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Factors That Influence Access To Credit For Micro,
Small, And Medium-Sized Enterprises In Ruiru Sub
County In Kenya
Samson Manjuru Mburu *, Dr. Lucy Wanjiru Njogu **
* Student, Jomo Kenyatta University of Agriculture and Technology, Kenya
** Ph.D., Lecturer, College of Human Resource Development (COHRED), Jomo Kenyatta University of Agriculture and Technology, Kenya
DOI: 10.29322/IJSRP.11.11.2021.p11918
http://dx.doi.org/10.29322/IJSRP.11.11.2021.p11918
Abstract- The study's general objective was to establish the factors
that influence access to credit for MSMEs in Ruiru Sub County.
Specifically, the study set out to determine the influence of cost of
credit, financial information asymmetry, type of lending
institution, and credit reference bureau on access to credit for
MSMEs in Ruiru Sub County in Kenya. The study adopted a
descriptive research design. The target population was 590
MSMEs registered for operations in Ruiru Sub County for the year
2021. The study employed a probability sampling design. The data
was collected using a closed-ended questionnaire administered to
a randomly selected sample of 238 respondents from the target
population comprising 590 registered MSMEs for the year ending
2021. The data collected was edited, coded, classified, tabulated,
and analyzed using SPSS. Non- parametric data analysis
techniques of ordinal regression and Spearman correlation were
performed. There was a high correlation between all the
independent variables and dependent variable access to credit.
Cost of credit was not a significant negative predictor, financial
information asymmetry was a significant positive predictor, the
type of financial lending institution was a significant positive
estimator, and credit reference bureau was a significant positive
predictor of access to credit. The study recommended that credit
reference bureaus be restructured to suit the emerging economy's
needs of Kenya, financial institutions to customize their credit
products to specific target markets, both parties' partisan to credit
transaction to practice full disclosure practices, financial
institutions to provide more financial training, and financial
institutions to work towards reducing the cost of credit.
Index Terms- Cost of credit, access to credit, financial
information asymmetry, type of financial Institution, credit
reference bureau
I. INTRODUCTION
ccess to financial services has been identified as one of the
means of creating employment, reducing poverty, and
promoting growth (Ndungu, 2016). Micro, Small, and Medium
Enterprises (MSMEs) play a very significant role in the economy
of any country, both developing and developed nations and
individuals. Silong and Gadanakis (2020) note that MSMEs play
a crucial role in creating dynamic market-oriented economic
growth, employing the growing workforce, alleviating poverty,
and promoting democratization in developing countries.
In recent times MSMEs have been lauded for contributing to
grassroots economic growth and equitable, sustainable
development. MSMEs have a significant contribution to the
national economy. Specifically, MSMEs contribution to Gross
domestic product (GDP) has recorded increases through time,
showing a gradual increase from 1993 to 2018 (World Bank,
2019a). MSMEs need access to credit to cover operational costs
and for their growth and expansion.
World Bank (2020) shows that close to 68% of Kenyan
MSMEs state access to finance as a challenge. World Bank (2020)
reports that approximately 70% of all MSMEs in developing
markets lack access to credit, making it difficult for them to
survive.
According to World Bank (2020), the credit gap varies
significantly between regions and countries and is particularly
wide in Asia and Africa. The credit access gap for formal MSMEs
was estimated at US$ 1.2, while that for the informal sector,
MSMEs stood at the U.S. $ 1.4 trillion (World Bank, 2020).
Therefore, enhancing credit access to MSMEs will improve their
capability to be established and thrive, expand governments'
development and growth, and increase employment opportunities.
1.2 Statement of the problem
Kenya aims to transform into a newly industrializing middle-
income country by 2030 as laid out in the country's development
blueprint covering the period 2008 to 2030 (GOK, 2007). To
achieve this goal, one of its focuses is encouraging the growth of
the Micro, Small, and Medium Enterprises (MSMEs), leading to
the formation of the Micro and Small Enterprises Authority
(MSEA). MSEA is a state corporation established under the Micro
and Small Enterprise Act No. 55 of 2012, capable of contributing
to the industrializing goal by 2030 (GOK, 2012).
According to the African Development Bank and
Government of Kenya (2018), MSMEs contribute to 40% of
Kenya's GDP, making the sector a significant contributor to socio-
economic development. MSMEs require finance for expansion,
productivity, and growth, yet the majorities self-finance their
businesses (Kenya Institute for Public Policy Research Analysis
(KIPPRA), 2020). Analysis of the World Bank Enterprise Survey
Data 2019 suggests that close to 68% of Kenyan enterprises state
access to finance as a challenge. According to the Survey, 50% of
A
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Kenyan MSMEs have never approached a bank, and only 36% of
Kenya's MSMEs have accessed loans, compared to the OECD
average of 51% (World Bank, 2019b)
Lack of capital is still the most significant constraint to
MSMEs growth in Kenya. The lack of money inhibits MSMEs'
capability to create jobs, pay taxes and provide goods and services.
Despite a landscape of service providers, including nine
microfinance banks, 23 credit-only MFIs, and 43 banks, there are
critical gaps in serving different MSME segments, especially the
enterprises in the missing middle that are too big for microfinance
and too small for private equity and debt (Kenya National Bureau
of Statistics (KNBS), 2018). The report further adds that MSMEs
are still struggling with the gap in funding between microloans and
more extensive commercial lending. Governments, NGOs, and the
private sector in Kenya attempt to fund that gap but only cater to
a small market share.
Despite increased efforts to lend to MSMEs, financial
inclusion analysis indicates that there is a total credit gap of over
US$ 5bn for MSMEs in Kenya (World Bank, 2020). If this
persists, the industrialization goal of vision 2030 will fall due. The
credit gap creates a need to investigate why there is a persistent
lack of access to credit despite all the stakeholder's efforts to
overcome the perennial problem of access to finance.
There is extensive literature on factors that influence access
to credit for MSMEs, mainly from the developed economies
whose applicability in an emerging economy is practically
impossible. In addition, there exists a gap in the existing
knowledge on the determinants of the cost of credit, financial
information asymmetry, type of financial lending institution, and
credit reference bureau. Therefore, this study will fill this gap by
providing relevant evidence from MSMEs in Ruiru Sub County
for 2021 in Kenya, an emerging economy.
1.3 Objectives
1.3.1 General Objective
The study's general objective was to determine the factors
that influence access to credit for MSMEs in Ruiru Sub County in
Kenya.
1.3.2 Specific Objectives
The study specifically aimed:
i. To determine the influence of cost of credit on
access to credit for MSMEs in Ruiru Sub
County in Kenya.
ii. To determine the influence of financial
information asymmetry on access to credit for
MSMEs in Ruiru Sub County in Kenya.
iii. To determine the influence of the type of
financial lending institution on access to credit
for MSMEs in Ruiru Sub County in Kenya.
iv. To determine the influence of the credit
reference bureau on access to credit for MSMEs
in Ruiru Sub County in Kenya.
1.4 Research Question
The following research questions guided the study:
1. What was the influence of cost of credit on
access to credit for MSMEs in Ruiru Sub
County in Kenya?
2. What was the influence of financial information
asymmetry on access to credit for MSMEs in
Ruiru Sub County in Kenya?
3. What was the influence of the type of financial
lending institution on access to credit for
MSMEs in Ruiru Sub County in Kenya?
4. What was the influence of the credit reference
bureau on access to credit for MSMEs in Ruiru
Sub County in Kenya?
II. LITERATURE REVIEW
2.1 Theoretical background to the study
2.2.1 Credit Rationing Theory
Credit rationing Theory by Stiglitz & Weiss (1981) is among
the most critical theories that focus on financing gap analysis.
Stiglitz & Weiss (1981) note that lenders seek to impose
quantitative restrictions on the amount of loan the borrower can
obtain, a state referred to as equilibrium quantity rationing of
credit, since high-interest rates may give additional impetus to
adverse selection and risk-taking. According to Kweyu (2017), the
model is based on imperfect markets characterized by information
asymmetry, making it costly for financial institutions to obtain
accurate information on the MSMEs (borrowers) and monitor their
actions.
2.2.2 Information Asymmetry Theory
The theory of information asymmetry developed in the
1970s and 1980s was attributed to Akerlof's (1970) paper: The
Market for lemons, quality uncertainty, and the market
mechanism. It postulates that an imbalance of information
between two parties transacting can lead to inefficient outcomes.
This imbalance can cause one party to enter into a transaction or
make costly decisions.
2.2.3 Financial Intermediation Theory
The theory of financial intermediation, according to Andries
and Cuza (2009), describes the process where savers (surplus
units) give funds through deposits to intermediaries (financial
institutions) who in turn channel out the funds to the borrowers or
spenders (deficit units). Andries and Cuza (2009) observe that
financial intermediation refers to transferring funds from the
entities with surplus to entities experiencing deficit through
financial intermediaries. Ndungu (2017) noted that financial
intermediaries are financial institutions specializing in purchasing
and selling financial capital.
2.2.4 Adverse Selection Theory
According to Stigliz and Weiss (1981), the adverse selection
theory is based on two assumptions. The two assumptions
postulate that lenders fail to distinguish between borrowers of
different risks, and loan contracts are subject to limited liability if
project returns are less than debt obligations. The borrower bears
no responsibility for honoring the end of their bargain. This
analysis applies to involuntary default and assumes that borrowers
repay loans when they have the means to do so.
2.3 Conceptual Framework
2.3.1 Cost of Credit
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Cost of credit refers to all the money involved in acquiring
credit from the lending financial institutions, namely SACCOs,
microfinance institutions, and Commercial banks (Chirchir,
2017). The cost of credit is indicated by legal fees, processing, and
interest charged on loans, negotiation fees, travel costs, insurance,
collaterals to acquire loans, and any charge related to accessing
credit (Chirchir, 2017).
2.3.2 Financial Information Asymmetry
The concept of information asymmetry was attributed to
Akerlof (1970). Two parties to a single transaction possess
different knowledge concerning the same trade; hence one will be
in an advantageous position compared to the other party. Leitner
(2006) defined financial information asymmetry as the situation
where the MSMEs (borrowers) have personal knowledge about
their ability to repay the loan and which the financial lending
institutions(lenders) cannot be able to know. This situation causes
the lending institutions to be unable to distinguish between good
and bad borrowers.
2.3.3 Type of Lending Financial Institution
The MSMEs can get loans from commercial banks, MFIs, or
Saccos. CBK (2020) defines the lending financial Institution as
formal entities that extend credit to micro, small, and medium-
sized enterprises categorized into commercial banks, microfinance
banks/Institutions, and Saccos. The type of lending financial
institution affects access to credit.
Studies show that the type of collateral and lending
requirements differ, thus dictating which financial Institution the
MSMEs will acquire credit from (World Bank, 2019a). Kinyua
(2014) shows that MFIs and Saccos are preferred most, while
Shikumo and Mwangi (2016) show that commercial bank loan
uptake is slow. Commercial banks prefer to lend to businesses
with accurate financial statements or records and sufficient
collateral in the form of tangible assets, which are difficult for
MSMEs to obtain (Ondieki et al., 2013).
2.3.4 Credit Reference Bureau
According to Mole and Namusonge (2016), the first known
credit bureau known as Mutual Information Society was
established in 1803 in London by a group of tailors to exchange
references on the paying habits of their consumers. In the United
States of America (USA), the authors note that the first credit
bureau was established in Brooklyn in 1869. In Kenya, the Central
Bank of Kenya (CBK, 2020) initiated and introduced CRB as a
policy to be implemented by all commercial banks in Kenya to
improve credit risk management in the banking sector.
2.3.5 Access to Credit
Access to credit is the ability of individuals and enterprises
to obtain external funding to enable them to ease cash flow
problems (Silong & Gadanakis, 2020). Chirchir (2017) points out
that lack of access to credit is universally accepted as the critical
problem for MSMEs. The author highlights that credit constraint
operates in various ways in Kenya, where the underdeveloped
capital markets force entrepreneurs to rely on self-financing or
family and friends, which is not enough to spur MSMEs
operations optimally. This lack of long-term credit forces MSMEs
to rely on high-cost short-term finance and even the unregulated
high-risk informal sources of credit.
Chirchir (2017) indicates no structured institutional
mechanisms to facilitate the flow of financial resources from the
formal sector through MFIs to MSMEs in Kenya. Mutinda (2018)
found out that most MSMEs are often unable to procure adequate
financial resources to procure machinery, equipment, and raw
materials in addition to meeting day-to-day expenses.
The main concern of this study is the external credit facilities
available to MSMEs. According to Buyinza et al., (2018), external
financing or credit facilities is a kind of finance provided by
individuals or entities other than the proprietor or partners of the
company, partnership, or sole proprietorship. Credit can be in any
of the following forms; overdrafts, trade creditors, lease financing,
debentures, loans, overdrafts, and can be either short-term or long-
term depending on the lender's assessment of the borrowers' ability
to repay.
Silong and Gadanakis (2020) have linked interest rates or
cost of finance, lack of financial management skills by MSMEs,
high fees charges, risk-averse behavior by MSMEs, lack of
accurate financial information by MSMEs, and stringent collateral
requirements as the factors that influence access to credit.
2.4 Empirical literature review
An empirical review of the objectives of the studies:
2.4.1 Influence of cost of credit on access to credit for MSMEs
Chirchir (2017) carried out a study on determinants of
financial accessibility by small and medium enterprises in Eldama
Ravine Sub County in Kenya. Using stratified sampling to draw a
sample size of 57 respondents and Pearson correlation and
regression analysis, the author found that cost of credit and access
to credit had a solid negative significant correlation implying an
increase in the cost of credit leads to a decrease in access to credit.
Subeyr and Muturi (2017) studied factors affecting access to credit
by microenterprises in Garowe in Puntland. The study used
comparative and quantitative research designs. They used
purposive, cluster sampling, and systematic sampling to determine
respondents. The study employed Spearman and regression
analysis for data analysis. The cost of credit was a significant
influencer of access to credit, and the authors conclude that the
cost of credit is a constraint to credit accessibility in Puntland.
Gichuki et al., (2014) carried a study in the Kangemi
Harambee market in Nairobi in Kenya on challenges facing
MSMEs in accessing credit facilities. They used a descriptive
research design and a sample size of 241 respondents by stratified
random sampling. The authors found that cost of credit was a
moderate influencer of access to credit. Therefore, high
transaction costs, high costs incurred in traveling to acquire the
credit facilities, insurance, and legal and taxation fees impacted
the access to credit, making it a challenge to access a loan.
2.4.2 Influence of financial information asymmetry on access
to credit for MSMEs
Asongu and Odhiambo (2019) investigate the reduction of
information asymmetry through information sharing on access to
credit. Using the generalized method of moments technique and a
sample size of 53 African Countries from 2004 – 2011, they found
that information sharing avenues are significant positive
predictors of access to credit.
Muli and Muli (2019) study the effect of information
asymmetry on borrowing costs among microfinance clients in
Kenya. The study adopted a descriptive research design. The study
used primary data collected using a structured questionnaire, and
multiple regression was performed on the data. It was found that
financial information asymmetry was a significant positive
predictor of access to credit. Specifically, borrowers' history of
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credit and information were significant predictors of access to
finance. Borrowers' proximity does not influence the access to
credit.
Huang et al., (2014) conducted a study in China to determine
financial difficulties for SMEs using information asymmetry
theories. The study adopted a comparative literature review
design. It was found that information asymmetry significantly
causes adverse selection and moral hazards, contributing to
lending institutions' high interest rates charged on credit access.
Information asymmetry causes lenders to take measures to
mitigate against the high risk associated with loan default.
2.4.3 Influence of type of lending financial Institution on access
to credit for MSMEs
Shikumo and Mwangi (2016) studied determinants of
lending to small and medium enterprises by commercial banks in
Kenya. The study undertook a census of the 43 commercial banks,
obtaining complete data for 36 institutions. Using secondary data
from the annually published reports of commercial banks in Kenya
for five years, they determined that bank size and liquidity
influence lending to MSMEs and that lending to SMEs by
commercial banks poses the most severe credit risks. Therefore,
commercial banks are reluctant to lend to MSMEs, exacerbating
the problem of access to credit.
Gangata & Matavire (2013) studied the challenges facing
MSMEs with access to credit from lending institutions and
established that the main reason why most MSMEs are turned
down on their request to access funding from financial firms was
failing to meet the lending requirements. The authors cite
collateral security required by commercial banks as the primary
determinant to access financing.
A study by Kinyua (2014) examined competition among
lending financial institutions and easy accessibility to credit by
MSMEs in Nakuru, Kenya, determined that MFIs and SACCOS
were the most preferred sources of credit for MSMEs.
Commercial banks are reluctant to loan to MSMEs, citing
the high risk of default. Hwarire (2012) study examined credit
management and loan repayment of MSMEs in South Africa
financial institutions. It concluded that 39% of loan repayments by
MSMEs were not made on time, while 28% defaulted. Some
studies have cited commercial banks' loan uptake as slow due to
the factors they consider to grant a loan, leading to the inability of
MSMEs to access funding from commercial banks.
Wangai & Omboi (2011) analyzed the factors that influence
credit demand among small-scale entrepreneurs in Meru Central
Business district, Kenya. They established that household income,
entrepreneur's level of education, and the number of entrepreneur's
dependent factors that influence commercial banks to lend to
MSMEs.
Vera and Onji (2010) state that for MSMEs to access finance
from commercial banks, they need quality audited financial
statements, which most MSMEs cannot have as it will increase
operations costs to hire auditors. Even most of them have a poor
educational background. As a result, commercial banks' rules and
procedures deter MSMEs from accessing their loans.
2.4.4 Influence of CRB on access to credit for MSMEs
Mole and Namusonge (2016) studied the factors affecting
access to credit by small and medium enterprises in Kitale
municipality. The study employed a descriptive survey design and
used Krejcie and Morgan formula to draw a sample size of 256
SMEs. They collected data using a questionnaire and interviews.
Descriptive statistics and correlation analysis established that,
among other factors, credit referencing bureau policies
significantly influence access to credit facilities by SMEs from
financial institutions. They recommend that information
asymmetry should be enhanced between CRB and the financial
institutions on the one hand and SMEs on the other hand.
Saliku (2015) studies the influence of credit reference
bureaus on access to credit by small and medium enterprises in
Kitale municipality in Trans Nzoia County. The findings revealed
that the CRBs have policies that guide their operations and that the
policies of CRBs have a significant impact on SMEs ' daily
operations. The study determined that SMEs are not aware of the
CRB policies lenders use to assess their viability for loans. The
study highlights the suspicion with which SMEs view CRB, with
a substantial proportion doubting their legality and mandate. Most
SMEs have failed to benefit from CRB policies because they have
defaulted on their loans and ended up being listed as serial
defaulters. The listing has diminished their chances of accessing
credit or increasing the cost of loans since they have low credit
ratings from CRB.
2.5 Suggestions for further research
Upon extensive review of the existing literature, this study
identifies research gaps on the effect of information,
communication, and technology (ICT) on access to credit. It is
established that given the nature of the modern world, computer
literacy is a prerequisite to operating any business enterprise. To
solve the perennial problem of access to credit, MSMEs operators
must embrace ICT. Hence there is a need to understand the
influence of ICT on access to credit for MSMEs.
The study was limited to determining the factors that influence
access to credit for MSMEs in Ruiru Sub County in Kenya. The
study found glaring gaps in access to credit which is the major
driving factor for the development of the Kenyan economy.
Studies should be carried out on emerging determinants of access
to credit, such as the impact of global pandemics on access to
credit for MSMEs, informal lending institutions' access to credit,
and the impact of cryptocurrencies on access to credit for MSMEs.
III. RESEARCH METHODOLOGY
3.1 Research Design
This study adopted a descriptive research design. The choice
of descriptive research design was guided by the need to collect
data on various variables through detailed descriptions. It ensured
that the research aim of exploring relationships was achieved
(Kothari & Garg, 2019). It was also consistent with other studies
(Buyinza et al.,2018; Balogun et al., 2018; Silong & Godanakis,
2020).
3.2 Population
The target population was all the 590 registered MSMEs for
operation in 2021 in Ruiru Sub County. The information was
obtained from Ruiru Sub-County offices located in Ruiru town in
Kenya. The choice of the target population was consistent with
prior researches (Chirchir, 2017, Balogun et al., 2018).
3.3 Sampling Frame
The sampling frame for this study was the database for the
department of business registration in Ruiru Sub County in the
ministry of trade of the Government of Kiambu County, archiving
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all the licensed MSMEs operators for 2021. The use of all the
items in the sampling frame was consistent with prior research
studies whose choice of sampling frame comprised all the listed
items in the department of the relevant registration body (Buyinza
et al.,2018; Balogun et al., 2018; Silong & Godanakis, 2020).
3.4 Sampling Design
The study employed a probability sampling design. Saunders
et al., (2019) explain that probability sampling aims to make
inferences from the sample and the population to answer the
research questions and meet the research objectives.
3.4.1 Sampling Technique
The study employed stratified random sampling. The
MSMEs are made up of 3 segments in line with the working
definition of MSMEs (MSE Bill, 2012). The segmentation makes
for three different sectors based on the number of employees and
business turnover. The MSMEs were licensed as single permit
businesses. Hence, the study used the stratified sampling
technique and simple random sampling to select the representative
sample.
3.4.2 Sample Size
The study used Slovin's Formula to determine the minimum
allowable sample size. Using Slovin’s formulae the representative
sample size was: 590 / [1+590(0.0025)] = 238.
3.5 Research Instrument
This study used a structured open and close-ended
questionnaire, which consisted of structured questions and a five-
point Likert scale where one was strongly disagreed, and five was
strongly agree to capture the relevant information for all the
variables.
3.6 Data Collection
This study employed primary data.
3.7 Model Specification
The data was transformed into a mean of variables and log
10 of transformed mean variables and tested for normality. The
Kolmogorov-Smirnov for the data set showed that all the
variables' values are less than 0.05 hence statistically significant.
Therefore, the data was found to be non-parametric and not
normally distributed (Osborne, 2017). Non-parametric data
analysis techniques, ordinal regression, and Spearmen Correlation
were carried out. The study adopted the ordinal regression model
(Liu & Koirala, 2012).
According to Liu and Koirala (2012), the ordinal logistic
regression equation was expressed as:
Logit [ π (Y ≤ j │X1, X2… Xp)] = In π (Y ≤ j │X1, X2…Xp) / π
(Y ˃ j │X1, X2…Xp)
= αj + (-β1Χ1 - β2Χ2 - … - βpΧp).
Where π (Y ≤ j │X1, X2, …, Xp) = Probability of being at or below
category j, given a set of inputs, j = 1, 2, … J – 1, αj = Cut points
or coefficients intercepts and β1, β2, … βp are logit coefficients.
The model determined the odds of being beyond a certain level
relative to being at or below that level. A positive logit coefficient
indicated that the variable was more likely to be at a higher level
than a lower level of the response variable.
IV. RESEARCH FINDINGS
4.1 Case Processing Summary
According to Osborne (2017), the case processing shows the
proportion of cases falling at each category of the response
variable access to credit.
Table 4.1.1 Case Processing Summary
Case Processing Summary
N
Marginal
Percentage
AC 1.6
1.8
2
2.2
2.4
2.8
3.4
3.6
3.8
4
4.4
4.6
27
9
27
27
27
9
9
9
9
18
27
27
12.0%
4.0%
12.0%
12.0%
12.0%
4.0%
4.0%
4.0%
4.0%
8.0%
12.0%
12.0%
Valid
225
100.0%
Missing
0
Total
225
Source: SPSS (2021)
According to table 4.4.1, eleven cases are falling at each
level of the outcome variable access to credit.
4.2 Tests of normality using the transformed mean of variables
The data collected from the questionnaire was transformed
by determining the mean of each variable, and it was checked for
normality before performing order analysis (Osborne, (2015,
2017). Table 4.2.1 shows the results for tests of normality using
the transformed mean of variables.
Table 4.2.1: Kolmogorov-Smirnova Test of Normality for the
transformed mean of variables
Tests of Normality
Statistic
df
Sig.
AC
.227
225
.000
COC
.179
225
.000
FIA
.178
225
.000
TLFI
.182
225
.000
CRB
.157
225
.000
a. Lilliefors Significance Correction
Source: SPSS (2021)
The Kolmogorov-Smirnov for data set above 100 shows that
all the values (p = .000) for the variable are less than (p = 0.05)
hence statistically significant. The Kolmogorov-Smirnov results
should not be statistically significant (Osborne, (2015, 2017).
Therefore, the data was not normally distributed.
4.3 Tests of normality using log 10 of the transformed
variables
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Since the data was not normally distributed, we further
determined log10 of the transformed variables (Osborne, (2015,
2017). Table 4.3.1 presents the results for tests of normality for
Log transformed values for all variables.
Table 4.3.1 Kolmogorov-Smirnova Tests of Normality for Log
10 of Transformed Variables
Tests of Normality
Statistic
df
Sig.
Log _ AC
.179
225
.000
Log _ COC
.168
225
.000
Log _ FIA
.171
225
.000
Log _ TLFI
.169
225
.000
Log _ CRB
.117
225
.000
a. Lilliefors Significance Correction
Source: SPSS (2021)
According to table 4.3.1, the Log transformed values (p =
0.000) for all the variables were less than (p = 0.05), which shows
that they are statistically significant hence not normally
distributed.
Since the Kolmogorov-Smirnov results for both means of
transformed variable and Log 10 of transformed variables are
statistically significant, the data collected from the questionnaire
is not normally distributed; hence the study performed non-
parametric data analysis techniques, which are ordinal regression
analysis and Spearmen Correlation analysis (Osborne, 2015,
2017).
4.4 Correlational Analysis
According to the normality tests, as shown in table 4.1.1 and
table 4.2.1, the data set is not normally distributed; hence the study
performs non-parametric correlation analysis using Spearman
Correlation (Osborne, 2015, 2017). Table 4.4.1 shows the non-
parametric correlations results for the data set.
Table 4.4.1 Non-parametric Correlations
AC
COC
FIA
TLFI
CRB
Spearman’s rho AC Correlation
Coefficient
Sig.(2-tailed)
N
1.000
225
.971**
.000
225
.974**
.000
225
.968**
.000
225
.961**
.000
225
COC Correlation
Coefficient
Sig. (2-tailed)
N
.971**
.000
225
1.000
225
.997**
.000
225
.995**
.000
225
.919**
.000
225
FIA Correlation
Coefficient
Sig. (2-tailed)
N
.968**
.000
225
.995**
.000
225
.998**
.000
225
1.000
.000
225
.917**
.000
225
TLFI Correlation
Coefficient
Sig. (2-tailed)
N
.968**
.000
225
.995**
.000
225
.998**
.000
225
1.000
225
.917**
.000
225
CRB Correlation
Coefficient
Sig. (2-tailed)
N
.961**
.000
225
.919**
.000
225
.920**
.000
225
.917**
.000
225
1.000
225
**. Correlation is significant at the level 0.01 level(2-tailed)
Source: SPSS (2021)
4.4.1 Correlation between Cost of credit and access to credit.
According to table 4.4.1, Spearman's Correlation between
the cost of credit and access to credit was 0.971, which shows a
high correlation between the two variables and was found to be
statistically significant with a significant value of .000, which was
less than 0.05.
4.4.2: Correlation between financial information asymmetry
and access to credit
According to table 4.4.1, Spearman's correlation between the
financial information asymmetry and access to credit was 0.974,
which shows a high correlation that was statistically significant at
a significant value of less than 0.05.
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4.4.3 Correlation between type of financial lending institution
and access to credit
According to table 4.4.1, Spearman's correlation between the
type of financial lending institution and access to credit was 0.968,
which shows a high correlation that was statistically significant
with a significant value of less than 0.05.
4.4.4 Correlation between credit reference bureau and access
to credit
According to table 4.4.1, Spearman's correlation between
credit reference bureau and access to credit was 0 .961, which
shows a high correlation that is statistically significant with a
significant value of .000, which is less than 0.05.
4.5 Ordinal Regression Analysis (ORL)
According to the normality tests, as shown in table 4.1.1 and
table 4.1.2, the data set is not normally distributed, and therefore
non-parametric data analysis was performed. Osborne (2015,
2017) notes that ordinal regression analysis is employed when
there are two categories for the dependent variables that are
ordered. This study uses a five-point Likert scale with five types.
The ORL yields only a single set of regression coefficients to
estimate the relationships between independent and dependent
variables. The study performed ordinal regression analysis to
show the relationship between the variables.
4.5. 1: Model fitting information
Model fitting information shows the -2 Log-likelihood for an
intercept only (or null) model and the full model, which contains
the complete set of inputs (Osborne, 2017). The author further
points out that a likelihood ratio chi-square test is carried out to
test whether there is a significant improvement in the fit of the
final model relative to the intercept-only model. Table 4.5.1 shows
the results for model fitting information
Table 4.5.1.1: Model fitting Information
Model Fitting Information
Model
-2 Log
Likelihood
Chi-
Square
df
Sig.
Intercept
Only
1067.590
Final
.000
1067.590
4
.000
Link Function: Logit
Source: SPSS (2021)
According to table 4.5.1.1, the model relevant information
for the study, there is a statistically significant improvement in the
fit of the final model over the null model [ Χ2(4) =1067.59,
p<.001]. We conclude that the data collected fits the model very
well.
4.5.2: Goodness of Fit
Osborne (2017) notes that the Goodness of fit table contains
the deviance and Pearson chi-square tests, which help determine
if the model exhibits a good fit to the data. Field (2018) shows that
non-significant tests results show that the model fits the data well.
Table 4.5.2.1 shows the Goodness of fit results
Table 4.5.2.1: Goodness of Fit
Goodness-of-fit
Chi-Square
df
Sig.
Pearson
188.344
216
.913
Deviance
201.502
216
.752
Link function: Logit
Source: SPSS (2021)
According to the findings presented on table 4.5.2.1, the
Pearson chi-square test is [Χ2(216) = 188.344, p=.913] and the
deviance test is [Χ2(216) =201.502, p=.752]. This implies that the
model fits the data very well.
4.5.3: Pseudo R-Square
Pseudo R-Square is used to indicate the percent changes in
the dependent variable due to independent variables
(Osborne,2017). Table 4.5.3.1 shows the results of Pseudo R-
Square for the data collected.
Table 4.5.3.1 Pseudo R-Square
Pseudo R-Square
Cox and Snell
.991
Nagelkerke
1.000
McFadden
1.000
Link function: Logit.
Source: SPSS (2021)
According to table 4.5.3.1, the Nagelkerke of the collected
data is 1.000, which implies that 100% changes in access to credit
result from cost of credit, financial information asymmetry, type
of financial lending institution, and credit reference bureau (Field,
2018).
4.5.4: Test of parallel lines
Osborne (2015, 2017) note that the proportional odds
assumption essentially states that the relationship between the
independent variable is the same across all possible comparisons
involving the response variable. The author notes that the non-
significance results indicate that the test of parallel lines is
satisfied. Table 4.5.4.1 shows the results for the test of parallel
lines.
Table 4.5.4.1 Test of parallel lines
Test of Parallel Lines
Model
-2 Log
Likelihood
Chi-
Square
df
Sig.
Null
Hypothesis
.000
General
.000
.000
40
1.000
Link function: Logit
Source: SPSS (2021)
According to table 4.5.4.1 results, the proportional odds
assumption is satisfied as p = 1.000 (Osborne, 2015, 2017)
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4.5.5 Parameter estimates
According to Osborne (2017) parameter estimates table
shows the regression coefficients and significance tests for each of
the predictor variables in the model. The author points out that the
regression coefficients were interpreted as the predicted change in
log odds of being higher instead of a lower category on the
outcome variable while controlling for the remaining predictor
variables per unit increase on the explanatory variable.
The positive estimate was interpreted to mean that for every
one-unit increase on an explanatory variable, there is a predicted
increase of a certain amount in the Log odds of falling at a higher
category of the response variable. The positive estimate indicates
that as scores increase on a predictor variable, there is an increased
probability of falling at a higher class on the output variable.
According to Osborne (2017), the negative estimates were
interpreted as every one-unit increase on an explanatory variable;
there is a predicted decrease of a certain amount in the Log odds
of falling at a higher category of the outcome variable. The
negative estimates mean that as scores increase on the predictor
variable, there is a decreased probability of falling at a higher class
on the response variable. Osborne (2017) note that the threshold
estimates given in the parameter estimates table are intercepts and
can be interpreted as the log odds of being in a particular category
or lower when scores on the other variables are zero. Table 4.5.5.1
shows the results of the parameter estimates.
Table 4.5.5.1 Parameter Estimates
Parameter Estimates
Estimate
Std.
Error
Wald
df
Sig.
95% Confidence
Interval (Lower
Bound)
95% Confidence
interval (Upper
Bound)
Threshold [AC = 1.60]
31.236
3.136
99.233
1
.000
25.090
37.382
[AC = 1.80]
32.900
3.260
101.820
1
.000
26.509
39.290
[AC = 2.00]
35.990
3.344
115.843
1
.000
29.436
42.544
[AC = 2.20]
43.511
4.256
104.507
1
.000
35.169
51.853
[AC = 2.40]
52.537
5.462
92.510
1
.000
41.831
63.243
[AC = 2.80]
59.131
5.894
100.650
1
.000
47.579
70.683
[AC = 3.40]
63.127
6.185
104.162
1
.000
51.004
75.250
[AC = 3.60]
64.726
6.233
107.835
1
.000
52.509
76.942
[AC = 3.80]
67.598
6.540
106.849
1
.000
54.781
80.416
[AC = 4.00]
75.002
7.326
104.812
1
.000
60.644
89.361
[AC = 4.40]
80.157
7.717
107.889
1
.000
65.032
95.282
Location COC
FIA
TLFI
CRB
-5.919
42.344
-25.731
7.633
4.244
6.831
4.111
.808
1.945
38.428
39.173
89.244
1
1
1
1
.163
.000
.000
.000
-14.236
28.956
-33.789
6.049
2.399
55.732
-17.673
9.216
Link function: Logit
Source: SPSS (2021)
Using the study's predictive model, we can determine our
predictive model values from table 4.5.5.1. The relationship
between the determinants of access to credit for MSMEs in Ruiru
Sub County can be modeled (Liu and Koirala, 2012): Ordinal
logistic regression uses Log-odds of cumulative probabilities.
Fj(X)=P (Y≤ j │X)
Lj (X) = logit (Fj(X)) = log((Fj(X)/(1-((Fj(X))
Model regression equation:
Lj(X) = αj – β1X1 – β2X2 - … - βp Xp;
Where (Y ≤ j │X1, X2, …, Xp) = Probability of being at or below
category j, given a set of inputs, j = 1, 2, … J – 1, αj = Cut points
or coefficients intercepts and β1, β2, … βp are logit coefficients and
the probability of being in at least the highest category is 1,
Logit (F Strongly Disagree) = 32.9 + 42.344COC – 42.344 FIA + 25.731
TLFI – 7.633CRB
Logit (F Strongly Agree) = 80.157 + 42.344COC – 42.344 FIA + 25.731
TLFI – 7.633CRB
Where:
COC = Cost of credit
FIA = Financial information asymmetry
TLFI = Type of lending financial institution
CRB = Credit reference bureau
V. CONCLUSIONS
The first objective sought to determine the influence of the
cost of credit on access to credit for MSMEs in Ruiru Sub County
in Kenya. The correlation between the cost of credit and access to
credit [ 0.971, p = 0.000] showed a high relationship between
access to credit and the cost of credit. From the correlation results,
we conclude that there is an association between the changes in
the cost of credit and access to credit. The parameter estimates
showed that cost of credit Estimate = -5.919, p = 1.63] was not a
significant negative predictor for access to credit. Hence, we can
conclude that there is not sufficient evidence to conclude that
increase in the cost of credit will result in a decrease in access to
credit.
The study's second objective sought to determine the
influence of financial information asymmetry on access to credit
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for MSMEs in Ruiru Sub County in Kenya. The correlation
between financial information asymmetry and access to credit
[0.974, p = 0.000] was high; hence we conclude that the changes
in financial information asymmetry cause the differences in access
to credit. The estimator parameters show that financial
information asymmetry was a significant positive estimator of
access to credit [estimate = 42.344, p = 0.000]. Hence, we
conclude that an increase in financial information asymmetry
increases access to credit. The biasness in the information markets
causes the MSMEs not to access credit.
The third objective of the study was to determine the
influence of the type of financial lending institution on access to
credit for MSMEs in Ruiru Sub County in Kenya. The correlation
between lending financial institutions and access to credit was [
0.968, p = 0.000]. We conclude that there is a strong significant
correlation between the variables. The changes in types of lending
institutions influence changes in access to credit. The parameter
estimates show that type of lending institution was a significant
negative predictor of access to credit [ Estimator = -25.731, p =
0.000]. We conclude that as the type of lending financial
institution increases, the access to credit reduces. Hence the
indicators of the type of lending financial institution affect the
access to credit.
The fourth objective of the study was to determine the
influence of the credit reference bureau on access to credit for
MSMEs in Ruiru Sub County in Kenya. The correlation results for
credit reference bureau and access to credit [ 0.961, p = 0.000]
were high. We conclude that there is an association between the
changes in the credit reference bureau and changes in access to
credit. The ordinal regression coefficient shows that credit
reference is a significant predictor of access to credit [Estimator =
7.633, p = 0.000]. Therefore, we conclude that changes in credit
reference bureau indicators cause an increase in access to credit
for MSMEs in Ruiru Sub County. Credit reference bureau
influences access to credit.
VI. RECOMMENDATIONS.
The study sought to determine the factors that influence
access to credit for MSMEs in Ruiru Sub County in Kenya. From
the summary and conclusions of the study, the study recommends
from the objective of determining the influence of cost of credit
on access to credit which was found to be statistically
insignificant, that the financial institutions work towards lowering
the cost of credit to enable MSMEs to gain access to access credit.
From the second objective of influence of financial information
asymmetry, the study recommends that the problem of financial
asymmetry can be resolved by both parties adhering to proper
financial disclosures practices, financial institutions to increase
training concerning the products they are offering the market and
to increase transparency in the process of a loan application.
From the third objective of influence of the type of financial
lending institution. The study recommends that SACCOs,
microfinance, and commercial banks align their lending terms to
standardize the lending process across all lending institutions. The
study also recommends that financial institutions customize their
credit products to specific target markets, such as developing loan
products targeting micro-enterprises or medium enterprises since
they exhibit different credit needs.
From the fourth objective of influence of credit reference
bureau, the study recommends that credit reference role should be
to increase access to credit rather than cause loss of access to credit
as the case is currently. The credit reference bureau should be
restructured to suit the emerging market's needs rather than
replicating a developed market model in the emerging economies.
The study further recommends credit reference bureau ensure that
they provide up-to-date credit information regarding MSMEs to
stop being a hindrance to access to credit.
REFERENCES
[1] African Development Bank and Government of Kenya. (2018). The State of
Kenya's Private Sector. African Development Bank and Government of
Kenya.
[2] Akerlof, G. A. (1970). The market for lemons: Quality uncertainty and the
market mechanism. The Quarterly Journal of Economics, 84(3), 488–500.
[3] Andries, A. M., & Cuza, A. I. (2009). Theories Regarding Financial
Intermediation and Financial Intermediaries – A Survey. The Annals of the
"Ştefan cel Mare," 9 (2), 254- 261.
[4] Asongu, S., & Odhiambo, N. M. (2018). Information asymmetry,
financialization, and financial access. International Finance.
DOI:10.2139/ssrn.3180506
[5] Balogun, O., Agumba, J., & Ansary. N. (2018). Evaluating credit
accessibility predictors among small and medium contractors in the South
African construction industry. Acta Structilia, 25(2), 69-
93. https://dx.doi.org/10.18820/24150487/as25i2.3
[6] Buyinza, F., Tibaingana, A., & Mutenyo, J. (2018). Factors affecting access
to formal credit by micro and small enterprises in Uganda. Institute for
Comprehensive Analysis of the Economy, Working Paper Serie-No 83.
[7] Central Bank of Kenya (2020) Bank Supervision Annual Report 2009
Retrieved on August 2020
https://www.centralbank.go.ke/images/docs/Bank%20Supervision%20Repo
rts/Annual%20Reports/bsd2020B.pdf
[8] Chirchir, S.J. (2017). Determinants of financial accessibility by small and
medium enterprises in Eldama Ravine Sub – County, Kenya; Unpublished
MBA, Jomo Kenyatta University of Agriculture and Technology.
[9] Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed).
Los Angeles: Sage.
[10] Gangata, K., & Matavire, E. M. (2013). Challenges facing SMEs in accessing
finance from financial institutions: The case of Bulawayo, Zimbabwe,
International Journal of Applied Research and Studies, 2(7).
[11] Gichuki, W., Njeru, A., & Tirimba. O. (2014). Challenges facing micro and
small enterprises in accessing credit facilities in Kangemi Harambee market
in Nairobi County, Kenya. International Journal of Scientific and Research
Publications, 4(12). 1-15.
[12] Government of Kenya. (2012). The Micro and Small Enterprises Act 2012.
Kenya Gazette Supplement No. 219 (Acts No.55). Nairobi, Government
Printer, Government of Kenya.
[13] Government of Kenya., (2007). Vision 2030. Nairobi, Government Printer,
Government of Kenya.
[14] Huang, C., When, Y. and Liu, Z. (2014). Analysis on financing difficulties
for SMEs due to asymmetric information. Global Disclosure of Economics
and Business, 3(2) ISSN 2305-9168
[15] Hwarire, C. (2012). Loan repayment and credit management of small
businesses: A case study of a South African commercial bank. A paper
presented at the African Development Finance Workshop 7-8 August 2012.
Cape Town, South Africa.
[16] Kinyua, A. N., (2014). Factors affecting the performance of small and
medium enterprises in the Jua Kali sector in Nakuru town, Kenya.
Unpublished MBA project. Egerton University.
[17] KIPPRA. (2020). Review of government Policies for the promotion of Micro
and Small-Scale Enterprises. Nairobi: Kenya Institute for Public Policy
Research Analysis.
[18] KNBS (2018). Economic Survey 2018. Nairobi, Kenya National Bureau of
Statistics, Government Printer.
International Journal of Scientific and Research Publications, Volume 11, Issue 11, November 2021 153
ISSN 2250-3153
This publication is licensed under Creative Commons Attribution CC BY.
http://dx.doi.org/10.29322/IJSRP.11.11.2021.p11918 www.ijsrp.org
[19] Kweyu, N. M. (2017). Determinants of access to credit by women-owned
SMEs in Migori Town, Kenya. Unpublished MBA project. United States
International University-Africa.
[20] Leitner, Y. (2006). Using collateral to secure loans. Federal Reserve Bank of
Philadelphia Business Review, Q2, 9-16.
[21] Liu, X & Koirala. H. (2012). Ordinal regression analysis: Using generalized
ordinal logistic regression models to estimate educational data. Journal of
Modern Applied Statistical Methods. 11(1). doi:
10.22237/jmasm/1335846000
[22] Micro and Small Enterprises Bill, (2012) Retrieved on August 20th, 2020
from: http://www.industrialization.go.ke/index.php/policies.
[23] Mole, A. & Namusonge, G. S. (2016). Factors affecting access to credit by
SMEs; A case of Kitale town. International Journal of Social Sciences and
Humanities Invention, 3(10), 2904-2917.
[24] Muli, E.N., & Muli. W.M. (2019). Effects of information asymmetry on
borrowing cost among microfinance in Kenya. International Journal of
Economics, Commerce, and Management. 7(8), 440 - 472.
[25] Mutinda, A. K. (2019). Financial factors affecting access to credit among
small and medium enterprises in Machakos town Sub County in Kenya.
Unpublished Master of business administration. South Eastern Kenya
University.
[26] Ndungu, C. W. (2016). Factors affecting credit access among small and
medium enterprises in Murang's County. Unpublished MBA project. The
University of Nairobi.
[27] Ondieki, N. S., Nashappi, N. G., & Moraa, O. S. (2013). Factors that
determine the capital structure among micro-enterprises: A case study of
micro-enterprises in Kisii town, American International Journal of
Contemporary Research, 3(7).
[28] Osborne, J.W. (2015). Best practices in logistic regression. Los Angeles:
Sage.
[29]
[30] Osborne, J.W. (2017). Regression and linear modeling: best practices and
modern methods. Thousand Oaks, CA: Sage
[31] Saliku, M. (2015). Influence of credit reference bureaus on access to credit
by small and medium enterprises in Kitale Municipality, Trans Nzoia County.
Unpublished MBA project. The University of Nairobi.
[32] Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for
business students. 8th ed. New York, New York: Pearson.
[33] Shikumo, D. H., & Mwangi, M. (2016). Determinants of lending to small and
medium enterprises by commercial banks in Kenya, 7(4), 57-63.
[34] Silong, A. & Gadanakis, Y. (2020) Credit sources, access and factors
influencing credit demand among rural livestock farmers in Nigeria.
Agricultural Finance Review, 80 (1). 68-90.
[35] Stiglitz, J. E., & Weiss, A. (1981). Credit rationing in markets with imperfect
information. The American economic review, 71(3), 393-410.
[36] Subeyr, M. H., & Muturi. (2017). Factors affecting access to credit by
microenterprises in Garowe, Puntland. International Journal of Economics,
Commerce, and Management, 5(3).
[37] Vera, D., & Onji, K. (2010). Changes in the banking system and small
business lending. Small Business Economics, 34(3), 293-308.
[38] Wangai, P. N., & Omboi, B. M. (2011). Factors that influence the demand
for credit among small-scale investors: A case study of Meru Central District,
Kenya. Research Journal of Finance and Accounting, 18-20.
[39] World Bank. (2019a). Doing business 2018: Understanding regulations for
small and medium-sized Enterprises. Washington, DC, World Bank Group.
[40] World Bank. (2019b). Doing business 2019: Training for reform. Washington
DC, World Bank Group.
[41] World Bank. (2020). Doing Business 2020: Comparing business regulation
in 190 economies. Washington DC, World Bank Group.
AUTHORS
First Author – Samson Manjuru Mburu, Student, Jomo
Kenyatta University of Agriculture and Technology, Kenya
Second Author – Dr. Lucy Wanjiru Njogu, Ph.D., Lecturer,
College of Human Resource Development (COHRED), Jomo
Kenyatta University of Agriculture and Technology, Kenya
