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

3.0                                                                                                  Methodology
3.1             Study Area:
The study was conducted in Ebonyi State, which is one of the five states in South Eastern Nigeria.  The state lies between latitudes 7031’E and 8030’E and longitudes 5040N and 6045 N.  It has boundaries on the North with Benue State, on the South with Abia, on the West with Enugu State and on the East with Cross River.  It has an area of 5933 square kilometres with a projected population of about 2.2 million people (NPC 2006).


The rainfall distribution pattern and the tropical equatorial climate characterised by high rainfall, high temperature and sunshine give rise to two distinct seasons – the rainy and the dry seasons.  Rainy season starts from April to October, while dry season lasts from November to March.

Ebonyi State with large expanse of arable land, large population and conducive environment is imbued with the resources to produce enough food for her people and for export as well as raw materials for food processing enterprises (Ebonyi State Blue Print Report, 2004).  According to Ebonyi State Blue Print 2004, on strategies for food processing, storage, preservation, marketing and distribution in Ebonyi State, thirteen (13) crops are produced in significantly large quantities, out of which Cassava, Rice, Yam, Groundnut, Oil Palm, Maize, Mango, Pepper, Oranges and Leafy vegetables were found to be currently produced at levels, which can sustain processing industries in some localities after the fresh food market needs are satisfied.

The State therefore is essentially agrarian with majority of the populace as small-scale farmers living in the rural areas, more than fifty percent (50%) of which are women.  They rear poultry and small ruminants like sheep and goats.  These farmers also engage in non-farm activities like trading, food processing and marketing.

The State has thirteen (13) Local Government Areas and is divided into three agricultural zones, North, Central and South Zones with their headquarters at Abakaliki, Onueke and Afikpo respectively.  Ebonyi North Zone comprises Abakaliki, Ebonyi, Izzi and Ohaukwu Local Government Areas.  Ebonyi Central has Ezza North, Ezza South, Ikwo Ishielu Local Government Areas, while Ebonyi South is made of Afikpo North, Afikpo South, Ivo, Ohaozara and Onicha Local Government Areas.


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3.2       Sampling Techniques:
From the purpose of this study, operational definition of micro and small-scale agro-processing enterprises include those enterprises with labour size of less than 10 workers or total cost of not more than 1.5 million, including working capital but excluding the cost of land for the micro-enterprise category while the small-scale enterprises have a labour size of between 10 – 49 workers or a total cost of not below 1.5 million and not more than 50 million.

The three micro/small agro-allied processing enterprises for the study were Cassava, Oil Palm Fruit processing and Rice milling enterprises.  Three of the 13 Local Government Areas were purposively selected from each zone, making a total of nine (9) Local Government Areas for the study.  The choice of the Local Government Areas was on the basis of predominance of the crops in such Local Government Areas.  The nine Local Government Areas selected for the study are Abakaliki, Izzi, Ohaukwu, Ikwo, Ezza North, Ishielu, Afikpo South, Ivo and Ohaozara.  It was also based on the number of processors in each Local Government Areas as provided by Ebonyi State Agricultural Development Programme.  One thousand and thirty nine processors (1039) Cassava, Rice and Oil palm fruit processors were identified in the selected nine Local Government Areas (EBADEP 2002).

A Focus Group Discussion (FGD) was conducted to find out the positive and/or negative externalities of the agro-processing enterprise on the people living within the enterprise locale.  The FGD was conducted on groups made up of males and females who have lived in the place for up to 2 years including the Entrepreneurs.  Each group was made up of 6 residents.

Table 3.1:      Production of Cassava, Rice and Oil Palm Fruit in Ebonyi State according to L.G.As in metric Tonnes

Local Government Area
Cassava
Rice
Oil Palm Fruit
1.
Abakaliki
36842
10481
2500
2.
Ebonyi
36842
9446
200
3.
Izzi
57558
12194
250
4.
Ohaukwu
08055
17450
375
5.
Ezza North
70063
17964
500
6.
Ezza South
36002
9231
350
7.
Ikwo
29722
7621
1500
8.
Ishielu
31168
7992
2500
9.
Afikpo North
31212
9285
15000
10.
Afikpo South
34698
10179
25000
11.
Ivo
22507
5771
4500
12.
Ohaozara
22860
5861
5500
13.
Onicha
27767
7119
6450

Source EBSG Blue Print (2004)

Table 3.2:      Number of identified processors according to Local Government Areas

Local Government Area
Number of identified Processors
1.
Abakaliki
38
2.
Afikpo North
65
3.
Afikpo South
198

4.
Ebonyi
38
5.
Ezza North
121
6.
Ezza South
35
7.
Ikwo
74
8.
Ishielu
37
9.
Ivo
187
10.
Izzi
30
11.
Ohaozara
198
12.
Ohaukwu
146
13.
Onicha
77

Source EBADEP – Service Providers in Ebonyi State (2002).
Five (5) communities were randomly selected from each Local Government, making a total of forty-five (45) communities.  In each community two processors for each of the three agro-processing enterprises were randomly selected.  Altogether, two hundred and seventy (270) copies of questionnaire were administered as interview schedules to the respondents but two hundred and sixty four (264) copies were properly filled.  Below is a table showing number of processors, communities and Local Government selected for the study.

Table 3.3:      Number of processors, communities and Local Government selected for the study

L. G. A.
Communities
Number of Processors
1.
Abakaliki
Ebia Unuhu, Nkaliki, Nkwagu, Enyigba, Ndiegu Okpoitumo
30
2.
Izzi
Agbaja, Igbeagu, Onuenyim, Ndezi, Ezza Inyimagu
30
3.
Ohaukwu
Amike, Ezzamgbo, Umuezeoka, Umuogudu Osha, Effium
30
4.
Ezza North
Umuoghara, Oriuzor, Umuezeokaha, Ogboji, Amawula
30
5.
Ikwo
Ndufu Alike, Ndufu Echara, Igbudu, Inyimagu, Ekpa-omaka
30
6.
Ishielu
Ntezi, Agba, Ohoffia, Ezillo, Nkalagu
30
7.
Afikpo South
Owutu, Oso, Amangwu Ufueseni, Okporojo
30
8.
Ivo
Mgbede Akaeze, Akaeze-Ukwu, Ogidi, Okue, Ihe (Ishiagu)
30
9.
Ohaozara
Okposi-Ukwu, Mgbom-Echara, Amata Uburu, Umunaga Uburu, Ugwulangwu
30

Source:  Field Data 2007
A structured questionnaire was administered to the processors to extract information about their socio-economic characteristics, availability, accessibility and functionality of infrastructure/amenities and agencies.  Questions asked aimed at extracting information on institutions such as Financial, Agriculture, Lands, Commerce/Industry, Transport, Judiciary etc.

The role of government institutions in micro/small agro-allied processing enterprise development was noted through information from the relevant Ministries.  Information was sought on the presence and number of water boreholes, tap water schemes, number of schools, hospitals and health centres, roads, electricity etc.  The presence and role of some poverty alleviation agencies like NAPEP, ADP and Community Based Poverty Reduction Agency etc was ascertained.

3.3             Method of Data Collection:
A reconnaissance survey was carried out to authenticate the list of existing functional processing enterprises, financial institutions and other relevant institutions as earlier enumerated.  The researcher sought the assistance of EBADEP Staff (Enumerators and Extension Agents) posted to these areas.  Where there was no EBADEP Staff, literate local leaders and teachers were used to cover the area.

Primary data were used for the study, which came from two sets of structured questionnaire, one was administered to the entrepreneurs while the other was to financial institutions in the area.

3.4             Nature of Data Collected:
Based on the objectives of the research, the following data were collected.  Objective One and Two: Cassava, rice and oil palm fruit processors were identified.  The related institutions were noted and they include formal, non formal, government and non government, rural and urban.  The socio-economic characteristics of these micro/small agro-allied processing entrepreneurs were collected and they include age, sex, size of family, years of schooling, type of enterprise, length of years in processing, years spent in skill acquisition, loan obtained and source, number of formal and informal credit organisation, distance from formal credit institutions, working capital of enterprise, number of organisations the entrepreneur belongs to, age of equipment, number of workers/employees, value of enterprise excluding land.

Objective Three:
Determining the issues and institutions that influence the establishment and development of agro-processing enterprises.  The following data and information were collected: availability, accessibility and functionality of infrastructure (Road, water, electricity, telephones), hospitals and health centres, credit facilities (formal and non formal) land, raw materials, schools and colleges, functional cooperatives (age grade groups, town union organisation, social clubs, churches, market organisation).  Taxes imposed by government (market, income, haulage) as they affect establishment and development of enterprises, urban markets for input and outputs, presence of facilitating agencies like Non – Governmental Organisations (NGOs), trade unions and processing associations.  Availability of equipment/tool repairing/servicing outfits.

Objective Four:
Assessing gender issues that influence institutional involvement in the development of agro-allied processing enterprises.  The following were examined: financial assistance to the genders both by formal and non formal credit institutions, land ownership opportunities for farming and development of agro-allied processing enterprises, literacy level of the gender and suitability of processing equipment to female entrepreneurs (female friendly equipment) services and incentives, government allocation of resources, political emancipation and participation.

Objective Five:
Determining how institutional performance affects/influences agro-allied processing enterprises development and poverty alleviation.  Improvement of the following due to existence of micro/small agro-allied in the area was noted:  increase in income (wages and self employment), raised agricultural productivity (input, seeds, fertilizer, tools and machinery), improved family healthcare, family nutrition and education, enhanced housing and re-investment in enterprises, enhanced technological capability, existence of repairing workshops, improved market network for sale of output, presence of subsidiary businesses – spare part shops, petrol/diesel/engine oil selling depots.

Objective Six:
Identifying the constraints to institutional performance in micro/small agro-allied processing enterprises development and discussing the implications of the finding on poverty alleviation issues.  Variables collected were on:  Availability and accessibility of finance, existence of infrastructure (water, road, electricity).  Issues relating to education and skill acquisition, availability of functional market and market information, appropriateness of machinery and equipment, types and level of policy implementation consistency, poverty indices of the entrepreneurs, level of judicial efficiency and sustainability of enterprise.

3.5             Method of Data Analysis:
3.5.1       Use of Descriptive Statistics:
Data collected for the study were subjected to analysis using descriptive statistical tools.  Frequency distribution tables, percentages, means and cross-tabulations were used to analyse objective I to VI.

3.5.2       Multiple Regression Model:
Multiple Regression Model was used in objective II to ascertain the influence of socio-economic characteristics of the processors on the amount of credit obtained from financial institutions and level of assistance from Government Agencies and Non Governmental Organisations.  Three functional forms were tested and the one with the best fit chosen.  These are ordinary linear function, semi-logarithmic and double logarithmic functions.  The model with the highest coefficient of determination R2 and showed many statistical significant variables was adopted following (Kmenta, 1971).  The implicit form of the regression model used is

Y         =  f  (X1, X2, X3 ………. X13 e)

Where dependent variables are:
Y1        =  Amount of credit obtained (Naira) from both formal and non-formal credit institutions.
2        = Level of assistance received (in percentage) from Government Agencies and Non Governmental Organisations (NGOs). 

Independent variables are:
X1        =          Income from enterprise (Naira)
X2        =          Age of Entrepreneur (Yrs)
X3        =          Gender of Entrepreneurs Dummy (M = 0, F = 1)
X4        =          Years of Schooling (Yrs)
X5        =          Distance from Formal Credit Organisation (Km)
X6        =          Number of Informal Credit Organisation
X7        =          Number of Organisations Entrepreneur belongs to
X8        =          Working Capital of Enterprise (Naira)
X9        =          Length of years in business (Yrs)
X10      =          Age of Equipment (Yrs)
X11      =          Number of Workers
X12      =          Years of Skill Acquisition (Yrs)
X13      =          Value of assets of Enterprise excluding land (Naira)
e          =          Error term.

Stepwise selection method was applied to the second regression analysis because none of the functional forms proved a good fit.  According to Madukwe (2004), stepwise selection method is used in instances when full set of independent variables contain excess information about group differences or when some of the variables are not very useful in discriminating among the groups.  By sequentially selecting the “next best” discriminator at each step, a reduced set of variables will be found, which is almost as good as and sometimes better than full set.  Excluding some variables have different resultant effect on dependent variable.

The a – priori expectation from the first regression is that Income from enterprise should have a positive relationship with Amount of credit obtained from financial institutions (Y,) because the higher the income of the processors, the more the willingness of credit institutions to extend credit.
 i.              Age of Entrepreneur should have a negative relationship since the older the processor, the less efficient, he becomes, because processing is laborious.  Credit institutions would be reluctant to extend credit to an old person due to the fear of death at old age.
ii.              Years in schooling is expected to have a positive relationship with credit acquisition for enterprise development.  Number of years spent in school enhances one’s knowledge and commands respect and confidence in accessing credit from either formal or non formal financial institutions.
iii.              Distance from formal credit organisation has an a priori assumption that the nearer the banks to the processors, the more credit they can access and vice versa.  Therefore it has a positive relationship because banks are one of the sources of credit.
iv.              Number of informal credit organisation should have positive relationship with amount of credit obtained since one can access more credit if they were many informal credit organisation.
v.              Number of organisation Entrepreneurs belongs to is expected to have positive relationship because one can get more credit in form of being a member of a cooperative.  Increased number would mean increased credit acquisition.
vi.              Working Capital of Enterprise has a positive relationship with credit acquisition because the higher the working capital, the more credit worthy, the more the willingness of credit institutions to extend credit.
vii.              Length of years in business is expected to have positive relationship with credit acquisition because practice makes perfect.  Consequently, the more years one is in processing, the more competent he is and the more confidence credit institutions would have on him.  Therefore the more eager they can extend credit to him.
viii.              Age of Equipment is expected to have negative relationship with credit acquisition from financial institutions.  The older the equipment, the less interested credit institution would be to extend credit due reduced performance of equipment.
ix.              Number of workers.  The more the number of skilled workers in an enterprise, the higher the rating and the more willing the credit institutions to extend credit because they are assumed to be more business conscious than those with few unskilled family members operating on subsistence level.  It therefore should have a positive relationship.
x.              Years in skill acquisition has a positive a priori expectation because knowledge is power.  Credit institutions are more disposed to extending credit to an expert than to a novice in any chosen career.
xi.              Value of assets of Enterprise has a positive relationship or influence on amount of credit from credit institutions.  The higher the value, the more serious the entrepreneur is considered to be, therefore the more willing credit institutions are in extending credit.

In the second regression, the dependent variable was the level of assistance received (in percentage) from Government Agencies and Non Governmental Organisation (NGOs).  Forms of assistance from agencies and NGOs included financial, Training (Technical/Business), Equipment Fabrication, Equipment Repairs/Maintenance and Provision of Business Land.  The identified assistances are in five categories as listed above.  If an entrepreneur got one, two or three assistances, it was measured as one, two or three out of five respectively (1/5, 2/5, 3/5 etc) and multiplied by 100 to get the percentage.

The independent variables are the same as in the first regression.  The a priori assumptions of the independent variables with the dependent variable are as follows:
 i.              Income from Enterprise:  It is expected that the higher the income, the more the assistance from government agencies or Non Governmental Organisations because it is believed that the enterprise would be sustainable.  Therefore there is a positive relationship between them.
ii.              Age of Entrepreneur.  The older the processor the less willing assistance could be extended to the person due to the fear of death which would result to the collapse of the enterprise.  It therefore has negative relationship.
iii.              Gender of Entrepreneur.  Female entrepreneurs are expected to get assistance from Government but due to their low educational status, they are not aware of many government programmes and Non Governmental Organisations that could be of benefit to them.  It is therefore assumed that being female entrepreneur will have a negative relationship with assistance from Government and NGOs.  Male entrepreneurs are supposed to have positive relationship due to higher educational level than the female processors.
iv.              Years in schooling:  The higher the number of years in school, the more awareness one has about Government Agencies and NGOs that can be of assistance.  Therefore there is a positive relationship.
v.              Distance from Formal Credit Organisation:  The nearer a processor is to bank, the easier it becomes to get assistance.  There is a positive relationship and a priori assumption is that nearness to banks would increase ease of obtaining assistance.
vi.              Number of Informal Credit Organisations:  The more the informal credit organisations, the more the chances of getting credit, and this will give an opportunity to benefit from these organisations.  There is a positive relationship.
vii.              Number of organisations entrepreneur belonged to:  The more the number, the higher the chances of getting assistance.  Therefore there is a positive relationship.
viii.              Working capital of Enterprise:  The higher the working capital, the more assistance that would be given.  There is a positive relationship
ix.              Length of Years in business:  It is assumed that the more the experience in processing, the more the confidence that would be reposed in the processor.  Therefore there is a positive relationship.
x.              Age of Equipment:  The older the equipment, the more problematic it becomes.  A priori expectation was that the older the equipment the less assistance that would be given to the processor.
xi.              Number of workers:  Government and NGOs encourage cooperative spirit in enterprises.  A priori expectation was that increased number of workers would mean increased assistance.
xii.              Years in skill acquisition:  Increase number of years in skill acquisition means higher expertise.  Therefore there is a positive relationship.
xiii.              Value of enterprise excluding land:  The higher the value of enterprise, the more sustainable it would be and the more assistance to the entrepreneur.
3.5.3       Test of Hypotheses:
For in-depth analysis of objective II, t and F tests were conducted for hypotheses 1A and 1B.  t – test was used to ascertain how the amount of credit obtained from credit institutions and assistance from Government Agencies and Non Governmental Organisations (NGOs) relate to the socio-economic characteristics of the entrepreneurs.  The individual t-values were tested to establish the significance of the concerned variable with amount of credit from formal and non formal credit organisations as well as assistance received from Government Agencies and Non Governmental Organisations.  F – test was used to establish the overall relationship between the socio-economic characteristics of the entrepreneurs with the amount of credit obtained and assistance received.

Inter-correlation matrix model:  This was used for objective II. to investigate how the selected socio-economic characteristics of the entrepreneurs are interrelated with one another.  Thirteen identified socio-economic variables relating to credit obtained from formal and informal credit organisations were inter-correlated in hypothesis 1A while the same socio-economic variables relating to assistance received from Government Agencies and Non Governmental Organisations were inter-correlated in hypothesis 1B.  It should be noted that if the inter-correlation matrix coefficient is less than 0.5, it is taken that there exist a poor relationship between the two but if greater than or equal to 0.5, there is strong relationship correlation of a variable with itself is 1.  (Bhattacharyya and Johnson, 1977).

The variables include:
X1        =          Income from Enterprise (Naira)
X2        =          Age of Entrepreneur (Yrs)
X3        =          Gender of Entrepreneurs Dummy (M = 0, F = 1)
X4        =          Years of Schooling (Yrs)
X5        =          Distance from Formal Credit Organisation (Km)
X6        =          Number of Informal Credit Organisation
X7        =          Number of Organisations Entrepreneur belongs to
X8        =          Working Capital of Enterprise (Naira)
X9        =          Length of years in business (Yrs)
X10      =          Age of Equipment (Yrs)
X11      =          Number of Workers
X12      =          Years of Skill Acquisition (Yrs)
X13      =          Value of assets of Enterprise excluding land (Naira)

Ugwuonah 2005, stated that non-existence of collinearity is one of the basic assumptions in using regression model and that high collinearity implies that the independent variables have strong relationship with each other.  A case of high multicollinearity is a problem because it suggests that more than one variable was used to represent a particular characteristics that the use of a single variable should have effectively represented.  It is synonymous with error of double counting in accounting.

3.5.4       Use of Cross – Tabulation
Many cross-tabulations were done to establish relationship between the three processing enterprises (Cassava, Rice and Oil Palm Fruits) and some variables associated with the entrepreneurs.  Cross-tabulation with chi – square (X2) output was used to test hypothesis two (II) and three (III).  Cross – tabulation with X2 output was used to investigate sets of relationships or association among variables.  The cross tabulation is in form of a two-way table as elucidated by Joliffe (1986).  The statistics X2 is defined as;
X2        =          Σ                      Σ(nij – eij)2
                I = 1                               eij
                j = 1

Based or (r – 1) (c – 1) degree of freedom, where the summation extends over all cells in the 2 x m contingency table.

nij       – Indicates the observed frequency in the 1 – jth cell.
eij        – Indicates the expected frequency in the 1 – jth cell.

The reason for the two-way table is to investigate whether a relationship changes, when values of other variables change.

3.5.5       Likert Scale Technique:
Likert scale analysis was used to analyse objective three (III) in determining influence of institutions on the establishment and development of agro-processing enterprises.  Likert analysis was also applied to analyse part of objective four (IV) – effects of institutional facilities to business environment and poverty reduction.

Likert is a tool used in making explicit decision on factors associated with a particular observed phenomenon from other possible factors or variables.

Likert formula Xs     =                      a  Σfn a
Where Xs  =  Mean score.       Nr.
             Σ    =  Summation
             n    =  Likert numerical value
             f     =  Frequency of each response pattern
             Nr  =  Number of respondents to each response category.

Decision Rule:  In the 4 point and 5 point likert scale, 2.5 and 3.0 are taken as cut – off mark respectively.  Five point likert scale was used for the study and 3.0 mean taken as the cut – off mark, (David, 2005).

3.5.6       Impact Assessment Model:
Impact assessment performance evaluation of the micro/small enterprises for the past three years was achieved using one-way – ANOVA Post Hoc multiple comparison with Scheffe Test.  It was used to analyse objective five (V) and to verify the level of significance of the processing enterprises and to determine the enterprise with the most impact.

Impact Assessment performance of the three processing enterprises on business and family, neighbourhood and self esteem of the processors was carried out to verify the level of significance of the impact after three years of operation.  This was used to measure physical achievement, income growth, financial efficiency and livelihood improvement indices.  According to Hulme (2000), intended beneficiary impact assessment focuses on the outcome at individual, household, enterprises and community level.  The commonly used outcome indicators include:
-          Economic Indicators:  Educational status, access to healthcare, family planning and nutritional levels etc.
-          Gender Empowerment includes control over assets, involvement in household or community decision making and social networks including political leadership.

One – Way – ANOVA – Procedure produces a One – Way – Analysis of variance for a quantitative dependent variable by a single grouping (independent) variable.  It is used to test the hypothesis that the means of the various groups are equal.  It is usually used when there are two or more groups.  In addition to determining that differences exist among the group means, it equally indicates, which means differ (Adekoya et al, 2004).  The Post Hoc performs further test after general test of variance, it allows for a wide option of test that determine, which of the groups in question are similar or different in terms of means.

Ugwuonah (2005) further stated that the Scheffe Post Hoc Test is the same as performing a t – test on two treatments and determines which of the groups have significantly different means by rating the various group means.  Usually homogenous groups are classified under the same subset.  Impact assessment of the three processing enterprises (Cassava, Rice, Oil Palm Fruit) on Business and family, neighbourhood and self esteem of the processors was carried out to verify the level of significance and to determine the enterprise with the highest impact.

3.5.7       Use of Factor Analysis:
Factor Analysis is a procedure of examining the possibility that a large number of items or variables have a smaller number of factors explaining their interrelationship.  The aim of the method is to account for the co-variances of the observed or manifested variables in terms of a small number of variables known as factors, where the factors are un-observable variables or theoretical concepts.  Analysis of objective six (VI) was done by the use of factor analysis to isolate the factors that act as constraints to institutional performance in micro/small agro-processing enterprises development in the State.

Kaiser (1958) developed a rule of thumb that variables with coefficient of 0.50 or more have high loading and may be used in naming a factor.  The rule has been generally applied (Ogunfiditimi, 1979; Alimba 1999). 



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