4.0
Results
Figures in parentheses are column percentages
X = Mean
Get The Complete
Work - See the Procedure
4.1 Socio-Economic Attributes of Micro/Small
Agro-Processors in Ebonyi State.
Some socio-economic characteristics of the
entrepreneurs were collected to identify their effects on enterprise
development.
4.1.1 Identification of Agro-allied processing
enterprise in the State and related institutions:
The three selected micro and small
agro-allied processing enterprises are Cassava, Rice and Oil Palm Fruits. Institutions comprise a wide range of formal
and informal relationships that enhance societal productivity by making
people’s interactions and co-operations more predictable and effective.
Agro-allied
Micro/Small Processing Enterprises and related Institutions.
Related
institutions included the following:
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i.
Government
Ministries: Education (formal/informal), Agriculture, Finance, Lands Survey
and Housing, Local Government and Rural Development, Commerce and Industry,
Works and Transport, Women Affairs and Social Development, Justice, Science
and Technology, Public Utilities.
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ii.
Government
Parastatal: Agricultural Development Programme (ADP)
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National
Poverty Alleviation Programme (NAPEP), National Directorate of Employment
(NDE), Community Based Poverty Reduction Agency (CPRA), Small and Medium
Enterprise Development Agency of Nigeria (SMEDAN)
|
iii.
Non Governmental
Organisation: British America Tobacco
Foundation, Sudan Mission, Global Initiative for Agricultural Development etc
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iv.
International Donor
Agencies: United Nations Development
Programme (UNDP), United Nations Industrial Development Organisation (UNIDO)
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v.
Community Based
Organisations: Town Unions, Age Grade
Groups, Social Club, Churches, etc
|
vi.
Business
Associations: All Farmers Association
of Nigeria (AFAN), Market Association, Trade Association, Processors
Co-operative Unions etc.
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vii.
Financial
Institutions (formal/informal): Banks,
Money lenders, Isusu etc.
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The institutions that are involved in the
establishment and development of micro/small agro-allied processing enterprises
in Ebonyi State are relevant in increased food production, extension services
delivery, security, entrepreneurship development programme, infrastructural
development, provision of adequate legal environment, fabrication of tool and
equipment. They equally create
opportunities for empowerment of women, youths and less privileged for skill
acquisition and self-reliance, and support community projects for poverty
reduction. They also provide fund for
micro/small processing enterprise development and linkage with market.
4.1.2 Micro/Small Agro-allied processing enterprise
and Banking Institutions in the State.
To determine the role of banking
institutions in the development of micro/small agro-allied processing
enterprises in the past 3 years a structured questionnaire was distributed to
project officers in the banks. The
following banks were involved: Nigerian
Agricultural Cooperative and Rural Development Bank (NACRDB) Afikpo, First Bank
of Nigeria PLC Ezzamgbo, Izzi Micro Finance Iboko, Nigerian Agricultural
Cooperative and Rural Development Bank (NACRDB) Abakaliki, Ngodo Community Bank
Abakaliki, United Bank of Africa (UBA) Ogoja Road and Zik Avenue Branches
Abakaliki, Ndiagu Community Bank Ogoja Road Abakaliki, Union Bank Abakaliki,
First Bank Water Works Road Abakaliki, Nigerian Agricultural Cooperative and
Rural Development Bank (NACRDB) Akaeze , United Bank of Africa (UBA) Onueke and
Union Bank Uburu.
Table 1: Percentage
Distribution of loanable and disbursed fund to Micro/Small Agro Processors by
Banks.
The banks involved included Commercial,
Community and Micro Finance Institutions in the study areas. They indicated the level of financial
commitment to the development of Micro/small agro-processing enterprises as shows
in Table 1.
Source:
Field Data, 2007
Data in Table 1 indicates that 20% and 40%
of the banks had less than 10% and between 51 –70% fund for micro/small
agro-processing enterprises development.
Effectively, 40% of the loanable fund was
disbursed to processors under the less than 10% category while 20% was
disbursed in the category of between 31 – 50%.
The mean percent loanable and disbursed fund is 37% and 8% respectively.
4.1.3 Socio-Economic Attributes (Characteristics)
of Micro/Small Agro – Processing Entrepreneurs in Ebonyi State:
Data from two hundred and sixty four (264) respondents
were used for the analysis. Some
Socio-economic attributes of the respondents considered are gender, age, household
size, years of schooling, years in processing, years spent in acquiring skill,
loan obtained and source. Others include
working capital, value of enterprise, Number of workers, monthly revenue,
Amount spent in purchasing equipment, Number of organizations entrepreneurs
belonged to, and assistance received from Government and Non Governmental
Organisations (NGOs). These variables
have in one way or the other influenced the establishment and development of
the selected agro-processing enterprises in Ebonyi State.
Source:
Field Data 2007
Table 2 shows that none of the entrepreneurs was less
than 20 years while those above 60 years were in the minority representing
3%. The mean age of the entrepreneurs is
44 years. The household size of the
entrepreneurs shows that 44% had between 6 – 10 persons while 2% had more than
20 persons with a mean of 9 persons.
Under marital status, 92% of the
respondents were married while less than 1% were separated or divorced. Fourteen percent (14%) of the processors did
not go to formal school, 31% had between 1 – 6 years of schooling while less
than 1% acquired 20 years of schooling.
The mean level of education is 9 years of schooling.
4.1.4 Years
in skill acquisition and processing:
Years in skill acquisition refers to the number of
years an individual served as an apprentice before establishing one’s
enterprise while years in processing refers to experience in agro-processing.
In table 3, 84% of the processors acquired skill for
processing between 1 – 2 years before starting their own enterprise while less
than 1% did not have any skill acquisition prior to establishment of their
business. Two years is the mean years in
skill acquisition for the entrepreneurs.
Majority of the entrepreneurs, 39% have been in business between 6 – 10
years while 7% have been into processing for the past 20 years. Mean number of years in processing is 12.
4.1.5: Enterprise
Characteristics.
The respondents were classified into males and females
and found to be engaged in various occupations.
The enterprises were located either in the urban or rural areas while
the three major enterprises include Cassava, Rice and Oil palm fruit
processing.
Table 4 indicates that 64% and 34% of male and female
processors respectively were involved in processing. Ninety-five percent (95%) and 5% of the
micro/small enterprises were located in the urban and rural areas
respectively. Majority of the
entrepreneurs 46% had farming as the primary occupation while only 1% was not
into farming. Of the 264 processors,
39%, 32% and 29% were involved in Cassava, Rice and Oil Palm Fruit processing
respectively.
4.1.6 Age of
Enterprise and Registration with Agencies
Age of enterprise refers to the number of years an
enterprise has been in existence or has been processing agro-product. Processing enterprises were registered with
state/local governments or union/Associations shown in table 5.
Table 5 indicates that 34% of the enterprises have
been in existence between 6 – 10 years and they are in the majority while 5%
have been processing for the past 20years.
The mean age of enterprise is 11 years.
Out of the 264 processors, 28% registered their enterprises while 72%
did not. None of the entrepreneurs
registered with corporate Affairs Commission.
Fifty-five percent (55%) and 23% registered with Local and State
Government respectively.
4.1.7 Sources of Capital at Inception of Business
Processors started their enterprises with capital
saved over a long period of time and/or borrowed money from formal or informal
credit institutions.
Data on table 6 reveal that 58% of the respondents
started their business with their own capital while 2% obtained fund from
Government and Non Governmental Organisations respectively.
4.1.8 Loan obtained and sources:
Processors require credit (loan) for their various
processing activities. This could be for
the purchase of equipment, building, raw materials, labour or even for
consumption. The sources of their loan
was either from formal or informal credit organisations. Formal Credit Institutions include banks,
cooperative societies, Government and Non Governmental Organisations (NGOs),
while non-formal credit organisations comprise loan from friends, relations,
money lenders and traders, Isusu/Thrift saving, unions and clubs.
4.1.9 Number of Informal Credit Organisations.
Informal credit organisations are groups of money
lenders without Central Bank regulation and are not officially registered as
financial institutions. Processors
accessed credit from these organisations.
Many micro and small agro-processing enterprises are located in the
rural areas where there is non-existence of banking services. Table 8 shows the number of the informal
credit organisation, in their neighbourhood and distance of enterprise location
from banks.
Data in Table 8 indicates that 12% of the processors
lived in a neighbourhood where they were no form of informal credit
organisation. Thirty-two (32%) had one
credit organisation while 1% had up to 6 of such organisations in their
communities respectively.
4.1.10
Distance
of enterprise location from Bank.
Many micro and small agro-processing enterprises are
located in the rural areas where there is non existence of banking
services. Table 9 indicates the distance
of enterprise location from bank.
Data in table 9 indicate that out of the 123
respondents (47%) who had banks in their locality, 40% were 2km away from bank
while 7% had their homes located more than 5km away from the bank.
4.1.11 Number and types of Organisation
Entrepreneurs belonged to.
The entrepreneurs belonged to some processing and
farmers groups like Cassava Processors, Rice Processors and Oil Palm Fruits
Processors Association, All Farmers Association of Nigeria (AFAN) etc. The processors were members of Community
Based Organisations such as Town Union, Age Grade Group, Social Clubs, Market
Organisations etc.
Table 10 shows that 56% of the entrepreneurs did not
belong to any processing group.
Twenty-seven percent (27%) belonged to one and they are in the majority,
while less than one percent (1%) belonged to four groups. Membership of entrepreneurs to community
based organisation indicate that less than 1% did not belong to any association
at all. Majority of them 36% belonged to
Farmer/Agro-Processors’ Associations while 3% belonged to social club and were
in the minority.
4.1.12
Size
of Working Capital and value of Enterprise Assets.
Working capital refers to the total amount used for
the running of the enterprise. Micro and
small enterprises have been summarized by Adelaja (2004) as enterprises with
small capital outlay of not more than N1.5 million for micro enterprises and
between N1.5 million and N50 million for small enterprises. Total value of enterprise includes value of
tools, machineries, building excluding the value of land. Monthly revenue refers to monthly income
realized from the sale of processed products and services rendered and paid for
by customers. Such customer services
include grinding/pressing of cassava, milling of rice and pressing of oil palm
fruit for specified fees since the study was on both the processors and people
who owned processing enterprises.
Data from table 11 indicate that majority of the
entrepreneurs 53.0% had between N10,000 – N50,000 as the size of working
capital. Less than 1% had more than
N500,000 while the mean size of working capital is N68.935. The mean value of total capital asset is
N195,204 with majority 40% having between N50,000 – N100,000 while less than 1%
had more than N500,000.
4.1.13 Monthly Revenue from Enterprise.
Majority of the processors 43% realized between
N11,000 – N30,000 as monthly revenue and less than 1% had monthly income of
more than N130,000. The mean monthly
revenue of both those who owned processing enterprises and those who got their
services was N33,379.
4.1.14
Age
and Cost of Agro-Processing Equipment.
Age of Equipment refers to the number of years an
equipment has been in use. The
agro-processing equipment include all the simple tools, utensils, machineries
used in processing of cassava, rice, oil palm fruits from its raw state to
finished stage. Some of the equipment
were not purchased new but were fairly used, before acquisition by the
processors. Micro and Small
agro-processing enterprises are operated by semi-skilled and non-skilled
individual. They have equipment which
include graters, press, soaking drums, sifter, fryer, mill, parboiling drums,
huller, storing tanks, plastic jerricans, knives, basins etc.
Less than 1% of the processors had equipment that were
less than one year. Majority of them 31%
had equipment aged between 11 – 15 years and minority 3% had equipment which
were more than 25 years old. The mean
years of usage of equipment was 9 years.
Table 13 also indicates that 45% of the processors had equipment that
cost between N10,000 – N50,000 while less than 1% had equipment between
N301,000 – N400,000. The mean cost of
equipment was N86,914.
4.1.15 Number of Employees engaged by
Entrepreneurs
Number of employees refers to number of persons who
are engaged in the processing outfit either as full time workers, apprentices,
casual or unpaid workers. They include
skilled and unskilled workers.
Data in table 14 indicates that 1.5% of the
respondents engaged no other worker.
Less than 1% employed between 13 – 15 and more than 15 persons
respectively. Majority of the
entrepreneurs 45% engaged between 4 – 6 workers. Mean number of workers employed was 4
persons.
4.1.16
Full
time workers, Apprentice, Casual and Unpaid workers engaged in Agro-processing.
Full time workers are those who work exclusively in
the processing outfit from Mondays to Saturdays without being engaged in other
jobs. Their livelihood depend mainly on
the money realized from processing.
Apprentices are workers who were in training, acquiring processing
skills. They were under the tutelage of
either the owner or manager of the enterprise.
Casual workers refer to people engaged in processing for specific period
of time or for adhoc purposes. They are
paid wages according to the hours spent or quantity of work done. They are not permanent workers. Unpaid labour on the other hand could be
casual or permanent but do not receive any financial remuneration. They are usually household members, friends
and relations who give helping hand to the enterprise as the need arises.
Sixteen percent (16%) and 87% of the entrepreneurs had
no full time worker and apprentice respectively. This means that the labour force comprised of
casual and unpaid workers. Forty-nine
percent (49%) and 6% had one full time and one apprentice respectively while 2%
and less than 1% had more than 3 full time and apprentice workers respectively.
About 35% and 33% of the respondents had no casual or
unpaid workers respectively. Twenty
three percent had one casual and unpaid worker respectively while less than 2%
and 7% of the processors had 4 casual and unpaid workers respectively. Mean number of full time, apprentice, casual
and unpaid workers was 1, less than 1, 1 and 2 persons respectively.
4.1.17 Assistance received by Processors
Entrepreneurs received assistance in the form of
training, fabrication of tools and machinery, repairs of equipment, land
business space and financial aid.
Of the 264 respondents, 66 (25%) got assistance either
from financial institutions, government agencies, Non – Governmental
Organisation and International Donor Agencies.
Forty-four percent (44%) received training pertaining to processing
through the relevant institutions as noted in table 16, while about 2% got
assistance through the repairs and maintenance of their equipment and tools.
4.1.18 Cross Tabulation Results:
Some social and economic variables were
cross-tabulated with the three agro-processing enterprises to establish
relationship among them for comparative analysis. These variables include gender and age of
enterprise owner, family size, years spent in school and processing, amount of
loan obtained and source, years in skill acquisition, working capital, total
value of capital, number of employees, monthly revenue, cost of equipment,
number of organisations entrepreneurs belonged to. Mean values and Chi Square tests were obtained
to establish relationships and independence of criteria among the three agro-processing
enterprises and the various variables.
Figures in parentheses are column percentages.
Chi Square Test.
X2 value 72.343 X2 tab = 4.605
df = 2 Sig
= 1%
* Reject if X2
Cal > X2 tab.
Source: Computed
from Field Data, 2007.
Data on table 17 show that there were 70%, 90% and 26%
males in Cassava, Rice and Oil Palm Fruits processing respectively while 30%,
10% and 74% females were in the three agro-processing enterprises respectively.
The Null hypothesis of no significant difference
between the number of males and females in the three agro-processing
enterprises was rejected and the alternative accepted since X2
calculated 72.343 is higher than X2 tabulated 4.605 at 1% significance
level. Therefore there is a significant
difference in the number of males and females in the three processing
enterprises.
Figures in parentheses are column percentages
Chi Square Test
X2 cal = 65.471 X2 tab =
90.531
df = 74 Sig = 5%
Source: Computed
from Field Data, 2007.
Data on table 18 show that 38%, 46% and 49% of
Cassava, Rice and Oil Palm Fruit processors were in the age bracket 41 – 50
years respectively while those in the minority were in the above 60 years
category with 2%, 5% and 1% for the three agro-processing enterprises
respectively. Mean age for the
processors is 44 years.
X2 calculated 65.471 is less than X2
tabulated 90.531 at 5% significance level, therefore Null hypothesis is
accepted. This means that there is no
significant difference between the age of enterprise owners in the three
agro-processing enterprises.
Figures in parentheses are column percentages
Chi Square Test
X2 Cal = 45.330 X2
tab = 55.759
df = 42 Sig = 5%
Source: Computed
from Field Data, 2007.
Family size of 6 – 10 persons category had the highest
number with 52%, 43% and 62% for Cassava, Rice and Oil Palm Fruits processing
respectively. The category with the
least number was those with more than 20 persons with 4%, 3% for Cassava and
Rice processing enterprises respectively and no respondent in Oil Palm Fruit
processing. The mean family size is
9.75, 9.38 and 7.93 for Cassava, Rice and Oil Palm Fruits processing
respectively with total mean of 9.11, which actually corresponded with the
category 6 – 10 persons that were in the majority for the three agro-processing
enterprises.
Since X2 calculated 45.330 is less than X2
tabulated 55.759 at 5% significance level, Null hypothesis is therefore
accepted. Consequently there is no
significant difference in the family size distribution in the three
enterprises.
Fourteen percent (14%) of the processors did not have
any formal education. Those who spent
between 1 – 6 years in school were in the majority with 29%, 24% and 40% for
Cassava, Rice and Oil Palm Fruit processors respectively. The group that spent more than 20 years in
school was in the minority with less than 1% in Cassava processing and no
person in both Rice and Oil Palm Fruit processing enterprises. The Mean level of education for Cassava, Rice
and Oil Palm Fruit processors is 11.15 years, 11.41 years and 9.20 years
respectively with the total mean of 10.60 years (11 years).
Chi Square (X2) test shows that X2
calculated 55.967 is more than X2 tabulated 43.773 at 5%
significance level, therefore the Null hypothesis is rejected and the
alternative accepted. It implies that
there is significant difference in the educational level of the processors in
the three enterprises. Those in Oil palm
fruit processing had the least number of 9 years.
Figures in parentheses are column percentages
Chi Square Test
X2 Cal = 65.609 X2
tab =
67.505
Df = 54 Sig = 5%
Source: Computed
from Field Data, 2007.
Data from table 21 indicate that 41%, 35% and 41% of
the processors in Cassava, Rice and Oil Palm Fruit processing respectively had
spent between 6 – 10 years and were in the majority. Those in the minority was the group that
spent more than 20 years with 12%, 6% and 3% for Cassava, Rice and Oil Palm
Fruit processing respectively. The Mean
experience is 12 years for the three processing enterprises with 12.41, 12.31
and 11.08 years for Cassava, Rice and Oil Palm Fruit processing respectively.
X2 calculated of 65.609 is less than X2
tabulated 67.505 at 5% significance level, the Null hypothesis is therefore
accepted. There is no significant
difference between the number of years spent in processing by the processors in
the three enterprises.
Figures in parentheses are column percentages
Chi Square Test
X2 Cal = 2.791 X2
tab =
5.991
Df = 2 Sig = 5%
Source: Computed
from Field Data, 2007.
Thirty-one percent (31%) of the respondents got loan
with 31%, 37% and 25% of Cassava, Rice and Oil Palm Fruit processors
respectively.
Since X2 calculated 2.791 is less than X2
tabulated 5.991 at 5% significance level, the Null hypothesis is accepted. It therefore means that there is no
significant difference between those who obtained loan in the three processing
enterprises.
Chi Square Test
X2 Cal = 2.353 X2
tab =
5.991
Df = 2 Sig = 5%
Source: Computed
from Field Data, 2007.
Data on table 23 show that out of 83 respondents that
obtained loan, 41% got from formal credit institutions with 37%, 50% and 32% in
Cassava, Rice and Oil Palm Fruit processing respectively. Fifty-nine percent (59%) got loan from
informal credit organisations with 62%, 50% and 68% of them in Cassava, Rice
and Oil Palm Fruit processing.
X2 test indicates that X2
calculated 2.353 is less than X2 tabulated 5.991 at 5% significance
level. The Null hypothesis is accepted,
implying that there is no significant difference between the number of
processors in the three enterprises who obtained loan either from formal or
informal credit sources. The level of
credit acquisition was generally poor both from informal or formal source.
Figures in parentheses are column percentages
Chi Square Test
X2 value = 51.977 X2
tab =
67.505
Df = 50 Sig = 5%
Source: Computer from Field Data, 2007.
Data on table 24 indicate that 14%, 50% and 58% of
Cassava, Rice and Oil Palm Fruit processors respectively got loan between
N10,000 – N50,000 and were in the majority.
The more than N200,000 category was in the minority with 0%, 3% and 5%
for Cassava, Rice and Oil Palm Fruit processing respectively. The mean amount obtained was N65,969,
N89,329, N63,619 by Cassava, Rice and Oil Palm Fruit processors respectively
with a total mean amount of N72,972.
The Chi Square result shows that X2
calculated 51.977 is less than X2 tabulated 67.505 at 5%
significance level, the Null hypothesis was accepted and the alternative
rejected. This implies that there was no
significant difference in the amount of loan obtained by the processors in the
three enterprises.
Figures in parentheses are column percentages
Chi Square Test
X2 Cal = 23.574 X2
tab =
21.026
Df = 12 Sig = 5%
Source: Computed
from Field Data, 2007.
Two percent (2%) of processor in cassava processing
and no one in Rice and Oil Palm Fruit processing had no formal training before
establishing their business. This means
that all the processors in Rice and Oil Palm Fruit processing had some years of
tutelage. Majority of the processors
91%, 73% and 85% in Cassava, Rice and Oil Palm Fruit processing respectively
established their enterprises after 1 – 2 years of skill acquisition.
X2 calculated value of 23.574 is more than
X2 tabulated 21.026 at 5% significance level, Null hypothesis is
rejected, implying that there is a significant difference in the years of skill
acquisition for the three enterprises.
Cassava processing does not require much formal training before
establishing one’s enterprise as is seen in table 25.
Figures in parentheses are column percentages
Chi Square Test
X2 Cal = 138.941 X2 tab
= 134
Df = 108 Sig = 5%
Source: Computed
from Field Data, 2007.
Data from table 26 show that majority of Cassava, Rice
and Oil Palm Fruit processors had between N10,000 – N50,000 as their working
capital corresponding to 52%, 39% and 70% respectively. Less than 1% in Cassava enterprise and no
processor in both Rice and Oil Palm Fruit processing had between N401,000 –
N500,000 as their working capital while less than 1% in Cassava, 1% and 0% in
Rice and Oil palm fruit processing respectively had more than N500,000. Mean working capital for Cassava, Rice and
Oil Palm Fruit processor recorded N74,029, N85,761 and N43,059 respectively
with a total mean amount of N68,935.
Chi Square test indicates that X2
calculated 138.941 is more than X2 tabulated 134 at 5% significance
level, Null hypothesis is rejected and it implies that there is a significant
difference between the size of working capital in the three processing
enterprises.
Figures in parentheses are column percentages
Chi Square Test
X2 Cal = 167.664 X2 tab
= 158.000
Df = 130 Sig = 5%
Source: Computed
from Field Data, 2007.
Data on table 27 indicate that majority of the
processors had between N50,000 – N100,000 as the total value of capital assets
with 36%, 27% and 59% for Cassava, Rice and Oil Palm Fruit processing
respectively. Less than 2% of Rice
processors had between N2,000,000 – N3,000,000 while no processor in both Cassava
and Oil Palm Fruit enterprises was in this category as shown in the table.
The mean total value of capital assets for Cassava,
Rice and Oil Palm Fruit processing is N184,058, N275,313 and N119,513
respectively with N195,204 as the mean for the three enterprises.
Figures in parentheses are column percentages
Chi Square Test
X2 Cal = 21.684 X2
tab =
38.885
Df = 26 Sig = 5%
Source: Computed
from Field Data, 2007.
Data on table 28 show that majority of the processors
38%, 52% and 49% of Cassava, Rice and Oil Palm Fruit had between 4 – 6
employees. Less than 1% in Cassava and
non in both Rice and Oil Palm Fruit processors had more than 15 employees. It should be noted that these employees could
be family members – casual, apprentices or full time workers. This normally included children who helped in
peeling or washing Cassava tubers, removal of Oil Palm Fruits from the bunches
etc. The mean number of employees for
Cassava, Rice and Oil Palm Fruit processing is 4.3, 4.7 and 4.1 respectively
with 4.4 for the three agro-processing enterprises.
Figures in parentheses are column percentages
Chi Square Test
X2 Cal = 142.742 X2 tab
= 124.342
Df = 100 Sig = 5%
Source: Computed
from Field Data, 2007.
Majority of the processors 44% had between N11,000 –
N30,000 as their monthly revenue with 57%, 29% and 45% of Cassava, Rice and Oil
Palm Fruit processors respectively. Only
1% of Rice processors had more than N130,000 every month with no processor in
both Cassava and Oil Palm Fruit processing enterprises. The general mean monthly revenue for the
three enterprises is N33,379 with N30,109, N45,633 and N23,900 for Cassava,
Rice and Oil Palm Fruit processing respectively.
Since X2 calculated 142.742 is more than X2
tabulated 124.342 at 5% significance level, the null hypothesis is
rejected. It therefore implies that
there is a significant difference between the monthly revenue of the three
enterprises.
Chi Square Test
X2 Cal = 217.911 X2 tab
= 208
Df = 176 Sig = 5%
Source: Computed
from Field Data, 2007.
Data on table 30 show that majority of the processor
(45%) had value of their equipment between N10,000 – N50,000 with 42%, 37% and
60% of Cassava, Rice and Oil Palm Fruit processors respectively. About 1% and 6% of Cassava and Rice
processors respectively had equipment that were worth more than N500,000. No Oil Palm Fruit processor belonged to this
group. The general mean amount N86,915
was spent on purchase of equipment for the three enterprises while N79,029,
N138,337 and 37,249 with mean amount spent on purchasing Cassava, Rice and Oil
Palm Fruit equipment respectively.
Since Chi Square calculated value of 217.911 is more
than X2 tabulated 208,000, null hypothesis is rejected. This means that there is a significant
difference in the cost of equipment in the three processing enterprises.
Figures in parentheses are column percentages
Chi Square Test
X2 Cal = 12.011 X2
tab =
15.507
Df = 8 Sig = 5%
Source: Computed
from Field Data, 2007.
Out of the 264 respondents, 43% belonged to
organisations while 57% did not belong to any organisation. Results on table 32 show that 66%, 59% and
62% of Cassava, Rice and Oil Palm Fruit processors respectively belonged to one
organisation and were in the majority.
Only 2% of Cassava processors belonged to 4 groups with none in Rice and
Oil Palm Fruit processing
Mean number of organisations Entrepreneurs belonged to
is 1.40, 1.45 and 1.45 for Cassava, Rice and Oil Palm Fruit processors respectively. The mean number for the three enterprises is
1.43.
Since X2 calculated 12.011 is less than X2
tabulated 15.507 at 5% significant level, the null hypothesis is accepted. This implies that there is no significant
difference in the number of organisations the entrepreneurs belonged to.
4.2.0
Results of Regression Analysis:
Multiple regression analysis was used to determine the
influence of some socio-economic characteristics of micro/small agro-processors
on the size of financial resources obtained from both formal and informal
credit institutions and level of Assistance received from both Government and
Non-Governmental Organisations (NGOs).
4.2.1 Summary of Regression Results:
The summary of the first regression result in table 32
was presented based on the three functional forms. Of the three functional forms tried, the
double – logarithm was preferred to others because it has the highest R2
and highest number of significant variables.
Results on table 32 show that Double Logarithm form
had the highest coefficient of determination R2 of 75.6% as against
51.3% and 27.5% for Linear and Semi Logarithm forms respectively and highest
number of significant variables. Also
the coefficients have more signs in line with a priori expectation than the
other functional forms. It had the
lowest error of 24% as against 49% and 73% for Linear and semi logarithm forms
respectively. The F – test was
statistically significant and therefore accepted.
Table 33: Summary
of Regression Result of the effects of the socio-economic characteristics of
entrepreneurs on Amount of Institutional Credit got for enterprise development.
Source: Computed
from Field data, 2007.
Note: Gender
of Enterprise Owner was deleted as a variable in the regression analysis by the
Computer because it indicated as a constant (dummy) in the double logarithm
form.
The regression function of the effects of some
socio-economic characteristics of the processors on the Amount of financial
resources they got is shown in table 33 with a coefficient of determination (R2)
of 76%.
This shows that these variables explain 76% of the
variations in the amount of financial resources obtained from financial
institutions and is considered high. The
estimated function can be regarded as a good fit, for according to Nwoko
(1989), as long as the R2 is up to 40% the regression is a good fit
at 90% confidence level or less.
Test of Hypothesis:
Ho1a: The
socio-economic attributes (characteristics) of agro-processing entrepreneurs do
not significantly influence the amount of credit got from financial
institutions for enterprise development.
The influence of each of the significant independent
variables on the amount obtained from both formal and non formal credit
institutions shows that years of schooling was significant at 10% but had
negative relationship with amount of financial resources obtained by the
processors. Number of years spent in
school was quite significant in accessing credit either from formal or non
formal credit organisations. This agrees
with a priori expectation. Education is
needed by processing entrepreneurs to identify the need for credit and to
process and access credit.
Working Capital of Enterprise was significant and had
positive relationship with credit acquisition of the processors. It had a positive influence on amount of financial
resources from credit institutions with regression coefficient of .650. This agrees with a priori expectation because
the higher the working capital of the processors, the more the willingness of
credit institutions to extend credit to them because the processors had
invested much and are taken to be credit worthy.
Value of assets of Enterprise was significant with
positive relationship or influence on amount of financial resources from credit
institutions. It had a regression
coefficient of .989. The higher the
value of the enterprise, the more serious the entrepreneur is considered to be,
therefore the more willing credit institutions are in extending credit. This equally agrees with a priori
expectation.
The F value of 3.868 was significant at 99% level of
confidence, indicating a strong influence of the independent variables
associated with socio-economic attributes of the entrepreneurs to the amount of
loan from financial institutions.
4.2.2 Result
of Second regression of effect of socio-economic characteristics of
entrepreneurs on the level of Assistance received from institutions:
In the second regression, the dependent variable was
level of assistance received (in percentages) from Government Agencies and Non
Government Organisations (NGOs). Forms
of assistance received from Agencies and NGOs included financial, Training
(Technical/Business), Equipment Fabrication, Equipment Repairs/Maintenance and
Provision of Business Land. If an
entrepreneur got one, two or three of such assistances, it was measured as one,
two or three out of five representing 20%, 40% or 60% respectively. The independent variables are the same as in
the first regression. All the three
functional forms were run but non proved a good fit. Consequently, a stepwise selection regression
method was run. Data on Table 35 shows
the model with the best fit.
Table 34: Summary
of Stepwise Regression of effects of the socio-economic characteristics of
entrepreneurs on the level of Assistance received from Institutions:
Source: Computed
form Field data, 2007.
Data in table 34 shows a coefficient of determination
(R2) of 85.8%, which is high enough and is regarded as a good
fit.
Test of Hypothesis:
Ho1b: The
socio-economic attributes (characteristics) of agro-processing entrepreneurs do
not significantly influence the level of assistance received from institutions.
With the exclusion of nine variables, the 4 remaining
variables had significant relationship with assistance obtained from Government
Agencies and Non Governmental Organisations (NGOs).
Gender of Enterprise Owner was significant at .033
(5%) but negatively related to assistance from Government Agencies and Non
Governmental Organisations. It has been
noted that women play important role in economic development and Nation
building but are usually disadvantaged due to low level of education and poor
financial status. Consequently,
government and NGOs advocate inclusion of women in many programmes in order to
elevate their social status. However it
had a negative influence on accessing micro/small agro-processing assistance
because majority of the women were not educated and found it difficult to
access assistance from government and NGOs.
Working Capital was significant at 0.000 (1%) but had
negative relationship with assistance to micro/small agro-processing
enterprises contrary to a-priori expectation.
This implies that the higher the working capital, the less the assistance
got because those who have high working capital seem not to be interested in
seeking government/NGO assistance.
Number of workers was significant at 5% and positively influence
assistance from agencies. Government and
NGOs place more emphasis on cooperatives than to individuals. Therefore processors are encouraged to form
cooperatives in order to enjoy benefits accruable to such groups.
Value of assets of enterprise excluding land was
significant at 1% with positive influence on assistance from agencies. This implies that the higher the value of
enterprise, the more the assistance because the entrepreneur is considered to
be business minded and serious in his investment.
The F – value of 84.739 was significant at 99% level
of confidence, thus indicating a strong influence of the independent variables
on assistance received from Government Agencies and Non Governmental
Organisations.
4.2.3 Result of
Inter – Correlation Analysis:
Inter-correlation matrix analysis was done in order to
establish how the selected socio-economic characteristics of the processors are
inter-related with one another. The
results of the analyses are presented in the tables I and II of the
Appendix.
In the first result, the correlation between Age of
Entrepreneur and length of years in processing was .761. This is expected considering the fact that
the length of years in business, which is experience acquired in processing
would normally reflect on the age of the enterprise owner. Gender of Enterprise Owner had .000 inter-correlation
coefficients with other variables and therefore was not reflected.
Working Capital of Enterprise and value of assets of
enterprise excluding land had .865 as inter-correlation coefficient in the
second result. This is understandable
because the working capital reflects on the value of the enterprise. If the working capital is low, it implies
that the value of the enterprise is equally low and vice versa.
Age of Equipment and length of years in business also
had .702. The two variables are
interrelated because the more the years in business the higher the age of
equipment.
4.3.0 Influence of Institutions on Establishment
and Development of Agro - Processing Enterprises:
Data were collected on the availability and
functionality of institutional/social facilities. It should be noted that the level of
availability and functionality of infrastructure and amenities in an area tend
to influence most other social and economic activities of the people in that
environment.
Level of availability and functionality of
infrastructure and production amenities and resources (Road, Water,
Electricity, Telephone), Hospital/Health Centres, Credit facilities (formal and
informal), Schools/Colleges, Land, Raw Materials, Cooperative, Markets (Input
and Output) and Equipment/Tool repairing outfits are shown in table 36.
Table 35: Distribution
of Respondents According to Availability and Functionality of Social
Infrastructure/Amenities.
Source: Field
data, 2007.
Data on Table 35 show the distribution of respondents according
to the percentage of entrepreneurs that agree and disagree on the availability
and functionality of the listed social infrastructure/amenities. From the table, 84% of the respondents agreed
that they had motorable roads but 47% agreed that the roads were functional
(passable) throughout the seasons.
Seventy-Three percent (73%) said that they had potable water while 50%
agreed that it was functional both in the dry and rainy seasons. Eighty-Four percent (84%) had
hospitals/health centres but 52% agreed that the facilities were functional in
the area while 32% reported that their health facilities were not
functional. Sixteen percent (16%) said
that such facilities were non existent in their locality.
Due to the absence of some facilities and poor
functioning of those that are available, processors provided their own
amenities as shown in table 36.
Table 36: Distribution
of Respondents According to Monthly Expenditure on Provision of own Amenities
and Services.
Source: Field
data, 2007.
Table 36 shows the monthly expenditure of the
agro-processors in the provision of electricity, water, disposal of waste and
security. It is recorded that 32% out of
the 264 respondents provide their own electricity apart from the irregular and
insufficient power supply by Power Holding Company of Nigeria PLC.
Out of this number, 32% spent less than N1,000
monthly. Thirty-Nine percent, 18% and 2%
spent between N1,000-N2,000, N2,001-N3,000 and N3,001-N4,000 respectively. Two percent (2%) spent more than N5,000 in
providing electricity on monthly basis.
The same pattern goes for the provision of water, waste disposal and
security services as shown in the table.
The above expenditure may have resulted in low savings which led to low
investment or expansion of enterprise.
Table 37: Likert
Scale Analysis of the availability and influence of the following
infrastructure/Amenities on the establishment and development of micro/small
Agro-Processing Enterprises.
Source: Field
data, 2007.
V.G = Very
Good
G = Good
P = Poor
V.P = Very Poor
N.E = Non Existent
X = Mean
Availability and influence of the amenities on the
establishment and development of Micro/Small Agro – Processing Enterprises was
done using a five point Likert Scale Analysis.
A mean of 3.00 was taken as the cut-off mark. Results obtained are shown on Table 37. Electricity and Banking Institutions had
means score of 2.09 and 2.23 respectively showing that they had no effect on
the establishment and development of the enterprises because they were below
cut-off mark of 3.00. Educational
Institution (Primary/Secondary), Security and Market business space had means
of 3.83, 3.82 and 3.66 respectively.
They had the highest scores, thus implying that they positively
influenced the development of Micro/Small Agro – Processing Enterprises. Informal Credit Organisations, Good Road,
Hospitals (Health Centres) and Potable Water had scores of 3.49, 3.49, 3.48 and
3.12 respectively, implying that they had positive effect on enterprise
establishment and development.
4.4.0 Assessment of Gender issues that influenced
institutional involvements in the development of Agro – Allied Processing
Enterprises:
The analysis was based on cross tabulation between
male and female entrepreneurs on their access to education, membership of
cooperative, processing Associations, Land, Acquisition of credit and source,
Reception of training relating to Enterprise, Possession of Raw Materials,
Availability of Market, Adequacy and Mechanization of Equipment, Ownership of
Bank Account, Application to Bank for credit, Registration of Enterprise.
4.4.1 Cross – Tabulation between the Genders.
Chi Square test was done to verify if there was any significant
difference in the level of access to institutional facilities by male and
female agro-processing entrepreneurs.
They were 168 and 96 of male and female entrepreneurs respectively.
Table 38: Cross
– Tabulation with X2 output of Gender with Access to Institutional
facilities.
Source:
Computed from Field data, 2007.
Data on table 38 indicate that 93% and 71% of male and
female processors had access to education while 7% and 26% of the male and
female processors had no access to formal education.
Chi Square Test indicates that there is a significant
difference between the male and female entrepreneurs in their educational
status. The Null hypothesis was
therefore rejected. Thirty-two percent
(32%) and 30% of male and female processors respectively had access to loan
while 68% and 70% of the male and female entrepreneurs respectively did not
have access to loan. The X2
result shows that there is no significant difference between the male and
female entrepreneurs in their loan acquisition.
The null hypothesis was accepted.
Out of the 83 entrepreneurs that got loan, 54 and 29
were male and female processors respectively.
Forty-four percent (44%) and 17% of the male and female processors
respectively got loan from formal source while 56% and 83% of the male and
female entrepreneurs sources loan from non formal credit organisations. Chi square test shows that there is a
significant difference between the male and female processors in their sources
of loan. As is observed on the table,
male entrepreneurs got more loan than their female counterparts from formal
credit institutions.
Seventy percent (70%) and 61% of male and female
processors respectively were members of functional cooperatives while 30% and
39% of male and female respondents respectively did not belong to any
cooperative society. Chi square result
shows that there is no significant difference between the male and female
processors in their membership to functional cooperative. The Null hypothesis was accepted.
It is observed that 50% and 32% of male and female
processor belonged to processing association while 50% and 68% respectively
were not members of any processing association.
Result from the chi square test shows that there is a significant
difference between the male and female processor in their membership to
processing association. The implication
is that the male processors belonged to many associations than their female
counterpart. The null hypothesis was
therefore rejected.
Thirty-five percent (35%) and 23% of male and female
processors had no difficulty in access land while 65% and 77% encountered
problem in land acquisition. Chi square
test indicates that there is a significant difference between the male and
female processors in relation to having access to land. The null hypothesis was rejected and the
implication is that female processors found it more difficult to access land
than their male counterparts.
Data from table 38 show that 51% and 47% of male and
female processors received training relating to their enterprise while 49% and
53% respectively did not receive any training.
Chi square test shows that there is no significant difference between
the gender in relation to access to training.
The null hypothesis was accepted.
Thirty-eight percent (38%) and 20% of the male and
female processors respectively agreed that they had enough raw materials for
processing while 62% and 80% respectively did not have enough. Chi square test shows that there is a
significant difference between the male and female processors in their access
to raw materials. Null hypothesis was
rejected, thus implying that the male processors had more access to raw
materials than their female counterpart.
Processors disposed off their products by selling in
their local markets, urban market, through middlemen. Forty-five percent (45%) and 46% of the male
and female processors respectively had market for their product while 55% and
54% respectively did not have access to available market to sell their product
at appreciable prices. Chi square test
shows that there was no significant difference in the availability of market
for product disposal among the male and female entrepreneurs. They had equal opportunity of selling their
products in the same market. The Null
hypothesis was therefore accepted.
Processors complained that they did not have enough and appropriate
equipment for processing. Eighteen
percent (18%) and 16% of male and female processors respectively had access to
adequate and appropriate equipment while 82% and 84% of male and female
respectively did not have access to adequate equipment. Chi square test shows that there was no
significant difference between the male and female processors in relation to
having access to adequate and appropriate equipment for processing. This implies that both the male and female
processors did not have adequate equipment.
The null hypothesis was therefore accepted.
Forty-percent (40%) and 5% of male and female
processors respectively had mechanized equipment while 60% of male and 95% of
female processors did not have mechanized equipment. Chi square test indicates that there is a
significant difference between the male and female processors in their
possession of mechanized equipment. Male
processors had more mechanized equipment than their female counterparts. Null hypothesis was rejected.
Some processors did not have account with banks
because there were no banks in their community or nearby village. Fifty-one percent (51%) of male and 33% of
female processors had account in the banks while 49% and 67% of male and female
processors respectively did not have account.
Chi square result shows that there is significant difference between
male and female processors in their ownership of bank account. More number of male processors had Bank
Account than female processors. Null hypothesis
was rejected.
Some processors applied to banks for loan and were
asked for collateral which included land, building, vehicle/equipment, 10
percent savings of loan amount demanded in their account. Generally, processors claimed that bank
officials have indifferent attitude towards micro/small scale entrepreneurs in
relation to loan disbursement.
Thirty-three percent (33%) of male and 23% of female
processors applied to bank for credit while 67% and 76% of male and female
respectively never applied to bank for credit.
Chi square test shows that there was no significant difference between
the male and female processors who applied to bank for credit. Null hypothesis was accepted.
Registration of Enterprise could be with Corporate
Affairs Commission (CAC), State/Local Government and processing Associations
etc. None of the entrepreneurs
registered with the Corporate Affairs Commission. However, some registered with the State
Ministry of Cooperative and Industry as Farmers Multipurpose Cooperative
Societies (FMCS), Micro/Small Scale Enterprise Cooperatives etc and with Local
Government under Trade Unions and Cooperative Societies. Thirty-two percent (32%) and 20% of male and
female processors respectively registered their enterprises. While 68% of male and 80% of female
processors did not register their enterprises.
Chie square test shows that there is a significant difference between
the male and female processors in the registration of their enterprises. The null hypothesis was rejected.
4.4.2 Problems of Female Agro-Processors and
Suggestions for Improvement.
Cross-tabulation results between the gender of
enterprises owner and access to institutional facilities show that many of the
female processors did not have enough access, implying that there was a
significant difference in the access to some institutional facilities between
the male and female processors. Female
agro-processors consequently enumerated their problems and proffered
suggestions for improvement/expansion of their enterprises as shown in table 39.
Table 39: Problems
of Female Agro-Processors and Suggestions for Improvement.
Source: Field
data, 2007.
Majority of the female agro-processors 39% agreed that
non-existence of credit facilities is one of their major problems in their
processing enterprise. This was followed
by absence of mechanized equipment and high cost of labour having 11% and 10%
respectively. Only 1% said that not
having enough land for expansion was their problem. They however gave suggestions for improvement
and expansion of their enterprises.
Thirty-seven percent (37%), 34% and 19% requested for provision of
micro-credit, Government’s assistance with favourable policies and
improved/mechanized equipment that are female friendly respectively. Only 1% of the respondents said that
provision of improved variety of input and availability of land would expand
their enterprises.
4.5.0
Effects or influences of Institutional
performance on Agro-Processing, Enterprise Development and Poverty Reduction:
The determination of the effects or influences of
institutional performance on Agro-Allied Processing Enterprise Development and
Poverty Reduction was based on physical achievement, income growth, financial
efficiency and livelihood improvement indices.
4.5.1 Causes of low Performance:
Some processors complained that they experienced low
performance in their agro-processing enterprises due to some reasons as shown
in table 40.
Table 40: Distribution
of Respondents According to causes of low performance in agro-processing
business.
Data on table 40 show that low capital outlay ranked
highest 22% among the reasons why some processors had low performance in their
processing business. Malfunctioning of
Amenities scored 5% which was the lowest.
4.5.2 Description of Business Growth and Factors
influencing growth of Agro-Processing Business.
Apart from some entrepreneurs 33% who claimed to have
low performance in their businesses, others recorded some levels of growth,
which are shown in table 41.
Table 41: Distribution
of Respondents According to description of Business growth and Factors
influencing growth of Agro – Processing Enterprises.
Source: Field
data, 2007.
In table 41, one of the manifestation or evidence of
growth was injection of more capital (fund) into the business which accounted
for 40%. Diversification into more
products had 4% which was the least agro-processors experienced as a result of
business growth.
Many factors influenced the growth of the processing
enterprises as recorded in table 41. The
most influential factor was high demand for product accounting for 26% while
improved extension services scored the least mark with 3%.
4.5.3 Effect of Enhanced Income on Agro-Processing
Business, Family, Neighbourhood and Self Esteem of Entrepreneurs:
Due to perceived increase in income which resulted to
enhanced economic status of the processors, they were able to take care of
their families, buy household property, acquire more processing equipment and
impacted positively on the lives of people in the neighbourhood. They equally felt a sense of increased self
worth in their communities as a result of increased income from agro-processing
enterprises.
Empowerment of the following issues due to existence
of micro/small agro-allied processing enterprise were evaluated in table
42. There are:
-
Increase
in Income (wages or self employment)
-
Improved
family nutrition and education
-
Improvement
on family healthcare
-
Enhanced
housing
-
Re-investment
in enterprises
-
Enhanced
technological capability
-
Existence
of repairing workshops
-
Presence
of subsidiary businesses (e.g spare parts, petrol/diesel/engine oil selling
depots)
Table 42: Distribution
of Entrepreneurs according to effects of increased Income from Agro-Processing
Business on Family, Neighbourhood and Self Esteem.
Source: Field
data, 2007.
Data on Table 42 show that 19%, 18% and 15% of the
processors were able to feed their families adequately, pay children’s school
fees and pay hospital bill with ease respectively. The least they could do was to save money in
the bank and build/renovate house(s) which had 3% and 2% respectively.
Improved business of the Entrepreneurs had multiplier
effect on the lives of people in the neighbourhood. People living in areas where agro-processing
outfits were located benefited immensely and this impacted positively on their
livelihood. Thirty-one percent (31%)
agreed that sale of their product to the public helped immensely in food
security of the people. Patronage to
equipment repairers had 5%. Improved
livelihood of processors increased their self esteem. They were able to participate and contribute
their own quote to their environment because of enhanced economic status. Thirty-three percent (33%) and 28% of the
processors helped the less privileged in the society and contributed to
community development respectively. Less
than 2% were active in political activities as a result of enhanced self worth.
4.5.4 Beneficiary Impact Assessment of
Enterprises:
Beneficiary impact assessment of the three processing
enterprises (Cassava, Rice and Oil Palm Fruit) on business and family,
neighbourhood and self esteem of the processors was carried out to verify the
level of significance of the impact (effect) of these enterprises after three
years of operation. One Way ANOVA and
Post Hoc Multiple Comparison using Scheffe Test was done to ascertain if
differences existed among the enterprise means and their level of significance
and the determination of the enterprise with the highest impact.
Table 43: Impact
Assessment of Agro-Processing on Business Development and Quality of life of
Family of Processors.
One Way ANOVA
Source: Computed
from Field data, 2007.
Table 43 shows One Way ANOVA with output displays of
between and within groups sum of square values as well as F – value with its
probability. The F – value shows that
there is a significant difference in the impact of the enterprises. An indication of significant difference alone
is deficient since it does not reveal the enterprise whose mean is responsible
for the difference. A Post Hoc
comparison was therefore carried out using Scheffe Test.
Post Hoc multiple comparison using scheffe test shows
that there is no significant difference in the impact between Cassava
Processing and Rice Processing on their family living standard and growth in
agro business. It has a non significant
value of .452. It is however observed
that there is a significant difference in the impact between Cassava Processing
and Oil Palm Fruit Processing, Rice Processing and Oil Palm Fruit Processing
with 1% level of significance respectively.
Further investigation on Table 43 shows that Rice
Processing had the highest Harmonic mean of 3.7558 and is placed in subset
2. This implies that Rice Processing had
the highest impact and was followed by Cassava Processing with a mean of 3.3922
in the same subset. Palm Oil Processing
had the least impact with 2.3553 mean and is in subset 1.
Impact Assessment of Enterprises on other people in
the neighbourhood:
People in the neighbourhood where the enterprises were
sited indicated that they benefited immensely and this impacted positively on
their livelihood. The presence of the
enterprises created employment for others.
There was food through the sale of the products to the public. Processors bought inputs from farmers,
petroleum products from dealers, and patronized spare part sellers and
equipment repairers. Table 44
established the level of significance and the enterprise that was the most
beneficial to the neighbourhood.
Table 44: Impact Assessment tests of Enterprises on
the Neighbourhood.
One Way ANOVA
Source: Computed
from Field data, 2007.
Data on Table 44 shows that there is a significant
difference in the impact of the enterprises on other people in the
neighbourhood. F-value of 7.206 has
significant level of 1%.
Comparisons of the three enterprises indicate that
there is no significant difference in the impact between Cassava Processing and
Rice Processing on the people. It has a
non significant value of .951. However,
there is a significant difference in the impact between Cassava Processing and Oil
Palm Fruit Processing, Rice processing and Oil Palm Fruit Processing with
significant values of 5% and 1% respectively.
Further test in table 44 proves that Rice Processing
had more impact because it has the highest mean of 2.5581 followed by Cassava
Processing with 2.5000 in subset 2. Palm
Oil Processing had the least mean of 1.8816 in subset 1.
Impact Assessment of Enterprises on Processors’ Self
Esteem:
The establishment of processing enterprises was
accepted to have improved economic status of the processors with resultant
enhanced self esteem or worth. The
processors agreed to have contributed to community development, helped the less
privileged in the society and participated in political and social
activities. Female Processors
participated in decision making in their families because of elevated financial
status. Data on tables 45 indicate the
level of their significance.
Table 45: Impact Assessment tests of Enterprises on
Processors’ Self Esteem:
One Way ANOVA
Source: Computed
from Field data, 2007.
Data on Table 45 indicate that F-value of 3.000 at
.052 significant level did not show any significant difference in the impact of
the enterprises on their self image because Multiple Range Test is significant
at 0.05 level in Scheffe Test (Ugwuona, 2005).
Post Hoc multiple comparison between the enterprises
shows that there is no significance difference in the impact of all the
processing enterprises. Cassava
processing is placed both in subset 1 and 2 because the highest and lowest mean
are not significantly different.
4.5.5 Respondents’ Perception of Relationship
between access to institutional facilities and implication on Poverty
Reduction.
The opinions of agro-processors were analysed to
determine how the provision of institutional amenities could improve enterprise
development which could aid poverty reduction.
Table 46: Perception
of Respondents to possible impact of Institutional facilities and implication
to Poverty Reduction.
Source: Computed
from Field data, 2007.
Data on Table 46 show that 99% of the processors
agreed that creation of friendly access to credit and improvement of quality of
infrastructure could reduce poverty.
Improvement on safety and security scored 98% and is considered
important because where there is war or conflict, there can never be progress
and this will lead to poverty. Reduction
of tax scored 77% and was the least issue considered to have implication on
poverty reduction. A chi-square result
shows that all the institutional facilities are highly significant at 1% level.
It shows that there is a significant relationship
between all the institutional facilities and poverty reduction, hence the Null
hypothesis was rejected and the alternative accepted. Respondents’ views on institutional
facilities were therefore found to be highly significant to poverty reduction
as is shown in Table 46.
To determine the degree of acceptance, a five point
Likert Scale Analysis was done with a mean of 3.0 as cut-off mark. Result obtained is presented in Table 47.
VGE = Very Great Extent 5
GE = Great Extent 4
ME = Moderate Extent 3
SE = Small Extent 2
VSE = Very Small Extent 1
Source: Computed
from Field data, 2007.
Data on Table 47 show that processors rated creation
of friendly access to credit to be first on the list with a mean of 4.9,
followed by improvement in safety and security with 4.38 as issues with implication
for poverty reduction. Creation of
Industrial Estate (clusters) for Small Scale Entrepreneurs (3.35) and Reduction
of Taxes (3.26) occupied the eighth and ninth positions respectively.
4.6.0
Results of Factor Analysis:
Factor analysis was used to identify and name those
factors that are considered constraints or discourage the performance of the
processing enterprises. Since the
purpose was to identify new factors, the variables having the highest loading
were chosen in naming each extracted factor.
They were classified into six factors according to their relationship
for critical consideration as shown in Table 48. The factors so identified are constraints to
the development and performance of Micro/Small Agro-Allied processing
Enterprises.
Table 48: Varimax
Rotated Factor Matrix of Factors that are considered hindrances to Micro/Small
Scale Enterprises Development in Ebonyi State.
Six factors were identified as constraints to the
establishment and development of micro/small agro-allied processing enterprises
and are shown below.
Source: Computed
from Field data, 2007
Data in Table 48 show that Inappropriate and High Cost
of Equipment was one of the limiting factors for enterprise development. Processors complained about processing
equipment not suitable to the infrastructure available in their
localities. Cost of equipment was
exorbitant for the resource – poor processors to acquire. Inappropriateness of equipment to available
infrastructure had a high regression weight of .809.
Sustainability and Business Environment issue was
factor two of the constraints to the performance of the processors. High cost of getting justice had a regression
weight of .889 and ranked highest among the other variables. Socio-infrastructural issue comprised
variables which had to do with physical infrastructure and social
amenities. Processors complained of poor
availability and high cost of infrastructure.
High cost of infrastructure had .714 as the regression weight.
Economic/Financial issue comprised of social
inhibitions and financial set back in agro-processing. They include lack of high cost of fund, which
militate against the development and performance of micro/small scale
agro-processing enterprises. Lack of
fund weighed .816 as the regression weight.
Processors complained of lack of market network or information. Competition from other processors and
importation of same product resulted to declining sale and glut in many cases,
thereby creating marketing problem in the disposal of products. Lack of market network/information had the
highest regression weight of .789.
Government policy issue was considered by the
processors as a constraint to the development of their businesses. They complained of harassment by tax
collectors and government officials who demand for evidence of registration of
enterprise and gratification. It had a
loading of .882.
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