CHAPTER FOUR: INSTITUTIONS IN MICRO AND SMALL AGRO – ALLIED PROCESSING ENTERPRISES: IMPLICATIONS FOR POVERTY ALLEVIATION IN EBONYI STATE, NIGERIA.

 4.0                                                                                                                     Results
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.



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Agro-allied Micro/Small Processing Enterprises and related Institutions.

Related institutions included the following:

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.
ii.                  Government Parastatal: Agricultural Development Programme (ADP)
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
iv.                 International Donor Agencies:  United Nations Development Programme (UNDP), United Nations Industrial Development Organisation (UNIDO)
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.
vii.               Financial Institutions (formal/informal):  Banks, Money lenders, Isusu etc.
 
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.

Figures in parentheses are column percentages
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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