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Go Back       IAR Journal of Business Management | IAR J Bus Mng, 2021; 2(1): | Volume:2 Issue:1 ( Feb. 10, 2021 ) : 136-142
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DOI : 10.47310/iarjbm.2021.v02i01.021       Download PDF       HTML       XML

An Investigation on the Effects of Asset-Based Lending on Credit Accessibility of Micro and Small Enterprises (MSEs) in Meru Town, Kenya


Article History

Received: 03.01.2021,Revision: 14. 01.2021, Accepted: 31. 01.2021, Published: 10. 02.2021

Author Details

Kobia Faith Kathure, Dr. Gabriel Waweru and Dr Mohamed Shano

Authors Affiliations

Department of Finance and Accounting School of Business Meru University of Science and Technology, Kenya

Abstract: Micro and Small Enterprises have been known to be drivers of socio-development. Despite the effort of governments in formulating and implementing policies intended to enhance entrepreneurship, a number of challenges are still being experienced by entrepreneurs with the major one being the requirement of lenders of different financial institutions. The aim of this paper is to investigate the Effects of Asset-Based Lending on Credit Accessibility of Micro and Small Enterprises (MSEs) in Meru Town. The paper adopts a descriptive research design. We targeted a study population of 800 different registered MSEs. Stratified sampling was applied on a target population of 800 registered MSEs that yielded a sample size of 80 respondents represented by owners or managers of these businesses. A regression results showed a positive correlation between asset-based lending with credit accessibility (r-0.93).

Keywords: Entrepreneurship; Asset-based; Credit-accessibility; Small-enterprises; lending; Financing.

Introduction

Financing is a very important aspect that aids the development and monetary performance of micro and small businesses. Amazingly, business financing from different financial institutions is one of the most challenging things that businesses face in modern economies. This makes admittance to finance by these MSEs a difficult venture in totality. According to (Kitili, 2015), credit accessibility is as a way of getting loan from a lender for use and paying it back with an interest after a period of time, including the means, ways and characteristics to obtain it by the borrower. Many scholars and researchers globally agree that credit accessibility is still limited and only 10 to 40 percent of the world’s population have access (Khandker, Samad and Ali, 2015). In a study done by (Hyytinen and Toivanen, 2015), poor people’s limited access to credit emanates from the fact that the majority have low education level to enable them to understand financial terms and requirements. According to (Honhyan, 2017), loan officers do not like giving credit to the poor because of their low transaction volume, which is relatively small compared to people living in rich neighborhood. Characteristically, MSEs face elevated transactional liabilities than bigger ventures when getting loans from bank (Berger & Udell, 2016; Lee, 2017). This phenomenon is experienced in European countries such as Britain, France and Germany. In her observation, while studying factors influencing accessibility to financial services by low income women business holders in Lometo, South Korea, (Andra, 2015), reported that access to loaning facilities from financial institutions had never been a walk in the park, as great attention was on obtaining maximum returns from any business transaction, given that banks are in the money business. Owing to the fact that loans are advanced against certain lending terms of reference such as security, cost of credit, amount of savings and ability to repay, she noted that women in small enterprises were unable to obtain adequate funds from the banks (Andra, 2015). Most African nations have completed modest efforts to reach MSEs as a result of challenges in monitoring and screening these Mses Due to high risks of defaulters (Zarook et al., 2015). Nevertheless, (Merrouche, Detragiache, & Demirgüç-Kunt, 2016), suggests that debt accounts for large amount of capital for MSEs in Kenya. In Meru County and specifically in Meru town there is a variety of MSEs as shown by Kenya Economic Survey, (2018), which includes; Barber shops, pharmacies, Agri-business, butcheries, supermarkets, bookshops, book stores, cyber, and wholesalers. Most of these businesses have been financially serviced by several banks and SACCOs by provision of saving and credit facilities to those who can afford. Meru County Government has continued to support the growth of MSEs through implementation of policy framework, provision of affordable finance, and support in access of tools and equipment, provision of market facilities and facilitation in creation of market linkages. MSEs within the County have continued to benefit with the capacity buildings programs offered by the Department of Trade, Tourism and Cooperatives. The launching of County Traders SACCOs in each of the Sub County has ensured quick and affordable access to finance for the entrepreneurs, (Meru County, 2018). However, Beaver (2015) points out that the need for easier access to credit is paramount to ensure there is growth in the MSEs in rural areas. It has been revealed by Government of Kenya, (2018) that a number of MSEs have contributed to employment and GDP development of Kenya boosting economy to some extent by about 15%.

Yet, over 50% of the MSEs underperform thus resulting to less disbursement of funds. There have been so many efforts by financial institutions including; Equity bank, Kenya Women Finance Trust, and of late Meru Microfinance Cooperation in bringing credit facilities closer to MSEs, yet no tangible signs of growth to the intended recipients have been seen. Many of the business survive because of the availability of credit facilities from financial institutions yet a closer look finds that many of these MSEs have characteristics that lenders find it hard to give those credits. One school of thought points out that many of the financial institutions put a bench mark on the ownership of assets that can be turned into liquid cash easily, and that these assets may not be available to the MSEs. For example according to (Meru County, 2018) report, Out of 200 applicants for credits, only 10% of them end up getting loans and this is due to either not fulfilling the requirements from the loaning institutions or negligence. To this level therefore, this research paper is aimed at investigating the effects of asset-based lending on credit accessibility of micro and small enterprises (MSEs) in Meru Town. This is in view that many of the studies done have been focusing on credit and performance of MSEs but none has researched on characteristics and accessibility of credits of MSEs in Meru town.

Review of Related Literature Review

(Cooper, 2016) defines asset-based lending as a form of giving credit to a business or an individual in agreement that something like an asset, account receivable records, inventory records are given to the lender before the money is dispatched to the borrower. (Berger et al., 2016) suggest that the lending technologies can be grouped into four main categories: financial statement lending (based on the evaluation of information from the financial statements); asset based lending (based on the provision of collateral and its quality); credit scoring lending (based on statistical techniques); relationship lending. The first three lending techniques are usually grouped together and labeled transaction lending because the risk evaluation is based on available factual and public information, collected independently from the quality of the relationship and includes loans that are mainly spot-like and for non-recurrent needs.

According to (Klapper et al., 2016) firm assets are based on age of the firm, type of the firm, and how much asset that is available to access loans. However, (Mensah and Agbekpornu, 2015) points out that the nature of credit advances to MSEs are basically in short-term basis, since the loans are always referred to as on demand basis.

However, many of the lenders will not accept an asset which has no value and in case the loan is sought out for the value of the asset, most cases the loan given out is always lower than the really or book value of the asset. This is for the reason that asset based lending is a riskier business and this valuation often covers the cases of defaults, which most of the MSEs may or might not have, for example cited assets were log books or land title deeds (Birch, 2016; Mulaga, 2015; Kamau, 2018). For the above reason, (Kira and He, 2018) observes that companies in industrial section can get financing a lot easier than MSEs in developing countries like Tanzania. For business-loan applications, the financial institution reviews the company's past cash flow statements to determine how much income is expected from operations. Capacity is also determined by analyzing the number and amount of debt obligations the borrower currently has outstanding, compared to money expected each month.

In another observation, (Beckz et al., 2016), argued that those firms that the government has shares in them always have more advantage in accessing funding because they can get finances from development banks or other public owned banks. This is in reference to the type of ownership of a firm compared to privately owned firms like MSEs who face fewer problems with collateral requirement and paperwork bureaucracy, (Demirgü-Kunt and Levine, 2015). However, (Birch, 2016) points out that many of these MSEs have a challenge in accessing funding for the reason that their collateral may not be enough or have enough value to get credit. Other factors hindering access to credit for MSEs are, low cash flowing in the business, exorbitant interest rates, borrowers credit history, period the business has been in operation among others.

Lenders and other financial institutions have insisted on other requirements for example fixed monetary amount. This consists of fixed monetary amounts in Kenyan currency held in the society. Up to 100% value of the deposit at the time of loan may be offered as collateral. However, financial statements issued by firms can be used to evaluate future performance and therefore determine whether borrowers are able to repay the interest and principal (Kira and He, 2015; Mulaga, 2015; Osei-Assibey et al., 2015; Safavian and Wimpey, 2017).

Credit constraint operates on diversity in Kenya and the fact that the stock market is not yet developed and expanded to other rural areas makes it hard for MSEs to go this way for funding hence the best option comes in as internal finances, through personal savings or relatives borrowing. Lack of assets forces the MSES to rely on other ways of funding like shylocks or informal lending which becomes high cost and difficult to deal with. These difficulties stem from the more formal lending institutions which led to rate all MSEs equally as un-credited worthy. However, the emergence of less formal institutions like microfinance lenders and SACCOs do not ease this burden.

They are also up against the cost of refinancing through the formal banking sector and have no central bank. However, empirical literature from (Siegel et al., 2015) suggests that MSEs that business which have steady asset and which are of value are the most one that are asset based borrowers and credit worth. In a nutshell large firms only borrow to offset short term money requirements

Research Methodology

The study employed a descriptive study design that allowed the use of questionnaire, which their result were described to see the situation at hand. This study aimed at taking two weeks in seeking information from respondents that was followed by data analysis of another two weeks. The population of this study constituted the registered MSEs in Meru town and those who were able, willing or had access to credit. In line with this study therefore MSEs were variously portrayed as those with at least 1 to 40 employees and with Kshs. 500,000 capital outlay. They had none or few branches. However the Meru County report indicated there are over 800 registered MSEs in Meru town (Meru County, 2018). The MSEs were categorized in the following; general shops, salons and Barber shops, cybercafés, wholesalers, supermarkets, Hotels & Restaurants/bars/wine & Spirits, Groceries & Dry Cereals, Stores, Boutiques and clothing shops as shown in table 1.

Sampling Design and Technique

The current study had a target population that was heterogonous hence it was better to use stratified sampling. The sample size of this current study was 10% of the targeted population of 800 respondents from managers/owners as respondents from general shops, salons and Barber shops, cybercafés, wholesalers, supermarkets, Hotels & Restaurants/bars/wine & Spirits, Groceries & Dry Cereals, Stores, Boutiques and clothing shops. According to (Cooper and Schindler, 2006) stratified sampling is a type of sampling method in which the total population is divided into smaller groups or strata to complete the sampling process. The stratum is formed based on some common characteristics in the population data. After dividing the population into strata, the researcher randomly selects the sample proportionally. The target population and sample sizes are shown in the table 1.

Table 1 Sample size and Target population

Target Group (Category)

Target Population

Sample

General Shops

250

25

Salons and Barber shops

150

15

Cyber cafes

20

2

Wholesalers

23

2

Supermarkets

10

1

Hotels & Restaurants/bars/wine & Spirits

253

25

Groceries & Dry Cereals Stores

55

6

Boutiques and clothing shops

38

4

Total

800

80

Data Source: Meru County Business Directorate.

Equation (1) was used to arrive at the sample size.

, Where: (1)

N = Sample size.

n = Target population.

This research proposed to engage a specialist in strategic change management who was tasked in assessing the correctness of the used variables. This was measured by use of five point Likert scale of 5 = strongly agree and 1= strongly disagree. A Cronbach’s Alpha was also used for reliability. The paper is based on the consideration that stresses that the rule in using Cronbach’s Alpha test in that those value things used in research should not be below 0.7. This is also agreed by Cooper and (Schindler, 2006), who stated that a coefficient of 0.7 is accepted worldwide. However, this current research proposed to use test-retest procedures which according to (Lindstrom, 2015), this is a test that tests the consistency of a measure across time: do you get the same results when you repeat the measurement? For example a group of participants complete a questionnaire designed to measure personality traits. If they repeat the questionnaire days, weeks or months apart and give the same answers, this indicates high test-retest reliability. This is why the current study did a pilot test in Arkrishna Wholesaler and that the same questions used for the rest of the targeted population to see if the same result shall be obtained. This however, was measured on internal consistency by use of Cronbach’s Alpha.

Data Analysis and Presentation

This current study strived to use descriptive data analysis, which inherently defines descriptive analysis as a summary statistic that quantitatively describes or summarizes features from a collection of information, while descriptive statistics is the process of using and analyzing those statistics. Descriptive statistics is distinguished from inferential statistics (or inductive statistics) by its aim to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent. This generally means that descriptive statistics, unlike inferential statistics, is not developed on the basis of probability theory, and are frequently non-parametric statistics. Use of dispensed information was applied in this study and according to (Hyndman, 2018) data dispensation comprises deciphering their responses on a survey form into method that can be handled to yield information which encompasses deletion, data admittance, and observing the entire data dispensation route. In this research however SPPS version 24.00 was used to analyze data herein through linear regression assumptions that tested if the dependent variable (credit accessibility) had any effect on the independent variable; asset based lending, and vice versa.

Statistical models

The study employed the follows statistical analysis model.


y= β0 + β1X1+ β2X2+ β3X3+ β4X4+ ε, In which: (2)

y is the dependent variable Credit accessibility of the MSEs.

X1, representing the explanatory variables Firm size.

X2, representing the explanatory variable Profitability.

X3, representing the explanatory variable Asset based lending.

X4, representing the explanatory variable Legal status.

β0 is the intercept coefficient, β1, β2, β3, and β4 are partial regression coefficients corresponding to the explanatory variables.

Ε is the disturbance term.

Equation (2) shows that the variable asset-based lending was investigated alongside other study variables; (firm size), (profitability), and, (legal status). The equations were used simultaneously in order to determine whether the explanatory variables considerably affects y (credit accessibility of the MSEs). (Maddalla, 2019) emphasizes that each of the models must be valid in terms of F statistic and P-value (<0.05).

Results and Discussions

The most important question in this category was the initial source of capital for starting various businesses. 60 percent of the respondents that chose bank credit as their source of capital fell in the medium category of business composed of wholesales, supermarkets, large-sized bars, hotels, restaurants and groceries and cereals businesses. These businesses are considered to have a significant asset base that can be used as security when securing a bank loan. The owners claimed that they used other property as collateral to access large amount of credit to start the businesses. 80 percent of small businesses such as wines and spirit, general shops, salons and barbers, cyber cafes, clothing shops and boutiques claimed that they started their businesses from personal savings. Personal savings were the best to start small businesses since they required small amount of capital. The owners of these businesses claimed that they could not access loan from banks due to lack of adequate collateral to cover the loan. 10 percent of the respondents claimed that they started their businesses from capital generated by friends and relatives contributions. 5 percent of the business owners said that they sold some of their property to get capital for their businesses. These observations are illustrated in tables (2-4) and figures (1-3).

Table 2: Assets owned by business owners in Meru

Stock as the only asset

72

Stock and premises as the assets

8

Asset-based Lending

The analysis results are as projected in table 3 and in figure 3 respectively.

Table 3. Sources of capital for business

Own saving

36

Bank credit

20

Sell of properties

4

Friends and family contributions

8

Others

12

Figure is Available in PDF Format

Figure 1. Pie-chart showing sources of capital for business start-up.

Figure is Available in PDF Format

Figure 2. Pie-chart showing types of assets owned by businesses in Meru.

90 percent of the respondents who owned small businesses claimed that their stock was the only asset they had. On the contrary, medium business owners claimed that the asset of their businesses was made up of stock and the business premises. 80 percent of the respondents said that the asset-based lending characteristic influence credit accessibility while 20 percent of the respondents disagreed with the statement. This shows that most people believe that lenders require assets to act as collateral when giving credit.

Table 4: Asset-based lending characteristic and credit accessibility

Yes

64

No

16

Figure is Available in PDF Format

Figure 3. Pie-chart showing asset-based characteristic and credit accessibility

Statements of Choice

The statement of choice analysis showed a similar trend that lenders require assets when giving loans to business owners. 80 percent of the business owners agreed strongly that lenders require asset for loan accessibility. 10 percent of the respondents agreed to this statement while 5 percent were neutral. Another 5 percent of respondents disagreed with this statement. 60 percent of the Mses Owners agreed that their access to credit was less than what they applied for. 5 percent of the respondents strongly agreed with the statement while 35 percent of the respondents disagreed with the statement. 50 percent of the respondents disagreed with the statement that ownership of MSE has no relationship with credit accessibility. This shows that most MSE business owners believe that they require credit to start their business. 95 percent of the respondents agreed that the security required is used as a vetting requirement of credit accessibility.

The remaining 5 percent of business owners strongly agreed with this statement. The results are consistent with the findings of Barbosa and (Moraes, 2004) who suggested that SMEs operators need to own more tangible assets to improve their borrowing security. 60 percent of business owners disagreed with the statement that their business has good reputation with credit lenders. The business owners might have defaulted on loan at some point. 65 percent of respondents strongly agreed that the conditions of the assets have an effect on credit accessibility. A further 20 percent of MSE owners disagreed with the statement. 80 percent of the business owners disagreed with the statement that insurance of collateral is a pre-requirement for credit accessibility. 90 percent of the respondents strongly disagreed with the statement that ownership of collateral has no relationship with credit accessibility.

Table 5: Statements of choice

Statements of choice

(5)

(4)

(3)

(2)

(1)

  1. Lenders require asset for loan accessibility

64

8

4

4

0

  1. MSEs access credit that is not or less what was applied for

4

48

0

28

0

  1. Ownership of MSE has no relationship with credit accessibility

5

10

10

40

15

  1. Security required is used as vetting requirement as credit accessibility

4

76

0

0

0

  1. Our business have good reputation with credit lenders

7

5

10

48

10

  1. The condition of asset has an effect oncredit accessibility.

52

10

2

16

0

  1. Insurance of collateral is pre-requirement for credit accessibility

0

12

4

64

0

  1. Ownership of the collateral has no relationship with credit accessibility

0

4

0

72

4

The statements support the notion that asset-based lending has an impact on loan accessibility for the micro and small-sized businesses in Meru town. The findings are well supported by existing literature. (Burkart and Ellingsen, 2004) found similar findings with this study that assets have a significant impact on loan accessibility for the small and medium sized businesses. (Bougheas, Mizen and Yalcin, 2005) observed similar findings by pointing out that collateral requirement are important in facilitating loan accessibility for the small and medium-sized enterprises. All banks and other institutional lenders in Kenya ask for collateral that can be sold to recover the full amount of loan in case of default by the borrower.

Conclusion

The study focused on the effects asset-based lending on credit accessibility for the micro and small enterprises in Meru town. The micro and small enterprisers that were considered in this study were; general shops, salons and barber shops, cyber cafes, wholesales, supermarkets, hotels and restaurants, bars, wines and spirits, boutiques and clothing shops, groceries and dry cereal stores. In total, the MSEs considered in this study were 80. The obtained results suggests that asset-based lending is crucial in loan accessibility in Meru town. As suggested earlier, assets are required by lenders because they act as collateral for the loans. Most micro and small enterprises lack loans from the lending institutions because they lack adequate collateral to cover the loans. This shows that credit accessibility increases with increase in asset base of micro and small enterprises in Meru town.

References

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