Socio-Economic Consequences ofTechnological Change on the Rural NonFarm Igbo Women Entrepreneurs ofSouth-Eastern Nigeria: Implications for
Farm and Non-Farm Linkages

Jonathan Okechukwu Alimba
Justina Uzoma Mgbada

ATPS Working Paper Series No. 40

Published by the African Technology Policy Studies Network, P.O. Box 10081, 00100 General Post
Office, Nairobi, Kenya.

© 2003 African Technology Policy Studies Network

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Table of Contents

Executive Summaryi

Chapter One
Introduction

1

Chapter Two
Review of Literature and Conceptual Framework

7

Chapter Three
Research Methodology

12

Chapter Four
Data Presentation and Discussion of Findings

18

Chapter Five
Summary of Findings, Conclusions and Recommendations

57

References

63

Appendices

65

Executive Summary
Technological change has affected many aspects of rural women’s non-farm enterprises with many
positive and negative socio-economic consequences on the women enterpreneurs and theirhouseholds. This study investigated the extent of this change, and the socio-economic effects of thechange on the rural Igbo women entrepreneurs engaged in cloth weaving, pot making, palm-oil
processing, garri processing and local brewing in south-eastern Nigeria. Specifically, it attempted toassess the technical change in these enterprises in relation to the perceived negative consequences,to find out if the change was neutral or biased to those consequences. An attempt was made to findout the implications of the identified consequences of technical change on rural inter-sectoral linkages.
A total of 250 non-farm women entrepreneurs randomly selected from 10 purposively chosencommunities in five out of nine Igbo-speaking states of south-eastern Nigeria provided the quantitative
data. One hundred and sixty other rural women entrepreneurs of various age groups from the chosencommunities provided the qualitative data. Data were collected using interview schedules and a
Focus Group Discussion (FGD) guide. Analysis of the data was by use of descriptive statistics, crosstabulations, multiple regression model, inter-correlation matrix model and factor analysis. Analysis ofqualitative data from FGD was by folk interpretation and verbatim quoting of discussants’ views.
The major findings of the study were that: rural women’s non-farm enterprises were dominated byageing entrepreneurs with low educational background, low operating capital, low income andconsumption expenditure; the rural areas from where they operated lacked basic social infrastructure
and amenities; out-migration of the rural to urban type was high among the female youths, andadoption and adaptation of some innovations was low or faulty; most of the socio-economic problemsof the entrepreneurs adversely affected the proper adoption of innovations rather than be the productof technical change.
Chi-square results from cross-tabulation analysis showed that some personal socio-economic
characteristics of entrepreneurs influenced their adoption behaviour which was associated with thenegative consequences of innovations on the women entrepreneurs; the coefficients of multiple
determination (R2) showed a high influence of the identified independent variables associated withtechnical change on the dependent, that is the level of technical change while inter-correlation
analysis established many useful inter-relationships among variables that were used to explain reasonsfor the nature of consequences of technical change.
Varimax rotated factor matrix of relevant variables was used to identify and name factors that will needurgent technology policy intervention and, based on it, technological change was assessed as neutralor biased to the socio-economic conditions of the women entrepreneurs, while the three types ofrural linkages identified in the area were consumption, backward and forward production linkages.
The implications of technical change on them were highlighted. Specifically, forward productionilinkage which involves use of farm outputs in the rural processing industries was identified as more
beneficial to the women because it involves minimal leakage of capital from the rural economy.
The study recommended, among other things: urgent solution to some social and economic attributesof the women entrepreneurs which tend to impede on proper adoption of innovation such as low
education, finance, and gender bias; provision of social infrastructure and amenities in the ruralvillages; encouragement of research and development of appropriate technologies that are resource
conserving, less women labour-displacing, and environment-friendly; establishment by state
governments of non-farm extension programmes; establishment of community polytechnics to improve
local technical know-how; restructuring of the micro-credit system to give it a rural face; encouragementof the formation of genuine rural women entrepreneur co-operatives and other associations; andproper exploitation of such rural linkages as the forward production linkage which has minimal
resource leakage out of the local economy.
The Poverty Alleviation Programme in Nigeria ought to be hinged on these recommendations if therural non-farm women entrepreneurs of this area are to benefit from it.

iiChapter One
Introduction
Background and Problem Statement
Rural women in Nigeria and indeed in most other developing countries are struggling with a problem
that is aggravated by the conventional rural development strategies. As observed by Carr (1984), since
the supplanting of the traditional barter system by cash economies, rural women have found it
increasingly difficult to satisfy their cash needs, more so, as their hold on cash income is eroded bytheir displacement from their traditional income-earning activities. Such indigenous enterprises
include clay pot-making, cloth weaving, cloth dyeing, palm-oil processing, garri processing and local
beer distilling. These activities used to provide not only employment to the women but also formed
good linkages with the farm that enhanced rural income and growth.
The struggle to find appropriate and acceptable ways of enabling women to spend their time as
productively as possible in support of their families should continue. This should not only be directed
to peasant farming activities but to other non-farm activities that are viable but capable of exploiting
the benefits of the linkages between the sectors.
Linkages are here used to describe the manifold interactions between agricultural (farm) andrural non-agricultural (non-farm) activities in a developing economy. Rural linkages therefore concern
the interactions between agricultural and non-agricultural enterprises in the rural economy (Rainsand Stewart et al, 1987). Two forms of rural linkages can be identified. These are the consumption
linkages where incomes generated by activities in one sector lead to demand for output of anothersector and, secondly, production linkages which may be forward or backward. Backward production
linkages occur where productive activity in one sector requires inputs from another, such as cotton orwool for cloth weaving. Forward production linkages occur where the production of a commodity
provides supplies for productive activities in other sectors. All these types of linkages must be nurtured
for integrated rural growth.
The Igbo women of south-eastern Nigeria are endowed with many skills in crafts and technology.
Examples include the traditional technology of clay pot-making which has been a big industry forcenturies. Pottery used to be an important industry in many Igbo communities because of the abundantdeposits of fine non-expanding clay mineral. The earthen pot is used for storage of grains, as a
cooking and household utensil, as a water container and local water ‘refrigerator’, as a container for
fermenting cassava, for storing and distributing palm-wine, for local brewing and distillery and as amusical instrument. Igbo women are also noted for their traditional weaving technology and artistry.
The Akwete women weavers have been famous since the 18th century for the popular “Akwete textile”(Ijere, 1988). In fact when Prince Charles and Lady Diana visited Nigeria in 1983, they were marvelled

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SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
at the level of entrepreneurship of the women and the high level of sophistication of the traditional
weaving technology. They called for the systematic upgrading of the technology and theencouragement of the women by government (Daily Times, May, 1982).
It is worthy of note that some of the rural crafts and technology industries provided the needs ofthe Igbo and others in south-eastern Nigeria during the Nigerian civil war (1967-1970), when thepeople were locked out of trade with other parts of Africa and the world. The cloth weaving industry, forexample, provided most of the clothing needs of the people, suggesting that people can live on theirown traditional crafts and technology.

Policy Issues and Programmes Relating to Women Entrepreneurship in Nigeria
The contemporary global economic and social upheaval led to the United Nations Declaration of1994 as the International Year of the Family. This led the then First Lady of the Federal Republic of
Nigeria to initiate the Family Support Programme (FSP). FSP’s Blueprint was launched on 4th
November, 1994 to replace the Better Life for Rural Women Programme (BLRW) initiated by theprevious First Lady, Mrs. Maryam Babangida.
According to its mission statement, FSP aims at improving on the experiences of women indevelopment programme by broadening its coverage and sharpening its focus. The specific objectivesof FSP, among others, include:i)
eradicating negative social, economic and cultural factors affecting women and childrenii) assisting families identify economically viable enterprises for income generation andproviding technical and financial support for their implementationiii) assisting rural families increase their agricultural productivity as well and improve theirnutritional status (FSP Blueprint, 1994).
It should be noted that both the Better Life for Rural Women Programme and the Family Support
Programme have been implemented for 15 years without much visible impact on women
entrepreneurs. The plight of the women continues unabated. Concerted efforts to correct theanomalies against women and to properly integrate them into the national development processcontinues. One such effort was through the Fourth World Conference on the Advancement of Womenorganised by the United Nations in Beijing, China, on September 4-15, 1995. At the end of theconference, a Platform for Action which comprised 12 critical areas of concern was adopted by thethen First Lady of Nigeria, Mrs. Mariam Abacha. This led to the initiation of the then Family Economic
Advancement Programme (FEAP), with the primary aim of activating the Family Support Programme.
It has other objectives which are to:i) provide loans directly to people at ward level with the capital needed to set up and run
cottage enterprisesii) provide opportunities for the training of ward-based business operationsiii) encourage the design and manufacture of appropriate plant, machinery and equipmentiv) create employment opportunities at ward levelv) improve the living standards of the peoplevi) encourage producers at ward level to form co-operative societies (Blueprint of FEAP,
1997).

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ATPS WORKING PAPER NO. 40
FEAP has however been integrated into other rural development programmes to become the
Poverty Elimination Programme of the current Obasanjo administration.
All these programmes emphasize in their blueprints the need for research in the areas covered bytheir objectives. The findings of such research would from time to time be used to focus the objectivesof the programmes properly for better results. But it seems that such research, especially as it concerns
rural women entrepreneurship, is lacking or not emphasized. For example, no programme canreasonably help the rural women identify other economically viable enterprises if attempts are notmade to find out the reasons why such women have abandoned or are not making profits from thetraditional enterprises that have hitherto sustained them economically. This, among other issues, willbe the focus of this research.

The Research Problem
The Igbo part of south-eastern Nigeria had for centuries been a centre of excellence for different
crafts and technologies that are now moribund. In recent years, as modern mass-produced products
have come to dominate world markets, most of the traditional Igbo handicrafts and industries are
disappearing because they seem not to compete favourably with imported commodities. Even
traditional skills that have not disappeared altogether have, in many cases, seriously deteriorated.
Just as machine textiles replace hand-woven cloth, and synthetics replace cotton, metal and plastic
utensils have come to replace earthen pots and ware. This process is intensified by the spread ofcapital intensive urban-based industries that threaten a wide range of indigenous cottage industries
and artisanal activities that have long provided income for rural women. Such rural cottage industries
include hand cloth weaving, rice pounding, garri processing, palm oil processing, pottery and others.
According to Dixon (1979), although these processes affect industries in rural areas, additional
forces work specifically against women. The new heavy industries typically demand male labour
while even in light instance – except when women are thought to be uniquely suited to certain skills- men often replace women as techniques are upgraded. For instance, the introduction of themodern kiln and the modern ceramic factory in Umuahia urban in eastern Nigeria favoured the maleworkers and shunted off women pot-makers of the nearby Isiagu community while the introduction ofmodern looms and the siting of textile factories in Aba urban did not integrate but rather devastated therural women weavers in the nearby Akwete community (Alimba 1990, Ikeme and Uvere, 1995).
Many rural women entrepreneurs have been abandoning their enterprises for lack of effective
competition with the modern urban-based industries and for lack of support and protection fromgovernment and other agencies. Most of these women now depend on unproductive peasant agriculture
for marginal employment while most of the landless ones have migrated to the urban areas. It is
common to see some of the migrated women in construction sites doing hard jobs such as mixing
and carrying concrete, street hawking and begging, or other menial jobs that can hardly sustain themand their families. Worse still, most of these women are economic household heads. The young girls
who could have found ready employment in some of these traditional industries are also forced tomigrate to the cities. While some go into apprenticeship in the already congested urban handicraft
and services such as hairdressing and cloth sewing, others are forced into socially undesirable
activities such as street hawking, prostitution and stealing. The magnitude of the socio-economic

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SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
problems associated with technological change, especially as they relate to the rural women are notwell understood.
No country or government can stop modern technology from overtaking the old. But the argument,
especially for developing countries, is that the negative socio-economic consequences of thetechnological change should not be a destabilizing factor to people in enterprises that use thetraditional technology, especially rural women. Rather, a system must be developed which aims at
integrating the new technology with the old, hence making the people relevant and competitive in thechanging environment. Only in this way will the much-needed rural linkages that engender sustainable
rural development and growth be achieved.
Previous efforts by Nigerian governments to provide competitive skills to rural women haveemphasized agrarian and technical skills that have had little or no traditional content. Examples
include the Better Life for Rural Women Programme of the Babangida administration, the Family
Support Programme and the Family Economic Advancement Programme of the past administration.
They were directed specifically towards increasing food production and acquisition of skills in suchenterprises as soap making and sewing while the traditional technical skills the women are familiar
with were ignored. Laudable as these programmes seem, not much has been achieved because theskills they teach are beyond the comprehension of the mostly illiterate rural women. Hence, thedisplacement of rural women from their traditional income yielding non-farm activities.
The specific research problem therefore is that the socio-economic consequences of technicalchange as they affect rural non-farm women entrepreneurs particularly in the area chosen for the
study do not seem to have been sufficiently established and used for improved technological policythat would engender rural linkages and enhance integrated rural development.

Research Questions
It is pertinent to find out the factors responsible for the situation in form of research questions. Could
it be that the base-line data and information required to make appropriate technology policies arelacking? What are the relevant data and information? Is there no way of reaching the rural women
entrepreneurs in order to understand their traditional production techniques and problems, to be able
to suggest how to adapt or upgrade them, along with modern equipment or scaled-down technology?
What are the levels of displacement and the socio-economic effects of this displacement or threat bymodern technology on the rural women entrepreneurs and their households? Should all the socioeconomic problems faced by rural non-farm women entrepreneurs be regarded as the consequencesof technological change or are there other causes? What are these other causes? Other relevant
questions follow. What are the implications of this technical change on the farm on one hand and, on
the rural economy as a whole, on the other? Are there appropriate policy variables that could be used
to achieve sustained technology policy for rural women entrepreneurs? What are they? Answers tothese questions were sought in the course of this investigation.

Research Objectives
General Objectives: The research is centrally aimed at assessing the socio-economic consequencesof technological change on the rural Igbo non-farm women entrepreneurs of south-eastern Nigeria
and the implications of such consequences on rural farm and non-farm linkages.

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ATPS WORKING PAPER NO. 40
Specific Objectives: The specific objectives of the research are to:i) characterize the socio-economic and cultural attributes of the rural Igbo non-farm women
entrepreneurs which should influence or be influenced by technological change;ii) identify and assess the level of change in the local production systems associated with modern
technology, and the perception by the women entrepreneurs over the change;iii) assess the effects of variables related to new technologies on some socio-economic attributesof the rural women in terms of employment generation, migration, standard of living, level ofincome, and the linkage effects of these variables in the rural economy;iv) find out the rate of entry of women into and exit from the traditional non-farm enterprises, thereasons for entry or exit and the level of apprenticeship by young women;v) find out the type of alternative employment for the displaced women and identify the factors thatmight continue to influence or discourage non-farm women entrepreneurs from adopting
innovations in the study area; andvi) indicate the policy implications of these findings for appropriate technology policy intervention.

Expected Results and Impacts
Not much attention has been paid or effort made to identify the reasons why the rural womencontinuously abandon, while the younger ones refuse to take up, traditional non-farm enterprises thatused to sustain them and their families. This research is expected to produce base-line data which
will reveal the magnitude of positive and negative effects of new technologies on the traditional ones
used by women in rural non-farm enterprises, and consequently, the effects on the socio-economic
lives of the women entrepreneurs.
Relevant policies and programmes that could address the socio-economic problems of the rural
non-farm women entrepreneurs, the displaced unemployed women, and the young girls, as a resultof the changing technologies could be based on empirical findings, and designed intervention rather
than policies ‘unencumbered’ by information as previously pursued in Nigeria. For example, theresearch will serve as a useful working tool to the government in redesigning the National Directorate
for Employment (NDE), which aims to carry out mass employment especially for young school leavers.
This programme was designed following an industrial training pattern rather than self-employment,
based on locally available resources. It may also assist such government and non-governmental
agencies, such as the Women Commission, the Family Support Programme (FSP), the Family
Economic Advancement Programme (FEAP), the new Poverty Alleviation Programme (PAP) and nongovernmental organizations in directing their assistance properly to the rural women through proper
focusing of programme objectives.
Both the local, State and the Federal Government will have a clearer focus and better appreciationof the fact that the integrated rural development being pursued could only be achieved through theharnessing of the linkages between farm and non-farm enterprises. This can best be achieved byencouraging the rural non-farm entrepreneurs and by systematically developing the traditional nonfarm technologies and enterprises hand-in-hand with the farm sector.

5Chapter Two
Review of Literature and Conceptual Framework
Literature Review
Although literature abounds in the area of traditional and indigenous technology, the socio-economic
impact of technological change on traditional industries involving rural women entrepreneurs in thedeveloping economies has not provoked much concern. Hence, it is difficult to find written informationon this area of knowledge especially relating to the area under study. Most literature on rural traditional
non-farm technologies and activities either concentrates on the state-of-the-art technologies or the
linkages of these technologies with the farm sector (Chuta and Liedholm 1979, Oludimu and Williams,
1987, Rains and Stewart et al 1987, Alimba 1990).
Literature has been reviewed on such areas as traditional and indigenous non-farm technology,
the consequences of technological change, and technology change and rural linkages.

Traditional and Indigenous Non-farm Technology
Traditional technologies are technologies developed in traditional set-ups and have in most cases
been transmitted unmodified through a particular lineage in the traditional setting. The community
technologies provided household goods and agricultural tools to the community (Ikeme and Uvere,
1995). According to Garg (1976), the rural areas were practically self-sufficient until the introductionof large-scale mechanized technologies which made an impact in three directions: on productselection, on technology, and on organizational patterns. Indigenous technologies, on the other
hand, are technologies which involved the exploitation of our indigenous human and material
resources, and the adaptation by our own people of imported (or foreign) technologies to suit ourenvironmental, social and economic needs (Umeh, 1982).
Therefore, indigenous technologies could be traditional or modified. According to Ikem andUvere (1985), Nigerians have at one time or another in their history displayed a high degree of
traditional technologies ranging from the agricultural and construction to medical and textile. They
cited examples of blacksmithing in Awka, pottery in Okigwe, carving and sculpture in Benin, boat
building in the riverine communities, and palm-oil processing in the eastern Nigerian villages.
Furthermore, they observed that some of the technological products such as hoes were used inagricultural production, hand looms for processing cotton into textile materials, while the cribs andearthen pots were used for grain storage. This technological capacity is crude, operates on a small
scale, and at cottage level.
Writing on the problem of technological displacement, Carr (1984) observed that the demand forthese products traditionally made by village women is declining as incomes rise. Foods and drinks

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ATPS WORKING PAPER NO. 40
processed from cereals, handspun cloth and earthenware goods, all have a low income elasticity ofdemand and are facing competition from “modern” products, often produced in urban-based factories.
The challenge is to take existing skills and adapt them (along with the help of upgraded equipment
or scaled-down modern technology) to the manufacturer of new products (e.g. china) which havehigh income elasticity of demand. Many of the products, which are currently imported into the village
from urban based factories or from overseas, could be made in the neighbourhood if the appropriatetechnology, training and retraining and other support services were available (Chuta, 1978).
In their study of rural non-farm enterprises in former Bendel State of mid-western Nigeria, (Oludimu
and Williams, 1987), and former Anambra State of south-eastern Nigeria (Alimba, 1990) identified
among others, metal works, blacksmithing, pottery, food and farm product processing, weaving,palm-wine tapping and gin distilling as some of the important rural non-farm activities performed in
the Igbo part of Nigeria. They noted that pottery, weaving, farm product processing (e.g. oil palm
processing) were dominated by women. They also noted that some of the traditional industries likepottery, weaving and cloth dyeing had been on the decline.

Consequences of Technological Change
Reflecting on the impact of modernization on rural women, Dixon (1979) observes that African women
who brew beer in their villages look forward to the day when a new road will carry their products to theregional centre, only to find to their dismay that the road brings them imported beer instead; those who
smoke and sell fish discover that new refrigerated warehouses and freezers undermine their business,while other locally produced foods and condiments give way to factory products. Furthermore, machine
textiles replace hand-woven ones, metal and plastic utensils replace earthenware, and synthetics
replace cotton.
Technical and vocational training is heavily biased towards the cities in locations, training, content
and methods in developing countries. The skills taught relate to imported technologies and knowledgesystem, and methods tend to be modelled on the industrial training used in developed countries,
which is geared to formal employment rather than self-employment. They also fail to take account oflocal patterns of production (Jazairy and Alamgir et al, 1990). As they further observed, a glance inmany villages in developing countries reveals a collection of workshops where a host of small-scaletechnical activities are carried out, often characterized by extra-ordinary ingenuity, in which disparate
equipment is cobbled together for a wide variety of purposes. Also observed, these enterprises are
particularly dynamic and the foundation of locally-attuned skills can be used to build a broad-based,
rural directed approach to technical and vocational training. These local skills can be harnessed
and upgraded to help alleviate poverty. The approach should be non-formal, rooted in the local
community, linked to practical learning and matched with local supply and demand.
Commenting on the reasons for the worsening poverty of the rural women, Alamgir (1988) asserted
that although there are many reasons for the worsening poverty of rural women, particularly importantare population increase, the present world-wide economic crisis, civil conflicts, natural calamities,
degradation of the environment, changes in traditional values, the disintegration of families due torising incidence of divorce, separation and desertion under duress, increased male migration, an
increase in households headed by women, and a weakening of household survival strategies. But
one of the most destabilizing factors affecting rural women is their massive displacement from income

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SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
yielding enterprises by modern technologies. The more relevant approach would be to identify which
non-farm activities rural women are already engaged in or used to be engaged in and then try to raise
their productivity of labour so as to increase their earnings or to help transform their subsistentactivities into income generating ones.
As variously observed, the rural poor suffers from a significant gap between their potential andactual productivity and the productivity gap of the poor rural women is much wider than that of poor
rural men. Rural women will gain proportionally more if investment allocations and development
efforts are shifted in their favour; they can go from a situation of being triply disadvantaged to one inwhich their contribution will have a multiplier effect in the household, in the community, nationallyand, most important, in the future generation (Ijere, 1988). An increase in the productivity of women
can lead to their economic empowerment and thereby raise the gross national product. (Okeke,1995).
On why rural women need a higher hold on cash income, Dhamija (1981) gave two reasons for
this: first, the number of households which are headed up by women are higher than most people
realize and are on the increase especially in Africa. Secondly, even in male-headed households, it
has been found that the interests of the family are often best served if women members have themeans to earn and control cash incomes of their own as men tend to commit more of their resources
to consumer goods, prestige or entertainment.

Technology Change and Rural Linkages
On the implication of improvement of non-farm enterprises on the farm in terms of linkages, Rainsand Steward et al (1987), Oludimu and Williams (1987) clearly stated that as far as linkage effects areconcerned, the magnitude of the rural non-farm sector depends on the increase in markets for farm
products, on improved supplies of inputs and technology and on modernizing influences on attitudes
to accumulation. Unless the non-farm sector of the rural economy is substantially uplifted, allocating
greater resources to agricultural development programmes may not substantially enhance rural
incomes and farmers’ standards of living.
In studies carried out in rural areas of India and the Philippines (Ranis and Stewart, 1987), andsome rural communities of eastern Nigeria (Alimba, 1990), some important roles played by traditional
non-farm employment were identified as sustaining employment and incomes in the face of rising
populations and threats of unemployment, providing seasonal occupation for farm workers during
the less busy time of the year, contributing to equality and poverty alleviation by increasing the incomesof the poor, contributing to the export earnings of the country concerned, and performing the linkage
functions thereby contributing to a dynamic and equitable growth cycle. Therefore, neglecting the
traditional technology-based rural enterprises or allowing them to go extinct because of theencroachment of modern technology, is a clear threat to rural linkages and ultimately to ruraldevelopment.
The current research will, based on the above reviewed work, aim to study the socio-economic
impacts of new technology on the traditional ones especially on rural women entrepreneurs of the
Igbo of south-eastern Nigeria, and highlight the implications of such impacts on rural linkages andgrowth.

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ATPS WORKING PAPER NO. 40
Theoretical and Conceptual Framework
Economic Analysis of Technological Change
It is worthwhile to clarify the economic tools which are used to conceptualize technological (technical)
change in order to focus the research properly. If the definition of technology as all the methods ofproduction that have been developed or could be developed with the existing state of scientificknowledge is accepted, then technological change would refer only to advances in scientificknowledge from which new production methods can be derived. But technology has to be distinguishedfrom technique. Some economists prefer to restrict it to mean only the techniques already developed
and available at a given moment in time from which a choice can be made (Ellis, 1988). Therefore,
this is a sub-set of those techniques which could be developed from existing scientific knowledge.
But technique refers to any single production method, that is, to a precise combination of inputs
used to produce a given output. A technique may be represented by a single point on an isoquant or
iso-product curve (Ellis, 1988). Technical (technological) change, in contrast, means a reduction in
the quality of resources required to produce a given output; or alternatively, more output for the samelevel of resources. For practical purposes, technical change is usually defined as the proportional
decrease in costs of production achievable by the innovation when both the old and the new techniques
operate at their combinational optimal and when factor prices are held constant (Binswanger, 1978).
The conceptional framework as summarised in the figure below shows that the technology
available to the rural entrepreneurs could be traditional or modern. Both could be farm or non-farm.
Traditional technology based on traditional knowledge and wisdom could be transformed to modern
technology which is based on alien knowledge and wisdom. This change could just be a blending by
the modern technology, or total, depending on the extent. Both types have some socio-economic
effects on the rural entrepreneurs, which may be positive or negative. They also have rural linkage
implication.
The socio-economic effects of modern technology on the people could be “biased” or “neutral”.
They are neutral when the technical change cannot be blamed for its (negative) effects such as
labour displacement by capital due to their relative prices. They could be biased if there is a favouringof one resource use against the other, with social and economic implications. The consequences on
the rural sectoral linkages that determine the rate of rural development and growth are of interest.
The research, therefore, focused on the extent of technological change in the study area, the formof the change (biased or neutral), the positive and negative consequences of the change, andimplications on rural linkages.

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SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
A Diagramatic Summary of the Conceptual Framework
Traditional - Technical(Farm and non-farm)Modern Technology(Farm and non-farm)

CHANGE(EXTENT)

Based on alienknowledge and wisom

Based on traditional
knowledge and wisdom
Blended

Total

SOCIO-ECONOMIC EFFECTS

Biased or Neutral

Negative

Positive

Negative

Time wasting
Poor quality
Low quantity

Increased employment
Increased income
Increase in households
headed by men
Reduced migration

Reduced labour cost
Increase in product quality
Time reducing
Positive expernalities

Low income
Increased
migration
Sustainable

High rate of apprenticeship by youth

Rural linkage types and effects?
Implications on rural linkages?

10

Positive
High labour displacement rate
Income reduction
Increase in migrration rate
Increase in households headed
by women
Reduction in the rate ofapprenticeship
Unsustainable
Negative externalities
Reduction in standard of living

Chapter Three
Research Methodology
The Study Area
The study covered the five core Igbo speaking states of south-eastern Nigeria. They are Abia, Anambra,
Ebonyi, Enugu and Imo States, all of which have 94 local government areas (LGAs). They have a
population of 11.7 million people with about 75 percent or 8.78 million in rural areas, of which about
51 percent are women (National Population Census, 1991). South-eastern Nigeria lies between
latitude 6 oE and longitude 4 oS and 7 oS. The occupation of the Igbo people in the region is basicallyfarming, combined mainly with non-farm activities in varying degrees. Some people in the delta area
are involved in artisanal fishing. Some communities are especially noted for their traditional non-farmenterprises which form their major occupation while some complement farming with non-farmenterprises. Some rural non-farm crafts and technologies common in the area include palm-wine
tapping, food and farm product processing such as palm-oil processing, cloth weaving and dyeing,
pottery and wood carving. While men are mainly involved in wine tapping, blacksmithing, wood
carving and metal work, the women are exclusively involved in agro-processing, cloth weaving anddyeing, pottery and local brewing. Some communities in the study area such as Akwete, Isiagu,
Enugu-Ezike, and Awka, among others, are noted for their unique traditional non-farm enterprises.
The research focused specifically on the following traditional women enterprises:i) cloth weavingii) pottery and clay workiii) local brewingiv) palm-oil processing, andv) garri processing.

Sampling Procedure
A multi-stage sampling technique was adopted by sampling, first, the local government areas (LGAs)
and secondly the communities in LGAs. The choice of the LGAs and communities was based on
purposive sampling technique in the sense that one LGA noted for any of the chosen women enterprises
was selected from each state. From each selected LGA, the community most noted for any of theenterprises was purposively selected. For example, in Abia State, Ukwa LGA was selected, and from
it Akwete, a community historically known for their artistry in cloth weaving. In all, five LGAs werechosen from the five Igbo States, out of which five communities were selected.
A compilation was done to list all the entrepreneur households. From the list, 50 women
entrepreneurs were chosen by the simple random sampling technique, and studied. Also 10 rural

11

SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
women who are no more engaged in the traditional enterprises were identified in each of thecommunities and interviewed. Altogether, 250 rural women non-farm entrepreneurs were selected
and studied in the five states. Panel survey was also adopted by forming panels from each group of
the women entrepreneurs out of which detailed panel data were collected. An entrepreneur selected
for the purpose of this study was a woman devoting up to 60 percent or more of her working time oravailable labour to the enterprise.

Data Collection Procedure
A reconnaissance survey was carried out in order to give the researchers an overview of the nature of
the environment in the study area, after which, a final survey was conducted to collect primary datausing structured interview schedule, and a focus group discussion (FGD) guide.
The FGD was used to collect qualitative information which was recorded on tape, and the groups
used for the discussion were carefully chosen to include both the entrepreneurs still active in theindustry, and those that had exited. Each group of discussants was made up of seven to nine womenof productive age. The old and retired entrepreneurs supplied information on the old order and theperceived transformation. Young girls supplied information on their general perception of the traditional
enterprises and their future employment plans.
The researchers were assisted by trained enumerators drawn from each village to interview thesampled respondents on a regular basis. Secondary information came from published andunpublished work that relates to the research and studies already done on these enterprises in thechosen areas.

Nature of Data Collected
The nature of data and information collected for each objective is given as follows:
Objective 1: The type of data collected for this objective includes the age of the women
entrepreneurs, the highest level of education attained, marital status, type of religion, family size,
headship position in the family, primary and secondary occupations, method of technical or
technological acquisition, number of years spent in the non-farm occupation, and level of income
from the non-farm activity. These data gave the general picture of the socio-economic and cultural
attributes of the rural women engaged in the non-farm enterprises. These socio-economic variables
were used to establish relationships with the other variables.
Objective 2: The objective sought information on the percentage change (if any) of the local
production systems associated with the new technology, how the women entrepreneurs perceive thechange associated with the new technology, and their own assessment of the new technology vis-àvis local technology in terms of time spent in production and quality of products. This information,
mainly qualitative, assisted the research in finding out reasons for the level of adoption or nonadoption of modern technology into the local production system, and also gave the researchers
insight into whether the women actually perceived the effects of modern technology as positive or
negative.
Objective 3: Information was sourced on the number of rural women employed as a result of the
adopted new technology in relation to the old one, the level at which people of the area had accepted

12

ATPS WORKING PAPER NO. 40
the products from modern industry in place of the local products, the changes in the income of thewomen in the area as a result of the new system adopted, the level of income of women still in the
traditional production system, and how their incomes flow into investment and consumption
expenditures in the other sectors. The information helped in comparing, quantitatively, the impact of
the new technology and production systems on the rural women entrepreneurs and their households.
Also, it helped in establishing the effects on backward and forward linkages in the rural economy.
Objective 4: The nature of information sought here was the number of women entrepreneurs whoenter into the traditional non-farm industry and the number that leave every year in the past two years.
From each of the villages under study, their reasons for entry or exit and alternative job for those who
exited and the rate of apprenticeship of young girls and women in these traditional industries wasdetermined. The information gave the research insight into the future of the local enterprises.
Objective 5: The information to be sought included the level of assistance in cash or in kind everreceived from both the government and non-governmental organizations by the women, either
individually or in groups (e.g. support from such government agencies like the Women Commission,
Better Life for Rural Women, Family Support Programme, or Family Economic Advancement
Programme), from church organizations, where the women who left the traditional occupation went,
the type of employment they engaged in, and other factors which continue to discourage women
entrepreneurs. This information helped the research to establish the level of encouragement, andwhat would continue to disencourage the women, and the consequences of their displacement fromthese traditional enterprises.

Data Analysis
Use of Descriptive Statistics
Data generated were analyzed in two stages. The first stage was the preliminary analysis, after which
a more elaborate analysis followed. The preliminary analysis involved use of descriptive statistics andalso cross-tabulation of the quantitative data in order to investigate sets of relationships among
variables. The cross-tabulation was in form of a two-way table as elucidated by Jolliffe (1986).
Preliminary analyses involving descriptive statistics were used to analyze parts of objective 1 to 5,
while cross-tabulation analysis was used on objectives 1, 2 and 3. It was necessary to conduct further
analyses since the study seeks to investigate sets of relationships among some variables that relate
to technological change and the consequences on the rural non-farm enterprises.

Inter-correlation Matrix Model
This was used to investigate how the selected socio-economic characteristics of the rural women
entrepreneurs are interrelated with variables associated with technological change. Objective 2, 3
and 4 were partly satisfied by using the inter-correlation matrix model. The inter-correlation specified
explicitly is of the form PxP correlation matrix of R.

13

SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
Where
P1

P2

P3

P4...Pm

R = P1

r11 = 1

r12

r13

r14...,r1p

P2

r21

r22=1

r23

r24

P3

r31

r32

r33=1

r34...,r3p

P4

r41

r42

r43

r44=1...,r4p

Pn

rP1

rP2

rP3

rP4...,rpp=1

Where
a)
P1,, P2, P3 ..... Pn, Pm are the exogenous variables which the inter-relationships are sought.
b)r11, r12, rr13 ..., rPm are the coefficients to be determined, with diagonal values - 1, notingthat correlation of a variable with itself is 1.
c) r11, r21, r31 ..., rPn, are the column coefficient determined as in b.
Note: If the inter-correlation matrix coefficient of rPm is less than 0.5, it is taken that there exists apoor relationship between the two variables, with such coefficients, but if greater than or equal to 0.5,
there is a strong relationship (Bhattacharyya and Johnson, 1977).
Twenty-five identified socio-economic variables relating to technical change were inter-correlated.
The variables include:
V1 = Age of entrepreneur
V2 = Educational attainment
V3 = Type of religion
V4 = Family size
V5 = Number of social infrastructure/amenities available and functional in the area
V6 = Amount of time spent in the enterprises per day (hrs)V7 = Average monthly income from enterprise (Naira)V8 = Average monthly expenditure on household (Naira)V9 = Number of female out-migrants from household
V10 = Age at time of departure (years)
V11 = Level of education prior to migration
V12 = Method of technical acquisition
V13 = Length of time in formal training
V14 = Amount of family labour used in enterprise (hours)
V15 = Number of apprentices in the past 2 years
V16 = Amount of working capital (Naira)
V17 = Level of technical change adopted (in percentage)
V18 = Number of years adopted
14

ATPS WORKING PAPER NO. 40
V19 =
V20 =
V21 =
V22 =
V23 =
V24 =
V25 =

Membership or non-membership of organised group or society.
Amount of financial assistance received in the past 2 years
Number of people that entered the enterprise in the past 2 years
Number of people that exited from the enterprise in past 2 years
Percentage of non-farm income invested in farm activities
Number of positive effects experienced after adoption
Number of negative effects encountered after adoption

Multiple Regression Model
Regression analysis was used in objective 3 to determine the influence of some variables on theentrepreneurs who have adopted new technology in relation to their levels of adoption. Three functional
forms were tested: the ordinary linear, the semi-logarithmic, and the double-logarithmic forms.
Whichever model that has the highest R 2 value and shows many statistical significance variables was
adopted following (Kmenta, 1971).
The stochastic implicit and explicit equation forms of the regression model were generally written
as g and h respectively as follows: Implicit as:
Esv = F (X1, X2, X,3 *-* XK, U) (g)Five regression equations would be analyzed to find out how the independent variables relating totechnical change explained the five critical dependent socio-economic variables identified. The fivedependent variables are:
YE = Level of income from enterprise (Naira)SL = Standard of living of entrepreneur in terms of monthly consumption expenditure (Naira)NE = Number of women employees or apprentices in the past two years
Ho = Number of households (female) out - migration in the past two years
IN = Amount of money invested from non-farm to farm in past two years (Naira)Each of the five socio-economic variables served as a dependent variable which was regressedagainst the nine identified independent (explanatory) variables related to technical change. The
explicit form of the regression equation is generally written as:
Esv = bo + b 1 X1 + b 2 x2 + b 3 x3 + b 6 x3 + b 4 x4 + b 5 x5 + b 6 x6 b7 x7 + b 8 x8 + b 9 x9 + U (h)Where
Esv = Each of the selected entrepreneurs socio-economic variable (dependent)
X1 = Level of technical change in enterprise (estimated in %)X2 = Level of technical change adopted by an entrepreneur (in %)X3 = Amount spent in repairs of adopted technology (Naira)X4 = Amount spent in procuring raw material of new technology (Naira)X5 = Extra labour requirement due to adopted change (man hours)
X6 = Number of entrepreneurs that exited due to technical change.
X7 = Level of entry or apprenticeship due to technical change
X8 = Number of positive effects of change on the production enterprise experienced.
X9 = Number of negative effects of change on enterprise experienced.
bo = The intercept of constant terms
bs = The coefficients of the independent variables
U = Stochastic error term with OLs properties.
15

SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA

Factor Analysis
Since many factors in the form of constraints were identified as being responsible for the poor growthof traditional technology industries or adoption of new technologies, and other factors as relating to
the consequences of technology change on the entrepreneurs, all of which are theoretical, it wasnecessary to do a factor analysis on objective 5. Factor analysis is a method for exploring thestructure of the data. The aim of the method is to account for the co-variances of the observed or
manifest variables in terms of a smaller number of variables known as factors; where the factors are
un-observable variables or theoretical concepts.
If n are observations on each of the variables x1, x2, x3..., Xp, and we suppose that m are theunderlying factors F 1, F 2,...F m, where m < p, then the factor analysis model assumes that each X1 (i =1,2,...P) can be written as a linear combination of the factors and as a residual variable. In effect, foreach, X1 takes the role of dependent variable and F1, F2,...Fm are like explanatory variables. For
example, in terms of observations, if Xij is the observation on variable Xj for the ith sample member
then:
Xij = ë 1 F i
1 + ë j 2 F i 2 + ..., + ë jm F im + e ij
(i = 1,2,...,n,j = 1,2,...,p)Where
F ik = The score on factor FK (k = 1,2,...m) andeij = The value on the residual variable Ej for the ith sample member.
F 1,F 2,...F m are known as common factors (since every Xij is written in terms of all of them) and Ej isknown as a specific factor since it corresponds to Xj. All of the Fik and eij are un-observable. The
weights ëji...ë jm are usually called the factor loading (Jolliffe, 1986). The assumptions made here
about the model, in order to estimate it, are that the common factors F 1, F 2,....,F m are independent of
one another, and the specific factors E1,E2,...,Ep are independent of one another and of the common
factors.
The suitable numbers of factors were selected subjectively, and the factors produced were rotatedwith the hope of finding a readily interpretable sets of factors.
All the multivariate analyses adopted were based on the Statistical Package for Social Sciences(SPSS) programme. The qualitative data complemented the quantitative. Analysis of the qualitative
data from the FGD guide was in terms of interpretation of the implicit and explicit folk concepts andverbatim quoting of the discussants’ views.

16Chapter Four
Data Presentation and Discussion of Findings
Socio-Economic Attributes of the Rural Non-farm Women Entrepreneurs ofSouth-eastern Nigeria
Some socio-economic attributes of the rural non-farm women entrepreneurs such as age, gender,
educational attainment, marital status, household size, type of religion, and type of non-farm enterprises
engaged in, income and expenditure patterns and facilities available and functional in the areas
constituted some of the independent variables in this study. These variables in one way or the othermay have influenced or were influenced by technological change in the traditional enterprises. Data
from 247 respondents were used for the analyses.

Age
Table 1: Age Distribution of Non-farm Women Entrepreneurs
Age Range
15 - 19
20 - 24
25 - 29
30 - 34
35 - 39
40 - 4445 - 49
50 - 54
55 - 59
60 - 64
65+
Total

Frequency
4
6
23
24
30
42
30
32
15
49
247

Percentage
(0.9)
1.6
2.4
5.2
9.6
12.1
16.9
12.1
12.9
6.0
19.8
100.0

Age cannot be influenced by changes in technology but the attitude of the entrepreneur to technologicalchange may be age-related. Table 1 shows that only four women entrepreneurs (1.6 percent) were ofage range 15-24 years. Majority of the women entrepreneurs were aged above 65 years (19.8 percent) suggesting a preponderance of the aged in those rural non-farm enterprises.
Table 2 is the cross-tabulation result of age range of the women entrepreneurs by the type of nonfarm enterprise they engaged in. Analysis shows that entrepreneurs in pottery and local distilling werepredominantly old, 60-65 years and above, while cloth weaving and garri processing had relatively

17

SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
younger entrepreneurs, 55 years and below. Generally, it can be seen that most traditional non-farm
women businesses were dominated by old retiring entrepreneurs. Continuity of these enterprises
then calls for questions as those aged entrepreneurs are gradually retiring. Also, a question arises as
to why younger people are reluctant to join some of the enterprises that help sustain their households.
Table 3 shows the age distribution of traditional non-farm women entrepreneurs by the level oftechnical change adopted in their enterprises. It is seen that adoption of technical changes involved
more of women aged from 40 to 59 years. This age range constituted the majority who adopted about
31 percent of the innovations exposed to them in their enterprises. In fact, a hypothesis of inverse
relationship between age and percentage adoption was accepted (as X2 cal = 25.5 < X 2 tab = 28.4 at
10 percent significance level). This implies that age was inversely related to level of adoption ofperceived change; that is, the higher the age of entrepreneurs the less innovations they adopted.
Table 2: Cross-tabulation of Age of Women Entrepreneurs by Type of Non-farm Enterprise Engaged
in
Age Range
20-29
30-34
35-39
40-44
45-47
50-54
55-59
60-64
65+

Total

Less than 20
1
(2.9)
2
(5.9)
3
(8.8)
2
(5.9)
5
14.7
8
23.5
11
32.4
2
(5.9)
34
(13.7)

20-30
1
(2.4)
7
(8.5)
5
(6.1)
11
(11.4)
14(17.1)
13(15.8)
19(23.2)
5
(6.1)
3
(3.6)
82
(33.2)

31-40
2
(4.9)
2
(3.3)
4
(6.6)
7
(11.5)
9(14.7)
12(19.7)
13(21.3)
7(11.5)
2(3.3)
61(24.7)

41-50
1
4
(4.5)
3
(6.7)
3
(13.3)
6(13.3)
5(11.1)
9(20.0)
6(13.3)
2(4.4)
45(18.2)

Above 50 Total
4
(4.0)
(2.0)
3
6
(12.0)
(2.4)
17
(6.9)
2
29
(8.0)
(11.7)
536(20.0)(14.6)
742(28.9)(17.0)
251(8.0)(20.6)
130(14.0)(12.1)
412(16.0)(4.8)
25
247(10.1)(100.0)Figures in parentheses are the column percentages.
X2 Value = 8.62
X2 tab = 18.3 *Reject if X2 cal > X2 tab.
d.f = 10Sig. = 5%

18

ATPS WORKING PAPER NO. 40
Educational Attainment of Women Entrepreneurs
The level of education attained by a traditional woman entrepreneur is a key variable that may affectthe attitude to change, assessment of change, and adoption behaviour. It may in turn influence thetype and level of benefits or problems an entrepreneur gets from a change in technology (Table 4).
Table 3: Cross-tabulation of Age of Entrepreneurs by Percentage Adoption of Technical Change
Age Range
20-29
30-39
40-49
50-59
60-69
70+

Total

Estimated Percentage Change Adopted
Less than 20 20-30
31-40
41-50
1
2
1
(3.1)
(5.3)
(11.1)
4
6
2
3(18.2)(18.8)(5.3)(33.3)
3
12
13
2(18.6)(37.5)(34.2)(22.2)
88
192(36.4)(25.0)(50.0)(22.2)
6
5
2
1(27.3)(15.6)(5.3)(11.1)
1
(4.5)
22
32
38
9
(19.0)(27.6)(32.8)(7.9)

Above 50 Total
4
3.4
4
19
(26.7)(16.9)
535(33.3)(30,5)
542(33.3)(36.2)
1
15
(6.7)
(12.9
15
(12.9)

116(100.0)Figures in parentheses are the column percentages
X2 Value = 8.62
X2 tab = 18.3
d.f = 10Sig. = 5%
Table 4: Distribution of Rural Non-Farm Women Entrepreneurs According to Educational Attainment
Highest Level of Education Attained
No schooling
Adult literacy
Primary incomplete
Primary complete
Secondary incomplete
Secondary complete
Tertiary/Post secondary
Total

Frequency
39
19
61
69
30
17
12
247

Source: Field data, 1999

19

Percentage
15.7
7.7
24.7
27.9
12.1
6.9
4.8
100

SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
Table 4 shows that up to 15 per cent of the respondents had no formal education, 27.9 per centcompleted their primary education, while only 5.7 per cent had one form of tertiary education or
another.
Table 5: Cross Tabulation of Entrepreneurs’ Level of Education and Ability to Assess Technical
ChangePerceived Technical Change
Total Change Blended
Don’t know
1
7
12
(3.4)
(5.9)
(45.3)
Adult literacy
2
23
3
(6.9)
(19.3)(5.9)
Primary education
1445
13(48.2)(37.8)(38.2)
Secondary education 8394(27.6)(32.8)(11.8)
Post secondary4
53(13.8)
(4.2)
(8.8)
29
119
34
Total
(15.9)(65.4)(18.7)
Age Range
No schoolingTotal
1(11.1)
27(14.8)
72(39.6)
51(28.0)
12(6.6)
182(100.0)Figures in parentheses are the column percentages.
X2 Value = 36.83
X2 tab = 23.2
d.f = 10Sig. = 1%
Source: Field data, 1999

The women’s ability to assess the type and magnitude of change available in their non-farmenterprises related to their levels of education. Up to 12 (35.3 per cent) women who had no formal
schooling did not know whether or not there was any change in their production system (Table 5).
Also, of the 182 entrepreneurs who agreed that technical change had taken place in their production
systems, 15.9 per cent reported a total change in all their production systems, 65.4 per cent said thechange was a blend of their traditional system (i.e. partial change), while 18.7 per cent could notestablish any form of change.
A null hypothesis of no relationship between entrepreneur’s level of education and ability to assess
technical change was rejected at 1 per cent level of significance, implying that level of education has
a relationship with an entrepreneur’s ability to correctly assess the magnitude of technical change.
Awareness and ability to know the magnitude of technical change in one’s enterprise may affect levelof adoption.

20

ATPS WORKING PAPER NO. 40
Table 6: Educational Level of Entrepreneurs by Percentage of Technical Change Adopted
Estimated Percentage Change Adopted
Less than 20 20-30
31-40
41-50
3
1
1
1
(11.1)
(2.7)
(2.3)
(7.1)
Adult literacy
1
6
10
9
(4.3)
(16.2)(23.3)(28.6)
Primary education
7
20
20
6
(25.9)(54.1)(46.5)(42.8)
Secondary
12983
education(44.4)(24.3)(18.6)(21.4)
Post secondary4
1
4(14.8)
(2.7)
(9.3)
27
37
43
14
Total
19.9
27.2
(31.6)(10.3)
Educational levelNo schooling

Above 50
1
(6.7)
2
(13.3)
7(46.7)
4(26.7)
1(6.7)
15(11.0)

Total
7
(5.1)
23
(16.9)
60(44.1)
36(26.5)
10(7.4)
136(100.0)Figures in parentheses are column percentages.
X2 Value = 25.95
X2 tab = 28.4
d.f = 20
Sig. = 10%
Source: Field data, 1999

Table 6 shows the distribution of educational levels of women entrepreneurs by the percentage oftechnical change adopted. It is seen that of the 27 entrepreneurs who adopted less than 20 per centof the change exposed to them, 11.1 percent had no formal education, while 44.4 per cent had
secondary school education. But of the 15 entrepreneurs who adopted 50 per cent and above of theinnovations they were exposed to, only 6.7 per cent of them had no formal schooling, while 46.7 percent had full primary education.
A null hypothesis of no positive relationship between level of education and percentage adoption
was accepted at 10 per cent level of significance (X2 cal < X 2 tab). This implies that educational level
was not the major factor that influenced adoption of technical change among the women
entrepreneurs studied.
This tends to support the oral information got from women especially from Akwete who worked as
cloth weavers that what influenced their adoption of any change in their weaving enterprise was thedecision of the officials of their local co-operative, after the officials had assessed such an innovation.

Marital Status
This tends to have implications on entrepreneurs household size, which is also affected by type of
marriage. The implication of household size on rural enterprises is usually in terms of unpaid householdlabour supply (Table 7).
Table 7: Distribution of the Entrepreneurs According to Marital Status

21

SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
Marital Status
SingleMarriedWidowedSeparated
Divorced
Total

Frequency
8
156
58
12
13
247

Percentage
3.2
63.2
23.5
4.9
5.3
100.0

Source: Field Data, 1999

Data from Table 3 show that up to 63.2 per cent of the women entrepreneurs were married, while only
3.2 per cent were single. Also, of interest is that up to 23.5 per cent of the women were widowed, which
in most cases made such women economic heads of their households.

Type of Religious Faith
Religion is a social variable capable of influencing most other variables in terms of enterprise choice,attitude to technological change and even adoption of technology (Table 8).
Table 8: Distribution of Respondents According to Type of Religion
Type of Religion
Traditional
MuslimCatholic
Protestants
Others
Total

Frequency
30
1
78
105
9
247

Percentage
15
0.4
31.6
42.5
3.6
100.0

Source: Field data, 1999

Table 8 shows that most women entrepreneurs (42.5 per cent) were Protestant, 31.6 per centwere Catholic, while 15 per cent were traditional. Only 0.4 percent were Muslim, while 3.6 per cent
did not disclose their faith.
Traditional religionists are usually influenced by traditional cosmovisions in what they do regarding
their enterprises. Since most of the entrepreneurs were not of the traditional faith, they may not havebeen influenced much by their religion with regard to behaviour towards technical change.

Household Size

22

ATPS WORKING PAPER NO. 40
Household size is one of the important determinants of household labour supply to rural enterprises.
Table 9: Distribution of Respondents According to Household Size
Household Size Range
Less than 4
4-8
9 - 12
Above 12
Don’t know
Total

Frequency
15
95
54
6
77
247

Percentage
6.1
38.5
21.9
2.4
32.2
100.0

Source: Field data, 1999

Table 9 shows that most of the entrepreneurs (38.5 per cent) had household sizes that ranged
between four and eight persons. Only 6.1 percent and 2.4 per cent had household sizes that were lessthan four and above 12 persons respectively. About 32.2 percent of the respondents refused to disclose
their household size on the reason that their culture does not permit counting of people in a household.
Table 10: Cross-tabulation of Household Size of Entrepreneurs by Number of Employees orApprentices in Enterprise in Past Two Years
Family Size Range
Less than 4

Number of Apprentices Enrolled
1-2
3-4
4
3
(23.5)
(7.9)
11
25
(6.5)
(65.7)
2
9(11.7)(23.7)
1(2.6)

Above 12

None
7
(9.6)
51
(69.8)
15(20.5)-

Total

73(43.4)

X2 Value = 91.62
d.f = 78

X2 tab = 96.6
Sig. = 10%4 - 8
9 - 12

17(10.1)

38(23.6)

5+31(77.5)
7(17.5)
2
(5.0)

Total
14
(8.3)
118
(70.2)
33(19.6)
3(1.8)

40(23.8)

168(100.0)Source: Field data, 1999

Analysis of data from Table 10 shows that large sized households of about nine persons and above
had less employees and apprentices enrolled in their enterprises than those with eight persons orless. This was because large-sized entrepreneur households seem to have enough labour that is
usually unpaid, but is either learning the trade or helping their parents. But a hypothesis of no positive
relationship between entrepreneurs household size and number of employees or apprentices was
23

SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
accepted, since: X2 cal < X2 tab at 10 per cent level of significance. This implies that apprenticeshipor employment levels into these enterprises did not grow according to growth in household size;
rather it decreased as household size enlarged.

Economic Headship of Households
Women can become economic heads of their households especially if they become widowed, or
divorced, or in cases where the husband becomes physically or financially handicapped. This situation
may influence her adoption of new technology. She may only adopt if the risk associated with it isrelatively low. This may be different for women whose husbands are the economic heads of thehouseholds.
Table 11: Percentage Distribution of Women Entrepreneurs According to Headship Patternof Respondents Households
HeadshipThe man (father)
The Woman (mother)
One of the children
Don’t know
Total:

Frequency
147
46
30
24
247

Percentage
57.5
18.6
12.1
9.7
100.0

Source: Field data, 1999

Table 11 shows that only 18.6 per cent of the women respondents were economic heads of theirhousehold, while 59.5 per cent had men as heads, and 12.1 per cent had their children as economic
heads.

Household Migration Records
The high migration rate especially of the rural-urban and overseas types could be an indirectconsequence of technical change, or a product of lack of change in the rural enterprise system of
production.
Table 12: Distribution of Entrepreneur Households According to Migration Records
Household Members
Male (sons)Females (daughters)
FatherMother
Total

1-2
71
(42.5)
67(98.6)
18
(100)
4
(100)

Distribution of Out-migrants in Households
2-4
5-6
7-8
9.10
52
26
13
4
(31.3) (15.5)(7.8) (2.3)
55
10
5
1
(39.8) (7.2)(3.6) (0.7)--

Figures in parentheses are column percentages.

24

-

-

10+
1
(0.6)
-

Total
167
(100)
138
(100)
18
(100)
4
(100)

ATPS WORKING PAPER NO. 40
Table 12 shows that 167 sons migrated from the households. Of this number, 71 (42.5 per cent)households had one to two of their male children out, while 67 (48.6 per cent) of them had one to twoof the female children out. Eighteen households had their fathers and four households their mothers
out, respectively.
Table 13: Distribution of Entrepreneurs’ Children According to Age at Time of Migration
Age Range
Less than 15 years
15 - 2031 - 45
46 - 60
Over 60 years
Total
Place Migrated to from Rural
Rural
Urban
Overseas
Total

Frequency
22
128
11
3

26
140
14
180

Percentage
11.8
68.4
12.3
5.9
1.6
100.0
14.4
77.8
7.8
100.0

Source: Field data, 1999

From Table 13, it is seen that majority of the migrants were of the age range 15 to 30 years, andmigration was mainly of the rural to urban type. This may have negative implications on the future of
the rural enterprises in the area.
Table 14: Estimated Percentage of Technical Change Adopted by Number of Family Out-migration
Estimated
Percentage AdoptionLess than 20
20 - 30
31 - 40
41 - 50
Above 50

Total

None
10
(40)
4
(16.0)
3(12.0)
3(12.0)
5(20.0)
25(26.0

No. of Migrants
<3
3-4
1
4
(5.0) (14.8)
5
7
(25)
(26)
7
8
(35)
(29.6)
2
4(16.0) (14.8)
5
2
(25)
(7.4)
20
27
(20.8) (28.1)

Figures in parentheses are column percentages.
X2 Value = 46.19
X2 tab = 36.7

25

5-6
8
(28.1)
13(61.9)-

7 and above
1
(20)
2
(40)
2
(40)
-

-

-

21
(21.8)

5
(5.2

Total
15
(16.7)
26(27.1)
33(34.4)
9
(9.4)
12
(12.5
96
(100.0)SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
d.f = 28
Sig. = 10%
Source: Field data, 1999
Data from Table 14 show that out of the 25 entrepreneurs who had zero migration from their households,
40 percent adopted only 20 percent of available technical changes, while only five percent adopted
technical change above 20 percent. On the other hand, of the five entrepreneurs that had seven andmore migrants, only 20 percent adopted less than 20 percent of the available technical change, while
none adopted 50 percent and above.

Occupation Distribution of Non-Farm Women Entrepreneurs
Of the 247 women entrepreneurs under study, (Table 15), 34 (13.7 percent) were engaged in pottery,
82 (33.2 percent) were in cloth-weaving, 61 (24.7 percent) were in palm-oil processing, 45 (18.2
percent) were in garri processing while 25 (10.1 percent) were in local brewing and distilling.
Table 15: Distribution of Respondents According to Type of Traditional Non-farm and Farm
Occupation
Non-Farm Business
Pottery
Cloth-weaving
Palm-oil processing
Garri processing
Local brewing/distilling
Total:
Farm Business
Crop farming only
Animal husbandry only
Both
Total:

Frequency
34
82
61
45
25
247

Percentage
13.7
33.2
24.7
18.2
10.1
100.0

70
7
35
112

62.5
6.3
31.2
100.0

Source: Field data, 1999

A total of 112 women combined non-farming with farming business, mainly as secondary occupation.
Of these, 70 (62.5 percent) were in crop farming only, seven (6.3 percent) in animal husbandry only,
while 35 (31.2 percent) combined both cropping and some animal husbandry activities such as
backyard poultry keeping or raising of small ruminants.

26

ATPS WORKING PAPER NO. 40
Employment and Apprenticeship Records in Rural Enterprises
The survival of rural enterprise may be related to the level in which people take up the enterprise andthe level of youth apprenticeship.
Table 16: Estimate of Employees and Apprentices in Women Enterprises
Apprentices

Employees

Profile of Employment/
Apprenticeship
None
1-3
4-7
8 - 10
10+
Don’t know

Frequency
121
86
16
5
2
17

%
49.0
34.8
6.5
2.0
0.8
6.9

Frequency
106
36
3
3
1
98

%
42.9
14.6
1.2
1.2
0.4
39.7

Total:

247

100.0

247

100.0

Source: Field data, 1999

From Table 16, 49 per cent of the women entrepreneurs had no apprentices learning the trade,while 42.9 per cent had not employed a helping hand. About 34.8 per cent had one to threeapprentices, while 14.6 per cent employed one to three persons. Up to 6.9 per cent said they would
not classify their children or relatives learning the trade as apprentices while 39.7 per cent would not
identify such people as employees in their enterprises.
Table 17: Wish of Women Entrepreneurs on their Daughters’ Adopting Enterprises
Decision Variable
Should take-up enterpriseShouldn’t engage in enterprise
UndecidedChild should decide
Total:

Frequency
65
67
103
12
247

Percentage
26.3
27.1
41.7
4.8
100.0

Source: Field data, 1999
Table 17 shows the women entrepreneurs’ response when asked to indicate their wish concerning
their children taking up their trade. Of the 247 women, 26.3 percent would wish their daughters took
up the trade as a profession, 27.1 percent said their daughters should not take up such enterprises,
41.7 percent were undecided, while 4.8 per cent said that the children should decide.
A woman pot-maker from Isiagu who was opposed to daughters taking up the pottery enterprise
had this to say: “I will be the last in this household to be engaged in this dirty and tedious job of pot-

27

SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
making. My daughters must finish their education and find a better civil service job. Unless thesuffering is lessened through modern ways, I will not recommend the job to even my enemy”. ( A
woman FGD discussant, (1999).
Table 18: Estimate of Percentage Technical Change Adopted by Number of Workers in Entrepreneur’s
Employment
Estimated
Percentage AdoptionLess than 20
20 - 30
31 - 40
41 - 50
Above 50

Total

None
9
(19.1)
12(25.5)
13(27.7)
5(10.6)
8(17.0)
47(62.7)

No. of Employees
1-3
4-7
11
(47.8)
2(8.7)
3(13.9)
7(30.4)
23(30.7)

7-9
1(33.3)

Above 10 +-1(100.0)2(66.7)
1
-

1
(1.3)

3
(4.0)

1
(1.3)

-

-

(100.0)Total
10(23.3)
23(30.7)
17(22.7)
10(13.3)
15(20.0)
75(106.0)Figures in parentheses are column percentages.
X2 Value = 29.46
X2 tab = 19.4
d.f = 16Sig. = 10%
Source: Field data, 1999

The percentage of technical change adopted seems to have no influence on the number of people
employed by women in their enterprises or vice versa. Table 18 shows of the 47 entrepreneurs who
did not employ any person in their enterprise, 19.1 percent adopted less than 20 percent, while 17
percent adopted 50 percent and above of the innovations they were exposed to. Only one cloth
weaver at Akwuete who employed up to 10 people and above had adopted 41 to 50 percent of thechange in the local weaving industry.
A hypothesis of no relationship between level of adoption and employment into rural enterprise by
women was accepted at 10 percent level of significance. The implication is that the women adoption
rate did not influence their level of employment in their enterprises.
Table 19 also shows that level of adoption of technical change did not affect the number of young
women apprentices that go into the traditional trades. For example, of the 131 respondents, 52 (39.7
percent) did not have any apprentice learning the trade. Of this number, 17 (32.7 percent) adopted 31
to 40 percent of the technical innovations exposed to them. Also, a hypothesis of no relationshipbetween adoption of technical change and number of apprentices in the local enterprises was
accepted at 10 per cent level of significance. This implies that the apprentices they had did not joinbecause of the change they noticed in the traditional enterprises.

28

ATPS WORKING PAPER NO. 40
Table 19: Estimated Technical Change Adopted by Number of Apprentices in Enterprises
Estimated
Percentage AdoptionLess than 20
20 - 30
31 - 40
41 - 50
Above 50

Total

None
8
(15.4)
14(26.9)
17(32.7)
8(15.4)
5(9.6)
52(39.7)

No. of Apprentices
<3
3-4
15
3
(23.1) (27.3)
183(27.7) (27.3)
212(32.3) (18.2)
42(6.2) (18.2)
7
1(10.8) (9.1)
65
11(49.6) (8.4)

5-6-

7 and above
-

-

-

-

-

-

1
(50.0)
11(100) (50.0)12(0.8) (1.5)

Total
26
(19.8)
35(26.7)
41(31.3)
14(10.7)
15(11.5)
131(100.0)Figures in parentheses are column percentages
X2 Value = 16.68
X2 tab = 19.4
d.f = 16Sig. = 10%
Source: Field data, 1999

Income and Expenditure Patterns of Women Entrepreneurs
It is widely accepted that income and expenditure distribution patterns of entrepreneurs in an area
are important determinants of adoption of new technology and attitude towards technical change(Ezeh, 1997, Kanu, 1997). Since most of the negative consequences of technical change relate to
the risk associated with the adoption of the change, and risk-taking has a relationship with income
size, it follows that income and expenditure patterns of the entrepreneurs are important variables inanalyzing the consequences of technical change.
Table 20 shows the income distribution patterns of women entrepreneurs studied. From theTable, out of the 247 women engaged in the various traditional non-farm primary occupations, 148(59.9 percent) earned less than N5,000, 57 (23 percent) earned N5,000 to N10,000, while only 42 (17percent) earned above N10,000 monthly from their operations. From the secondary occupation
which was mainly farming, 171 (90.5 percent) of the entrepreneurs earned less than N5,000, while
only 3 (1.6 per cent) earned above N10,000 from such activities. Other earnings, mainly remittancesfrom relatives and friends, and gifts, were also generally low. It can be seen that what accrued to these
women as income from all their yearly economic activities was generally low; and this may haveaffected their potential to adopt and use new technologies available in their enterprises.

29

SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
Table 20: Distribution Entrepreneurs Households According to Yearly Income Earning Pattern
Income Distribution Level (Yearly (N)Income Earning Items
Primary occupation
Secondary occupation
Remittances
Gifts
Others<5000
148(59.9)
171(91.5)
91(94.8)
116(99.1)
5(100)

5000-10,000
57(23.0)
15
(7.9)
4
(4.2)
-

10,000+42(17.0)
3
(1.6)
1
(1.0)
1
(0.9)
-

Total
247
(100)
189
(100.0)
96
(100)
171
(100)
5
(100)

Figures in parentheses are the column percentages
Source: Field data, 1999

Table 21 shows the yearly expenditure patterns of the women entrepreneurs on their households on
the listed items. It is seen for example, that 88.95 percent of the women spent less than N5,000 while
only 1.7 percent of them spent above N10,000 on food. Savings were generally low among theentrepreneurs as about 95.4 percent of those who saved at all had less than N5,000 as averageannual savings. Low savings may have led to low investment, including investment on new technology
used in production.

Technical Change Availability, Adoption and Utilization
The level of technical change available, and the adoption and utilization levels will determine the typeand level of consequences an entrepreneur may get. If the change in technology is never madeavailable to the entrepreneur, such entrepreneurs may not have a direct consequence from suchtechnology.
Table 22 shows that modern looms for cloth weaving were available to only 33.6 per cent of theentrepreneurs, but only 3.6 per cent adopted the innovation for use. Innovations such as clay mixing
and moulding machines, and modern kilns, were never exposed to the entrepreneurs for possible
adoption and use. The impact of such innovation on the rural entrepreneurs’ may be indirect, on the
demand side.
Table 23 shows that of the innovations exposed to the women entrepreneurs less than 20 per centwere used by 10.9 per cent, while 44.9 per cent of the women did not adopt any innovation at all. Of
this percentage are women entrepreneurs who were not exposed to any technical change at all.

30

ATPS WORKING PAPER NO. 40
Effect of Technical Change on Income of Entrepreneurs After Adoption
Assessment of the effects of some innovation on adopters and non-adopters will help show the effects
of new technology.
Table 21: Distribution of Households According to Yearly Expenditure Patterns
Expenditure Items <5000Food
160(88.9)
Clothing
120(95.2)
Rent
44(97.8)
Education
93(71.2)
Health care
192
(93.9)
Transport
112(97.4)
Electricity
33(100)
Fuelwood/Kero161(99.4)
Water
38
(100)
Extended family 119(99.9)
Remittance/gifts
131(99.3)
Entertainment
131(99.3)
Savings
83(95.4)

Expenditure Distribution (N)5000-10,000
17
(9.4)
5
(4.0)
1
(2.2)
34
(26.2)
2
(0.1)
2
(1.7)
-

10,000+
3
(1.7)
1
(0.8)
3
(2.3)
1
(0.8)
-

1
(0.6)
-

-

2
(0.1)
1
(0.7)
1
(0.7)
4
(4.6)

-

-

-

Total
180
(100)
126
(100)
45
(100)
130
(100)
194
(100)
115
(100)
33
(100)
162
(100)
38
(100)
121
(100)
132
(100)
87
(100)

Figures in parentheses are the column percentages.
Source: Field data, 1999

Table 24 shows that women entrepreneurs who indicated non-adoption of technical changes exposedto them had 54.4 per cent of them earning below N2000 from their enterprises monthly, while only
41.2 per cent of the adopters earned less than N2000. Nine of the non-adopters (6.6 per cent) earned
income in excess of N10,000, while 11 (8.1 per cent) earned income in excess of N10,000 from theirenterprises. This is, however, a loose indication of the positive effects of technical change on therural non-farm entrepreneurs.

31

SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
Table 22: Technical Changes in Non-Farm Enterprise Availability and Level of Adoption and Use by
Entrepreneurs in South-eastern Nigeria
Technology Available
Modern looms for cloth weaving
Other modern weaving
materials/techniques
Garri processing machines andother techniques
Palm-oil processing machines
Clay mixing and moulding
Modern kilns
Modern distilling system
Credit facilities
Modern marketing system(eg through co-ops)

Available %33.6

Adopted and Used by (%)
3.6

100

73.2

72.2
80
None
None
20
61

66.7
54.1
8.0
48.2

92

54.6

Table 23: Estimated Percentage of Technical Change Adopted by Women Entrepreneurs
Level of Change (%)
Less than 20
20 - 30
31 - 40
41 - 50
Above 50
Not adoption
Total

Frequency
27
37
43
14
15
111
247

Percentage
10.9
15.0
17.4
5.7
6.1
44.9
100.0

Source: Field data, 1999

Table 24: Estimate of Average Monthly Income of Entrepreneur Adopters and Non-Adopters ofTechnology
Monthly Income Level(Naira)Less than 2000
2000 - 5000
6000 - 9000
10,000 +Total

Non-Adopters
Adopters
Freq.
%
Freq.
74
54.4
56
31
22.8
43
22
16.2
26
9
6.6
11
136
100.0 136

Source: Field data, 1999

32

%
41.2
31.6
19.1
8.1
100.0

ATPS WORKING PAPER NO. 40
Table 25: Estimated Percentage of Technical Change Adopted by Average Monthly Income of
Women Entrepreneurs
Estimated
Percentage
AdoptedLess than 20
20 - 30
31 - 40
41 - 50
Above 50

Total

Average Monthly Income Distribution (Naira)> N2000
13(50.0)
4(15.4)
4(15.4)
3(11.5)
2(7.7)
52(20.5)

2000-5000
6
(5.6)
12
(22.2)
21(38.9)
7(13.0)
11(20.4)
54(42.5)

5001-9005(13.5)
15(40.5)
13(35.1)
3
(8.1)
1
(2.7)
37
(29.1)

10,000-13,000 14001 +Total
1
1(12.5)(100.0)4(50.0)
2
1(25.0)(100.0)1(12.5)
8
(6.3)

1
(0.8)

23
(18.1)
35(27.6)
41(11.0)
14(11.0)
14(11.0)
127(100.0)Figures in parentheses are column percentages
X2 Value = 43.66
X2 tab = 37.6
d.f = 20
Sig. = 1%
Source: Field data, 1999

The relationship between the percentage of technical change adopted by women entrepreneurs
and their average monthly income is presented in Table 25. From the cross-tabulation, there seems
to be no relationship between these two factors. However, the hypothesis that percentage change oftechnology adopted does not relate to the average income size realized by entrepreneurs was rejected(X2 cal 43.66 > X2 tab 37.6) at 1 percent level of significance. This implies that percentage adoption
positively related to income level from non-farm enterprises.
The percentage of technical change adopted by women entrepreneurs does not seem to influence
their sources of finance as seen from the cross tabulation (Table 26). It was expected that the moreof modern techniques and technologies adopted by entrepreneurs, the more such entrepreneurs
may qualify for loans from formal financial organizations.
Table 27 shows the relationship between percentage of technical change adopted by women
entrepreneurs and total employment in enterprises in the past two years. Chi-square result showsthat the hypothesis of no relationship between the two variables was accepted since X 2 cal 16.33 < X 2
tab 18.5 at 10 percent level of significance. This implies that any other factor could have influenced
employment into the enterprises, not the level of adoption of technical change.

33

SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
Table 26: Percentage of Technical Change Adopted by Source of Financial Assistance toEntrepreneurs
Percentage
Adoption
Less than 20
20 - 30
31 - 40
41 - 50Government(20.0)
1(50.0)-

Above 5001
(50.1)
-

Total

2
(3.4)

Sources of Financial Assistance
Non
Prof. Formal
Government
Group Inst.
3
4(44.4)(9.1)
6
2
1(40.0)(18.2)(11.1)
47
4(26.7)(63.6)(44.4)
2
1(13.3)
(9.1)
1
(9.1)
15
11
9
(25.4)(18.6)(15.3)

Non-formal
Group
2
4
(18.2)
3(13.6)
4(18.2)
9(40.9)
22(37.2)

Total
9(15.3)
14(23.7)
18(30.5)
8(13.6)
10(16.9)
59(100.0)Figures in parentheses are column percentages.
X2 Value = 34.27
d.f = 16
Source: Field data, 1999

X2 tab = 26.3
Sig. = 5%

Table 27: Percentages Technical Change Adopted by Total Employment in Enterprises in Past 2
Years
Percentage
ChangeLess than 20
20 - 30
31 - 40
41 - 50
Above 50
Total
X2 Value = 16.33
d.f = 12Source: Field data, 1999

Number of Workers Employed in Past 2 Years
None
1-4
5-9
15
5
6
(23.9)(15.2(27.3)
1412
4(22.2)(36.4)(18.2)
25
8
5(39.7)(24.2)(22.7)
4
2
5
(6.3)
(6.1)
(22.7)
5
62(7.9)(18.2)
(9.1)
63
33
22
(48.5)(25.4)(16.9)
X2 tab = 18.5
Sig. = 10%

34

10+
1
(8.3)
3
25.0)
3
(25.0)
3(25.0)
2(16.7)
12
(9.2)

Total
27
(20.8)
33(25.4)
41(31.5)
14(10.8)
15(11.5)
130(100.0)ATPS WORKING PAPER NO. 40
Table 28: Percentage of Technical Change Adopted by Farm Size (Hectares) Cultivated by
Entrepreneurs
Percentage
Adoption
Less than 20

Above 50< 2.0
3(23.1)
5(38.5)
4(30.8)
1
(7.7)
-

Total

13
35.1

20 - 30
31 - 40
41 - 50

X2 Value = 19.21
d.f = 24
Source: Field data, 1999

Farm Size (Ha)2-3
3(60.0)
1(20.0)
1(20.0)(10.0)
5(13.5)

4-5
2(20.0)
4(40.0)
3(30.0)-

Above 51
(11.1)
5(55.6)
3(33.3)-

1-

10(27.0)

9(24.3)

Total
9(24.3)
11(37.8)
11(29.7)
2
(5.4)
1
(2.7)
37
(100.0)X2 tab = 19.0
Sig. = 10%

Most of the rural women entrepreneurs combined non-farm activities with farming, leading to a flowof resources from one to the other. Table 28 shows a cross-tabulation of farm size by percentage of
non-farm innovations adopted by the entrepreneurs. It was expected that the more non-farm innovations
adopted by entrepreneurs, the more the farm size. But X2 cal. 19.21 > X2 tab 19.0 led to a rejection ofa hypothesis of no relationship between the two variables. This implies that percentage of technicalchange adopted influenced farm size of entrepreneurs.
Table 29 shows a cross tabulation of percentage of technical change adopted and the income
from non-farm activities that was invested in farming. Chi-square result shows that level of adoption
influenced income from non-farm enterprises invested in farming (X2 cal 16.21 > X2 tab 15.3) at 10
percent level of significance leading to a rejection of the hypothesis of no relationship. This implies amore linkage of farm with non-farm enterprises as non-farm enterprises are modernized and improved.

Availability and Functionality of Social Facilities
It should be noted that the level of availability and functionality of infrastructures and amenities in anarea tends to influence most other social and economic attributes of people in that area. Suchvariables as educational level, level of income, household expenditure, and adoption behaviour,among others, are affected by the level of infrastructural facilities available and functional in that area
(Table 30).

35

SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
Table 29: Percentage Technical Change Adopted by Income Flow from Non-farm to Farm Enterprises
Percentage
Adoption
Less than 20
20 - 30
31 - 40
41 - 50
Above 50

Total

< 10%
5
33.3
3
(20.0)
4(26.7)
2(13.3)
1(6.7)
15(18.9)

X2 Value = 16.21
d.f = 16
Source: Field data, 1999

Percentage Income Invested in Farming
10-30%
31-50%> 50%
91(19.1)
6.3
16
6(34.0)(37.5)
157
1(31.9)(43.8)(100.0)5
2(10.6)(12.5)
2
(4.3)
47
16
1
(59.5)
20.3
(1.3)

Total
15
(19.0)
25(31.6)
27(34.2)
9(11.4)
3
(3.8)
79
100

X2 tab = 15.3
Sig. = 10%

Table 30: Distribution of Respondents According to Availability and Functionality of Social Facilities
Percentage
Adoption

Percentage of Respondents
Available & Available/
Not
Available
Functional Not Available Available
Postal facilities
86.2
83.0
2.4
11.7
Health (clinics/hospitals)
91.1
77.7
4.9
5.3
Electricity
63.6
15.4
58.7
20.6
Portable water source
56.7
23.9
50.2
27.9
Good roads
24.3
25.1
46.2
56.3
Primary schools
91.9
76.9
5.3
2.4
Secondary schools
92.7
74.9
6.5
2.8
Daily markets
52.1
48.2
26.3
25.5
Four daily markets
65.2
53.8
24.3
21.5
Agro-processing industries 59.5
47.8
32.4
31.2
Agro-service centres
17.8
13.0
48.7
51.8
Bank facilities
55.9
24.3
53.8
27.9
Source: Field data, 1999

Table 30 shows the distribution of respondents according to the percentage of entrepreneurs thatagreed or disagreed on the availability and functionality of the listed social infrastructure. From thetable, for example, 91.1 percent of the respondents agreed they had medical facilities such as clinics
but only 77.7 percent agreed that the facilities were functional in their areas. About 4.9 percent
36

ATPS WORKING PAPER NO. 40
reported that such facilities were available but not functional, while 5.3 percent reported that such
facilities were not available to them at all.
The level of social facilities in the rural area can influence level of adoption of technical innovations
and the benefits from such adoption. It can also affect rural linkages between farm and non-farmenterprises.

Results of Regression Analysis
Multiple regression analysis was undertaken to determine the influence of some independent variables
related to technical change on some dependent variables associated with the effects of technicalchange on the non-farm women entrepreneurs. In all, five regressions, based on three functional
forms, were run. The linear regression estimates were selected because they showed manystatistically significant variables beyond the 10 percent confidence level, and they also had the highest
values for the coefficient of determination (R2).

Significance and Interpretation of Coefficients of Regression Data
To determine the impact of the independent variables on the annual income realized by farm
entrepreneurs, t-values were computed from their corresponding regression coefficients and standard
errors (Tables 31-35). The inter-correlation metrics were computed and were used to test for multicollinearity. The problem of multi-collinearity did not arise in the study ( see Appendix 1). According
to Olayemi and Olayide (1981) any inter-correlation value of less than 0.5 can be considered asstrong evidence of independence between the two variables; while any inter-correlation of 0.68 andabove is strong evidence of the existence of multi-collinearity between the two variables.
All the coefficients in the inter-correlation table as shown in Appendix 1 are less than 0.5, thus theabsence of multi-collinearity.

Summary of Regression Results
The summary of the regression results is reported in Tables 31-35 based on the linear functional
forms which have the lowest standard error of estimates and invariably the highest number of significant
variables.

37

SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
Table 31: Summary of Regression Results of the Effects Technical Change Variables on the Levelof Income of Non-farm Women Entrepreneurs of South-eastern Nigeria
Variables
X1
X2
X3
X4
X5
X6
X7
X8
X9

Regression
Coefficients-.2077-.0365-.2354
.2078
.2386
.4923
7.1100-.2383-.0806

Standard
Errors
.0952
.0261
.1238
.1305
.0649
.3531
4.1613.1121.0514

t-Value-2.181-1.395-1.200
1.592
4.289
1.394
1.709
2.125
-1.567

Level of
Significance
.05
NS
.10
NS
.01
NS
.01
.05
NS

Constant b = 1.4857
Degree of freedom (d.f) = 9
NS = Not significant at 10% level of confidence
R2 = 0.421
R2 = 0.325
Fvalue = 4.3684
SE of the Estimate 0.7021

The regression function of the effects of technical change variables on level of income of non-farm
women entrepreneurs (Table 31) shows that the coefficient of determination R 2 was 42 percent. This
percentage shows that the proportion of the observed variability (in levels of income generated by the
women entrepreneurs) explained by the combined effects of the independent variables, is high. The
estimated function can be regarded as a good fit, for according to Nwoko (1989), as long as the R 2 isup to 40 percent, the regression is a good fit at 90 percent confidence level or less.
It should be noted that the F-value of 4.3684 was significant at 99 percent level of confidence, thus
indicating a strong influence of the nine independent variables associated with the income that
accrued to different level of adopters of new technology (Appendix 1).
A cursory examination of the influence of each of the significant independent variables on the levelof income realized by the entrepreneurs shows that: the level of technical change adopted by anentrepreneur - in percentage - (X2) positively influenced the annual income from the enterprise. Also
the amount spent in procuring raw materials for the new technology - in Naira - (X4) and the extra
labour requirements due to the adopted change (X5) both had a negative influence on the level ofincome that accrued to the women entrepreneurs. The number of positive effects experienced fromnew technology (X8) had positive influence, while the number of negative effects experienced byentrepreneurs as a result of technical change (X9) had negative influence on their income.

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ATPS WORKING PAPER NO. 40
Table 32: Summary of Regression Results of Effects of Technical Change Variables on Expenditure
Levels of Women Entrepreneurs
Variables
X1
X2
X3
X4
X5
X6
X7
X8
X9

Regression
Coefficients
5299.0393
86.0353
3320.0692-2648.9424-305.1778
9456.3711
1.52366653.9697-710.5515

Standard
Errors2935.6525
642.0467
3148.9500
3571.7886
1944.3674
4993.5385
1.67952638.4856
1101.0843

t-Value
1.805
.134
1.054
-.742
.157
1.894
.907
-2.522-.645

Level of
Significance
.10
.10
NS
NS
.10
NS
NS
.05
NS

Constant b = 125.2064
R2 = .651
R2 = .302
F = 1.864
SE = 7622.102

Table 32 shows that the regression function of the effects of technical change variables on expenditurelevels of women entrepreneurs on consumption goods (taken as index of standard of living) has thecoefficient of determination R2 as 65 percent. This percentage shows that the proportion of theobserved variability (in levels of expenditure of the women entrepreneurs) explained by the combined
effects of the independent variables, is quite high, and can be regarded as a good fit.
The F-value of 1.864 was significant at 90 percent level of confidence, thus indicating a strong
influence of the nine independent variables associated with the level of expenditure (as index of thestandard of living) of the entrepreneurs.
Further examination of the influence of each of the independent variables that were significant on
the level of expenditure shows that both the level of estimated change in a given enterprise (X1), andestimated level of technical change adopted by an entrepreneur (X2) had positive influence on thestandard of living of the entrepreneurs in terms of their monthly expenditure. Extra labour requirement
as a result of the adopted change - usually male labour – (X5) had an inverse effect on the level ofconsumption expenditure by the women, while the number of positive effects from the change (X8) as
identified by the women, had a significant and positive relationship with their consumption of
expenditure.
From Table 33, the regression function of the effects of technical change variables on level of
employment in non-farm enterprises in the past two years shows a coefficient of determination (R2) of
23 percent. This shows that the proportion of the observed variability (in level of employment into thenon-farm women enterprises) explained by the combined efforts of the independent variables, is low,
and cannot be regarded as a good fit. This is justified by Nwoko (1989), who maintained that as longas the R 2 is up to 40 percent, the regression is a good fit at 90 percent confidence level or less.

39

SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
However, the level of estimated technical change in an enterprise (X1), and the level of technicalchange adopted by an entrepreneur (X2) had positive and significant influence, respectively, on thelevel of employment in the enterprises.
Table 33: Summary of Regression Results of Effects of Technical Change Variables on Level of
Employment in Non-Farm Enterprises in Past Two Years
Regression
Coefficients
.3582
.1212
.0381
-.2544.1939.2792-9.01388.0742-.1017

Variables
X1
X2
X3
X4
X5
X6
X7
X8
X9

Standard
Errors
.1915
.0529
.2445
.2652
.1367
.6143
.6378
.2274
.1040

Level of
Significance
.10
05
NS
NS
NS
NS
NS
NS
NS

t-Value
1.870-2.288
.156
-.959
1.419
.454
-1.044.327.978Constant 2.4119
R2 = .232
R2 = .113
F.ratio = 1.947
SE = 1.468

Table 34: Summary of Regression Results of Effects of Technical Change Variables on Female Outmigration from Entrepreneurs’ Households
Variables
X1
X2
X3
X4
X5
X6
X7
X8
X9

Regression
Coefficients.0045-.0690-.1035-.7879-.5556
1.9668-.7754-.63581.9503Standard
Errors
.2873
.0678
.3207
.3213
.1619
.9795
2.1193.2876.1448

Level of
Significance
.10
05
NS
NS
NS
NS
NS
NS
NS

t-Value.0020-.1293-.0475
.3086
.4407
.2561
-.0986-.3224.2495

Constant b = -19504
R2 = .474
R2= .367
NS = Not significant at 10 percent level of confidence.

40

F=

4.405

SE

= 1.615

ATPS WORKING PAPER NO. 40
Table 34 shows that the combined effects of the nine variables related to technical change on femalemigration from entrepreneurs’ households has a coefficient of determination R 2 of 47 percent. This
percentage is high enough and can be regarded as a good fit. The F-value of 4.405 was significant
at 99 percent level of confidence, thus indicating a strong influence of the independent variables
associated with level of female out-migration from households.
From the six significant variables, level of technical change adopted (X2), was inversely related tolevel of out-migration from households. Also the extra amount spent on raw materials due to adoption(X4) and the extra amount of labour needed after adoption (X5), inversely influenced the rate ofhousehold out-migration. The number of positive contributions from adoption (S8) and the number ofnegative effects associated with adoption (X9) had inverse and positive influences on the level ofhousehold migration, respectively.
Table 35: Summary of Regression Results of Technical Change Variables on the Flow of Income
from Non-farm to Farm by Women Entrepreneurs
Variables
X1
X2
X3
X4
X5
X6
X7
X8

Regression
Coefficients
5780.0988
124.1420
4018-7244-3795-8964-144.9992
19314.8091
1.6092-6599.5768

Standard
Errors2755.3339
620.3906
2869.4082
3006.4279
1761.1627
4670.5443
1.62462559.0356

t-Value
2.098
.200
1.401
-1.263
-.082
2.208
.991
-2.579Level of
Significance
NS
.10
NS
NS
NS
NS
.10
.05

Constant = -4359.3732
R2 = .635
R2 = .342
F = 2.172
SE = 7396.360

The combined influence of eight variables related to the effects of technical change on the flow ofincome from non-farm to farm by women entrepreneurs shows a coefficient of determination of 63.5
percent. The F-value was significant at 90 percent level of confidence, thus indicating a strong
influence of the independent variables on the dependent.
From the three variables that were significant, the level of technical change adopted by theentrepreneurs was positively related to the amount of money invested from non-farm to farm enterprise.
Also the income that accrued to the entrepreneurs as a result of adoption positively influenced theincome the entrepreneur invested in the farming business. The number of positive results experienced
by entrepreneurs (X8) negatively influenced the fund flow from non-farm to farm activities. This may be
because the more comfortable the women were with what they got from their non-farm business, theless they were willing to invest in farming.

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SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA

Result of Inter-correlation Analysis
Inter-correlation matrix analysis was done in order to establish how the variables related to newtechnologies affected or were affected by some socio-economic attributes of the women
entrepreneurs.
The highlights of the results show that in terms of employment as indicated by the number ofapprentices within the past two years in relation to percentage change adopted, there was a low but
positive relationship (r = .333). The level of relationship between level of adoption of technical change
and the number of female out-migration from entrepreneur households was low but positive (r =.200). However, the level of technical change adopted by women entrepreneurs showed a strong
relationship with the average number of women that had entered the enterprises in the past two years(r = 0.536). Also, there was a low but positive relationship between level of technical change adopted
and average monthly expenditure on entrepreneur’s household as a measure of standard of living (r= 0.419). Average monthly income from enterprise had a high and positive relationship (r = .512) withthe level of adoption of technical change. The analysis also shows that the less the level of adoptionof changed technology, the less the entrepreneurs invested in the farm (r = -394). This may be
because entrepreneurs who adopted more of a changed technology were likely to invest more in the
non-farm business, leaving little for investment in the farm.

Results of Factor Analysis
Factor analysis was used to identify and name those factors that might continue to encourage or
discourage the activities of the traditional women entrepreneurs in relation to adoption of technicalchange. Since the purpose was to identify new factors, the interpretation boils down to identifying thevariables having the highest loading for each factor. The variables loading highest were used innaming each extracted factor. Kaiser (1958) developed a rule of thumb: variables with a coefficientof 0.30 or more have high loading and may be used in naming a factor. The rule has generally been
applied (Child, 1978, Ogunfiditime 1979, Alimba, 1999).

Results of Varimax Rotated Factor Matrix of Positive Effects of New
Technology Adoption Variables
The variables that loaded high for the naming of the factors considered positive as perceived by nonfarm women technology adopters are shown on Table 36. From these variables, three factors wereidentified for critical consideration.
Factor 1 that has Vo 2 - quantity of the product (.716), Vo 3 - quality of the product (.752), and V06 - costof production (.514) loading highest was named “production and output relationship”. Factor 2 wasnamed “social and economic relationship” because it has Vo1 - level of income generated (.725), Vo7- household quality of life (-.858), and Vo8 - household labour demand (-.667) loading highest. After
careful consideration, factor 3 was named “employment and future of enterprises relationship” because
Vo4 - number of workers employed – (.645), Vo5 - number of apprentices that join enterprise yearly(.601), and interest shown by children (.698) variables were loading very high.

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ATPS WORKING PAPER NO. 40
Table 36: Varimax Rotated Factor Matrix of Positive Effects of New Technology Adoption Variables
on Non-farm Women Entrepreneurs
Positive EffectVariables
V01 Level of income generated(yearly)V02 Quantity of products
V03 Quality of products
V04 Number of workers employed
V05 Number of apprentices thatjoin (yearly)
V06 Cost of production
V07 Household quality of life
V08 Household labour demand
V09 Interest shown by children% of explained variation

Factor 1
Production& Output
Relationship

Factor 2
SocioEconomic
Relationship

Factor 3
Employment andFuture of Enterprise
Relationship

.017
.716
.752
.063

.725
.555
.445
-.535

.363
-.123
-.098
-.645

.023
.514
.073
.052
.215
40.5

-.507
.067
-.858
-.667
-.245
20.1

.601
-.095
.078
-.455
.698
11.2

Coefficients in the table represent regression weights.
For the factor “production and output relationship” the entrepreneurs identified quantity and qualityof products produced, and reduced cost of production as relating to the improved production system
adopted. This accounted for 40.5 percent of the issues relating to the positive effects of new technology
adopted. On the factor named “socio-economic relationship”, the level of income generated improved
quality of life in relation to higher expenditure level, and reduced household labour demand of theenterprises was identified as the positive social and economic benefit of adoption as perceived by theentrepreneurs. The factor accounted for 20.1 percent of the positive effects of adoption on theentrepreneurs.
For the factor named “employment and future of enterprises relationship” women entrepreneurs
who adopted improved production techniques perceived an improvement in the number of workers
employed, number of apprentices that join yearly and the interest shown in their enterprises as
positive indicators, and that continued improvement in their production techniques would further
enhance these variables. This factor accounted for 11.2 percent of the adoption of innovation by the
women entrepreneurs that adopted and used aspects of new technology in production.

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SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
Table 37: Varimax Rotated Factor Matrix of Negative Effects Associated with New Technology
Adoption Variables on Non-farm Women Entrepreneurs
Negative
EffectsVariables

Xo1 Complexity of New
Technology
Xo2 Quality of product
Vo3 Issue of repairs andmaintenance
Vo4 Labour displacing
Vo5 Marketing of productproblems
Vo6 Technology issues
Vo7 Issue of cost of newtechnology
Vo8 Environmental issues
Vol9 Input availability% of explained variation

Factor 1 (Inappropriate
Technology Issues)

Factor 2(Social and Marketing
Issues)

Factor 3(Sustainability &Environmental
Issues

.891
.178

.206
.892

.208
.090

.244
.852

.111
.036

.865
-.018

.021
-.048

.803
.840

.025
.101

.787
.311
.318
44.7

.183
.297
.139
30.9

.142
-.658
.654
16.3

Coefficients in the table represent regression weights.
Table 37 shows the results of the variables that loaded high with the three factors identified as thecritical negative factors that affected adopters of changed technologies in their rural enterprises.
Factor 1 had Vo1 - complexity of new technology (.891), Vo4, - labour displacing (.852) and Vo7 cost of new technology (.787) loading highest, and was subsequently named “inappropriate technology
issues”. Factor 2 was named “social and marketing issues” because it arose from variables Vo2 quality of products, (.892), Vo5 - marketing of product problems (.803) and Vo6 - technology extension
issues (.840) which loaded highest. “Sustainability and environmental issues “ was the name given
to factor 3 because the variables relating to it, Vo3 - issues of maintenance and repairs (.865), Vo8 environmental issues (-.658), and Vo9 - input availability (.654) loaded highest.
Factor 1 which accounted for 44.7 percent of issues relating to negative effects of new technology,
was identified as relating to inappropriate technology issues because of the common complaint ofentrepreneurs that some of the technologies exposed to them (such as the modern loom) were very
complex. Some complained that some of the new technologies they adopted tended to displacesome women entrepreneurs with men’s labour, and others raised the negative issue of high cost of
acquiring such new technologies which in most cases was beyond their financial capability.
The social and marketing issues the women entrepreneurs perceived as negative consequencesof adoption accounted for 30.7 percent of the negative effect issues and were related to the quality ofthe products got from the new technology. Some women traditional cloth weavers of Akwete, Abia
State, maintained that the product from the modern loom and thread adopted by some of them wereinferior to the one weaved using traditional methods. Their traditional product was better priced in the

44

ATPS WORKING PAPER NO. 40
market, while the one done in the modern method had marketing problems. Also the quality of palmoil processed using machine technology was perceived as inferior in quality to the traditionallyprocessed product and more acceptable in the local market. Problems of marketing also faced themass-production of such products as woven cloth, as the rural women lacked modern marketing
capability. Usually, the rural market capacity was hardly enough to buy up the products, while someof the products could not compete with urban and imported foreign products in urban markets.
The “substainability and environmental” issues identified related to the problem of repairs andmaintenance of modern machinery when it broke down. It accounted for 16.3 percent of issues
relating to negative effects of new technology. Some cloth weavers complained that whenever themodern loom broke down, they had to wait for the repairmen from town who took too long to come; forthe locally fabricated looms, women could easily carry out the repairs. Other complaints related toeffluent and noise pollution from the processing machines as in the palm-oil and garri processing
set-ups.

Results Varimax Rotated Factor Matrix of Reasons for Non-Adoption of New
Technology by Non-Farm Women Entrepreneurs
Several reasons that continuously hindered adoption of non-farm technologies, regarded as variables,
were identified and rotated and the variables with the highest loadings used in naming the two major
factors that tended to continually inhibit rural women entrepreneurs from adopting changed technology(Table 38).
Table 38: Varimax Rotated Factor Matrix of Reasons For Non-Adoption of New Technology Variables
by Some Non-farm Women Entrepreneurs

Constraint Variables
Vo1 Cost of new technology
Vo2 Issue of adaptation
Vo3 Non-awareness of non- technology
package
Vo4 Low capital for adoption
Vo5 Perception of traditional technology
as betterVo6 Associated infrastructure issues% of explained variation

Factor 1(Adoption and Adaptation
Relationship)
.992
.798

Factor 2(Extension and Rural
frastructureRelationship)
.123
.602

-.123
-.123

.994
.992

.995
.289
64.1

.921
.323
35.9

Coefficients in the table represent regression weights.
The first factor had Vo1 – cost of new technology (.992), issue of adaptation to new technology (.788),
and Vo5 – perception of old technology as better (.995) having the highest loads were collectively
named “cost of adoption and adaptation relationship”. Factor 2 had Vo 3 – non-awareness oftechnological package (.994), Vo4 – low capital for adoption (.992) and Vo6 – “associated infrastructure

45

SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
issue” (.323) loading highest. Factor 2 was therefore named “extension and rural infrastructure
relationship”.
Policy issues relating to factor 1 “adoption and adaptation relationship” which accounted for 64.1
percent of the issues should concentrate on issues relating to development of cost-effective
technologies for the local women entrepreneurs, understudying the traditional technology the women
entrepreneurs are used to, and developing `appropriate technologies’ that would adapt to what they
are used to. Also effective credit system targeted at women groups can help the women procure such
relevant technologies.
For the second factor named “extension and rural infrastructure relationship” which accounted
for 35.9 percent relating to reasons for non-adoption, it should have the policy thrusts as awarenesscreation through a well-packaged appropriate technology extension programme, capable of proving
to the rural women that such new technology was superior to the ones they were used to. Furthermore,issues relating to complementary infrastructure in the rural areas should be addressed, as they are
critical to effective adoption of improved technologies.

Social and Technical Problems of Adoption of Change in Non-farm Technology
Among Women Entrepreneurs in South-eastern Nigeria
The study attempted to identify those perennial problems rural non-farm women entrepreneurs ofsouth-eastern Nigeria continue to encounter in an attempt to adopt technical change. These problems
have given rise to the perceived negative consequences of non-farm technology change. They
continue to arise because most of the technologies are not resource-conserving and sustainable,
while some social factors linked to the women entrepreneurs’ personal characteristics also contributed.
These factors are summarized under ten headings as follows:

Ten Identified Social and Technical Problems of Adoption of Change on Women’s
Non-farm Technology in Southeastern Nigeriai) Inappropriate innovation
Some of the technologies did not address the main problems of the non-farm women entrepreneurs.
Some were labour displacing, while some were labour intensive.
ii) General unawarenessMany mechanical, chemical and management innovations were not made known to the entrepreneurs
sufficiently well. If adopted at all, adaptation was usually faulty.
iii) Incorrect identification of adoption domains
Researchers and developers misunderstood reasons for local practices and indigenous knowledge
systems, and targeted innovations inappropriately.
iv) Local practices perceived better
Sometimes, traditional technologies and methods were adjudged better by local entrepreneurs. Thistended to create confusion in entrepreneurs minds when adopting innovations, leading to haphazard
adoption patterns.

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ATPS WORKING PAPER NO. 40v) Generation of new problems
Many innovations which local non-farm entrepreneurs adopted had generated new and more criticalproblems. Some mechanical technologies used male labour instead of female; some were difficult
to repair and maintain, while others produced products inferior to the local technologies.
vi) High cost
The high cost of most of the chemical and mechanical technologies affected adoption. Sometimes,
the payback period of these technologies were too long for the women entrepreneurs.
vii) Poor extension
Inadequate and at times, incorrect demonstration of a technology to end-user entrepreneurs diminishedtheir interest and undermined the credibility of such innovation. In most cases, there was no non-farm
technology extension teaching at all.
viii) Low operating capital
Most rural non-farm entrepreneurs operated with very low working capital. Credit was not available tothem and sometimes they feared to owe. This inhibited effective demand for innovations.
ix) Poor infrastructure
Incentives such as rural infrastructure and social amenities which should complement technology
change such as access roads, electricity, modern markets, credit, and portable water were notavailable. This hindered adoption of innovation that could elevate the women entrepreneurs.
x) Unsustainable technology
Most of the non-farm technologies were not resource-conserving (e.g. energy conserving machinery);most of them were not flexible for the entrepreneurs to adapt, adjust or manipulate to their benefit andcontinued use.

Assessment of Negative Socio-economic Consequences of Non-farmTechnological Change in South-eastern Nigeria
Based on the identified social and technical problems that critically affected adoption of changed
technologies in rural non-farm enterprises of south-eastern Nigeria, an assessment can be made as
to whether the technologies are neutral or biased vis-à-vis the negative effects. It should be noted thatan innovation is adjudged neutral when the technical change cannot be blamed for the perceived
negative effects of the change. Technical change is biased to the negative effects if it can be blamed
for the effects that occurred, such as favouring one resource use more than the other (Ellis, 1988).

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SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
Table 39: Assessment of Negative Socio-economic and Environmental Consequences of Non-farmTechnical Change Among Women Entrepreneurs of South-eastern Nigeria
Type of Consequencei) Difficult to adapt technology
to production due tocomplexity:ii) Lower quality of product frominnovation:iii) Difficulty in repair andmaintenance:iv) Labour displacement
leading to rural out-migration
especially of youths.
v) Problem of marketing of Rural
products.

vi) Difficulty in awarenesscreation and adoption of
innovation.
vii) High cost of technicalinnovation.

viii) Reduction in household
standard of livingix) Negative externalities

Description
Inappropriateness oftechnology to the problem ofentrepreneurs
Inappropriateness of technology.
Also wrong use of the technology
in question.
Unsustainable technical change.
Displacement of labour may be dueto their relative prices. Other social
factors may cause out-migration.
markets may be small forthe large quantity of products ofof new technology. Also other social
infrastructure that aid marketing
may be lacking. Problems of low
income elasticity of demand.
Limitation is imposed by lack of non-farm technology extension aimed atawareness creation and adoption
stimulation.
Rural entrepreneurs are mainly
resource-poor and need credit
and other financial assistance,
which are not easily available tothem. Attitude of co-operation topull resources together is lacking.
Investment in technicalinnovation in rural enterprises
usually do not give sufficient
returns due mainly to low income
elasticity of demand of products,
poor marketing outlets, poor
competition with urban massproduced goods etc; hence leading
to low income and expenditure.
This may have been in form ofeffluent, noise and other forms ofpollution, especially from processing
techniques.

48

Verdict (Neutral/Biased)
Technical change is biased

Technical change is neutral andbiased.
Technical change is biased.

Technical changes is neutral.

Technical change is neutral.

Technical change is neutral.

Technical change is neutral.

Technical changes is neutral
and biased

Technical changes isbiased.

ATPS WORKING PAPER NO. 40
Discussion of the Igbo Traditional Non-Farm Women Enterprises Studied inSouth-eastern Nigeria
It is necessary to attempt a brief discussion on each of the Igbo traditional non-farm women enterprises
studied. The enterprises are pottery and clay work, cloth weaving, local beer distilling, palm-oil
processing and garri processing

Pottery and Clay Work
The study on this village industry was carried out using the Isiagu community of Ivo Local Government
Area (LGA) of Ebonyi State, South-eastern Nigeria. The community was purposively selected out ofothers that engaged in the enterprise because it is traditionally the most famous for pottery industry inthis part of Nigeria.
For the community, pottery and other clay work used to be the primary occupation for more than90 percent of the women, while farming was the secondary occupation. There was a large deposit offine non-expanding clay suitable for this industry. Today, only about 40 percent of the women are
engaged in this rural enterprise.
Clay was collected from local deposits, formed by hand into any shape intended without a well,and fired at low temperature without glazing. The women complained of the high cost of clay and the
additive soil called “ujiji” because it is men that mine it.
Because of this skill among the Isiagu women, the then government of the Eastern Region of
Nigeria, under Dr. M. I. Okpara established a modern ceramic industry in the community in the late
1950s.
By the time some of the women were about getting trained to adopt and adapt to modern pottery
and ceramic technology, the factory was relocated to a more distant community, Umuahia, in theearly 1960s. Though Umuahia equally had good clay raw material, the women there were not endowed
traditionally in pottery. It may have been done out of political exigency as the then premier of theregion was from Umuahia.
An aged woman potter in Isiagu when contributing to the Focus Group Discussion said: “Theremoval of that modern pottery factory from Isiagu was devastating to the women then, especially theyoung girls who had started becoming interested in the enterprise. Now, we are about the lastgeneration who may practice this skill, as you can see that no young woman is interested. I do notblame them; the work is arduous and dirty for our young more educated women”.
A few women producers hire lorries to take their goods to the distant market especially since therail transport in Nigeria got moribund; but most of them sell to visiting traders. Output and income areirregular since open-air work depends on the weather, and skill, especially in the past, is passed onin the family.
In the 1990s, the State government promised to encourage local pot makers through the Better
Life for Rural Women programme. The same promise continued under the Family Economic
Advancement Programme (FEAP). The women were asked to form themselves into co-operatives,
as a pre-condition for receiving government loans and other assistance. This they did, but no assistancecame. They accused the wives of military heads of putting the money to personal use. This was how
one of the leaders of the co-operatives put it:

49

SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA“We were tricked into forming a co-operative and spending a lot of money in bribes to register it in
the State capital in our effort to secure a loan. All our efforts to get even a kobo aborted as we got only
promises. We only hear on radio that several millions of Naira have been disbursed to women’s cooperatives in the State, but no women co-operative got anything. The wives of the military administratorsand people in local government were the beneficiaries”.
Policy inconsistency as one administration succeeded another seemed to rule out the possibilityof women’s groups getting any assistance either individually or collectively. The incumbent government
with Mrs Olusegun Obasanjo as First Lady does not emphasize rural women’s entrepreneurship.
The National Poverty Alleviation Programme seems not to have been well focused to address the
problems of such rural women.

Cloth Weaving
The Akwete community of the Ukwa-West LGA was purposively chosen for the study. This is becauseof the women’s weaving artistry, which dates back to the 12th century. Akwete women are the mostnotable weavers in Eastern Nigeria, with the famous and unique “akwete” cloth.
According to a women’s leader who has retired from active weaving, the weaving skill was acquired
time immemorial through a divine vision shown to a blind woman settler in the area. Prior to thatvision, the people of the area had always begged God to bless them with a skill they could use to feed
their children as they lived on unproductive soil, and their men were only involved in artisanal fishing.
God heard their prayer.
According to the women, the spirit that exposed the skill to them forbade them from teaching it toa non-Akwete. If an Akwete weaver teaches the skill to a non-indigene, she goes blind and the skill
vanishes from the memory of the non-indigene. This oath of secrecy is renewed every year, with aperformance they call “iku-odu”. The women cited many instances of calamities that befell women
who broke the oath. Hence, no other community near or far has been able to learn the Akwete
weaving skill.
The weaving technology consists of a fabricated wooden loom and locally ginned wool.
On the survival of the weaving industry, a women leader said: “The technology and techniques of
Akwete weaving have survived and will continue to survive because it is compulsory for every Akwete
girl to learn the skill. In fact, once you are born a girl in the community, you are already a weaver
whether you are taught the skill or not because the spirit must teach you in your dream. You can seethat our young girls from all walks of life are engaged in the weaving”. (Discussion with the leader ofthe women’s weavers co-operative).
On extent of technological change in the industry, the women reported that when loans were
given to them by the wife of the then Imo State, Governor Chief Sam Mbakwe, that technology was
rejected because the product from it lacked “Akweteness”. It differed from the texture of the cloth
woven in the older technology. The major technology in wide adaptation by the weavers was theindustrially ginned wool. Though they complained among other things that the wool, once used,makes a product lack original quality, it is still good enough. It enables them to weave faster, andproduce cloth of varied designs; it was however costly. Young weavers preferred industrial wool, while
the older women preferred the original wool.

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ATPS WORKING PAPER NO. 40
Traditional Palm-Oil processing
Traditional processing of palm-oil and other buy-products is commonly carried out by women in
Igboland, South-east Nigeria. Women from Nkanu and Enugu-Ezike communities of Enugu state
were chosen for the study because the enterprise formed their major occupation. They used the
traditional method to produce the “soft oil”.
The process involves boiling the palm fruit, after separating it from the bunches. It is then pounded
in a wooden mortar with a pestle, or with the feet in a concrete pit, until the mass containing oil, fibreand unbroken nuts is obtained. After kneading, the mass is sieved to remove the nuts and fibre and theliquid is then boiled to separate the oil and water. Oil then floats to the surface and is skimmed off.
This process yields oil with lower fatty acid that is more acceptable for use in cooking.
The traditional methods of palm oil production have an extraction efficiency of less than 50
percent, is labour intensive and time-consuming. Fats and oils from palm oil are critical to the wellbeing of many rural communities as a source of energy. The production of palm-oil provides animportant source of income for the rural women of south-eastern Nigeria not only in direct production
work but also through processing of secondary products such as soap, snack foods, palm kernel
cakes and cosmetics.
The major problem perceived by many of the women entrepreneurs in traditional oil processing
is the tiring nature of the work. This has made young women reluctant to go into the business or evento help their mothers in the production. A girl in Akpugo community has this to say concerning taking
up the enterprise as an occupation:“My God cannot allow me to remain in this village as a palm-oil processor. The whole stage of
production is suffering from fetching firewood for boiling the palm fruits to pounding in mortar or withthe legs. It can make a young girl become instantly old and scraggy – so that no man will want tomarry her. I would rather go to do any menial job in the town rather than stay in this village as a palmoil processor”. (FGD with girls at Akpugo Community, Nkanu East LGA, 1999).
However, some oil mills have been established in most of the rural areas, but these seem to bepresenting a threat to the women’s incomes. This is because the men (husbands of the women) who
traditionally possess the ownership rights to the palm trees, now sell the palm fruits and bundles
directly to the mills for instant cash, instead of leaving it to their wives for processing. Their major
reason is that women (their wives) do not give them accurate accounts of the palm oil sold after
processing. Also, women are deprived of the income from palm kernel which traditionally was thewomen’s personal reward for processing palm fruits into oil. It also denied the household the income
from the palm oil, as the men tended to keep the money from the sale of the palm fruits for themselves.
In order for the women to compete with the larger mills, and for the younger women to stay inbusiness, it is crucial that they have access to improved but scaled-down oil extraction technologies.
This would lead to enhanced rural employment, improve the women’s income and relieve some of
their arduous tasks.
Micro credit and other support services would help in the purchase of raw materials, even fromtheir husbands, and after processing generate higher incomes. Of importance is the fact that thetechnology designed for the rural women should not be the type that reduces or changes the qualityof the palm oil they produce which consumers perceive as better than that from the mills. If this is notachieved, women will lose the local advantage they enjoy and hence lose income.

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SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
Cases of loss of jobs by the rural women as a result of introduction of modern oil presser was notwidely reported as a major consequence of change. The major problem has been that of reduced
income; because many women (especially young ones) are reluctant to join the enterprise. However,
the technology recommended for adoption at the rural level should involve the rural craftsmen indesign and construction, as they can make it sustainable by providing local maintenance. This was
the type of success story from Senegal palm oil processing (Jacobi, 1983). But the marketing problems
similar to those suffered by women in Senegal as a result of bad roads, which exposed them to
exploitative middlemen, should be tackled. This is because, most of the palm-oil processing
communities visited lacked all-weather roads, which exposed them also to similar exploitation.

Local Brewing
Brewing of local beer by women is not, strictly speaking, a traditional enterprise of the Igbo women ofSouth-eastern Nigeria. The skill was learnt from women from the middle-belt and northern Nigeria,
who traditionally brewed a beer called Burukutu. Due to an interchange of culture, some Igbo womenlearnt it, and it is becoming popular in many communities.
Burukutu is brewed by fermenting millet for about five days. It is then ground, sieved and boiled
overnight in a large earthen pot or metal drum. The product, watery but thicker than refined factorybeer, has a similar intoxicating effect. Many consumers prefer it to factory beer, as it is more naturaland does not have the side-effects of factory beer.
This Burukutu has come to provide a lot of women with jobs, especially those living in rural andperi-urban areas. The annual growth rate in women employment in the enterprise is about 8 percent,while young girls and older women are keen on learning the trade. Many young girls learn it when
helping their mothers in the local enterprise. A woman in this enterprise confirmed the lucrative natureof the business: “Our problem is how to produce this Burukutu (BKT) in sufficient quantities as thedemand is high. You know that factory beer is becoming very costly for the poor and even for the
middle-income consumers, as one bottle of the least quality beer is above N50. But a similar bottle of
BKT is just N20, yet is gives you the same effect. Again, unlike your beer, it has no side effects; rather
it nourishes the body”. (Woman focus group discussant, Enugu South LGA 1999).
The major problem with BKT productions relates to the short shelf-life. If not consumed within a
few days of production, it loses its taste or goes sour, especially where there is no refrigeration, as inmany rural areas. This has affected the marketing of the product, especially in places far from thepoint of production.
However, it is a micro-enterprise which ought to be supported with credit, and other facilities. In
addition, the production technology should be upgraded. This is especially with regard to use of
firewood in boiling, which apart from adding to the production costs, is not environmental friendly.
The major technology change in this enterprise has been the introduction of grinding machines
for the millet. Initially, grinding was done with stone, which was extremely arduous. Though thegrinding machines were mainly operated by men, they have helped in increasing the production levelof the women brewers, hence giving them higher incomes. Technical change at that level cannot be
blamed for any problem related to such production.

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Garri Processing
Garri, which is processed from cassava tuber, is one of the major foods traditionally produced by Igbo
women of south-eastern Nigeria. Virtually all women in every Igbo community know the skill of garri
production. However, some communities are noted to produce garri more than others for commercial
purposes. Such communities include Nenwe and Nomeh in Enugu State, and Nkalagu in Ebonyi
State. Data and information on this important women’s enterprise were collected from thesecommunities.
Garri production involves peeling, washing and grating of cassava roots. The grated mass isplaced in a large cloth or fibre bag and weight is placed on the bag to press out the liquid. It is allowed
to ferment for three to four days in the sun. When sufficiently dry (about 50 percent water content) the
pulp is removed from the sack and sieved to remove fibrous material.
After sieving, the product is roasted, by constantly stirring a small amount in a shallow pan overheat. The roasted product is spread out to dry further and cool, producing a light, crisp, free-flowing
granular powder which is creamy white in colour – garri. Shelf-life ranges from a few weeks ormonths to more than one year depending on packaging and moisture content.
The problem with the traditional method of processing is that the peeling and grating techniques
are time consuming. Knife cuts on hands and from hand-grater are frequent. Also during roasting, thewoman who stirs the garri on a fire endures a lot of heat and smoke. She is also exposed to cyanide
gas for long periods.
Improved technologies for garri processing have emerged in the area, especially in the past two
decades with positive and negative consequences.
Diesel powered mechanical graters and hydraulic de-watering presses have replaced handgrating methods. The positive effect of these on the women processor is that they have reduced
processing time and the drudgery associated with the traditional methods. The mechanical graterand press introduced in the villages studied had increased garri production. The time needed for
grating, considered one of the most laborious steps in the process, had been greatly reduced. Also,de-watering time of three to four days had been reduced to half an hour. A woman processor has thisto say of the positive effects of these technologies: “Garri processing nowadays is one of the easiest
enterprises a women can embark on. We suffered when some of us were producing garri some 40
years ago, and even after the civil war in the 1970s when no such machines existed. The problem wehave now has to do with roasting as the fire tends to smoke us like fish.” (Woman focus group
discussant from Nenwe in Aninri LGA)
However, the high cost of the technologies resurfaces as only men own and operate the machines.
It is only in a few communities that women’s co-operatives benefited from a UNESCO rural womendevelopment programme when they received processing machines. Such women now hire a male
operator. But communities who were not so lucky find themselves exploited by men who own all themachines, and who are, in most cases, in unions that fix prices for any operation. At times the locationof the processing factory is too far from the village.
One of the most negative consequences of the introduction of the garri processing graters andpresses was that work traditionally done by women became men’s work. This transfer of control
resulted in transfer of income. Women seemed to have lost an important income source as one of thewomen FGD respondents says:

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SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA“Before the introduction of this mechanical grater and press, many indigenous women who could
not start their own garri processing enterprise did the peeling, grating and de-watering for others at afee. Such women cannot get the employment. Technology has also adversely affected the quality ofgarri we produce. This one-hour de-watering before frying without fermentation is not good enough”.
(Woman discussant from Nomeh in Nkanu East LGA, 1999)
According to the women, the short time for de-watering has made for more production of garri, of
less quality. Fermentation enhances the quality of garri by reducing the cyanide content. Such garriis rarely found in the urban markets where the demand for garri is usually higher than supply. It can
only be got in rural markets, but the production is discouraging because it does not attract acommensurate price.
Many young women are involved in garri processing and marketing, hence the need for promotionof the garri industry as a women’s enterprise.

Farm and Non-Farm Linkage Implications of Changes in Traditional Non-Farm
Women Technologies in South-eastern Nigeria
This study has identified varied technological changes that have occurred in the non-farm sector of
the rural economy of the area studied. But it seems that these technical changes have not led to
sufficient and effective rural inter-sectoral linkages with the farm sector that are capable of engenderingrural economic growth, and hence higher quality of life for the rural entrepreneurs’ households.
The reasons for this may be associated partly with the perceived nature and structure of linkagesin the rural economy of south-eastern Nigeria, the negative consequences of technical change andother social factors neutral to technical change – both of which hindered rural linkage effects. Notethat rural linkages are here used to describe the manifold interactions between farm and rural nonfarm activities in a developing economy (Renis and Stewart, 1987). The linkage types identified in thestudied communities and the problems that tend to hinder their development and exploitation of theirbenefits are discussed.
1) Consumption Linkages – Where incomes generated by activities in one sector lead to demand for
output in another sector. This may operate from farm to non-farm and conversely. The fact that the
regression function of the effects of technical change variables on expenditure level of women
entrepreneur has a coefficient of determination R2 as 65 percent shows that there were highconsumption linkages as a result of technical change.
However, much of such consumption expenditure was not on rurally produced goods and services.
For example, more than 88.9 percent of the women households spent less than N5,000 on food, as
most of the households produced much of what they consumed. The study estimated that over 80
percent of the rural consumption expenditure was on urban manufactured goods and services,
indicating a high rural income leakage to the urban areas, thus retarding rural inter-sectoral
consumption linkages.
For example, the women pot-makers of Isiagu in Ebonyi State are under threat from plastic
products which households seem to prefer to earthen ware for domestic use. Women in Abia Stateprefer textiles from Aba factory to the Akwete hand-woven cloth because the former is cheaper.
Therefore, much of the rural generated income was spent on urban mass-produced goods, with theobvious result of weakening rural consumption linkages through such rural income leakages.

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2) Production Linkages – This may be backward or forward. Backward production linkages,
which occur where productive activity in one sector requires input from another such as hoes andmachetes, fertilizer for farming and earthen pots, was high in favour of urban industrial outputs. For
example, fertilizer and other agro-chemicals and biological technologies represented over 75percentof the cost of variable farm inputs in the area studied. Given the high social and economic costassociated with the use of such technologies in agriculture, effective backward linkages between
farm and non-farm activities can hardly be achieved. The high economic cost of using thesetechnologies instead of resource-conserving ones represented a leakage out of the rural economy
and a weakening of backward linkages. Also, the occasional displacement of such rural entrepreneurs
as blacksmiths and rural labour force by tractorization has adverse social effects on the inter-sectoral
linkages and on rural economic growth.
Forward Production Linkages – which occur where the production of a commodity provides
supplies for the productive activities in other sectors, was also identified in the areas. This was mainly
in form of processed and semi-processed agricultural products such as palm-oil, garri, and locallybrewed beer, most of which serve as inputs to other productive activities. Forward production linkages
tend to be the most visible in the area and if well harnessed, could lead to high rural economic growth.
The reason being that most of the production processes here made less use of urban purchased
inputs, and the products have less direct competition with the urban mass-produced industrial products.
Rather, such rural products enhance backward linkages with urban industries which yield income forthe rural economy. Forward production linkages should be encouraged in the area, and the problems
associated with technology change in the enterprises which tend to hinder the growth should betackled.

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Chapter Five
Summary of Findings, Conclusions andRecommendations
Summary of Findings
Changes in technology in traditional non-farm women enterprises have occurred over the years withpositive and negative socio-economic consequences on the rural entrepreneurs. But technical change
has been blamed for almost all the problems of the rural entrepreneurs wrongly or rightly. Researchneeded to be done, therefore, to identify the socio-economic problems of the rural women
entrepreneurs and isolate those which can be linked to technical change and those which are
related to both the women entrepreneurs’ personal characteristics, agencies or institutions. This
could help in formulating appropriate technology policy interventions. The main thrust of this research,
therefore, was the identification of the socio-economic consequences of technological change on
the Igbo traditional non-farm women entrepreneurs of south-eastern Nigeria.
A total of 250 rural non-farm women entrepreneurs in the five Igbo-speaking States of Nigeria,
namely, Abia, Anambra, Ebonyi, Enugu, and Imo supplied the quantitative data, while about 50
women of different ages supplied the qualitative data. Data collection involved use of descriptive
statistics, cross-tabulations, the multiple regression model, the inter-correlation matrix model andfactor analysis. Analysis of qualitative data from the FGD was by interpretation of implicit and explicit
folk concepts, and verbatim quoting of the discussants views. The major findings for the research are
as follows:a)
Most of the women entrepreneurs were above 45 years of age, with various levels ofschooling but mostly primary education. Most of the women were married and were mainlyof Christian religious faith. Household size was mostly 4-8 persons, while most households
were headed by a man (father). Household out-migration was mainly of male children,
and age at migration was mainly 15-20 years and the areas migrated to were mainly urban.
Households lived in areas purely designated as rural, where social facilities were mostly
unavailable or available but not functional or both. Most entrepreneurs were engaged
purely in non-farm activities while some combined both farm and non-farm activities invarious degrees. Only about 17 percent of the women earned N10,000 and above fromtheir rural non-farm activities. Most of the incomes earned were spent on consumption
goods and services with negative saving in most cases.
b)Technical changes have occurred in many rural enterprises. Still, they were not available
to some. The most available change was in cloth weaving, where up to 73.2 percent of thewomen had adopted the changes in one form or another. Generally, up to 44.9 percent of
the non-farm women entrepreneurs had not adopted any change in their production

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c)

d)

e)

f)

processes. Up to 27.1 percent of the women entrepreneurs did not wish their daughters
would take up their enterprise as an occupation, while 41.7percent of them were undecided.
Chi-square results from cross-tabulation analysis show that the percentage adoption rate
positively related to income level realized from the non-farm enterprise. Also X2 results
show that age was inversely related to level of adoption of available changes, while
educational level did not have a positive relationship with level of adoption, hence implyingthat educational level was not the major factor that influenced adoption of technical change
among the women entrepreneurs. It was also found from the X2 results that there was no
significant relationship between adoption level of technical change and number ofapprentices in the local enterprise. The percentage of technical change adopted was also
found to influence farm size for women who combined farm and non-farm activities. This
has linkage implications between the two rural sectors especially as it was also found that
there was a positive relationship between the percentage of technical change adopted
and the level of income flow from non-farm to farm enterprises.
The coefficient of multiple determination (R2) resulting from regression analysis of theeffects of technical change variables on the level of income of women entrepreneurs was
42.1 percent. This percentage shows that the proportion of the observed variability (inlevels of income generated by women entrepreneurs) explained by the combined effectsof the independent variables was high, and was significant at 99percent level of confidence.
It shows that the level of technical change adopted by an entrepreneur – in percentage –(X2) positively influenced the annual income from the enterprise. Also the amount spent inprocuring raw materials for the technology and the extra labour requirements due to theadopted change both had a negative influence on the level of income that accrued to thewomen entrepreneurs. From the regression results, the number of positive effectexperienced from new technology had positive and significant influence on income, while
the number of negative effects experienced by entrepreneurs as a result of technicalchange, had a negative influence on their incomes.
On the effects of technical change variables on expenditure levels of women entrepreneurson consumption goods (taken as index of standard of living), the coefficient of determination
R2 was 65 percent; which was a good fit. From the result, the estimated technical change
in a given enterprise and the estimated level of technical change adopted by an entrepreneur,both had positive and significant influence on the standard of living of the entrepreneurs interms of their monthly expenditure on consumption goods and services. Extra labour
requirements as a result of the adopted change (usually male labour) had a significant but
inverse relationship on the level of consumption expenditure of the women, while thenumber of positive effects as perceived by a change, had a significant and positive
relationship with their consumption expenditure.
The coefficient of multiple determination of the effects of technical change variables onlevel of employment on non-farm women enterprises in the past two years was low (R2 =23%). This cannot be regarded as a good fit. However, the level of estimated technicalchange in an enterprise, and the level of technical change adopted by an entrepreneur, alland positive and significant influence on the level of employment in the enterprise.

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SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
g)

h)

i)

j)

k)

On the effects of technical change variables on the female out-migration rate fromentrepreneurs households, the coefficient of determination R2 was 47 percent. This
percentage is high enough and can be regarded as a good fit since it was also significant
at 99 percent level of confidence. The six significant variables were level of technicalchange which was inversely related to household out-migration, extra amount spent on
raw materials as a result of adoption and extra amount of labour needed after adoption,
both of which inversely influenced the rate of household out-migration. The number of
positive contributions from adoption and the number of negative effects associated withadoption, had inverse and positive, but significant, influences on the level of household outmigrations, respectively.
The income flow from non-farm to farm as a result of technical change variables shows acoefficient determination of 63.5 percent; which was an indication of a strong influence,
and was significant at 90 percent confidence level. From the result, the level of technicalchange adopted by the entrepreneurs showed a positive and significant relationship withthe amount of money invested from non-farm to farm. Also, the income that accrued to theentrepreneurs as a result of adoption shows a positive and significant influence on theincome that flowed from non-farm to farm; while the number of positive effects as apercentage by the entrepreneurs inversely related to income that flows from non-farm to
farm.
Inter-correlation analysis was done in order to establish how the variables related to technicalchange influenced some socio-economic attributes of the rural women entrepreneurs.
Most of the variables showed a high level of relationship among themselves. For example,
the level of technical change adopted by women entrepreneurs showed a strong
relationship with the average number of women that entered the enterprises in the past two
years. Also, the average monthly income from enterprise had a high and positive relationshipwith the level of adoption of technical change.
Varimax rotated factor matrix identified a production output relationship, a socio-economic
relationship and an employment and future of enterprises relationship as the three criticalfactors that suggest positive effects of technology change on the women entrepreneurs inthe study area. On the other hand, inappropriate technology issues, social and marketing
issues and sustainability and environmental issues were identified as critical factors that
related to the negative effects of adoption of innovations in traditional non-farm women’senterprise. Furthermore, adoption, adaptation, extension and rural infrastructure issues
were named as the sensitive factors that hindered and may continue to hinder adoption of
new technology by the women entrepreneurs of the area. Policy issues to address each of
the identified factors have been suggested.
Ten social and technical problems which tend to inhibit adoption of innovations in nonfarm enterprises by women entrepreneurs and which relate to negative consequences ofadoption in the area were identified to include: inappropriateness of technology, generalunawareness, incorrect identification of adoption domain, better local practices, generationof new problems, high cost of innovation, poor infrastructure, and unsustainability ofinnovation. Based on these problems, technical change was assessed either as neutral or

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l)m)biased to these problems and their associated socio-economic consequences on thefarm entrepreneurs.
Three types of non-farm rural linkages were identified in the study area as consumption,
backward and forward production linkages. The one that appeared to be more beneficial
to rural entrepreneurs was forward production because it had minimum leakage effects on
the rural economy. Implications of each type of linkage as they related to technical changes
in south-eastern Nigeria were highlighted.
A discussion on the five rural enterprises studied was done with findings on flow to makethem more relevant to the people of the area, particularly women.

Conclusion
Being dynamic, technology has resulted in varied changes in many aspects of non-farm production
processes, especially in the last century. Faster changes are occurring and will continue to occur inrural enterprises. The challenge of the new millennium must border on intensive and extensive
production processes in the non-farm sector which must be achieved if the needs of rural women
entrepreneurs in the Third World are to be met and if the younger generation are to make a living fromthe enterprises. The answer is in rapid technological change. The consequences of technologicalchange affect people and nations differently depending on the method of adoption and adaptation,and on the manipulation of other social and economic variables relating to technical change. For
example, chemical, electrical and mechanical technical change gave rise to the Asian Tigers, butleft most parts of Africa confused. Part of the Asian success story stemmed from the efficient andeffective harnessing of inter-sectoral rural linkages. While not asserting that non-farm technical change
has not got negative socio-economic consequences (some of which the research has identified), it
is neutral to most of the perceived negative consequences. Rather, most of the socio-economic
problems of the rural entrepreneurs are mainly systemic, relating mainly to factors not associatedwith technical change. Rather, they affect the effective and efficient adoption, adaptation and use ofinnovation for the better welfare of the entrepreneurs, especially in the rural areas.

Recommendations
Based on the findings and conclusions, the following recommendations are made:i)
The identified social issues relating to the rural women entrepreneurs, such as low level of
education, gender bias, high rural out-migration rate of young girls, and poor social
infrastructures that affect the efficient and effective exploitation of the benefits of technicalchange should be tackled holistically by government and non-governmental organizations(NGOs) in their rural development programmes. Specifically, there is need for a more
efficient and effective adult literacy programme in the rural areas; vigorous campaigns
against all forms of gender bias, and policies and programmes to stem the tide of rural outmigration of youths for urban opportunities. The poverty alleviation programme (PAP) of theincumbent government is called upon to address these issues.
ii)
The positive consequences of technical change rooted in efficient adoption and adaptation,and effective rural linkages can only be exploited by provision of adequate and vital rural
infrastructures and amenities such as all-season roads, electricity supply, portable water,

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SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA

iii)

iv)

v)

vi)

vii)

viii)

among others. Past attempts at rural enterprise development where government agencies
and non-governmental organizations install, for instance, modern processing machinesthat are electricity dependent in some communities for the rural women, without electricity
for its use, is an example of conscious misplacement of priority, which cannot lead topositive harnessing of technical change.
For technical change to be appropriate and relevant, it must address the major problems
which the old technology failed to address. This means that researchers and developers
especially in Nigeria, must have the end-users of technology in mind and must not ignore
the indigenous technical knowledge system of the local people, or their reasons for local
practice.
While it is assumed that technical change is neutral to economic cost of adoption,
researchers and developers in Nigeria should aim at resource-conserving, and cost-effective
technologies, with high marginal revenue productivity, especially for the women. This is
more so as some technologies yield benefits only in the long future.
Most of the problems associated with innovation result from faulty adoption processes, due
mainly to poor extension services. Inadequate information on the credibility and benefits ofinnovation tend to obliterate hope for the entrepreneurs. Non-farm extension programmes
are imperative for rural entrepreneurship. Non-farm extension programmes similar to
those of the farm should be established at all levels of government in Nigeria to aid the nonfarm women entrepreneurs.
Research and development practitioners must pay attention to non-farm technologies thatare environment friendly, sustainable, those that do not displace women, and do not exploit
the labour of women and children. Some of the machines such as the garri processing
machines were found to be too heavy and complex for the women to operate, leading to
men taking up the enterprise at the disadvantage of the women.
Radical revision of the nation’s technological policy to recognise and favour the indigenous
technical and craft industries is imperative. This will enhance the development of simple,
energy-conserving machines that are suitable to local conditions, and are less labour
displacing. This study recommends the establishment of community polytechnics to exploit
the indigenous technical knowledge especially of communities noted for technical knowhow. Such polytechnics should be linked to the relevant research institutes, and institutionsof higher learning, and other agencies for effectiveness. This has been the practice insome successful Asian countries.
Rural credit and other forms of financial assistance are critical to group adoption of
innovation. Unfortunately, the rural credit (micro-credit) system as practised in Nigeria in
the past by programmes such as Better Life for Rural Women and Family Economic
Advancement Programme (FEAP) were found to be faulty and ineffective. They used the
big commercial banks that in the past failed in rural credit delivery, rather than community
banks and other rural agencies. Again, the programmes were not sincere in efforts toalleviate the financial problems of the rural women entrepreneurs. A more sincere approachis recommended.

60

ATPS WORKING PAPER NO. 40
ix)

x)

xi)

The importance of genuine rural associations and societies in women’s enterprisedevelopment has been identified. To this end, the States and local authorities should
provide the enabling environment for the formation of such groups in the rural areas, basedon their mutual felt needs and philosophy. Past attempts where groups were formed basedon the government’s laid down conditions before qualification for loans was faulty. Most
women’s enterprise groups in the study area could not secure loans mainly as a result.
A discussion on the five rural enterprises studies based on FGD exposed the opinions andperceptions of the women entrepreneurs, the aged and the young on the rural enterprises
in terms of the problems, prospects, and some imperatives on how to improve and preserve
those enterprises for the future.
Forward production linkages which were identified as more beneficial in the area should
be encouraged. To this effect, rural enterprises such as palm-oil processing, garriprocessing, among others, that provide supplies for productive activities in other sectors
should be encouraged and exploited. These linkage effects, if well harnessed, could leadto high rural economic growth. This must be by tackling most of the problems associatedwith technical change in such enterprises.

61

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63

APPENDICES

Appendix 1: Testing for Multi-Collinearity By Use of Correlation Matrix of Independent Variables Related
X1

X2

X3

X4

X5

X6

X7

X8

X9

X1

1.000

X2

-.084

1.000

X3

.248

-.333

1.000

X4

.366

-.046

-.188

1.000

X5

-.020

-.282

.291

-.007

1.000

X6

-.161

-.027

-.048

-.122

-.225

1.000

X7

.164

-.108

-.026

-.367

.006

-.102

1.000

X8

-.194

-.243

.416

-.253

.190

.057

.031

1.000

X9

-.103

.077

-.005

.199

.086

.128

-.339

-.098

1.000

X1 0

-.130

-.311

.137

-.059

.461

.052

.172

.290

-.157

Appendix 2:

1.000

ANOVA for Testing the Combined Efforts of the Nine Independent Variables Related to Monthly
Income of Non-farm Women Adopters of Technologies in South-eastern Nigeria
ANOVA

DF

Sum of Sq

Mean SquareF

Multiple R 0.6491

Regression

919.3807

2.15344.3684R Square 0.4213

Residual

54

26.6192

0.49290.0003Adjusted RS Sq. 0.3248

TotalStandard Error 0.7021

ANOVA = Analysis of Variance
DF = Degree of Freedom
F = F-statistics
Significant at 1%
Source: Field data

X963

Appendix 3: ANOVA for Testing the Combined Efforts of the Nine Independent Variables Related to Technical Change

on Level of Expenditure by Non-farm Women Entrepreneurs
ANOVA

DF

Multiple R .8057

Regression

R Square .6508

Residual

Adjusted R2 .3017

Total

9

9

Sum of Sq

Mean SquareF

974703839.40

108300426.60

1.864

522868043.11

58096449.23

18

Standard Error 7622.102

ANOVA = Analysis of Variance
DF = Degree of Freedom
F = F-statistics
Significant at 10%
Source: Field data

Appendix 4:

ANOVA for Testing the Combined Efforts of the Nine Independent Variables Related to Female
Out-imigration from Entrepreneurs Household
ANOVA

DF

Sum of Sq

Mean SquareF

Multiple R .8057

Regression

9103.478

11.497

4.405

R Square .6508

Residual

44

114.8363.609

0.0004

Adjusted R2 0.3667

Total

Standard Error 1.6155

ANOVA = Analysis of Variance
DF = Degree of Freedom
F = F-statistics
Significant at 1%
Source: Field data

53

Appendix 5: ANOVA for Testing the Combined Efforts of the Nine Independent Variables Related to Technical Change
on Sever of Employment in Non-Farm Enterprises
ANOVA

DF

Sum of Sq

Mean SquareF

Multiple R0.4817

Regression

937.797

4.199

1.947

R Square 0.2321

Residual

58

125.085

2.157

.0627

Adjusted R2 0.1128

Total

67

Standard Error 1.6155

ANOVA = Analysis of Variance
DF = Degree of Freedom
F = F-statistics
Significant at 10%
Source: Field data

A ppendix 6: ANOVA for Testing the Combined Efforts of the Nine Independent Variables Related to Technical Change

on Flow of Income From Farm to Non-Farm Enterprises
ANOVA

DF

Sum of Sq

Mean SquareF

Multiple R 0.7967

Regression

8950510289.895

118813786.236

2.172

R Square 0.6347

Residual

10547061592.631

54706159.263.125

Adjusted R2 0.3425

Total

Standard Error 2.1718

ANOVA = Analysis of Variance
DF = Degree of Freedom
F = F-statistics
Not Significant at 10%
Source: Field data18

Appendix 7: Inter-correlation Matrix of Selected Variables on Consequences of Adoption of Non-Farm Technical
ChangeVariables V1 V2

V3

V4

V5

V6

V7

V8

V9

V10

V11

V12

V13

X1

1.000 -.364

X2-.264 1.000

X3-.116 .442 1.000

X4.204 -.081 -.090 1.000

X5-.050 .058 -.091 -.076 1.000

X6-.097 .122 .163 -.050 .077 1.000

X7.187 -.162 .021 .520 -.314.177 1.000

X8.117 -.034 .060 .303 -.041.247 .542 1.000

X9.094 -.137 -.187 .021 -.087 -.090 .013

.081

X10

.304 -.099 .045 .030 -.004.190 - .041 1.000

X11-.257 .162 -.124 -.274 .249 -.096 -.491 -.407 -.134 -.115 1.000

X12-.184 -.051 .041 -.007 -.186 -.225 -.054 -.010 -.043 -.015 -.305 1.000

X13.055 -.066 -.086 .141 -.019 -.296 -.103.159.248 -.017 -.162 .425 1.000

X14-.002 -.033 .167 -.072 -.263

.137

.114

.300

.021 -.004 -.306

X15.099 .099 .117 .066 .125 -.023.105.183 -.006 -.118 -.160

Variables V16 V17

V18

V19 V20.029 .198

V16

V22

V23

V14

V15

V16

1.000

V24

V25

V26

V27

.291 .221 1.000.106 .233 .124 1.000
V28

V29 V30 V31

X16.038 .164 .112 .258 -.054

.361

.307

.441

.011 -.051 -.415.148 .005 .138 .233 1.000

X17-.185 -.198 -.254 .041 .251 -.015

.512

.419

.200 -.219 -.073.117 .087 -.060 .333 -.0135 1.000

V18-.035 -.095 -.079 -.015 -.001.107 -.015 -.088-.163 .054 .132

.108 -.188 -.023 -.308 -.107 -.157 1.000

V19-.141 .088 -.220 .095 .172

.007 -.157 -.045.001 -.304 .108

.123 .027 -.017 .282 .199 .261 -.053 1.000

V20-.138 .028 .129 -.252 .417

.105 -.404.242-.428 -.063 .253

.123 -.036.073 .331 -.0129 .316 .237 .140 1.000

V21-.126 -.088 -.051 .021 .150

.093 -.020.178-.018 -.190 .046

.124 .082.089 .179 -.118 .536 -.069 .039 .3261 1.000

V22-.037 -.249 -.069 -.035 .218 -.022 -.055.178-.096 -.053 -.088 -.061 .014 -.218 .046 -.013 .210 .225 .079 .212 .201 1.000

V23.054 .060 .026 -.098 -.099.105-.213 .011 -.113 -.226 .184

V24-.132 .260 .1386 -.063 -.118 .106 -.018 .075

V1
V2‘
V3
V4
V5
V6
V7
V8

.001

.074

.045 .159 -.079 -.394 -.117 .107 -.076 .141 .142 1.000.136 -.094 -.109 .365 .185 .098 .174 .284 .065 .050 .180 .064 .059 -.107 .026 1.000

Age of entrepreneur
Educational attainment
Type of religion
Family size
Number of social infrastructure in an area
Amoung of time spent in entreprise per day (hrs)
Average monthly income from enterprise (Naira)
Average monthly expenditure on household (Naira)

V18 Number of years adopted
V19 Membership/Non-membership of a society
V20 Amount of financial assistance received
V21 Number of people that entered enterprise
V22 Number of people that left enterprise
V23 Percentage of non-farm income invested in farm business
V24 Number of positive effedts experienced after adoption

SOCIO-ECONOMIC C ONSEQUENCES OF T ECHNOLOGICAL C HANGE ON THE R URAL N ON-FARM I GBO W OMEN OF SOUTH-EASTERN N IGERIA
V9
V10
V11
V12
V13
V1 4
V1 5
V1 6
V1 7

Number of female out-migrations from household
Age at time of departure (years)
Level of education prio to migration
Method of Technical acquisition
Length of time in formal technical training
Amoung of family labour used in enterprise
Number of apprentices in the past 2 years
Amount of working capital (Naira)Level of technical change adopted

68

ATPS WORKING PAPER SERIES
No.Title Publication

Authors

1

The Effect of Economic Reform on Technological Capability

2

Methodological Issues in Science and Technology Policy Research

4
5
6
8

Rehabiliation in the Manufacturing Sector in Tanzania
Agricultural Policy and Technology in Sierra Leone
Effectiveness of Agricultural Research Extension in Sierra Leone
Generation and Utilization of Industrial Innovation in Nigeria

T. Adeboye
M. S. D. Bafachwa
O. A. Bamiro
T. Adeboye
N. Clark
S. M. Wangwe
C. Squire
A. K. Lakoh
O. Oyeyinka
G. O. A. Laditan
A. O. Esubiyi

9

Irrigation in the Tuli Block, Botswana Water Conservation Techniqes
or Optimal Strategies
Endogenous Technology Capacity and Capability Under Conditionsof Economic Policies of Stabilization and Structural Adjustment
Technology and Female-Owned Business in the Urban Informal
Sector of South-West Nigeria
Technology Innovations Used to Overcome the Problem of Resource
Scarcity in Small Scale Enterprises
Financing of Science and Technology Institutions in Kenya During
Periods of Structural Adjustment
Impact of Economic Liberization on Technologies in Enterprises
Processing Agricultural Produce
Technology and Institutions for Private Small and Medium Firms
Institutiona Reform, Price Deregulation and Technological Change
in Smallholder Agriculture
Adoption of Agricultural Technologies by Rural Women under the
Women-in-Agriculture Program in Nigeria
Electrical Power Utilities and Technological Capacity Building in
Sub-Saharan Africa
Investigation into Factors that Influence the Diffusion and Adoptionof Intentions and Innovations from Research Institutes andUniversities in Kenya
The effects of Economic Reforms on the Quality on the Quality ofTechnological Manpower Development in Nigeria
Issues in Yam Minisett Technology Tranfer to Farmer in Southern
Nigeria

10
11
12
13
14
15
16
17
18
19

20
21

22
23

Technological Respone to Telecommunication Development: A
Study of Firms and Instituions in Nigeria
Gender Differences in Small Scale Rice Farmers’ Access toTechnological Inputs in Enugu State of Nigeria

I. N. Masonde
S. E. Chambua
R. O. Soetan
C. W. Ngahu
M. Mwamadzingo
P. Madaya
B. Oyeyinka
E. C. Eboh
D. S. Ugwu
Brew-Hammond
Bwisa, H. M. andGachuiH. E. Nnebe
Michael C. Madukwe
Damain Ayichi
Ernest C. Okoli
Adeyinka F. Modupe
David Nwoye Ezeh

24
25
26

27

28
29
30

31

32

33

34.

35.

36.

37.

38.

Adoption of Sustainable Palm Oil Mini-Processing Technology in
Nigeria
Domestic Energy Situation in Nigeria: Technological Implications
and Policy Alternatives
Technological Capability in the Nigerian Leather Industry: A
Firm-Level Case Study

Agricultural Research and Delivery in the South-Eastern Highlandsof Ethiopia: A Case Study of the SG-2000 Approach in Hitosa
District
Impact of Computer Technology Adoption on Banking Operations
in Nigeria
Agricultural Technology Adoption on Small-Holder Farmers: Policy
Policy Options for Nigeria
Donor Funding and Sustainability of Rural Water Supply andSanitation Technologies in Swaziland
Promotion of Production and Utilization of Ceramic Roofing
Materials in the Informal Housing Industry in Sierra Leone
Technology Transfer and Acquisition in the Oil Sector and
Government Policy in Nigeria

Acquisition of Technological Capability in Africa: A Case Studyof Indigenous Building Materials Firms in Nigeria

Analysis of Indigenous Knowledge in Swaziland: Implicationsfor Sustainable Agricultural Development
Analysis and Comparison of the Agricultural Development Programmeand University Agricultural Technology Transfer Systems in Nigeria

Extension Services and Enterprise Development: Study of Technology
Extension Services to Small nd Medium Size Manufacturing Firms in Naiorbi

Nkechi Mbanefoh
Olabisi I. Aina
Uka Enenwe,Enwere dike,
Anthony Ihuoma,
Mike Duru.

G. Yiemene
A. I. Odebiyi
J. O. Olusi
Aja Okorie,
M. Mabuza,
U. Aja-Okorie.
Tamba Jamiru

R.I. Chima
E.A. Owioduoki
R.I. Ogoh
Yomi Oruwari
Margaret Jev
Opuene Owei
Musa A. Dube
Patricia J. Musi
M.C. Madukwe
E.C. Okoli
S.O. Eze
Frederick U. Ngesa
Justus M. Ombati
Milcah Mutuku

Women and Engineering in Nigeria: Towards Improved Policy Initiatives andIncreased Female Participation

A.J. Badekale

The Gender Variable in Agricultural Technology: A Case of Rural Farmers in
Machakos District-Eastern Kenya

Esther Njiro