Factors Influencing Risk Management Decision of Small and
Medium Scale Enterprises in Ghana
By
Anselm Komla Abotsi1
Gershon Yawo Dake (PhD)
Richard Abankwa Agyepong
University of Education, Winneba, Ghana
ICBE-RF Research Report N0. 61/13
Investment Climate and Business Environment Research Fund
(ICBE-RF)
www.trustafrica.org/icbe
Dakar, October 2013
1
Contact : [email protected]
This research was supported by a grant from the Investment Climate and Business Environment (ICBE) Research
Fund, a collaborative initiative of TrustAfrica and IDRC. It’s a working paper circulated for discussion and comments.
The findings and recommendations are those of the author(s), and do not necessarily reflect the views of the ICBERF Secretariat, TrustAfrica or IDRC
Abstract
Despite the dynamic role of small and medium scale enterprises in the country’s development,
operators of these enterprises in the country face several risks in their businesses. The occurrence
of these risks may have disastrous effects on the entrepreneurs’ toiled effort for business success
if not managed. The research therefore seeks to study the factors that stimulate or prevent owners
of SMEs in Ghana in taking risk management decisions.
The study was conducted in four regions in Ghana. The researchers adopted a quantitative
approach and employed STATA 10 and SPSS version 20 in the analysis. Stratified and simple
random sampling techniques were used to select the sample units. The probit model was used in
the analysis of data. In all 447 SMEs were sampled for the study with at least 111 from each
selected region.
The probit results show that the demographic factors indicate positive influence on the likelihood
of the managers to take risk management decision. All the business related demographic factors
are significant at various levels and positive apart from the obvious risk loving. Economic related
factors such as the estimated amount at risk, estimated cost of risk management and estimated total
monthly income after tax all have positive influence on risk management decision taking. Results
on Government and tax policies indicate that these factors are perceived to negatively influence
risk management decision by managers.
It is recommended among others that institutions working closely with SMEs should get expertise
to train managers of SMEs on risk management practices.
ii
Acknowledgements
We thank the entire staff of Faculty of Social Sciences (UEW), the Office of External Funds
(UEW) and the University of Education, Winneba (UEW) as whole, Dr. Ben S.O. Bempah (DABB
Consult Limited), Dr. Jim Weiler and the Field Supervisors:
Mr. Lawrence Quarshie
Mr. I.Y. Dadson
Mr. Anthony Agbolosu
Mr. Mohammed Adam
Mr. Francis Nyantakyi
Mr. Nicholas Essah
Mr. George Hutchful
Mr. George Okine
Mr. Samuel Poatob
Special thanks go to the ICBE (joint project of TrustAfrica and IDRC) for funding the study.
iii
Table of Content
ABSTRACT .................................................................................................................................................. ii
ACKNOWLEDGEMENTS ......................................................................................................................... iii
LIST OF TABLES ....................................................................................................................................... vi
LIST OF FIGURES ..................................................................................................................................... vi
ACRONYMS .............................................................................................................................................. vii
INTRODUCTION ........................................................................................................................................ 1
1.1 Background ......................................................................................................................................... 1
1.2 Statement of the Problem .................................................................................................................... 2
1.3 Specific Objectives ............................................................................................................................. 3
1.4 Justification ......................................................................................................................................... 4
1.5 Scope ................................................................................................................................................... 4
CONCEPTUAL FRAMEWORK AND LITERATURE REVIEW ............................................................. 5
2.0 Conceptual Framework ....................................................................................................................... 5
2.2 Literature Review................................................................................................................................ 8
2.2.1
Individual Demographic Factors ........................................................................................... 8
2.2.2
Business related demographic factors ................................................................................... 9
2.2.3
Economic Factors.................................................................................................................. 9
2.2.4
Government Policies ........................................................................................................... 10
2.2.5
Business Characteristics ...................................................................................................... 10
2.2.6
Factors that increase the effectiveness of risk management procedures ............................. 10
METHODOLOGY ..................................................................................................................................... 11
3.1 Research Design................................................................................................................................ 11
3.1.2 The Unit of Analysis .................................................................................................................. 11
3.1.3 Sample Selection ........................................................................................................................ 11
3.1.4 Sampling Technique .................................................................................................................. 12
3.1.5 Data Collection and Survey Administration .............................................................................. 13
3.1.6 Validity and Reliability Measurement ....................................................................................... 13
3.2 Data Presentation, Analysis and Discussion ..................................................................................... 13
3.3 Model and Data Analysis .................................................................................................................. 14
3.4 The Study Hypotheses ...................................................................................................................... 14
3.4.1 Demographic Factors ................................................................................................................. 15
3.4.2 Business Related Demographic Factors ..................................................................................... 15
iv
3.4.3 Economic Factors....................................................................................................................... 16
3.4.4 Government Policies/Tax ........................................................................................................... 17
3.4.5 Business Characteristics ............................................................................................................. 17
DESCRIPTIVE STATISTICS .................................................................................................................... 18
4.1 Descriptive Results of Demographic Variables ................................................................................ 18
4.2 Descriptive Statistics of Economic Variables ................................................................................... 19
4.2.1 Funding Sources......................................................................................................................... 20
4.3 Descriptive Statistics of Business Characteristics............................................................................. 21
4.4 Knowledge in Risk Management ...................................................................................................... 21
4.5 Cross Tabulations.............................................................................................................................. 22
4.5.1 Cross Tabulation of Type of Government Policies and Specific Effect on Business ................ 22
4.5.2 Cross tabulations of Business characteristics............................................................................. 23
4.6 Risk Management Decision and the Various Sectors ....................................................................... 24
4.7 Type of risks businesses are exposed to............................................................................................ 25
4.8 Definition of Variables used in the Regression Analysis.................................................................. 26
4.8.1 Descriptive Statistics of Variables used in the Regression Analysis ......................................... 27
4.9 Probit Regression Results and Discussion ........................................................................................ 28
4.9.1 Individual Demographic Factors ................................................................................................ 29
4.9.2 Business related demographic factors ........................................................................................ 29
4.9.3 Economic Factors....................................................................................................................... 29
4.9.4 Government Policies and Tax on RMDM ................................................................................. 30
4.9.5 Business characteristics.............................................................................................................. 33
4.10 Dprobit Results ............................................................................................................................... 33
4.11 Results of Hypotheses Testing ........................................................................................................ 35
5.1 CONCLUSION AND RECOMMENDATIONS .................................................................................. 36
REFERENCES ........................................................................................................................................... 39
v
List of Tables
Table 1: Respondents Characteristics ......................................................................................................... 18
Table 2: Respondents Characteristics cont'd ............................................................................................... 19
Table 3: Descriptive Statistics of Business Characteristics ........................................................................ 20
Table 4: Source of capital ........................................................................................................................... 20
Table 5: Cross tabulation of Sources of capital .......................................................................................... 21
Table 6: Region, Economic Sector and location of business ...................................................................... 21
Table 7: Knowledge on Risk Management ................................................................................................. 22
Table 8: Cross tabulation of type of Government policies and Specific effect on business ....................... 23
Table 9: Cross tabulation of Region and Economic Sector ........................................................................ 24
Table 10: Cross tabulation of Location of business and Economic Sector ................................................. 24
Table 11: Cross tabulation of Risk Management Decision and Economic Sector...................................... 25
Table 12 : Cross Tabulation of Risk Management Decision and Type of Risk .......................................... 26
Table 13: Variables used and labels ............................................................................................................ 27
Table 14: Descriptive statistics of variables used in the analysis ............................................................... 28
Table 15: Probit Regression Results ........................................................................................................... 32
Table 16: Dprobit Regression Results ........................................................................................................ 34
Table 17: Hypothesis results ....................................................................................................................... 36
List of Figures
Figure 1: Conceptual Framework of Factors Affecting Risk Management Decision ................................... 8
Figure 2 : Sampling Distribution ................................................................................................................ 12
vi
Acronyms
AGI
ASSI
CoC
ERM
ERP
GDP
GEDC
MASLOC
MoTI
NBSSI
OBP
RMDM
SMEs
UNIDO
Association of Ghana Industries
Association of Small Scale Industries
Chamber of Commerce
Enterprise Risk Management
Economic Recovery Programme
Gross Domestic Product
Ghana Enterprise Development Commission
Microfinance and Small Loans Centre
Ministry of Trade and Industries
National Board for Small Scale Industries
Office of Business Promotion
Risk Management Decision Making
Small and Medium Scale Enterprises
United Nations Industrial Development Organization
vii
1. Introduction
1.1 Background
Risk management can be described as the process of determining the maximum acceptable level
of overall risk of engaging in a proposed activity. It involves using risk assessment techniques to
determine the initial level of risk and, if it is excessive, developing a strategy to ameliorate
appropriate individual risks until the overall level is reduced to an acceptable level. Risk
management approaches differ from one firm to the next, which partly reflects different risk
management goals. Large firms in general tend to manage risk more actively than small firms.
This is perhaps surprising as small firms are generally viewed to be more risky. This phenomenon
may be due to the fact that smaller firms have limited access to derivatives markets and furthermore
lack staff with risk management skills.
According to Saeidi, Sofian, Abdul Rasid, Saeidi, Saeidi (2013), a paradigm change has occurred
in how organizations view risk management towards a holistic view instead of looking at risk
management from a silo-based perspective. Saeidi et al. (2013) acknowledged that the frequently
used definition of Enterprise Risk Management (ERM) is:
“Enterprise risk management is a process, effected by an entity’s board of directors, management
and other personnel, applied in strategy setting and across the enterprise, designed to identify
potential events that may affect the entity, and manage risk to be within its risk appetite, to provide
reasonable assurance regarding the achievement of entity objectives” (COSO, 2004, p. 2).
Small and Medium Scale Enterprises (SMEs) in Ghana have gone through a series of
transformation since the early 1970s in terms of development and promotion. One of the
institutions set up to champion this course was the Office of Business Promotion (OBP) and the
present Ghana Enterprise Development Commission (GEDC). Apart from the support from
Economic Recovery Programme (ERP) instituted in 1983, the National Board for Small Scale
Industries (NBSSI) was also established within the then Ministry of Industry, Science and
Technology to meet the needs of small and medium scale businesses.
The Ghana Statistical Service (GSS) basing on number of employees defines small scale
enterprises as firms with less than 10 employees as small, and those with over 10 employees as
medium and large-sized enterprises. The National Board for Small Scale Industries uses the
1
concept of fixed assets and number of employees to define SMEs as having more than 9 workers
with plant and machinery (excluding land, buildings and vehicles) valuing, 10 million Cedis (US$
9506, using 1994 exchange rate). Generally, SME in Ghana is defined as:

Micro enterprises: Those employing up to 5 employees with fixed assets (excluding realty)
not exceeding the value of $10,000

Small enterprises: Employ between 6 and 29 employees with fixed assets up to $100,000

Medium enterprises: Employ between 30 and 99 employees with fixed assets of up to $1
million.
According to Bigras (2004), a typical profile of SMEs is dominated by one person, with the
owner/manager taking all major decisions. The entrepreneur possesses limited formal education,
limited access to and use of new technologies, limited market information, access to credit from
the banking sector is severely limited and management skills are also weak. In addition, this SMEs
experiences extreme working capital volatility and the managers lack technical know-how and
their inability to acquire skills and modern technologies impede growth opportunities. This inhibits
the development of a strategic plan for sustainable growth and management.
1.2 Statement of the Problem
Many non-financial constraints inhibit the success of SMEs (Buckley, 1998). According to Tetteh
(2001) SMEs owners are reluctant to be transparent or open up involvement of their businesses to
outsiders. As such, they seem to be unaware of or oblivious to the obligations and responsibilities
they have toward capital providers. Therefore, the need to seek support for technical services such
as accounting, management, marketing, strategy development and establishment of business
linkage are neglected.
Despite their dynamic role in the country’s development, operators of small, medium and large
scale enterprises in the country face several risks in their businesses. The risks, external or internal,
threatens the performance, profitability and sustainability of business. The external risk includes:
natural disasters (e.g. flooding and earthquakes), wars, political crises, and government policies.
The internal risks pertain to the running of the business and include: risk of reduced demand for
products, competition, high labor turnover, injury, financial profitability and growth. Noteworthy
is that some of the risks that SMEs face can be controlled through the use of appropriate actions,
2
whereas others are unpredictable and uncontrollable. The occurrence of any of these risks may
impact disastrous effect on the entrepreneurs’ toiled effort for business success, which may
adversely lead to bankrupt the owners and further deny the country of the expected contribution to
national growth.
The ability of operators of SMEs to deal with the dynamics of emergent market of the global
economy is also largely influenced by their ability to carefully identify and analyse the type of risk
their business face and then examine the factors that need to be taken into account for control.
Management of an enterprise including risks the enterprise is exposed to and other support services
are perceived to be cost prohibitive and non-value adding (Mambula, 2002). Also, there is the lack
of institutional and legal structures that facilitate the management of SMEs lending risk (Mensah,
2004).
It is based on this notion that the research seeks to study the factors that stimulate or prevent owners
of SMEs in Ghana in taking risk management decisions. This study is of particular relevance as it
provides policy makers and owners of SMEs with critical information pertaining to factors that
influence the extent of managers’ decision to undertake risk management practices. The study
further makes modest contribution to the existing literature on risk management in Ghana as well
as building and testing generic models that can be adapted and applied to ascertain risk
management decision behavior of managers in other developing countries.
1.3 Specific Objectives
The objectives of this research are to identity the factors that enhance or preclude owners of small
and medium scale enterprises in managing risk to which their enterprises are exposed in Ghana.
These factors could be demographic, economic, government policies, social, business
characteristics among others. More specifically, the research will:
1.3.1
Determine the influence of individual demographic factors such as age, education,
gender, marital status and family size of manager on Risk Management Decision
Making (RMDM).
1.3.2
Determine the influence of business related demographic factors of manager such
as experience of managers, knowledge on risk management, risk attitudes of
managers, owning business elsewhere on RMDM
3
1.3.3
Determine the influence of economic factors such as capital at risk, cost of risk
management, monthly income and source of capital on RMDM.
1.3.4
Determine the influence of government policies and tax on RMDM.
1.3.5
Determine the influence of business characteristics such as type of business, staff
strength, number of owners of the business and location of business on RMDM.
1.4 Justification
The choice of small and medium scale enterprises is based on the propositions that large scale
industry has not been an engine of growth (Romanco, 1989) and a good provider of employment
even though they already receive enormous support through general trade, finance, tax policy and
direct subsidies. Small and medium scale enterprises have been one of the major areas of concern
to many policy makers.
Small and medium scale enterprises mobilise idle funds, are labour intensive, employing more
labour per unit of capital than large enterprises; promote indigenous technological know-how; are
able to compete (but behind protective barriers); use mainly local resources, thus have less foreign
exchange requirements; cater for the needs of the poor and adapt easily to customer requirements
(flexible specialisation).
These enterprises have been recognised as the engines through which the growth objectives of
developing countries can be achieved. They are potential sources of employment and income in
many developing countries. Risk management should therefore be an integral part of small and
medium scale enterprises (Hill, 2000). It has also been found that a portfolio of firms using risk
management outperform a portfolio of firms that did not, when other aspects of the portfolio were
controlled for. Therefore, an in depth knowledge on factors that influence the decisions making of
managers of small and medium scale enterprise in Ghana (entrepreneurs) with respect to taking
risk management ventures would influence policy decisions which will aid the development and
growth of SMEs in Ghana.
1.5 Scope
The scope of this research was the various sectors of the economy. These are agriculture which
employs about 60% of the labour force and contributes about 46% to the GDP and is characterised
by micro and small-scale operations, the services sector which is the second largest employer
4
(about 25% of the labour force), accounting for over 40% of real GDP from trade and public sector
services, and the industrial sector which accounts for 14% of GDP and employment. The study
was conducted in four regions in Ghana including Greater Accra Region, Ashanti Region, Western
Region and the Northern Region.
2. Conceptual framework and literature review
2.0 Conceptual Framework
This study modeled the business manager’s choice of risk management strategies in an expected
utility framework. This model is adopted from Velandia, Rejesus, Knight, and Sherrick, (2009) in
their study to find factors affecting farmers’ utilization of agricultural risk management tools. This
framework assumes that different business managers assess their end-of-period expected utilities
from their own business specific risk and risk preferences. This approach further assumes that the
risk management decision made fundamentally affects the net return distribution of each business
manager.
The business manager then examines his or her net return distribution by considering the certainty
equivalent for risk management decision made and calculating its associated reservation cost. The
reservation cost is the amount that would make the business manager indifferent to take a risk
management decision.
The business manager then compares the reservation cost with the actual cost of adoption of a risk
management strategy and then adopts a risk management strategy if the reservation cost is larger
than the actual cost. This is equivalent to having a larger certainty equivalent net return with the
risk management strategy relative to without the risk management strategy.
More formally, consider a business manager making the decision of whether or not to adopt a risk
management strategy i, available to him or her (i = 1, ..., m). This business manager evaluates each
of these risk management strategies by considering its effect on the returns distribution to a set of
assets, A, used in production. These assets have a stochastic rate of return 𝑟̃𝐴 , with mean 𝑟̅ , and
variance 𝜎𝐴2 , reflecting overall business risks. Financial risk is introduced through the use of debt
capital. Utilizing the accounting identity that assets are equal to debt plus equity, (A = D +E) and
assuming a fixed cost of debt, CD , the expected rate of return to equity (𝑟̃𝐸 ) and the variance of
the return to equity (𝜎𝐸2 ) can, respectively, be expressed as:
5
𝐴
𝐷
𝑟̃𝐸 = 𝑟̃𝐴 (𝐸) − 𝐶𝐷 (𝐸 )…………………………………………………………………(1)
𝜎𝐸2 = (𝐴/𝐸)2 𝜎𝐴2 ……………………………………………………………………….(2)
Given the stochastic environment above, the business manager’s certainty equivalent of end-ofperiod wealth can be approximated as follows (under known sufficient conditions):
2
̅ − 𝜌𝜎𝑊
𝑊𝐶𝐸 = 𝑊
……………………………………………………………………….(3)
̅ is the
Where 𝑊𝐶𝐸 is the business manager’s certainty equivalent of end-of-period wealth (W), 𝑊
2
mean of W, and 𝜎𝑊
is the variance of W, and 𝜌 is the parameter reflective of risk preferences.
Maximizing the certainty equivalent rate of return on equity(𝑟𝐶𝐸 ) is equivalent to maximizing 𝑊𝐶𝐸
which can be defined as:
𝑟𝐶𝐸 = 𝑟̅ − 𝜌𝜎𝐸2 …………………………………………………………………………..(4)
From “Equation 1” and “Equation 2”, the expression in “Equation 4” can be rewritten as:
𝐴
𝐴 2
𝐷
𝑟𝐶𝐸 = 𝑟̃𝐴 (𝐸) − 𝐶𝐷 (𝐸 ) − 𝜌 (𝐸) 𝜎𝐴2 ……………………………………………………(5)
The effects of using risk management strategies are then assumed to be embodied in the changes
in the mean and variance of the asset return distribution, and in the costs (C) of using these
strategies for managing risks. Given this cost, the effect of using a particular risk management
strategy is to reduce the rate of return to equity by 𝐶⁄𝐸 . Taking this reduction into account, for
every risk management strategy i available to the business manager, the certainty equivalent rate
of return to equity can then be redefined as:
𝐴
𝐷
𝐶
𝐴 2
𝑖
2
𝑟𝐶𝐸,𝑖 = 𝑟̃
𝐴,𝑖 (𝐸 ) − 𝐶𝐷 (𝐸 ) − ( 𝐸 ) − 𝜌 (𝐸 ) 𝜎𝐴,𝑖 …………………………………………(6)
The amount that implicitly equates the expected utilities from using and not using the risk
management strategy is the highest cost that a manager is willing to incur for the use of risk
management strategies (i.e., the reservation cost 𝐶𝑖∗ ). Hence, by “Equation 5” and “Equation 6”
the reservation cost can be calculated based on:
𝐴
𝐷
𝐴 2
𝐴
𝐷
𝐶∗
𝐴 2
2
𝑖
{𝑟̃𝐴 (𝐸) − 𝐶𝐷 (𝐸 ) − 𝜌 (𝐸) 𝜎𝐴2 } = {𝑟̃
𝐴,𝑖 (𝐸 ) − 𝐶𝐷 (𝐸 ) − ( 𝐸 ) − 𝜌 (𝐸 ) 𝜎𝐴,𝑖 }…………..(7)
6
Solving for 𝐶𝑖∗ , we obtain the following expression;
𝐴
2
2
𝐴
𝐶𝑖∗ = 𝐴(𝑟̃
𝐴𝑖 − 𝑟̃
𝐴 ) − 𝜌 (𝐸 ) (𝜎𝐴𝑖 − 𝜎𝐴 )…………………………………………………(8)
the expression in “Equation 8” suggests that variables related to asset (A), risk attitudes (r),
leverage (A/E), as well as variables that determine how the risk management strategies affect the
2
2
mean and variance of the return to assets (𝑟̃
𝐴,𝑖 − 𝑟̃
𝐴 ) and (𝜎𝐴,𝑖 − 𝜎𝐴 ), all help determine the
manager’s reservation cost for i.
Using “Equation 8”, it is assumed that the manager will then decide to adopt a risk management
strategy if the difference between the reservation cost and the actual cost of using i is greater than
zero (𝑐̂ 𝐷 >0); where 𝑐̂ 𝐷 = (𝐶𝑖∗ − 𝐶𝑖𝐴𝑐𝑡𝑢𝑎𝑙 ). The difference (𝑐̂ 𝐷 ) is a latent variable, but the adoption
decision (𝑌𝑖 ) is observable such that;
1 𝑖𝑓 𝑐̂ 𝐷 > 0,
𝑌𝑖 = {
……………………………………………………………………….(9)
0 𝑖𝑓 𝑐̂ 𝐷 < 0
Where 𝑌𝑖 = 1 if the manager adopts a risk management strategy and 𝑌𝑖 = 0, otherwise.
The formulation in “Equation 9” makes it empirically tractable to estimate the factors influencing
the adoption of a risk management strategy. This means that once a risk management strategy is
adopted it implies a risk management decision has been made.
The study is an empirical examination of the relationship that exists between factors affecting
managerial decision to undertake risk management. The factors hypothesized to affect risk
management decisions of SMEs in Ghana are; individual demographic, business related
demographic, economic, government policies and business characteristics as shown in Figure 1.
The literature reviewed equipped the researchers to develop a holistic conceptual framework of
factors affecting risk management decision of managers/owners of SMEs.
7
Individual Demographic
factors
+/-
Business related
demographic factors
+/-
Economic factors
+/-
Government
Regulations/tax
+/-
Business characteristics
+/-
Take Risk
Management
Decision
Yes or No
Designed by researchers
Figure 1: Conceptual Framework of Factors Affecting Risk Management Decision
2.2 Literature Review
There has been some research done on the factors influencing the risk management decisions of
entrepreneurs.
2.2.1 Individual Demographic Factors
Kouamé (2010) for example used a multivariate probit approach to show the importance of
individual risk aversion, farm size, household size, household’s head literacy among others as
factors that increase the likelihood of adopting risk management strategies. Education and age
have been found to significantly affect the adoption of a risk management tool such as Crop
Insurance, Forward Contracting and Spreading Sales (Velandia, Rejesus, Knight, and Sherrick,
2009). For example, age was found to decrease the likelihood of adopting a risk management tool,
education had both increasing and decreasing likelihood of adopting a risk management tool, and
farm size was found to increase the likelihood of adopting a risk management tool. Valentia et al
(2009) indicated that the negative marginal effect for education in the crop insurance was
consistent with the assertion of Shapiro and Brorsen (1988) that farmers become less risk-averse
as they gain more education, thus decreasing the likelihood of using crop insurance as a risk
reducing strategy. In contrast, the marginal effect of education on the forward contracting and
spreading sales is positive and significant.
8
2.2.2Business related demographic factors
It has been posited that risk perception is an indispensable component of financial decision making
and other risk-taking behaviors (Gärling, Kirchler, Lewis & van Raaij, 2010). Literature explaining
the general effects of socio-demographic factors on risk taking has been reviewed extensively by
Gärling et. al (2010) where they indicated that Byrnes, Miller, and Shafer (1999) posited in a metaanalysis that men are generally more risk taking than women. Parenthood seems to reduce risk
taking and older people show a lower risk propensity. The argument is supported by other findings
which suggest women to be more risk averse in making financial decisions than men (Donkers &
Van Soest, 1999; Powell & Ansic, 1997; Weber, Blais, & Betz, 2002). Also Jianakoplos &
Bernasek (1998) posited that women tend to own less risky assets than single men or married
couples and reduce their risky assets when the number of children increases. It is also indicated
that older people tend to take less financial risks than younger people (Jianakoplos & Bernasek,
2006). Studies have shown that measured risk aversion affects occupational and human capital
investment decisions. For example, less risk averse individuals are more likely to pick private
sector jobs (Pfeifer, 2008) and are more likely to become entrepreneurs (Ahn, 2009). The least risk
averse are apparently those who can best assess and manage risks (Cho & Orazem, 2011).
Literature reviewed suggests that very little work has been done in establishing the influence of
economic factors, government policies/tax and business characteristics in risk management
decision making by mangers. This therefore implies that there is still much knowledge gap in
factors that affect the risk management decision of managers of SMEs and this gap is what the
research seeks to close.
2.2.3 Economic Factors
It is envisaged that the larger the capital base of the enterprise, the more likelihood its risk will be
managed. This is because the amount that would be lost could be large. Also depending on the
type of risk, the risk might not affect the entire capital base. It is only a portion that will be affected.
Therefore, if the capital at risk is large, it will positively influence risk management decision.
Depending on where the business sought its capital, the owner may decide either to manage risk
or not. If the capital was sourced with collateral, then it is expected to have a positive influence on
risk management decision. It is expected that daily sales/income influence risk management
decision. The larger the income/sales, the positive the influence on risk management decision.
9
Proportion of owned acres, off-farm income and level of business risks has been found to
significantly affect the adoption of a risk management tool (Velandia, Rejesus, Knight, and
Sherrick, 2009).
2.2.4 Government Policies
Government policies may either positively or negatively influence risk management decision. For
example on the one hand a high tax may influence risk management decision positively so as to
minimise losses in order to get more revenue to pay tax. On the other hand, the tax competes with
cost of risk management and other business expenditure and so with high tax, managers are more
likely to compromise cost of risk management since government tax is statutory. Analytically,
Hutter and Jones (2006) identified two sources of policies which are autonomous and independent
from the state, namely the economic sector and civil society as external influences on business risk
management. The work of Hutter and Jones (2006) recognizes that the state has an important role
to play (Wolswijk, 2007) but that it also has its limitations which may be mitigated by other
influences beyond the state.
2.2.5 Business Characteristics
Exposure to some risks is also dependent on the location of the enterprise. If the area is risk prone,
then it will positively influence risk decision. On the issue of number of staff, the higher number
of workers employed, depicts the larger capital base of the enterprise. Therefore the higher the
staff strength, the positive influence it has on risk management decision. If the type of business is
less risky, it is expected that it will have positive effect on risk management decision.
2.2.6 Factors that increase the effectiveness of risk management procedures
Ranong and Phuenngam (2009) found a set of seven critical success factors which can be used as
a guideline on how to increase the effectiveness of risk management procedures. These factors are
commitment and support from top management, communication, culture, information technology
(IT), organizational structure, training and trust. These seven factors, Ranong and Phuenngam
(2009) posits can increase the effectiveness of risk management procedures from the perspective
of the financial industry in Thailand. A holistic approach toward managing an organization’s risk
is generally known as enterprise risk management (ERM) (Gordon, Loeb and Tseng, 2009). Saeidi
10
et. al (2013) posit that trust is one of the tools of driving impressive risk management and that
ERM could be enhanced by improving and maintaining of organizational trust.
3. Methodology
The study was conducted in four regions in Ghana including Greater Accra Region, Ashanti
Region, Western Region and the Northern Region. The population for the study includes all SMEs
in these regions.
3.1 Research Design
Research method embraces the examination and explanation of a phenomenon which takes
account of the underlying philosophical ideologies assumption, research design and analysis
(Myers and Avison (2002:7). There are several factors underlying the choice of a research method
which includes; the unit of research, location of research and the research tool employed (Gray,
2004). Taking cue from this notion this research was designed to take into account resources
available and time constraint vis-à-vis the need to ensure reliability and validity of the study results.
The research adopted a quantitative approach through the use of survey responses from
onwners/managers of SMEs in Ghana and analysed by employing STATA 10 and SPSS version
20.
3.1.2 The Unit of Analysis
The unit of analysis of this study was the individual SMEs surveyed in the selected regions. The
owners/managers in the various businesses surveyed were purposely given the opportunity to
respond to the questions because they have the best knowledge of the historical data and
information about the set up and management of the firms. It was therefore taken that they have
the information needed for the study.
3.1.3 Sample Selection
The application of a particular sample design and how it mimics the population has a considerable
effect on the study results. It is therefore important that the sample possesses results in close
characteristics of the population. According to Bartlett, Kotrik and Higgins (2001) to choose a
sample size that gives an alpha level of 0.5 a researcher requires a minimum of 75 from a
population of 200. The research after employing frequencies and cross tabulation of the data,
11
utilizes probit regression as its key analytical technique and by conversional principle large sample
size is required based on the number of predictor variables. To run a regression analysis, a total of
10 cases for each variable is required. In order to calculate the required sample size the formula
given is 50+8m where m= the number of variables in the study. This formula guided the choice of
the sample size for the study.
3.1.4 Sampling Technique
Stratified sampling technique (shown in figure below) was used to put SMEs which are
homogeneous in their output or operations into subgroups since the nature of risk vary across firms.
Then a simple random sampling technique was used to select the sample units. As stated earlier,
the strata include agriculture, service and the industrial sectors of the economy. The agriculture
sector employs about 60% of the labour force and contributes about 46% to the GDP and is
characterised by small-scale operations, the services sector is the second largest employer (about
25% of the labour force), accounting for over 40% of real GDP from trade and public sector
services, and the industrial sector accounts for 14% of GDP and employment.
448 SMEs
4 Regions
GAR
WR
ASH
112 SMEs
112 SMEs
NR
112 SMEs
112 SMEs
Sample Unit
Based on proportion of labour force each sector employs
Agric
(60%)
= 50 SMEs
Service
(25%)
=44 SMEs
Figure 2 : Sampling Distribution
12
Industry (14%)
=16 SMEs*
3.1.5 Data Collection and Survey Administration
A pilot test of the questionnaire was initially conducted where 20 SMEs were conveniently
selected and surveyed. The field staff presented their reports which were further used to revise the
questionnaire. The final administration of the questionnaire for the survey was undertaken by
trained personnel. The duration for the questionnaires administration lasted for about three weeks
in all. On the average, it took between 45 minutes to one hour for a questionnaire to completely be
administered. After administering the questionnaires, the questionnaires were passed through a
series of scrutiny and cleaning before the data were entered into the statistical software package
(SPSS) after which the descriptive phase of the data analysis begun.
3.1.6 Validity and Reliability Measurement
Validity and reliability are critical to research. Validity underscores whether the research truly
measures that which it is intended to measure. In other words how truthful the research results are.
In order to ensure validity often specialist are relied upon to check and appraise the research
instruments and then critically and constructively direct it to ensure the appropriate measurement
of the study construct. For the assessment of factors affecting risk management decision of SMEs
in Ghana, construct validity was first ensured through thorough literature review to identify the
underlying study construct. Additionally, experts from the Economics Department of University
of Education, Winneba were tasked to review the questionnaire after which a consultant was
further assigned to fine-tune the questionnaire after pilot survey had been conducted. Thus the
pilot test enhanced the assurance of reliability and validity of the research instrument. The pilot
test was undertaken for 20 SMEs and the data collected was analysed through the use of SPSS
version 20 to check for reliability of the study constructs.
3.2 Data Presentation, Analysis and Discussion
After the gathering of empirical data it was processed for the required analysis by entering it into
Stata 10 and SPSS (Version 20) which was used for subsequent analysis. This section presents the
descriptive results with the main focus on statistics of key components of the study variables. The
aim is to present a general account of the responses on values of the variables and their components
as provided by the survey respondents. According to Sekaran (2000), there are three important
objectives of data analysis which are:
13

A feel for the data: This involves assessing measures of central tendency and dispersion
through the use of mean, range and standard deviation. This enables the researcher to have
in-depth insight into how the respondents answered the questionnaire and how well the
questions are used to garner the appropriate responses. The measures of central tendency
also show the clustering and dispersion of variables.

Showing the goodness of the data

Testing of research hypotheses
3.3 Model and Data Analysis
The probit model was used in the analysis of data. The probit model is often motivated in terms of
a latent variable specification. This assumes that there is some continuous latent variable y* that
determines decision to take risk management strategy. We can think of y* as a business manager
decision to take risk management strategies. If y* is positive, the business manager will choose to
take risk management strategy and the observed binary outcome equals 1. Otherwise, the business
manager will not take risk management strategy and the observed value equals 0. Then the latent
variable y* is modelled by a linear regression function of the independent variables x and assume
that the error term in this equation has a standard normal distribution. The probit model is typically
estimated by the method of maximum likelihood estimation.
More specifically, the model is of the form;
𝑌𝑖 = 𝑋 ′ 𝑖 𝛽𝑖 + 𝜀𝑖 ……………………………………………………………………………….(10)
Where the dependent variable (𝑌𝑖 ) represents whether a risk management decision has been made
or not, and the independent variables (𝑋𝑖 ) include individual demographic, business related
demographic economic factors, government and business factors, 𝛽𝑖 is a vector of unknown
parameters (to be estimated) and 𝜀𝑖 is the stochastic error term.
3.4 The Study Hypotheses
The study is an empirical examination of the relationship that exists between factors affecting
managerial decision to undertake risk management. The factors hypothesized to affect risk
management decisions of SMEs in Ghana are; individual demographic, business related
demographic, economic, government policies and business characteristics. The literature reviewed
14
equipped the researchers to develop a holistic conceptual framework of factors affecting risk
management decision of managers/owners of SMEs.
The hypotheses developed are as follows:
3.4.1 Demographic Factors
HA1: Age of business owner/manager has a positive effect on decision to manage risk. This
hypothesis is founded on the premise that as managers grow in age their probability to undertake
risk management practices increases.
HA2: The educational level of business managers has a positive effect on their decision to
undertake risk management (That is the higher the managerial educational level the more likely
they are to take risk management decisions)
HA3: The marital status of managers has positive relationship with managerial decision to
undertake risk management (Managers in stable marital relationship are willing to undertake risk
management decisions than their other counterparts because of the need to support and sustain
their family obligations)
HA4: Gender: Males are more likely to undertake risk management decisions than their
female counterparts. (Hence there is an association between gender and decisions to undertake risk
management)
HA5: Family size has a positive effect on decision to undertake risk management. (The
higher the family size, the more likely managers are willing to undertake risk management
decisions. This is attributed to the need for resources to take care of large family size.)
3.4.2 Business Related Demographic Factors
HB1: The number of years as a manger in the business has a positive influence on
managerial decision to undertake risk management. (That is, as managers engage in business for a
long time, they are more likely to undertake risk management decisions as a result of their
experience.)
15
HB2: Managerial knowledge in risk management has a positive effect on the decision to
undertake risk. (That is, managers who have knowledge about risk management are more likely to
undertake risk management actions than those without.)
HB3: Risk aversion has a positive effect on decision to undertake risk. (Risk aversion is a
strong motivation to risk management practices.)
HB4: Managers owning other businesses elsewhere are less likely to manage risk. (This is
because attention will be divided and this may have a negative managerial effect.)
3.4.3 Economic Factors
HC1: The amount of capital at risk has a positive effect on managerial decision to undertake
risk management. (This hypothesis is formulated on the grounds that the higher the amount of
business capital is exposed to the risk, the higher the managerial probability or willingness to
undertake risk management.) Hence, there is an association between the vulnerability of risk of a
particular business sector of operation and management decision to undertake risk management
action.
HC2: The source of business capital has a positive relationship with managerial decision to
undertake risk management. This is formulated on the basis that there is an association between
source of business capital and management decision to implement risk control measures.
HC3: The cost of risk management has negative effect on the decision to manage risk by
managers. (This hypothesis is linked to the concept that in so far as the cost of risk management is
less than the capital at risk, managers are motivated to prevent the lost of the capital and thus more
likely to influence their risk management decisions.)
HC4: The size of business monthly income has a positive effect on decision to undertake
risk management. (Higher monthly income is presumed to have a positive impact on the profit
margin of a firm and as a result increases the probability of managers to undertake risk
management decisions.)
16
3.4.4 Government Policies/Tax
HD1: Government policies that have an effect on businesses have a positive effect on
managerial decision to undertake risk management control measures. (This hypothesis is founded
on the premise that favourable government policies and enabling environment created by
government will lead to improved capacity of managers.)
HD2: Government tax has a negative effect on decision to undertake risk management
decision. This is based on the premise that tax compete with cost of risk management and other
business expenditure and so with high tax, managers are more likely to compromise cost of risk
management since Government tax is statutory.
3.4.5 Business Characteristics
HE1: Business location has a positive effect on decision to undertake risk management.
(Businesses located in urban areas are more likely to undertake risk measures than their
counterparts located in peri-urban and rural areas, hence there is an association between location
of business and decision to undertake risk management. This is partly due to the fact that most
insurance companies are situated in the urban areas).
HE2: The sectoral type of business being operated has a positive effect on managerial
decision to undertake risk management. (Firms operating in the industrial and commercial sector
are more likely to undertake risk management than those in the agriculture sector). Therefore, there
is an association between the sector of business operation and management decision to undertake
risk management action.
HE3: The number of staff has a positive effect on decision to manage risk. (The hypothesis
is founded on the premise that as the size of firm increases, they employ more staff and are able to
expand their operations and therefore undertake risk management measures.)
HE4: The number of persons owning the business has a positive influence on decision to
undertake risk management. (All business owners necessarily share the profits, the liabilities and
the decision making process. Where partners have different skills, they can work well together and
this may have a positive influence on decision to undertake risk management).
17
4. Descriptive Statistics
4.1 Descriptive Results of Demographic Variables
In relation to the characteristics of respondents, Table 1 shows that 54.0% were males. Regarding
marital status, 79.0% of the managers were married; 11 % single, 7.0% divorced and 4.0% were
widows. Majority of the managers (43%) had up to 12 years of education, 6% had no education
and 14% had 16 or more years of education.
Table 1: Respondents Characteristics
Demographic Background
Marital Status
Component
Single
Married
Divorced
Widowed
Frequency
49
351
29
17
Percent
11
78.7
6.5
3.8
Gender
Male
Female
243
204
54.4
45.6
Years of education
0
<=9
<=12
<=15
16+
27
87
192
78
63
6.0
19.5
43.0
17.4
14.1
Source: Field data
In terms of age, about 25.0% of the respondents were below 35years old and 20.0% between 47 55years. The mean age was 44.54, with minimum of 20years, maximum 79 and a standard
deviation of 12.14 which shows a wide dispersion. The years of working as manager were also
taken as an important factor to complement managerial experience in business management. Out
of the respondents surveyed, the majority representing 23% had worked over 16–20years, whilst
22% had worked less than 5years and 22% for 11-15years.The mean number of years working as
a manager was 12.96 years with minimum being 2years and maximum 43 with a standard deviation
of 8.57 indicating a wide dispersion. This is shown in Table 2.
18
Table 2: Respondents Characteristics cont'd
Component
Frequency
Percent
<= 5years
6 - 10years
11 - 15years
16 - 20years
21years+
98
85
98
103
63
21.9
19
21.9
23
14.1
<=35years
36 - 40years
41 - 46years
47 - 55years
66years+
111
84
79
89
84
24.8
18.8
17.7
19.9
18.8
<=4
5-8
113
134
25.3
30
Years working as Manager
Age of Managers
Household Size
9-12
51
11.4
12-16
77
17.2
16+
72
16.1
SD
8.57
Min
2
Max
43
Ẋ
12.96
12.14
20
79
44.54
3.27
1
18
6.66
Source: Field data
In relation to household size, the 30% recorded between 5 and 8, followed by 0 -4, 25% then 12 16, 17%. The least was between 9 and 12 which recorded 11%. The smallest household size was
1 and maximum 18 with the mean 3.92 with a standard deviation of 2.04 which indicates a wide
dispersion.
4.2 Descriptive Statistics of Economic Variables
The income of businesses has a considerable influence on the decision of managers to undertake
risk management practices. The total minimum monthly business income after tax was GH₡40.00,
maximum GH₡12,200.00 with a mean of GH₡1432.12 and a standard deviation of 1789.90
indicating wider disparity (Table 3).
19
Table 3: Descriptive Statistics of Business Characteristics
Business Related
Factors
Total Monthly Business
Income After Tax
Component
<= 500.00
501.00 - 1000.00
1001.00 - 1500.00
1501.00 - 2000.00
2001.00+
Frequency
Percent
150
129
57
28
83
94
110
82
72
89
Min
Max
X
1789.90
40.00
12,200
1432.12
1,864.46
40.00
6,800.00
2,885.29
33.6
28.9
12.8
6.3
18.6
Estimated Amount at
Risk? GH (Cedis)
<= 700.00
701.00 - 1400
1401.00 - 2100.00
2101.00 - 2800.00
2801.00+
SD
21
24.6
18.3
16.1
19.9
Source: Field data
Data gathered also showed that for the estimated amount that businesses risk if they did not take
needed measures for its management, about 25% majority ranged between GH₡701.00 GH₡1400.00. The mean amount at risk was GH₡2,885.00, the least being GH₡40.00 and highest
of GH₡6,800.00.
4.2.1 Funding Sources
The source of funding was taken as one of the important elements that affect managerial decision
to undertake risk management interventions. It was realized that the majority (54%) of the source
of capital of the businesses was from earnings from other jobs. This is followed by the Bank
(37.6%) and then microfinance (28.2%). This is shown in Table 4.
Table 4: Source of capital
Frequency
Percent
Valid
Source: Field data
Bank
168
37.6
447
Credit Union
90
20.1
447
Micro Finance
126
28.2
447
Job Earnings
245
54.8
447
Friends
119
26.6
447
Government
94
21.0
447
It is clear from Table 4 that the source of capital was not from a single source. The businesses
solicit for capital from more than a single source. For example, 125 of the correspondents sourced
for capital from both earnings from other jobs and the Bank, 99 from Bank and Microfinance, 102
from earnings from other jobs and friend and so on (Table 5).
20
Table 5: Cross tabulation of Sources of capital
Bank
Credit Union
Micro Finance
Job Earnings
Friends
Government
Source: Field data
Bank
168
81
99
125
84
85
Credit Union
81
90
80
82
79
80
Micro Finance
99
80
126
94
80
80
Job Earnings
125
82
94
245
102
87
Friends
84
79
80
102
119
80
Government
85
80
80
87
80
94
4.3 Descriptive Statistics of Business Characteristics
In all 447 SMEs were sampled for the study with at least 111 from each selected region. Within
each region, as stated earlier, the sample units were expected to be selected based on the proportion
of labour force each sector employs but because some respondents were hesitant in giving out
information for fear of being taxed, this criterion was relaxed. The result as shown in Table 6
shows that on the average each region had 25 % out of the total 447 sample surveyed.
Table 6: Region, Economic Sector and location of business
Demographic Background
Region
Component
Greater Accra
Ashanti
Western
Northern
Frequency
112
112
112
111
Percent
25.1
25.1
25.1
24.8
Sector
Agriculture
Industry
Service
185
115
147
41.4
25.7
32.9
Location
Urban
Rural
Peri-rural
281
159
7
62.9
35.6
1.6
Source: Field data
Table 6 further provides information about the SMEs surveyed with respect to sectoral distribution
in the economy. Agriculture is high at 185 (41.4%), followed by service 147 (32.9%) and then
industry 115 (25.7%). In terms of business location, 62.9% of the businesses surveyed were located
in the urban areas, 35.6% rural and 1.6% in peri-rural.
4.4 Knowledge in Risk Management
In order to ascertain the knowledge of managers in taking risk management decisions, respondents
were asked whether they take risk management decisions or not. About 54% of the respondents
positively affirmed to taking risk management decisions. A further probing question was asked as
to whether their businesses had had the opportunity to conduct risk management assessment. It
21
was noted that whilst many respondents agreed to taking risk management decisions, the
percentage of businesses which had conducted formal risk assessment to enable them undertake
appropriate control measures is small. Only about 36% of the managers interviewed responded
positively to having a formal risk assessment conducted for their business in order to inform
appropriately the needed mitigating measures to put in place. The study also elicited from
managers the extent of knowledge on risk management practices on the scale of 1 (lowest
knowledge) to 5 (highest knowledge). About 25% rated their extent of knowledge on risk
management practices as high knowledge whilst about 5 % rated lowest and almost 23% did not
rate which is an indication of no knowledge. Additionally, when enquired about respondents
awareness of insurance policies as risk management option, about 60% responded positively,
whilst 40% attested to not being aware of existing insurance policies pertaining to their areas of
operation. This therefore implies that there is still much gap in the knowledge base of managers of
SMEs in so far as risk management is concern. This is shown in Table 7.
Table 7: Knowledge on Risk Management
Do you take risk management practices
Valid
Yes
No
Total
Risk assessment conducted for your business
Valid
Yes
No
Total
Extent of knowledge in Business risk management
Valid
1.00
2.00
3.00
4.00
5.00
Total
Missing
Applicable
Total
Awareness of insurance policies provided by insurance companies
Valid
Yes
No
Total
Source: Field data
Frequency
239
208
447
Percent
53.5
46.5
100.0
162
285
447
36.2
63.8
100.0
23
43
98
112
69
345
102
447
5.1
9.6
21.9
25.1
15.4
77.2
22.8
100.0
268
179
447
60.0
40.0
100.0
4.5 Cross Tabulations
4.5.1 Cross Tabulation of Type of Government Policies and Specific Effect on Business
Table 8 shows the cross tabulation of the same government policies which are perceived as bad by
some group and also perceived as good by another group and its specific effect on business. The
22
results shows that out of a total of 7 businesses whose exports and imports are perceived to have
been affected by government policies, 6 representing 86% is due to taxation and only 1 is due to
what they claimed; bad policy. On the issue of profit, 19% blamed it on taxation whilst 93
representing 81% attributed it to perceived bad policy. With business security, 5% blamed it on
taxation and 95% on bad policy. 11 respondents representing 100% attributed the difficulty in loan
acquisition to bad policy. It is also worth noting that 52 respondents representing 11.6% of the
entire sample indicated a positive impact on their business due to perceived good government
policy.
Table 8: Cross tabulation of type of Government policies and Specific effect on business
Taxation
Specific
effect
on
busines
s
Affect
export and
import
Affect
business
profit
Affect
business
security
Affect
loan
acquisition
Not
applicable
Affect
business
positively
Total
Type of Government policies
Perceived
Not
Perceived
Bad
applicabl
Good
Government
e
Government
policy
policy
1
0
0
14.3%
0.0%
0.0%
.8%
0.0%
0.0%
.2%
0.0%
0.0%
93
0
0
80.9%
0.0%
0.0%
73.8%
0.0%
0.0%
20.8%
0.0%
0.0%
21
0
0
95.5%
0.0%
0.0%
16.7%
0.0%
0.0%
4.7%
0.0%
0.0%
11
0
0
100.0%
0.0%
0.0%
8.7%
0.0%
0.0%
2.5%
0.0%
0.0%
0
231
0
0.0%
100.0%
0.0%
Total
Count
% within Specific effect on business
% within Type of Government policies
% of Total
Count
% within Specific effect on business
% within Type of Government policies
% of Total
Count
% within Specific effect on business
% within Type of Government policies
% of Total
Count
% within Specific effect on business
% within Type of Government policies
% of Total
Count
% within Specific effect on business
6
85.7%
20.7%
1.3%
22
19.1%
75.9%
4.9%
1
4.5%
3.4%
.2%
0
0.0%
0.0%
0.0%
0
0.0%
% within Type of Government policies
0.0%
0.0%
96.3%
0.0%
51.7%
% of Total
Count
% within Specific effect on business
% within Type of Government policies
% of Total
Count
% within Specific effect on business
0.0%
0
0.0%
0.0%
0.0%
29
6.5%
0.0%
0
0.0%
0.0%
0.0%
126
28.2%
51.7%
9
14.8%
3.8%
2.0%
240
53.7%
0.0%
52
85.2%
100.0%
11.6%
52
11.6%
51.7%
61
100.0%
13.6%
13.6%
447
100.0%
% within Type of Government policies
100.0%
100.0%
100.0%
100.0%
100.0%
6.5%
28.2%
53.7%
11.6%
100.0%
% of Total
7
100.0%
1.6%
1.6%
115
100.0%
25.7%
25.7%
22
100.0%
4.9%
4.9%
11
100.0%
2.5%
2.5%
231
100.0%
Source: Field data
4.5.2 Cross tabulations of Business characteristics
The description of the regional distribution and the economic sector is shown in Table 9. Out of
the 447 sample units (SMEs) from all the regions, 185 (41.4%) were from agriculture sector, 147
(32.9%) from the service sector and 115 (25.7%) from the industrial sector.
23
Table 9: Cross tabulation of Region and Economic Sector
Name of Region * Economic Sector Cross tabulation
Name of
Region
Greater
Accra
Ashanti
Region
Western
Region
Northern
Region
Total
Economic Sector
Agriculture
55
49.1%
29.7%
52
46.4%
28.1%
39
34.8%
21.1%
39
35.1%
21.1%
185
41.4%
100.0%
Count
% within Region
% within Economic Sector
Count
% within Region
% within Economic Sector
Count
% within Region
% within Economic Sector
Count
% within Region
% within Economic Sector
Count
% within Region
% within Economic Sector
Total
Industry
21
18.8%
18.3%
28
25.0%
24.3%
18
16.1%
15.7%
48
43.2%
41.7%
115
25.7%
100.0%
Service
36
32.1%
24.5%
32
28.6%
21.8%
55
49.1%
37.4%
24
21.6%
16.3%
147
32.9%
100.0%
112
100.0%
25.1%
112
100.0%
25.1%
112
100.0%
25.1%
111
100.0%
24.8%
447
100.0%
100.0%
Source: Field data
The distribution of the location of business and the economic sector is shown in Table 10. The
results show that 281 (62.9%) of the respondents are in urban areas, 159 (35.6%) in the rural and
7 in the peri-rural. Also 115 of the agriculture businesses sampled are located in the rural area
whilst 118 of the service businesses are located in the urban areas.
Table 10: Cross tabulation of Location of business and Economic Sector
Crosstab
Location of business
Total
Urban
Rural
Peri-rural
Economic
Agriculture
Count
64a
115b
6b
185
Sector
% within Economic Sector
34.6%
62.2%
3.2%
100.0%
% within Location of business
22.8%
72.3%
85.7%
41.4%
Industry
Count
99a
16b
0a, b
115
% within Economic Sector
86.1%
13.9%
0.0%
100.0%
% within Location of business
35.2%
10.1%
0.0%
25.7%
Service
Count
118a
28b
1a, b
147
% within Economic Sector
80.3%
19.0%
.7%
100.0%
% within Location of business
42.0%
17.6%
14.3%
32.9%
Total
Count
281
159
7
447
% within Economic Sector
62.9%
35.6%
1.6%
100.0%
% within Location of business
100.0%
100.0%
100.0%
100.0%
Each subscript letter denotes a subset of Location of business categories whose column proportions do not differ significantly
from each other at the .05 level.
Source: Field data
4.6 Risk Management Decision and the Various Sectors
Table 11 depicts the distribution of the various sector of the economy with respect to taking risk
management decision.
24
Table 11: Cross tabulation of Risk Management Decision and Economic Sector
Take Risk Management Decision * Economic Sector Cross tabulation
Economic Sector
Total
Agriculture
Industry
Service
Count
89a
57a
62a
208
% within Risk Management Decision
42.8%
27.4%
29.8%
100.0%
No
Take Risk
% within Economic Sector
48.1%
49.6%
42.2%
46.5%
Management
Count
96a
58a
85a
239
Decision
% within Risk Management Decision
40.2%
24.3%
35.6%
100.0%
Yes
% within Economic Sector
51.9%
50.4%
57.8%
53.5%
Total
Count
185
115
147
447
% within Risk Management Decision
41.4%
25.7%
32.9%
100.0%
% within Economic Sector
100.0%
100.0%
100.0%
100.0%
Each subscript letter denotes a subset of Economic Sector categories whose column proportions do not differ significantly
from each other at the .05 level.
Source: Field data
Within the agriculture sector, 51.9% out of 185 respondents took risk management decision on
their enterprise, 50.4% out of 115 in the industry and 57.8% out 147 in the services sector. The
result show that in terms of management decision on the enterprise, the service sector is the highest
followed by agriculture and then the industry. It means that a little below 50% are not managing
risks their business are exposed to and this could have implications on the profitability of the
business enterprises.
4.7 Type of risks businesses are exposed to
The type of risk perceived by managers to be exposed to in their businesses and how they ranked
these various types of risks in terms of the extent of vulnerability/exposure to the risk is presented
in Table 12. The risk of reduced demand for products and service (285) was the highest risk
businesses are exposed to followed by risks posed by competitors (188), risk of weather failure
(153), risk of escalating operating costs (147), occupational health and safety risks (144), fire risk
(134), then risk of lack of accessible credit (120) and so on. This provides a fair idea on the areas
of concerns of the business managers to all stakeholders.
25
Table 12 : Cross Tabulation of Risk Management Decision and Type of Risk
Risk Management Decision * Type of Risk Cross tabulation
Risk of reduced demand for products and service
Risks posed by competitors
Weather failure
Risk of escalating Operating Costs
Occupational health and safety risks
Fire risk
Risk of lack of accessible credit
Commodity price risk (fluctuations in commodity prices)
Financial risks(late payments)
Risk of supplier failure to deliver on the time agreed
Credit risk (risk that debtors will be unable to pay)
Vehicular accidents
Interest rate risk (movements in interest rates will affect the business
Technology risks/Communication Technologies (ICT)
Foreign exchange risk (fluctuations in the value of foreign currency)
Risks posed by the market or the economy
Liquidity risk(Enough funds to pay its debts)
Unexpected exit of the business owner
High staff turnover
Source: Field data
Valid
N
Cases
285
188
153
147
144
134
120
91
82
69
62
38
34
24
21
17
10
10
6
Percent
Not Applicable
N
Percent
Total
N
63.80%
42.10%
34.20%
32.90%
32.20%
30.00%
26.80%
20.40%
18.30%
15.40%
13.90%
8.50%
7.60%
5.40%
4.70%
3.80%
2.20%
2.20%
1.30%
162
259
294
300
303
313
327
356
365
378
385
409
413
423
426
430
437
437
441
36.20%
57.90%
65.80%
67.10%
67.80%
70.00%
73.20%
79.60%
81.70%
84.60%
86.10%
91.50%
92.40%
94.60%
95.30%
96.20%
97.80%
97.80%
98.70%
447
447
447
447
447
447
447
447
447
447
447
447
447
447
447
447
447
447
447
Percent
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
4.8 Definition of Variables used in the Regression Analysis
The regression function includes two types of explanatory variables. The first type can be treated
as though they were continuous variables and this include the age of manager(s)/ owners, years of
education, household size, number of years as a manager, log of estimated amount at risk, log of
estimated cost of risk management, log of estimated total monthly income after tax, total number
of employees and number of business ownership.
26
Table 13: Variables used and labels
VARIABLE NAME TYPE
Dependent variable
Dep_V
double
Demographic factors
Q2_Demo_Ag~n
YearEdu
mar_mar
Male_Gender
Q7_Demo_Hs~e
Business related demographic factors
Q4_Demo_Yr~g
have_RM_KNGE
Risk_Loving
Risk_Averse
Owns_Bus_E~h
Economic factors
log_Amt_At~k
log_Cost_o~t
log_Incom
Bank_Sourc~p
CreditU_So~p
Gov_Source~p
Government policies/tax
Govt_Tax_E~t
Gov_Reg_Ef~t
Business characteristics
urban_loc
peri_urban~c
Agric_Sec
Industry_Sec
Q11_Demo_T~s
Q19_Bus_Ow~m
VARIABLE LABEL
take risk management decision; Yes/No
Age of manager(s)/Biz owners
years of education
married
Male
Household size
Number of years as a manager
have knowledge on risk management
risk loving manager
risk averse manager
Owns business elsewhere
log of Estimated Amount at Risk
log of Estimated cost of risk management
log of Estimated Total monthly income after tax
source of capital - Bank
source of capital - credit union
source of capital - government
Government tax affect decision to manage risk
Government Policies affect decision to manage risk
urban location of business
peri urban location of business
Agriculture sector
Industry sector
Total number of employees
Number of business ownership
All of the other explanatory variables are binary or dummy variables. These take the value 1 if the
individual has a particular characteristic and 0 otherwise. The meaning of the variables used in the
analysis is shown in Table 13.
4.8.1 Descriptive Statistics of Variables used in the Regression Analysis
The descriptive statistics of variables used in the analysis is shown in Table 14. The results show
that 53% of the managers have made a risk management decision even though 72% of them have
some knowledge on risk management practices. The average age of managers, years of education
and years as manager (experience) is 44.5, 11.7 and 12.9 respectively.
27
Table 14: Descriptive statistics of variables used in the analysis
VARIABLE
OBS
Dependent variable
Dep_V
447
Demographic factors
Q2_Demo_Ag~n
447
YearEdu
447
mar_mar
447
Male_Gender
447
Q7_Demo_Hs~e
447
Business related demographic factors
Q4_Demo_Yr~g
447
have_RM_KNGE
447
Risk_Loving
447
Risk_Averse
447
Owns_Bus_E~h
447
Economic factors
log_Amt_At~k
447
log_Cost_o~t
447
log_Incom
447
Bank_Sourc~p
447
CreditU_So~p
447
Gov_Source~p
447
Government policies/tax
Govt_Tax_E~t
447
Gov_Reg_Ef~t
447
Business characteristics
urban_loc
447
peri_urban~c
447
Agric_Sec
447
Industry_Sec
447
Q11_Demo_T~s
447
Q19_Bus_Ow~m
447
Source: Field data
MEAN
STD. DEV.
MIN
MAX
0.5347
0.4994
0
1
44.5347
11.7785
0.7852
0.5436
6.6555
12.1385
3.7760
0.4111
0.4987
3.2720
20
0
0
0
1
79
16
1
1
18
12.9620
0.72036
0.2640
0.4295
0.4989
8.5716
0.4493
0.4413
0.4956
0.5006
2
0
0
0
0
43
1
1
1
1
7.5816
6.0457
6.7418
0.3758
0.2013
0.2103
1.0841
1.4337
1.0153
0.4849
0.4015
0.4080
3.6889
2.3026
3.6889
0
0
0
8.8247
10.223
9.4092
1
1
1
0.3691
0.5749
0.4831
0.4949
0
0
1
1
0.6286
0.0157
0.4139
0.2573
8.3356
1.3154
0.4837
0.1243
0.4931
0.4376
6.5176
0.6470
0
0
0
0
2
1
1
1
1
1
53
5
The maximum household size is 18 with the mean being 7. Almost 50% of the managers own
business elsewhere and about 42% are risk averse. About 57% of the managers affirm that
Government policies affect their decision to manage risk and 63% of the businesses are located in
the urban areas.
4.9 Probit Regression Results and Discussion
In order to differentiate between occupational safety risk measures and business risk measures, the
responds on the extent of knowledge on risk management practices, the conduct of risk assessment
of the enterprise and the adoption of a risk management strategy (ref. Table 7 above) were scored
on the scale of 1 to 4. Any score above 2 implies the manager has been taking risk management
decisions. Also, once a risk management strategy is adopted, it implies a risk management decision
has been made. Table 15 shows the probit regression results. The coefficients on the X variables
tell us how this probability changes with changes in the manager’s characteristics. A negative
28
coefficient means that managers with those attributes are less likely to take risk management
decision, and a positive coefficient means they are more likely to take risk management decision.
4.9.1 Individual Demographic Factors
All the demographic factors indicate positive influence on the likelihood of the managers to take
risk management decision. Apart from years of education and gender, the rest are not significant.
Years of education is significant at 10% meaning that managers with more years of education are
more likely to take risk management decision than those with less years of education. Male
managers are more likely to take risk management decision than their female counterparts and this
is significant at 1%. These findings are consistent with earlier findings (Shapiro and Brorsen, 1988;
Kouamé, 2010; Velandia, Rejesus, Knight, and Sherrick, 2009).
4.9.2 Business related demographic factors
All the business related demographic factors are significant at various levels and positive apart
from the obvious risk loving. Number of years as manager is significant at 1% indicating that
managers with more experience are more likely to take risk management decision. Knowledge on
risk management is also significant at 1% meaning that managers with some level of knowledge
on risk management are more likely to take risk management decision. At 1% level of significance,
managers who own other businesses elsewhere are inclined to take risk management decision.
These managers are most likely to be risk averse and as part of their risk management plan, decide
to establish other businesses elsewhere. Managers who are risk loving are found to be less likely
to adopt a risk management strategy. These assertions are consistent with that posited by Cho &
Orazem (2011).
4.9.3 Economic Factors
Economic related factors such as the estimated amount at risk, estimated cost of risk management
and estimated total monthly income after tax all have positive influence on risk management
decision taking. The cost of risk management has a positive influence on managerial decision with
regards to risk management at 5% level of significance. This means that cost involved in risk
management is not a deterrent to risk management decision and so in so far as the cost of risk
management is less than the capital at risk, managers are motivated to prevent the lost of the
capital. The sources of capital have different influences on risk management decision. Whereas
29
capital source from credit union have significantly negative influence on risk management
decision, capital source from the government is positive and significantly more likely to influence
risk management decision taking. Capital source from the bank is negative but not significant.
Loans from the banks and credit unions are either insured and/or with strict collateral requirements
and this may influence risk management decision negatively due to the exhibition of moral hazards
on the part of the business managers. Government of Ghana’s (GoG) microfinance programmes
targeted at reducing poverty, creating jobs and wealth is being implemented by Microfinance and
Small Loans Centre (MASLOC). In addition to disbursement of micro and small loans to the
identified poor in the various sectors of the Ghanaian economy, MASLOC also provides business
advisory services, training and capacity building for small and medium scale enterprises (SMEs)
as well as collaborating institutions, to provide the beneficiaries with efficient and effective skills
and knowledge required in managing their businesses. No tangible security is required from
applicants apart from the group solidarity guarantee. The capital source from the government
having a positive and significant influence on risk management decision can be alluded to all this.
4.9.4 Government Policies and Tax on RMDM
Results on Government policies and tax indicate that these factors negatively influence risk
management decision taking of managers. Government tax and Government policies affect
decision to manage risk negatively at 1% and 10% level of significance respectively. An increase
in tax paid by businesses on their profits in the year (corporate tax) will not lead to a change in
output and prices in the short run all things equal. In the long run however, an increase in tax will
put firms out of business if they were earning just normal profits before the tax increment and this
will negatively influence risk management decision. Tax stamp which is a type of tax collected
from small-scale self-employed persons in the informal sector on quarterly basis in Ghana with its
rates determined according to both type and size of business may influence risk management
decision negatively if the tax rate is considered high for the simple reason that tax is statutory and
so managers may sacrifice risk management for tax obligations. The importance of tax revenue to
the Government cannot be overemphasized. This notwithstanding, the consideration of short- and
long-term elasticities of tax revenues with respect to their bases must not be ignored in tax research
(Wolswijk G., 2007). This will help in identifying the most efficient method in increasing tax
revenue either by broadening the tax base or by raising tax rates. To the extent that tax base30
broadening reduce distortions to economic decisions on saving, investment, consumption and other
business related variables, tax base-broadening reforms are identified as growth-oriented reforms
(OECD).
Results from Table 8 show that the managers attributed taxation and bad policy to have affected
their profit and business security (on the issue of profit, 19% blamed it on taxation whilst 81%
attributed it to bad policy and with business security, 5% blamed it on taxation and 95% on bad
policy). A further probing question to ascertain how taxation and bad policy may have negatively
affected their business pointed to import policies, high taxes, increase in import duties and
exchange rate, increase in bank interest rates, rising cost of business operations among others.
The pursuit to rationalize salaries by government led to the implementation of the Single Spine
Salary Structure (SSSS) in 2010 and this had an impact on the economic variables. For example,
SSSS led to an increase in the wage bill which culminated in the increase in money supply. The
broad money supply (M2+) grew by 33.2% in December 2011 and by 24.3 % in December 2012
(BOG). The financial institutions especially the banks failed to take advantage of the increase in
liquidity despite the fact that the bulk of bank’s sources of funds come from deposit mobilization
from individuals and businesses in the form of foreign and domestic money market deposit
accounts (CEPA, 2011) and this led to the increased demand for foreign exchange. The Ghana
cedi started depreciating and this made imports relatively expensive with a devastating effect on
the expenditure of business enterprises.
31
Table 15: Probit Regression Results
DEP_V
COEF.
STD. ERR.
Z
P>Z
Demographic factors
Q2_Demo_Ag~n
0.000
0.009
0.030
0.979
YearEdu
0.052
0.026
2.010**
0.045
mar_mar
0.233
0.225
1.040
0.300
Male_Gender
2.391
0.325
7.360***
0.000
Q7_Demo_Hs~e
0.022
0.030
0.710
0.475
Business related demographic factors
Q4_Demo_Yr~g
0.047
0.012
3.920***
0.000
have_RM_KNGE
1.718
0.237
7.260***
0.000
Risk_Loving
-0.863
0.254
-3.400***
0.001
Risk_Averse
0.376
0.216
1.740*
0.081
Owns_Bus_E~h
0.711
0.197
3.610***
0.000
Economic factors
log_Amt_At~k
0.102
0.085
1.210
0.226
log_Cost_o~t
0.177
0.073
2.400**
0.016
log_Incom
0.133
0.118
1.130
0.259
Bank_Sourc~p
-0.167
0.238
-0.700
0.484
CreditU_So~p
-1.069
0.393
-2.720***
0.006
Gov_Source~p
0.758
0.404
1.880
0.060*
Government policies/tax
Govt_Tax_E~t
-1.092
0.324
-3.370
0.001***
Gov_Reg_Ef~t
-0.344
0.197
-1.750
0.080*
Business characteristics
urban_loc
-0.625
0.245
-2.550
0.011**
peri_urban~c
-0.455
0.769
-0.590
0.554
Agric_Sec
-0.241
0.263
-0.920
0.360
Industry_Sec
-0.025
0.247
-0.100
0.918
Q11_Demo_T~s
0.019
0.020
0.920
0.355
Q19_Bus_Ow~m
-0.043
0.153
-0.280
0.778
_cons
-5.899
1.294
-4.560
0.000
Probit regression
Number of obs
=
447
LR chi2(24)
=
349.67
Prob > chi2
=
0.000
Log likelihood = -133.92652
Pseudo R2
=
0.5662
Source: Analysis of Field data ***, **, * represent significance at 1%, 5% and 10% respectively
[95% CONF.
INTERVAL]
-0.017
0.001
-0.208
1.754
-0.038
0.017
0.103
0.675
3.027
0.081
0.024
1.254
-1.361
-0.047
0.325
0.071
2.181
-0.366
0.798
1.097
-0.064
0.033
-0.098
-0.634
-1.838
-0.033
0.268
0.321
0.365
0.300
-0.299
1.550
-1.726
-0.730
-0.457
0.042
-1.105
-1.962
-0.756
-0.509
-0.021
-0.344
-8.436
-0.145
1.052
0.275
0.458
0.058
0.258
-3.362
The quest to stabilize the Ghana cedi resulted in the benchmark 91-day Treasury bill rate increasing
from 10.7% in December 2011 to 22.4% in June 2012 and then marginally in the second half of
the year to 23.1%. The 182-day bill increased from 11.1% in December 2011 to 22.7% in
December 2012.The 1-year fixed note went up from 11.3% in December 2011 to 22.9% in
December 2012 (BOG). Interest rates took an upward trend in response to efforts to stabilize the
Ghana Cedi. The increase in cost of funds, strict collateral requirements and inflation expectations,
resulted in the tightening of credit stance for both enterprises and households. This made the
business environment not conducive with its subsequent negative influence on the managerial
decisions on risk management. This goes to buttress the point by Hutter and Jones (2006) that the
32
state has an important role to play but that it also has its limitations which may be mitigated by
other influences beyond the state.
4.9.5 Business characteristics
Apart from total number of employees, all the business characteristics factors negatively influence
risk management decision by managers. Rather weirdly, at 5% level of significance, businesses in
the urban areas are less likely to take risk management decision than those in the rural areas. Out
of the 281 businesses in the urban areas, 64 (22.8 %) are agricultural, 99 (35.2%) industrial and
118 (42.0%) are services. Also out of the 159 in the rural areas, 16 (10.1%) are industry, 28 (17.6%)
are service and 115 (72.2%) are agriculture (refer Table 10). It can therefore be inferred that the
managers of agricultural businesses are better than their counterpart in the industry and service
sectors in terms of risk management decisions.
4.10 Dprobit Results
In order to interpret the quantitative implications of the results, there is the need to compute
marginal effects for continuous explanatory variables and average effects for binary explanatory
variables. For continuous explanatory variables, the marginal effect of the explanatory variables
on the probability of risk management decision was regarded. For the dummy variables, the
average effect of the explanatory variables on the probability of risk management decision was
also regarded.
The dprobit results (Table 16) depicts that the probability of a manager taking risk management
decision increases by 0.02 for every additional year of education and experience. Even though not
significant, an increase in economic related factors such as the estimated amount at risk, estimated
cost of risk management and estimated total monthly income after tax is likely to increase the
probability of managers taking risk management decision by 0.04, 0.07 and 0.05 respectively.
33
Table 16: Dprobit Regression Results
DV
dF/dx
Std. Err.
Demographic factors
Q2_Dem~n
0.000
0.003
YearEdu
0.021
0.010
mar_mar*
0.093
0.089
Male_G~r*
0.768
0.063
Q7_Dem~e
0.009
0.012
Business related demographic factors
Q4_Dem~g
0.019
0.005
have_R~E*
0.592
0.057
Risk_L~g*
-0.333
0.090
Risk_A~e*
0.148
0.083
Owns_B~h*
0.276
0.073
log_Am~k
0.041
0.034
log_Co~t
0.070
0.029
log_In~m
0.053
0.047
Bank_S~p*
-0.066
0.095
Credit~p*
-0.399
0.125
Gov_So~p*
0.281
0.133
Government policies/tax
Govt_T..*
-0.415
0.111
Gov_Re..*
-0.135
0.076
Economic factors
urban_~c*
-0.241
0.090
peri_u~c*
-0.179
0.290
Agric_~c*
-0.095
0.104
Indust~c*
-0.010
0.098
Q11_De~s
0.007
0.008
Q19_Bu~m
-0.017
0.061
Probit regression, reporting marginal effects
z
P>|z|
x-bar
[ 95% C.I. ]
0.030
2.010
1.040
7.360
0.710
0.979
0.045
0.300
0.000
0.475
44.535
11.779
0.785
0.544
6.655
-0.007
0.000
-0.082
0.644
-0.015
0.007
0.041
0.268
0.892
0.032
3.920
7.260
-3.400
1.740
3.610
1.210
2.400
1.130
-0.700
-2.720
1.880
0.000
0.000
0.001
0.081
0.000
0.226
0.016
0.259
0.484
0.006
0.060
12.962
0.720
0.264
0.430
0.499
7.582
6.046
6.742
0.376
0.201
0.210
0.009
0.480
-0.509
-0.016
0.132
-0.025
0.013
-0.039
-0.252
-0.644
0.020
0.028
0.705
-0.156
0.311
0.420
0.107
0.127
0.145
0.119
-0.154
0.541
-3.370
-1.750
0.001
0.080
0.369
0.575
-0.632
-0.285
-0.197
0.014
-2.550
-0.590
-0.920
-0.100
0.920
-0.280
0.011
0.554
0.360
0.918
0.355
0.778
0.629
-0.418
0.016
-0.747
0.414
-0.299
0.257
-0.202
8.336
-0.008
1.315
-0.136
Number of obs
LR chi2(29)
Prob > chi
Pseudo R2
-0.065
0.389
0.108
0.182
0.023
0.102
Log likelihood = -133.92652
=
=
=
=
447
349.67
0.0000
0.5662
Source: Analysis of Field data
Government tax and policies have the propensity to plummet the probability of managerial
decision on risk management practices by 0.42 and 0.14 respectively. This means that government
prudential policies should be aimed at preventing distortions to economic decisions on saving,
investment, consumption and other business related variables.
On the average male managers have a probability of 0.77 of taking risk management decision
more than the female managers. Also on the average risk averse managers have a probability of
0.15 of taking risk management decision more than the risk loving and neutral managers. Managers
with knowledge on risk management practices have a probability of 0.59 of taking risk
management decision more than those without knowledge on risk management practices and this
call for training of managers of SMEs on risk management practices. Managers who own
businesses elsewhere have a probability of 0.28 of taking risk management decision. On the
34
average, businesses in the urban areas have a probability of 0.24 of not taking risk management
decision compared with those in the peri urban and rural areas.
4.11 Results of Hypotheses Testing
The factors hypothesized to affect risk management decisions of SMEs in Ghana are; individual
demographic, business related demographic, economic, government policies and business
characteristics. The results are shown in Table 17. The results show that only the educational level
of business managers and gender (males) hypotheses were accepted at 5% and 10% level of
significance respectively under the demographic factors to positively influence risk management
decisions. All the hypotheses under the business related demographic factors were all accepted at
various levels of significance. Under economic factors, the hypothesis on cost of risk management
is rejected at 5% level of significance and Government source of capital is accepted at 10% level
of significance. The hypothesis on credit union is rejected at 1% level of significance meaning that
capital source from credit union negatively influence risk management decisions. The hypothesis
on government policies is rejected and that on tax is accepted at 10% and 1% levels of significance
respectively.
35
Table 17: Hypothesis results
Null hypothesis on decision to manage risk
Demographic factors
HA1: Age of business owner/manager
HA2: The educational level of business managers
HA3: The marital status of managers
HA4: Gender: Males
HA5: Family size
Business related demographic factors
HB1: The number of years as a manger
HB2: Managerial knowledge in risk management
HB3: Risk aversion
HB4: Managers owning other businesses elsewhere
Economic factors
HC1: The amount of capital at risk
HC2: The source of business capital
Bank
Credit union
Government
HC3: The cost of risk management
HC4: The size of business monthly income
Government policies/tax
HD1: Government policies
HD2: Government tax
Business characteristics
HE1: Business location (urban)
HE2: The type of business
HE3: The number of staff
HE4: The number of person owning the business
Source: Analysis of Field data
Decision
Reject
Accept at 5% level significance
Reject
Accept at 1% level of significance
Reject
Accept at 1% level of significance
Accept at 1% level of significance
Accept at 10% level of significance
Accept at 1% level of significance
Reject
Reject
Reject at 1% level of significance
Accept at 10% level of significance
Reject at 5% level of significance
Reject
Reject at 10% level of significance
Accept at 1% level of significance
Reject at 5% level of significance
Reject
Reject
Reject
5.1 Conclusion and Recommendations
Small and medium scale enterprises are the engine of growth of the economy and a good provider
of employment and therefore have been one of the major areas of concern to many policy makers.
The findings show that apart from years of education and gender, the rest of the demographic
variables are not significant and all the business related demographic factors are also significant at
various levels and positive apart from the obvious risk loving. Government policies and tax
negatively influence risk management decision taking by managers. It is also the case that 170 of
the managers (40%) are not aware of insurance policies being offered by insurance companies in
the country.
Based on the findings from the research on factors influencing risk management decisions, the
following recommendations are made to all stakeholders;
1. The managers of SMEs should recognize that a holistic approach toward managing
business risk has generally been recommended to ensure effective risk management.
36
Research has identified seven factors that can increase the effectiveness of risk
management procedures. These factors are commitment and support from top
management, communication, culture, information technology (IT), organization structure,
training and trust. These factors must serve as a guide to all managers in taking risk
management decisions.
2. Government of Ghana’s (GoG) microfinance programmes targeted at reducing poverty,
creating jobs and wealth is being implemented by Microfinance and Small Loans Centre
(MASLOC). These programmes must be enhanced through capacity building, provision of
logistics and making enough funds available to cater for as many SMEs as possible.
3. The banks, credit unions and other financial intermediaries should add to their loan
packages, business advisory services, training and capacity building for SMEs emphasizing
on risk management practices.
4. Ministry of Trade and Industries (MoTI), National Board for Small Scale Industries
(NBSSI), Association of Small Scale Industries (ASSI), Chamber of Commerce (CoC),
Association of Ghana Industries (AGI), Banks and other support institutions working
closely with SMEs should get expertise on risk management practices to train managers of
SMEs through periodic workshops and conferences. It must be stated that women are more
vulnerable in terms of risk management and so need more attention.
5. Insurance companies should make provision for SMEs in all sectors (agriculture, industry
and service) in terms of policies and make these packages very attractive in order to
persuade managers of SMEs to patronize these packages. Insurance companies should
increase the creation of awareness of the existence of such policies provided.
6. In order not to distort tax revenues inflows, to ensure high tax elasticities and prevent
distortions to economic decisions on saving, investment, consumption and other business
related variables, the Revenue Authority is encouraged to adopt a broad base – low rate
approach to taxation. This approach will help managers of SMEs to manage business risk
which will culminate in the growth of SMEs in Ghana.
7. Government prudential policies that have an effect on SMEs should be carefully
formulated and these policies should be well articulated and explained to stakeholders so
that it is not seen as deterrent to business development in Ghana.
37
8. Bank of Ghana through the Monetary Policy Committee should collaborate with the
financial institutions to reduce interest rates in order to ameliorate the negative effect of
high interest rates on business investment and growth.
9. Information on interest rates by the various financial institutions should be made available
by National Board for Small Scale Industries to prospective business men and women so
that they can make informed decisions on when and where to borrow.
38
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