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Go Back       IAR Journal of Business Management | IAR J Bus Mng, 2020; 1(3): | Volume:1 Issue:3 ( Oct. 31, 2020 ) : 163-170
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DOI : 10.47310/iarjbm.2020.v01i03.012       Download PDF       HTML       XML

Credit Access and Performance of Micro and Small Enterprises in Nakuru County, Kenya



Article History

Received: 05.10.2020; Revision: 24. 10.2020; Accepted: 27. 10.2020; Published: 29. 10.2020

Author Details

Silas Peter Mwiathi*1 and Mwangi, Rahab Wanjiru2

Authors Affiliations

1Lecturer, Kenyatta University, Kenya

2Student, Kenyatta University, Kenya


Abstract: Entrepreneurship contributes to the achievement of development goals by encouraging economic growth and improving the standards of living of the people. The Government of Kenya has established various funds in an effort to enhance equity among socially disadvantaged groups like women, the disabled and unemployed youth. Despite these efforts, the uptake of these funds remains low due to unfavourable lending conditions, leaving a financing gap. This study employed a non-experimental research design, conducted in four randomly selected administrative sub-counties in Nakuru County. From an approximated sampling frame of 650 MSEs, a random sample of 248 was selected. A structured questionnaire, with a Cronbach Alpha coefficient of 0.72, was administered to the sample. Results of a linearized logarithmic regression showed that credit amount and challenges faced in accessing credit accounted for close to 14.0% (R2 = 0.139) of performance of the MSEs in Nakuru County. The study concluded that credit access had a positive significant effect on the performance of MSEs. Policy implication was that the Central bank, in collaboration with commercial banks, should ease borrowing terms to increase MSEs’ access to credit. Also, the government should offer incentives to lending institutions so that they can lower the cost of borrowing.


Keywords: Credit Access; Performance of MSEs; Credit availability to MSEs; Small Business Financing.


Introduction

Entrepreneurship serves as a major tool in achieving the Sustainable Development Goals (SDGs) to eradicate poverty and promote gender equality. It contributes to local economies by being a source of employment, public revenue and helps promote equity and foster innovation Muturi (2015). Entrepreneurship enhances equity by providing lucrative opportunities to socially disadvantaged groups like women, the disabled and unemployed youth. Entrepreneurship is also observed to reduce the rate of recidivism by offering opportunities to ex-offenders, who would otherwise not be marketable in the job market. As a result, entrepreneurship is widely viewed as a tool for the achievement of development goals like Kenya Vision 2030, as it not only encourages economic growth, but also improves the standards of living of the people.


However, according to the Deloitte Kenya Economic Outlook (2016), MSEs face numerous challenges, notably, limited access to credit, lack of entrepreneurship skills, and low uptake of new technologies. A gap in the small business-financing sector exists due to the inability of these firms to attract investors and their lack of creditworthiness. Yet, Kariuki and Omwenga (2017) observe that the success of MSEs is greatly hinged on their ability to access financing. It is noted that poor management practices, the high default rate on loan repayment and poor financial management by small entrepreneurs are to blame for the limited access to credit by small entrepreneurs (Kariuki & Omwenga, 2017). In addition, Ogeta (2016) observed that high interest rates and collateral requirements by financial institutions increased the cost of borrowing, and this deterred the small entrepreneurs from borrowing.


Cheap short-term loans are proven to improve the profitability of a business. However, small entrepreneurs have limited access to sources of credit. According to Schoof (2006), this is because small entrepreneurs often lack sufficient credit history and collateral. These factors also hinder their ability to attract external sources of financing like venture capital and commercial bank loans. The pecking order theory that explains an entrepreneur’s preference for the cheapest source of financing supports the need for more affordable sources of credit for entrepreneurs (Myers & Majluf, 1984). Therefore, traditional banking methods are not suited to the unique needs of these enterprises that require low cost financing and fewer collateral demands.


A review of empirical literature revealed that credit access is the main factor that affects the growth of the MSEs. Musavi (2018) observed that entrepreneurs favour personal savings and “chamas” (table-banking groups), as sources of credit due to their negative view of financial institutions’ lending terms. Chirchir (2017) observed that key among the factors that limited the access to credit were transactional costs because they increased the cost of borrowing, tedious loan application process and inflexible repayment terms. Suryadevara (2017) noted that Micro Finance Institutions (MFIs) loans were becoming popular because of their flexible “group-lending model” that reduced collateral requirements and cost imposed on an individual. The study observed that favourable lending terms that include low interest rates, low security requirements and fewer collateral requirements improved the uptake of loans.


Wairimu (2017) notes that the establishment of MFIs helped to fill the gap left by banks in financing the low-income population mainly consisting of MSEs. In addition, the enactment of the Movable Property Security Rights Act in 2017, permitting the use of log-books as collateral for credit facilities; led to the establishment of numerous logbook-based loans offered by MFIs around the country. This move has exponentially increased the uptake of credit by small entrepreneurs, and seen to increase innovations in the small business-financing sector.


The Government of Kenya has established government funds in an effort to combat the problem of low financing of MSEs in the country. These are the Youth Enterprise Development Fund (YEDF), and The Women Enterprise Fund (WEF) established in 2007; and The Uwezo Fund established in 2013. These funds were created with the aim of availing low-cost financing to small entrepreneurs, predominantly the youth and women. The funds were also aimed at reducing the unemployment rate and building the economy. Despite these efforts, the uptake of these funds remained low, reportedly due to the group-lending system and high default rate on loan repayment. Due to the rotating nature of the fund, the low repayment rates meant that fewer funds were available to be levied out. The issue of corruption and misappropriation of funds by fund administrators is also noted as a major impediment to the success of these funds. Thus, recently, the government proposed a consolidated fund, the new Biashara Fund, proposed to have a lower interest rate and fewer security requirements targeted at small business financing. The effects of the fund are yet to be assessed.


Mobile banking in the country has stood out as a source of fast and cheap loans for entrepreneurs with minimal security and collateral requirements. This method of financing is becoming popular due to its wide reach and low operating costs for financiers. Services like M-PESA have been revolutionary in easing business transactions and promoting business. Consequently, young entrepreneurs tend to favour non-traditional forms of banking like mobile baking, for its ease of operating and less bureaucracy. Mobile loan applications like TALA have also helped to increase the access to credit by people. However; these loans are reported to have high interest rates and short repayment periods, making them unsuitable for small entrepreneurs. The Central Bank of Kenya (CBK) established the new mobile loan application named Stawi in 2019 targeted at MSEs’ Financing; to be run by five commercial banks in the country. The fund is proposed to have lower interest rates and collateral requirements.


Despite many initiatives by the government and other organisations, Sharu and Guyo (2015) revealed that 60 percent of small enterprises fail within three years of being in operation, their ability to survive the market conditions being very low. Small enterprises lack the advantages larger companies have in their economies of scale, access to credit and skilled personnel. According to Cowan (2019), the financing gap in small enterprise financing popularly referred to as the ‘missing middle’ in Kenya persists despite developments in micro financing and government initiatives. This financing gap exists due to unsuitable financial products and unfavourable lending terms by financial institutions in the country, due to investor’s risk aversion as they give preference to high performing enterprises. A gap in MSE financing is therefore apparent, and the need arises for the establishment of financial institutions specifically targeted to MSEs.


The performance of a business can be appraised in a number of ways. Financial measures of performance include profit and turnover. Non-financial measures include the number of employees, long-term growth and survival of the business. Chirwa (2008) observed that MSEs’ performance is measured by the profitability index or the growth in employment around the world. However, the turnover of a business is one of the most accurate measures of its performance. Nakuru Town is on the fast track to becoming a City in Kenya due to its high cosmopolitan population, developed infrastructure and its strategic location in Kenya. It is also one of the major agricultural towns in Kenya, home to several game parks and the geothermal power plant seeing to increased commercial activity in the Town. These developments have attracted numerous investors, which led to the growth of numerous micro and small enterprises in the town. A number of financial institutions including banks and microfinance institutions have been established targeting MSEs in Nakuru. Thuo (2014) noted that the performance of MSEs in Nakuru town is derailed largely by high interest rates on available credit and the collateral requirements by financial institutions, thereby lowering access to credit by entrepreneurs. This proved the need for further research in the area to determine the problems in small business financing and how to solve them, hence the gap this study aimed to fill.


MATERIALS AND METHODS

This research is firmly founded on the philosophy of Equity. Equity theory focuses on determining whether the distribution of resources is fair to all parties. Considered one of the justice theories, equity theory was first developed in the 1960s by J. Stacy Adams, who asserted that people seek to maintain equity between the inputs that they bring to a job and the outcomes that they receive from it (Adams, 1963). According to Equity Theory, in order to maximize individuals' rewards, we tend to create systems where resources can be fairly divided amongst members of a group. In our view, MSEs have to be treated equitably as far as access to credit is concerned.


This study employed a non-experimental research design. The main objective of the study was to determine the effect of credit on the performance of micro and small enterprises in four towns of Nakuru County, Kenya. The study used cross-sectional primary data collected via self-administered questionnaires. According to the County Integrated Development Plan (CIDP, 2014), Nakuru has an average population estimated to be about 1,603,325 people based on the 2009 population census (KNBS 2009). Nakuru County is divided into nine administrative sub-counties namely Naivasha, Gilgil, Nakuru Town, Rongai, Nakuru North, Subukia, Njoro, Molo and Kuresoi. Nakuru County is noted to have 118,200 licensed MSEs with the majority of them located in Nakuru Town (KNBS 2018). This study was conducted in four randomly selected administrative sub-counties namely Nakuru Town, Njoro, Gilgil and Rongai. The target population of this study is composed of representatives of the various industries including the mechanics, dairy, retail shops, jua kali (artisans), salons, boutiques and small manufacturing companies in Nakuru County. From an approximated sampling frame of 650 MSEs in the four sub-counties, and using Yamane’s formula (1967), a sample of 248 MSEs was randomly selected.


The researcher developed a structured questionnaire, which was administered through the drop and pick later method. This gave the respondents adequate time to respond to the questions owing to their busy schedules. In some instances, the researcher conducted interviews and filled the questionnaire so as to ensure a high response rate. The primary quantitative research data was collected from the owners of the MSEs. Closed ended questions were used in an effort to conserve time and money as well as to facilitate an easier analysis as they are in immediate usable form. Reliability of the questionnaire was determined using Cronbach alpha and found to be 0.72. Construct validity was shown by the relevance of the instrument to the stated research objectives.


The data was analysed using the multivariate regression equation so as to determine the relative contribution of each variable to the performance of the MSEs. The equation is given as:

Where:

Y = Performance of the MSEs

= Constant

β1 = Régression coefficients

= Amount of credit

Challenges in borrowing

= Frequency of borrowing

Ɛ =Error term

Results and Discussion

There is quite a wide range of businesses in Nakuru County. The most prominent ones include service provision (17.8%), retail shops (16.2%), hospitality places (15.8%) and boutiques (13.7%). A unique one is that of financial services (0.4%). This is where an entire shop is dedicated to the provision of financial services in the form of M-Pesa, as well as various Banks’ agency.


Descriptive Statistics on Credit

The researcher used descriptive statistics (frequencies and percentages) to analyze the sources of start-up capital, credit, frequency and challenges to borrowing. Results for the sources of start-up capital are presented in Table 1.


Table 1: Sources of Start-up Capital


Frequency

n=241

Percent

Source of credit

Personal Savings

102

42.3

Friends and Family

105

43.6

Bank, Micro or SACCO

33

13.7

Governmental Agencies

1

0.40

Total

241

100.0

Source: Research Data 2019

Most of the entrepreneurs represented by 43.6 percent of the sample, borrowed from friends and close family members to obtain capital for starting and sustaining their enterprises. A further 42.8 percent reported to have used their personal savings while a minority of 13.7 and 0.40 percent respectively borrowed from financial and government institutions. This is in agreement with the pecking order theory (Myers & Majluf, 1984), which states that an entrepreneur will give preference to internal sources of financing, and use external sources like loans, as a last resort. This is because external sources of credit are expensive due to the interest rates accrued. Other terms of borrowing imposed by financial institutions are also prohibitive to the micro and small entrepreneurs. By making credit sources cheaper and more accessible to entrepreneurs, more potential entrepreneurs can be reached.


Once the businesses are up and running, entrepreneurs seek credit financing in order to maintain their businesses. Sources of such credit were analysed and reported in Table 2.

Table 2: Sources of Credit Financing


Frequency

n=241

Percent

Source of Credit

Commercial Banks

35

14.5

SACCO

30

12.4

Micro-Credit

72

30.0

Mobile Money

43

17.8

Chama

61

25.3

Total

241

100.0

Source: Research Data 2019

According to the pecking order theory, an enterprise will opt for credit in an attempt to improve its performance in a case where its internal sources of financing are inadequate. 30.0 percent, the majority, of MSE owners borrowed from Micro Finance Institutions (MFIs), and another 25.3 percent from table banking schemes (chamas). Commercial banks and SACCOs were observed to be less popular with 14.5 and 12.4 percent of small entrepreneurs respectively seeking credit from them. This is due to their stringent requirements on guarantors, collateral and high interest rates on loans. This is in agreement with Ogeta (2016) and Musavi (2018).


Mobile banking is increasingly becoming popular with small entrepreneurs as 17.8 percent of them borrowed from mobile banking sources. Mobile banking sources are however reported to be expensive due to their high interest rates on loans, and also offer inadequate loan amounts. This is in agreement with Cowan (2019), who observed mobile banking as a means to bridge the small business-financing gap in the country. However the study recommended a reduction in the interest rates levied on these loans.


Most of the MSEs set up business using very small amounts of money as capital. This amount ranged from 5,000 Kenya Shillings to 40,000. The bigger businesses required more than 40,000 to start their businesses, making slightly over 50% of the businesses studied. The amount that could not be raised through personal savings and internal family borrowing was taken as external debt from the various financial institutions. This information is presented in Table 3.

Table 3: Amount of Credit Borrowed


Frequency

n=241

Percent

Amount of Credit Borrowed

Below 5000

13

8.8

5000-10000

17

11.6

10001-20000

12

8.2

20001-30000

22

15.0

30001-40000

9

6.1

Above 40000

74

50.3


Total

147

100.0

Source: Research Data 2019

The amounts taken depended on the size of the business as well as the amount availed by the lending institutions. Mobile loans were usually lower than those extended by other conventional institutions. The study sought to determine the frequency of borrowing by the business owners. This information was then analysed and results presented in Table 4.

Table 4: Frequency of Loan Borrowing


Frequency

n=241

Percent

Frequency of loan borrowing

Very often

25

10.4

Often

141

58.5

Rarely

53

22.0

Never

22

9.1

Total

241

100.0

Source: Research Data 2019

A total of 58.5 percent of the entrepreneurs were observed to borrow money often. Taking these and the 10.4% who borrowed very often, over 68% were frequent borrowers, proving the inadequacy of internal sources of financing and the need for external sources of financing. Only a small minority (9.1 percent) of the entrepreneurs reported to have never accessed loans.


The reasons for this were partly due to lack of awareness and partly due to negative attitudes towards loans.

In addition, the study looked at the perceived major challenges to accessing credit, as expressed by the entrepreneurs. Results of the descriptive analysis of this information are presented in Table 5.

Table 5: Challenges Accessing Credit


Frequency

n=241

Percent

Challenges

No challenges

64

26.6

No Collateral

28

30.3

Recently opened Bank Account

10

4.1

Non-existent business

9

3.7

Lack of Guarantors

73

11.6

No resources

8

3.3

No credit history

5

2.1

Too many requirements

7

2.9

Too much paper work

3

1.2

High interest paid

12

5.0

Short repayment period

4

1.7

Amount availed too little

18

7.5

Total

241

100.0

Source: Research Data 2019

Access to credit by entrepreneurs is cited as the dominant growth factor of MSEs in Nakuru County. Access to credit is limited largely by the lack of collateral and lack of guarantors both representing 30.3 and 11.6 percent, respectively. The challenges of high interest rates on loans and insufficient amounts of accessed credit by small entrepreneurs in Nakuru were also noted. This is consistent with Thuo (2014) and Chirchir (2017).


An interesting category of entrepreneurs representing 26.6% of the population, reported having no challenges at all in accessing credit. The possible explanation for this could be that their businesses have been in place for a long time, or they have built confidence with the financiers due to regular borrowing and prompt repayment. In addition, maybe they are in low risk, high returns kind of businesses, which credit-giving bodies would gladly take to.


The study used descriptive statistics (frequencies and percentages) to analyze average output of enterprises and number of employees as indicators of performance. Data on average output was analysed and reported in Table 6 for MSEs in Nakuru County.

Table 6: Average Output of MSEs in Nakuru County


Frequency

n=241

Percent

Average Output

(Ksh.)

Below 50000

89

36.9

50000-100000

85

35.3

100000-150000

34

14.1

150000-250000

15

6.2

250000-500000

12

5.0

Above 500000

6

2.5

Total

241

100.0

Source: Research Data 2019

36.9 percent of MSEs reported an average output of less than Ksh. 50,000 compared to only 2.5 percent reporting an average output of above Ksh. 500,000. This is representative of a large number of these businesses being micro enterprises with low turnover and low employment capacity. This is proof of the need for improvement of the macro-economic environment in the county to encourage the growth of these businesses from micro to small and then medium enterprises.

As mentioned earlier, both the performance and the size of a business can be indicated by the employment capacity of that business. This information was sought, and the results of analysis are reported in Table 7.


Table 7: Number of Employees in the various MSEs in Nakuru County


Frequency

n=241

Percent

Number of employees

None

75

31.1

Less than 2

85

35.3

2-5

64

26.6

5-10

15

6.2

Over 10

2

0.8

Total

241

100.0

Source: Research Data 2019


Micro enterprises are businesses with up to 10 employees, while small businesses have up to 50 employees. 99.2 percent of the total sample was comprised of micro enterprises, with small enterprises forming only 0.8 percent of the sample. Approximately 66% of these enterprises were run by one or two people, indicating a low capacity of these businesses to absorb labour. This illustrates low levels of growth in enterprises and calls for further interventions by the government and other stakeholders to improve the business environment in these towns.


Effect of Credit on Performance

The objective of this study was to establish the effect of credit on the performance of MSEs in Kenya. Credit represented the independent variable while the dependent variable was represented by the total output accrued by the MSE’s. The effect of credit on the performance of the business was analyzed using the multivariate regression equation given earlier. The results are presented in Table 8.

Table 8: Regression Analysis of the Effect of Credit on Performance

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

0.373a

0.139

0.128

1.178

a. Predictors: (Constant), Challenges, Amount, Loan frequency

b. Dependent Variable: AVG Output


ANOVA

Model

Sum of Squares

Df

Mean Square

F

Sig.

1

Regression

53.108

3

17.703

12.760

.000b

Residual

328.809

237

1.387



Total

381.917

240




a. Dependent Variable: Average Output


COEFFICIENTS


Unstandardized Coefficients

Standardized Coefficients




Model

B

STD Error

Beta

t

Sig

(Constant)

1.741

.338


5.143

.000

Loan Amount

.225

.042

.335

5.398

.000

Challenges

-.074

.026

-.197

-2.877

.004

Frequency

-.130

.114

-.080

-1.140

.255

a. Dependent Variable: Average output

Source: Research Data 2019


According to the regression results in Table 8, the linear regression model specifies that credit amount and challenges faced in accessing credit accounted for close to 14.0% (R2 = 0.139) of performance of the MSEs in Nakuru County. The un-standardized beta coefficient of the variable amount of credit accessed ( = 0.225, p < 0.05) was a good predictor of performance of the MSEs. This means that those businesses that access higher amounts of credit will tend to perform better than those that can only access small amounts of credit. It is therefore hypothesised that a unit increase in credit amounts causes an increase of 0.225 units in performance.


The challenges faced in accessing credit had a coefficient of (β= -0.074) indicative of the negative effects of limitations to credit access on the performance of MSEs. An increase in challenges in accessing credit causes a decrease in the performance of MSEs. Fewer and less stringent terms of borrowing will lead to better performance of MSEs in Nakuru County.


Frequency of loan borrowing was also shown to have a negative effect on business performance. The frequency of borrowing has a coefficient of (-0.130), which is indicative of the fact that frequency of borrowing does not necessarily translate to better performance of the business. The possible explanation for this could be that not all the money borrowed is injected into the business, and some funds borrowed are poorly invested or put into other non-core uses; and hence do not improve the performance of a business. The linear regression results above generally show that the amount of credit accessed has a statistically significant positive influence on performance of the MSEs in Nakuru County.

This is consistent with Ogeta (2016), Chirchir (2017) and Musavi (2018) among other researchers who noted that access to credit by small entrepreneurs improved the performance of their businesses. Businesses that had better access to credit facilities fared better than those without. While challenges like high interest rates and collateral requirements and the poor investment and management of borrowed funds, derailed the performance of the business.


Conclusion

The study concludes that credit access has a positive and significant effect on the performance of MSEs. This means that MSEs with access to sufficient loan amounts and conducive borrowing terms perform better than those without similar conditions.


Based on the findings, the amount of credit and the challenges of accessing credit were observed to be significant variables. Empirical findings indicate that the amount of credit accessed has a positive and significant effect on the performance of MSEs. Insufficient loan amounts offered to small entrepreneurs may not be adequate for the effective implementation of a business idea and plan. On the other hand, the larger the loan size, the better the success of business project, efficient repayment, increased profits and growth in firm assets.


The challenges faced in accessing credit facilities by these firms have been highlighted as a significant variable in the study. They have negative effects on the performance of MSEs due to limitations to credit access. The lack of guarantors, lack of sufficient collateral and high interest rates on loans are among the dominant challenges faced by small entrepreneurs in the County.


The mobile banking industry is noted to be increasingly popular with entrepreneurs in Nakuru for its convenience and reach. However, it should be regulated to cap the interest rates levied on loans. These loans were found to have high interest rates and offer inadequate amounts of accessible credit.


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