Description
The importance of small scale industry sector (SSI) has been increasingly recognized in developing countries as a solution to the problem of scarcity of capital, widespread unemployment and poverty (see for example, Romijn 2001 and Junejo, et al 2007). In Ghana Rural Small-Scale Industries (RSSIs) play a very important role in the socio-economic life of majority of the people who live in rural areas where agriculture is the dominant economic activity.
Journal of Science and Technology © KNUST August 2011
MANAGEMENT AND GROWTH PARADOX OF RURAL
SMALL-SCALE INDUSTRIAL SECTOR IN GHANA
S. E. Edusah
Bureau of Integrated Rural Development (BIRD), KNUST, Kumasi, Ghana
ABSTRACT
It has been argued that Small Scale Industries (SSIs) scarcely grow rather they stagnate and
most of them eventually die off mainly due to poor management. The situation has been blamed
on a number of factors and therefore a systematic analysis of the key influential factors will give
better understanding of the phenomenon. Data was mainly collected through interviews of own-
ers/managers of small industrial units in the Mfantseman District of Central Region of Ghana.
Logistic regression was used to distinguish the factors influencing management and growth of
the RSSI sector and to estimate the impact of each explanatory variable in the equations. The
study shows that record keeping and banking, which are key ingredients of good management
practices are largely influenced by the age of the firm, the type of technology used, how proprie-
tors acquired their skills, the production level and the source of investment capital. Similarly,
growth of firm is influenced by the gender of the proprietor, the sources of raw materials and
how proprietors acquired their skill.
Keywords: Rural Small-Scale Industries, firm growth, management, proprietors, workforce
INTRODUCTION
The importance of small scale industry sector
(SSI) has been increasingly recognized in de-
veloping countries as a solution to the problem
of scarcity of capital, widespread unemploy-
ment and poverty (see for example, Romijn
2001 and Junejo, et al 2007). In Ghana Rural
Small-Scale Industries (RSSIs) play a very im-
portant role in the socio-economic life of ma-
jority of the people who live in rural areas
where agriculture is the dominant economic
activity. Although other sectors of the economy
such as mining, manufacturing and services
have improved considerably since the begin-
ning of the decade, agriculture still remains the
most important economic activity and employs
the bulk of the rural workforce. In spite of its
importance, agriculture in Ghana remains rain-
fed. Therefore, the pronounced seasonality of
rainfall has significant effects on labour use in
that sector which tends to be concentrated in
peak periods of the farming cycle i.e. land
© 2011 Kwame Nkrumah University of Science and Technology (KNUST)
Journal of Science and Technology, Vol. 31, No. 2 (2011), pp 57-67 57
RESEARCH PAPER
Journal of Science and Technology © KNUST August 2011
preparation, cropping and harvesting
(Bagachwa and Steward, 1992). This pattern of
labour use in agriculture permits rural dwellers
to engage in other economic activities particu-
larly in RSSIs. Consequently, the RSSI sector
plays four important roles in the rural economy
by:
• providing additional regular employment
and income opportunities;
• providing seasonal and part-time employ-
ment for farm workers during off-peak
farming times of the year;
• increasing incomes of marginal farmers
and farm workers; and
• generating linkages, which are the basis for
growth in agriculture and in the rural econ-
omy.
In order for the RSSI to continue to play these
roles there would be the need for proper man-
agement of the sector to stimulate growth
among the numerous firms operating in the
sector. This is because the sector has been de-
scribed as being characterised by limited spe-
cialisation in management. Managerial defi-
ciencies of proprietors manifest in many forms
prominent among which are poor or lack of
formal record keeping and poor accounting and
banking practices. Junejo et al. (2007) in their
study of SSIs in Sindh, Pakistan reported that
23.25 percent of 83 units sampled did not sur-
vive because of lack of good management. The
managerial handicap often works against the
growth of the firms. Storey (1997) states that
there is a numerically dominant group of small
firms even if they survive would remain small
operations due to their managerial bankruptcy.
While there are enormous amount of literature
on the economic and financial performance of
the SSI very little academic work could be
found on the analysis of the RSSI and the influ-
ential factors of the sector. As part of the eco-
nomic and financial analysis some of the influ-
ential factors have been casually discussed by
some researchers, (see for example Storey
1997). Rani (1996) for example used a combi-
nation of frequency tables and cross-tabulations
in her work on women entrepreneurs in India.
However, in the real life situation many socio-
demographic and socio-economic variables like
age, gender, education, marital status, skill ac-
quisition and sources of capital are partially
correlated with one another. Consequently, it is
often not clear and at times difficult to compre-
hend from a series of cross-tabulations the
magnitude of the influential factors. There is
therefore compelling advantage in employing a
more sophisticated statistical analysis to esti-
mate the independent effects of the influential
factors. Regression techniques are important
statistical tools because they estimate the im-
pact of each explanatory variable after allowing
for variation that can be attributed to each other
factor in the equation.
There are therefore enough reasons to examine
the influential factors of the management and
the growth of the sector. Two key indicators,
record keeping and banking have been used as
proxies for good management practices for
analysis. Record keeping is a measure of good
management practices because it is the system-
atic procedure by which the records of industry
are captured, maintained and disposed off. It
ensures the preservation of records for eviden-
tial purposes, accurate, efficient updating and
timely availability. Similarly banking is a
measure of good management practice because
it ensures financial discipline, good control of
industries and facilitates access to capital for
growth.
RESEARCH METHOD
The data used in this study form part of data
collected from population of firms in the RSSI
sector through the use of a structured question-
naire survey administered in rural settlements
in the Mfantseman District in the Central Re-
gion of Ghana. A total of 215 proprietors were
interviewed. The survey was carried out over a
period of six months between March and Sep-
tember 2009. Questions were asked about the
characteristics of the proprietor, such as the
age, gender, marital status and educational
level. At the firm level questions were asked
Edusah 58
Journal of Science and Technology © KNUST August 2011
about the age of the firm, production technol-
ogy, size of the firm and sources of raw materi-
als. Questions were also asked about the nature
of the market such as the source of the market,
size of sales, fluctuation of sales and competi-
tion.
This paper uses the statistical analysis of the
nature and strength of the association and the
relationship between variables and explores in
more detail the causal and influential factors to
gain proper understanding of the management
of the RSSI sector. The analysis is quantitative
and uses multivariate techniques to incorporate
more than a single factor in examining the rela-
tionships within the RSSI sector. This is be-
cause the relationships are often complex, re-
quiring more than one element for better expla-
nation. In the analysis logistic regression sta-
tistical technique which is appropriate for the
analysis of association and influential factors
has been used and the summary results are pre-
sented in Table 3.
In logistic regression, an outcome is coded as a
zero-one or yes-no variable. As a result the
apparent determinants of that outcome can be
measured in terms of the quantitative impact of
the outcome event occurring. Although logistic
regression is relatively new to researchers lin-
ear and multiple regression statistical tech-
niques have been used extensively in a number
of industry studies. Harris (1970) suggested the
procedure for estimating a single equation of
linear regression in investigating hypotheses
empirically. Liedholm and Chuta (1996) used
the technique in their Sierra Leone study. Gupta
(1992) used a number of linear regressions in
his work on “rural-urban migration, informal
sector and development policies” and Kumar
(1992) used linear regression statistical tech-
niques in analysing the determinants of indus-
trial production in India.
Specification of the Models
Logistic regression adopts automatic computa-
tion by strength of association and as a result
automatically eliminates the more weakly cor-
related factors. For that reason there is the pos-
sibility of falsely eliminating some important
factors that may display weak significance lev-
els. For instance, the variables education and
age are found not significant in the equations.
These were allowed to drop from their equa-
tions. Also some thought was given to the
sources of initial capital available to proprie-
tors. It is recognised that personal and family
capital correlate somehow with each other. For
that reason both were tested for the simple rea-
son that they seem to have substantially differ-
ent additive effects.
In the presentation, comments on various logis-
tic regression equations have been made. In
each equation the estimation of the substantive
effect of each independent variable on the de-
pendent variables has been made. For zero-one
variables like gender, a positive coefficient
indicates that the odds increase if that factor is
present. In the case of the continuous variables
such as age of respondents, age of firm and
sales the interpretation is less simple. In the
analysis the coefficient (B) shows the size of
the effect on the “logit”, which is the logarithm
of the odds. Consequently, negative effects are
interpreted as an inverse effect and positive as
direct effect on the equation. It must be noted
that the final result is presented as the coeffi-
cient Exp(B)=e
B .
This has been done to avoid
the use of the logarithms. In logistic regression,
Exp(B) measures the multiplicative effect on
the predicted odds. For example there is a posi-
tive effect when Exp(B) is greater than one. If it
is less than one, there is an inverse or negative
effect. It must be emphasised that Exp(B) is
always above 0. In the analysis a variable is
dropped from the equation if its Exp(B) is not
significantly different from 1.
Some technical issues about the models are
clarified below to help in their interpretation
and understanding as follows:
• Most of the models contain dummy vari-
ables and these are coded ‘0’ for no event
and ‘1’ for event or ‘0’ for no or ‘1’ for
Management and growth paradox ... 59
Journal of Science and Technology © KNUST August 2011
60 Edusah
yes. For example gender is coded 1 = male
and 0 = female while competition is coded
1= yes and 0 = no.
• There may be some Interaction effects of
some variables in the models but these
have not been measured. All variables that
did not achieve any significant levels have
been dropped from the various equations.
• The significant levels used are 1 per cent, 5
per cent and 10 per cent, which is the cut-
off level for the removal of variables from
the models.
Management of the RSSI Sector
Record keeping and banking were used as
proxies to assess the management practices of
proprietors. The reason is that the two variables
are good management practices that are easy to
assess. Poor record keeping and banking prac-
tices among proprietors of RSSIs have often
been cited among the major managerial impedi-
ments that hinder the development and growth
of RSSI sector (Yaffeh, 1992). This has been
attributed to a number of factors. A careful
study of the influential factors would give bet-
ter understanding of the phenomenon. Influen-
tial factor for keeping or not keeping formal
records and poor banking practices may in-
clude, sex and age of proprietor, educational
level of proprietor, skills, technology, level of
sales and age of the firm.
The operation of the RSSI sector is strongly
related to gender. This is because male and
female proprietors have different capabilities,
aspirations and orientation. Tinker (1987) ar-
gues that women may have different goals and
employ different business strategies to men. It
has been recognised that female proprietors are
usually found in less productive ventures. This
has been attributed to high level of illiteracy
and low motivation among them and inade-
quate access to credit facilities (Storey,1997).
As a result, considerable attention has been
focused upon the uneven opportunities avail-
able to male and female in the sector in recent
times. It is of interest to examine whether
source of skill acquisition is an important influ-
ential factor on management of the RSSIs. This
is because skill, management and success of a
business are interwoven. The type of technol-
ogy i.e. minimal or no use of equipment and
modern technology – semi-automated or auto-
mated equipment (such as mills and extractors)
adopted by proprietors is an important factor in
the operation of RSSIs. It is asserted that the
technological progress of SSIs particularly
RSSIs tends to be slower than that of large in-
dustries (Chan Onn, 1990). In view of that it is
important to assess the impact of technology
upon the management of the sector.
To examine the influential factors of record
keeping and banking as important managerial
skills and practices, two logistic regression
models of the following types have been
adopted and their relationship estimated as:
1a. Record Keeping Model:
a. Reck = a + b
1
Age + b
2
Gend + b
3
Agefirm + b
4
Edu +
b
5
Tech + b
6
Skill+ b
7
Sales + b
8
Prod + b
9
Szf + b
10
K
………………..
(1a)
1b. Banking Model:
b. Bank = a + b
1
Age + b
2
Gend + b
3
Agefirm + b
4
Edu +
b
5
Tech + b
6
Skill+ b
7
Sales + b
8
Prod + b
9
Szf + b
10
K
………… (1b)
Where
Reck = record keeping (yes – 1; no - 0)
Bank = banking (yes – 1; no – 0)
a = constant
Age = log of age of the respondents
Gend = sex of respondents (male -1, female -0)
Agefirm = age of firm
Edu = educational level (primary 9, secondary – 12, tertiary
–16)
Skill = skill acquisition (within the RSSI sector – 1, Out-
side the sector – 0)
Tech = type of technology (traditional – 1, modern – 0)
Sales = log of sales
Prod = production level (all year – 1; seasonal – 0)
SzF = size of firm (number of workers)
K= sources of initial capital of respondents (own funds – 1;
others – 0)
Reck =3.30+0.34Agefirm-1.05Tech-1.55Skill-0.36Sales+0.
Journal of Science and Technology © KNUST August 2011
Management and growth paradox... 61
.99Prod +1.09K ……………….. (1c)
Bank = 2.46-1.38Gend-1.20Skill+0.54K ………. (1d)
Growth of firms
The growth of a firm may be assessed using
different parameters but for the purpose of this
paper the following parameters were used; (i)
increase in workforce, (ii) increase in sales and
(iii) increase in production. In this paper in-
crease in workforce has been used to mean
growth of firms. It has been argued that once
established SSIs do not grow but stagnate and
eventually die off. Storey (1997) argues that
there are numerically dominant groups of small
businesses, which are small today and even if
they survive, are always likely to remain small-
scale operations. The situation has been blamed
on a number of factors and therefore a careful
study of the influential factors of the phenome-
non would give a better understanding of SSIs.
Influential factors for growth or non-growth
among firms in the survey include, age and sex
of proprietor, education, type and age of firm.
This will be investigated empirically with a
logistic model as:
Growth model:
Growth = a + b
1
Age + b
2
Gend+ b
3
Edu +
4
Maris + b
5
Agef
+b
6
Skill + b
7
Flsales + b
8
SzF +
9
Rawmat + b
10
Sale +
b
11
Prod ................ (2a)
Where
Growth = growth of firm (increased workforce -1, no in-
crease – 0)
a = constant
Age = log of age of the respondents
Gend = sex of the respondents (male -1, female – 0)
Edu = educational level (primary -1, secondary – 2, tertiary
–3)
Maris = marital status (yes-1, no - 0)
Agefirm = age of firm
Skill = skill acquisition (within the RSSI sector – 1, Out-
side the sector – 0)
Flsales = fluctuation of sales (yes - 1, no- 0)
SzF = size of firm (number of workers)
Rawmat = sources of raw material (local - 1, imported - 0)
Sales = log of sales
Prod = production level (all year – 1, seasonal – 0)
Growth=-2.56+1.12Gend+1.23Rawmat+0.80Skill .…. (2b)
RESULTS AND DISCUSSION
Characteristics of Firms
The study shows that the RSSIs sector covers a
wide range of activities and forms a very im-
portant part of the rural off farm activities.
These activities include (1) crafts, (pottery,
woodwork, straw work, leather work, gold and
black smithing), (2) artisan (carpentry, tailor-
ing/dress making etc.) and (3) processing (oil
extraction, powder making, fish processing,
cassava processing, vegetable/fruit processing,
brewing etc).
The study revealed that the Processing Industry
(PI) sub-sector dominates the RSSI sector ac-
tivities and accounted for more than half of all
firms. The Artisan Industry (AI) and the Craft
Industry (CI) sub-sectors followed in that order.
The dominance of PI was not unexpected given
the economic activities of the study area which
is based strongly on farming and fishing. The
relatively strong position of AI sub-sector was
also expected because the sub-sector serves as a
safe haven for school dropouts in the rural ar-
eas. The magnitude and the overall distribution
of firms under CI, AI and PI sub-sectors cate-
gorisation is summarised in Table 1, where they
are arranged by gender and industry sub-sector.
The Management and Growth Factors
Discussion of the results of the logistic regres-
sion analysis follows from here on. This is to
help the understanding and the determination of
the factors that best explain the nature of RSSI
sub-sectors in the survey. The statistics re-
ported is the wald significant levels. The logis-
tic regressions present the collective impact of
independent variables on the dependent vari-
ables: 1. Management; and 2. Growth of firms.
The description of the dependent variables and
the summaries of the analyses are presented in
Tables 2 and 3 respectively. The figures pre-
sented in the summary are the wald statistics at
1, 5 and 10 per cent significant levels. The re-
sults show in detail how different variables
affect the RSSI sector in the study area. In each
instance, the results indicate that the equations
Journal of Science and Technology © KNUST August 2011
Edusah 62
Industry Sub-sector
Firm Level Activity
Total Gender of Proprietors
Male Female
% %
Crafts
Pottery 7 28.6 71.4
Woodcarving 7 100 -
Rattan 9 88.9 11.1
Leather-work 5 100 -
Goldsmithing 1 100 -
Blacksmithing 2 100 -
Net-making 6 100 -
Sub Total 37 83.8 16.2
Artisan
Carpentry 15 100 -
Dressmaking 18 - 100
Tailoring 11 100 -
Smelting and Foundry 3 100 -
Block-making 5 80.0 20.0
Canoe Building 9 100 -
Shoe Making 2 100 -
Sub Total 63 69.8 31.2
Processing
Cassava Processing 6 16.7 83.3
Edible Oil Extraction 9 22.2 77.8
Soap making 7 28.6 71.4
Grain milling 16 100 -
Baking/Confectionery 7 - 100
Food Processing 11 - 100
Charcoal Production 12 100 -
Brewing 4 - 100
Distilling of Alcoholic Beverages 19 95.0 5.0
Beekeeping and Honey Processing 6 100 -
Fish Processing 9 - -
Talc Extraction and Processing 8 100 -
Total 215 65.6 34.4
Table 1: Magnitude of RSSI Sector by sub-sector activities and by gender of proprietors
Source: Field Survey (2009)
Journal of Science and Technology © KNUST August 2011
have provided a reasonably good estimate of
the influencing factor for the dependent vari-
ables. It is important to note that unlike multi-
ple regression there is no single indicator of
strength of overall explanation of the equation
in the logistic regression. The regression per-
formed reasonably well than the 50 per cent by
just blind guessing. The two models fit the data
quite well. This is because the equations
achieved goodness of fit of the following lev-
els: (1) the Management models; 1a. Record
Keeping – 76 per cent, 1b. Banking – 74 per
cent and (2) the Growth model; a. Growth – 68
per cent. The individual equations and influen-
tial factors would now be examined in turn.
pectively. This is an indication that skill acqui-
sition has inverse relationships with record
keeping and banking. These suggest that pro-
prietors who acquire their skills outside the
RSSI sector, probably through formal education
or by working with a medium or large-scale
firm are likely to keep record and to operate
bank account for their businesses. On the other
hand Proprietors who acquired their skills from
within the RSSI sector are less likely to keep
records and to operate bank account for their
businesses. In many senses, this outcome is
expected since formal education and prior Me-
dium and Large Scale Industry experience instil
the habit of sound business management in-
cluding record keeping and banking
Management and growth paradox... 63
Measurement 1a. Record Keeping 1b. Banking 2. Growth
Mean
0.64
0.33
0.39
Std. Deviation
0.48
0.47
0.49
Table 2: Description of dependent Variables
Source: Field Survey (2009)
Management of Firms
The results of regressions 1a and 1b show that
there is no relationship between gender and
record keeping but there is a strong negative
relationship between gender and banking at the
1 per cent level. The non-significant relation-
ship between gender and record keeping sug-
gests that gender is not a key influencing factor
on record keeping and the negative coefficient
is an indication of inverse relation which sug-
gests that female proprietors are more likely to
practice banking. The explanation for this may
be that male proprietors more than their female
counterparts plough back their profit to expand
their firms and for housekeeping.
The regression shows that there is a very strong
relationship between source of skills, record
keeping and banking in the survey. The coeffi-
cients are negative and significant at the 1 per
cent levels for record keeping and banking res-
in those who go through them. Proprietors who
acquire their skills from within the RSSI resort
to informal record keeping which is very unre-
liable.
The results reveal that there are strong relation-
ships between source of capital, record keeping
and banking. The table reveals that source of
capital is positively and significantly related to
record keeping and banking at 1 and 10 per
cent respectively. The positive coefficients sug-
gest that proprietors who started their busi-
nesses with their own capital were more likely
to keep records and operate bank accounts. This
may be due to the fact that self-financed pro-
prietors have better control of their firms and
incomes and are able to take decisions about
their incomes.
The statistics show that the age of the firm,
which represents the experience of proprietors,
Journal of Science and Technology © KNUST August 2011
INDEPENDENT VARIABLES
Management Growth
1a 1b 2
Record Keeping Banking Growth
1.Characteristics of the Proprietor
Gender --- 0.25*** 3.06***
Age --- --- ---
Education --- --- ---
Marital status --- --- ---
Skill Acquisition 0.21*** 0.13*** 2.23**
Source of Capital 2.98*** 1.72* ---
2. The Firm
--- --- ---
Age of Firm 1.41** --- ---
Production 2.69** --- ---
Technology 0.35*** ---
Sources of Raw Materials --- --- 3.42***
3. Market
Source of Market --- --- ---
Sales 0.70*** --- ---
Fluctuation --- --- ---
Competition
Constant Term
---
3.30
---
2.46
---
-2.56
Number of Respondents 215 215 215
Edusah 64
Table 3: Logistic Regression for factors influencing the management and growth of RSSI
Source: Field Survey (2009)
Note:
The coefficient reported here are Exp (B), reflecting the multiplicative effect on the predicted odds of the event occurring.
Positive factors have coefficients above 1. Inverse factors have coefficients less than 1.
*** = Wald test significance < 1%
** = Wald test significance < 5%
* = Wald test significance < 10%
is positive and significantly related to record
keeping at the 5 per cent confidence level. This
suggests that the older the firm or the more ex-
perienced the proprietor is the more likely for
him or her to keep records. On the other hand,
the non-significant level for banking indicates
that experience of proprietors is not an influen-
tial factor for banking. This is quite worrying
Journal of Science and Technology © KNUST August 2011
because it was the expectation that older and
experienced proprietors would cultivate the
habit of banking as a good managerial practice.
The statistics show that production was posi-
tively significant to record keeping but non-
significant to banking. The positive coefficient
suggests that proprietors who operate through-
out the year were more likely to keep records.
The non-significant coefficient of production
for banking suggests that the form of produc-
tion was not an influential factor on banking by
the proprietors.
From regression 1a and 1b, the statistics show
that technology was significantly related to
record keeping at 1 per cent level but non-
significant with banking. The negative coeffi-
cient indicates that technology may be in-
versely related to record keeping and reveals
that proprietors who use modern technology
(i.e. Proprietors who use powered equipment
instead of relying on manpower) were more
likely to keep records. The non-significant co-
efficient of technology to banking suggests that
technology, either manpower or powered
equipment, was not a key influence on banking.
The statistics show that sales of firms are nega-
tive and significantly related to record keeping
at the 5 per cent level but non-significant with
banking. The negative coefficients are indica-
tions that sales were inversely related to record
keeping and this suggests that the higher the
sales the less likely that the proprietors may
keep records. This seems to give credence to
the fact that a sizeable number of the respon-
dents in the survey keep informal records. It is
also recognised that it is easy to keep informal
records of small size of sales than to do so
when sales are high. Higher level of activity
requires more formal approach to operations
and management. The non-significant coeffi-
cient of sales to banking is evidence that size of
sales does not influence banking. A look at the
result of equation 2b shows that gender and
growth are positively related. The coefficient
shows that gender is positive and significant at
1 per cent levels. This is an indication that gen-
der is a very important influential factor on the
growth of firms and suggests that male proprie-
tors were more likely to experience growth in
their businesses. This may be due to the fact
that male proprietors enter into those businesses
that have chances to grow. On the other hand
female proprietors were not too keen on the
growth of their enterprises rather they were
satisfied to generate regular income to supple-
ment the household income (see also Storey,
1997; Baud and Bruijine et al, 1993). Again
while male proprietors may concentrate all their
efforts on one enterprise the tendency is that
female proprietors may dissipate their energy
over more than one activity and therefore hin-
der the growth of any of them. In rural Ghana,
households tend to engage simultaneously in
survival and income-mobility strategies on a
gender basis. As a result of this women gener-
ally assume survival strategies with men prac-
tising mobility or growth-oriented strategies.
Therefore women’s low but steady income al-
lows men to seek greater absolute returns at
heightened risk (see Schmink, 1984). The
growth model would now be analysed.
Growth of Firms
The regression 2b shows that gender, sources of
raw materials and skills are the key influential
factors of growth of firms. The statistics show
that source of skill acquisition is positively and
significantly related to growth of firms in the
study area. The coefficient is significant at 5
per cent level and this is an indication that
sources of skill acquisition are important influ-
ential factors in the growth of the RSSI sector.
It suggests that proprietors who acquired their
skills from within the RSSI sector were more
likely to experience growth in their operations.
This may be due to the fact that those who ac-
quired their skills from within the RSSI sector
may have better understanding of their opera-
tions as a result of accumulated knowledge
passed on from parents to children or from the
master craftsman to the apprentices. There may
also be continuity of operations since firms
may be passed on from parents to children,
Management and growth paradox... 65
Journal of Science and Technology © KNUST August 2011
which may be helpful to firm growth.
The regression shows that source of raw mate-
rials is positively and significantly related to
growth at 1 per cent level of significance. The
positive relationship between raw materials and
growth suggests that firms that have access to
local raw materials are more likely to grow.
This may be due to the fact that there are many
difficulties associated with purchase and supply
of imported raw materials such as high cost and
erratic supplies of essential materials. These
among other things affect performance and
stifle growth of firms that rely on imported
materials as inputs.
CONCLUSION
The study shows that the RSSIs sector covers a
wide range of activities and forms a very im-
portant part of the rural off farm activities in
the Mfantseman District. It was apparent from
the results that record keeping and banking by
proprietors were largely influenced by the age
of the firm, the type of technology used, how
proprietors acquired their skills and also the
production level of the firms and the source of
the investment capital. The influential factors
for the growth of firms in the study were the
gender of the proprietor, the sources of raw
material and how proprietors acquired their
skill. All the three factors showed a positive
relationship with growth of firms. It is recom-
mended that prospective RSSI entrepreneurs
attach themselves to old and experienced firms
to undergo training to acquire good managerial
practices before establishing their own.
REFERENCES
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Rural Industries and Rural Linkages in Sub-
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(1993). Gender, Small-Scale Industry and
Development Policy. Exeter, SRP.
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dustries in Malaysia: Economics, Efficiency
and Entrepreneurship, Journal of Develop-
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Gupta M. R. (1992). Rural–Urban Migration,
Informal Sector and Development Policies.
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–151
Harris, J. R. (1971). Nigerian Entrepreneurship
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(2007). Sickness in Small-Scale Industries
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nomics of Rural and Urban Small-Scale
Industries in Sierra Leone, Michigan State
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Rani, D. Lalitha (1996). Women Entrepreneurs,
APH Publishing Corporation, New Delhi
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(1): 58–79
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Storey, D. J. (1997). Understanding the Small
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doc_760239413.pdf
The importance of small scale industry sector (SSI) has been increasingly recognized in developing countries as a solution to the problem of scarcity of capital, widespread unemployment and poverty (see for example, Romijn 2001 and Junejo, et al 2007). In Ghana Rural Small-Scale Industries (RSSIs) play a very important role in the socio-economic life of majority of the people who live in rural areas where agriculture is the dominant economic activity.
Journal of Science and Technology © KNUST August 2011
MANAGEMENT AND GROWTH PARADOX OF RURAL
SMALL-SCALE INDUSTRIAL SECTOR IN GHANA
S. E. Edusah
Bureau of Integrated Rural Development (BIRD), KNUST, Kumasi, Ghana
ABSTRACT
It has been argued that Small Scale Industries (SSIs) scarcely grow rather they stagnate and
most of them eventually die off mainly due to poor management. The situation has been blamed
on a number of factors and therefore a systematic analysis of the key influential factors will give
better understanding of the phenomenon. Data was mainly collected through interviews of own-
ers/managers of small industrial units in the Mfantseman District of Central Region of Ghana.
Logistic regression was used to distinguish the factors influencing management and growth of
the RSSI sector and to estimate the impact of each explanatory variable in the equations. The
study shows that record keeping and banking, which are key ingredients of good management
practices are largely influenced by the age of the firm, the type of technology used, how proprie-
tors acquired their skills, the production level and the source of investment capital. Similarly,
growth of firm is influenced by the gender of the proprietor, the sources of raw materials and
how proprietors acquired their skill.
Keywords: Rural Small-Scale Industries, firm growth, management, proprietors, workforce
INTRODUCTION
The importance of small scale industry sector
(SSI) has been increasingly recognized in de-
veloping countries as a solution to the problem
of scarcity of capital, widespread unemploy-
ment and poverty (see for example, Romijn
2001 and Junejo, et al 2007). In Ghana Rural
Small-Scale Industries (RSSIs) play a very im-
portant role in the socio-economic life of ma-
jority of the people who live in rural areas
where agriculture is the dominant economic
activity. Although other sectors of the economy
such as mining, manufacturing and services
have improved considerably since the begin-
ning of the decade, agriculture still remains the
most important economic activity and employs
the bulk of the rural workforce. In spite of its
importance, agriculture in Ghana remains rain-
fed. Therefore, the pronounced seasonality of
rainfall has significant effects on labour use in
that sector which tends to be concentrated in
peak periods of the farming cycle i.e. land
© 2011 Kwame Nkrumah University of Science and Technology (KNUST)
Journal of Science and Technology, Vol. 31, No. 2 (2011), pp 57-67 57
RESEARCH PAPER
Journal of Science and Technology © KNUST August 2011
preparation, cropping and harvesting
(Bagachwa and Steward, 1992). This pattern of
labour use in agriculture permits rural dwellers
to engage in other economic activities particu-
larly in RSSIs. Consequently, the RSSI sector
plays four important roles in the rural economy
by:
• providing additional regular employment
and income opportunities;
• providing seasonal and part-time employ-
ment for farm workers during off-peak
farming times of the year;
• increasing incomes of marginal farmers
and farm workers; and
• generating linkages, which are the basis for
growth in agriculture and in the rural econ-
omy.
In order for the RSSI to continue to play these
roles there would be the need for proper man-
agement of the sector to stimulate growth
among the numerous firms operating in the
sector. This is because the sector has been de-
scribed as being characterised by limited spe-
cialisation in management. Managerial defi-
ciencies of proprietors manifest in many forms
prominent among which are poor or lack of
formal record keeping and poor accounting and
banking practices. Junejo et al. (2007) in their
study of SSIs in Sindh, Pakistan reported that
23.25 percent of 83 units sampled did not sur-
vive because of lack of good management. The
managerial handicap often works against the
growth of the firms. Storey (1997) states that
there is a numerically dominant group of small
firms even if they survive would remain small
operations due to their managerial bankruptcy.
While there are enormous amount of literature
on the economic and financial performance of
the SSI very little academic work could be
found on the analysis of the RSSI and the influ-
ential factors of the sector. As part of the eco-
nomic and financial analysis some of the influ-
ential factors have been casually discussed by
some researchers, (see for example Storey
1997). Rani (1996) for example used a combi-
nation of frequency tables and cross-tabulations
in her work on women entrepreneurs in India.
However, in the real life situation many socio-
demographic and socio-economic variables like
age, gender, education, marital status, skill ac-
quisition and sources of capital are partially
correlated with one another. Consequently, it is
often not clear and at times difficult to compre-
hend from a series of cross-tabulations the
magnitude of the influential factors. There is
therefore compelling advantage in employing a
more sophisticated statistical analysis to esti-
mate the independent effects of the influential
factors. Regression techniques are important
statistical tools because they estimate the im-
pact of each explanatory variable after allowing
for variation that can be attributed to each other
factor in the equation.
There are therefore enough reasons to examine
the influential factors of the management and
the growth of the sector. Two key indicators,
record keeping and banking have been used as
proxies for good management practices for
analysis. Record keeping is a measure of good
management practices because it is the system-
atic procedure by which the records of industry
are captured, maintained and disposed off. It
ensures the preservation of records for eviden-
tial purposes, accurate, efficient updating and
timely availability. Similarly banking is a
measure of good management practice because
it ensures financial discipline, good control of
industries and facilitates access to capital for
growth.
RESEARCH METHOD
The data used in this study form part of data
collected from population of firms in the RSSI
sector through the use of a structured question-
naire survey administered in rural settlements
in the Mfantseman District in the Central Re-
gion of Ghana. A total of 215 proprietors were
interviewed. The survey was carried out over a
period of six months between March and Sep-
tember 2009. Questions were asked about the
characteristics of the proprietor, such as the
age, gender, marital status and educational
level. At the firm level questions were asked
Edusah 58
Journal of Science and Technology © KNUST August 2011
about the age of the firm, production technol-
ogy, size of the firm and sources of raw materi-
als. Questions were also asked about the nature
of the market such as the source of the market,
size of sales, fluctuation of sales and competi-
tion.
This paper uses the statistical analysis of the
nature and strength of the association and the
relationship between variables and explores in
more detail the causal and influential factors to
gain proper understanding of the management
of the RSSI sector. The analysis is quantitative
and uses multivariate techniques to incorporate
more than a single factor in examining the rela-
tionships within the RSSI sector. This is be-
cause the relationships are often complex, re-
quiring more than one element for better expla-
nation. In the analysis logistic regression sta-
tistical technique which is appropriate for the
analysis of association and influential factors
has been used and the summary results are pre-
sented in Table 3.
In logistic regression, an outcome is coded as a
zero-one or yes-no variable. As a result the
apparent determinants of that outcome can be
measured in terms of the quantitative impact of
the outcome event occurring. Although logistic
regression is relatively new to researchers lin-
ear and multiple regression statistical tech-
niques have been used extensively in a number
of industry studies. Harris (1970) suggested the
procedure for estimating a single equation of
linear regression in investigating hypotheses
empirically. Liedholm and Chuta (1996) used
the technique in their Sierra Leone study. Gupta
(1992) used a number of linear regressions in
his work on “rural-urban migration, informal
sector and development policies” and Kumar
(1992) used linear regression statistical tech-
niques in analysing the determinants of indus-
trial production in India.
Specification of the Models
Logistic regression adopts automatic computa-
tion by strength of association and as a result
automatically eliminates the more weakly cor-
related factors. For that reason there is the pos-
sibility of falsely eliminating some important
factors that may display weak significance lev-
els. For instance, the variables education and
age are found not significant in the equations.
These were allowed to drop from their equa-
tions. Also some thought was given to the
sources of initial capital available to proprie-
tors. It is recognised that personal and family
capital correlate somehow with each other. For
that reason both were tested for the simple rea-
son that they seem to have substantially differ-
ent additive effects.
In the presentation, comments on various logis-
tic regression equations have been made. In
each equation the estimation of the substantive
effect of each independent variable on the de-
pendent variables has been made. For zero-one
variables like gender, a positive coefficient
indicates that the odds increase if that factor is
present. In the case of the continuous variables
such as age of respondents, age of firm and
sales the interpretation is less simple. In the
analysis the coefficient (B) shows the size of
the effect on the “logit”, which is the logarithm
of the odds. Consequently, negative effects are
interpreted as an inverse effect and positive as
direct effect on the equation. It must be noted
that the final result is presented as the coeffi-
cient Exp(B)=e
B .
This has been done to avoid
the use of the logarithms. In logistic regression,
Exp(B) measures the multiplicative effect on
the predicted odds. For example there is a posi-
tive effect when Exp(B) is greater than one. If it
is less than one, there is an inverse or negative
effect. It must be emphasised that Exp(B) is
always above 0. In the analysis a variable is
dropped from the equation if its Exp(B) is not
significantly different from 1.
Some technical issues about the models are
clarified below to help in their interpretation
and understanding as follows:
• Most of the models contain dummy vari-
ables and these are coded ‘0’ for no event
and ‘1’ for event or ‘0’ for no or ‘1’ for
Management and growth paradox ... 59
Journal of Science and Technology © KNUST August 2011
60 Edusah
yes. For example gender is coded 1 = male
and 0 = female while competition is coded
1= yes and 0 = no.
• There may be some Interaction effects of
some variables in the models but these
have not been measured. All variables that
did not achieve any significant levels have
been dropped from the various equations.
• The significant levels used are 1 per cent, 5
per cent and 10 per cent, which is the cut-
off level for the removal of variables from
the models.
Management of the RSSI Sector
Record keeping and banking were used as
proxies to assess the management practices of
proprietors. The reason is that the two variables
are good management practices that are easy to
assess. Poor record keeping and banking prac-
tices among proprietors of RSSIs have often
been cited among the major managerial impedi-
ments that hinder the development and growth
of RSSI sector (Yaffeh, 1992). This has been
attributed to a number of factors. A careful
study of the influential factors would give bet-
ter understanding of the phenomenon. Influen-
tial factor for keeping or not keeping formal
records and poor banking practices may in-
clude, sex and age of proprietor, educational
level of proprietor, skills, technology, level of
sales and age of the firm.
The operation of the RSSI sector is strongly
related to gender. This is because male and
female proprietors have different capabilities,
aspirations and orientation. Tinker (1987) ar-
gues that women may have different goals and
employ different business strategies to men. It
has been recognised that female proprietors are
usually found in less productive ventures. This
has been attributed to high level of illiteracy
and low motivation among them and inade-
quate access to credit facilities (Storey,1997).
As a result, considerable attention has been
focused upon the uneven opportunities avail-
able to male and female in the sector in recent
times. It is of interest to examine whether
source of skill acquisition is an important influ-
ential factor on management of the RSSIs. This
is because skill, management and success of a
business are interwoven. The type of technol-
ogy i.e. minimal or no use of equipment and
modern technology – semi-automated or auto-
mated equipment (such as mills and extractors)
adopted by proprietors is an important factor in
the operation of RSSIs. It is asserted that the
technological progress of SSIs particularly
RSSIs tends to be slower than that of large in-
dustries (Chan Onn, 1990). In view of that it is
important to assess the impact of technology
upon the management of the sector.
To examine the influential factors of record
keeping and banking as important managerial
skills and practices, two logistic regression
models of the following types have been
adopted and their relationship estimated as:
1a. Record Keeping Model:
a. Reck = a + b
1
Age + b
2
Gend + b
3
Agefirm + b
4
Edu +
b
5
Tech + b
6
Skill+ b
7
Sales + b
8
Prod + b
9
Szf + b
10
K
………………..
(1a)
1b. Banking Model:
b. Bank = a + b
1
Age + b
2
Gend + b
3
Agefirm + b
4
Edu +
b
5
Tech + b
6
Skill+ b
7
Sales + b
8
Prod + b
9
Szf + b
10
K
………… (1b)
Where
Reck = record keeping (yes – 1; no - 0)
Bank = banking (yes – 1; no – 0)
a = constant
Age = log of age of the respondents
Gend = sex of respondents (male -1, female -0)
Agefirm = age of firm
Edu = educational level (primary 9, secondary – 12, tertiary
–16)
Skill = skill acquisition (within the RSSI sector – 1, Out-
side the sector – 0)
Tech = type of technology (traditional – 1, modern – 0)
Sales = log of sales
Prod = production level (all year – 1; seasonal – 0)
SzF = size of firm (number of workers)
K= sources of initial capital of respondents (own funds – 1;
others – 0)
Reck =3.30+0.34Agefirm-1.05Tech-1.55Skill-0.36Sales+0.
Journal of Science and Technology © KNUST August 2011
Management and growth paradox... 61
.99Prod +1.09K ……………….. (1c)
Bank = 2.46-1.38Gend-1.20Skill+0.54K ………. (1d)
Growth of firms
The growth of a firm may be assessed using
different parameters but for the purpose of this
paper the following parameters were used; (i)
increase in workforce, (ii) increase in sales and
(iii) increase in production. In this paper in-
crease in workforce has been used to mean
growth of firms. It has been argued that once
established SSIs do not grow but stagnate and
eventually die off. Storey (1997) argues that
there are numerically dominant groups of small
businesses, which are small today and even if
they survive, are always likely to remain small-
scale operations. The situation has been blamed
on a number of factors and therefore a careful
study of the influential factors of the phenome-
non would give a better understanding of SSIs.
Influential factors for growth or non-growth
among firms in the survey include, age and sex
of proprietor, education, type and age of firm.
This will be investigated empirically with a
logistic model as:
Growth model:
Growth = a + b
1
Age + b
2
Gend+ b
3
Edu +
4
Maris + b
5
Agef
+b
6
Skill + b
7
Flsales + b
8
SzF +
9
Rawmat + b
10
Sale +
b
11
Prod ................ (2a)
Where
Growth = growth of firm (increased workforce -1, no in-
crease – 0)
a = constant
Age = log of age of the respondents
Gend = sex of the respondents (male -1, female – 0)
Edu = educational level (primary -1, secondary – 2, tertiary
–3)
Maris = marital status (yes-1, no - 0)
Agefirm = age of firm
Skill = skill acquisition (within the RSSI sector – 1, Out-
side the sector – 0)
Flsales = fluctuation of sales (yes - 1, no- 0)
SzF = size of firm (number of workers)
Rawmat = sources of raw material (local - 1, imported - 0)
Sales = log of sales
Prod = production level (all year – 1, seasonal – 0)
Growth=-2.56+1.12Gend+1.23Rawmat+0.80Skill .…. (2b)
RESULTS AND DISCUSSION
Characteristics of Firms
The study shows that the RSSIs sector covers a
wide range of activities and forms a very im-
portant part of the rural off farm activities.
These activities include (1) crafts, (pottery,
woodwork, straw work, leather work, gold and
black smithing), (2) artisan (carpentry, tailor-
ing/dress making etc.) and (3) processing (oil
extraction, powder making, fish processing,
cassava processing, vegetable/fruit processing,
brewing etc).
The study revealed that the Processing Industry
(PI) sub-sector dominates the RSSI sector ac-
tivities and accounted for more than half of all
firms. The Artisan Industry (AI) and the Craft
Industry (CI) sub-sectors followed in that order.
The dominance of PI was not unexpected given
the economic activities of the study area which
is based strongly on farming and fishing. The
relatively strong position of AI sub-sector was
also expected because the sub-sector serves as a
safe haven for school dropouts in the rural ar-
eas. The magnitude and the overall distribution
of firms under CI, AI and PI sub-sectors cate-
gorisation is summarised in Table 1, where they
are arranged by gender and industry sub-sector.
The Management and Growth Factors
Discussion of the results of the logistic regres-
sion analysis follows from here on. This is to
help the understanding and the determination of
the factors that best explain the nature of RSSI
sub-sectors in the survey. The statistics re-
ported is the wald significant levels. The logis-
tic regressions present the collective impact of
independent variables on the dependent vari-
ables: 1. Management; and 2. Growth of firms.
The description of the dependent variables and
the summaries of the analyses are presented in
Tables 2 and 3 respectively. The figures pre-
sented in the summary are the wald statistics at
1, 5 and 10 per cent significant levels. The re-
sults show in detail how different variables
affect the RSSI sector in the study area. In each
instance, the results indicate that the equations
Journal of Science and Technology © KNUST August 2011
Edusah 62
Industry Sub-sector
Firm Level Activity
Total Gender of Proprietors
Male Female
% %
Crafts
Pottery 7 28.6 71.4
Woodcarving 7 100 -
Rattan 9 88.9 11.1
Leather-work 5 100 -
Goldsmithing 1 100 -
Blacksmithing 2 100 -
Net-making 6 100 -
Sub Total 37 83.8 16.2
Artisan
Carpentry 15 100 -
Dressmaking 18 - 100
Tailoring 11 100 -
Smelting and Foundry 3 100 -
Block-making 5 80.0 20.0
Canoe Building 9 100 -
Shoe Making 2 100 -
Sub Total 63 69.8 31.2
Processing
Cassava Processing 6 16.7 83.3
Edible Oil Extraction 9 22.2 77.8
Soap making 7 28.6 71.4
Grain milling 16 100 -
Baking/Confectionery 7 - 100
Food Processing 11 - 100
Charcoal Production 12 100 -
Brewing 4 - 100
Distilling of Alcoholic Beverages 19 95.0 5.0
Beekeeping and Honey Processing 6 100 -
Fish Processing 9 - -
Talc Extraction and Processing 8 100 -
Total 215 65.6 34.4
Table 1: Magnitude of RSSI Sector by sub-sector activities and by gender of proprietors
Source: Field Survey (2009)
Journal of Science and Technology © KNUST August 2011
have provided a reasonably good estimate of
the influencing factor for the dependent vari-
ables. It is important to note that unlike multi-
ple regression there is no single indicator of
strength of overall explanation of the equation
in the logistic regression. The regression per-
formed reasonably well than the 50 per cent by
just blind guessing. The two models fit the data
quite well. This is because the equations
achieved goodness of fit of the following lev-
els: (1) the Management models; 1a. Record
Keeping – 76 per cent, 1b. Banking – 74 per
cent and (2) the Growth model; a. Growth – 68
per cent. The individual equations and influen-
tial factors would now be examined in turn.
pectively. This is an indication that skill acqui-
sition has inverse relationships with record
keeping and banking. These suggest that pro-
prietors who acquire their skills outside the
RSSI sector, probably through formal education
or by working with a medium or large-scale
firm are likely to keep record and to operate
bank account for their businesses. On the other
hand Proprietors who acquired their skills from
within the RSSI sector are less likely to keep
records and to operate bank account for their
businesses. In many senses, this outcome is
expected since formal education and prior Me-
dium and Large Scale Industry experience instil
the habit of sound business management in-
cluding record keeping and banking
Management and growth paradox... 63
Measurement 1a. Record Keeping 1b. Banking 2. Growth
Mean
0.64
0.33
0.39
Std. Deviation
0.48
0.47
0.49
Table 2: Description of dependent Variables
Source: Field Survey (2009)
Management of Firms
The results of regressions 1a and 1b show that
there is no relationship between gender and
record keeping but there is a strong negative
relationship between gender and banking at the
1 per cent level. The non-significant relation-
ship between gender and record keeping sug-
gests that gender is not a key influencing factor
on record keeping and the negative coefficient
is an indication of inverse relation which sug-
gests that female proprietors are more likely to
practice banking. The explanation for this may
be that male proprietors more than their female
counterparts plough back their profit to expand
their firms and for housekeeping.
The regression shows that there is a very strong
relationship between source of skills, record
keeping and banking in the survey. The coeffi-
cients are negative and significant at the 1 per
cent levels for record keeping and banking res-
in those who go through them. Proprietors who
acquire their skills from within the RSSI resort
to informal record keeping which is very unre-
liable.
The results reveal that there are strong relation-
ships between source of capital, record keeping
and banking. The table reveals that source of
capital is positively and significantly related to
record keeping and banking at 1 and 10 per
cent respectively. The positive coefficients sug-
gest that proprietors who started their busi-
nesses with their own capital were more likely
to keep records and operate bank accounts. This
may be due to the fact that self-financed pro-
prietors have better control of their firms and
incomes and are able to take decisions about
their incomes.
The statistics show that the age of the firm,
which represents the experience of proprietors,
Journal of Science and Technology © KNUST August 2011
INDEPENDENT VARIABLES
Management Growth
1a 1b 2
Record Keeping Banking Growth
1.Characteristics of the Proprietor
Gender --- 0.25*** 3.06***
Age --- --- ---
Education --- --- ---
Marital status --- --- ---
Skill Acquisition 0.21*** 0.13*** 2.23**
Source of Capital 2.98*** 1.72* ---
2. The Firm
--- --- ---
Age of Firm 1.41** --- ---
Production 2.69** --- ---
Technology 0.35*** ---
Sources of Raw Materials --- --- 3.42***
3. Market
Source of Market --- --- ---
Sales 0.70*** --- ---
Fluctuation --- --- ---
Competition
Constant Term
---
3.30
---
2.46
---
-2.56
Number of Respondents 215 215 215
Edusah 64
Table 3: Logistic Regression for factors influencing the management and growth of RSSI
Source: Field Survey (2009)
Note:
The coefficient reported here are Exp (B), reflecting the multiplicative effect on the predicted odds of the event occurring.
Positive factors have coefficients above 1. Inverse factors have coefficients less than 1.
*** = Wald test significance < 1%
** = Wald test significance < 5%
* = Wald test significance < 10%
is positive and significantly related to record
keeping at the 5 per cent confidence level. This
suggests that the older the firm or the more ex-
perienced the proprietor is the more likely for
him or her to keep records. On the other hand,
the non-significant level for banking indicates
that experience of proprietors is not an influen-
tial factor for banking. This is quite worrying
Journal of Science and Technology © KNUST August 2011
because it was the expectation that older and
experienced proprietors would cultivate the
habit of banking as a good managerial practice.
The statistics show that production was posi-
tively significant to record keeping but non-
significant to banking. The positive coefficient
suggests that proprietors who operate through-
out the year were more likely to keep records.
The non-significant coefficient of production
for banking suggests that the form of produc-
tion was not an influential factor on banking by
the proprietors.
From regression 1a and 1b, the statistics show
that technology was significantly related to
record keeping at 1 per cent level but non-
significant with banking. The negative coeffi-
cient indicates that technology may be in-
versely related to record keeping and reveals
that proprietors who use modern technology
(i.e. Proprietors who use powered equipment
instead of relying on manpower) were more
likely to keep records. The non-significant co-
efficient of technology to banking suggests that
technology, either manpower or powered
equipment, was not a key influence on banking.
The statistics show that sales of firms are nega-
tive and significantly related to record keeping
at the 5 per cent level but non-significant with
banking. The negative coefficients are indica-
tions that sales were inversely related to record
keeping and this suggests that the higher the
sales the less likely that the proprietors may
keep records. This seems to give credence to
the fact that a sizeable number of the respon-
dents in the survey keep informal records. It is
also recognised that it is easy to keep informal
records of small size of sales than to do so
when sales are high. Higher level of activity
requires more formal approach to operations
and management. The non-significant coeffi-
cient of sales to banking is evidence that size of
sales does not influence banking. A look at the
result of equation 2b shows that gender and
growth are positively related. The coefficient
shows that gender is positive and significant at
1 per cent levels. This is an indication that gen-
der is a very important influential factor on the
growth of firms and suggests that male proprie-
tors were more likely to experience growth in
their businesses. This may be due to the fact
that male proprietors enter into those businesses
that have chances to grow. On the other hand
female proprietors were not too keen on the
growth of their enterprises rather they were
satisfied to generate regular income to supple-
ment the household income (see also Storey,
1997; Baud and Bruijine et al, 1993). Again
while male proprietors may concentrate all their
efforts on one enterprise the tendency is that
female proprietors may dissipate their energy
over more than one activity and therefore hin-
der the growth of any of them. In rural Ghana,
households tend to engage simultaneously in
survival and income-mobility strategies on a
gender basis. As a result of this women gener-
ally assume survival strategies with men prac-
tising mobility or growth-oriented strategies.
Therefore women’s low but steady income al-
lows men to seek greater absolute returns at
heightened risk (see Schmink, 1984). The
growth model would now be analysed.
Growth of Firms
The regression 2b shows that gender, sources of
raw materials and skills are the key influential
factors of growth of firms. The statistics show
that source of skill acquisition is positively and
significantly related to growth of firms in the
study area. The coefficient is significant at 5
per cent level and this is an indication that
sources of skill acquisition are important influ-
ential factors in the growth of the RSSI sector.
It suggests that proprietors who acquired their
skills from within the RSSI sector were more
likely to experience growth in their operations.
This may be due to the fact that those who ac-
quired their skills from within the RSSI sector
may have better understanding of their opera-
tions as a result of accumulated knowledge
passed on from parents to children or from the
master craftsman to the apprentices. There may
also be continuity of operations since firms
may be passed on from parents to children,
Management and growth paradox... 65
Journal of Science and Technology © KNUST August 2011
which may be helpful to firm growth.
The regression shows that source of raw mate-
rials is positively and significantly related to
growth at 1 per cent level of significance. The
positive relationship between raw materials and
growth suggests that firms that have access to
local raw materials are more likely to grow.
This may be due to the fact that there are many
difficulties associated with purchase and supply
of imported raw materials such as high cost and
erratic supplies of essential materials. These
among other things affect performance and
stifle growth of firms that rely on imported
materials as inputs.
CONCLUSION
The study shows that the RSSIs sector covers a
wide range of activities and forms a very im-
portant part of the rural off farm activities in
the Mfantseman District. It was apparent from
the results that record keeping and banking by
proprietors were largely influenced by the age
of the firm, the type of technology used, how
proprietors acquired their skills and also the
production level of the firms and the source of
the investment capital. The influential factors
for the growth of firms in the study were the
gender of the proprietor, the sources of raw
material and how proprietors acquired their
skill. All the three factors showed a positive
relationship with growth of firms. It is recom-
mended that prospective RSSI entrepreneurs
attach themselves to old and experienced firms
to undergo training to acquire good managerial
practices before establishing their own.
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