Management on the Profitability of 
fulfillment of 
~cien\:e in 
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School of Finance and Applied Economics 
StTathmore University 
ii 
Abstract 
This study seeks to provide empirical evidence about the impact of Working Capital 
Management (WCM) on the profitability of Kenyan listed firms. This is necessary since 
Working Capital Management affects both liquidity and profitability of firms and thus it is 
necessary for firm survival. A sample of 20 firms is chosen from five sectors of the NSE, based 
on market capitalization as at 2014. The study uses the fixed effects model in order to show the 
relationship between working capital management components and profitability and also show 
the effect of aggressiveness of working capital management strategies on profitability of firms 
listed in the Nairobi Securities Exchange. The study finds that individual working capita1 
management components affect profitability but the effect is not significant to all sectors. Also, 
the relationship can either be negative or positive between different firms. Therefore, firm 
managers should employ proper working capital management strategies in order to yield the best 
results depending on what is best for them. An improvement in the firm's performance shall 
increase the firm value and eventually shall be of great importance to the shareholders of the 
company. Both the level of aggressiveness and conservativeness affect profitability of firms 
listed in the NSE. Therefore, firm managers should employ the best strategy for their firm in 
order to increase their profits. 
iii 
List of Abbreviations 
WCM- Working Capital Management 
GOP - Gross Operating Profit 
GOI - Gross Operating Income 
CCC - Cash Conversion Cycle 
ACP -Average Collection Period 
f\PP - Average Payment Period 
ICP - Inventory Conversion Period 
ROTA - Return on Total Assets 
ROE- Return on Equity 
NSE -Nairobi Securities Exchange 
iv 
Contents 
Abstract ..................................................................................................................................................... iii 
List of Abbreviations ................................................................................................................................ iv 
1.0 CHAPTER ONE ................................................................................................................................. 1 
1.1 Background of the study. .................................................................................................................. 1 
1.2 Problem Statetnent. ........................................................................................................................... 2 
1.3 Research Objectives .......................................................................................................................... 3 
1. 4 Research Questions ........................................................................................................................... 3 
1.5 Significance ofR~search ........................... t ........................................................................... : ........... 3 
2.0 CHAPTER TWO: LITERATURE REVIEW .................................................................................. 4 
2.1 Introduction ....................................................................................................................................... 4 
2.2 Theories ofWorking Capital Manage1nent. ...................................................................................... 4 
2.3 Components of Working Capital Management ................................................................................. 4 
2.4 Etnpirical Work ................................................................................................................................. 6 
2.5 Summary of the Findings from previous research ........................................................................... lO 
2.6 Research Gap .................................................................................................................................. 11 
2.7 Conceptual Framework ................................................................................................................... 11 
3.0 RESEARCH METHODOLOGY ..................................................................................................... 12 
3.1 Introduction ..................................................................................................................................... 12 
3.2 Research Design .............................................................................................................................. 12 
3.3 Population and Srunple size. ........................................................................................................... 12 
3.4 Data Collection ............................................................................................................................... 12 
3.5 Choice ofVariables ......................................................................................................................... 13 
3.6 Reseru·ch Model. ............................................................................................................................. 14 
3.7 Data Analysis. ................................................................................................................................. 15 
4.0 DATA ANALYSIS, RESULTS AND DISCUSSION ...................................................................... 16 
4 .I Introduction ..................................................................................................................................... 16 
4.2 Descriptive Analysis ....................................................................................................................... 16 
4.3 Regression Analysis ........................................................................................................................ 17 
4. 4 Sector-wise Regression ................................................................................................................... 17 
4.4.5 Regression Interpretation ............................................................................................................. 27 
5.0 SUMMARY, CONCLUSIONS AND RECOMMENDATIONS .................................................... 31 
5.1 Introduction ..................................................................................................................................... 31 
v 
5.2 SUilllnruy ......................................................................................................................................... 31 
5.3 Conclusion ...................................................................................................................................... 32 
5.4 Motivation and extending current literature .................................................................................... 32 
5.5 Limitations ofthe study .................................................................................................................. 32 
5.6 Recotnmendations ........................................................................................................................... 32 
5.7 Areas of further research ................................................................................................................. 33 
4.0 Bibliography ..................................................................................................................................................... 34 
vi 
1.0 CHAPTER ONE 
1.1 Background ofthe study. 
Every organization, whether profit oriented or not, requires a necessary amount of working 
capital. Working capital is the difference between current assets and current liabilities in a 
business. A current asset is any asset which is expected to be converted into cash within one year 
(Raheman & Nasr, 2007). Examples of current assets include cash, short 'term investments, 
accounts receivable, inventory, marketable securities and prepaid expenses. In the balance sheet, 
current assets are presented in order of liquidity, which means that the most liquid items are 
shown first. Current assets are important because they are used to finance day to day activities of 
a business. A current liability is a company's obligation that is due within one year. Examples of 
current liabilities include short tenn debt, accounts payable and accrued liabilities. It is generally 
assumed that if a company has excess current assets over its current liabilities, it will be able to 
finance its short tenn obligations. 
Working capital management (WCM) is the management of short term investing and financing 
of a company. It is how current assets and current liabilities are managed in a company (Garcia-
Teruel & Martinez-Solano, 2007). The aim of working capital management is to ensure that 
companies have sufficient cash flow to continue with their operations and that they have 
sufficient funds to satisfy both maturing short-tenn debt and upcoming operational expenses. 
WCM affects both liquidity and profitability of the firms. Therefore, effective management of 
working capital is very important for every organization in order to establish a tradeoff between 
liquidity and profitability. (Deloof, 2003), emphasized that working capital management is a key 
part of corporate strategy and the way it is managed can have a significant impact on the 
liquidity and the profitability of the company. 
The most important financial objective of any organization is to earn profit. As a result, more 
attention is given on the profitability of the business rather than on its liquidity. This is due to the 
fact that a finn cannot survive for a long time if it is not making profits. Thus, the managers of a 
company thus lay more emphasis towards profitability, and neglect liquidity which is the ability 
1 
of a company to honor short tenn financial obligations. If the company is not able to honor its 
short-term financial obligations, it moves a step towards bankruptcy. Thus, liquidity is equaily 
important as well. 
For many businesses, the components of working capital represent the largest items on the 
balance sheet. Despite this, they tend not to be seen as issues demanding strategic consideration 
or top management attention (Deloof, 2003) 
In Kenya, all the listed firms maintain a certain level of working capital which maximizes their 
profits as well as maintaini~g the right liquidity .levels. Firms differ in their requirements of 
working capital to reflect their methods of doing business and what they are selling. Uchumi 
supermarket for example, will have a lot of cash sales, few credit sales and minimal trade 
debtors. Manufacturing companies on the other hand, maintain high levels of stock and inventory 
to ensure continuous operations. Thus, the managers of the firms should know the optimum level 
of working capital to maintain in order to increase profitability as well as maintaining the right 
liquidity levels. 
1.2 Problem Statement. 
Proper management of working capital is very important in the survival of any company 
(Padachi, 2006). According to (Smith K. V., 1973), Poor management of working capital can 
lead to the downfall of a business since it will not be able to honor its short tenn obligations. 
Working capital affects both liquidity and profitability of a firm. Liquidity in the context of 
working capital management is the ability of a company to satisfy its short-term obligations 
using assets that are readily convertible into cash (current assets). Excessive liquidity on one 
hand indicates the accumulation of idle funds that don't fetch any profits for the finn (Smith K. 
V, 1980) whereas insufficient liquidity might deteriorate finn's credit standings. (Raheman & 
Nasr, 2007), concluded that the final goal for any firm is to maximize the profitability of the finn 
by preserving the liquidity. However, increasing profits at the cost of liquidity might cause 
serious problems to the finn which might lead to the firm's insolvency. Thus, an effective 
working capital management would be needed to strike a balance between the two core 
objectives of the firm. (Smith K. V, 1980), found that a balance between both goals is important 
for firms to survive. This is called a trade-off Finns focusing on maximizing profitability will 
most likely reduce the liquidity of the firm and conversely firms focusing on maximizing 
2 
liquidity will most likely reduce the profitability of the firm (Shin & Soenen, 1998). Therefore, 
the trade-off between profitability and liquidity is the key to working capital management. 
1.3 Research Objectives 
1. To determine the effect of working capital management components on profitability of finns 
in Kenya. 
2. To examine how the level of aggressiveness in working capital affects profitability of firms in 
Kenya. 
1.4 Research Questions 
1. Do working capital management components affect profitability of firms in Kenya? 
2. To what extent does the level of aggressiveness affect profitability of firms in Kenya? 
1.5 Significance ofResearch 
i. To the Managers 
The study will provide a better insight to managers on how working capital components and 
strategies affect profitability of firms in Kenya and the measures they can take to create an 
optimal working capital in order to increase profitability. 
ii. To the Government and policy makers 
·This study would also assist the Government and policy-makers to implement new set of policies 
regarding the working capital market to ensure continuous economic growth and increased 
profitability of firms. Capital Market Authority as regulator of listed firms should ensure that 
good corporate governance is maintained to promote the value and interest of stakeholders. 
iii. To the academics: 
This study will help in understanding the relevance of working capital management and provide 
knowledge base upon which further studies and research can be undertaken to explain 
phenomena and create model and theories that can adequately account for business operations in 
the dynamic business environment. 
3 
2.0 CHAPTER TWO: LITERA TORE UEVIEW 
2. 1 Introduction. 
This chapter presents a review of the literature on working capital management. Many studies on 
working capital and firm profitability have been done from different views and in different 
environments leading to different results. 
2.2 Theories ofWorking Capital Management. 
Firms need to determine the optimal trade-off between profitability and liquidity. The type of 
working capital policy operated will ,be influenced by factors such as the growth rate of the 
company, its size, the nature of the industry it operates in and the risk attitude of the firm's 
management. The optimal policy adopted by firms should be the one which maximizes their 
returns and minimizes their risks. 
Aggressive Themy. 
An aggressive approach in working capital means that there is low investment in working 
capital. This means that the outcome of current assets minus current liabilities is negative. 
According to (Caballero & Garcia-Teruel, 2012), investing in an aggressive approach leads to 
high risk and conversely high returns for a business. This is because there are less idle funds in 
the business which do not generate return. Likewise, the unavailability current assets means that 
the company can fall into bankruptcy once it is unable to honor its short tenn obligations. 
Conservative Theory 
In the conservative approach, there is a high investment in current assets which can be able to 
support any levels of sales and production. According to (Caballero & Garcia-Teruel, 20 12), a 
conservative working capital approach is characterized by lower risk and return. This is because 
the company has excess amounts of cash which do not earn much of a return, thus leading to 
decreased profitability. The cash should be invested in, in order to generate returns for the 
business. Most companies in seasonal industries such as tourism, farming or construction might 
adopt a conservative working capital model in order to buffer against risk. 
2.3 Components of Working Capital Management 
Previous works done in working capital management have used inventory conversion period, 
receivables conversion period and payables conversiOn period as the main components of 
4 
working capital. The three components form the cash conversion cycle which is a comprehensive 
measure of working capital. (Afeef, 2011 ). 
Inventory Conversion Period (ICP) 
This refers to the time taken to convert inventory into sales. The longer the time taken the more 
cash the company has locked up in inventory. There are certain costs involved with keeping 
inventory. They include costs of warehousing and also insurance costs. (Deloof, 2003), found 
that having more inventory reduces the risk of a stock out thus ensuring a continuous production 
process and consequently increasing profits. (Jose, Lancaster, & Stevens, 1996) contradicted that 
by saying that having low inventory may cause loss of sales due to a stock out thus causing 
reduced profitability. Managers should thus ensure a proper level of inventory in order to 
maximize sales. 
Average Collection Period (ACP) 
This refers to the time taken to collect cash from accounts receivables. Firms provide trade credit 
to customers in order to win customers (Lazaridis & Tryfonidis, 2006) through strengthening 
long tenn relationships with them and consequently increasing sales. On the other hand, 
providing trade credit to customers can lead to liquidity problems since the firm might run short 
of cash to honor its short term obligations. 
Average Payment Period (APP) 
It refers to the time taken to pay firm's creditors. According to (Deloof, 2003), delaying 
payments to suppliers allows a firm to assess the quality of the products bought and also the finn 
can reserve some cash which was to be used to pay the suppliers and use it in other operations 
which will maximize profits. On the other hand, (Padachi, 2006)found that delaying payments to 
suppliers can be very costly especially if there is a trade discount for early payment and also can 
ruin the credit reputation for the firm in the long run. 
Cash Conversion Cycle (CCC) 
The CCC is a comprehensive measure of WCM. It focuses on the time span between the 
expenditure for purchasing resources and the collection of cash for the goods sold (Eljelly, 
2004 ). The length of the CCC detennines how much money is locked up in working capital. The 
length can be positive and negative. A negative CCC shows that a firm already collected its 
5 
receivables before the finn pays its suppliers (Uyar, 2009) and working capital is a source of 
funds. A positive CCC shows that working capital is a use of funds, which needs to be financed 
(Caballero & Garcia-Teruel, 2012). A longer CCC can lead to higher sales and thus increase 
profitability, but it may also decrease profitability when the cost of holding more inventory 
and/or granting trade credit to customers outweigh the benefits (Raheman & Nasr, 2007) If a 
firm has a positive CCC of 50 days, the firm has to finance an amount equivalent to the daily 
cost of sales multiplied with 50 days (Eljelly, 2004). 
2.4 Empirical Work 
The papers done on WCM have been done in different environs thus leading to different results. 
Working Capital and Profitability. 
Deloof (2003) tested the relationship between working capital management and corporate 
profitability for a sample of 1,009 large Belgium non-financial firms for the period 1992- 1996. 
Profitability is measured by Gross Operating Income (GOI) which is the dependent variable. The 
length of a firm's CCC is measured as the independent variable. He uses Pearson correlation to 
regress Gross operating income against the three components of cash conversion cycle. He found 
a significant negative relation between GOI and the nmnber of days accounts receivable, 
inventories and accounts payable of Belgian firms. These results show that managers can 
increase value for their shareholders by reducing the nmnber of days receivable and inventories 
to a reasonable minimmn. 
Lazaridis & Tryfonidis (2006) investigated the relationship between corporate profitability and 
working capital management of 131 companies listed in the Athens Stock Exchange for the 
period of 2001 - 2004. Firm's profitability is measured as the dependent variable using Gross 
Operating Profit (GOP). The length of a firm's CCC is measured as the independent variable. 
Accounts receivable turnover, accounts payable turnover and inventory management were the 
three components of cash conversion cycle used in the study. As control variables in the 
regressions, Lazaridis & Tryfonidis (2006) used fixed financial assets, the natural logarithm of 
sales and the financial debt ratio. Using Pearson's correlation and regression, he found a highly 
significant negative relationship between GOP and the CCC, accounts receivable and accounts 
payable. The negative relationship between ICP and gross operating profit however was not 
6 
statistically significant. The results meant that a low GOP is associated with an increase of 
number of days payable, since firms want to take advantage of credit period granted to them by 
their suppliers. Likewise, the negative relationship between number of days inventory and 
corporate profitability suggests that a drop in sales accompanied by mismanagement of inventory 
will lead to tying up excess capital at the expense of profitable operations. (Lazaridis & 
Tryfonidis, 2006) Concluded their study by saying that managers can increase company profits 
by properly handling the cash conversion cycle and keeping each different component (accounts 
receivables, accounts payables, inventory) to an optimum level. 
Raheman & Nasr (2007) tested the relationship between working capital management and 
profitability of 94 Pakistani firms listed in the Karachi Stock exchange in a period of six years 
from 1999- 2004. Using Pearson's correlation and pooled regression, the results showed negative 
relation between working capital management's components and profitability. This results 
support the finding of (Deloof, 2003) and (Lazaridis & Tryfonidis, 2006). They also found a 
negative relationship between liquidity and profitability of the finns and thus the firms must set a 
trade-off between the two objectives so that neither of the two suffers. 
Contradicting Results. 
Padachi (2006) examined the trends in WCM and its impact on profitability on small 
manufacturing firms in Mauritius. A sample of 58 small manufacturing firms is used for a period 
of six years (1998- 2003). Profitability is measured by return on total assets (ROTA) and CCC 
is used as a comprehensive measure of working capital. (Padachi, 2006), found a negative 
correlation between ROTA and the individual components of the cash conversion cycle, but 
found a positive correlation between ROTA and the cash conversion cycle as a whole. The 
positive relationship can be attributed with the view that resources are blocked at the different 
stage of the supply chain, thus prolonging the operating cycle. This might increase profits due to 
increased sales, especially where the costs of tied up capital is lower than the benefits of holding 
more inventories and granting more trade credit to customers. The regression analysis shows that 
the relationship between ROTA and individual cash conversion cycle components is statistically 
negative except for inventory management, which is negative but not highly significant. 
(Padachi, 2006), conducted his research at an opportune time when Mauritius Government was 
7 
deploying resources to help the SME sector and thus, through a proper management of working 
capital, the SME can positively contribute to the Mauritian Economy. 
Gill, Biger, & Mathur (20 10) tested the relationship between working capital management and 
firm profitability for 88 American manufacturing firms listed on the New York Stock Exchange 
for a period of 3 years (2005 - 2007). Firm profitability is the dependent variable and is 
measured using GOP. The length of a firm's CCC and its components are used as the 
independent variables and finn size, financial debt ratio and fixed financial assets as the control 
variables. Contrary to prior research done by (Laza~idis & Tryfonidis, 2006) and (Raheman &· 
Nasr, 20b7) where they found a negative relation between number of days payable and 
profitability, (Gill, Biger, & Mathur, 2010) found no significant relationship between the two. 
They also found no significant relationship between the number of days of inventory and 
profitability of firms, contrary to (Lazaridis & Tryfonidis, 2006), (Raheman & Nasr, 2007) and 
(Padachi, 2006) who found a negative relationship between the two. Likewise, he found a 
positive relationship between cash conversion cycle and profitability. Thus, the higher the cash 
conversion cycle, the higher the profitability. 
Afeef (20 11) tested the impact of working capital management on the profitability of 40 Pakistan 
SME's listed on the Karachi Stock Exchange for a period of 6 years (2003 - 2008). (Afeef, 
2011 ), found no significant relationship between CCC, the number of days payable and firm 
profitability which was measured using return on assets. 
Sharma & Kumar (2011) tested the relationship between working capital management and 
profitability of 263 Indian non-financial firms listed on the Bombay Stock Exchange in a period 
of 9 years (2000 - 2008). Contrary to prior studies, (Sharma & Kumar, 2011) found a positive 
relationship between number of days receivable and profitability. This implies that in the Indian 
companies, managers can increase profitability by increasing the credit period granted to their 
customers. In addition to that, (Sharma & Kumar, 2011) found a positive relationship between 
CCC and profitability of the Indian finns. This is in contrast with the theoretical work, where 
cash conversion cycle and profitability of firms should be negatively related. 
8 
Empirical Research in Kenya 
Nzioki, Kirwa, Riwo, & Mwende (2013) conducted a study which analyzed the effects of 
working capital management on the profitability of manufacturing firms listed on the Nairobi 
Securities Exchange. The results from the study revealed that gross operating profit was 
positively correlated with average collection period and average payment period but negatively 
correlated with cash conversion cycle. The relationship between inventory turnover in days and 
gross operating profit was insignificant. They recommended that managers should focus on 
reducing cash conversion cycles and try to collect receivables as soon as possible 
Makori & Jagongo (2013) conducted a research on impact of working capital management on 
profitability of Kenyan firms listed on the NSE. They found that the management can create 
value for their shareholders by increasing the finns' inventories to a reasonable level. Also, finns 
should take long to pay their creditors in as far as they do not strain their relationships with these 
creditors. They also found that firms are capable of gaining sustainable competitive advantage by 
means of effective and efficient utilization of the resources of the organization through a careful 
reduction of the cash conversion cycle to its minimum. By doing so, the profitability of the firms 
is expected to increase 
Working Capital Management Strategies. 
Weinraub & Visscher (1998) conducted a research to examine the relative relationship between 
aggressive/ conservative practices of ten diverse industry groups for ten years for the period 
between 1984 and 1993 in the US firms. Results showed that the industries had significantly 
different management strategies and an aggressive working capital was balanced by a relatively 
conservative working capital in order to hedge against risk. 
Filbeck & Krueger (2005) also examined the aggressive/ conservative working capital policies 
using data of the companies from the CFO's magazine's annual working capital management 
survey. They found out that there is a significant difference in working capital policies between 
industries and that the working capital policy adopted by an industry changes significantly over 
time due to factors such as changes in interest rate and competition. 
Nazir & Afza (2009) studied the impact of aggressive working capital management on firm 
performance of204 non-financial Pakistan firms listed in Karachi Stock Exchange for 8 years for 
9 
the period (1998 - 2005). Secondary data was used and data included firms in 17 different 
sectors of the Karachi Stock Exchange. The ratio of current assets to total assets ratio was used 
as a measure of conservative WCM and the ratio of current liabilities to total assets was used as a 
measure of aggressiveness. They found that firms with more aggressive working capital provided 
low levels of profit which was measured by ROE and ROA. They also found that investors give 
weight to stocks of those firms that adopt an aggressive approach to managing short tenn 
liabilities. 
2.5 Summary of the Findings from previous research 
between 
profitability profitability and profitability and profitability and 
CCC 
(Lazaridis 
Positive 
! Not significant 
• Negative Not significant • Not significant 
• Positive . Negative . Positive 
2011) 
'table i: Surrunmy ofFindings. Source: Auti!~r 
10 
2.6 Research Gap 
From the above literature, it is evident that different working capital components have different 
effects on profitability. However, there's little research done on other firms listed in the NSE 
other than those in the manufacturing and construction industry. This paper will seek to study 
more firms listed on the NSE in order to give conclusive results. This paper will also seek to find 
whether the level of aggressiveness adopted by a firm has any significant impact on profitability 
of firms listed. on NSE. 
2. 7 Conceptual Framework. 
Independent Variables 
Average Collection Period Dependent Variable 
Average Payment Period 1 __P_ro_fitabil\_ __ity_ __)  
Inventory Conversion Period 
Level of Aggressiveness 
Figure I: Conceptual framework 
Source: Author 
Control Variables 
Firm Size 
Current Ratio 
Debt Ratio. 
11 
3.0 RESEARCH METHODOLOGY 
3.1 Introduction. 
This chapter describes the research methodology of the study. It describes the research design, 
sampling design, target population, data collection procedures regression models and analysis. 
3.2 Research Design. 
The study adopts an explanatory research design in order to show the relationship between 
working capital management components and profitability and also show the effect of 
aggressiveness of working capital management strategies on profitability of finns across the 
different firms listed in the Nairobi Securities Exchange (NSE) 
Explanatory research attempts to connect ideas, in order to understand cause and effect between 
the independent variables and the dependent variables. 
3.3 Population and Sample size. 
A population is the target group, from which the researcher wishes to take their sample from. 
The population in this study comprises of all the companies in Kenya. The sample size shall 
consist of companies listed in the NSE, except for firms listed in the banking. investment. 
Automobile and insurance sectors. 
The remaining sectors include; Agricultural, Construction, Energy and Petroleum, manufacturing 
and commercial and service industries. (Appendix 4). A total of20 companies is used. 
3.4 Data Collection. 
Due to the nature of this study, the data to be used is secondary. The data shall be obtained from 
the financial statements of the companies. The duration will be of9 years for the period 2006-
20 14 so as to reflect the latest data and draw conclusive results. 
12 
3.5 Choice of Variables. 
The choice of variables is affected by prior research done on working capital management as 
seen in the literature review. The independent variable of working capital management is the 
cause, while the dependent variable for firm profitability is the effect 
Variables Description 
Dependent variables 
ROA It is a good measure since it relates a finn's profitability to its 
asset base. 
Net Income 
ROA= 
Total Assets 
Independent variables 
Average Collection Period It shows the time taken to collect cash from debtors. 
(ACP) Debtors 
ACP == * 365 days 
Sales 
Average Payment Period It shows the time taken to pay firm's creditors. 
(APP). Creditors 
APP== Co st o fS a l es *365days 
Inventory Conversion Period It shows the time taken to convert inventory to sales. 
(ICP). Inventory 
ICP = Co st of S als  * 365 days. 
Cash Conversion Cycle It shows the time span between the expenditure for purchasing 
resources and the collection of cash for the goods sold. 
(Debtors+ Inventory)- Creditors 
CCC== * 365 
Sales 
13 
Level of Aggressiveness A high ratio indicates a high level of aggressive working capital 
management. 
Current Liabilities 
Agg= 
Total Assets 
Level of Conservativeness A high ratio indicates a high conservative approach used 
Current Assets 
Cons= 
Total Assets 
Control Variables 
Firm Size Measured by logarithm of sales. Ln (sales) 
Current Ratio Measures the ability of finns to honor their short term 
obligations. 
Current Assets 
CR = Current Liabilities 
Debt Ratio Measures firm leverage. 
Total Liabilities 
DR= 
Total Assets 
.. 
fable 2: Vanablcs dcscnptlon 
Source: Author 
3.6 Research Model. 
The research model adopts an OLS regression equation which is written as: 
Profitability= f(working capital management components, firm characteristics) 
Profitability = f(level of aggressiveness) 
Modell 
ROA =a+ Pl(ACP) + P2(APP) + p3(ICP) + B4 (CCC) +B5(S) + P6(CR) + P7(DR) + c: 
14 
Model2 
ROA =a+ Bl(CNTA) + B2(CL/TA) + B5(S) + B6(CR) + B7(DR) + z 
The first model will answer the first objective of the research by finding the relationship between 
working capital components and profitability of firms in the NSE. 
The second model will show how the level of aggressiveness of working capital affects 
profitability of the firms listed in the NSE. 
3.7 Data Analysis. 
The study will first start by conducting a descriptive analysis of the variables being used. 
Descriptive statistics are used to describe and discuss characteristics of a data set more generally 
and orderly than using raw data alone. They are often used where there is a significant amount of 
qualitative and quantitative data. In order to get the relationship between WCM and profitability 
and also the relationship between the financing theory and profitability, the models are run in 
STATA  STAT A software is picked since it is the best for analyzing panel data. The first step is 
to conduct both the fixed effects and Random Effects models in order to get the association 
between the explanatory variables and profitability. In order to choose the best model between 
the two, a Hausman Test is performed. The test gives you the most appropriate model for your 
analysis. Based on the model picked, the working capital management components are regressed 
independently against ROA. Likewise, the level of aggressiveness and conservativeness are 
regressed against ROA in order to answer the second objective of the study. 
15 
4.0 DATA ANALYSIS, RESULTS AND DISClJSSION 
4.1 Introduction. 
The chapter presents the empirical findings of this study. Each section is based on the tests that 
were carried out towards achieving the research objectives of the study. A fixed effects model 
was used to run the tests for the study after conducting a Hausman test 
4.2 Descriptive AHalysis 
The table provides the mean average of the collected variables of 20 Kenyan Listed firms in five 
sectors from the year 2006 to 2014. 
Agriculture 65.58 75.73 77.68 63.16 0.408 2.889 14.44 0.134 0.278 
Construction 66.79 90.11 96.795 70.25 0.525 1.61 15.90 0.252 0.367 
Manufacturing 53.60 94.73 96.53 59.48 0.5 1.58 16.10 0.35 0.522 
Services 67.27 179.8 68.04 23.96 0.547 1.33 15.60 0.345 0.403 
Energy 56.18 87.12 45.45 24.83 1.36 0.63 17.69 0.41 0.48 
Interpretation of the Results 
The credit period that firms grant their customers range between 53 and 67 days for the firms 
listed in the Nairobi Securities exchange. Generally, the services sector gives a longer credit 
period of 67 days whereas the manufacturing sector gives the shortest period of 53 days. 
The services sector takes the longest number of days to pay their bills while the agriculture sector 
takes the shortest time to pay their bills. A longer number of days imply that the firms prefer to 
postpone their payments to their creditors. 
The manufacturing and construction sectors have the highest number of days inventory, that is, 
96 days. This means that the finns in this sector, on average keep their raw materials, work in 
progress and/or finished goods for a period of 96 days in stock. 
16 
All the sectors of the NSE have a positive CCC, which means that most of the firms in the 
sample do not finance themselves with the trade credit provided by their suppliers but provide 
trade credit to their customers and take the risk of keeping the inventory. 
The energy sector has the highest debt ratio of 1.36 which means that firms in this sector prefer 
financing their total assets with liabilities and not equity. 
The agriculture sector has the highest current ratio of2.889. The firms in this sector have current 
assets which are 288.9% oftheir current liabilities. This means that the firms are able to pay off 
their short term debts in time. 
The energy sector uses a more aggressive approach whereas the manufacturing sector uses a 
more conservative approach of w?rking capital management. 
4.3 Regression Analysis. 
In order to investigate the impact of working capital management on the profitability of firms, a 
multivariate regression analysis is used. Both the fixed effects( Appendix 1) and random effects 
model(Appendix 2) were carried out and the fixed effects model was chosen after carrying out 
the Hausman test. The P value of the fixed effects model was less than 0.05 thus making it 
appropriate for data analysis. (Appendix 3) 
4.4 Sector-wise Ret,rression 
Five regression models were run per sector in order to meet the objectives of this study by 
establishing the relationship between working capital management components and profitability 
and also by finding out whether the aggressive or conservative approaches of working capital 
affect profitability of the firms. Doing a sector-wise regression will yield better results. 
17 
1. The Agriculture Sector 
Relationship between ROA and APP 
Fixed Effects Model 
Group variable: Company 1 
Rsquared Within= 0.8605 
Between= 0.4085 
Overall= 0.7665 
Number of Obs = 36 
Prob > f = 0.0000 
ROA .coeff Std Err · p>ltl 
APP 0.0023 0.0006 0.003 
LN SALES 0.015 0.035 0.671 
CR 0.0197 0.007 0.01 
DR 0.347 0.027 0 
Relationship between ROA and ACP 
Fixed Effects Model 
Group vru.iable: ComJ:>any_l 
Rsquared Within= 0.8063 
Between= 0.8654 
Overall= 0.7966 
Number of Obs = 36 
Prob > f = 0.0000 
ROA Coeff Std Err P>ltl 
ACP -0.000047 0.0009 0.961 
LNSALES 0.055 0.038 0.166 
CR 0.00622 0.006 0.377 
DR 0.348 0.032 0.00 
Relationship between ROA and ICP 
Fixed Effects Model 
Group variable: Company 1 
Rsquarcd Within= 0.8522 
Between= 0.6109 
Overall = 0. 7 564 
Number ofObs = 36 
Prob > f = 0.0000 
ROA Coeff Std Err P>ltl 
ICP 0.00178 0.0006 0.006 
18 
LNSALES 0.045 0.034 0.196 
CR 0.0086 0.006 0.166 
DR 0.3359 0.0287 0.00 
Relationship between CCC and ROA 
Fixed Effects Model 
Group variable: Company 1 
Rsquared Within= 0.8066 
Between= 0.8560 
Overatl = 0.7969 
Number of Obs = 36 
Prob > f == 0.0000 
ROA Coe:ff Std Err P>ltl 
CCC 0.00019 0.0008 0.82 
LNSALES 0.057 0.039 0.159 
CR 0.005 0.007 0.44 
DR 0.348 0.03 0.00 
Objective 2: Aggressive or Conservative? 
Fixed Effects Model 
Group variable: Company I 
Rsquared Within= 0.8139 
Between= 0.8207 
Overall= 0.7963 
Number ofObs = 36 
Prob > f = 0.0000 
ROA Coeff Std Err P>ltl 
CONS 0.20 0.42 0.528 
AGG -0.44 0.32 0.306 
LNSALES 0.04 0.04 0.342 
CR -0.005 0.01 0.690 
DR 0.38 0.07 0.000 
19 
2. The Energy and Petroleum Sector 
Relationship between ROA and APP 
Fixed Effects Model 
Group variable: Company 1 
Rsquared Within= 0.1190 
Between= 0.0101 
Overall= 0.0375 
Number of Obs = 36 
Prob > f = 0.0000 
ROA Coeff Std Err P>ltl 
APP '-0.0000277 ·o.ooo2 0.913 
LNSALES -0.0304 0.016 0.077 
CR 0.0028 0.01 0.854 
DR 0.008 0.032 0.803 
Relationship between ROA and ACP 
Fixed Effects Model 
Group variable: Company 1 
Rsquared Within= 0.1333 
Between= 0.0013 
Overall= 0.0367 
Number of Obs = 36 
Prob > f = 0.0000 
ROA Coeff Std Err P>lt! 
ACP -0.00017 0.0002 0.496 
LNSALES -0.0323 0.016 0.060 
CR 0.00612 0.01 0.671 
DR 0.01 0.034 0.631 
Relationship between ROA and ICP 
Fixed Effects Model 
Group variable: Company 1 
Rsquared Within= 0.1187 
Between= 0.0138 
Overall= 0.0361 
Number of Obs =3 6 
P•·ob > f = 0.0000 
ROA Coeff Std Err P>ltl 
ICP -0.0000233 0.0004 0.958 
LNSALES -0.03000 0.016 0.076 
20 
0.0031 0.01 I 0.7951 
0.007 0.813 
Relationship between ROA and CCC 
Fixed Effects Model 
Group variable: Company 1 
Rsquared Within= 0.1254 
Between= 0.0150 
Overall= 0.0342 
Number of Obs =3 6 
Prob > f = 0.0000 
ROA Coeff Std Err P>ltl 
CCC -0.0001 0.0002 0.644 
LNSALES -0.029 0.016 0.081 
CR 0.007 0.016 0.648 
DR 0.011 0.033 0.728 
Objective 2: Aggressive or Conservative? 
Fixed Effects Model 
Group variable: Company 1 
Rsquared Within= 0.1254 
Between= 0.0150 
Overall= 0.0342 
Number of Obs =3 6 
Prob > f = 0.0000 
ROA Coeff Std Err P>lt! 
CONS 0.3817 0.1285 0.006 
AGG -0.45 0.161 0.009 c------
LN SALES -0.03 0.015 0.059 
CR -0.02 0.014 0.158 
DR 0.05 0.07 0.415 
21 
3. The Manufacturing Sector 
Relationship between ROA and ICP 
Fixed Effects Model 
Group variable: . Company 1 
Rsquared Within= 0.5202 
Between= 0.3871 
Overall= 0.3064 
Number of Obs = 36 
Prob > f = 0.0000 
ROA Coeff Std Err P>ltl 
ICP -0.0006 0.0004 0.106 
LNSALES 0.119 0.04 0.02 
CR 0.03 0.02 0.07 
DR -0.32 0.09 0.003 
Relationship between ROA and ACP 
Fixed Effects Model 
Group variable: Company 1 
Rsquared Within= 0.5689 
Between= 0.5719 
Overall= 0.4314 
Number ofObs = 36 
Prob > f = 0.0000 
ROA Coeff Std Err P>ltl 
ACP -0.002 0.0008 0.018 
LNSALES 0.137 0.043 0.003 
CR 0.02 0.01 0.175 
DR -0.289 0.092 0.004 
Relationship between ROA and APP 
Fixed Effects Model 
Group variable: Company 1 
R squared Within= 0.5144 
Between= 0.3290 
Overall= 0.2905 
Number of Obs = 36 
Prob > f = 0.0000 
ROA Coeff Std Err P>!tl 
APP -0.0006 0.0004 0.13 
LN SALES 0.129 0.04 0.01 
22 
0.021 0.021 0.221 
-0.32 0.09 
Relationship between ROA and CCC 
Fixed Effects Model 
Group variable: Company 1 
Rsquared Witllin = 0.1190 
Between= 0.0101 
Overall= 0.0375 
Number of Obs =3 6 
P•·ob > f = 0.0000 
ROA Coeff Std Err P>ltl 
CCC -0.00046 0.0004 0.350 
LNSALES 0.142 0.04 0.005 
CR 0.03 0.221 0.089 
DR -0.314 0.100 0.004 
Objective 2: Aggressive or Conservative? 
Fixed Effects Model 
Group vruiable: Company 1 
Rsquared Within= 0.6021 
Between= 0.4478 
Overall= 0.3796 
Number of Obs = 36 
Prob > f = 0.0000 
ROA Coeff Std Err P>ltl 
CONS 0.26 0.30 0.212 
AGG -0.82 0.20 0.011 
LN SALES 0.10 0.04 0.003 
CR -0.06 0.05 0.261 
DR -0.15 0.10 0.167 
4. Th~ Stn'Vi~tllj St~ctor 
R~lutlon§lllp btltwtHm UOA nntl AJlP 
FLO!i4!d Effe4}t!l Modlll 
- Onm17 vnrinblt~; Ct:1lllRtlllY 1 --
R squared Within= 0.1190 
Between= 0.0101 
Overall= 0.0375 
23 
Number of Obs = 36 
Prob > f = 0.0000 
ROA Coeff Std Err P>jtj 
APP -0.00045 0.0004 0.333 
LNSALES 0.122 0.07 0.099 
CR 0.168 0.08 0.057 
DR -0.062 0.089 0.492 
Relationship between ROA and ACP 
Fixed Effects Model 
Group variable: Company 1 
Rsquared Within= 0.4199 
Between= 0.8376 
Overall = 0.4 724 
Number of Obs = 36 
Prob > f = 0.0000 
ROA Coeff Std Err P>ltl 
ACP -0.0003 0.0017 0.858 
LNSALES 0.12 0.07 0.109 
CR 0.18 0.08 0.035 
DR -0.07 0.09 0.392 
Relationship between ROA and ICP 
Fixed Effects Model 
Group variable: Company_ I 
Rsquared Within = 0.4192 
Between = 0.8740 
Overall= 0.4830 
Number of Obs == 36 
Prob > f = 0.0000 
ROA Coeff Std Err P>ltl 
ICP 0.000006 0.0008 0.996 
LNSALES 0.12 0.073 0.110 
CR 0.188 0.083 0.032 
DR -0.077 0.090 0.399 
24 
Relationship between ROA and CCC 
Fixed Effects Model 
Group vruiable: Company 1 
R squared Within= 0.5204 
Between= 0. 7872 
Overall= 0.4724 
Number of Obs = 36 
Prob > f = 0.0000 
ROA Coeff Std Err P>ltl 
CCC 0.0024 0.0009 0.022 
LN SALES 0.08 0.06 0.241 
CR 0.132 0.07 0.106 
DR -0.05 0.08 0.40 
Objective 2: Aggressive or Conservative? 
Fixed Effects Model 
Group variable: Company 1 
Rsquared Within= 0. 7477 
Between= 0.9663 
Overall= 0.6112 
Number ofObs = 36 
Prob > f = 0.0000 
ROA Coeff Std Err P>ltl 
CONS 1.55 0.244 0.006 
AGG -0.72 0.266 0.000 
LNSALES 0.03 0.05 0.476 
CR -0.12 0.07 0.129 
DR .0.15 0.1 0.178 
5. The Construction Sector 
Relationship between ROA and APP 
Fixed Effects Model 
Group vru-iable: Com~ru1y 1 
R squared Within= 0.8251 
Between = 1. 000 
Overall= 0.8240 
Number ofObs = 36 
Prob > f = 0.0000 
25 
ROA Coeff Std Err P>jtj 
APP -0.001 0.004 0.031 
LN SALES -0.03 0.03 0.358 
CR -0.02 0.4 0.517 
DR -0.36 0.27 0.2 
Relationship between ROA and ACP 
Fixed Effects Model 
Grou_Q variable: Companyl 
Rsquared Within= 0.7438 
Between = 1. 00 
Overall = 0.5244 
Number of Obs == 36 
Prob > f = 0.0000 
ROA Coeff Std Err P>ltl 
ACP -0.000466 0.0009 0.615 
LNSALES -0.0254 0.44 0.579 
CR -0.018 0.49 0.716 
DR -0.536 0.342 0.143 
Relationship between ROA and ICP 
Fixed Effects Model 
Group variable: Company 1 
Rsquared Within= 0.7471 
Between = 1. 00 
Overall= 0.5197 
Number of Obs =3 6 
Prob > f = 0.0000 
ROA Coeff Std Err P>l!l 
ICP -0.0003 0.0004 0.525 
LN SALES -0.02 0.04 0.578 
CR -0.015 0.04 0.758 
DR -0.5 0.3 0.084 
26 
Relationship between ROA and CCC 
Fixed Effects Model 
Group variable: Company 1 
R squared Within= 0.7485 
Between= 1.00 
Overall= 0.4936 
Number of Obs = 36 
Prob > f = 0.0000 
ROA Coeff Std Err P>ltl 
CCC 0.000525 0.0007 0.495 
LNSALES -0.008 0.04 0.85 
CR !0.019 0.04 0.69 
DR -0.67 0.3 0.053 
Objective 2: Aggressive or Conservative? 
FiXed Effects Model 
Group variable: Company 1 
Rsquared Within= 0.7943 
Between= 1.000 
Overall = 0.4 722 
Number ofObs == 36 
Prob > f = 0.0000 
ROA Coeff Std Err P>ltl 
CONS 0.601 0.367 0.130 
AGG -0.097 0.31 0.758 
LNSALES -0.002 0.04 0.951 
CR -0.57 0.074 0.440 
DR 0.365 0.306 0.089 
4.4.5 Regression Interpretation. 
Relationship between ROA and APP 
A negative relationship exists between ROA and APP for all the sectors in the study other than 
the agricultural sector, which has a positive relationship. The results are however only significant 
for firms in the agriculture sector since the P value (0.003) is below 0.005. The significance 
results suggest that the APP is a critical factor to consider when taking decision to improve 
profitability of firms in the agriculture sector. The positive relationship suggests that an increase 
in number of days payable by 1 day leads to an increase in profitability of the firms. This 
27 
positive relationship can be explained by the fact that firms in the agriculture sector take longer 
to pay their suppliers so as to take advantage of the funds in order to meet their working capital 
needs. Also, this relationship can be interpreted as; a finn which takes longer to settle its bills has 
a higher level of working capital reserves which it uses to increase its profitability. However. 
these finns should be careful not to spoil the business-supplier relationship when they delay 
these payments. 
The negative relationship between ROA and APP means that an increase in number of days 
payable will reduce profitability of the firms. This can. be interpreted as, finns that take a longer 
period to pay their suppliers fail to take advantage of early trade discounts given to them and 
thus end up depleting their working capital reserves thus leading to a lower profit. 
Relationship between ROA and ACP 
There exists a negative relationship between ACP and finn profitability in all the five sectors in 
study. However, this relationship is only significant to the manufacturing companies (P =0.018). 
The significance suggests that the ACP is a critical factor to consider when taking decision to 
improve profitability of firms in the manufacturing sector. 
The negative relationship is consistent with findings from (Deloof, 2003), (Raheman & Nasr, 
2007) and (Garcia-Teruel & Martinez-Solano, 2007). The negative co-efficient of the ACP 
implies that an increase in the number of days of accounts receivable by 1 day is associated with 
a decline in financial performance of the finns listed in the NSE. 
These results suggest that firms can improve their profitability by reducing the number of days 
accounts receivable. This means that the less time it takes for debtors to pay their bills, the more 
cash is available to replenish inventory thus leading to more sales and eventually increased 
profitability. 
The implication of these results is that managers can increase finn profitability by reducing the 
trade credit period granted to their customers. A more restrictive credit policy should be adopted 
by firm managers in order to increase their profitability. 
28 
Relationship between ROA and ICP 
The relationship between ICP and ROA is positive for both the agriculture and the services 
sector and negative for the manufacturing, energy and construction section. However, this 
relationship is only significant in the agriculture sector since it has a p value of 0.003, that is 
( <0. 05). For both the agriculture and services sectors, maintaining high levels of inventory 
reduces the cost of possible interruptions in the production process and the loss of business due 
to scarcity of products. Maintaining high levels of inventory is a form of hedging against price 
fluctuations due to adverse macroeconomic factors. 
For the manufacturing, energy and construction sectors, the relationship is negative but not 
significant. The negative relationship can be interpreted as; an increase in the number of days 
inventory by 1 day will decrease profitability of the firms. 
Relationship between ROA and CCC 
There exists a positive relationship between CCC and ROA of firms in the agriculture, services 
and construction sectors and a negative relationship exists between CCC and ROA of firms in 
the energy and manufacturing sectors. The results are however significant for firms in the 
services sector alone, which has a P value of 0.02. A positive relationship means that a longer 
cash conversion cycle leads to increased profits. An explanation for this can be the fact that firms 
in this sector have a high level of accounts receivables due to their generous trade credit policy. 
The negative relationship between CCC and ROA means that firms with a shorter cash 
conversion cycle have a higher profitability. The negative relationship between the CCC and 
finn profitability can be explained by the fact that reducing the investment in current assets can 
help in increasing profits. This ensures that liquid cash is not kept in the business for too long but 
instead it is used to generate more income for the firm. 
Aggressive Financing 
All the sectors in the study had a negative relationship between aggressiveness and profitability. 
The results are however significant for firms in the energy (P value 0.009), manufacturing (P 
value 0.011) and the services sector (P value 0.000). 
29 
An aggressive approach in working capital means that there is low investment in working 
capital. The negative relationship between aggressiveness and profitability of firms means that 
the more the firm engages in risky activities, the lower the profitability. This is because the firms 
have low levels of current assets which mean that the companies can fall into bankruptcy when 
they are unable to honor their short term debts. 
Conservative Financing 
The relationship between level of conservativeness and profitability of all the firms in the study 
is positive. This relationship is only signifi~ant to firms in the ~nergy (P value 0.000) and 
services sectors (P value 0.006). 
The positive association between the level of conservativeness and profitability can be explained 
by the fact the firms have a high level of investment in current assets which can be able to 
support any levels of sales and production. This in turn ensures there is a continuous production 
process and thus increased profitability. In theory, however, this approach is characterized by low 
risk and return since the firms have excess amounts of cash which do not earn much of a return, 
thus leading to decreased profitability. The cash should be invested in, in order to generate 
returns for the business. 
30 
5.0 SUMIV!ARY, CONCLUSIONS AND RECOIVlMENDATIONS 
5.1 Introduction 
This chapter presents the summary of the study, the conclusion, the limitations of the study, the 
recommendations and suggestions for further research. 
5.2 Summary 
The purpose of this study was to determine the relationship between working capital 
management components and profitability and also to determine whether the level of 
conservativeness or aggi-essiveness affects financial performance of finns listed in the Nairobi 
Securities Exchange. 
In order to address the above objectives, panel data was collected for four companies from five 
sectors of the economy for a period of 9 years between 2006 and 2014. The justification of the 
four companies was based on firm market capitalization as at 2014. The big four companies in 
each sector were thus selected. The population comprised 20 listed companies from the 
Agriculture, Energy, Services, Manufacturing and Construction sectors. 
The study used the fixed effects model for data analysis after carrying out a Hausman Test. The 
working capital management components are run independently in the model in order to give the 
relationship. Likewise, the approaches to financing are also run differently in order to answer the 
second objective of the study. The results showed a negative relationship between the number of 
days receivable and profitability of all the finns in the study. These results were however 
significant for the manufacturing sector. For the inventory conversion period, the energy, 
manufacturing and construction sectors had a negative relationship with profitability whereas the 
agriculture and services sectors had a positive relationship with profitability. These results were 
only significant for firms in the agriculture sector. For the average payables period, all the finns 
other than finns in the agriculture sector have a negative relationship with profitability. These 
results were only significant to finns in the agriculture and construction sectors. For the cash 
conversion cycle, a negative association is present for finns in the energy and manufacturing 
sectors. For agriculture, services and construction sectors, the relationship is negative. All the 
firms in the study had a negative relationship between the level of aggressiveness and 
profitability and the results were only significant to finns in the energy and services sectors. For 
31 
conservativeness, all the firms had a positive association with profitability and the results were 
only significant to firms in the energy and services sector. 
5.3 Conclusion 
In conclusion, the study finds that individual working capital management components affect 
profitability but the effect is not significant to all sectors. Also, the relationship can either be 
negative or positive between different finns. Therefore, firm managers should employ proper 
working capital management strategies in order to yield the best results depending on what is 
best for them. An improvement in the firm's performance shall ii;lcrease the finn value and 
evehtually shall be of great importance to the shareholders of the company. 
Both the level of aggressiveness and conservativeness affect profitability of finns listed in the 
NSE. Therefore, firm managers should employ the best strategy for their finn in order to increase 
their profits. 
5.4 Motivation and extending current literature 
This study can be used as a foundation for existing literature as well as for future research on the 
relationship between WCM and firm profitability. This research extends existing research by 
incorporating five sectors of the NSE unlike prior research where only one sector was studied. 
5.5 Limitations of the study 
The study is only limited to 20 companies listed on the NSE. This sample size could have 
affected the findings of the study. All the companies in each sector should be put in 
consideration in order to give the best results. 
Also, working capital is dynamic and it keeps changing from time to time depending on 
prevailing economic conditions and product market demand. The findings therefore, may not 
reflect the true effect of working capital on financial performance of the firms for a period 
considered. 
5.6 Recommendations 
Firm managers should employ proper working capital management strategies in order to yield 
the best results depending on what is best for them. An improvement in the firm's performance 
32 
shall increase the finn value and eventually shall be of great importance to the shareholders of 
the company. 
The finns in the study prefer a conservative approach to an aggressive approach. In the long run, 
the firms should employ a more aggressive approach since it yields more income. 
5. 7 Areas of fmiher research 
Further research can be done between WCM and small scale business enterprises so that the 
managers can know how to manage working capital effectively so that the business can grow. 
Also, an extension of this, study can be done but now with more companies and also a longer 
time frame. 
33 
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35 
Appendix 1: Fixed Effects Model 
Fixed-effects (within) regression Number of obs 180 
Group variable: Companyl Number of groups 20 
R-sq: within ~ 0.3455 Obs per group: min ~ 9 
between ~ 0.0432 avg ~ 9.0 
oventll ~ 0.2094 IUiJX ~ 9 
F(9,151) 8.86 
corr(u_i, Xb) ~ -0.3421 Prob > E' 0.0000 
ROA CoeL Std. Err. t P>!tl [95% Conf. IntervatJ 
APP .000261 .0005853 0.45 0.656 -.0008953 .0014174 
ICP -.0007177 .000685 -1.05 0.296 -.0020712 .0006350 
CCC .0012109 .0009574 1.26 0.208 -.0006807 .0031026 
ACP -.0009402 .000994 -0.95 0.342 -.0029123 .0010158 
1\GG -. 7688487 .1286227 -5.98 0.000 -1.022981 -.51471.6 
CONS .5610467 .0940997 5.96 0.000 .3751245 .7469600 
LNSALES -.0001959 .0189619 -0.01 0. 992 -.0376607 • 037269 
CR -.0308365 .0089105 -3.46 0.001 -.0484419 -.013231 
DR .21627H .01!21053 5.13 0.000 .1329247 .2996241 
cons .0551279 .3115099 0.18 0.860 -.560353 .6706089 
sigrua_u .07253728 
sigrua_e .09183470 
rho . 38419402 (fraction of variance due to u_i) 
F test that all u i~O: F (19, 151) ~ 3.1!9 Prob > F ~ 0.0000 
36 
Appendix 2: Random Effects Model 
Random-ettects GLS regression Number ot obs 180 
Group variable: Cornpanyl Number of groups 20 
R-sq: within = 0.3334 Obs per group: min= 9 
between = 0.2409 avg = 9.0 
overall - 0.2897 max- 9 
Wald chi2 (9) 77.00 
corr(u_i, X) = 0 (assumed) Prob > chi2 0.0000 
ROA Coef. Std. Err. z P>lzl [95% Conf. Interval] 
APP .0004502 .0003027 1.49 0.137 -.0001432 .0010435 
ICP - .. 0005562 .0004777 -1.16 0.244 -.0014924 .00038 
CCC .0009115 .000654 1.39 0.163 -.0003704 . 0021933 
ACP -.0007618 .0006497 -1.17 0.241 -.0020352 • 0005116 
AGG -.7415924 .1026793 -7.22 0.000 -.9428401 -.5403447 
CONS .4527599 .0824107 5.49 0.000 .2912378 . 614282 
!JlSAf,ES .0001742 .0092515 0.02 0.985 -.0179584 .0183068 
CR -.021855 . 0084186 -2.60 0.009 -.0383551 -.0053548 
DR .2268798 . 0360034 6.30 0.000 .1563144 .2974453 
cons .0349535 .155614 0.22 0.822 -.2700443 . 3399513 
sigma_u .04205172 
sigma_e .09183478 
rho .17333385 (fraction of variance due to u_i) 
. estimates store random 
37 
Appendix 3: Hausman Test 
. hausman fixed . 
- Coefficients -
(b) (B) (b-B) sqrt {diag (V _b-V _B)) 
fir.ed random Difference S.E • 
APP . 000261 • 0004502 -.0001892 .0005009 
ICP -. 0007177 -. 0005562 -. 0001615 . 000491 
CCC .0012109 . 0009115 . 0002995 . 0006992 
ACP -. 0009482 -.0007618 -.0001864 .0007523 
ii.GG -.7688487 -. 7415924 -. 0272563 .0774647 
CONS .5610467 .4527599 .10e2S6S . 0454228 
LNS.~LES -.0001959 .0001742 -.0003701 .0165518 
CR -.0308365 -. 021855 -. 0089815 • 0029198 
DR .2162744 .2268798 -. 0106054 .0219853 
b = consistent under Ho and Ha; obtained from ;:treg 
B = inconsistent under Ha 1 efficient under Ho; obtained from ;:treg 
Test: Ho: difference in coefficients not systematic 
chi2(9) = (b-B)'[(V_b-V_BI''(-l)](b-B) 
44.29 
Prob>chi2 = 0.0000 
(V_b-V_B is not positive definite) 
Appendix 4: List of Companies ill the Study 
Sector List of Companies 
Agriculture Kakuzi, Sasini, Williamsons Tea,Rea Vipingo. 
Services Nation Media, TPS EA, Uchumi, Scan Group 
Manufacturing EABL, Mumias Sugar, Unga group, Eveready EA. 
Construction Athi River Mining, Bamburi cement, EA Cables, EA Portland 
Energy and Petroleum Kengen, Kenol Kobil, Total, KPLC 
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