THE EFFECT OF CAPITAL STRUCTURE ON THE FINANCIAL

PERFORMANCE OF SMALL AND MEDIUM ENTERPRISES IN

THIKA SUB-COUNTY

BY

EDWIN MARANGA BIRUNDU

D63/61327/2013

A RESEARCH PROJECT SUBMITTED IN PARTIAL

FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD

OF THE DEGREE OF MASTER OF SCIENCE IN FINANCE,

SCHOOL OF BUSINESS OF THE UNIVERSITY OF NAIROBI

NOVEMBER 2014

ii

DECLARATION

This research project is my original work and has not been presented in any other

examination body. No part of this research project should be produced without my

consent or that of the University of Nairobi.

Signature………………………..… Date:…………………………………

Name: Edwin Maranga Birundu.

REG: D63/61327/2013

DECLARATION

This research project has been submitted with the approval of the University of

Nairobi

SUPERVISOR

Signature………………………………… Date…………………………………….

Name: Mr. Mwangi W. Mirie

Lecturer, Department of Finance and Accounting

School of Business, University of Nairobi.

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ACKNOWLEDGMENT

I would like to first and foremost thank God for helping me through and giving me the

strength to achieve this. My special thanks to my supervisor, Mr. Mwangi W. Mirie

for his continued advice and guidance over the research period. I also thank the

lecturers, administration staff and support staff of the University of Nairobi for their

support through the entire program period.

To my parent, friends and classmates for supporting me throughout the course

especially where matters of group work and revision were concerned. Last I wish to

thank the entire fraternity of PKF Kenya for their supports God bless them. May the

Almighty God bless you all greatly!

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DEDICATION

This paper is dedicated to my Mother Mrs Elizabeth Kemunto Nyantika for her

support, to my sibling Ryan Momanyi and my sister Leoninda Birundu I encourage

them work hard in school, and to my wife Rosebell wahu for her support and

encouragement to see me through.

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TABLE OF CONTENTS

DECLARATION .......................................................................................................... ii

ACKNOWLEDGMENT.............................................................................................. iii DEDICATION ............................................................................................................. iv LIST OF TABLES ...................................................................................................... vii LIST OF FIGURES ................................................................................................... viii LIST OF ABBREVIATIONS ...................................................................................... ix

CHAPTER ONE: ...........................................................................................................1 INTRODUCTION .........................................................................................................1 1.1. Background of the Study ...............................................................................1 1.1.1. Capital Structure ...........................................................................................3 1.1.2. Firms Financial Performance .......................................................................5

1.1.3. Capital Structure and Financial Performance ..............................................7 1.1.4. Small and Medium Size Enterprises in Thika Sub-County ...........................8

1.2. Research Problem ........................................................................................10

1.3. Research Objective ......................................................................................11 1.4. Value of the Study .......................................................................................12 CHAPTER TWO: ........................................................................................................13

LITERATURE REVIEW ............................................................................................13 2.1 Introduction .................................................................................................13 2.2. Theories of Capital Structure .......................................................................13

2.2.1. Capital structure Irrelevance Theory ...........................................................13 2.2.2. Static Trade-off Theory ................................................................................14

2.2.3. Agency Costs Based Theory .........................................................................15 2.2.4. Pecking Order Theory ..................................................................................17 2.3. Determinants of Financial Performance of SMEs .......................................18

2.3.1. Asset Turnover ..............................................................................................18

2.3.2. Asset tangibility ............................................................................................18 2.3.3. Profitability ...................................................................................................19 2.3.4. Firm Size .......................................................................................................20 2.3.5. Firm Growth..................................................................................................20

2.3.6. Liquidity ........................................................................................................21 2.3.7. Non-Debt Tax Shields...................................................................................21 2.4. Empirical Evidence .......................................................................................22 2.5. Summary of the Literature Review ...............................................................26 CHAPTER THREE .....................................................................................................29

RESEARCH METHODOLOGY.................................................................................29 3.1 Introduction ...................................................................................................29 3.2. Research Design............................................................................................29 3.3. The Population ..............................................................................................29 3.4. Sample Design ..............................................................................................30

3.5. Data Collection .............................................................................................30 3.6. Data Analysis ................................................................................................31

3.6.1. Research Model ............................................................................................31 3.6.2. Measurement of Variables ............................................................................32 3.6.2.1. Independent Variables ..................................................................................32 3.6.2.2. Dependent Variables .....................................................................................33 CHAPTER FOUR ........................................................................................................34 DATA ANALYSIS, RESULTS AND DISCUSSION ................................................34

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4.1 Introduction ...................................................................................................34

4.2. Response Rate ...............................................................................................34 4.3. Data Validity .................................................................................................34 4.4. Descriptive Statistics .....................................................................................34 4.4.1. Debt Ratio .....................................................................................................34

4.4.2. Asset turnover ...............................................................................................35 4.4.3. Asset tangibility ............................................................................................36 4.4.4. Financial performance of SMEs ...................................................................36 4.5. Correlation Analysis .....................................................................................37 4.6. Regression Analysis and Hypothesis testing ...............................................38

4.7. Discussion of Research Findings ..................................................................40 CHAPTER FIVE .........................................................................................................43 SUMMARY, CONCLUSION AND RECOMMENDATIONS ..................................43 5.1 Introduction ...................................................................................................43 5.2. Summary of the Findings ..............................................................................43

5.3. Conclusions ...................................................................................................44 5.4. Recommendations .........................................................................................45

5.5. Limitations of the Study................................................................................46 5.6. Suggestions for Further Research .................................................................47

REFERENCES ............................................................................................................49 APPENDICES ..............................................................................................................57

Appendix I: Debt Ratio .................................................................................................57 Appendix II: Asset Turnover ........................................................................................59 Appendix III: Asset Tangibility ....................................................................................61

Appendix III: Return on Asset ......................................................................................63

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LIST OF TABLES

Table 4.1: Correlation Matrix…………………………………………………….….28

Table 4.2: Model Summary……………………………………………………….….28

Table 4.3: Analysis of Variance (ANOVA)…………………………………….……29

Table 4.4: Coefficients of Determination…………………………………………....29

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LIST OF FIGURES

Figure 4.1: Debt Ratio of SMEs in Thika……………………………………………26

Figure 4.2: Asset Turnover of SMEs in Thika………………………………….……27

Figure 4.3: Asset tangibility of SMEs in Thika……………………………………....27

Figure 4.4: Return on assets of SMEs in Thika……………………………………....28

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LIST OF ABBREVIATIONS

ANOVA - Analysis of Variance

GDP - Gross Domestic Product

GPM - Gross Profit Margin

KBS - Kenya Bureau of Statistics

MM - Modigliani and Miller

NDTS - Non Debt Tax Shield

NPS - Net Profit Margin

NSE - Nairobi Security Exchange

OLS - Ordinal Least Square

ROA - Return on Asset

ROCE - Return on Capital Employed

ROE - Return on Equity

SDTA - Short-Term Debt to Total Assets Ratio

SMEs - Small and Medium Enterprises

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ABSTRACT

The objective of this research is to determine the effect of capital structure on the

financial performance of small and medium enterprises in Thika sub-county. In most

cases, it is expected the capital structure of a firm should have some effects on the

performance of SMEs. The study was conducted on 40 SMEs in Thiks sub-county

which were in operation for the five years of study from 2009 to 2013. The various

ratios and analysis of these SMEs were computed from the various data collected and

extracted from their financial statement for the period. The data was then analyzed

using linear regression models using SPSS20 to establish if there were any significant

effect of capital structure and the financial performance of these SMEs. The finding of

the analysis concluded that there were no significant effect between the capital

structure and the financial performance of SMEs in Thika sub-county based on the

variable factored during this study. There was very minimal effect which is negligible

and therefore it was concluded that there is minimal effect between capital structure

and financial performance of SMEs in Thika sub-county. Therefore we recommend

that additional research should be conducted in other areas and factoring other

variable which were not factored and identify which are the major factors that affect

the performance of their industry. This will enable them to control these factors to

ensure maximum profitability is attained and sustained for the growth of the industry.

1

CHAPTER ONE:

INTRODUCTION

1.1. Background of the Study

The contribution of Small and Medium Enterprises (SMEs) to an economy has been

viewed from the point that all consumers would prefer products that are more

personalized (Roshanak, 2013), this has create inherent pressures towards making

markets smaller and smaller same as to say, more and more particularized to the

demands of individual consumers. The managerial costs of satisfying the demands of

small markets are high as compared to big and generalized markets. SMEs serve an

economy by satisfying the demands of small markets for which there are no or lower

scale economies of production or distribution. SMEs also serve an economy by

satisfying demands where the managerial costs of large business are greater than the

market transaction costs of dealing by contract rather than by control within a firm

(Mazur, 2007).

The way small business mobilize and structure the capital is a subject of interest.

Capital refers to the resources that a business owns. These resources can have the input

of the owner(s) and or non-owner(s) or debtor(s). The input of the owner(s) is called

equity and the input of non-owner(s) for the purpose to repay with interest is called debt

(Gunasekaran, 2010). The composition of capital with respect to debt and equity is

referred to as capital structure. Both debt financing and equity financing have very

different potential incentive problems. To understand how SMEs finance their

operations, it is necessary to examine the determinants of their financing or capital

structure decisions. SMEs financing decisions involve a wide range of policy issues.

2

The relationship between capital structure and financial performance is one that

received considerable attention in the finance literature. How important is the

concentration of control for the company performance or the type of investors exerting

that control are questions that authors have tried to answer for long time The study the

impacts of capital structure or financial performance, will help us to know the potential

problems in finance performance and capital structure (Matibe, 2005).

Capital structure has been a major issue in financial economics ever since Modigliani

and Miller showed in 1958 that given frictionless markets, homogeneous expectations

capital structure decision of the firm is irrelevant. SMEs may face difficulties in raising

finance (debt component) due to information asymmetry and other inefficiencies in loan

markets. Inevitably, this has a serious impact on their capital structures. Taking

cognizance of exceptions, asymmetric information can also explain the dominance of

debt financing over equity issues in practice, as the bulk of external financing is

expected to come from commercial banks and micro-finance institutions (Bebczuk,

2003). Strong financial systems, which provide loans/credits to investors/businesses,

can directly and indirectly create employment and alleviate poverty in an economy

(Honohan and Beck, 2007).

Credit system also facilitates the process of job creation in which some will become

self-employed entrepreneurs while others involved in other business related activities

(Thomas, 1992). Economists and financial researchers have sought to establish the

factors that determine the capital structure. It has been researched in various locations

but with varied results as will been shown in the next charter, Literature Review. The

factors that appear to determine capital structure are many but this research will still

within the parameters of three factors including profitability, growth, size, asset

3

structure and age (Wald, 1999). Profitability refers to the net income with respect to

capital-net income to capital ratio.

This means that the higher the net income to capital ratio, higher the profitability and

vise-versa. Growth shall be defined as a consistent increase in the number of

employees. Size refers the number of employees (Mazur, 2007). Asset structure refers

to the value of fixed asset with respect to capital. Finally, age is the number of years of

existence with respect to the years being studied (Roshanak, 2013). Financing and

investment are two major decision areas in a firm. In the financing decision the

manager is concerned with determining the best financing mix or capital structure for

his firm. Capital structure decision is the mix of debt and equity that a company uses to

finance its business (Damodaran, 2001).

(Berger & di Patti, 2006) concluded that more efficient firms were more likely to earn a

higher return from a given capital structure, and that higher returns can act as a cushion

against portfolio risk so that more efficient firms are in a better position to substitute

equity for debt in their capital structure. This is an incidental of the trade-off theory of

capital structure where differences in efficiency enable firms to alter their optimal

capital structure either upward or downwards.

1.1.1. Capital Structure

A firm’s capital structure refers to the mix of its financial liabilities. As financial capital

is an uncertain but critical resource for all firms, suppliers of finance are able to exert

control over firms (Harris and Raviv, 1991). Debt and equity are the two major classes

of capital, with debt holders and equity holders representing the two types of investors

in the firm. Each of these is associated with different levels of risk, benefits, and

control. It is the way the corporation finances its assets through some combination of

4

equity, debt, or hybrid securities. A firm’s capital structure is then a composition or

structures of its liabilities.

Over the past years much of the capital structure research has advanced theoretical

models to explain the capital structure pattern and also to provide empirical evidence

concerning whether the theoretical models have explanatory power when applied to the

real business world. The focus of both academic research and practical financial

analysis has been on those large corporations with publicly traded debt and equity

securities that dominate economic life throughout the developed world. Capital

structure research has become increasingly internationalized in recent years, which

provides researchers the opportunity to make cross-sectional comparisons between

countries and between various industries around the world.

(Wald, 1999) examined characteristics of firms that were not similarly correlated with

leverage across countries. He demonstrated that institutional differences could

contribute to differences in capital structure. His results indicate that institutions may

significantly influence firms’ capital structure decision and that agency and monitoring

problems, while existing in every country, may create different outcomes. (Booth,

2001) provided the first empirical study to test the explanatory power of capital

structure models in developing countries. The study used data from 10 developing

countries to assess whether capital structure theory was portable across countries with

different institutional structures. It investigated whether the stylized facts, which were

observed from the studies of developed countries, could apply only to these markets or

whether they had more general applicability.

The results were somewhat skeptical of this premise. They provided evidence that

firms’ capital choice decisions in developing countries were affected by the same

5

variables as they were in developed countries. This study will use debt ratio as a

measure of the independent (explanatory) variable. This will serves as the proxy for

capital structure. However, a number of factors may impact on firm performance,

hence, the need for controlled variables to be included in the model.

1.1.2. Firms Financial Performance

A firm’s financial performance, in the view of the shareholder, is measured by how

better off the shareholder is at the end of a period, than he was at the beginning and this

can be determined using ratios derived from financial statements mainly the balance

sheet and income statement, or using data on stock market prices (Berger and Patti,

2002). These ratios give an indication of whether the firm is achieving the owners’

objectives of making them wealthier, and can be used to compare a firm’s ratios with

other firms or to find trends of performance over time. The main objective of

shareholders in investing in a business is to increase their wealth. Thus the

measurement of performance of the business must give an indication of how wealthier

the shareholder, has become as a result of the investment over a specific time.

Performance having different meanings depending on the user perspective of financial

information, a company can be categorized as global performance if it can satisfy the

interests of all stakeholders (Roshanak, 2013). The financial performance of SMEs can

be measured using a number of indicators firms size, profitability and growth rate. The

performance is a general term applied to a part or to all the conducts of activities of an

organization over a period of time often with reference to past or projected cost

efficiency, management responsibility or accountability. Thus, not just the presentation,

but the quality of results achieved refers to the performance. Performance is used to

indicate firm’s success, conditions, and compliance.

6

The recommended measures for financial analysis that determine a firm’s financial

performance are grouped into five broad categories: liquidity, solvency, profitability,

repayment capacity and financial efficiency (Mazur, 2007). It is important to remember

that past and present financial information are not the only factors affecting a firm’s

financial performance. Liquidity measures the ability of the firm/business to meet

financial obligations as they come due, without disrupting the owner equity, using the

market value of assets and including deferred taxes in the liabilities. Three widely used

financial ratios to measure solvency are the debt-to-asset ratio, the equity-to-asset ratio

(sometimes referred to as percent ownership) and the debt-to-equity ratio (sometimes

referred to as the leverage ratio).

The debt-to asset ratio expresses total farm liabilities as a proportion of total farm

assets. The higher the ratio, the lower the performance of the firm and the greater the

risk involved (Gunasekaran, 2010). Profitability measures the extent to which a

business generates a profit from the factors of production, labor, management and

capital. Profitability analysis focuses on the relationship between revenues and expenses

and on the level of profits relative to the size of investment in the business. Repayment

capacity method measures the ability to repay debt from both firm and non-firm

income. It evaluates the capacity of the business to service additional debt or to invest in

additional capital after meeting all other cash commitments (Roshanak, 2013).

The analysis of financial statements is an important aid to financial performance

analysis. Financial performance analysis includes analysis and interpretation of

financial statements in such a way that it undertakes full diagnosis of the profitability

and financial soundness of the business. (Metcalf and Titard, 1976) claims that the

financial performance analysis identifies the financial strengths and weaknesses of the

7

firm by properly establishing relationships between the items of the balance sheet and

profit and loss account.

The study will employs return on Assets (ROA) as the two dependent variables, and

measures of firm financial performance (Metcalf and Titard, 1976). Although there is

no unique measurement of firm performance in the literature, ROA were chosen

because they are important accounting – based and widely accepted measures of

financial performance. ROA can also be viewed as a measure of management’s

efficiency in utilizing all the assets under its control, regardless of source of financing.

1.1.3. Capital Structure and Financial Performance

(Hutchinson, 1995) in his scholarly works argued that, financial leverage had a

positive effect on the firm’s return on equity provided that earnings’ power of the

firm’s assets exceeds the average interest cost of debt to the firm. (Taub, 1975) also

found significantly positive relationship between debt ratio and measures of

profitability. (Nerlove, 1968), (Baker, 1973), and (Petersen and Rajan, 1994) also

identified positive association between debt and profitability but for industries. In

their study of leveraged buyouts, (Roden and Lewellen, 1995) established a

significantly positive relation between profitability and total debt as a percentage of

the total buyout-financing package.

However, some studies have shown that debt has a negative effect on firm

profitability. (Fama and French, 1998), for instance argue that the use of excessive

debt creates agency problems among shareholders and creditors and that could result

in negative relationship between leverage and profitability. (Majumdar and Chhibber,

1999) found in their Indian study that leverage has a negative effect on performance.

(Gleason, 2000) support a negative impact of leverage on the profitability of the firm.

8

In a polish study, (Hammes, 1998) also found a negative relationship between debt

and firm’s profitability.

In another study, (Hammes, 2003) examined the relation between capital structure and

performance by comparing Polish and Hungarian firms to a large sample of firms in

industrialized countries. He used panel data analysis to investigate the relation

between total debt and performance as well as between different sources of debt

namely, bank loans, and trade credits and firms’ performance measured by

profitability. His results show a significant and negative effect for most countries. He

found that the type of debt, bank loans or trade credit is not of major importance, what

matters is debt in general. (Mesquita and Lara, 2003), in their study found that the

relationship between rates of return and debt indicates a negative relationship for

long-term financing.

They however, found a positive relationship for short-term financing and equity.

(Abor, 2007) in his scholarly works on debt policy and performance of Medium Sized

Enterprises found the effect of short-term debt to be significantly and negatively

associated with gross profit margin for both Ghana and South African firms. This

indicated that increasing the amount of short-term debt would result in a decrease in

the profitability of the firms.

1.1.4. Small and Medium Size Enterprises in Thika Sub-County

Thika Sub-County is a home to large industries in Kenya including tanneries textiles,

footwear, food processing, motor vehicle assembly and cigarette manufacturing and

over a hundred light industries. Majority of the enterprises in Thika Sub-County are

SMEs, some are faced with challenges of accessing fund to finance their business and

therefore the adequate financial knowledge remain a constraints within the region.

9

Small and Medium Enterprises (SMEs) contribute greatly to the economies of all

countries, regardless of their level of development, it is the major source of

employment, it generation domestic and export earnings and are a key instrument in

poverty reduction (Mephokee, 2004). In Kenya, the SMEs sector employs 74% of the

labor force and contributes over 18% of the country’s gross domestic product (GDP),

(Ngugi, 2012). Generally, SMEs are defined by the number of workers employed, value

of assets and sales turnover (Garikai, 2011).

The term SMEs covers a wide range of perceptions and measures, varying from country

to country and between the sources reporting SME statistics. Some of the commonly used

criterions are the number of employees, total net assets, sales and investment level.

However, the most common definitional basis used is employment, many researchers

define Small and Medium Enterprises in terms of the numbers of people employed.

(Storey, 1994), for example, defines micro-enterprises as those with 0 to 9 employees,

those with 10 to 99 workforces as small business, and medium sized enterprises as having

100 to 499 employees. (Gunasekaran & Kobu, 2000), however, states that Small and

Medium Enterprises have to be defined within the context of the economies in which they

operate.

(Waweru, 2007) posits that SMEs in Kenya are characterized by the ease of entry and exit

the small scale nature of activities self-employment with a high proportion of family

workers and apprentices the little amount of capital and equipment. Further, they have

labour intensive technology, low level of skills and low level of organization with little

access to organized markets. Other observations by (Waweru, 2007) are their unregulated

and competitive markets, their limited access to formal credit, the existent low levels of

education and training and the limited access to services and amenities.

10

1.2. Research Problem

The continued poor performance coupled with closure of medium sized enterprises

has raised more questions than answers to researchers and practitioners. It is also

pointed out that the increase from 6.7% to 10.4% in June 2013 in commercial

institutions’ non-performing assets was attributable by small and medium firms’

failure to service their loans due to insufficient financial resources (RSM, 2013

banking survey), the capital structure employed by such firms could be a reason

influencing their financial performance trends, this issue has not been given much

attention as expected.

According to (Agn, 1992), small businesses are not engaged in the problems, as well

as opportunities, of large firms. However, small firms face different complexities,

such as the presence of tax, shorter expected life than large firms, intergenerational

transfer problems, and prevalence of implicit contracts. Moreover, (Pettit and Singer,

1985) argued that standard problems like asymmetric information and agency cost is

more severe in small firms than large firms.

Studies on the failure of the SMEs reveal that financial leverage is a main cause of

decline (Otieno, 1987). SMEs borrowing decisions are different form large

companies, due to the borrowing constraints they face. (Metha, 1981) argued that

"resource poverty" is one of the most frequently cited reasons for business failure.

Bigger business can seize the opportunity and win the market. External forces such as

government regulations and tax laws are felt more acutely by small ventures than by

large ones. Frequently, small ventures cannot afford the professional expertise of

accountants like large firms can.

(Kuria, 2010) found that profitability and tangibility are significantly negatively

related to leverage as also liquidity growth and taxation but are insignificant. While

11

risk was seen to have a significant positive relationship but an insignificant one for

dividend policy and non-debt tax shield. (Kiogora, 2000) indicates that there is a

complex array of factors that influence SMEs owners/managers' financing decisions.

These processes are influenced by firm owners' attitudes toward the utility of debt as a

form of funding as moderated by external environmental conditions (e.g., financial

and market considerations). The form of business also has an impact on the owners'

attitudes towards the utility of debt as a form of funding. For example, sole

proprietorships and partnerships are sensitive to the risk of unlimited liability. A

number of other factors have been shown to influence financing decisions including

profitability, growth prospect, assets structure, size and age.

(Kinyua, 2005) established that profitability, company size, asset structure,

management attitude towards risk and lenders’ attitude towards the company are key

determinants of capital structure for small and medium enterprises in Kenya. Despite

SMEs using different sources of financing some of them are still stagnated and others

are failing. This could be attributed to lack of knowledge on the best sources of

financing with majority of SME owners having no ideas on how debts and internal

sources of finance influence their financial performance. There is little that has been

done to provide viable solutions on which side of financing will benefit financial

performance of SMEs especially in Kenya. Thus, this study sought to fill this research

gap by answering, the effect of capital structure on the financial performance of SMEs

in Kenya particularly in Thika Sub-County?

1.3. Research Objective

The objective of this study was to determine the effect of capital structure on the

financial performance of SMEs in Thika Sub-County.

12

1.4. Value of the Study

The study will assist policymakers in formulating effective strategies and policies to

curb under performance of SME. Scholars and researcher’s knowledge and

information realized through this research undertaking will benefit other future

scholars who wish to study the same area as it provides an insight of what has not

been examined.

SME capital structure is rapidly growing as a field of practice. Many business

leaders believe that there is need to make effective financial decisions. The findings

will inform appropriate policy making and implementation that could spur the

growth of SMEs into medium-sized companies. In addition, the study provided

information to the SME' owners on the problems that generally face them and on

how best they can be able to solve the challenges. Prospective entrepreneurs might

find the conclusions on the challenges that face the SME sector useful on how best

they can surmount them upon entry into business. The research helped to elucidate

on how well capital structure could explain the growth of SMEs within Thika Sub-

County.

13

CHAPTER TWO:

LITERATURE REVIEW

2.1 Introduction

This chapter will present a review of the theoretical and empirical literature on the

effect of capital structure on the financial performance of SMEs in Thika Sub-

County. The section starts with the capital structure theories, empirical reviews and

then to the determinants of financial performance. The conceptual framework,

incorporate scholarly works and theories, the rationale of the study is to ascertain

the role capital structure played in determining financial performance.

2.2. Theories of Capital Structure

Finance theory has made major advances in understanding effect capital structure on

financial performance of SMEs, the following are some of modern financial theories

on capital structure.

2.2.1. Capital structure Irrelevance Theory

The initial theory of capital structure was first developed in 1958 by economists

Franco Modigliani and Merton Miller known as MM Theory. The “Irrelevance

Theory” showed that a firm's value is independent of its ratio of debt to equity

financing with the assumptions that, neutral taxes, no capital market frictions (i.e.

no transaction costs, asset trade restrictions or bankruptcy costs), symmetric access

to credit markets (i.e. firms and investors can borrow or lend at the same rate) and

firm financial policy reveals no information. Cost of capital does not affect capital

structure, particularly debt then not effect on firm value In other words, the value of

levered firm equals the value of unlevered firm.

14

Subsequently in their 1963 paper, Modigliani and Miller relaxed the assumptions by

introducing taxes into their model in which case the method of financing becomes

relevant. In the relaxation of the assumptions of the Irrelevance Theory, (Modigliani

and Miller, 1963), suggests that capital structure can alter the value of a firm in the

world of corporate tax and a firm can maximize it value by the use of debt which

provides an interest tax shield. A firm has more value if it uses debt financing

because debt reduces the corporate tax. The savings due to the use of debt adds to

the value of the firm. The firm that uses more debt saves more in the form of

corporate tax shield.

This suggests that debt is a preferable source of financing for less taxation is laid on

debt. (Modigliani and Miller, 1963). Therefore the theory acknowledge that if

capital structure is optimal at 100% debt financing it will minimize the weighted

average cost of capital and maximizes firm performance. However, according to the

theory there is a positive relationship between firm’s leverage and its performance

but the theory has not taken into consideration other factors that affect leverage and

the different sizes of the firm.

2.2.2. Static Trade-off Theory

Static Trade-off suggests that a firm sets a target debt-equity ratio and gradually

follows it. Debt has an advantage of tax shield (Modigliani and Miller, 1963).

However, debt cannot be indefinitely used as the source of financing as there is a

trade-off between tax shield advantage on one hand and bankruptcy cost and financial

distress on the other hand (Jensen and Meckling, 1976). Debt financing has one major

advantage over equity financing-the interest on debt is deducted before corporate tax

is paid. But debt also increases financial risk.

15

This makes debt-financing not emphatically less costly than equity-financing. A firm

that considers static trade-off, threats debt-equity decision as a “give and take”

between the cost of financial distress and tax shield of debt respectively. “Give and

take” as use here means cost and benefit. Capital structure reflects tax rates, assets

type, business risk, profitability and bankruptcy costs (Myers, 1984). Generally, if the

cost of debt is low and the corporate tax rate is high to the extent that the firm benefit

significantly from debt financing, the form will use more debt since the marginal tax-

rate on debt is less than the corporate tax rate.

This will lead the firm to a positive net tax advantage if it uses debt-financing. Here

the firm’s optimal capital structure will involve the trade-off between the tax

advantage of debt and various leverage-related costs (Niu, 2008). Distinction in firms’

characteristics leads to variation in the target debt-equity ratio. The trade-off theory

predicts that safe firms, firms with more physical/tangible assets and higher tax rate will

have higher debt-equity ratio. Firms that are risky (firms with more non-

physical/intangible assets) ought to have more equity-financing (Niu, 2008).

Static Trade-off theory suggests that a firm that is profitable is likely to have more debt as

it would want to shield its income from taxes. This means that a firm that in its profitable

period will use more debt-financing. Static Trade-off theory therefore suggests that there

is a positive relationship between the firm’s leverage and performance. However there is

no clear consensus on the link between capital structure and firms financial performance.

2.2.3. Agency Costs Based Theory

(Berle and means, 1932) put forward the agency theory which also contributes to the

capital structure decision. The theory argues that conflicts arise from the possible

divergence of interests between shareholders (principals) and managers (agents) of

16

firms. The primary duty of managers is to returns to shareholders thereby increasing

the profit figures and cost cash flows (Elliot and Chiber, 2002). However, (Jensen and

Meckling, 1976) and (Jensen and Ruback, 1983) argue that managers do not always

run the firm to maximize returns to shareholders. As a result of this, managers may

adopt non-profitable investments, even though the outcome is likely to be losses for

shareholders. They tend to use the three cash flow available to fulfill their personal

interest instead of investing in positive not present value projects that would benefit

the shareholders.

(Jensen, 1986) argues that the agency cost is likely to exacerbate in the presence of

free cash flow in the firm. In an effort to mitigate this agency conflict, (Pinegar and

Wilbruch, 1989) argue that capital structure can be used through increasing the debt

level and without causing any radical increase in agency costs. This will force the

managers to invest in profitable ventures that will be of benefit to the shareholders. If

they decide to invest in non-profitable projects and they are unable to pay the interest

due to debt holders, the debt holders can force the firm to liquidation and managers

will lose their decision rights or possibly their employment.

Agency theory contributes that leverage firms are better for shareholders as debt level

can be used for monitoring the managers (Boodhoo, 2009). Thus, higher leverage is

expected to lower agency costs, reduce inefficiency and thereby lead to improvement

in a firm’s performance. Empirical supports for the relationship between capital

structure and firm performance from the agency perspective are many and in support

of negative relationship. (Zeitun and Tian, 2007), using 167 Jordanion companies

over fifteen year period (1989 – 2003), found that a firm’s capital structure has a

significant negative impact on the firm’s performance indicators, in both the

17

accounting and market measures. (Rao and Syed, 2007) also confirm negative

relationship between financial leverage and performance.

Their results further suggest that liquidity, age and capital intensity have significant

influence on financial performance. Hence the disjunction at this level has posted a

challenge that there is no consensus between capital structure and firm’s financial

performance.

2.2.4. Pecking Order Theory

The pecking order theory was developed by (Myers, 1984) stated that firms prefer

internal sources of finance they adapt their target dividend payout ratios to their

investment opportunities although dividends and payout ratios are gradually

adjusted to shifts in the extent of valuable investment opportunities. Pecking Order

theory tries to capture the costs of asymmetric information. It states that companies

prioritize their sources of financing (from internal financing to equity) according to

the law of least effort, or of least resistance, preferring to raise equity as a financing

means “of last resort”.

Hence internal financing is used first when that is depleted, then debt is issued and

when it is no longer sensible to issue any more debt, equity is issued. This theory

maintains that businesses adhere to a hierarchy of financing sources and prefer

internal financing when available, and debt is preferred over equity if external

financing is required (equity would mean issuing shares which meant 'bringing

external ownership' into the company). Thus, the form of debt a firm chooses can

act as a signal of its need for external finance.

The pecking order theory is popularized by (Myers, 1984) when he argues that

equity is a less preferred means to raise capital because when managers (who are

18

assumed to know better about true condition of the firm than investors) issue new

equity, investors believe that managers think that the firm is overvalued and

managers are taking advantage of this over-valuation. As a result, investors will

place a lower value to the new equity issuance. (Myers, 1984) and (Macan and

Lucey, 2011)

2.3. Determinants of Financial Performance of SMEs

A number of empirical studies have identified firm level characteristics that affect

the capital structure of firms and these include.

2.3.1. Asset Turnover

Asset turnover is defined as the ratio of sales to total assets. Assets play important

role in leverage level of firms. A firm with large amount of fixed assets can borrow

at relatively lower rate of interest by providing the security of these assets. Having

the incentive of getting debt at lower interest rate, a firm with higher percentage of

fixed asset is expected to borrow more as compared to a firm whose cost of

borrowing is higher because of having less fixed assets. Tangible assets are less

subject to informational asymmetries and usually they have a greater value than

intangible assets in the event of bankruptcy. The trade-off theory predicts a positive

relationship between measures of leverage and the proportion of tangible assets.

Relative to this theory, (Bradley, Javrell and Kim, 1984).

2.3.2. Asset tangibility

Asset tangibility is defined as the ratio of net tangible asset to total assets. Assets

play important role in leverage level of firms and its turnover. A firm with large

amount of fixed assets can borrow at relatively lower rate of interest by providing

the security of these assets also its associated that if the assets are used efficiently

19

they will increase its turnover. Having the incentive of getting debt at lower interest

rate, a firm with higher percentage of fixed asset is expected to borrow more as

compared to a firm whose cost of borrowing is higher because of having less fixed

assets. Tangible assets are less subject to informational asymmetries and usually

they have a greater value than intangible assets in the event of bankruptcy.

2.3.3. Profitability

There are two opposite views relating relationship between profitability and leverage.

(Myers, 1984) in his pecking order theory predicts that firms prefer raising capital

from retained earnings, then from debt, then from issuing equity. The cost of capital

dictates the rank of the pecking order under asymmetric information and market

imperfections. If pecking order applies, then, higher profitability will correspond to a

lower debt ratio holding other things equal. As a result, pecking order theory assumes

negative relationship between leverage and profitability. Studies conducted by

(Danbolt and Bevan, 2001) empirically proved negative relation between leverage and

profitability.

In the trade off theory, agency costs, taxes and bankruptcy costs push more profitable

firms toward higher book leverage. First, expected bankruptcy costs decline when

profitability increases. Secondly, the deductibility of corporate interest payments

induces more profitable firms to finance with debt. In a trade-off theory framework,

when firms are profitable, they prefer debt to benefit from the tax shield. In addition,

if past profitability is a good proxy for future profitability, profitable firms can borrow

more, as the likelihood of paying back the loans is greater. In the agency models of

(Jensen and Meckling, 1976), higher leverage helps control agency problems by

20

forcing managers to pay out more of the firms excess cash. Accordingly, the trade-off

theory predicts a positive relationship between profitability and leverage.

2.3.4. Firm Size

There are two conflicting viewpoints about the relationship of size to leverage of a

firm. According to trade off theory, larger firms are well diversified, having stable

cash flows and their chances of bankruptcy are less as compared to small firms.

Therefore, large firms prefer leverage and are having high level of leverage (Myers

and Majilu, 1984). Due to the large size, high level of fixed assets, economies of

scale, stable cash flow and creditworthiness larger firms have the bargaining power

over lender and can borrow at relatively lower rate. Thus, large firms are expected to

hold more debt in their capital structure than small firms. Following this, one may

expect a positive relationship between size and leverage of a firm.

Second, contrary to first view, (Rajan and Zingales, 1995) argue that there is less

asymmetrical information about larger firms. This reduces the chances of

undervaluation of the new equity issue and thus encourages the large firms to use

equity financing. This means there is negative relationship between size and leverage

of a firm.

2.3.5. Firm Growth

Empirically, there is much controversy about the relationship between growth rate and

level of leverage. According to pecking order theory hypothesis, a firm will use first

internally generated funds which may not be sufficient for a growing firm so the next

option is for the growing firms to use debt financing which implies that a growing

firm will have a high leverage (Drobetic and Fix, 2003). Hence, pecking order theory

assumes positive relationship between leverage and growth.

21

On the other hand, agency costs for growing firms are expected to be higher as these

firms have more flexibility with regard to future investments. The reason is that

bondholders fear that such firms may go for risky projects in future as they have more

choice of selection between risky and safe investment opportunities. Because of that

bondholders will impose higher costs at lending to growing firms. Growing firms,

thus, facing higher cost of debt will use less debt and more equity. (Rajan and

Zingales, 1995) find a negative relationship between growth and leverage. In this

study, growth is taken to have a positive relationship with leverage.

2.3.6. Liquidity

There are two opposite views relating the relationship between liquidity and leverage.

According to trade off theory, the more liquid firm would use external financing due

to their ability of paying back liabilities and to get benefit of tax shields, resulting in

positive relationship between liquidity and leverage. Pecking order theory assumes

that the more liquid firm could use first its internal funds and would decrease level of

external financing, resulting in negative relationship between liquidity and leverage.

Most studies have found the negative relationship (Mazur, 2007). In this study

negative relationship between liquidity and leverage is expected. Not many studies

have tested the effect of liquidity on the choice of capital structure. (Mazur, 2007) and

(Ullah, 2011) measured liquidity as the ratio of current assets to current liabilities. In

this study, Liquidity will also be measured as the ratio of current assets to current

liabilities.

2.3.7. Non-Debt Tax Shields

The effective tax rate has been used as a possible determinant of the capital structure

choice. According to (Modigliani and Miller, 1963), if interest payments on debt are

22

tax deductible, firms with positive taxable income have an incentive to issue more

debt. That is, the main incentive for borrowing is to take advantage of interest tax

shields. Other items apart from interest expenses, which contribute to a decrease in

tax payments, are labelled as non-debt tax shields (NDTS), for example the tax

deduction for depreciation and investment tax credits.

(Angelo and Masulis, 1980) argue that non-debt tax shields are substitutes for the tax

benefits of debt financing and a firm with larger non-debt-tax shields, ceteris paribus,

is expected to use less debt. Therefore, the relation between non-debt tax shields and

leverage should be negative. (Angelo and Masulis, 1980) measured non-debt-tax

shields as depreciation divided by total assets as in most studies. Depreciation divided

by total assets is used in order to proxy for non-debt tax shield in this study.

2.4. Empirical Evidence

This section discusses studies which have been conducted locally and internationally,

which examined the impact of capital structure on financial performance. (Chode,

2003) studied impacts of capital structure of public enterprises in Kenya on its

financial performance. His period of study was between 1994 and 1998. He used

regression analysis and found out that enterprises depended on public funding and

also found a positive relationship between debt and financial performance of the

organisation, which he categorized as debt. He also concluded public enterprises did

not endeavour to maximize profits in a competitive market and their managers did not

have the motivation to respond to competition.

(Kinyua, 2005) studied the impacts of capital structure of small and medium-sized

enterprises in Kenya on its firm financial performance. In his study which covered

five years, between 1998 and 2002, he used multiple regression and correlation to

23

analyse the collected data. He established that profitability, company size, asset

structure, management attitude towards risk and lenders’ attitude towards the

company are key impacts of capital structure for small and medium enterprises in

Kenya. The study found a positive relationship between internally generated fund and

the firm performance.

(Matibe, 2005) set out to study the relationship between capital structure for listed

companies in Kenya and their financial performance. The study covered five years,

between 1998 and 2002. Correlation analysis was used to analyse the collected data.

The study found out that firms owned by the state are more likely to borrow than

those owned by individuals, institutions or foreign investors. He concluded that state-

owned firms have more access to debt than firms owned by individuals and foreign

investors. This study did not consider the effect of bankruptcy of organisation

although it has indicated that there is a positive relation between leverage and firm

performance.

(Mustafa and Osama, 2006). The study investigated the effect of capital structure on

the performance of the public Jordanian firms listed in Amman stock market. The

study used multiple regression model represented by ordinary least squares (OLS) as a

technique to examine what is the effect of capital structure on the performance by

applying on 76 firms (53 industrial firms and 23 service corporation) for the period

(2001-2006). The results of the study concluded that capital structure associated

negatively and statistically with firm performance on the study sample generally. In

addition, the study found out that there was no significant difference to the impact of

the financial leverage between high financial leverage firms and low financial

leverage firms on their performance.

24

Finally, the study also showed that the effect of financial leverage on the basis of the

growth that there is no difference between the financial leverage of high growth firms

and low growth firms on the performance, which it was negatively and statistically.

Although the study has illustrate that there was a positive relationship between

leverage and firm’s performance other factors like the asset structure and profitability

were not factored in the study.

(Osuji and Odita, 2010) did a study on impact of capital structure on financial

performance of Nigerian firms using a sample of thirty non-financial firms listed on

the Nigerian Stock Exchange during the seven year period, 2004 – 2010. Panel data

for the selected firms were generated and analysed using ordinary least squares (OLS)

as a method of estimation. The result shows that a firm’s capital structure surrogated

by debt ratio has a significantly negative impact on the firm’s financial measures

(Return on Asset ROA). The study of these findings, indicate consistency with prior

empirical studies and provide evidence in support of Agency cost theory.

(Mwangi, 2010) did a study on capital structure on firms listed at the Nairobi Stock

Exchange also tried to look on the relationship between capital structure and financial

performance. Data was collected using structured questionnaires. The study identified

that a strong positive relationship between leverage and return on equity, liquidity,

and return on investment existed This hypothesis is also supported by a number of

studies, to them the benefits of debt financing are less than it’s negative aspects, so

firms will always prefer to fund investments by internal sources (Jensen and

Meckling, 1976) all found a significant and negative impact of capital structure on

performance.

25

(Kehinde, 2012) in his study conducted between 2010 and 2012 examined the

relationship that exists between the capital structure mix of the SMEs and the overall

performance of the firm over the years in Nigerian. The study made use of

questionnaire a survey method for data collection and chi-square a non-parametric

method for data analysis. The study revealed that most SMEs have an all equity

finance structure and has a less debt finance to equity finance. It also revealed that the

earning, survival and growth of the SMEs is strongly influence by the capital structure

mix. It was recommended that the government should design a home grown and

SMEs friendly debt financing structure and managers of SMES should also seek

professional advice when approaching financial institutions for debt finance.

(Roshanak, 2013). The study of the Impact of Capital Structure Determinants on

Small and Medium size Enterprise Leverage in Iran. The study used deductive

approach with the unique set of data gathered from 201 SMEs in Iran over the period

of 2006 to 2010, the statistic panel data regression is used to analyse the empirical

data picked up from different manufacturing industries in Iran.

The result of this research reveals that the impacts of capital structure determinants on

SMEs leverage levels are different in terms of both magnitude and direction. The

result indicates that profitability has a strong impact on SMEs borrowing decisions.

Besides profitability, size and asset structure appear to have an impact on leverage

level in compare with other determinants. The research finding shed lights on the

necessity of using the maturity structure of debt (short-term debt and long-term debt)

as dependent variables.

Firms are more willing to finance their projects with short term debt, rather than long

term debt. Long term debt is costly, and the probability of bankruptcy is higher with

26

long term debt. Although long term debt is riskier for SMEs, but it shows the

management confident in the firm’s future since it obliges the firm’s management to

make legally binding future payments of interest. However, the empirical result of this

study shows that all the determinants have an effect on the level of leverage in SMEs.

(Wambugu, 2013) effects of working capital management practices on profitability of

small and medium enterprises in Nairobi County, Kenya. The objective of this study

was to determine the effect of working capital management practices on profitability

of Small and Medium Enterprises (SMEs) in Nairobi County The study adopted a

cross-sectional survey research design and also, a linear regression model was used to

analyze quantitative data and was developed and tested to explain the relationship

between various proxies of working capital management practices and profitability of

SMEs of Nairobi County. The study results of the regression analysis indicated that

the dependent variables are significant and have an effect on profitability of SMEs.

The study concluded that managers of SMEs should adopt the correct working capital

management practices and identifying critical areas that may improve the profitability

of SMEs.

2.5. Summary of the Literature Review

Initially (1958) Modigliani and Miller Theory posited that firm value is independent

of its financial structure, subsequently (1963), taking into account the corporate tax,

they underscored the effects of benefits of the tax shield of debt recognising that

leverage can reduce the payment obligations related to corporate tax. In 1960s-1970s,

research shifted towards studying the way in which firms manage to balance the

bankruptcy costs with the benefits of tax shields, derived from taking on debt these

27

works were grouped under the “static trade-off theory.” According to the theory, there

was a positive relationship between the firm’s leverage and performance.

In the mid-1970s, research turned to agency costs, focusing on two categories of

conflicts of interest between managers and shareholders. The research was predicated

on the assumption that optimal capital structure represents a compromise between the

effects of interest tax shield, financial distress costs and agency costs. In the first half

of the 1980s, the emphasis was mainly placed on information asymmetries among

investors and firms, which defined the pecking order theory.

Thus, according to pecking order theory, more profitable firms generate higher

earnings that can serve for self-financing, enabling them to opt less for debt financing

conversely, less profitable firms do not enjoy the same opportunity, being compelled

to take on debt in order to finance their on-going activity. Consequently, the theory

asserts a negative correlation between the debt level and firm performance. During the

1990s the focus on the disjunctive-hypothetical reasoning to provide arguments in

favour of or against the two theories proposed, i.e. trade-off theory and pecking order

theory, respectively

The disjunction at this level of theoretical and empirical research has posted challenge

that there is no consensus on the link between capital structure and firm performance

(for instance, static trade-off theory admits a positive relation between the firm’s debt

level and its performance. Agency cost theory recognises that higher leverage, in the

context of lower agency costs, reduces inefficiency and thereby leads to enhanced

company performance pecking order theory estimates a negative correlation between

the firm’s debt level and its performance).

28

In Kenya previous studies have focused on effect of capital structure on financial

performance of companies listed in NSE (Matibe, 2005), only three studies have been

done on SMEs in Kenya and since SMEs are becoming the major contribution in a

country economy all research have been concluded and suggested that a more tailored

research on SMEs need to be conducted. This study therefore comes in to fill the gap

in the effect on capital structure and firm performance of the SMEs in Thika Sub-

County.

29

CHAPTER THREE

RESEARCH METHODOLOGY

3.1 Introduction

This chapter presents the methods and procedures that will be followed in conducting

research with the aim of evaluating the effect of capital structure on financial

performance of SMEs in Thika Sub-County. The chapter will thus outlined into

research design in section, population and sample in section, data collection

procedures in section, research models in section and data analysis in section.

3.2. Research Design

The research adopted descriptive research design. This design gives a description of

phenomenon, characteristics and association of the research variables. It is appropriate

for the study as it will enable high level analysis such as correlation and regression

analysis that will allow to establish the nature and the extend of the effect of capital

structure on financial performance of SMEs.

3.3. The Population

There has been not convictional definition for SMEs. What was agreed upon is the

factor size which has been measured by the number of employees. What has been

practiced is the customization of definitions to suit the conditions of a particular

economy or country. Base on this practice, Kenya Bureau of Statistics (KBS) has

defined SMEs as businesses that have less than fifty (50) employees. Hence the SMEs

being studied in this research are businesses with less than fifty (50) employees (KBS,

2013). According to (KBS, 2013) Statistics Business Register, a total of

30

approximately 1,890 registered SMEs operated in Thika, hence our population frame

was 1,890.

3.4. Sample Design

The sample size was determined using (Krejcie, 1970) who developed a formula for

estimating the sample size and a table for determining the sample size based on

confidence level needed from a given population. Based on a population of 1,890

SMEs in Thika, the recommended sample size will be 40. Simple random sampling

was used to select the sample from the population. (Mugenda & Mugenda, 2003)

suggested that for correlation research, 30 cases or more are required.

Since this study involved determining the effect of capital structure on financial

performance, a sample of 40 SMEs was considered sufficient. This study used simple

random sampling technique to select sample because the technique minimized bias

and increases the chances of representativeness.

3.5. Data Collection

Secondary data was collected from the annual financial reports of SMEs in Thika. The

data was collected from the financial year 2009 – 2013. This included statement of

profit and loss, statement of financial position and statement of cash flows. The

annual reports was requested from the organisation management or data published

with the Kenya Bureau of Statists (KBA). The data collected included, total turnover

for the period, Net profit after tax, total fixed asset, and total current asset, capital

structure variables which include debt, equity, current liabilities and non-current

liabilities.

31

3.6. Data Analysis

The data collected for this study was cleaned, edited and tested for completeness. This

was done to ensure that the data used were adequately reflective, accurate and reliable

for conclusion and realization of the research objective of this study. SPSS software

was used to carry out the analysis of the data obtained. The study used three

independent variables. The researcher constructed a regression model to analyze the

reliance leverage (the dependent variables) on the independent variable outlined below.

Regression has become one of the most widely used techniques in the analyzing such data

(Bryman, 1998).

3.6.1. Research Model

The model adopted by this study was the multiple regression models. “Multiple

regressions” is a technique that allows many factors to enter the analysis separately so

that the effect of each can be estimated. It was valuable for quantifying the impact of

various simultaneous influences upon a single dependent variable.

Data collected on the variable of interest within the period of study were analyzed

through descriptive statistics. Further multiple regressions and correlation analysis

were used to explain the nature and significance of relationship between changes in

the response variables and change in the prediction variables (determinants) identified

in the study. The regression equation model was as below:

Y=β0+ β1X1+ β2X2+ β3X3+Ɛ

Where:

Y= Dependent Variable - ROA

X1=Debt Ratio (ratio of total debt to total assets)

32

X2= Asset turnover (ratio of sales to total assets)

X3= Asset tangibility (Net tangible assets to total assets)

β0 = Constant

Ɛ = error term that the residual cannot be explained by the independent variables

β1 - β3=regression coefficients define the amount by which Y was changed for every

unit change in predictor variables. ROA represents the dependent variable. On the

other hand, Debt ratio, Asset tangibility and asset turnover represents the independent

variables. This indicates that X1, X2 and X3 are all factors that influence ROA. ROA is

an accounting measure for evaluating the firm’s financial performance. Independent

variable will be represented by the debt ratio (DR), asset tangibility and asset

turnover. Debt ratio is the representative variables while asset turnover and assets

tangibility are controlled variables.

3.6.2. Measurement of Variables

The capital structure was measured using the debt ratio, asset turnover and asset

tangibility whiles the firm financial performance was measured using the return on

asset.

3.6.2.1. Independent Variables

The value of debt was arrived at using the unconventional formula, where the total

liabilities (both long term and short term) were expressed as a proportion of the total

funding. The value for the indebtedness was computed as below.

Debt ratio = total debt / Total asset

Asset turnover = Sales / Total asset

33

Asset tangibility = Net tangible asset/Total asset

3.6.2.2. Dependent Variables

The performance of a company was considered as the return on equity. The benefit or

return to the shareholder was expressed as the ratio of the net profit after taxes to the

shareholders’ funds. The net profit after tax was arrived at after deducting all

obligatory expenses of the business including interest and taxes. The shareholder’s

fund included share capital, retained profits and other reserves. This ratio expressed

the return in shillings for each shilling of the shareholder’s funding. This was

expressed mathematically by:

ROA = Profit after tax / Total asset

Greatest advantage with regression analysis was that the parameters will be estimated

to show causality between explanatory variables and regressors. Parameters estimated

suggest magnitude and direction the independent variables have on the explanatory

variables. In order to test the significance of the model in measuring the relationship

between independent and dependent variable, this study conducted an Analysis of

Variance (ANOVA). On extracting the ANOVA statistics, the researcher looked at

the significance value. The study was tested at 95% confidence level and 5%

significant level. If the significance number found to be less than the critical value (a)

set, then the conclusion will be that the model is significant in explaining the

relationship.

34

CHAPTER FOUR

DATA ANALYSIS, RESULTS AND DISCUSSION

4.1 Introduction

This chapter presents analysis and findings of the study as set out in the

research objectives and methodology. The study findings are presented on the effects

of capital structure on the financial performance of SMEs in Thika sub-county. The

Study begins by showing descriptive statistics and then finish with regression results

from ordinary least square estimates. The specific variables discussed in this chapter

include Financial performance as measured by Return on Assets, Debt ratio, asset

tangibility and asset turnover.

4.2. Response Rate

The response rate was 100% since the study targeted 40 respondents from SMEs in

Thika and all responded and gave their management accounts for the period of our

study. Therefore the response rate was considered ideal and reliable.

4.3. Data Validity

We obtained secondary data which was obtained from financial reports presented to

us and therefore data validity is not applicable under this study.

4.4. Descriptive Statistics

4.4.1. Debt Ratio

The study collected secondary data on the performance of the SMEs in Thika as

regards the levels of debt ratio for five years starting 2009 to 2013. From the findings

of the study, the industry debt ratio started at 88% in 2009 then reduced to 58% in

2010 before picking a downward trend in 2011 to hit a low of 53% in 2012, the debt

35

ratio level reduced further to 52% before reducing further in 2013 to 46%. These

findings are well illustrated using a graph shown in figure 4.1 below and appendix I.

Figure 4.1: Debt Ratio of SMEs in Thika

4.4.2. Asset turnover

From the findings of the study, the industry debt ratio started at 91% in 2009 then

reduced to 75% in 2010 before picking a downward trend in 2011 to hit a low of 66%

in 2012, the debt ratio level reduced further to 52% before reducing further in 2013 to

47%. These findings are well illustrated using a graph shown in figure 4.2 below and

appendix II.

Figure 4.2: Asset Turnover of SMEs in Thika

0%

20%

40%

60%

80%

100%

2009 2010 2011 2012 2013

Percentage

Period (years)

Debt Ratio

Percentage

0%

20%

40%

60%

80%

100%

2009 2010 2011 2012 2013

Percentage

Period (years)

Asset Turnover

Percentage

36

4.4.3. Asset tangibility

The study computed the tangibility of the SMEs by dividing net tangible assets by

total assets. Tangibility was used to measure the level of tangible assets owned by the

SMEs in relation to total assets. From the findings, the level of tangibility started at a

low of 46% in the year 2009 then it increased to 51% in the following year 2010. In

the year 2011, the level of tangible assets reduced further to reach 39%. The ratio

increased in 2012 to 51% which further increased to 53% in 2013. These findings are

well illustrated using a line curve in the figure 4.3 below and the data in appendix III.

Figure 4.3: Asset tangibility of SMEs in Thika

4.4.4. Financial performance of SMEs

The financial performance of the SMEs was measured using return on assets. From

the research findings, return of assets of the SMEs under study stood at 12% in the

year 2009. It decreased to 10% in 2010. In 2011 the ratio increased to 13% before

again a further reduction to 9% in 2012. In 2013 the return increased to 20%, and this

can be well illustrated using a line curve in the figure 4.4 below and the data in

appendix IV.

0%

20%

40%

60%

2009 2010 2011 2012 2013

Percentage

Period (years)

Percentage

Percentage

37

Figure 4.4: Return on assets of SMEs in Thika

4.5. Correlation Analysis

Correlation coefficient indicates strength and direction between variables.

Specifically, partial correlation coefficient shows correlation between two variables

holding others constant. Table 4.1 shows Pearson correlation coefficients of variables

of our interest.

Table 4.1: Correlation Matrix

Correlations

ROA

Debt

ratio

Asset

turnover

Asset

tangibility

ROA 1

Debt ratio -.315 1

Asset turnover -.405 .894* 1

Asset tangibility .117 -.236 -.369 1

*. Correlation is significant at the 0.05 level (2-tailed).

In table 4.1 above shown that there is a negative correlation between returns on asset

(ROA) with variables of Debt ratio of -0.315 with a probability value of 0.606 asset

turnover ratio of -0.405 with a probability value of 0.499, while there is a positive

0%

5%

10%

15%

20%

25%

2009 2010 2011 2012 2013

Percentage

Period (years)

Return on Assets

Percentage

38

correlation of asset tangibility of 0.177 with a probability value of 0.852. Although

there was a correlation between the variables at 5%, they were not significant.

4.6. Regression Analysis and Hypothesis testing

In addition to the above analysis, the researcher conducted a multiple regression

analysis so as to test the relationship among independent variables. The researcher

applied the Statistical Package for Social Sciences (SPSS Version 20) aid in

computation of the measurements of the multiple regressions for the study. The

findings are as shown in the table 4.2 below:

Table 4.2: Model Summary

Model Summary

Model R R Square Adjusted R Square Std. Error of the

Estimate

1 .422a .178 -2.286 .07744

a. Predictors: (Constant), Asset tangibility, Debt ratio, Asset turnover

The coefficient of determination explains the extent to which changes in the

dependent variable (Financial Performance of SMEs) can be explained by the change

in the independent variables (debt ratio, asset turnover and asset tangibility). The three

independent variables that were studied, explain only 17.80% of the changes in the

financial performance of SMEs in Thika as represented by the adjusted R2.

The R column represents the multiple correlation coefficients which measures the

quality of the prediction of dependent variable. In this case the value of R is 0.422

which shows a weak level of prediction. However, the R2 which is the coefficient of

determination is 0.178 indicating that only 17.8% of the financial performance of

SMEs in Thika can be explained by debt ratio, asset turnover and asset tangibility, the

other 82.2% can be explained by other variables which were not in the model.

39

Table 4.3: Analysis of Variance (ANOVA)

ANOVAa

Model Sum of

Squares

df Mean Square F Sig.

1

Regression .001 3 .000 .072 .966b

Residual .006 1 .006

Total .007 4

a. Dependent Variable: ROA

b. Predictors: (Constant), Asset tangibility, Debt ratio, Asset turnover

To test for the existence of a linear relationship between capital structure and financial

performance variables, Analysis of Variance was employed. The results from the

analysis of variance as per table 4.2 shows that the regression relationship between

ROA and the independent variables is not statistically significant at 5% level of

significance (F (3,1) = 0.072, p-value = 0.966 > 0.05), meaning that there is no

significant effect of the debt ratio, asset turnover and asset tangibility to financial

performance of SMEs in Thika. This can be shown by the significant level which is

0.966 which is more than 0.05.

Table 4.4: Coefficients of Determination

Coefficientsa

Model Unstandardized

Coefficients

Standardized

Coefficients

t Sig.

B Std. Error Beta

1

(Constant) .214 .443 .484 .713

Debt ratio .068 .533 .264 .127 .920

Asset turnover -.157 .511 -.665 -.307 .811

Asset tangibility -.050 .757 -.066 -.066 .958

a. Dependent Variable: ROA

The researcher conducted a multiple regression analysis so as to determine the effects

of capital structure on the financial performance of SMEs in Thika, although the

regression relationship between ROA and the independent variables is not statistically

40

significant at 5% level, the regression equation was not necessary. However, from our

findings the variables indicated that when all the factors are held at zero the ROA will

increase by 0.214 units. In this model, it can be observed that there is a positive

relationship between the financial performance and debt ratio meaning that as the debt

ratio increase by one unit, the ROA ratio will tend to increase by 0.068.

However, there is a negative relationship between the assets turnover and financial

performance as can be indicated by the coefficient of the assets turnover which is -

0.157. This means that as the assets turnover decreases by one unit, the ROA ratio

will tend to decrease by -0.157. Also there is a negative relationship between the

assets tangibility and financial performance as can be indicated by the coefficient of

assets tangibility which is -0.05. This means that as the assets tangibility decreases by

one unit, the ROA ratio will tend to decrease by -0.050.

4.7. Discussion of Research Findings

Capital structure decisions are vital decisions with great implication for the firm's

financial performance, the study used both descriptive (measure of central tendency)

and inferential statistics (correlations and regression) to undertake data analysis. The

findings in this study indicate that there is no significant relationship between the

three factors analysed in determining the financial performance of SMEs in Thika.

The study however established that there is a positive relationship between debt ratio

and the financial performance of SMEs although not statistically significant. As more

funds for debt are available SMEs will be able to generate more sources of capital to

enable them to expand on their activities. As more opportunities will be exploited for

better performance. These results are consistent with the agency theory postulated by

Jensen & Meckling (1976) and extended by Elliots (2002). The agency theory

41

postulate that the use of debt in the capital structure can be used to mitigate the

agency conflict by forcing managers in invests in profitable ventures that benefit the

shareholders. We noted that since SMEs in Thika had a lot of opportunity for growth

and shareholders have segregated their duties to managers to take charge in the

management of the business.

However, the positive effects can be realized when usage of borrowed funds increases

the earning ability of the firms performance in the industry by reinvesting in

opportunities as and when they arise, thereby increasing their profitability. The study

also established that there is a negative relationship between assets turnover and

financial performance. The more the amount held in as assets will have negative

effects on the financial performance of SMEs. Since the asset turnover measured how

efficient are the assets were used to generate the sales of the business.

This is an indication that the SMEs still they do not have the capability of

maximisation of their assets hence leading to misuse which leads to negative effects

between the asset turnover and the financial performance of the SMEs in Thika. The

study has well established that there exists a negative relationship between tangibility

and financial performance of SMEs. As the level of tangible assets increase, the level

of funds tied up in the assets increases, hence limiting the ability of the industry to

utilize the resources in new opportunities as and when they arise.

Since the ratio of net tangible assets to total assets has a negative effects on the

financial performance of the organisation. This is an indication that assets are not

optimally used to generate maximum benefit to the business, hence having negative

effect to the overall financial performance of the organisation. From the descriptive

result we have noted that the debt ratio as per the study of the industry started at a

42

high of 88% then it reduced to reach a low of 46%, and from our regression analysis

we have seen that debt ratio has a positive relationship with the financial performance.

Several studies have been conducted on debt financing that indicate either positive or

negative relationship on financial performance of the firm.

Abor (2007) examined the effect of capital structure on the financial performance of

firms, his model used a regression model. The results of his study indicate a positive

association between total debt ratio and financial performance. Hence from our study

it can be concluded that over the business period less debt is preferred compared to

equity. The Asset turnover as per the study of the industry started at a high of 91%

then it reduced to reach a low of 47%, and from our regression analysis we have seen

that asset turnover ratio has a negative relationship with the financial performance.

The Asset tangibility as per the study of the industry started at a low of 46% then it

increased to reach a high of 53%, and from our regression analysis we have seen that

asset tangibility has a negative relationship with the financial performance. It was

observed that the model was not a very strong predictor of financial performance

since it can only explain 17.8% of the variables that causes changes in the financial

performance of SMEs. Further from the analysis it was also found that there is no

significant effect of the debt ratio, asset turnover and asset tangibility to financial

performance of SMEs in Thika.

43

CHAPTER FIVE

SUMMARY, CONCLUSION AND RECOMMENDATIONS

5.1 Introduction

This chapter presents a summary of the findings, conclusions and recommendations.

The summary is presented on the effects of capital structure on the financial

performance of SMEs in Thika. The chapter also has a section which makes

suggestions for further study.

5.2. Summary of the Findings

The performance of SMEs in Thika is affected by different factors as reviewed in

this study. The Debt ratio as per the study of the industry started at a high of 88%

then it reduced to reach a low of 46%, and from our regression analysis we have seen

that debt ratio has a positive relationship with the financial performance. The Asset

turnover as per the study of the industry started at a high of 91% then it reduced to

reach a low of 47%, and from our regression analysis we have seen that asset turnover

ratio has a negative relationship with the financial performance.

The Asset tangibility as per the study of the industry started at a low of 46% then it

increased to reach a high of 53%, and from our regression analysis we have seen that

asset tangibility has a negative relationship with the financial performance. It was

observed that the model was not a very strong predictor of financial performance

since it can only explain 17.8% of the variables that causes changes in the financial

performance of SMEs. Further from the analysis it was also found that there is no

significant effect of the debt ratio, asset turnover and asset tangibility to financial

44

performance of SMEs in Thika. This can be shown by the significant level which is

0.966 which is more than 0.05.

5.3. Conclusions

It can therefore be concluded that capital structure do have effects to financial

performance of SMEs although the effect is very minimal and hence negligible.

Therefore it can also be concluded that there are other major factors which affect the

performance of SMEs in Thika more than its capital structure. These other factors

may have major effects on financial performance of SMEs in Thika hence they should

be included in the other studies relating to financial performance of SMEs in Thika.

From the empirical analysis, it was noted that there were significant relationship

between debt and financial performance of SMEs. Capital structure is an important

determinant of firm’s financial performance our study has revealed that debt ratio,

asset turnover and asset tangibility cannot significantly explain the extent of the

variability of the financial performance of SMEs in Thika.

From the empirical study the expectations that there is a positive and significant effect

between the debt ratio and ROA as a measure of financial performance of SMEs in

Thika has proved negligible. We have noted that although the result were not

statistically significant, there was a positive relationship of 0.68 between the financial

performance and debt ratio meaning that as the debt ratio increases by one unit the

return on Asset will tend to increase by 0.068 if all other factors are kept constant.

Therefore our theoretical study of irrelevancy theory still holds.

In conclusion, the empirical evidence from this study suggests that the debt ratio has

minimal and positive significant effect on firm financial performance. Asset

tangibility and asset turnover has both minimal and negative effects on firms financial

45

performance. Amongst these theories, the major contending are Static Trade-off theory

and Pecking Order theory. Static Trade-off theory argues the existence of an optimum

capital structure which the management of a firm will choose. Oppose to Static Trade-off

theory, Pecking Order theory argues that a firm does not observe an optimum capital

structure rather it would only solicit debt when there is no internal generate funds

(retained earnings) partly siding with Pecking Order theory.

5.4. Recommendations

In this study, it can be observed that the capital structure have very minimal effect on

the performance of SMEs in Thika. Therefore we recommend that, Performance

standards should be established and identifying weaknesses of investment may be best

one to improve the firm’s financial performance, because it indicates the area which

decision should be taken.

Identifying weaknesses of investment may be best one to improve the firm’s financial

performance, because it indicates the area which decision should be taken and

motivating the investors to help to achieve the high level of firm’s financial

performance. The management of SMEs should take into account the industry norms

when developing their financial policies. Capital structure of comparable companies

in the industry should be considered because it might reflect the unique risks inherent

in that industry.

The study also recommends that even though firms cannot possibly avoid use of loans

in its capital structure, the firm should try to operate using little amounts of loans

compared to equity to avoid higher finance costs which could otherwise be used to

invest in other profitable ventures for the firm. Regarding debt it’s recommended that

even if increased liabilities enlarge repayment obligations restrain free cash flow, a

46

right balance between debt and assets should established to avoid bankruptcies that

can be caused by over-investment since over-investment reduces cash flow.

From the descriptive analysis, we noted that the rate of borrowing reduced over time,

the reduction could be caused by the challenges the SMEs faces during the processing

of requesting for such loan. Hence we recommend that capacity building need to be

done and SMEs educated on how to reduce such challenge and access funds whenever

there is a need. SMEs should also prioritise the use trade credit before they decide to

use loan. Trade credits improve the financial performance as compared to loans.

5.5. Limitations of the Study

There were various limitations which related to this study and which need to be

mentioned to ensure that a researcher puts them into consideration when planning for

a research project. Some of these limitations include. The study used only three

measures of capital structure and this does not seem to have much effects on the

financial performance and hence there is need to carry out the study with other

different factors both qualitative and quantitative in order to be able establish which

are the major factors that affect the financial performance of SMEs in Thika.

SMEs are not obliged to disclose their financial information. Those SMEs that

disclose their financial statement may decide to disclose partially and incomplete

financial records, this could lead to understating of the revenues, overstate the

expenditure and also they may not disclosing all the bank accounts. The study

therefore could not independently verify the information given by management but

only relied on financial reports that were not audited hence we could not guarantee for

the accuracy of the figures used in the research.

47

The research was done over a period of 5 years, and over that period of five years

there has been tremendous change within our economy. In 2009 to 2011 Kenya was

still experiencing high inflation rate compared to 2012 and 2013. Hence the analysis

done in these two segments may lead to contradicting sequence flow of information as

the factors that influenced the decision were different. Also we could not differentiate

the SMEs in term of their stages of operation, as at different stages business differ in

terms of decision and challenges they face, hence combining them could hamper the

accuracy of result derived.

5.6. Suggestions for Further Research

This study advocates that further studies can be done in this areas such studies may

include identifying various other factors which affect the performance of SMEs in

Thika. The study may be carried using various other measures of financial

performance such as return on equity instead of return on asset. The study may also

use different variables from those which have been used in this study. Another area of

study which can be considered is researching on the impact of other factors such as

marketing. This may enable various stakeholders to understand if there are other

major factors which affect the performance in major way with exception of capital

structure.

Only secondary data were collected and used in this study analysis, hence further

researchers should be done based on primary data collection method. Capital structure

is a puzzling concept especially so in emerging markets like SMEs. Further study can

be conducted by adding sales growth and business risk as independent variables. To

clarify the results of our study more variables for performance measurement may be

helpful. Data of long time series may also be used for reliability of results. Future

48

research can be conducted by comparing the capital structure and firm performance of

small and large firms.

49

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57

APPENDICES

Appendix I: Debt Ratio

2013 2012 2011 2010 2009

Name of the

SME Total Assets Total Debt

Debt

Ratio Total Assets Total Debt

Debt

Ratio Total Assets Total Debt

Debt

Ratio Total Assets Total Debt

Debt

Ratio Total Assets Total Debt

Debt

Ratio

1 Intel Fire Group

Of Companies 245,801 432,039 1.76 232,655 370,132 1.59 239,228 359,865 1.50 225,369 287,546 1.28 235,763 362,396 1.54

2 Saana Shoes L 82,475,652 82,475,652 1.00 57,557,855 40,099,084 0.70 29,927,789 22,459,566 0.75 17,945,951 13,269,131 0.74 27,639,835 28,139,189 1.02

3 Thika Cloth

Mills Ltd 36,091,446 8,764,551 0.24 29,009,843 7,008,401 0.24 27,191,540 2,566,524 0.09 27,426,109 2,083,654 0.08 28,227,937 12,632,310 0.45

4 Imara

Enterprises Ltd 11,080,986 4,315,266 0.39 14,280,510 2,853,067 0.20 12,680,748 3,584,167 0.28 11,586,954 2,869,541 0.25 12,407,300 3,405,510 0.27

5 Ruiru Hardware

Store Ltd 22,788,440 15,302,985 0.67 19,545,260 12,353,117 0.63 18,555,555 12,072,170 0.65 15,218,077 9,733,688 0.64 12,218,198 8,576,777 0.70

6 Uhuru Flowers

Limited 1,169,846,108 123,210,830 0.11 735,694,081 174,139,777 0.24 894,561,251 184,326,925 0.21 330,837,064 168,223,092 0.51 285,455,964 195,279,628 0.68

7 Waridi

Garments 97,669,376 - - 125,276,216 21,592,310 0.17 319,132,876 209,214,260 0.66 752,474,086 460,679,438 0.61 416,294,165 493,120,806 1.18

8 Savage

Holdings ltd 1,133,072 385,813 0.34 1,133,122 331,303 0.29 1,133,131 276,793 0.24 1,133,131 222,283 0.20 1,133,154 167,773 0.15

9 Alliance One

Tobacco Ltd 35,538,505 15,121,985 0.43 33,702,437 12,435,379 0.37 23,669,793 8,177,847 0.35 27,921,767 12,957,409 0.46 28,551,117 19,229,373 0.67

10 Alpha Knits Ltd 13,848,498 14,909,100 1.08 14,770,736 14,141,160 0.96 10,896,058 10,401,914 0.95 5,858,907 5,024,633 0.86 5,919,453 5,856,725 0.99

11 Macushla House

Limited 15,999,848 10,807,654 0.68 17,268,425 13,391,767 0.78 17,518,858 14,787,184 0.84 18,172,340 16,409,310 0.90 18,929,670 17,984,369 0.95

12 Flame Tree

Security Ltd 4,569,135 397,849 0.09 4,143,858 594,969 0.14 3,153,838 741,656 0.24 2,351,911 628,582 0.27 1,738,431 570,760 0.33

13 summit guest

house 2,072,620 141,022 0.07 2,140,593 75,000 0.04 2,173,634 76,000 0.03 4,310,924 2,316,171 0.54 2,465,101 1,890,843 0.77

14 Centrofood

Industries Ltd 245,025,477 163,011,956 0.67 239,712,929 166,315,698 0.69 206,177,439 150,891,648 0.73 153,563,703 101,258,673 0.66 145,977,392 119,207,507 0.82

15 Dawaline

Pharmaceuticals 11,248,902 13,808,399 1.23 57,503,333 58,220,307 1.01 54,289,323 58,230,430 1.07 49,150,109 46,337,071 0.94 56,302,622 49,241,026 0.87

16 Kenya Tanning

Extract Co Ltd 2,889,131 409,319 0.14 2,977,762 438,794 0.15 1,534,860 1,952,096 1.27 1,003,049 1,079,097 1.08 857,498 904,553 1.05

17 Ready Timber

Merchants 3,524,539 2,899,475 0.82 3,075,304 2,573,612 0.84 2,503,653 2,843,097 1.14 2,323,789 2,326,426 1.00 2,856,821 2,660,652 0.93

18 Salama Clothing

Manufacturers 22,372,152 3,955,609 0.18 21,903,325 3,350,301 0.15 21,440,125 2,691,173 0.13 16,688,369 3,202,784 0.19 15,234,038 3,617,687 0.24

19 United Textile 152,749,105 224,098,613 1.47 86,052,020 205,254,418 2.39 131,214,371 207,268,148 1.58 290,632,375 223,089,640 0.77 243,312,798 234,465,890 0.96

20 Central

Computers 348,651,695 178,416,348 0.51 236,219,762 145,551,765 0.62 236,219,762 145,551,765 0.62 92,652,234 26,990,505 0.29 94,539,323 79,113,804 0.84

21 Popular

Industries Ltd 193,931,997 193,931,997 1.00 200,683,546 26,387,216 0.13 195,069,986 100,925,192 0.52 203,629,664 27,962,254 0.14 182,034,738 155,419,303 0.85

22 Colorchase

Limited 70,217,445 19,594,405 0.28 77,819,421 29,525,528 0.38 79,694,203 43,996,526 0.55 85,622,750 62,083,820 0.73 85,117,197 64,782,351 0.76

23 Marks Photo

Studio 67,553,855 46,261,331 0.68 65,300,134 65,300,134 1.00 74,917,615 74,917,615 1.00 72,835,053 25,054,759 0.34 57,162,618 19,772,816 0.35

58

2013 2012 2011 2010 2009

Name of the

SME Total Assets Total Debt

Debt

Ratio Total Assets Total Debt

Debt

Ratio Total Assets Total Debt

Debt

Ratio Total Assets Total Debt

Debt

Ratio Total Assets Total Debt

Debt

Ratio

24 Blue Post Hotel 18,835,390 46,395,947 2.46 18,835,390 46,395,947 2.46 19,784,154 47,133,285 2.38 18,037,095 45,109,309 2.50 18,684,921 46,945,247 2.51

25 Edkan

Enterprises 61,088,417 35,213,577 0.58 61,725,524 36,640,843 0.59 62,742,707 38,614,223 0.62 61,852,216 36,822,881 0.60 59,865,234 35,698,789 0.60

26 Prisma Electric

Limited 34,359,831 42,730,467 1.24 39,087,678 45,963,341 1.18 36,771,230 44,661,558 1.21 36,739,580 44,451,789 1.21 25,897,456 36,587,214 1.41

27 Crystal Valuers

Ltd 121,419,937 127,245,389 1.05 87,491,712 119,632,581 1.37 70,069,755 79,709,859 1.14 38,555,169 41,899,746 1.09 32,812,202 30,061,721 0.92

28 Mama Millers

Limited 13,241,770 13,460,991 1.02 11,980,473 8,938,119 0.75 11,846,459 7,031,534 0.59 12,356,234 9,810,215 0.79 11,156,234 8,710,215 0.78

29 Milkon & Sons

partners acc 36,091,446 8,764,551 0.24 29,009,843 7,008,401 0.24 27,191,540 2,566,524 0.09 27,426,109 2,083,654 0.08 28,227,937 12,632,310 0.45

30 Bempee Ltd 18,835,390 46,395,947 2.46 17,589,658 45,289,741 2.57 19,784,154 47,133,285 2.38 18,037,095 45,109,309 2.50 18,684,921 46,945,247 2.51

31 Tilkones

Limited 187,258,741 193,931,997 1.04 200,683,546 26,387,216 0.13 195,069,986 100,925,192 0.52 203,629,664 27,962,254 0.14 182,034,738 155,419,303 0.85

32 Oliver Clearing

Service Limited 152,749,105 224,098,613 1.47 86,052,020 205,254,418 2.39 131,214,371 207,268,148 1.58 290,632,375 223,089,640 0.77 243,312,798 234,465,890 0.96

33 Wajibu Mobil

Service Station 4,569,135 397,849 0.09 4,143,858 594,969 0.14 3,153,838 741,656 0.24 2,351,911 628,582 0.27 1,738,431 570,760 0.33

34 Messanine Oil 11,080,986 4,315,266 0.39 14,280,510 2,853,067 0.20 12,680,748 3,584,167 0.28 11,880,867 3,949,716 0.33 10,258,965 2,256,874 0.22

35 Thika Motor

Dealers (K) Ltd 15,999,848 10,807,654 0.68 17,268,425 13,391,767 0.78 17,518,858 14,787,184 0.84 18,172,340 16,409,310 0.90 18,929,670 17,984,369 0.95

36 Wishwaste

Limited 1,169,846,108 123,210,830 0.11 735,694,081 174,139,777 0.24 894,561,251 184,326,925 0.21 330,837,064 168,223,092 0.51 285,455,964 195,279,628 0.68

37 Nelka Agencies 82,475,652 82,475,652 1.00 57,557,855 40,099,084 0.70 29,927,789 22,459,566 0.75 17,945,951 13,269,131 0.74 27,639,835 28,139,189 1.02

38 Century Oil

Trading Co Ltd 13,848,498 14,909,100 1.08 14,770,736 14,141,160 0.96 10,896,058 10,401,914 0.95 5,858,907 5,024,633 0.86 5,919,453 5,856,725 0.99

39 Samerchant

Construction 4,569,135 397,849 0.09 4,143,858 594,969 0.14 3,153,838 741,656 0.24 2,351,911 628,582 0.27 1,738,431 570,760 0.33

40 The Coconut

Gril 13,848,498 14,909,100 1.08 14,770,736 14,141,160 0.96 10,896,058 10,401,914 0.95 5,858,907 5,024,633 0.86 5,919,453 5,856,725 0.99

Totals 4,571,641,672 2,112,312,977 0.46 3,461,089,029 1,803,769,798 0.52 3,921,158,433 2,080,801,149 0.53 3,286,087,077 1,903,585,983 0.58 2,702,887,775 2,380,383,011 0.88

59

Appendix II: Asset Turnover

2013 2012 2011 2010 2009

Name of the

SME Total Sales Total Assets

Asset

turno

ver Total Sales Total Assets

Asset

turno

ver Total Sales Total Assets

Asset

turno

ver Total Sales Total Assets

Asset

turno

ver Total Sales Total Assets

Asset

turn

over

1

INTEL FIRE

GROUP OF

COMPANIES 289,219 245,801 1.18 238,899 232,655 1.03 264,059 239,228 1.10 158,659 225,369 0.70 237,709 235,763 1.01

2 Saana Shoes

Ltd 135,693,840 82,475,652 1.65 101,241,062 57,557,855 1.76 93,881,332 29,927,789 3.14 72,247,042 17,945,951 4.03 56,119,312 27,639,835 2.03

3 Thika Cloth

Mills 19,634,375 36,091,446 0.54 3,693,213 29,009,843 0.13 1,050,000 27,191,540 0.04 1,320,500 27,426,109 0.05 4,260,000 28,227,937 0.15

4 Imara

Enterprises 50,360,643 11,080,986 4.54 44,783,651 14,280,510 3.14 47,572,147 12,680,748 3.75 40,589,625 11,586,954 3.50 45,826,517 12,407,300 3.69

5

Ruiru

Hardware

Store Ltd 33,562,920 22,788,440 1.47 31,498,965 19,545,260 1.61 24,414,134 18,555,555 1.32 20,778,110 15,218,077 1.37 19,339,796 12,218,198 1.58

6 Uhuru Flowers

L 302,247,545 1,169,846,108 0.26 235,769,097 735,694,081 0.32 177,297,273 894,561,251 0.20 132,579,960 330,837,064 0.40 92,169,477 285,455,964 0.32

7 Waridi

Garments 50,911 97,669,376 0.00 1,149,675 125,276,216 0.01 884,756,110 319,132,876 2.77 671,361,871 752,474,086 0.89 385,747,506 416,294,165 0.93

8 Savage

Holdings L 761,819 1,133,072 0.67 816,338 1,133,122 0.72 870,858 1,133,131 0.77 925,381 1,133,131 0.82 960,393 1,133,154 0.85

9 Alliance One

Tobacco Ltd 106,337,103 35,538,505 2.99 107,207,167 33,702,437 3.18 80,960,144 23,669,793 3.42 82,219,567 27,921,767 2.94 77,965,691 28,551,117 2.73

10 Alpha Knits

Ltd 10,938,516 13,848,498 0.79 9,678,453 14,770,736 0.66 15,879,210 10,896,058 1.46 42,641,511 5,858,907 7.28 39,861,790 5,919,453 6.73

11 Macushla

House L 21,000,667 15,999,848 1.31 22,483,231 17,268,425 1.30 20,096,284 17,518,858 1.15 18,012,262 18,172,340 0.99 13,646,379 18,929,670 0.72

12 Flame Tree

Security Ltd 926,613 4,569,135 0.20 1,125,455 4,143,858 0.27 819,196 3,153,838 0.26 722,262 2,351,911 0.31 818,801 1,738,431 0.47

13 summit guest

house 3,240,000 2,072,620 1.56 3,482,000 2,140,593 1.63 2,891,250 2,173,634 1.33 2,567,199 4,310,924 0.60 3,030,512 2,465,101 1.23

14 Centrofood

Industries Ltd 137,101,137 245,025,477 0.56 126,056,154 239,712,929 0.53 124,635,822 206,177,439 0.60 108,166,996 153,563,703 0.70 91,611,329 145,977,392 0.63

15

Dawaline

Pharmaceticals

Ltd 12,190,667 11,248,902 1.08 36,877,415 57,503,333 0.64 59,830,303 54,289,323 1.10 62,748,105 49,150,109 1.28 76,943,751 56,302,622 1.37

16

Kenya

Tanning

Extract Ltd 6,729,077 2,889,131 2.33 5,149,870 2,977,762 1.73 165,726 1,534,860 0.11 - 1,003,049 - - 857,498 -

17 Ready Timber

Merchants 2,610,300 3,524,539 0.74 2,885,483 3,075,304 0.94 4,665,697 2,503,653 1.86 4,516,770 2,323,789 1.94 3,669,562 2,856,821 1.28

18

Salama

Clothing

Manufacture 34,931,233 22,372,152 1.56 29,638,685 21,903,325 1.35 26,111,417 21,440,125 1.22 21,628,474 16,688,369 1.30 19,919,965 15,234,038 1.31

19 United Textile

Industry Ltd, 89,754,720 152,749,105 0.59 25,458,504 86,052,020 0.30 42,273,246 131,214,371 0.32 167,405,334 290,632,375 0.58 327,074,736 243,312,798 1.34

20 Central

Computers 256,280 348,651,695 0.00 4,907,504 236,219,762 0.02 4,907,504 236,219,762 0.02 1,417,957 92,652,234 0.02 2,308,570 94,539,323 0.02

21 Popular

Industries 9,623,171 193,931,997 0.05 11,240,437 200,683,546 0.06 52,276,154 195,069,986 0.27 24,939,983 203,629,664 0.12 163,301,027 182,034,738 0.90

22 Colorchase

Limit 93,201,059 70,217,445 1.33 143,572,987 77,819,421 1.84 140,453,709 79,694,203 1.76 163,374,020 85,622,750 1.91 161,666,769 85,117,197 1.90

60

2013 2012 2011 2010 2009

Name of the

SME Total Sales Total Assets

Asset

turno

ver Total Sales Total Assets

Asset

turno

ver Total Sales Total Assets

Asset

turno

ver Total Sales Total Assets

Asset

turno

ver Total Sales Total Assets

Asset

turn

over

23 Marks Photo

Studio 91,911,031 67,553,855 1.36 166,048,487 65,300,134 2.54 83,344,952 74,917,615 1.11 66,697,750 72,835,053 0.92 51,552,936 57,162,618 0.90

24 Blue Post

Hotel 21,893,650 18,835,390 1.16 21,893,650 18,835,390 1.16 21,203,495 19,784,154 1.07 21,666,422 18,037,095 1.20 22,811,032 18,684,921 1.22

25 Edkan

Enterprises 5,895,000 61,088,417 0.10 5,760,000 61,725,524 0.09 5,255,000 62,742,707 0.08 5,636,667 61,852,216 0.09 4,562,365 59,865,234 0.08

26

Prisma

Electric

Limited 39,108,455 34,359,831 1.14 42,128,911 39,087,678 1.08 32,298,298 36,771,230 0.88 37,845,221 36,739,580 1.03 37,845,221 25,897,456 1.46

27 Crystal

Valuers Ltd 176,905,186 121,419,937 1.46 58,484,090 87,491,712 0.67 92,424,218 70,069,755 1.32 71,326,223 38,555,169 1.85 62,981,371 32,812,202 1.92

28 Mama Millers

Limited 47,095,877 13,241,770 3.56 53,160,419 11,980,473 4.44 53,056,364 11,846,459 4.48 51,104,220 12,356,234 4.14 52,104,400 11,156,234 4.67

29

Milkon &

Sons partners

account 19,634,375 36,091,446 0.54 3,693,213 29,009,843 0.13 1,050,000 27,191,540 0.04 1,320,500 27,426,109 0.05 4,260,000 28,227,937 0.15

30 Bempee Ltd 21,893,650 18,835,390 1.16 20,856,589 17,589,658 1.19 21,203,495 19,784,154 1.07 21,666,422 18,037,095 1.20 22,811,032 18,684,921 1.22

31 Tilkones

Limited 9,623,171 187,258,741 0.05 11,240,437 200,683,546 0.06 52,276,154 195,069,986 0.27 24,939,983 203,629,664 0.12 16,330,102 182,034,738 0.09

32

Oliver

Clearing

Service

Limited 89,754,720 152,749,105 0.59 25,458,504 86,052,020 0.30 42,273,246 131,214,371 0.32 167,405,334 290,632,375 0.58 327,074,736 243,312,798 1.34

33

Wajibu Mobil

Service

Station 926,613 4,569,135 0.20 1,125,455 4,143,858 0.27 819,196 3,153,838 0.26 722,262 2,351,911 0.31 818,801 1,738,431 0.47

34 Messanine Oil 50,360,643 11,080,986 4.54 44,783,651 14,280,510 3.14 47,572,147 12,680,748 3.75 48,966,395 11,880,867 4.12 36,897,523 10,258,965 3.60

35

Thika Motor

Dealers (K)

Ltd 21,000,667 15,999,848 1.31 22,483,231 17,268,425 1.30 20,096,284 17,518,858 1.15 18,012,262 18,172,340 0.99 13,646,379 18,929,670 0.72

36 Wishwaste

Limited 302,247,545 1,169,846,108 0.26 235,769,097 735,694,081 0.32 177,297,273 894,561,251 0.20 132,579,960 330,837,064 0.40 92,169,477 285,455,964 0.32

37 Nelka

Agencies 135,693,840 82,475,652 1.65 101,241,062 57,557,855 1.76 93,881,332 29,927,789 3.14 72,247,042 17,945,951 4.03 56,119,312 27,639,835 2.03

38

Century Oil

Trading Co

Ltd 10,938,516 13,848,498 0.79 9,678,453 14,770,736 0.66 15,879,210 10,896,058 1.46 42,641,511 5,858,907 7.28 39,861,790 5,919,453 6.73

39 Samerchant

Construction 926,613 4,569,135 0.20 1,125,455 4,143,858 0.27 819,196 3,153,838 0.26 722,262 2,351,911 0.31 818,801 1,738,431 0.47

40 The Coconut

Gril 10,938,516 13,848,498 0.79 9,678,453 14,770,736 0.66 15,879,210 10,896,058 1.46 42,641,511 5,858,907 7.28 39,861,790 5,919,453 6.73

Totals 2,128,189,883 4,571,641,672 0.47 1,783,563,401 3,461,089,029 0.52 2,582,700,644 3,921,158,433 0.66 2,468,463,535 3,286,087,077 0.75 2,469,006,658 2,702,887,775 0.91

61

Appendix III: Asset Tangibility

2013 2012 2011 2010 2009

Name of the

SME

Net Tangible

Assets Total Assets

Ass

tang

Net Tangible

Assets Total Assets

Ass

tang

Net Tangible

Assets Total Assets

Asse

tang

Net Tangible

Assets Total Assets

Asse

tang

Net Tangible

Assets Total Assets

Asse

tang

1

INTEL FIRE GROUP OF COMPANIES 69,849 245,801 0.28 14,972 232,655 0.06 13,287 239,228 0.06 12,458 225,369 0.06 27,642 235,763 0.12

2 Saana Shoes Ltd 13,754,389 82,475,652 0.17 3,304,199 57,557,855 0.06 4,976,614 29,927,789 0.17 3,346,974 17,945,951 0.19 7,395,890 27,639,835 0.27

3 Thika Cloth Mills L 7,815,312 36,091,446 0.22 7,946,073 29,009,843 0.27 8,076,834 27,191,540 0.30 8,207,595 27,426,109 0.30 8,338,356 28,227,937 0.30

4 Imara Enterprises 68,515 11,080,986 0.01 78,303 14,280,510 0.01 73,409 12,680,748 0.01 65,214 11,586,954 0.01 71,360 12,407,300 0.01

5

Ruiru Hardware Store Ltd 4,345,793 22,788,440 0.19 4,186,354 19,545,260 0.21 4,057,626 18,555,555 0.22 3,896,845 15,218,077 0.26 3,009,092 12,218,198 0.25

6 Uhuru Flowers L 870,735,908 1,169,846,108 0.74 523,997,934 735,694,081 0.71 408,912,699 894,561,251 0.46 152,836,363 330,837,064 0.46 146,506,850 285,455,964 0.51

7 Waridi Garments - 97,669,376 - - 125,276,216 - - 319,132,876 - 587,279,208 752,474,086 0.78 298,978,823 416,294,165 0.72

8

Savage Holdings limited 1,133,072 1,133,072 1.00 1,133,122 1,133,122 1.00 1,133,131 1,133,131 1.00 1,133,141 1,133,131 1.00 1,133,154 1,133,154 1.00

9

Alliance One Tobacco Ltd 5,775,505 35,538,505 0.16 6,898,967 33,702,437 0.20 5,316,634 23,669,793 0.22 5,119,333 27,921,767 0.18 5,859,522 28,551,117 0.21

10 Alpha Knits Ltd 625,758 13,848,498 0.05 625,758 14,770,736 0.04 743,318 10,896,058 0.07 266,792 5,858,907 0.05 255,512 5,919,453 0.04

11 Macushla House L 15,577,406 15,999,848 0.97 16,229,712 17,268,425 0.94 16,818,410 17,518,858 0.96 17,422,581 18,172,340 0.96 18,112,659 18,929,670 0.96

12

Flame Tree Security 145,043 4,569,135 0.03 165,880 4,143,858 0.04 189,741 3,153,838 0.06 217,078 2,351,911 0.09 248,412 1,738,431 0.14

13

summit guest house 446,133 2,072,620 0.22 532,958 2,140,593 0.25 550,998 2,173,634 0.25 583,685 4,310,924 0.14 674,086 2,465,101 0.27

14

Centrofood Industries Ltd 36,277,866 245,025,477 0.15 39,221,021 239,712,929 0.16 37,248,663 206,177,439 0.18 33,155,269 153,563,703 0.22 32,403,506 145,977,392 0.22

15

Dawaline Pharmaceuticals L 600,442 11,248,902 0.05 718,911 57,503,333 0.01 869,024 54,289,323 0.02 593,099 49,150,109 0.01 764,567 56,302,622 0.01

16

Kenya Tanning Extract Co Ltd 1,482,991 2,889,131 0.51 1,393,334 2,977,762 0.47 1,055,554 1,534,860 0.69 983,010 1,003,049 0.98 831,544 857,498 0.97

17

Ready Timber Merchants 1,907,483 3,524,539 0.54 2,009,460 3,075,304 0.65 1,321,810 2,503,653 0.53 1,391,183 2,323,789 0.60 1,657,484 2,856,821 0.58

18

Salama Clothing Manufacturers 15,894,815 22,372,152 0.71 15,012,229 21,903,325 0.69 13,954,296 21,440,125 0.65 9,229,345 16,688,369 0.55 8,778,786 15,234,038 0.58

19

United Textile Industry (K) Ltd 18,375,011 152,749,105 0.12 45,386,713 86,052,020 0.53 91,805,198 131,214,371 0.70 99,021,100 290,632,375 0.34 108,055,622 243,312,798 0.44

20

Central Computers - 348,651,695 - - 236,219,762 - - 236,219,762 - - 92,652,234 - 58,422,659 94,539,323 0.62

21 Popular Industries 188,646,978 193,931,997 0.97 192,366,965 200,683,546 0.96 150,862,546 195,069,986 0.77 196,368,661 203,629,664 0.96 26,067,583 182,034,738 0.14

22

Colorchase Limited 1,241,688 70,217,445 0.02 1,507,105 77,819,421 0.02 1,677,589 79,694,203 0.02 1,814,750 85,622,750 0.02 2,146,813 85,117,197 0.03

23

Marks Photo Studio 28,050,443 67,553,855 0.42 29,544,681 65,300,134 0.45 28,367,104 74,917,615 0.38 29,440,854 72,835,053 0.40 24,849,133 57,162,618 0.43

24 Blue Post Hotel 6,037,818 18,835,390 0.32 6,037,818 18,835,390 0.32 5,757,192 19,784,154 0.29 6,064,719 18,037,095 0.34 6,291,544 18,684,921 0.34

62

2013 2012 2011 2010 2009

Name of the

SME

Net Tangible

Assets Total Assets

Ass

tang

Net Tangible

Assets Total Assets

Ass

tang

Net Tangible

Assets Total Assets

Asse

tang

Net Tangible

Assets Total Assets

Asse

tang

Net Tangible

Assets Total Assets

Asse

tang

25 Edkan Enterprises 60,374,586 61,088,417 0.99 61,251,789 61,725,524 0.99 62,135,654 62,742,707 0.99 61,254,010 61,852,216 0.99 52,458,965 59,865,234 0.88

26

Prisma Electric Limited 4,197,013 34,359,831 0.12 3,880,812 39,087,678 0.10 3,807,474 36,771,230 0.10 3,961,766 36,739,580 0.11 2,985,647 25,897,456 0.12

27 Crystal Valuers Ltd 4,355,546 121,419,937 0.04 4,146,550 87,491,712 0.05 3,188,664 70,069,755 0.05 3,795,061 38,555,169 0.10 457,499 32,812,202 0.01

28 Mama Millers Ltd 3,967,963 13,241,770 0.30 4,295,587 11,980,473 0.36 4,831,911 11,846,459 0.41 4,365,154 12,356,234 0.35 3,465,154 11,156,234 0.31

29

Milkon & Sons partners account 7,815,312 36,091,446 0.22 7,946,073 29,009,843 0.27 8,076,834 27,191,540 0.30 8,207,595 27,426,109 0.30 8,338,356 28,227,937 0.30

30 Bempee Ltd 6,037,818 18,835,390 0.32 5,896,847 17,589,658 0.34 5,757,192 19,784,154 0.29 6,064,719 18,037,095 0.34 6,291,544 18,684,921 0.34

31 Tilkones Limited 188,646,978 187,258,741 1.01 192,366,965 200,683,546 0.96 150,862,546 195,069,986 0.77 196,368,661 203,629,664 0.96 126,067,583 182,034,738 0.69

32

Oliver Clearing Service Limited 18,375,011 152,749,105 0.12 45,386,713 86,052,020 0.53 91,805,198 131,214,371 0.70 99,021,100 290,632,375 0.34 108,055,622 243,312,798 0.44

33

Wajibu Mobil Service Station 145,043 4,569,135 0.03 165,880 4,143,858 0.04 189,741 3,153,838 0.06 217,078 2,351,911 0.09 248,412 1,738,431 0.14

34 Messanine Oil 68,515 11,080,986 0.01 78,303 14,280,510 0.01 73,409 12,680,748 0.01 70,962 11,880,867 0.01 68,515 10,258,965 0.01

35

Thika Motor Dealers (K) Ltd 15,577,406 15,999,848 0.97 16,229,712 17,268,425 0.94 16,818,410 17,518,858 0.96 17,422,581 18,172,340 0.96 18,112,659 18,929,670 0.96

36

Wishwaste Limited 870,735,908 1,169,846,108 0.74 523,997,934 735,694,081 0.71 408,912,699 894,561,251 0.46 152,836,363 330,837,064 0.46 146,506,850 285,455,964 0.51

37 Nelka Agencies 13,754,389 82,475,652 0.17 3,304,199 57,557,855 0.06 4,976,614 29,927,789 0.17 3,346,974 17,945,951 0.19 7,395,890 27,639,835 0.27

38

Century Oil Trading Co Ltd 625,758 13,848,498 0.05 625,758 14,770,736 0.04 743,318 10,896,058 0.07 266,792 5,858,907 0.05 255,512 5,919,453 0.04

39

Samerchant Construction 145,043 4,569,135 0.03 165,880 4,143,858 0.04 189,741 3,153,838 0.06 217,078 2,351,911 0.09 248,412 1,738,431 0.14

40 The Coconut Gril 625,758 13,848,498 0.05 625,758 14,770,736 0.04 743,318 10,896,058 0.07 266,792 5,858,907 0.05 255,512 5,919,453 0.04

Totals 2,414,456,264 4,571,641,672 0.53 1,768,677,230 3,461,089,029 0.51 1,546,894,399 3,921,158,433 0.39 1,716,131,942 3,286,087,077 0.52 1,242,092,730 2,702,887,775 0.46

63

Appendix III: Return on Asset

2013 2012 2011 2010 2009

Name of the

SME

Profit After

Tax Total Assets ROA

Profit After

Tax Total Assets ROA

Profit After

Tax Total Assets ROA

Profit After

Tax Total Assets ROA

Profit After

Tax Total Assets ROA

1

INTEL FIRE GROUP OF COMPANIES (51,629) 245,801 (0.21) 28,441 232,655 0.12 (11,594) 239,228 (0.05) 14,856 225,369 0.07 (4,982) 235,763 (0.02)

2

Saana Shoes Ltd 29,926,425 82,475,652 0.36 9,990,548 57,557,855 0.17 7,368,223 29,927,789 0.25 5,176,173 17,945,951 0.29 513,227 27,639,835 0.02

3

Thika Cloth Mills L 11,406,337 36,091,446 0.32 (257,186) 29,009,843 (0.01) 219,617 27,191,540 0.01 539,429 27,426,109 0.02 3,512,366 28,227,937 0.12

4

Imara Enterprises 10,587,767 11,080,986 0.96 9,299,471 14,280,510 0.65 9,943,619 12,680,748 0.78 7,859,632 11,586,954 0.68 9,422,622 12,407,300 0.76

5

Ruiru Hardware Store Ltd 255,157 22,788,440 0.01 688,417 19,545,260 0.04 1,088,333 18,555,555 0.06 1,633,414 15,218,077 0.11 349,316 12,218,198 0.03

6

Uhuru Flowers Limited 348,602,108

1,169,846,10

8 0.30 148,806,017 735,694,081 0.20 129,821,797 894,561,251 0.15 49,820,984 330,837,064 0.15 (20,862,461) 285,455,964 (0.07)

7

Waridi Garments (6,014,530) 97,669,376 (0.06) (6,234,260) 125,276,216 (0.05) 202,564,938 319,132,876 0.63 259,018,846 752,474,086 0.34 153,779,178 416,294,165 0.37

8

Savage Holdings limited (54,560) 1,133,072 (0.05) (54,519) 1,133,122 (0.05) (54,520) 1,133,131 (0.05) (54,523) 1,133,131 (0.05) (35,012) 1,133,154 (0.03)

9

Alliance One Tobacco (Kenya) (850,538) 35,538,505 (0.02) 5,775,112 33,702,437 0.17 527,588 23,669,793 0.02 5,642,614 27,921,767 0.20 (5,602,229) 28,551,117 (0.20)

10 Alpha Knits Ltd (28,800) 13,848,498 (0.00) (215,279) 14,770,736 (0.01) (335,111) 10,896,058 (0.03) 616,013 5,858,907 0.11 (727,908) 5,919,453 (0.12)

11

Macushla House Limited 1,322,946 15,999,848 0.08 1,155,882 17,268,425 0.07 984,341 17,518,858 0.06 839,988 18,172,340 0.05 195,333 18,929,670 0.01

12

Flame Tree Security Ltd 652,899 4,569,135 0.14 1,136,706 4,143,858 0.27 688,856 3,153,838 0.22 476,940 2,351,911 0.20 235,940 1,738,431 0.14

13

summit guest house 67,005 2,072,620 0.03 534,177 2,140,593 0.25 247,481 2,173,634 0.11 (647,740) 4,310,924 (0.15) 325,573 2,465,101 0.13

14

Centrofood Industries Ltd 8,619,326 245,025,477 0.04 18,105,133 239,712,929 0.08 6,213,061 206,177,439 0.03 25,535,147 153,563,703 0.17 3,195,382 145,977,392 0.02

15

Dawaline Pharmaceutica (1,842,523) 11,248,902 (0.16) (1,789,777) 57,503,333 (0.03) (1,740,235) 54,289,323 (0.03) (4,248,558) 49,150,109 (0.09) 2,122,235 56,302,622 0.04

16

Kenya Tanning Extract Co Ltd (59,155) 2,889,131 (0.02) 201,204 2,977,762 0.07 (341,188) 1,534,860 (0.22) (28,992) 1,003,049 (0.03) (17,852) 857,498 (0.02)

17

Ready Timber Merchants 98,280 3,524,539 0.03 561,352 3,075,304 0.18 (273,389) 2,503,653 (0.11) (249,582) 2,323,789 (0.11) 34,165 2,856,821 0.01

18

Salama Clothing Manufacturers 151,519 22,372,152 0.01 (483,928) 21,903,325 (0.02) 5,263,367 21,440,125 0.25 1,869,234 16,688,369 0.11 615,599 15,234,038 0.04

19 United Textile 47,852,890 152,749,105 0.31 (16,933,517) 86,052,020 (0.20) (45,417,464) 131,214,371 (0.35) (38,750,625) 290,632,375 (0.13) (2,753,601) 243,312,798 (0.01)

64

2013 2012 2011 2010 2009

Name of the

SME

Profit After

Tax Total Assets ROA

Profit After

Tax Total Assets ROA

Profit After

Tax Total Assets ROA

Profit After

Tax Total Assets ROA

Profit After

Tax Total Assets ROA

Industry (K) Ltd

20

Central Computers (10,108,492) 348,651,695 (0.03) (4,149,474) 236,219,762 (0.02) (4,149,474) 236,219,762 (0.02) (2,800,274) 92,652,234 (0.03) 3,190,896 94,539,323 0.03

21

Popular Industries (301,561) 193,931,997 (0.00) (997,909) 200,683,546 (0.00) 43,800,208 195,069,986 0.22 17,016,615 203,629,664 0.08 159,483,691 182,034,738 0.88

22

Colorchase Limited 2,343,194 70,217,445 0.03 4,599,355 77,819,421 0.06 10,418,296 79,694,203 0.13 3,316,967 85,622,750 0.04 31,413,668 85,117,197 0.37

23 Marks Photo (12,693,093) 67,553,855 (0.19) 5,448,811 65,300,134 0.08 2,077,032 74,917,615 0.03 (35,647,408) 72,835,053 (0.49) (22,650,805) 57,162,618 (0.40)

24 Blue Post Hotel (190,704) 18,835,390 (0.01) (190,704) 18,835,390 (0.01) (369,384) 19,784,154 (0.02) 1,234,629 18,037,095 0.07 (1,437,357) 18,684,921 (0.08)

25

Edkan Enterprises 790,159 61,088,417 0.01 956,197 61,725,524 0.02 123,924 62,742,707 0.00 623,427 61,852,216 0.01 569,874 59,865,234 0.01

26 Prisma Electric (1,494,973) 34,359,831 (0.04) 914,865 39,087,678 0.02 (7,890,528) 36,771,230 (0.21) (2,823,545) 36,739,580 (0.08) (1,452,365) 25,897,456 (0.06)

27

Crystal Valuers Ltd 26,315,417 121,419,937 0.22 (28,796,292) 87,491,712 (0.33) (1,481,363) 70,069,755 (0.02) (5,494,176) 38,555,169 (0.14) 2,049,599 32,812,202 0.06

28

Mama Millers Limited (3,261,575) 13,241,770 (0.25) (1,772,572) 11,980,473 (0.15) (76,994) 11,846,459 (0.01) (1,703,714) 12,356,234 (0.14) (1,403,714) 11,156,234 (0.13)

29

Milkon & Sons partners account 11,406,337 36,091,446 0.32 (257,186) 29,009,843 (0.01) 219,617 27,191,540 0.01 539,429 27,426,109 0.02 3,512,366 28,227,937 0.12

30 Bempee Ltd (190,704) 18,835,390 (0.01) (125,698) 17,589,658 (0.01) (369,384) 19,784,154 (0.02) 1,234,629 18,037,095 0.07 (1,437,357) 18,684,921 (0.08)

31

Tilkones Limited (301,561) 187,258,741 (0.00) (997,909) 200,683,546 (0.00) 43,800,208 195,069,986 0.22 17,016,615 203,629,664 0.08 15,948,369 182,034,738 0.09

32

Oliver Clearing Service Limited 47,852,890 152,749,105 0.31 (16,933,517) 86,052,020 (0.20) (45,417,464) 131,214,371 (0.35) (38,750,625) 290,632,375 (0.13) (2,753,601) 243,312,798 (0.01)

33

Wajibu Mobil Service Station 652,899 4,569,135 0.14 1,136,706 4,143,858 0.27 688,856 3,153,838 0.22 476,940 2,351,911 0.20 235,940 1,738,431 0.14

34 Messanine Oil 10,587,767 11,080,986 0.96 9,299,471 14,280,510 0.65 9,943,619 12,680,748 0.78 10,265,693 11,880,867 0.86 8,548,963 10,258,965 0.83

35

Thika Motor Dealers (K) Ltd 1,322,946 15,999,848 0.08 1,155,882 17,268,425 0.07 984,341 17,518,858 0.06 839,988 18,172,340 0.05 195,333 18,929,670 0.01

36

Wishwaste Limited 348,602,108

1,169,846,108 0.30 148,806,017 735,694,081 0.20 129,821,797 894,561,251 0.15 49,820,984 330,837,064 0.15 (20,862,461) 285,455,964 (0.07)

37 Nelka Agencies 29,926,425 82,475,652 0.36 9,990,548 57,557,855 0.17 7,368,223 29,927,789 0.25 5,176,173 17,945,951 0.29 513,227 27,639,835 0.02

38

Century Oil Trading Co Ltd (28,800) 13,848,498 (0.00) (215,279) 14,770,736 (0.01) (335,111) 10,896,058 (0.03) 616,013 5,858,907 0.11 (727,908) 5,919,453 (0.12)

39

Samerchant Construction 652,899 4,569,135 0.14 1,136,706 4,143,858 0.27 688,856 3,153,838 0.22 476,940 2,351,911 0.20 235,940 1,738,431 0.14

40 The Coconut Gril (28,800) 13,848,498 (0.00) (215,279) 14,770,736 (0.01) (335,111) 10,896,058 (0.03) 616,013 5,858,907 0.11 (727,908) 5,919,453 (0.12)

Totals 902,493,702 4,571,641,672 0.20 299,106,735 3,461,089,029 0.09 506,267,886 3,921,158,433 0.13 337,114,563 3,286,087,077 0.10 316,741,282 2,702,887,775 0.12