FACTORS AFFECTING ACCESS TO CREDIT BY SMALL AND MEDIUM ENTERPRISES IN KENYA

FACTORS AFFECTING ACCESS TO CREDIT BY SMALL AND MEDIUM ENTERPRISES IN KENYA: A CASE STUDY OF LIMURU MUNICIPALITY, KIAMBU COUNTY.

MILIKAH NJERI WAWERU
1030094

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A Research project Submitted in Partial Fulfillment of the Requirement for the Bachelor of commerce, Finance option.

CATHOLIC UNIVERSITY OF EASTERN AFRICA

JULY 2018

STUDENT’S DECLARATION
I, the undersigned, declare that this is my original work and has not been submitted to any other college, institution or university other than the Catholic University of Eastern Africa for academic credit

Signed……………………………….. Date………………………………….

Milikah Njeri Waweru (1030094)

This proposal has been presented for examination with my approval as the appointed supervisor
Signed…………………………………. Date………………..…………………

Ms. Caroline Ndirangu

Signed…………………………………… Date…………………………………

HOD, Finance

ABSTRACT
The purpose of the study was to evaluate factors affecting access to credit by Small and Medium Enterprises (SMEs) from financial institutions in Kenya, a case study of Limuru Municipality Kiambu County. The research was guided by the following objectives: to determine the influence of firm’s size of access to credit, to determine whether the financial status of a business affects its access to credit, to establish the influence of managerial expertise of the entrepreneur in access to credit.

The study concluded that small SME’s experience a challenge accessing loans from banks as compared to big SME’s, access to finance is not influenced by networking and SME’s have adequate book keeping records which have made it easy for them to access credit.
ACKNOWLEDGEMENT
My special appreciation goes to the almighty God for his divine strength and wisdom.
I have taken efforts in this project. However, it would not have been possible without the kind support and help of many individuals. I would like to extend my sincere thanks to all of them.
I gratefully appreciate my Supervisor Ms. Caroline Ndirangu who worked closely with me in carrying out this research study. I would also like to thank my parents for their financial and motivational support.
Finally my gratitude goes to all respondents from the SME’s in Limuru municipality who agreed to participate to fill the questionnaire and provided me with the necessary information.
Table of Contents
STUDENT’S DECLARATION i
ABSTRACT ii
ACKNOWLEDGEMENT iv
LIST OF TABLES vii
LIST OF FIGURES vii
1 CHAPTER 1: INTRODUCTION 1
1.1 Background study. 1
1.2 Statement of problem 1
1.3 General objective of the study 2
1.4 Specific objectives 2
1.5 Research questions 2
1.6 Significance of study 3
1.7 Scope of the study 3
1.8 Theoretical Framework 3
1.9 Agency Theory 4
1.10 Growth Cycle Theory 4
1.11 Entrepreneurship theory 4
2 CHAPTER TWO 5
2.1 Literature Review 5
2.2 Firm’s size influence on access to Credit 5
2.2.1 Size of the firm. 5
2.2.2 Age of the firm 6
2.2.3 Location of firm. 7
2.2.4 Industry sector 8
2.2.5 Number of employees 8
2.3 Financial status of the firm influence on access to Credit 8
2.3.1 Financial statements 8
2.3.2 Asset Base / Firm’s Collateral 9
2.3.3 Business performance 10
2.4 Managerial expertise effect on access to credit 10
2.4.1 Education background 10
2.4.2 Entrepreneur’s experience 11
2.4.3 Networking 11
3 CHAPTER THREE -RESEARCH METHODOLOGY 13
3.1 Introduction 13
3.2 Research Design 13
3.3 Population and Sampling Design 13
3.3.1 Population 13
3.3.2 Sampling Design 14
3.3.3 Sampling Frame 14
3.3.4 Sampling Technique 14
3.3.5 Sample Size 14
3.4 Data Collections Method 15
3.5 Research Procedures 15
3.6 Data Analysis Method 16
3.7 Chapter summary 16
4 CHAPTER FOUR 17
4.1 Data Analysis and Interpretation 17
4.1.1 Introduction 17
4.1.2 Response Rate 17
4.2 Demographical Factors 17
4.2.1 Gender 17
4.2.2 Age 17
4.2.3 Level of Education 18
4.2.4 Years Worked in the Organization 18
4.2.5 Loan Application 19
4.3 Effects of Access to Credit 20
4.3.1 Descriptive of Effects of Access to Credit 20
4.3.2 Experience in Accessing Credit 22
4.4 Size of firm affecting access to credit 23
4.4.1 Size of firm affecting access to credit 23
4.5 Other factors 24
4.6 Financial status of the firm influence access to Credit 25
4.6.1 Financial status of the firm influence access to Credit 25
4.6.2 Types of Collateral or Securities Accepted 26
4.7 Entrepreneur’s Characteristics on SME’s Access to Credit 26
4.7.1 Managerial skills of the entrepreneur affecting access to credit 27
4.8 Chapter Summary 22
5 CHAPTER FIVE 27
5.1 Discussion, Conclusion and Recommendation 27
5.2 Introduction 27
5.3 Summary of Findings 27
5.4 Discussion 28
5.4.1 Effect of Firm’s sizes on SME’s Access to Credit 28
5.4.2 Effect of Financial status of a firm on SME’s Access to Credit 29
5.4.3 Effect of managerial expertise on SME’s Access to Credit 29
5.5 Conclusions 30
5.5.1 Firm’s size effect on SME’s Access to Credit 30
5.5.2 Financial status of a firm effect on SME’s Access to Credit 30
5.5.3 Managerial expertise effect on SME’s Access to Credit 31
5.6 Recommendations 31
5.6.1 Recommendation for Improvement 31
5.6.2 Recommendation for Further Studies 31
6 REFERENCES 32
APPENDIX I: INTRODUCTION LETTER 36
APPENDIX II: QUESTIONNAIRE 37
APPENDIX III: WORK PLAN 40
APPENDIX IV: RESEARCH BUDGET 40

LIST OF TABLES
Table 1: Population 14
Table 2: Sample Size 15
Table 3: Response Rate 17
Table 4: Age 18
Table 5: Level of Education 18
Table 6: Years worked in the organization 19
Table 7: Descriptive of Effects of Access to Credit 21
Table 8: Size of firm affecting access to credit 22
Table 9: Financial status of the firm influence access to Credit 24
Table 10: Managerial skills of the entrepreneur affecting access to credit 25

LIST OF FIGURES
Figure 1: Proportion of loan application 19
Figure 2: Reason for not accessing loans 20
Figure 3: Experience in Accessing Credit 22
Figure 4: Other Factors: 23
Figure 5: Types of Collateral or Securities Accepted 25

1 CHAPTER 1: INTRODUCTION
1.1 Background study.
Small and medium-size enterprises are businesses that maintain revenues, assets or a number of employees below a certain threshold. Every country and economic organization has its own definition of what is considered a small and medium-sized enterprise. In the European Union, a small-sized enterprise is a company with fewer than 50 employees, while a medium-sized enterprise is one with fewer than 250 employees (Zheng, O’Neill ; Morrison, 2009; Cunningham ; Rowley, 2007).
Research suggests that SMEs account for 95% of firms in most countries. They create jobs, contribute to GDP, aid industrial development, satisfy local demand for services, innovate and support large firms with inputs and services.
In Africa, SMEs create 80% of employment, establishing a new middle class and stimulating demand for new goods and services. In Kenya, SMEs play a key role in economic development and job creation. In 2014, 80% of jobs created were dominated by SMEs. The term micro and small enterprises (MSEs) or micro, small and medium enterprises (MSMEs), is used to refer to SMEs in Kenya. Under the Micro and Small Enterprise Act of 2012, micro enterprises have a maximum annual turnover of KES 500,000 and employ less than 10 people. Small enterprises have between KES 500,000 and 5 million annual turnovers and employ 10-49 people. Medium enterprises are not covered under the act, but have been reported as comprising of enterprises with a turnover of between KES 5 million and 800 million and employing 50-99 employees (Microfinance and Small Enterprise Act, 2012).
1.2 Statement of problem
The SME sector is the largest provider of employment in most countries, especially of new jobs; SMEs are a major source of technological innovation and new products. They are essential for a competitive and efficient market. SMEs with high turnover and adaptability play a major role in removing regional and sector imbalances in the economy. Easy entry and exit of SMEs make economies more flexible and more competitive.
SMEs are important for poverty reduction; they tend to employ poor and low income workers. These businesses are sometimes the only source of employment in poor regions and rural areas and SMEs play an important role in developing countries where poverty is most severe.
SMEs have the potential to contribute substantially to the economy and can provide a strong foundation for the growth of new industries as well as strengthening existing ones; they are drivers of wealth creation and productivity in the economy. We also know from research that high-growth firms, many of which are small businesses, are crucial in terms of driving innovation.
Of all the major problems facing SMEs in Africa, Kenya has identified two as the most critical. The first one is lack of a supportive governance framework. SMEs suffer due to lack of legal framework that protects interests, harassment from local authorities’ unsupportive tax regime and exposure to corruption. The second reason is lack of adequate access to credit. SMEs have little access to finance, which thus prohibit their emergence and eventual growth. This has not been easy for SMEs due to the stringent credit terms offered by financial institutions. Most of small businesses do not have access to finance due to lack of minimum requirements from commercial banks in Kenya, most commercial banks have been hesitant in lending to the small business due to lack of collateral, credit history, financial statement and banking history. Their main sources of capital are retained earnings, borrowings from friends, family capital and informal savings, which are unpredictable, not very secure and have little scope for risk sharing. Access to final finance is poor because of the high risk of default among SMEs and due to inadequate financial facilities. It is upon this statement that the study sought to find out the challenges that face SMEs in accessing finances.
1.3 General objective of the study
The general objective of this research is to assess factors affecting accessibility of credit by SMEs in Kenya, a case study of Limuru Municipality, Kiambu County, Kenya.
1.4 Specific objectives
1) To examine the effect of a business size on SMEs’ access to finance.
2) To evaluate the influence of financial status on SMEs’ funding.
3) To check the effect of the management roles and expertise on SMEs access to loans.
1.5 Research questions
1) Does the business size have an influence on SMEs’ access to finances?
2) Does the finance status of a business have an effect on its accessibility to funding?
3) What influence does the management role and expertise have on SMEs’ access to loans?

1.6 Significance of study
This study will have its significance to Microfinance institutions as the research study will be carried out in their area of jurisdiction. The challenges discussed will enable them to take the necessary measures in access of loans and in policy making.

The study will also be of benefit of small businesses since issues raised will help them know what to do in order to gain better access to credit and also know the pitfalls to avoid when trying to get credit from microfinance institutions.

The study will have its significance to commercial banks since this will provide an area in which they can tailor products to meet that segment of the market and improve the access by providing competition in the microfinance sector.

The Government of Kenya will also benefit from this study since microfinance is important in providing support to small businesses which help in growing the economy of the country. This makes it key sources of governments’ revenues. The findings of this research will help the government in the area of policy in the microfinance sector. This research is both an important field of study for the Kenyan government, which in line with Vision 2030, in working towards a sustainable economy that achieves the Millennium Development Goals like solving the problem of unemployment and poverty.

Educational institutions and non-governmental organizations will also be able to focus more on the needs of small and medium entrepreneurs, to educate on practical aspects of business and for the NGOs to be able to know areas to offer grants or donations and training the less fortunate. It is hoped that the recommendations and measures will help in counter checking whether the government is still on the truck in realization of the Kenya vision 2030.

Lastly the study will also benefit other researchers working on microfinance and small businesses by adding on to the literature that is available in that field.
1.7 Scope of the study
This study was carried out in Limuru area in Kiambu County. The study will confine itself only to small businesses and no other businesses. Limuru area has a mix of different small businesses and also microfinance institutions and is easily accessible.
1.8 Theoretical Framework
This study will examine the Agency theory, Growth cycle theory and Entrepreneurship theory.

1.9 Agency Theory
Jenson and Meckling (1976) explain the principal-agent relationship between equity holders and debt holders. In a principal-agent framework, the business is the agent and the finance provider is the principal. This theory asserts that principals have higher agency costs because equity- controlled firms have a tendency to invest sub optimally to expropriate wealth from debt holders that in turn results in incremental risk for the principal. That is, business owners invest on high risk projects that the cost of debt should have been higher to share on the marginal return on investment.
1.10 Growth Cycle Theory
Berger and Udell (1998) initiated the Growth Cycle Theory of small business financing. This theory illustrates the dynamic financial needs, as the small business becomes more experienced and enhanced informational transparency. In this theory ,the firm gets better access to venture capital as a source of equity and midterm-loans as a source of debt yet, as the firm gets older and information-wise transparent it tends to have better access to public equity and long-term financing. Gregory, Rutherford, Oswald and Gardiner (2005) found that only firm size, as measured by total employees, could significantly determine the decision of whether to use insider financing instead of going for public equity or long-term financing.
1.11 Entrepreneurship theory
Entrepreneurship theory (Shane, 2003) stated that entrepreneur’s ability to discover and exploit opportunity for entrepreneurial activity differs between individuals and depends on individual’s attitude towards risk taking. For instance, a risk-averse individual is less likely to exploit entrepreneurial opportunity (Shane, 2003). As such, a person may not search for or discover entrepreneurial opportunity if he/she has a negative attitude towards risk-taking. In the same vein, an individual may have an innovation business or service idea, and great likelihood to access micro-finance but may not utilize the opportunity if he/she fears risk.
2 CHAPTER TWO
2.1 Literature Review
This chapter reviews various theories on factors affecting access to credit by SMEs. It will look on various literatures previously done by other scholars. The study is guided by research specific objectives. This particularly focuses on effects of size of the firm, financial status of the business and managerial expertise on access to credit. These will be considered as being the pillars of the study.
2.2 Firm’s size influence on access to Credit
They have been categorized into five variables which include location of the firm, physical size of firm, age of the firm, industry sector and number of employees.
2.2.1 Size of the firm.
Firm size is one of the most important variables in literature related to access to credit. Numerous studies have discussed that small and medium-sized enterprises are financially more constrained than large firms (Carpenter & Petersen, 2002). With SME’s, there is a high risk involved because small firms have high failure rate compared to large firms. For example, Schiffer and Weder (2001) sampled firms across a number of countries and found that there was a negative relationship between the size of a business and the risk it might pose for a lender. These factors make it very difficult for financial institutions to lend to SME’s and as a result impact on their performance.

According to a study done by Fatoki and Asah (2011) on the impact of firm and entrepreneurial characteristics on access to debt finance by SMEs in King Williams’ town found that firm size impacts SMEs access to debt finance from commercial banks whereby small enterprises are less favoured to large firms. Consequently, it’s hypothetical existence of a positive association between the firm size and SMEs access to debt financing.

According to a research done by Oliveira and Fortunato (2006) on firm growth and liquidity constraints found that small firms face greater financial constraints hence having a negative impact on their growth. In addition, medium-sized firms face greater financial constraints than large firms. Small firms cannot exploit economies of scale in the same way as large firms can. These authors claim that since young companies have not accumulated sufficient cash flow and are unable to rely on bank financing, they have to depend on the original equity investment of their owners.

Generally, in either developing and developed economies, rate of credit access is a very important factor in accelerating investments and creating job thus, transforming small businesses into strong enterprises. For example, the United Kingdom governments have made such efforts possible by working in close links with lenders and this has resulted into consumers having access to credit at any time (Merton & Poll, 2008). Traditionally, in many African states, access to credit has been prioritized towards corporate bodies, therefore leaving out individuals and SMEs even though they make up a huge mass of consumers. However, countries like Egypt, Nigeria and Libya have worked hard towards enabling individuals and SMEs have access to credit facilities in banks.
In Kenya, the case hasn’t been any better as access to credit has been complicated by the stringent conditions levied by commercial banks. For banks to be able to gain more confident towards small borrowers, the Credit Information Sharing mechanism was launched in Kenya following Credit Bureau Regulations 2008 in July 2010. The Regulations demanded for the Deposit Protection Fund and institutes certified under the Banking Act to share data on Non-Performing Loans through the licensed credit reference bureaus (FSD Kenya, 2008).

2.2.2 Age of the firm
A firm’s age is an important factor in the study of SMEs’ financing decisions, especially regarding variations and/or adjustments of debt. Being in the business for many years suggests that the firms are at least competitive on average. It can be argued that being an older firm means there is lower informational opacity. The reason is information required by the lenders to evaluate and process applications is readily available because these businesses have an established reputation or track record. The studies conducted in the past have found that the financing constraints are particularly severe in start-ups enterprises and relatively young firms (three years old or less) (Pandula, 2010).

According to Kimuyu and Omiti (2000) states that age is associated with access to credit. Older entrepreneurs are more likely to seek out for credit. Lore (2007) also reveals that younger entrepreneurs are less likely to access loans from banks in Kenya. Age is an indicator of useful experience in self-selecting in the credit market. In addition, Lore (2007) asserts that older entrepreneurs also tend to have higher levels of work experience, education, wealth and social contacts. These resources are important in developing key competencies. Therefore, superior age leads to higher levels of entrepreneurial orientation.

Firms’ sources of finance change with time. For instance, a firm which starts off as a family business by utilizing internal financing like personal and family savings may grow to obtain funds from its suppliers. When it has established a good track record, developed accounting systems and reputable legal identity, it may qualify and obtain a loan from a banks or other financial institution. Therefore, the firm age is very important. In addition, the growth stage where the SME is at can also have a significant impact on its accessibility to finance. Previous studies done have established that financing constraints are particularly severe in start-up and relatively young enterprises of under three years in age. For example, Aryeetey (1994) early 90s survey of 133 firms in various industries in Ghana found out that only 10 percent of the start-up were able to obtain bank loans contrary to older firms who were offered credit three times more. A similar survey by Levy (1993) in Sri Lanka and Tanzania reported that 80 percent of small firms with 6 or more years in operation are easily able to access bank loans, compared to the success rate of around 55 percent for similar firms with fewer employees of similar age, and decline with the composition.

Moreover, older firms gain from their established relations with banks to reduce asymmetric information problems (Berger &Udell 2002). In addition, bank financing may be limited for research and design for projects run by young firms and this is due to the high default risk of the firms (Fritsch et al. 2006). Czarnitzki and Kraft (2007) also emphasize that young firms lack a track record and such uncertainty in their prospects results into low rating, thus bank loans would be considered too expensive for them. This study is set to determine whether a firm age affect credit accessibility by SMEs in Limuru sub county.
2.2.3 Location of firm.
There is some evidence to suggest that some SMEs in rural environments may face additional difficulties. First, there may be an absence of financial institutions in these rural areas. Sometimes, there may be a single bank branch available to the location, which may enjoy a monopoly power in the area, and small firms may not have much financing alternatives available. Due to this, they may end up paying high interest on bank loans or may have to adhere to restricted covenants such as collateral and other conditions (Pandula, 2011). Second, banks may be reluctant to lend to small firms located in rural areas, as the assets offered as collateral by these firms may have less market value, and in case of default, they may find it difficult to realize these assets (Pandula, 2011).

According to a research done by Fatoki and Asah (2011) on the impact of firm and entrepreneurial characteristics on access to debt finance by SMEs in King Williams’ town found that SMEs located in urban are successful in access to debt financing compared those located in rural areas. Physical closeness between lenders and borrowers produce an improved form of environmental scrutinize that aid SMEs to access credit from lenders. Consequently, there is a positive relationship between firm’s location and access to debt financing by SME.
2.2.4 Industry sector
The lending banks may prefer lending to industry sectors that are growing. Firms in certain sectors will also require more credit to invest in equipment, machinery, buildings, labour and raw materials than firms in other industry sectors. For instance, the industries with more capital requirements may face proportionately greater constraints (Pandula, 2010).

The demand side studies suggest that; whilst overall the majority of SMEs appear not to have difficulties obtaining external finance, there is evidence to indicate that a number of groups and sectors do face distinct challenges in accessing finance. Manufacturing firms were found to exhibit twice the proportion of problems per bank application (30.3%) when compared to all other sectors SMEs in competitive sectors may find it difficult to raise finance, especially if they are operating in ways that do not fit the banks’ own internal guides on benchmarking for the sector (Deakins et al, 2008).

Ayodeji and Balcioglu (2010) found that some SMEs in some sub-sectors were able to generate more financing than others. This was largely due to some structural defects in the nation’s economic scene meaning that the industry that the business is in will have a direct effect on the access to loans.
2.2.5 Number of employees
Small, medium, and large businesses utilize debt financing for a range of reasons from securing working capital to making longer-term investments. For micro businesses— small entities with less than five employees—this is no less true (Pollinger et al, 2007)
SMEs tend by their very nature to show a far more volatile pattern of growth and earnings, with greater fluctuations, than larger companies. Their survival rate is lower than for larger companies. One analyst found that manufacturing firms with fewer than 20 employees were five times more likely to fail in a given year than larger firms (OECD, 2006).

2.3 Financial status of the firm influence on access to Credit
2.3.1 Financial statements
When the firm is small, most of the time it is owned and operated by the entrepreneur himself and there is no such legal requirement to regularly report financial information and many firms do not maintain audited financial accounts. Audited financial statements are very useful in accessing credit from financial institutions. Often, banks require audited financial statements before granting credit. However, most of the SMEs in the South Asia have difficulty in getting credit from the formal financial institutions because they lack proper financial records. Most of the businesses in these countries often keep multiple sets of books and do not have audited financial statements based on reliable accounting standards. On the other hand, these firms end up getting loans at higher interest rates because banks considered them as high risk borrowers. Combined with the absence of information on their financial records, this makes difficult to lenders to assess lending proposals submitted by new firms (Pandula, 2010).

Banks usually cannot lend to SMEs as much as would be warranted if firms do not report reliably their full financial activity on their financial statements. Furthermore, informality implies that the firm has unrecorded, contingent senior liabilities to the government and its own employees. Faced with the risk that tax and labour authorities could cause such liabilities to materialize, banks would lend less to SMEs or charge a higher risk premium (Soledad, Sergio and Schmukler, 2008).

Tagoe, Nyarko, and Anuwa-Amarh (2005) carried out studies that showed that good information management and the ability to use information to present a well-crafted business case for financing reduced risk perception and increased the chance of SMEs getting adequate funds. Therefore, SME’s should seek to improve their information management practices and investor relations skills to improve their access to credit.

2.3.2 Asset Base / Firm’s Collateral
Collateral refers to the extent to which assets are committed by borrowers to a lender as security for debt payment (Gitman, 2003). The security assets should be used to recover the principal in case of default. SMEs in particular provide security in form of properties (houses, the businesses, the car, and anything that could actually bring back the principal) in case of default on loans (Garrett, 2009). Security for loans must actually be capable of being sold under the normal conditions of the market, at a fair market value and also with reasonable promptness. However, in most banks, in order to finance SMEs and to accept loan proposals, the collateral must be 100% or more, equal to the amount of credit extension or finance product (Mullei & Bokea, 2000).

According to a survey done by Kamau (2009), found that collateral security is a major constraint to credit access. In addition, 92% of enterprises studied had applied for loans, and were rejected while others had decided not to apply since they knew they would not be granted for lack of collateral security. McMahon (2005) stats that other factors held constant, firms with more intangible assets need to borrow less compared to firms with more tangible assets because of collateral factor. SMEs have fewer collateral and sable assets than large firms. Banks have always adopted a risk adverse attitude towards small firms, with an accompanying inability to focus on the income generating potential of the venture, when analysing the likelihood of loan repayment (Beaver, 2002).

According to Cressy and Toivanen (2001), states that better borrowers are able to get larger loans and lower interest rates and provision of collateral and loan size helps reduce interest rate that will be paid when a firm borrows. In addition, banks use qualitative and quantitative information in the structuring of loan contracts to small businesses. In order to protect the funds of savers, banks approach the lending process in a risk-averse way hence turn down a number of propositions perceived to be ‘riskier’. Agricultural enterprises faces unfavorable factors hence financial service providers classify farmers as high risk clients who cannot use their farms as collateral for credit (Rahaji & Fakayode, 2009; De Klerk, 2008).

According to a study done by Vuvor and Ackah (2011) on challenges faced by small & medium enterprises (SMEs) in obtaining credit in Ghana findings revealed that SMEs in Ghana like most SMEs in other countries are faced with major challenges in accessing credit. These challenges include; the inability of SMEs to provide collateral and other information needed by banks such as audited financial statement couple with high cost of loan in terms of high interest rates make it extremely difficult to access bank loans. Fatoki and Asah (2011) suggested that operators of SMEs have to own more tangible assets that can create higher value on their firm to accelerate borrowing security. Because, the higher the value of assets the lower the interest rates of the debt to be secured by those assets. Consequently, it is hypothetical of a strong positive relationship between collateral and access of debt financing by SMEs.

2.3.3 Business performance
Although it is difficult to construct the measures for firm performance in the SME sector, many studies have attempted to do this and found that greater sales and profits are associated with greater access to credit (Pandula, 2010).
2.4 Managerial expertise effect on access to credit
2.4.1 Education background

Education has always been considered important for entrepreneurs determining the type of business to be started or even the networks that will be formed. Bosworth (2009) found a strong correlation between education and forward thinking in business. There was also a strong link between education and the use of internet which is associated with embracing technology so as to benefit the business. Other studies also show evidence supporting a positive and significant relationship between the level of general education and entrepreneurial performance, whether performance is measured as growth, profits or earning power of the entrepreneur (Dickson, Solomon, Weaver, 2008).

The evidence linking general education to selection into entrepreneurship could however not be classified as either positive or negative. It would appear that there is sufficient evidence to suggest that the level of educational attainment by entrepreneurs is significantly and positively associated with entrepreneurial performance. The review also notes the ambiguous findings regarding the links between general education and selection into entrepreneurship.

The research appears to suggest that personal characteristics do make some difference to the ability of entrepreneurs to raise finance. Not surprisingly, graduates have the least difficulty in raising finance. Education appears to make little difference to sources of finance, except that those educated (only) to A levels seem more likely to use friends and family and to mortgage their home (Irwin, 2006)
Ayodeji and Balcioglu (2010) also indicated that the entrepreneur’s capacity (educational) development level has significant effect on the financing of Small and Medium Enterprises in Kwara State of Nigeria.

2.4.2 Entrepreneur’s experience
The accrual of experience in that business sector during the learning phase will provide both the requisite business knowledge as well as the skills to do the work. With these attributes, an individual is well placed to identify a business opportunity and to turn that opportunity into reality (Bosworth, 2009).

2.4.3 Networking

The statistical data confirmed that higher education qualifications were associated with higher levels of networking and use of business advice services (Bosworth, 2009) where a business owner lacks skills; these can often be overcome with strong support networks. The presence of other entrepreneurs in networks of social relationships can also reduce the ambiguity associated with entrepreneurial activity. Not all business owners have access to networks that are rich in human capital or entrepreneurial ability. This was reflected in the statistics, which showed that business owners with higher education qualifications were the most likely to have used a variety of business advice and networking organizations.

Belonging to a professional body helps gain a competitive advantage in a business. Being a member of an industry association implies that one is serious about the business they do. This could also help in networking and obtaining of business information (Bowen et al 2009). Ayodeji and Balcioglu (2010) also showed that the membership of business/trade organizations significantly affected the financing of the SMEs in Kwara State in Nigeria. Most of the SMEs in Kwara State did not belong to any organization and the SMEs that belonged to one or more had not made tangible contributions in the organization.
3 CHAPTER THREE -RESEARCH METHODOLOGY
3.1 Introduction
This chapter presents research methodology that will be used in the study. It also discusses research design, population and sample size, data collection methods, research procedures and data analysis and the presentation methods to be used in this research.
3.2 Research Design
Research design is the arrangement of conditions for collection and analysis of data in a manner that aims to combine relevance to the research purpose with economy in procedure (Chandran, 2004). On the other hand it is a detailed plan of how a research study is going to be conducted starting from data collection to data analysis of the research (Cooper & Schindler 2014).

This study used a descriptive research design. Burns and Bush (2010), state that a descriptive research design is a set of methods and procedures that describe variables. In addition, a descriptive research is also used to answer the question what, how and why (Sekaran & Bougie, 2013). Quantitative research will be used to gain better knowledge and in depth understanding of the results. The dependent variable is access to credit whereas the independent variables are firm’s size, financial status of the business and entrepreneurs’ managerial expertise. The main objective of this study is to determine factors affecting access to credit by small and medium enterprises in Limuru.
3.3 Population and Sampling Design
3.3.1 Population
Population refers to the entire group of individuals, events or objects having common observable characteristics. It is the aggregate of all that conforms to a given specification (Mugenda and Mugenda, 2003). According to Cooper and Schindler (2008), population is the total of all elements (an element is the subject on which measurement is being taken) upon which inferences can be made. The study population is 200 SMEs in Limuru Municipality. This information is obtained from Kiambu municipal county database. The study targeted dairy, food, crop and cash crop farmers.

Name of SME’S Frequency % Distribution
Dairy 34 17
Cash crop 66 33
Food crop 100 50
Total 200 100

Table 1: Population
3.3.2 Sampling Design
According to Sekaran and Bougie (2013), in probability sampling, the elements in the population have some known, nonzero chance or probability of being selected as sample subjects. This design is used when the representatives of the sample is of importance in the interest of wider generalize ability. This is the design that the study will adopt, as the sample will be inferred to the population.

3.3.3 Sampling Frame
Cooper and Schindler (2003) define a sampling frame as a list of sampling units from which the sample will be drawn. Saunders (2016) a sampling frame is a comprehensive list of all individuals or unit in a population from which a sample will be drawn.
The sample frame of the study is the SMEs in Limuru.

3.3.4 Sampling Technique
According to Cooper ;Schindler (2014) a sampling technique is a method of selecting elements from the population that represent the population. In addition, it is usually used to determine whether the sample of the study is a true representative of the whole population from which it is drawn or not. Stratified random sampling technique will be used. Stratified random sampling divides a population into subgroups or strata, and random samples are selected from each stratum (Mbwesa, 2006).
3.3.5 Sample Size
The sample size is a smaller set of the larger population (Cooper ; Schindler, 2014). A sample is a set of entities drawn from a population with the aim of estimating characteristic of the population (Siegel 2003). It is a fraction or portion of a population selected such that the selected portion represents the population adequately.

Where n = number of samples, N = total population and e = error margin / margin of error.

N= 200
(1+ 200 (0.1) = 67

Type of SME Frequency % Distribution
Dairy 12 17
Cash crop 2 33
Food crop 30 50
TOTAL 67 100

Table 2: Sample Size

3.4 Data Collections Method

Data collection instrument is a device used to collect data in an objective and a systematic manner. Primary data will be collected using structured questionnaire. According to Sandakos (2005), use of questionnaire is appropriate because questionnaires are stable, consistent, and uniform. The questionnaires are self-administered.

Questionnaires were collected immediately after respondents were done answering hence reduced bias. Likert scale was used. This is because Likert scale is easy to understand. The questionnaire will be subdivided into five sections. The first section addressed demography, the second, third and fourth sections addressed research objectives and the fifth section dependent variable.
3.5 Research Procedures

According to Creswell (2003) research procedure is a method of collecting and analysing data for a particular purpose. A pilot study will be conducted. Pilot testing is important in ascertaining the validity and the reliability of the questionnaire. The pilot testing also helps the researcher to identify errors in the questionnaire, to identify areas where the respondents will find difficulty in answering. According to Barnett (1991), a pre-test is essential in checking that the questions are properly understood and interpreted. The study used five respondents to conduct the pilot study.
3.6 Data Analysis Method

Data analysis is a process of transforming a mass of raw data into tables, charts, with frequency distribution and percentages (Saunders ; Thornhill, 2009). Data collected from the field was sorted and summarized in tables and charts. The Statistical Package of Social Sciences (SPSS) was used to analyse data. The process of data analysis involved several stages. Completed questionnaires were edited for completeness and consistency. The data was then checked for any errors and omissions (Kaewsonth ; Harding, 1992). Quantitative data collected was analysed using inferential statistics. Mean and standard deviation was used to measure the central tendencies of the variables. The formula (Y= ß0+ ß1X1+ ß2X2 + ß3 where Y represents the dependent variable access to credit, X1 represents firm’s size, X2 represents the business financial status and X3 represents entrepreneur managerial expertise was used to determine the correlation between the variables. Bar graphs and pie charts were used to present findings.
3.7 Chapter summary

This chapter has addressed research methodology that was used in the study. It also discussed research design, target population and sampling design and technique, research procedure, design and data analysis methods that were used. Chapter four presents the analysis and findings of the study.

4 CHAPTER FOUR
4.1 Data Analysis and Interpretation
4.1.1 Introduction
This chapter presents the results established from the study. The chapter presents results on demographic data of the respondents such as gender, age, level of education, number of years in the organization, and ever applied for a loan. The chapter gives results based on specific objectives.
4.1.2 Response Rate
The response rate is utilized to find out the statistical authority of a test and the higher the response rate the higher the statistical power. In this study, the researcher distributed 67 questionnaires and only 59 were filled and returned. This represents a response rate of 88% as shown in Table 3: Response Rate below.

Questionnaires Number Percentage
Filled and collected 59 88
Non responded 8 12
Total 67 100
Table 3: Response Rate

4.2 Demographical Factors
The research analysed data with regard to the demographic factors and the results were presented as follows:
4.2.1 Gender
To establish gender of the respondents, findings revealed that majority of the respondents were female this represents 66% of the total population followed by male representing 34% of the population. This shows that there are more females operating SME’s in Limuru Municipality as compared to the males.
4.2.2 Age
To analyse age of the respondents, the study established that 23 respondents who are between 21-30 years representing 39% of the population, 30 respondents were between 31-40 years this represents 51% of the respondents and 6 respondents were above 40 years representing 10% of the population as shown in Table 4: Age below. This shows that most of the employees in the agriculture SME’s are in the age bracket of 31 – 40 years.
Table 4: Age
Age Frequency Percentage
21 – 30 years 23 39%
31 – 40 years 30 51%
Above 40 years 6 10%
Total 59 100%

4.2.3 Level of Education

To analyse the highest level of education the result established that 1 respondent was a form four lever representing 2% of the total population, 4 respondents have a certificate representing 7% of the total population, 9 respondents have a diploma representing 15% of the population, 34 respondents have a degree this represents 58% of the total population, 11 respondents have post graduate representing 19% of the total population as shown in Table 5: Level of Education below.

Type Frequency Percentage
Form four 1 2%
Certificate 4 7%
Diploma 9 15%
Degree 34 57%
Post graduate 11 19%
Total 59 100%
Table 5: Level of Education

4.2.4 Years Worked in the Organization
To establish the year’s respondents have been in the organization, findings revealed that 22 respondents have been in the organization for less than a year representing 37% of the population, 26 respondents have been in the organization for 1-5 years this represents 44% of the population, 9 respondents have been in the organization for 6-10 years representing 15% of the population and 2 respondents have been in the organization for over 10 years representing 3% of the population as shown in Table 6: Years worked in the organization below.

No. of years Frequency Percentage
Less than an year 22 37%
1 – 5 years 26 44%
6 – 10 years 9 15%
Over 10 years 2 3%
Total 59 100%
Table 6: Years worked in the organization

4.2.5 Loan Application
To investigate if respondents have ever applied for a loan from the bank 61% said “Yes” whereas 36% said “No” and 3% was missing as shown in Figure 1: Proportion of loan application below.
Figure 1: Proportion of loan application

4.2.5.1 If no Say why
To analyse why SME’s have not accessed loan from a bank 12% of the respondents stated that it was due to high interest rate, 7% of the respondents said it was due to lack of lack of collateral, 5% said it was because banks are expensive, 3% of the respondents said it was because of lack of guarantors and another 3% said it was time limitation, 2% said it was because they prefer using SACCO than banks this represents and 68% of the respondents never answered as shown in Figure 2: Reason for not accessing loans below.

Figure 2: Reason for not accessing loans

4.3 Effects of Access to Credit
The study set to establish effects of access to credit. Respondents were asked a set of questions to indicate to what extent they agree or disagreed with statement related access to credit. Using a five point Likert scale where 1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, and 5-Strongly Agree the findings revealed that 51 respondents strongly disagreed and 53 strongly agree as shown in Table 7: Descriptive of Effects of Access to Credit.
4.3.1 Descriptive of Effects of Access to Credit
Majority of respondents agreed that they prefer personal loans from family and friends (4.15). There was however uncertainty on respondents have received loan from a bank where personal contacts existed (3.62), it is very difficult accessing credit (3.34). In addition, respondents disagreed on whether they consider loans from banks or other financial institutions as being expensive (2.35) and undergone bankruptcy before starting the current enterprise (1.52) as shown in Table 7: Descriptive of Effects of Access to Credit below.
On analysis of the standard deviation they prefer personal loans from family and friends had the highest deviation of (1.286) whereas they had undergone bankruptcy before starting the current enterprise (0.773) had the lowest standard deviation. This means that there was little variation amongst respondents on those who agreed, disagreed and neutral.
Table 7: Descriptive of Effects of Access to Credit
Variable 1 2 3 4 5 Mean Standard deviation
It’s difficult accessing credit 2 16 12 20 8 3.34 1.122
I have only received credit from places personal contacts existed. 7 22 9 15 6 3.62 1.244
I prefer loans from family and friends. 5 10 7 20 17 4.15 1.286
I prefer loans from banks and financial institutions expensive. 0 5 6 26 22 2.35 0.899
I had undergone bankruptcy before starting the current enterprise. 37 12 9 0 0 1.52 0.772

1- Strongly disagree, 2- Disagree, 3- Neutral, 4- Agree, 5- Strongly agree.
4.3.2 Experience in Accessing Credit
To analyze SME’s experience when accessing credit 42% of the respondents agreed that it was difficult accessing credit due to time limitation given to them to repay back the loan, 36% of the respondents was due to interest capping, 7% of the respondents stated that it was because of bureaucracy, and another 7% due to high transaction cost, 5% of the respondents agreed that collateral was required, and 3% of respondents agreed that they prefer using mobile money as shown in Figure 3: Experience in Accessing Credit below.
Figure 3: Experience in Accessing Credit

4.4 Size of firm affecting access to credit
The first objective set to establish the effect of size of the firm on SME’s access to credit. Respondents were asked a set of questions to indicate to what extent they agree or disagreed with statement related to effect of firm’s size on SME’s access to credit. Using a five point Likert scale where 1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, and 5-Strongly Agree the findings revealed that 6 respondents strongly disagreed and 58 strongly agree as shown in Table 8: Size of firm affecting access to credit.
4.4.1 Size of firm affecting access to credit
Majority of respondents agreed that small firms have problems in accessing loans than big firms (4.41), SME’s located in urban are successful in access to debt financing compared those located in rural areas (4.28) and older firm (more than 3 years) have more experiences of applying for loans than younger firms below 3 years (4.30). There was however uncertainty on credit enables SMEs to meet their expansion plan (3.97), younger firms (less than 3 years) face challenge accessing loans as compared to older firms (3.88) and banks are unwilling to lend to small firms located in rural areas (3.85) as shown in table
On analysis of the standard deviation SMEs located in urban are successful in access to debt financing compared those located in rural areas had the highest deviation of (1.765) whereas Older firm (more than 3 years) have more experiences of applying for loans than younger firms below 3 years had the lowest deviation of (0.525). This means that there was little variation amongst respondents on those who agreed, disagreed and neutral.
Table 8: Size of firm affecting access to credit
Variable 1 2 3 4 5 Mean Standard deviation
Small firms find more difficulty in accessing credit compared to big firms. 0 2 30 27 0 4.41 0.581
Credit enables SME’s to meet their expansion plan. 0 4 7 35 13 3.97 0.784
Older firms (more than 3 years) have more experience in applying for loans than small firms below 3 years. 0 0 2 38 19 4.30 0.525
Younger firms experience challenges in accessing loans than older firms. 1 6 10 26 16 3.88 0.985
Banks are unwilling to lend to smaller firms in rural firms. 0 2 14 33 10 3.85 0.728
SME’s in urban areas are successful in access to debt financing compared to those located in rural areas. 5 2 15 6 0 4.28 1.765
1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, and 5-Strongly Agree
4.5 Other factors
To analyze other factors that influence access to credit on basis of firm’s size, findings revealed that 4 respondents stated that management affects access to finance this represents 7% of the population, 2 respondents stated that government policy affects access to finance this represents 3% of the population, 14 respondents stated that bank statement affects access to finance this represents 24% of the population, 8 respondents stated that networking/political ties affects access to finance representing 14% of the population, 7 respondents stated that nature of business affects access to finance representing 12% of the population, 11 respondents stated that assets affects access to finance representing 19% of the population, 6 respondents stated that risk assessment affects access to finance this represent 10% of the population, as shown in figure 4.4.
Figure 4: Other Factors:

4.6 Financial status of the firm influence access to Credit
The second objective set to establish the influence of financial status of a firm on SME’s access to credit. Respondents were asked a set of questions to indicate to what extent they agree or disagreed with statement related to influence of financial characteristics on SME’s access to credit. Using a five point Likert scale where 1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, and 5-Strongly Agree the findings revealed that 46 respondents strongly disagreed and 81 strongly agree
4.6.1 Financial status of the firm influence access to Credit
Majority of respondents agreed that they have adequate book keeping records which have made it easy for them to access credit (4.33), audited financial statements are needed before a loan is approved (4.08) and lack of collateral affects access to finance (4.05). There was however uncertainty on firms that do not generate profits experience challenges accessing credit (3.91) and financial institutions are reluctant to provide long term finance to SME’s (3.47). In addition, respondent also disagreed on credit has a positive effect on business performance and growth (1.24) as shown in Table 9: Financial status of the firm influence access to Credit below.
On analysis of the standard deviation financial institutions are reluctant to provide long term finance to SME’s had the highest deviation of (1.765) whereas Credit has a positive effect on business performance and growth had the lowest deviation of (0.432). This means that there was little variation amongst respondents on those who agreed, disagreed and neutral.
Table 9: Financial status of the firm influence access to Credit
Variable 1 2 3 4 5 Mean Standard deviation.
Lack of collateral affects access to credit. 0 3 7 34 15 4.41 0.773
Financial institutions are reluctant to provide finances to SMEs 4 6 12 31 6 3.47 1.026
I have adequate book keeping records which has made it easy for me to access credit. 0 1 6 28 24 4.33 0.709
Audited financial statements are needed before a loan is approved. 0 2 8 32 18 4.08 0.771
Firms that do not generate profit have challenges accessing credit. 1 3 13 22 18 3.91 0.955
Credit has a positive effect on business performance and growth. 41 15 0 0 0 1.24 0.432
1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, and 5-Strongly Agree
4.6.2 Types of Collateral or Securities Accepted
To analyse the type of collateral or securities accepted, the study revealed that 71% of the respondents agreed that title deed is required as part of collateral/security when accessing credit and 23% also agreed that fixed assets is also considered as part of collateral as shown in Figure 5: Types of Collateral or Securities Accepted.
Figure 5: Types of Collateral or Securities Accepted

4.7 Entrepreneur’s Characteristics on SME’s Access to Credit
The third objective set to determine entrepreneur’s characteristics on SME’s access to credit. The respondents were asked a set of questions to indicate to what extent they agree or disagreed with statement related to determine entrepreneur’s characteristics on SME’s access to credit. Using a five point Likert scale where 1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, and 5-Strongly Agree, 39 respondents strongly disagreed and 49 strongly agree.
4.7.1 Managerial skills of the entrepreneur affecting access to credit
Majority of the respondents agreed that banks prefer women to men when issuing credit (4.25). There was however uncertainty on use of networking influences access to finance (3.74). In addition, the respondents disagreed on applying a loan as a group is easy because of access to co guarantors (2.94), use of political ties helps an entrepreneur access finances (2.91), level of education/training affects access to finance (2.73) and banks consider training and skills one has to access credit (2.24) as shown in Table 10: Managerial skills of the entrepreneur affecting access to credit.
On analysis of the standard deviation banks prefer women to men when issuing credit had the highest deviation of (1.524) whereas Credit level of education / training affects access to finance had the lowest standard deviation of (0.953). This means that there was little variation amongst respondents on those who agreed, disagreed and neutral.
Table 10: Managerial skills of the entrepreneur affecting access to credit
Variable 1 2 3 4 5 Mean Standard deviation.
Through networking I have been able to access credit. 0 10 11 21 17 3.74 1.042
Applying for loan as a group is easy because I can get guarantors. 5 18 19 13 4 2.94 1.036
Use of political ties helps an entrepreneur access credit. 7 14 19 16 3 2.91 1.106
The level of education/training has an effect on the accessibility to finances. 4 21 22 10 2 2.73 0.953
Banks consider the training and skills one has to assess credit. 18 13 14 4 18 2.24 0.978
Banks prefer women to men in issuing credit. 5 3 7 19 5 4.25 1.524
1-Strongly Disagree, 2-Disagree, 3-Neutral, 4-Agree, and 5-Strongly Agree
4.8 Chapter Summary
This chapter discusses results and findings. The first part provided an analysis of demographic data of the respondents, the second section dealt with data on access to finance, the third part looked at the data on firm’s size, the fourth part covered issues on financial status of a firm and the fifth part discussed issues on managerial expertise of an entrepreneur. Chapter five will present discussion, conclusion and recommendation.
5 CHAPTER FIVE
5.1 Discussion, Conclusion and Recommendation
5.2 Introduction
This chapter presents a summary of findings of the study. Findings are discussed in relation to previous literature review. This will be organized based on the specific research objectives which sought to establish firm characteristics, financial; characteristics and entrepreneur characteristics affected access to finance.
5.3 Summary of Findings
The purpose of the study was to evaluate the factors affecting access to credit by Small and Medium Enterprises (SMEs) from financial institutions in Kenya, a case study of Limuru Municipality, Kiambu County. The research was guided by the following objectives: to determine the influence of firm’s size on SMEs access to credit, also to determine effect of managerial skills on access to credit and to establish the influence of financial status of an SME access to credit in Limuru Municipality, Kiambu County, Kenya.
A descriptive research design was employed in this study to gather quantifiable information through use of open and close-ended questions. The target population was 84 SMEs in that have been in operation for more than 3 years. Stratified random sampling was used to select a sample size of 67. Data was analysed using descriptive statistics and Statistical Package of Social Sciences (SPSS). Data obtained was coded according to different variables and descriptive statistics such as frequencies, mode, mean percentiles, variances and standard deviations. Tables, figures and charts were used for analysis and interpretation of data. Pearson correlation and regression analysis was done to determine the influence of independent variables on the dependent variable.
The findings on firm’s size factors and access to credit revealed majority of the respondents agreed that small firms size have problems in accessing loans than big firms, SME’s located in urban are successful in access to debt financing compared to those located in rural areas, older firm (more than 3 years) have more experiences of applying for loans than younger firms below 3 years. There was however uncertainty on credit enables SMEs to meet their expansion plan, younger firms (less than 3 years) face challenge accessing loans as compared to older firms and banks are unwilling to lend to small firms located in rural areas.
The findings on financial status of a business and access to credit revealed that respondents agreed that they have adequate book keeping records which have made it easy for them to access credit, audited financial statements are needed before a loan is approved and lack of collateral affects access to finance. There was however uncertainty on firms that do not generate profits experience challenges accessing credit and financial institutions are reluctant to provide long term finance to SME’s. In addition, respondent also disagreed on credit has a positive effect on business performance and growth.
The findings on managerial expertise factors and access to credit revealed that majority of respondents agreed that banks prefer women to men when issuing credit. There was however uncertainty on use of networking influences access to finance. In addition, respondents disagreed on applying a loan as a group is easy because of access to co guarantors, use of political ties helps an entrepreneur access finances, level of education / training affects access to finance and banks consider training and skills one has to access credit.
5.4 Discussion
5.4.1 Effect of Firm’s sizes on SME’s Access to Credit
The study revealed that small firms size have problems in accessing loans than big firms and older firm (more than 3 years) have more experiences of applying for loans than younger firms below 3 years. This is in support to study done by Berger and Udell (2002), and Fatoki and Asah (2011) who indicated that firm size influences SME’s access to finance. This is because smaller and younger SME’s are less favored by banks hence facing higher cost of financing as compared to big and older firms. In their study, they also revealed that there was a positive relationship between firm size and SMEs access to debt financing. In addition, Oliveira and Fortunato (2006) indicated that smaller firms faces a challenge accessing finance hence affecting their growth because of lack of sufficient cash flow and are unable to rely on bank financing.
The study revealed that SME’s located in urban areas are able to easily access debt financing as compared to those located in rural areas. This is in agreement with a research done by Fatoki and Asah (2011) in their research findings revealed that there was a positive relationship between location and access to debt financing by SME’s.
The study revealed banks are willing to lend to small firms located in rural areas. This is in support to a study done by Rand (2007) who indicated that SME’s in rural areas are able to access credit from banks because most government bank credit are located towards rural areas. However this is in contrast study done by Pandula (2011) who indicated that banks are more reluctant to lend to small firms located in rural areas because most SME’s have collateral that have less market value and in case of default, they may find it difficult to realize these assets.
The study revealed younger firms do not (less than 3 years) face challenge accessing loans as compared to older firms. In contrast study done by Klapper (2010) and Ngoc, Le and Nguyen (2009) it was established that firms face hardship and more costs in accessing external financing from lenders because information asymmetry. In addition, it was also revealed that there was a positive relationship between firm’s age and access to debt financing by SME’S.
5.4.2 Effect of Financial status of a firm on SME’s Access to Credit
The study revealed that respondents have adequate book keeping records which have made it easy for them to access credit and audited financial statements are needed before a loan is approved. This is line with study done by Pandula (2010) and Nanyondo, (2014) who indicated that audited financial statements and quality of financial statement has a significant positive association with access to finance. However, In contrast, a study done by Sarapaivanich and Kotey (2006) indicates that young and small firms experience a challenge accessing credit due to lack of well-established record keeping system and readily available audited financial statements.
The study revealed that respondents agreed that lack of collateral affects access to finance. This is in agreement with research done y done by Kamau (2009) and Vuvor and Ackah (2011) which indicated that SME’s faces a challenge accessing credit due to lack of collateral.
The study revealed that financial institutions are reluctant to provide long term finance to SME’s, credit has a positive effect on business performance and growth and firms that do not generate profits experience challenges accessing credit. This is in line with study done by Rahaji and Fakayode, (2009); De Klerk, (2008) who indicated that agricultural SME’s faces unfavorable factors which hinders them from accessing finance and also financial service providers classify farmers as high risk clients who cannot use their farms as collateral for credit.
This was also in line with study done by Wagenvoort, (2003); Beck, et al (2005); Khandker et al (2013) indicated that SME’s performance and growth is affected due to lack of accessing formal finance. Study done by Shinozaki (2012) revealed that access to credit has a positive effect on SMEs growth whereas a study done by Malesky and Taussig (2009) revealed that there was no relationship between access to credit and firm performance.
5.4.3 Effect of managerial expertise on SME’s Access to Credit
The study revealed that respondents agreed that banks prefer women to men when issuing credit. This is in line with a study done by Demirgüç-Kunt, Beck, and Honohan, (2008), Cole and Mehran (2009) who indicated that women entrepreneurs face more challenge accessing finance compared to male entrepreneurs. It is also in line with study done by Roper & Scott (2009) and Mijid (2009), in their findings it was established that that gender is a factor in the demand for and availability of credit. However, in contrast a study done by Beck and Cull (2014) and Mama and Ewoudou (2010) findings revealed that gender is not a factor in access to credit.
The study revealed that use of networking influences does not influence access to finance, applying for a loan as a group is easy because of access to co guarantors and use of political ties helps an entrepreneur access finances. In contrast, according to Atieno (2009), Andula, (2011) findings established that networks help to provide advice, information and capital to small firms, social ties or professional associations allows SME operators to establish relations with bankers. This was also in line with studies done by Vos, (2004); Pandula, (2011), Kumah, (2011), McKenzie, (2009) and OECD, 2000 who indicated that group liability is preferred by financial institutions, membership with an association increase SMEs’ access to finance, group lending increases a firm’s access to credit and political surroundings exert a huge impact on the performance of SMEs.
The study revealed that level of education/training does not affect access to finance and banks consider training and skills one has to access credit. In contrast according to Zarooket al. (2013); Slavec and Prodan (2012) it was revealed that educational level of owners has big tremendous correlation with access to financial institution loan. This was also in line to study done by Abdesamed and AbdWahab (2012) Kira (2013) and Mukiri (2012) which indicated that that academic education level of the entrepreneur has positive effect on getting entry to finance and also that SMEs with proprietor/manager who have instructional qualification of training and past are much more likely to be favored by banks to get admission to credit score.
5.5 Conclusions
5.5.1 Firm’s size effect on SME’s Access to Credit
Small firms experience a challenge accessing loans from banks as compared to big SME’s, location of a firm also affects access to finance, older firms that have been around for a long time have more experience in accessing finance than younger firms. Credit does not enable SME’s achieve or meet their expansion plan.
5.5.2 Financial status of a firm effect on SME’s Access to Credit
SME’s have adequate book keeping records which have made it easy for them to access credit; audited financial statements and collateral are needed before a loan is approved. SME’s are not able to generate profit due to challenges accessing credit, SME’s are not able to access long term financing because financial institution consider them as being risky and credit does not have a positive effect on performance and growth.
5.5.3 Managerial expertise effect on SME’s Access to Credit
Banks prefer lending to women than men, access to finance is not influenced by networking, applying as a group, political ties, level of education and training and skills entrepreneurs have.
5.6 Recommendations
5.6.1.1 Firm’s size effect on SME’s Access to Credit
It is recommended that financial institutions should develop products that will target SME’s located in rural areas. Awareness should also be created. Through this SME’s will be encouraged and maotivated to access credit from financial institutions.
5.6.1.2 Financial status of a firm effect on SME’s Access to Credit
SME’s should ensure that they have collateral and sufficient documentations before accessing credit. Through this they will be able to access finance. Through this financial institutions will be less reluctant to give them long term loan. Access to credit will also enable SME’s grow and invest.
5.6.1.3 Managerial expertise effect on SME’s Access to Credit
SME’s should be encouraged to access credit as a group or chama. This is because that will be able to easily get guarantors hence making it easy to access loans. Entrepreneurs should be encouraged to attend training and seminars that will enable them network, share ideas and gain more knowledge of how to prepare a marketable business plan and access credit.
5.6.2 Recommendation for Further Studies
The study only focused access to credit by SME’s in the agricultural sector. It is recommended that other studies be done to determine other factors that affect access to finance. Further studies should be conducted on the role of financial sector in development of agriculture sector.
Research can also be done on the financial market deepening to look at the reachability of the financial services to the informal sector due to the impact SMEs have on our GDP.

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APPENDIX I: INTRODUCTION LETTER

Milkah Njeri Waweru
Catholic University of Eastern Africa,
P.O. Box 14634, 00800,
Nairobi. Kenya.
June 13, 2018

Dear Respondent,

RE: GRADUATE RESEARCH QUESTIONNAIRE

I am a student at Catholic University of Eastern Africa, pursuing Bachelor of commerce degree and majoring in Finance. As part of the requirements of the program I am undertaking a research on “Factors Affecting Access to Credit by SMEs in Limuru Municipality, Kiambu County.

You have been selected to participate in the study. I therefore request you to complete the attached questionnaire. It is estimated that it will take 15-20 minutes to complete. The information and data provided is needed for academic purposes only and will be treated strictly confidential and no instance will your name be mentioned in this report thereof.

Thank you for your anticipated kindest response.

Yours Sincerely,

Milkah Waweru. ?
APPENDIX II: QUESTIONNAIRE
FACTORS AFFECTING ACCESS TO CREDIT BY SMALL AND MEDIUM
ENTERPRISES IN KENYA: A CASE OF SME’s IN LIMURU MUNICIPALITY, KIAMBU COUNTY.

This questionnaire assists in data collection for academic purpose. The research intends to give an analysis of firm’s characteristics, financial characteristics, and entrepreneur’s characteristics in relation to access to finance. All information obtained, will be handled with high level of confidentiality. Do not incorporate identification or names in the questionnaire.
Please answer every question as in outlined by using either a cross(x) or (ticking) in the option that applies.

SECTION A: DEMOGRAPHIC DETAILS
1. Gender
Male
Female

2. Age
Below 20 years 21–30 years 31 – 40 years above 40 years

3. What is your highest education level?
Form 4 Certificate Diploma Degree Post graduate

5. How long have you been working in the organization
Less than one year 1-5 years 6-10 years over 10 years

6. Have you ever applied for loan from a bank? Yes No
If no say why …………………………………………………………………………………………………………………………………………………………………………………………..

SECTIONE B: Access to Credit
Please indicate your opinion as per the level of disagreement or agreement with the outline statement using 1 to 5 scale guideline. 1= Strongly Disagree, 2= Disagree,
3= Neutral, 4 =Agree, 5= Strongly Agree.

Statement 1 2 3 4 5
It is very difficult accessing credit.
I have only received a loan from a bank where personal contacts existed.
I prefer personal loans from family and friends.
I consider loans from banks or other financial institutions as being expensive.

In your opinion what is your experience in accessing credit?
_____________________________________________________________________
_____________________________________________________________________

SECTION C: Size of the firm affecting access to credit

Please indicate your opinion as per the level of disagreement or agreement with the outline statement using 1 to 5 scale guideline. 1= Strongly Disagree 2= Disagree, 3= Neutral, 4 = Agree, 5= Strongly Agree
Statement 1 2 3 4 5
Small firms size have problems in accessing loans
than big firms
Credit enables SMEs to meet their expansion plan
Older firm (more than 3 years) have more experiences of applying for loans than younger firms below 3 years.
Younger firms (less than 3 years) face challenge accessing loans as compared to older firms.
Banks are unwilling to lend to small firms
located in rural areas
SMEs located in urban are successful in access to
debt financing compared those located in rural areas

In your own opinion what other firm characteristics influence access to credit?
_____________________________________________________________________
_____________________________________________________________________

SECTION D: Financial status of the firm affecting access to credit
Please indicate your opinion as per the level of disagreement or agreement with the outline statement using 1 to 5 scale guideline. 1= Strongly Disagree 2= Disagree, 3= Neutral, 4 = Agree, 5= Strongly Agree.

Statement 1 2 3 4 5
Lack of collateral affects access to finance.
Financial institutions are reluctant to provide long term finance to SME’s
I have adequate book keeping records which has made it easy for me to access credit.
Audited financial statements are needed before a loan is approved.

Firms that do not generate profits have challenges accessing credit.
Credit has a positive effect on business performance and growth

What collateral / securities are commonly acceptable by financial institutions for SMES seeking credit?
_____________________________________________________________________
_____________________________________________________________________

SECTION E: Managerial skills of the entrepreneur affecting access to credit

Please indicate your opinion as per the level of disagreement or agreement with the outline statement using 1 to 5 scale guideline. 1= Strongly Disagree 2= Disagree,
3= Neutral, 4 = Agree, 5= Strongly Agree.

Statement 1 2 3 4 5
Through networking I have been able to access credit
Appling a loan as a group is easy because I can get co guarantors
Use of political ties helps an entrepreneur access
Finances
The level of education / training one has affects in
accessing finance
Banks consider the training and skills one has to access credit

According to you are there other factors that affect SMES credit access?
_____________________________________________________________________
_____________________________________________________________________

Thank you for your participation

APPENDIX III: WORK PLAN
Activity Month
Selection of topic Jan, 2018
Topic approval March, 2018
Research proposal writing April- May, 2018
Questionnaire formulation June, 2018
Project submission July, 2018
APPENDIX IV: RESEARCH BUDGET
Research Item Cost
Flash disk 1,500
Stationery 7,000
Transport 3,000
Internet cost 4,000
Research assistant 2,000
Total 17,500

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