kimani (revised on 12 oct 2012)(1)

7/30/2019 Kimani (Revised on 12 Oct 2012)(1)

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AN INVESTIGATION INTO INDUSTRY OPTIMAL GEARING

RATIO TARGETING BY FIRMS IN KENYA

BY

KIMANI RUHANGA

REG NO: D53/RI/11519/2004

A RESEARCH PROPOSAL SUBMITTED TO THE

SCHOOL OF BUSINESS, KENYATTA UNIVERSITY

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR

THE AWARD OF A DEGREE OF MASTERS OF BUSINESS

ADMINISTRATION

KENYATTA UNIVERSITY

JULY 2012


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DECLARATION

This research proposal is my original work and has not been carried out in any other

institution for examination purposes

Signed: . Date:

KIMANI RUHANGA

REG NO: D53/RI/11519/2004

The research proposal has been submitted for defence with my approval as the student

supervisor

Mr. F. NDEDE

Signed: . Date:

ii


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ACKNOWLEDGEMENTS

It has been a long journey since I started my course work and it would be impossible

not to remember those who in one way or another, directly or indirectly, have played

a role in the realization of this research project. Let me, therefore, thank them all

equally.

I am indebted to the God for his blessings which have enabled me reach this far. I am

deeply obliged to my supervisor for his exemplary guidance and support without

whose help; this project would not have been a success. Finally, yet importantly, I

take this opportunity to express my deep gratitude to my loving family and friends

who are a constant source of motivation and for their never ending support and

encouragement all through.

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DEDICATION

I dedicate this study to my family, and especially my wife, for the support,

understanding and encouragement provided over the years of my studies and as I

prepared and worked on this project.

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ABSTRACT

Despite significant theoretical and empirical developments in the capital structure

literature, the question of optimal gearing ratio still remains a big source of debate,

and more so the existence and targeting of an optimal gearing ratio by firms. Review

of literature on capital structure indicates arguments for the existence of an optimal

ratio, implying a target ratio, while empirical findings show that gearing ratios vary

significantly by industry and that different industries have different optimal gearing

ratios that are a function of their business risk. Other research rationalizes

industry norm targeting behaviour, by arguing that firms choose gearing ratios which

suit their particular business risk. The objective of this study is to investigate industry-

optimal gearing ratio targeting by firms in Kenya. The study will be conducted by

researching into the movement of the capital gearing ratios, over the period 2006 to

2011, of firms whose shares are quoted on the Nairobi Securities Exchange, in order

to determine whether the firms target an optimal capital gearing ratio over time. The

study will purposively sample firms in two sectors; manufacturing and those in

communication and technology.The study is expected to contribute new knowledge

to this debate and benefit scholars by providing additional knowledge on capital

structure and gearing ratios, enlighten investors and managers on short-term and long-term gearing and financing decisions and in addition influence government and policy

makers in policy development.

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TABLE OF CONTENT

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

ACKNOWLEDGEMENTS ..........................................................................................iii

DEDICATION ..............................................................................................................ivABSTRACT ...................................................................................................................v

CHAPTER ONE ............................................................................................................ 1

INTRODUCTION ......................................................................................................... 1

1.1 Background of the study .......................................................................................... 1

1.2 Statement of the problem ........................................................................................ 2

1.3 Objective of the study .............................................................................................. 3

1.4 Hypothesis ................................................................................................................3

1.4.1 Research hypotheses testing ..................................................................................3

1.5 Significance of the study ..........................................................................................3

1.6 Scope of the study ....................................................................................................4

1.7 Limitations of the study ........................................................................................... 4

1.8 Assumptions of the study ......................................................................................... 5

LITERATURE REVIEW .............................................................................................. 6

2.1 Theoretical rreview ................................................................................................ 6

2.2 Empirical review .................................................................................................... 7

2.2.1 Effect of an industry-optimal gearing ratio targeting .......................................... 9

2.3 Knowledge gap .....................................................................................................10

CHAPTER THREE ..................................................................................................... 12

RESEARCH DESIGN AND METHODOLOGY ....................................................... 12

3.1 Introduction ............................................................................................................12

3.2 Research design .....................................................................................................12

3.3 Population .............................................................................................................. 13

3.4 Sampling techniques .............................................................................................. 13

3.5 Sample size ............................................................................................................14

3.6 Data collection .......................................................................................................14

3.7 Data analysis .......................................................................................................... 14

Regression Model with control variables .....................................................................15

REFERENCES ............................................................................................................ 16

APPENDIX I: TIME PLAN ........................................................................................21

APPENDIX II: BUDGET ............................................................................................22

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CHAPTER ONE

INTRODUCTION

1.1 Background of the study

Since the arguments put forward in the capital structure irrelevancy proposition by

Modigliani and Miller (1958), research and debate on capital structure have been

intense but somewhat inconclusive, despite significant theoretical and empirical

developments in the capital structure literature. The question of optimal gearing ratio

still remains a big source of debate, and more so the existence and targeting of an

optimal gearing ratio by firms. Review of literature on capital structure indicates

arguments for the existence of an optimal ratio, implying a target ratio, while

empirical findings show that gearing ratios vary significantly by industry and that

different industries have different optimal gearing ratios that are a function of their

business risk. Some researchers rationalize industry norm targeting behaviour, by

arguing that firms choose gearing ratios which suit their particular business risk, while

other research indicates that accounting for debt tax shields and financial distress

costs counters the capital structure irrelevancy proposition and leads to an optimal

gearing ratio which maximises firm value Bradley et al. (1984). Other researchers

argue that industry classification impacts significantly upon the firm's gearing

decision and that each firm targets the average (or norm) gearing ratio of its industry,

since the benefits and costs of debt vary significantly across industries. Ang (1976)

argues that the existence of an optimal gearing ratio implies the existence of a target

ratio. More recently, Antoniou et al. (2002) find that firms adjust their debt ratios

towards targets, but at different speeds depending on their industry, suggesting that

environmental conditions are important drivers of targeting behaviour.

There is no consensus on how capital structure ratios and their components should be

defined. Certain ratios stress the importance of market rather than book values, whilst

other ratios emphasise the importance of balance sheet values, especially if

substantially different from market values. Bowman (1980) and Marsh (1982) find

that book measures of gearing are statistically indistinguishable from market value

measures. However, book and market gearing ratios are conceptually different. Book

measures are by definition backward-looking due to their reliance on accounting

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data whereas market values are generally held to be forward looking. Thus, there is

no reason why these two concepts should match Barclay et al., (2006). Harris and

Raviv (1991) and Rajan and Zingales (1995) employ gearing ratios in their empirics

using book values for debt and market values for equity, a compromise employed

widely in the empirical literature.

Another important issue is whether short-term debt should be included in a definition

of gearing as its omission may lead to an understatement of financial distress risk.

Rajan and Zingales (1995) and Tucker (1995) find that while there is large variation

across industries,

Most capital structure studies to date are based on data from developed countries.

Antoniou et al. (2002) analyse data from the UK, Germany and France, Rajan and

Zingales (1995) use data from the G-7 countries, Bevan and Danbolt (2000) from the

UK. Only a few studies provide evidence from developing countries. Booth et al,

(2001) analyse data from ten developing countries (Brazil, Mexico, India, South

Korea, Jordan, Malaysia, Pakistan, Thailand, Turkey and Zimbabwe), Pandey (2001)

uses data from Malaysia, Chen (2004) utilise data from China, Omet and Nobanee

(2001) use data from Jordan and Al-Sakran (2001) analyses data from Saudi Arabia.

This study attempts to reduce the gap by analysing a capital structures from firms in

the developing world, and more specifically Kenya. Booth et al, (2001) state that, In

general, debt ratios in developing countries seem to be affected in the same way and

by the same types of variables that are significant in developed countries. However,

there are systematic differences in the way these ratios are affected by country factors,

such as GDP growth rates, inflation rates, and development of capital markets. This

study is also intended to provide further evidence of the capital structure theories

pertaining to a developing country.

1.2 Statement of the problem

The study is an investigation into industry optimal gearing ratio targeting by firms in

Kenya. Though scholars have over the years hinted on the existence of an optimal

capital gearing ratio and the endeavour by firms to target an optimal gearing ratio, the

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research so far is inconclusive and therefore the existence of a gap for research. Most

studies on capital structure and gearing ratios are based on data from developed

countries. This study attempts to reduce the gap by analysing a capital structures from

firms in the developing world, and more specifically Kenya.

1.3 Objective of the study

The objective of this study is to investigate industry-optimal gearing ratio targeting by

firms in Kenya.

The specific objectives are:

i. To establish the effect of short-term debt on optimum gearing ratio;

ii. To establish the effect of optimum gearing ratio on financial distress risk; and

iii. To establish the effect of optimum gearing ratio targeting on capital structures.

1.4 Hypothesis

By testing the movement in capital gearing ratios over time we are implicitly testing

the hypothesis of the targeting of an industry optimal capital gearing ratio.

1.4.1 Research hypotheses testing

The study will test the following hypotheses:

Hypotheses I:

H0: Short-term debts have an insignificant effect on the optimum gearing ratio

H1: Short-term debts have a significant effect on the optimum gearing ratio

Hypotheses II:

H0: The optimum gearing ratio has an insignificant effect on financial distress risk

H1: The optimum gearing ratio has a significant effect on financial distress risk

Hypotheses III:

H0: Optimum gearing ratio targeting has an insignificant effect on capital structure

H1: Optimum gearing ratio targeting has a significant effect on capital structure.

1.5 Significance of the study

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1.8 Assumptions of the study

The study hypothesis assumes that short-term debts have an insignificant effect on the

optimum gearing ratio, the optimum gearing ratio has a significant effect on financial

distress risk, while optimum gearing ratio targeting has a significant effect on capitalstructure. This study also assumes that the firms listed on the Nairobi Stock Exchange

are representative of firms in Kenya and the findings of this study can be generalised

to firms in Kenya.

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CHAPTER TWO

LITERATURE REVIEW

2.1 Introduction

This section provides a review of the relevant theoretical and empirical literature.

Theoretical arguments suggest the existence of optimal gearing ratios and the implicit

endeavour by firms by attain the optimal gearing ratio.

2.1 Theoretical rreview

Review of literature on capital structures indicates that past research findings broadly

support the existence of optimal gearing ratios implying the incidence of targetgearing behaviour within firms. Central to this understanding of financing behaviour

is the trade-off theory which asserts that an optimal gearing ratio is reached by the

firm at the point where the marginal benefits of employing debt, such as interest tax

shields, equals its marginal costs such as financial distress and bankruptcy costs Kim

(1978). Further, an optimal gearing ratio may also be reached by trading off the

agency costs and benefits of debt Jensen and Meckling (1976). Francis and Leachman

(1994), Leary and Roberts (2005) and Flannery and Rangan (2006) for US firms, and

Ozkan (2001), Antoniou et al. (2002), Bunn and Young (2004) and Beattie et al.

(2006), amongst others, provide evidence in support of targeting behaviour.

Bradley et al. (1984) and Flannery and Rangan (2006) argue that the optimal gearing

ratio varies for firms in different industries because it is the typical asset structures

and the stability of earnings which determine inherent risks vary across industries.

Firms in cyclical sectors such as leisure firms will suffer greater variability in

profitability, while other firms, such as information technology firms, are subject to

technological risks and typically employ firm-specific non-accounting assets which

have value only when employed by a going concern entity. Further, they suggest

intuitively that firms characterised by high operating risk are more susceptible to

financial distress. Additionally, Brealey and Myers (2001) argue that high growth

sectors such as biotechnology may experience high agency costs through restrictions

imposed by lenders to reduce the greater opportunities for asset substitution. The

degree of competition can impact upon the capital structure decisions of the firm.

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Therefore, the impact of determinants on capital structures may vary significantly

across industries. Optimal gearing ratios will vary across industries not just in terms

of magnitude but also in terms of ratio definition. For example, the optimal total debt

to total assets employed ratio for the textile industry may be, say 40 percent while for

the oil industry it may be 50 percent. However, the optimal ratio to target for

information technology firms may be, say 10 percent as measured by the total debt to

market value equity ratio.

The proposition of the existence of a target ratio of course raises the issue of optimal

adjustment policy. Fischeret al. (1989), Mauer and Triantis (1994), Dissanaike et al.

(2001) and Flannery and Rangan (2006) argue that some researchers fail to

acknowledge the multiple time periods required by firms to achieve their target capital

structure ratios. This particular dynamic problem lies at the heart of the capital

structure debate: do firms fully adjust their debt ratios to new information within one

accounting period (typically one year) or does adjustment take longer? To address this

issue, a functional form that permits partial adjustment of the firm's initial gearing

ratio to its target must be specified. Estimating target gearing in a simple cross-

sectional regression implicitly assumes that firms always attain their target gearing

ratios within one time period. However, if adjustment costs are non-trivial,

unrealistically restricting the adjustment speed to equal unity will bias coefficient

estimates.

While Flannery and Rangan (2006) found that US firms have target gearing ratios,

they also found that the sample average debt ratio over the period 1966-2001 is very

volatile. Fischeret al. (1989) developed a model of dynamic optimal gearing choice

and demonstrated that debt ratios are characterized by wide swings. These findings

suggest that firms do not identify a strict, single optimal capital structure ratio as such,

but rather a range over which their capital structures are allowed to vary.

2.2 Empirical review

Review of studies on targeting behaviour suggests that instead of a unique gearing

ratio to which firms immediately adjust, firms may engage in a multi-period

adjustment if actual gearing ratios are outside the optimal range. This implies that

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gearing studies should employ methods capable of modelling the dynamic adjustment

of capital structures undertaken by firms in the real world. It may be understood that

that gearing decisions would improve if econometric methodologies enabled some

synthesis of competing capital structure theories. The approach taken in this study

facilitates the testing of such a synthesis model.

Not only do we seek to determine empirically the nature of the targeting behaviour of

firms but also which industries are expected to be more prone to targeting behaviour.

A further question then, concerns which ratios we might expect to observe as being

targeted. We might expect firms in cash rich, mature industries with abundant cash

flows to take on more debt and target industry ratios as a form of management

discipline Jensen (1986). Additionally, firms with unique and/or specialised products,

tangible and liquid assets, and high asset turnover would be expected to target their

gearing ratios Titman and Wessels (1988). Firms in these industries should, in theory,

target book value gearing ratios and/or total debt to total assetsemployed ratios. Well-

established may attempt to target, or should monitor, market-value gearing ratios as

they recognize that establishment brings with it know-how and extra earning power

which is difficult to capture in a gearing measure otherwise; hence the market value of

the firm would be significantly different from its book value.

Industries dominated by small firms, such as the leisure industry, and cyclical

industries or industries with longer operating cycles, such as the building industry, are

more likely to target total debt gearing ratios Titman and Wessels (1988). On the

other hand, industries which are characterized by large firms which employ

significant amounts of fixed (tangible) assets rather than current assets, such as the

utilities and real estate industries will typically monitor and target ratios with long-

term debt and total assets employed as components. This is due to the easier access of

member firms to bond markets at non-prohibitive costs, that is, they enjoy significant

debt issue cost economies Bevan and Danbolt, (2000).

Industries which as considered as young such as information technology, with

significant non-accounting assets, are expected to target ratios with total debt as a

component according to the well-accepted maturity matching principle that tangibles

are best financed with long-term debt and growth opportunities with short-term debt

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Myers, (1977); Barclay et al. (1995). In industries where non-accounting assets are

the most important asset class, we would expect firms to target market value gearing

ratios as opposed to book value ratios. However, because the common practice in

these industries is to retain rather than to distribute earnings, we would expect to find

some evidence of firms targeting ratios which include retained earnings (i.e. ratios

which include total equity rather than base equity capital alone). However, the more

heterogeneous an industry is, the less likely it is that the results will conform to

expectations.

2.2.1 Effect of an industry-optimal gearing ratio targeting

The objective of this study is to investigate whether industry-optimal targeting arises

in the long run while a hierarchy of financing arises in the short run. The relationship

between measures of corporate capital structure is investigated using the Johansen co-

integration test. The application of this technique represents an improvement upon the

cross sectional tests often employed in the capital structure literature as it tries to

capture the financing behaviour of firms in a dynamic rather than static framework.

Further, it allows for variability in gearing ratios and adjustment to a target in a multi-

period framework, a common finding in the literature. Examining the target gearingbehaviour of firms at the industry level necessarily involves a trade-off between

information loss and results generalisation. It is easier to generalise results if we

aggregate the data at the market level, though we may lose useful information

regarding firm-level gearing correction because at this level any changes will be very

small. While there are limits to the change in capital sources from year to year (for

instance, bank borrowing may well remain stable), the variation of gearing ratio

components for individual firms will be much greater than the variation of the

aggregated gearing ratio components. Conversely, while firm-level analysis retains all

information available, it is not clear how generalisable the results may be. Further,

given that business risk and other gearing determinants are often assumed to be

similar for firms in a given industry, by aggregating at the industry level, we retain the

most essential information while also retaining results generalisation.

There is no consensus on how capital structure ratios and their components should be

defined. Certain ratios stress the importance of market rather than book values, whilst

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other ratios emphasise the importance of balance sheet values, especially if

substantially different from market values. Bowman (1980) and Marsh (1982) find

that book measures of gearing are statistically indistinguishable from market value

measures. However, book and market gearing ratios are conceptually different. Book

measures are by definition backward-looking due to their reliance on accounting

data whereas market values are generally held to be forward looking. Thus, there is

no reason why these two concepts should match Barclay et al., (2006). Harris and

Raviv (1991) and Rajan and Zingales (1995) employ gearing ratios in their empirics

using book values for debt and market values for equity, a compromise employed

widely in the empirical literature.

Another important issue is whether short-term debt should be included in a definition

of gearing as its omission may lead to an understatement of financial distress risk.

Rajan and Zingales (1995) and Tucker (1995) find that while there is large variation

across industries,

2.3 Knowledge gap

Most capital structure studies to date are based on data from developed countries.Antoniou et al. (2002) analyse data from the UK, Germany and France, Rajan and

Zingales (1995) use data from the G-7 countries, Bevan and Danbolt (2000) from the

UK. Only a few studies provide evidence from developing countries. Booth et al,

(2001) analyse data from ten developing countries (Brazil, Mexico, India, South

Korea, Jordan, Malaysia, Pakistan, Thailand, Turkey and Zimbabwe), Pandey (2001)

uses data from Malaysia, Chen (2004) utilise data from China, Omet and Nobanee

(2001) use data from Jordan and Al-Sakran (2001) analyses data from Saudi Arabia.

This study attempts to reduce the gap by analysing a capital structures from firms in

the developing world, and more specifically Kenya. Booth et al, (2001) state that, In

general, debt ratios in developing countries seem to be affected in the same way and

by the same types of variables that are significant in developed countries. However,

there are systematic differences in the way these ratios are affected by country factors,

such as GDP growth rates, inflation rates, and development of capital markets. This

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study is also intended to provide further evidence of the capital structure theories

pertaining to a developing country.

2.4 Conceptual frameworkThe researcher has developed the framework outlined below to explain the

relationships of the variables under investigation and the overall study objective.

Independent Variable Moderating Variables Dependent Variable

Figure 1

Figure 1

11

Short-term debt to

equity

Distress risk

Long-run total debt to

total assets


market value of equity


total equity

Finance costs

Co-lateral

Government policy

Shareholder preferences

Optimum gearingratio


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CHAPTER THREE

RESEARCH DESIGN AND METHODOLOGY

3.1 Introduction

This chapter discusses the research methodology that will be adopted in order to meet

the objectives of the study. Included in this chapter are the research design, study

population, sampling method, data collection tools, and data analysis tools to be used

to be used in the study.

3.2 Research design

The study will use a cross-sectional survey design as this allows the observation of the

full population, or a sample thereof, at a specific point in time. This method has been

selected because the researcher seeks to collect data from a cross-section of firms at

one point in time. It is important to note that cross-sectional studies involve data

collected at a defined time and they often rely on data originally collected for other

purposes. In this case the researcher will study financial data presented on annual

audited financial statements of the firms selected.

The study involves the analysis of the movement of each firms gearing ratio over

time and to capture the financing behaviour of firms in a dynamic rather than static

framework. The researcher will improve on the cross sectional tests by applying co

integration analysis, to allow for the variability of gearing ratios and the adjustment to

a target in a multi-period framework, results commonly found in the literature

Dissanaike et al., (2001); Flannery and Rangan, (2006). Implicitly, this technique tests

a synthesis model of capital structure determination where the long-run gearing ratio

is determined by the trade-off theory while the short-run variations may be driven by

the pecking order theory.

3.2.1 Definition and measurement of variables

The study involves the analysis of the movement of the capital gearing ratios of firms

over time to determine the existence of optimum capital gearing ratio targeting. The

various components of gearing ratios are shown in the table below.

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Table 1: Gearing ratio constituents

Variable Label Datastream

code

Definition

Book value of equity BE 305 (Ln of) Book value of equity

at balance sheet date

Market value of equity ME HMV (Ln of) Market value of

equity at balance sheet date

Total assets employed A 391 (Ln of) The sum of all assets

less current liabilities

Book value of equity plus

reserves

TE 307 (Ln of) Book value of equity

and reserves at balance sheet

date

Long-term debt D 321 (Ln of) All loans repayable

in more than one year

Short-term debt SD 318 (Ln of) All loans payable in

one year or less

Total debt TD (Ln of) The sum of long-

term and short-term debt

Book value of total capital

employed

TDBE (Ln of) The sum of total

debt and book value ofequity

Book value of total capital

employed and reserves

TDTE (Ln of) The sum of total

debt and total equity

Market value of total

capital employed

TDME (Ln of) The sum of total

debt and market value of

equity

3.3 Population

The study will be conducted by researching into the movement of the capital gearing

ratios of firms whose shares are listed on the Nairobi Securities Exchange.

3.4 Sampling techniques

In order to sample different determinants of gearing ratios, whether based on asset

book values or market values the study will purposively sample listed firms in two

sectors; manufacturing and technology.

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3.5 Sample size

Out of the 56 firms listed on the Nairobi Securities Exchange the researcher will

purposively sample those in two sectors; manufacturing and technology, which are 7

in number. In the opinion of the researcher manufacturing firms are characterised by

significant amounts of tangibles assets, and those likely to base their optimal gearing

ratios on total assets to debt, while those in technology are likely to hold insignificant

amounts in tangible assets and may prefer to base their optimum gearing ratio on the

market value of the firm.

3.6 Data collectionThe study will use secondary data, and involves the collection of quantitative data

from published audited annual financial statements of the firms sampled and data

obtained from the Nairobi Securities Exchange database.

The data required is:

i. The balance sheet amounts of the companies equity, liabilities and assets; and

ii. Daily stock prices to compute the market value of the firms equity.

The data will be collected by the use of tables and spreadsheets.

3.7 Data analysis

The data will be analyzed using regression analysis. This will be aided by the use of

Statistical Package for the Social Sciences (SPSS), a computer statistical analysis

program. The data will be analysed using descriptive statistics. The results will be

tested using student T- test to test the significance of the data. Regression analysis

will be carried out to establish the relationship and correlation of the variables. The

model to be used is the one that was successfully used by Mamoghli and Dhouibi

(2009) to study optimum gearing ratio and insolvency risk in emerging markets.

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The researcher will use the following regression model to analyze the data collected:

Regression Model with control variables

The Regression Model will test for the number of the characteristic roots that are

significantly different from unity can be constructed using the following two test

statistics:

+=

=n

ri

itrace Tr

1

^

)1ln()( (1)

)1ln()1,( 1^

max +=+ rTrr (2)

1 1 1, 1 2, 1 1( )t t t t x a x x e = + (3)

2 2 1, 1 2, 1 2( )t t t t x a x x e = + (4)

The general form of the panel model can be specified more exactly as: to check the

consistency of the ECM results, in addition to TD/(BE+RE) ratio, we also examine

the adjustment speed coefficients ofD/(BE+RE), TD/BEandD/BEratios

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Al-Sakran, S. (2001). Leverage Determinants in the absence of Corporate Tax

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Ang, J. (1976). The Intertemporal Behaviour of Corporate Debt Policy, Journal of

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Antoniou, A., Guney, Y. and Paudyal, K. (2002). Determinants of Corporate Capital

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Barclay, M. and Smith, C. (1999). The Capital Structure Puzzle: Another Look at the

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Barclay, M., Morellec, E. Smith Jr., C. (2006) On the Debt Capacity of Growth

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Beattie, V., Goodacre, A. and Thomson, S. (2006). Corporate Financing Decisions:

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Bevan, A. and Danbolt, J. (2000). Capital Structure and its Determinants in the UK -

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Bowman J. (1980). The Importance of a Market Value Measurement of Debt in

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APPENDIX I: TIME PLAN

Activity JUN JUL AUG SEPT

Proposal development

Approval

Data collection

Data analysis/ Project write up

Project submission

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APPENDIX II: BUDGET

Proposal development 3,000.00

Stationery 5,000.00

Typing and printing proposal 5,000.00

Photocopy (proposal questions ) 2,000.00

Transport 15,000.00

Miscellaneous expenses (15%) 4,500.00

Total 34,500.00

kimani (revised on 12 oct 2012)(1)

Documents