more effective in reaching the target and evaluating credit risk than formal financial institutions (Ibtissem and Bouri 2013). Madestam (2013) commented that formal banks have unlimited access of funds but do not have the capability to control how the borrowers use those funds. On the contrary, informal lenders are more concerned in the early screening instead of the usage of those funds (Togba 2012). Many researchers have found that banks consider that SMEs bear higher risks, thus, prefer to provide loans to large scale enterprises (Kaduoamai 2013). Early screening from banks requires higher costs. Asymetric information bears high transactional costs, which causes the limitation of credit by formal institutions, inducing low income household enterprises to exercise informal source of funds (Togba 2012). Studies in 5 provinces in Indonesia (South Sumatera, Central Java, Bali, South Kalimantan, and South Kalimantan) show that 87.4% of SMEs have a high expectation of being able to obtain loans from formal financial institutions, especially banks (Situmorang 2007). Capital issue commonly is due to the fact that micro enterprises are individually that rely on self-funding with limited resources to access to formal financial institutions such as banks. Many businesses in rural area still rely on informal source of funds (Maldonado 2004). These micro-enterprise households acquire the loan from money lenders because of the ease of requirement, exercising trust as the collateral but higher interest costs must be paid (Pradhan 2013). Khoi, et al., (2013) commented that interest rates from informal institutions are five times higher than formal institution. In the past, banks were reluctant to provide loans to the economically deprived people because of their inability to provide collaterals (Li, et al. 2011). Asymmetric information and moral hazard reason such as expensive fees and unpaid risks, banks abandon these enterprises instead. Mallick (2011) in Northern Bangladesh found that money lenders’s interest rate will be lower if the loan is spent for productive investment, thus, there is no issue in repayment. On the other hand, if the demand of loan to the micro financing institutions rises and these demands are not met, household business will switch to money lender and the interest rate will be raised. A number of subjects do not seek for loan since it is not needed or they are risk averse. According to Maldonado (2004), not all household loan applications in rural areas are approved even though they are willing to pay higher interest rate. Credit rationed applied due to as follows 1) lack of information from lenders, 2) inefficiency in law enforcement, 3) high transaction cost due to the absence of institutional infrastructures. Maldonado identifies that those who apply the credit, but rejected, namely as fully quantity rationed. Those who approved, but not fully is assumed as the partial quantity rationed. People who their application approved it all called non quantity rationed. In turn, the households who never apply categorized as non credit rationed. The reasons for not applying for a loan by households are grouped into 4 categories as; 1) no need, the households that do not face difficulties, including domestic capital. They usually have a low scale of economic; 2) risk averse. Some households think that the credit risk and do not feel comfortable having a debt; 3) high cost. Household in this category recognizes that credit is available, but does not match the terms of the contract are considered expensive; 4) Self selected out. They are already a priori and they think even though they are applying for a loan will certainly be rejected. Helsen and Chmelar (2014) said that the micro enterprises are self-conscious upon their capacity to acquire the credit, thus many micro enterprises do not apply credit. This paper will discuss the results of research on the people business credit program (KUR) and factors influencing the micro enterprises to access formal micro credits. The purpose of this study is also to determine whether micro credit institutions doing credit constrained to the micro enterprise households. The result of this study is expected to be utilized by the government to evaluate KUR in achieving the goal of inclusive financial. 2.

ACCESS TO PEOPLE BUSINESS CREDIT PROGRAM (KUR) IN INDONESIA

People business credit program (KUR) is a financing for working capital and investment purposes that provided by banks to feasible but un-bankable micro enterprise. These micro enterprises have positive prospects and the ability to repay the loan, but do not fulfill the administrations, requirements or collaterals regulated by banks. In this micro-financing scheme, the government allocates state budget or APBN through interest subsidy, while the executing banks will provide 100% of the loan principal. The government will also bear the loan insurance in the International Journal of Economic Perspectives ISSN 1307-1637 © International Economic Society http://www.econ-society.org

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International Journal of Economic Perspectives, 2015, Volume 9, Issue 2, 57-70. amount of 70% of total principal. KUR is not the government grant to the public; therefore, micro enterprises receiving this facility have the obligation to repay their loan to the banks. By December 31, 2012 KUR’s credit limit totaled to IDR 97.6 trillion with total debtors reaching 7.68 million. The average loan per customer is approximately IDR 12.7 million. The largest loan provider was BRI, taking up to 60.6% of proportion. From this total, micro segment is the largest recipient about 47.79 percent of total BRI’s KUR. In term of the number of customers, BRI served 7.0 million people or 91.84% from total customers throughout Indonesia. The table 1 below shows the realization of KUR, micro enterprises and debtor from the period 2009 to 2012. Table 1. KUR Realization, Debtor and Micro Enterprises Year

KUR (Rp Bill)

Debtor

Micro-entrepreneurs (units)

Employees

2008/9

17,189.3

2,374,908

52,176,796

90,012,694

2010

17,228.6

1,437,650

53,207,500

93,014,759

2011

29,002.6

1,909,880

54,559,969

94,957,797

2012

34,230.5

1,962,153

55,254,899

95,877,900

Total

97,651.0

7,684,591

Source: Committee-KUR, 2012

BRI’s micro debtors maintain good credit quality, as seen from the NPL (non-performing Loan) ratio of 1.7% far below the government’s requirement of 5%. The table 2 below shows the realization of KUR and NPL. Table 2. Realization of KUR and NPL as of 31 December 31, 2012 Realization of KUR Credit Limit IDR (Mill)

Outstanding Rp (Mill)

Average of Credit

No

Bank

Debtors

IDR (Mill)

NPL (%)

1

BNI

10,679,291

4,172,265

153,050

69.8

7.3

2

BRI (KUR Ritel)

12,626,671

5,436,204

79,084

159.7

3.1

3

BRI (KUR Micro)

46,670,190

14,448,280

7,057,766

6.6

1.7

4

Bank Mandiri

10,796,762

3,795,445

210,453

51.3

2.0

5

BTN

3,273,465

1,366,075

19,181

170.7

5.8

6

Bukopin

1,479,878

395,402

10,149

145.8

6.3

7

Syariah Mandiri

2,761,083

1,267,617

35,263

78.3

4.9

8

BNI Syariah

41,756

31,425

136

307.0

0.0

9

BPD

9,321,986

3,413,742

119,509

78.0

6.3

Total

97,651,082

34,230,456

7,684,591

12.7

3.6

Source: Committee-KUR, 2012

Previously, loan schemes have been launched in Indonesia, however, they did not perform as expected, for instance the agricultural extension loans (Bimas/Inmas), the agricultural enterprise credit program (KUT) and the food security credit program (KKP). Their Drawbacks such as complex procedures, high interest and collaterals, as well as high costs on late repayments, lead to the discontinuation of the programs.

International Journal of Economic Perspectives ISSN 1307-1637 © International Economic Society http://www.econ-society.org

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International Journal of Economic Perspectives, 2015, Volume 9, Issue 2, 57-70. 3.

LITERATURE REVIEW

For the purpose of this KUR study, accessibility of micro enterprises to micro financing is defined as the micro enterprises’ ability to obtain loan from bank (Li, et al. 2011). Studies regarding access to loan and its determinant by households have been conducted (Mohamed, et al. 2009; Li, et al. 2011; Quoc 2012). Li includes demographic factors (ages, gender, education level, family dependent level), socialeconomic factors (asset, revenue, farmland size, family dependent ratio, self-employment, official worker, bank account holder), and other factors such as distance, attitude toward debt, and access to credit alternative. Factors such as age, gender, education and family size were also used by Mohamed (2009) as the demographical factor. Other factors used are main activities in agriculture, financial statements, land area, market’s integrity level, value of productive assets, income and leadership role. In Indonesia many studies found that access to credit are normally leaning to informal loan or micro financing institutions. Grants programs have been conducted, however these programs are not continued since the funds were not paid back and considered as donation. Studies on formal bank loans to micro enterprise are rarely conducted in Indonesia, considering the fact that formal bank loans are only accessible for medium to big scale businesses. This study thereupon is interested to explore further on micro enterprises’ determinant factors to the access of financial institutions with the KUR program. There are two types of financing institutions in rural areas: formal and informal loan institution (Nuryartono 2005). Ghosh et al. (1999) states the characteristics of informal loan markets: (1) loan is usually based on oral agreement due to low transactional cost; (2) loan markets are commonly segmented; (3) the average of its interest rate is relatively higher than formal loan market; (4) informal loan markets are interconnected with other counterparties such as land lords, work force and market; (5) there is a tendency of monopolistic borrower; (6) there is significant credit rationing. Vitor and Abankwah (2012) conclude that both formal and informal loans are imperfect substitutes. The formal credit will reduce but not eliminate the informal loans. Formal and informal credit is complementary. Madestam (2013) says that both formal and informal credit can be as a complement or substitute. 4.

METHODOLOGY

This study was conducted in Pati District in Central Java Province as the largest KUR recipient. The decision on location is based on multistage sampling purposive. There are 350 respondents, of which the research unit is the micro enterprises household located in 30 villages spreading across two districts. Respondents from respective villages were selected randomly or simple random sampling, given the assumption that diversity of micro enterprises in small villages is homogenous. Binary logistic regressions were used to identify factors influencing micro enterprises to apply for KUR. This method was used by other fellows in loan access research (Chauke and Anim 2013; Ololade and Olagunju 2013; Li et al. 2011). Another different method is probit used by (Mohamed, et al. 2009; Behr and Sonnekalb 2012). Logistic regression analysis is a technique to analyze data with two or more controlling variables and one or more independent variables with continuous scale. Juanda (2009) explains to estimate multiple logistic regression model with k-1 of dependent variable, the score of

P(Y=1|x = as P(x then the model as follows;

g(xi) = In P(xi) =

(

(1)

− (

+

= −(

(

(2)

+ (

Generally, if there are dummy variable, logit transformation model becomes; g(xi) = which is: u Dju

(3)

: 1,2,3…,kj-1 : kj-1 dummy variable


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International Journal of Economic Perspectives, 2015, Volume 9, Issue 2, 57-70. : dummy variable coefficien : independent variable Parameter estimation used maximum likehood estimation (MLE). ℓ(β) = ∑(x1)∑(x2)…∑(xn) =

(4)

Using transformation with natural logaritme, thus function of log likehood as follows; ℓ(β) = In ℓ(β) =

(5)

To determine estimation of β which maximizes ℓ(β), we differentiate above equation for each parameter of α and β, then equated with 0. This testing is either simultaneously or partially. According Juanda (2009), likehood ratio testing is done with hyphotesis as follows; H0 : β1 = …= βk = 0 H1 : minimum any of βj ≠0, for j = 2, 3,…k Statistically G testing formulated as follows; G = -2In[

≈

= 2In[

= 2[In (likehood_ModelUR) – In (likehood_ModelR) H0 rejected if G >

(6) (7)

.

Partially parameter testing using Wald testing which formulated as follows; W=

(8)

H0 rejected if |W|>Zα/2 Odds ratio used to interprete coefficient in this binary logistic regression model which formulated as follows;

Ψ=

+ /( ++

/( ++

/

/( +

/( +

=

(9)

In this study, an empirical approach is used to refer to (Li, et al. 2011). The dependent variable or g (xi) is a dichotomous variable indicating whether the household microenterprises access KUR (1 if yes, and 0 otherwise). As for the explanation variables (Xj) consist of household demographic characteristics of micro enterprises (age, gender, education), socioeconomic characteristics (distance, business length, the barriers of business, working capital, bank account ownership, side job, the spousal working and other loan sources). The description of variables in the table 3. 5.

RESULT AND DISCUSSION

5.1 Descriptive statistics The household characteristics of micro enterprise used in this sample are summarized in Table 4. Total Household micro enterprises are eligible for processing as much as 332 respondents, consisting of micro enterprises that are not participants or KUR's borrower as much of 177 respondents (53.31%) and the partisipants are 155 respondents (46.69%). The majority of KUR borrowers are men around 78.07 percent. In turn, the majority of non participants are women about 65.54 percent. An average of men and women’s age are 39.25 years old and 43.71 years old, respectively. the level of education for participants are mostly senior high school about to 37.42 percent, meanwhile for non partisipants are only primary school around 39.55 percent. It seems that micro enterprise that had received higher education still less than 3 percent. International Journal of Economic Perspectives ISSN 1307-1637 © International Economic Society http://www.econ-society.org

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International Journal of Economic Perspectives, 2015, Volume 9, Issue 2, 57-70. Table 3. Description of Variables in Logistic Model Variable Name

Variable Type

Explanation

g(xi)

Binary

Micro enterprise household (1= access to KUR, 0=not)

Age

Continuos

Age of micro enterprise owner (MBO), (in years)

Gender

Binary

Gender of MBO (1 = male, 0 = female)

Demographics;

Edu

Educational level of MBO

Edu_1

Binary

1=primary school graduation, 0 is otherwise

Edu_2

Binary

2=yunior high school graduation, 0 is otherwise

Edu_3

Binary

3=senior high school graduation, 0 otherwise

Distance to bank

Continuos

business location to bank (in Km)

Business length

Continuos

How many years the business built (in years)

Business barrier

Binary

Obstacles faced by micro enterprise (1= capital, 0 otherwise)

Working capital

Continous

Working capital need per week (Rp million)

Bank account

Binary

Bank account ownership (1 = yes, 0 otherwise

Side job

Binary

Do micro-entrepreneurs have a side job (1=yes,0 is otherwise)

Spouse working

Binary

Does spouse have a job (1=yes, 0 is otherwise)

Other loan source

Binary

Having other credit sources? (1=yes, 0 is otherwise)

Social-economic;

The socioeconomic characteristics of micro enterprise households in this study summarized in table 5. Of the total of 332 micro enterprises are able to absorb 209 workers or 62.95 percent. An average of the partisipants is having more labor about 0.89, compared to micro enterprises without KUR of 0.40. Unfortunately, not all workers are paid due to the reason as the family member. Thus, the average of paid labor that can be absorbed in the micro enterprise amounted to only 20.34 per cent for non-KUR and 34.34 per cent for micro enterprise that borrow KUR. The average of the business length of non KUR micro enterprise is 9.08 years. This is longer than KUR micro enterprise of 5.47 years. As micro enterprises typically categorized as informal businesses, there is no limitation of working hours. Overall, the working hours of informal business are approximately 59.85 hours per week. This amount is much longer than the formal working hours are usually only up to 40 hours per week. On the average of working hours per week for non-KUR micro enterprise is longer about 61.33 hours compared to KUR micro enterprise of 58.15 hours per week. Average of working hours can be prolonged, if accumulated with a side job that they do. Of the total of 332 micro enterprise in this study, about 26.51 percent are having a side job. Longer working hours can show the higher the income to be earned, but it can also indicate the lack of efficiency of a business. Trade and retail sector dominate micro enterprise about 60.2 percent. In this study this business includes informal business such as daily grocery stalls, meatball and noodle stalls, and other businesses in rural area. These businesses characterized as the low barrier to entry, even neglected instead, however, these businesses having a high return reaching 70 percent (Grimm, et al. 2011). The data show that the agricultural sector is the lowest sector which getting KUR. Agriculture is assumed as the high risk sector, hence the formal institution avoids to finance this sector by the reason as follows; high transaction cost, asymetric information, low profit, lack of collateral, low education and low finance literate. Generally, banks do not want to finance agricultural due to the fluctuative production and the risk of uncontrollable prices (Wenner 2010). Studies by Togba (2012) in Cote d’Ivoire explains the highest loan statistically disbursed not to agricultural sector but retail sector about 30 percent. International Journal of Economic Perspectives ISSN 1307-1637 © International Economic Society http://www.econ-society.org

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International Journal of Economic Perspectives, 2015, Volume 9, Issue 2, 57-70. Table 4. Demographics Characteristics Description

Non-KUR (N1=177) Amount (n1)

% to N1

KUR (N2=155 ) Amount (n2)

All Respondents (N3=332)

% to N2

Sub-Total (N4=n1+n2)

%to N3

Demographics; GENDER Male Female

61

34.46

121

78.07

182

54.82

116

65.54

34

21.93

150

45.18

AGE Male (Mean), years

40.67

38.53

39.25

Female (Mean), years

43.96

42.85

43.71

EDUC Primary school

70

39.55

46

29.68

116

34.94

Yunior high school

49

27.68

46

29.68

95

28.61

Senior high school

55

31.07

58

37.42

113

34.04

3

1.70

5

3.22

8

2.41

College or University

Source: Data processed.

Based on the data collected, of the total of 332 respondents, there are 166 households or 50 percent applied to KUR. Of this total, about 134 households or 80.72 percent are all approved in accordance with the amount requested. This group means non quantity rationed credit. The application of about 21 households or 12.65 percent agreed, however, not all approved. Those categorized as partially quantity rationed credit. The remaining of 11 households or 6.63 percent of the application is rejected by the bank without explaining the reason. This group restricted or fully quantity rationed credit. Of the total of 332 households that have never applied is about 166 households or 50 percent. Of this total, there are 40 households (24.1%) giving the reason of no need capital or credit. This group included as non quantity rationed credit. Micro enterprise households that the reason could not develop economies of scale and limited customers also non quantity rationed credit. Mostly the reason not applied KUR are they do not the program and procedure to apply due to lack of information amounted to 67 households or 40.36 percent. This group categorized as fully quantity rationed credit. Another reason not filed KUR is the fear of credit risk (risk averse), which is about 56 households or 33.73 percent. Some households also give reason to fear the bank is related to inconvenience themselves in debt. Finally, there are 3 households that do not apply for credit due to they believe that they will be rejected (subjective self-selected out). The last two reasons do not apply for credit belongs to the group fully credit-rationed quantity. From the description above, the amount of non-rationed quantity credit is 174 (= 134 and 40 households) or 52.41 percent and partially credit-rationed quantity of 21 units (6.33%) as well as fully quantity rationed 137 (= 11, 67, 56 and 3 units) or 41.26 percent. See table 6 below. Studies about credit-rationed also carried out by (Dufhues and Buchenrieder 2005; Fletschner 2008; Mohamed and Retrieval 2009; Shoji, et al. 2012; Helsen and Chmelar 2014). Shoji classifies as credit constrained (15.9%) and credit unconstrained (84.1%) to access credit. Based on data from the Household Finance and Consumption Survey (HFCS), Helsen and Chmelar writes that 8.1% of Europeans households are credit constrained and 5-7% are not applying for a loan because they thought would be rejected. 5.2. Econometric Estimation Estimation of logistic regression model with STATA to identify the main factors of household enterprises to access KUR is presented in table 7. According to likehood ratio (LR) 126.24 with the degree of freedom 13 or p-value 0,000 under the significant level (α = 5%), it can be drawn that the above logistic regression model is valid to explain or elaborate the decision of household enterprises to receive KUR. International Journal of Economic Perspectives ISSN 1307-1637 © International Economic Society http://www.econ-society.org

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International Journal of Economic Perspectives, 2015, Volume 9, Issue 2, 57-70. Table 5. Social-Economic characteristics Description

Non-KUR (N1=177) Amount (n1) % to N1

KUR (N2=155 ) Amount (n2) % to N2

All Respondents (N3=332) Sub-Total (N4=n1+n2) % to N3

Social economics; labor (people) Paid labor Average

of

71

40.11

36

20.34

138

89.03

209

62.95

78

50.32

114

34.34

business

9.08

5.47

7.4

working

61.33

58.15

59.85

length (year) Average

of

hours/week Business line: trade/retail

130

73.45

70

45.16

200

60.24

Production

23

12.99

26

16.77

49

14.76

Services

16

9.04

30

19.36

46

13.86

Farming

5

2.83

12

7.74

17

5.12

Fishing

3

1.69

12

7.74

15

4.52

Agricultural

0

0.0

5

3.23

5

1.50

Distance: Distance to market (km)

4.83

4.27

4.58

Distance to bank (km)

4.23

4.23

4.23

Products sold: At home

140

79.10

121

78.06

261

78.62

at market

28

15.82

23

14.84

51

15.36

Selling around

9

5.08

11

7.10

20

6.02

Business barrier: Capital

65

36.72

102

65.81

167

50.30

Marketing

112

63.28

53

34.19

165

49.70

Avergae

of

working

2,163,412

3,748,194

2,903,304

capital (Rp)/week Bank account: Yes

42

23.73

89

57.42

131

39.46

No

135

76.7

66

42.58

201

60.54

Yes

48

27.12

40

25.81

88

26.51

No

129

72.88

115

74.19

244

73.49

Yes

99

55.93

80

51.61

179

53.92

No

78

44.07

75

48.39

153

46.08

Yes

73

41.24

38

24.52

111

33.43

No

104

58.76

117

75.48

221

66.57

Side job

Spousal working

Other loan sources

Source: Data processed


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International Journal of Economic Perspectives, 2015, Volume 9, Issue 2, 57-70. Table 6. Application of Households’ Credit Total micro enterprise households N = 332

Not apply KUR (N1=166) Amount

% to N1

Apply KUR (N2=166) Amount

% to N2

The requested amount is approved

134

80.72

The requested amount is not all approved

21

12.65

The requested amount is all rejected

11

6.63

Reasons not apply KUR Not knowing program/application process

67

40.36

No need

40

24.10

Fear of risk/risk averse

56

33.73

Apriori to bank

3

1.81

Source: Data processed.

To test the key factors influencing the decision of household enterprises in getting KUR based on p-value (α = 5%). Variables which show significant positive trends are gender, business barrier, and ownership of bank account. Inversely, variables which show significant negative trend with confidence level of 5% are business length and alternative funding sources.Working spouse also gives significant positive effect for confidence level of 10%. While age factor, education level is not significant in the decision of applying KUR or not. Other insifinicant level in decision of getting KUR is distance, working capital and side job. Gender. Gender represents men and women, which show the significance in influencing household micro-credit in acquiring KUR. Men have the possibility of 6.56 times higher to access KUR than women. This result is in line with the study conducted by (Mpuga 2008; Messah and Wangai 2011; Ololade, et al. 2013). Ololade says that being a woman declines the probability of having access to credit by 71.3 percent. This study shows that the objective of inclusive finance and women empowerment are not prioritized in Indonesia. Formal loans have not made women as their main target to increase the revenue of micro-credit in supporting other women empowerment programs. Women have insufficient tangible assets to be collateralized to receive formal loans. This circumstance relates to property rights which are commonly controlled by men in household enterprises. Education level and financial literacy also become significant factors for women to have access to formal loans. Majority of women’s education level in this study is primary school level with the proportion of 47.33%. Roomi & parrott (2008) identified that women has less access to capital, technology and strong male dominance in a patriarchal society that are likely to affect investment decisions (Akpalu, et al. 2012). According to Okojie et al. (2010), the women could not access to the bank because they do not have formal savings accounts, warranties and do not know the credit application procedures. By contrast, access too informal loans were dominated by women (Syukur 2002). Out of 332 household respondents in this study, more than 45% of the micro enterprises are controlled by women, however only 10.2% of proportion has the access to KUR.


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International Journal of Economic Perspectives, 2015, Volume 9, Issue 2, 57-70. Table 7. Estimation of Logistic Regression Model Logistic regression

Log likelihood =-166.27634

No.of obs

=

332

LR chi2 (13)

= 126.24

Prob>chi2

= 0.0000

Pseudo R2

= 0.2752

Kur

Coef

Odds Ratio

Std. Err

Z

P>

[95% Conf. Interval]

Gender

1.881237

6.561618

2.046902

6.03

0.000

3.560229

12.09328

Age

.0129872

1.013072

.0193272

0.68

0.496

.9758907

1.05167

Educ_1

.3388249

1.403298

1.305779

0.36

0.716

.2265166

8.693597

Educ_2

-.1460037

.8641545

.7929406

-0.16

0.874

.1430660

5.219711

Educ_3

-.1632431

.8493847

.7717567

-0.18

0.857

.1431189

5.040945

Distance

-.0045834

.9954271

.0392788

-0.12

0.908

.9213437

1.075467

Business_l~h

-.0814168

.9218094

.0277119

-2.71

0.007

.8690645

.9777557

Business_b~r

.7996556

2.224775

.6393548

2.78

0.005

1.266683

3.907545

Working_ca~k

1.98e-08

1

3.52e-08

0.56

0.574

1

1

Bank_account

1.169159

3.219285

.9990799

3.77

0.000

1.752248

5.914569

-.3822496

.6823247

.2253536

-1.16

1.247

.3571593

1.303527

_spouse_w~g

.5576558

1.746573

.5146384

1.89

0.058

.9803366

3.111706

Other_loan~e

-1.027931

.3577464

.1109003

-3.32

0.001

.1948522

.656818

Side_job

Source: Data processed

Business barriers. This research identifies two business barriers: capital and marketing barrier. This factor is significant and explain that micro enterprise which face capital barrier have 2.22 times greater probability to access KUR in comparison with micro enterprise which face marketing barrier. Micro enterprise which faces capital barrier commonly has the opportunity to grow larger when the barrier is removed. On the other hand, if it is marketing barrier, the business will have difficulty to grow larger due to lack of capability to attract new customers. Bank account. Ownership of bank account is one of the significant factors for micro enterprise to access KUR. Micro enterprise with bank account will have 3.32 times greater probability to access KUR in comparison with micro enterprises without bank accounts. Owning bank account will have an information access faster of saving account and loan products due to direct interaction with bank employees. Data shows that the strongest reason (40.36%) of micro enterprise not accessing KUR is because of not knowing the program. This result is contradictory with study of (Li, et al. 2011; Khoi, et al. 2013) that household micro-busness with saving accounts will be able to fulfill their capital requirements by utilizing existing savings, thus bank loans are not needed. Business length. Business tenure is also a significant factor for micro enterprise to access KUR. With significance level of 5%, a conclusion can be drawn that micro enterprise with longer the tenure have 0.92 times probability not to access KUR. The longer the tenure of the business, the greater the ability to gather capitals, assets and profits. Spousal working. With a significance level of 10%, household micro enterprise who having spousal working have the 1.75 times more probability to access KUR than household micro enterprise which the spouse does not have working. Households whose partner works the revenue increase. If associated with a person's level of risk, International Journal of Economic Perspectives ISSN 1307-1637 © International Economic Society http://www.econ-society.org

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International Journal of Economic Perspectives, 2015, Volume 9, Issue 2, 57-70. then the household courage to take the credit increases in line with income. From the bank's perspective too, safer funds distributed to households who are both working. Alternative funding sources. There are several alternative funding sources in rural areas such as national community empowerment program (PNPM), cooperation, Baitul Mal Wattamwil (BMT), neighbors, relatives or money lenders. Based on logistic estimation, alternative funding sources significantly influences the decision of household micro enterprise not to exercise KUR. Micro enterprise with alternative funding sources other than KUR has 0.36 times probabilities, not to access KUR in comparison with micro enterprise without alternative funding sources. In other words, micro enterprise without alternative funding sources have 2.78 times probability to access KUR than micro enterprises with alternative funding sources. Micro enterprise which have loans from alternative funding sources and have fulfilled their capital needs, will not borrow from formal sources due to its inflexible requirements. Based on logistic estimation results in this study, age factor does not significantly influence the decision of micro enterprises to access KUR. Age factor seemingly do not become the consideration of the banks, given that it is within productive age range. The result of this study is contradictory with the result from (Chau, et al. 2012; Khoi, et al. 2013) that the older the head of the family, the greater the probability of access to loans due to greater network. Khoi assumes that age as the level of accountability and commitment to pay back the loan. Distance to the banks does not influence the micro enterprise to access KUR either. In this study, distance is less significant due to improved infrastructure and transportation facility. People ease to come to the banks. This result is inversed with the result from (Quoc 2012; Vitor and Abankwah 2012) that distance or location is very significant and negatively correlated. Longer distance will reduce the access to formal loans. 6.

CONCLUSION

KUR program was launched by the government to achieve inclusive financing, especially for micro enterprise in funding their working capitals and investment from formal bank loans. Out of 332 respondents from household micro enterprise, 46.7% of respondents have the access to KUR and the remaining proportion does not use KUR to fund their businesses. There are alternative funding sources such as PNPM, cooperation, union, BMT, neighbors, money lenders or relatives. There are 111 units or 33.4% of respondents access to other loan sources. Household micro enterprises which received KUR as requested (134 units) and household businesses which did not apply for KUR (40 units) are categorized as fully non-credit rationed. This category constitutes 52.4% of proportion or equivalent to 174 units. While partially non-credit rationed category makes up about 6.3% or 21 units. The remaining 41.3% or 137 units are categorized as credit rationed, comprising household micro enterprises which are rejected or do not apply for KUR for the reason of not knowing such program, fear or a priori with banks. As a whole, there is a huge gap between demand for loan and micro enterprises which have the ability to access loans. The rejection of loan application should provide objective reasons to the customers so that they can improve themselves. Product information has not reached to the whole level of society because there is a fraction of the population not knowing such information. Chau, et al. (2012) commented that information from lenders is critical to borrower to receive loans. Loan information can be inseminated by staffs or local organizations. The result of this study divulges the determinant factors which significantly influence the household micro enterprise access to KUR. Gender plays significant role in the accessing KUR in household micro enterprise, of which men, having a higher occurrence in accessing KUR than women. Asset and property ownership under the husband’s name lessens the role of the wife in getting loans. Women lack interaction with formal institutions and signing loan contracts due to having little experience or low level of education. The longer the tenure of the business and the accessibility to alternative funding sources become the detrimental factors in their accessibility to KUR. The underlying reason is that the needs for capital are fulfilled without the need to access KUR from banks. Another case if the barriers faced by households because of limited marketing micro, low economic scale, thus KUR is not the right solution.


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