Co-Value: Jurnal Ekonomi, Koperasi & Kewirausahaan
Volume 14, Nomor 10 Maret 2024
p-ISSN: 2086-3306 e-ISSN: 2809-8862
Sisca Contesa
THE EFFECTS OF SME'S ACCESS TO FINANCE ON NON-BANK
FINANCIAL INSTITUTIONAL LOANS
Sisca Contesa
Fakultas Ekonomi dan Bisnis Universitas Widya Dharma Pontianak
siscacontesa@gmail.com
Abstract
This study aims to investigate the effect of access to finance on non-bank financial
institutional (NBFI) loans. The tenet is that SMEs with less access to finance will pursue
loans provided by the NBFI despite its higher interest rates. Using World Bank Enterprise
Survey data, the findings show the positive relationship between SMEs' access to finance
and NBFI loans. It affirms our hypothesis and aligns with Pecking Order Theory. This
research contributes to the literature by revealing NBFI loans as alternative financing for
SMEs due to their severe access to finance. Practically, the policymakers should consider
giving more incentives on NBFI loans; hence, Indonesian SMEs have more room to finance
their operation.
.
Keywords: Access to Finance; Non-Bank Financial Institutions; Loans; SMEs; Capital
Structure.
PENDAHULUAN
Small and medium-sized enterprises (SMEs) are fundamental assets to Indonesia's
economy (Pramono et al., 2021). They contribute 61% to the country's GDP and employ
97% of its workforce (Suhaili & Sugiharsono, 2019). However, despite their economic
significance, Indonesian SMEs have faced a persistent challenge in accessing funding
since 2009 (Bell, 2015). Research shows that only 25% of Indonesian SMEs have full
access to finance for their business operations (Bell, 2015).
Securing financing for small and medium-sized enterprises (SMEs) in Indonesia
poses significant challenges due to their size and credit constraints (Widnyana et al., 2021).
It forces SMEs to seek alternative financing, such as family or other traditional financing
channels (Nguyen et al., 2022; Prijadi et al., 2020). This research proposes non-bank
financial institutions, such as pawn shops, credit unions, venture capital, microfinancing,
and peer-to-peer lending, as the alternative financing of SMEs due to their financial
exclusion.
NBFI offers higher borrowing costs, yet SMEs still consider it alternative financing
(Khowaja et al., 2021). One main reason is due to limited access to traditional funding
sources. The logic is that when SMEs desperately need financing, they turn to NBFI
because of its leniency. When external options are scarce, SME entrepreneurs often seek
support from family or relatives, which is perceived as a more cost-effective financing
option than other alternatives. However, the family and relatives have limited funds to
assist the request from SMEs.
This research argues that severe financial access will lead to non-bank financial
institutions rather than family or relatives for three reasons. First, non-traditional financing
is a reasonable alternative to getting the necessary funding when traditional financing
resources are unavailable (Nguyen et al., 2022). The NBFI has more lenient requirements
despite higher costs. Further, NBFI offers greater flexibility regarding repayment
schedules or collateral requirements, accommodating SMEs' unique needs and
The Effects of Sme's Access to Finance on non-Bank
Financial Institutional Loans
e-ISSN: 2809-8862
p-ISSN: 2086-3306
Sisca Contesa
circumstances. Second, NBFI typically provides larger funding than family and relatives
due to their ability to pool resources from various investors or sources (Khowaja et al.,
2021). This allows them to offer substantial loans that meet SMEs' financing needs,
surpassing what individual families or relatives can provide, aligning with SMEs'
expectations for adequate capital infusion. Third, NBFI is also regulated by the
government, offering protection against fraudulent practices or exploitation. (Trapanese,
2021).
In short, this research aims to examine the relationship between obstacles in
accessing financing and NBFI loans. The argument is that if the SMEs are hard to access
the traditional banking, they would pursue NBFI loans despite its higher interest rate. This
research frames the association using Pecking Order Theory to explain the theoretical
relationship between those two dimensions. Through empirical analysis, the study seeks
to provide evidence supporting the hypothesized relationship and offer insights into the
financing behavior of SMEs in Indonesia.
2. Literature Review
2.1 Theoretical Arguments
Pecking Order Theory is a financial theory that provides insights into how firms,
particularly small and medium-sized enterprises (SMEs), determine their capital structure
and financing choices (Martinez et al., 2021). According to this theory, firms have a
hierarchy of preferred financing sources and tend to prioritize internal financing over
external financing (Martinez et al., 2021). If internal funds are insufficient, firms turn to
less costly and risky external financing options, such as debt.
From the perspective of Pecking Order Theory, small and medium-sized enterprises
(SMEs) may opt for alternative financing from non-bank financial institutions (NBFIs)
despite higher interest rates due to several factors. Firstly, internal financing options may
be limited or insufficient to meet the firm's funding requirements because many SMEs have
limited cash holdings (Chaklader & Padmapriya, 2021). Secondly, traditional bank
financing may be difficult to obtain for SMEs due to stringent collateral requirements, lack
of credit history, or risk perceptions by lenders (Casey & O’Toole, 2014). In such
situations, SMEs resort to alternative financing sources like NBFIs, which may offer more
accessible and flexible funding options, albeit at higher costs (Khowaja et al., 2021;
Nguyen et al., 2022). Additionally, the asymmetric information between SMEs and
traditional lenders may lead to adverse selection costs, making alternative financing more
attractive despite the higher interest rates (Brahmana et al., 2022). Furthermore, NBFIs may
provide faster approval processes and fewer bureaucratic hurdles than banks, making them
a preferred choice for SMEs needing quick capital injection (Khowaja et al., 2021). Overall,
the decision to opt for alternative financing from NBFIs despite higher interest rates reflects
SMEs' prioritization of maintaining financial flexibility and avoiding adverse selection
costs, as proposed by the Pecking Order Theory.
2.2 Hypothesis Development
Theoretically, firms favor financing activities using relatively cheaper bank debt
(Casey & O'Toole, 2014). However, if the firms meet bank lending constraints, they are
more inclined to borrow from more expensive non-institutional sources, provided that
investment returns exceed the cost of funding from alternative credit providers (Casey &
O'Toole, 2014). Consequently, the obstacle in accessing financing leads firms to pursue
alternative financing, such as non-bank financial institutions (NBFI).
It is affirmed by the seminal work of Petersen and Rajan (1997), who find that firms
tend to pursue NBFI, like trade credit, when traditional financing is constricted. Similar
Vol. 14, No. 10, Maret, 2024
https://journal.ikopin.ac.id
findings are presented in Nilsen (2002), where small firms are shown to substitute trade
credit for bank credit in the face of bank lending shocks. In East Asia, Love et al. (2007)
have the same conclusion, where firms pursue NBFI due to lending shocks during the
financial crisis. In a more recent study, Casey and O'Toole (2014) found that European
SMEs pursue alternative financing due to the financing constraints in accessing traditional
bank loans. There is also Nguyen et al. (2020), who found that Vietnamese SMEs pursue
alternative financing due to the obstacles in accessing traditional banking loans.
Therefore, this research hypothesizes:
H
1
: Higher obstacles in accessing financing have a positive relationship with the SME's
behavior in pursuing Non-bank financial institutional loans.
METODE PENELITIAN
This research follows Calabrese et al. (2021) and Khowaja et al. (2021), as the
baseline model. The variables such as Sales, Size, Age, and Legal status are the main
functions of the loans. The Collateral Size and Province are introduced to the baseline for
robustness reasons. Those factors are the control variables to isolate the main effect on the
dependent variable. Meanwhile, the main effect is the obstacle to accessing finance
(ACCESS_OBS). It is a categorical variable, where 0 denotes no obstacle, and 4 denotes
extreme obstacle. This categorization is made by the World Bank.
Therefore, the estimation model is as follows.
…………. (1)
Where NBFI is a loan taken from the non-bank financial institution. ACCESS_OBS
is the obstacle to access financing. Meanwhile, Collateral, Sales, Size, Age, Legal, and
Province are the control variables in the model estimation. The details of the measurement
are in the next section.
The variable definition in this research follows the metadata guideline the World
Bank provided, and it matches previous research. The NBFI is a dummy variable for
whether or not the SMEs took loans from non-banking financial institutions. Examples of
NBFI are pawn shops, multifinance, credit unions, venture capital, microfinancing, and
peer-to-peer lending. Meanwhile, for the alternative measurement, this research uses NBFI
loans, the ratio of total NBFI borrowing to total debt.
For the independent variables, ACCESS_OBS is the obstacle for SMEs in accessing
financing. The SMEs were asked by the World Bank how difficult it is to access financing
for their operation. There are five answers: "No Obstacles," "Minor Obstacles," "Moderate
Obstacles," "Major Obstacles," and "Very Severe Obstacles." Each answer is coded using
a Likert-rating approach from 0 (No Obstacles) to 4 (Very Severe Obstacles).
Meanwhile, the control variables are also defined in the WES. COLLATERAL is
the total assets that are used as collateral by the SMEs. This research takes the lognormal
form of it as the measure. Sales is the lognormal of total sales made in the financial year.
Size is the lognormal of total employees. Age is the lognormal of the establishment year of
the SMEs. Legal is the legal status of the SMEs. Province is categorical of SME's location.
This study utilizes data from the World Bank Enterprise Survey (WES), which
compiles cross-sectional data from their field survey. The sampling frame was 1,320
Indonesian SMEs, although only 424 SMEs have complete data for hypothesis testing. It
is important to note that the WES is conducted every four years, with the latest available
The Effects of Sme's Access to Finance on non-Bank
Financial Institutional Loans
e-ISSN: 2809-8862
p-ISSN: 2086-3306
Sisca Contesa
surveys from 2015, 2019, and subsequent years. Consequently, panel data is not employed
in this research due to the absence of certain years.
HASIL DAN PEMBAHASAN
Results
4.1 Descriptive Statistics
Table 1 presents summary statistics for all variables in the estimation models. The
findings indicate that the mean value of access to finance (ACCESS_OBS) is 1.48. This
suggests that Indonesian SMEs encounter moderate obstacles in accessing finance,
hindering their funding. Data from the World Bank's WES survey reveals that 24% of
Indonesian SMEs encounter severe obstacles in accessing financing, with 4% facing
extreme challenges. However, 21% of Indonesian SMEs face moderate obstacles, affirming
the descriptive statistics.
Regarding Non-Bank Financial Institutions (NBFI), the average value is 0.17 or
17%. This indicates that 17% of Indonesian SMEs obtained loans from NBFI. The average
loan amount from NBFI is 4.28%, suggesting that, on average, Indonesian SMEs have a
gearing portion of 4.28% from NBFI, with a maximum of 40%.
Regarding the control variables, the collateral size of Indonesian SME assets
averages 9.78 in lognormal form, equivalent to IDR 4.4 billion in nominal value. Sales
average 22.92 in lognormal form, and the average number of hired employees is 3.45, or
30 employees in nominal terms. The average Age is 3.22 in lognormal form or 25 years old
in nominal terms. Lastly, the data has an average value of 2.43 for LEGAL, suggesting a
dominance of either Sole Proprietorship or Partnership (Commanditaire Vennootschap /
CV).
Table 1 Descriptive Statistics
Variable
Mean
Std. Dev.
Min
Max
ACCESS_OBS
1.48
1.29
0.00
4.00
NBFI (DUMMY)
0.17
0.37
0.00
1.00
NBFI_LOAN (%)
4.28
7.91
0.00
40.00
COLLATERAL (LN)
9.78
6.28
0.00
28.04
SALES (LN)
22.92
2.91
16.52
31.64
SIZE (LN)
3.45
1.55
0.00
8.52
AGE (LN)
3.22
0.41
1.95
4.51
LEGAL (CATEGORICAL)
2.43
0.65
1.00
4.00
N
424
Table 2 displays the correlation matrix, illustrating the individual relationships
among the variables. The findings are consistent with expectations, as all signs are negative
for NBFI and NBFI Loans. This suggests that lower access to financing, smaller collateral
sizes, decreased sales, smaller size, and younger SMEs are associated with higher loans
from NBFI. Simply put, Indonesian SMEs with limited financing access, smaller collateral
sizes, reduced sales, smaller sizes, and younger ages are more inclined to seek NBFI loans
compared to their counterparts.
Table 2 Correlation Matrix
ACCESS_OBS
NBFI
NBFI_LOAN
COLLATERAL
SALES
SIZE
AGE
LEGAL
Vol. 14, No. 10, Maret, 2024
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ACCESS_OBS
1
NBFI
0.382
1
NBFI_LOAN
0.159
0.441
1
COLLATERAL
-0.064
-0.265
-0.129
1
SALES
-0.041
-0.168
-0.063
-0.054
1
SIZE
-0.052
-0.189
-0.054
-0.016
0.706
1
AGE
-0.078
-0.145
-0.108
-0.005
0.321
0.328
1
LEGAL
0.140
0.207
0.128
0.050
-0.285
-
0.374
-0.228
1
4.2 Classical Linear Regression Assumption Tests
This study utilizes cross-sectional data, where each observation is independent of the
others, so autocorrelation tests are not typically conducted (Gujarati, 2021). However, other
diagnostic tests are performed using STATA software. For instance, the multicollinearity
test is conducted using the Variance Inflation Factor (VIF). Table 3 indicates that both
NBFI and NBFI loan models have VIF values below 10, suggesting the absence of
multicollinearity.
In addressing heteroscedasticity, which occurs when the variance of errors in a
regression model varies across observations, this research employs the Breusch-Pagan test.
The results indicate that all probability values exceed 5%, implying no heteroscedasticity
issues.
Although a normality test is not essential for the distribution of this research data, as
it follows a Gaussian distribution, a normality Z test is conducted in this study. The results
further affirm the robustness of the data, with all probability values exceeding 5%.
Table 3 Classical Linear Regression Model Assumption Tests
VIF
Breusch-Pagan test for heteroskedasticity
Normality Z-Test
NBFI
1.45
0.53
0.07
NBFI_LOAN
1.42
0.44
0.10
4.3 Regression Results
Table 4 displays the results of hypothesis testing using robust OLS logistic
regression. In this study, the standard errors were clustered using the White-test approach
to enhance maximum likelihood estimation. The findings support the hypothesis (H1),
revealing a positive relationship between obstacles in accessing finance and NBFI (β=0.996
p-value<1%). Because the higher ACCESS_OBS implies severe financial accessibility, the
results indicate Indonesian SMEs with higher obstacles in financing traditional bank loans
will have a higher probability of pursuing NBFI as the alternative financing. In practical
terms, each unit increase in financial access obstacles for SMEs corresponds to a 0.996%
increase in the probability of seeking NBFI loans.
The findings align with the Pecking Order Theory, positing when SMEs require
additional funds beyond what they can generate internally, they turn to external financing
options. However, because SMEs face obstacles or constraints in accessing traditional bank
loans, such as stringent collateral requirements, lengthy approval processes, or limited
credit availability, they may turn to NBFI loans as an alternative source of financing. NBFI
loans may offer more flexible terms, quicker approval times, or less stringent requirements
than traditional bank loans.
Another rationale for this finding lies in the comparative advantages provided by
NBFI. Despite potentially higher interest rates, NBFI's relaxed credit scoring criteria
The Effects of Sme's Access to Finance on non-Bank
Financial Institutional Loans
e-ISSN: 2809-8862
p-ISSN: 2086-3306
Sisca Contesa
positions them as viable financing alternatives for SMEs. It matches the pragmatic
approach of Indonesian SMEs in exploring alternative financing avenues. Consequently,
this may lead to a debt burden for Indonesian SMEs, increasing their default risk from low
to high.
Table 4 Regression Results
β-COEFFICIENT
STANDARD ERROR
ACCESS_OBS
0.996***
0.151
COLLATERAL
-0.178***
0.034
SALES
-0.128
0.089
SIZE
-0.105
0.166
AGE
-0.282*
0.154
LEGAL
0.781***
0.287
PROVINCE
0.009
0.026
CONSTANT
1.597*
0.812
PSEUDO R
2
0.154
F-VALUE
10.84
Note: *, **, and *** denote significance level at 10%, 5%, and 1% respectively.
4.3 Robustness Check: Alternative Measurement
This research employs a robustness check to ensure the robustness of the hypothesis
testing. The earlier result uses a dummy variable of NBFI as the measure. Yet, another
measurement to define NBFI is the percentage of NBFI loans to the total loans. This
research calculates the ratio and re-estimates the model under robust OLS regression. Table
5 presents the results.
The results show consistency across all independent variables. For instance, all
control variables have the same inference. Collateral size, Age, and legal status have
significant effects on NBFI loans. Meanwhile, other control variables such as Sales, Size,
and Province remain with trivial effects. Collateral and Age have a negative relationship
with NBFI loans, the same as the earlier finding in Table 4. Meanwhile, Legal status has
positive effects.
Turning to the main effect of obstacles in accessing financing (ACCESS_OBS),
Table 5 shows a significant positive relationship with NBFI loans (β=0.805, p-value<1%).
This indicates that as obstacles to traditional bank loans increase, there is a corresponding
rise in the proportion of NBFI loans in SMEs' debt structure. In practical terms, a one-unit
increase in the obstacle level (ACCESS_OBS) results in a 0.805% increase in NBFI loans.
Therefore, the conclusion remains intact.
Table 5 Robustness Check: Alternative Measurements
β-COEFFICIENT
STANDARD ERROR
ACCESS_OBS
0.805***
0.296
COLLATERAL
-0.164***
0.061
SALES
0.003
0.031
SIZE
-0.244
0.361
AGE
-0.128**
0.064
LEGAL
1.246*
0.636
PROVINCE
0.003
0.028
CONSTANT
1.609*
0.86
Vol. 14, No. 10, Maret, 2024
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ADJ. R
2
0.144
F-VALUE
2.74
Note: *, **, and *** denote significance level at 10%, 5%, and 1% respectively.
KESIMPULAN
This study proposes a positive relationship between access to finance and reliance
on non-bank financial institution (NBFI) loans. It suggests that small and medium-sized
enterprises (SMEs) turn to alternative financing options like NBFI due to difficulties
obtaining loans from traditional banks. Using data from Indonesian SMEs, the results
confirm this hypothesis, indicating that the greater the obstacles in accessing financing, the
more likely SMEs are to opt for NBFI loans despite their higher interest rates. This finding
aligns with the Pecking Order Theory, which posits that SMEs seek alternative financing
avenues when faced with funding challenges. The rationale is straightforward: SMEs
require funding for their operations, prompting them to choose NBFI loans despite their
higher financing costs as long as they align with their capital budgeting calculations.
This study makes a notable contribution to the body of knowledge in two key
aspects. Firstly, it empirically validates the applicability of Pecking Order Theory in the
context of small and medium-sized enterprises (SMEs) in Indonesia. By demonstrating a
positive relationship between obstacles in accessing traditional bank loans and SMEs'
uptake of non-bank financial institution (NBFI) loans, the research provides empirical
evidence supporting the theory's relevance in understanding SMEs' financing behavior.
Secondly, the study sheds light on the role of NBFI loans as an alternative financing source
for SMEs facing challenges in accessing traditional bank financing. By uncovering the
pattern where higher obstacles in traditional bank lending lead to increased reliance on
NBFI loans, the research enhances our understanding of SMEs' financing decisions and
offers insights into the factors driving their choice of financing sources.
Practically, this research offers several implications for policymakers. First, to
mitigate SMEs' reliance on higher-cost NBFI loans, policymakers could implement
incentives aimed at both SMEs and NBFIs. For SMEs, incentives might include providing
tax breaks or subsidies for accessing traditional bank financing or offering preferential loan
terms for qualifying SMEs. Simultaneously, policymakers could introduce measures to
incentivize NBFIs to offer more competitive rates and terms, such as providing tax
incentives or regulatory support for offering loans to SMEs at lower interest rates.
Additionally, policymakers may consider measures to promote a competitive environment
in the NBFI sector, ensuring that SMEs can access diverse and affordable financing options
beyond traditional banks.
Three notable limitations of this study require attention for future research. Firstly,
the use of cross-sectional data limits the establishment of causality and the consideration
of temporal effects. Utilizing panel data would facilitate a more comprehensive analysis
of changes over time and provide better insight into dynamic relationships. Secondly, the
study overlooks governance factors, such as agency issues or risk-shifting, which may
influence SMEs' financing decisions. Future research could explore these factors to
enhance understanding of the barriers faced by SMEs in accessing traditional bank
financing. Lastly, the study does not differentiate between various types of NBFIs, such as
credit unions, pawn shops, peer-to-peer lending platforms, or microfinance institutions.
Investigating SMEs' preferences and experiences with different NBFI sources could yield
valuable insights into their financing behaviors and preferences. Addressing these
limitations would enrich our understanding of SME financing dynamics and inform more
targeted policy interventions to support SMEs in accessing affordable financing options.
The Effects of Sme's Access to Finance on non-Bank
Financial Institutional Loans
e-ISSN: 2809-8862
p-ISSN: 2086-3306
Sisca Contesa
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