TITLE
PAGE
THE IMPACT OF HUMAN CAPITAL ON LABOUR
PRODUCTIVITY IN MANUFACTURING INDUSTRIES IN
ENUGU AND ANAMBRA STATES, NIGERIA.
By
Anumudu, Charles Nnamdi
PG/Ph.D/2000/28777
Being a Ph.D Thesis Dissertation Submitted to the Department of
Economics, Faculty of Social Sciences, University of Nigeria, Nsukka,
in Partial Fulfillment of the requirement for the degree of Doctor of
Philosophy in Economics.
June 2010
i
APPROVAL PAGE
This Ph. D Dissertation has been approved for the Department of
Economics, Faculty of Social Sciences, University of Nigeria, Nsukka.
By
Professor F.E Onah
Professor C. C Agu
Supervisor
Head of Department
Professor L. C Ezeaku
Professor [Mrs.] P.C Onokala
External Examiner
Dean, Faculty of Social Sciences
ii
DEDICATION
This Dissertation is dedicated to:
My mother, Mrs J. A. Anumudu
And my wife, Mrs E.N Anumudu
iii
ACKNOWLEDGEMENT
How can one ever acknowledge academic debts satisfactorily? Knowledge is
as a result of a cumulative process spanning over many years and during
these periods, the individual passes through many people, institutions and
ideas. It is difficult to categorize these people, ideas and institutions and
where the influence of one stops, that of the other beings. But all the same, I
wish to express my profound gratitude and apparent happiness to my
Supervisor- Professor Felix. E Onah who had always read the manuscript
carefully and thoroughly and made specific suggestions as regards
improving the organization, style and clarity of materials in this work.
My gratitude goes to Professor Charles Chukwuma Soludo for the
approval of the topic before his meritorious appointment as the Economic
Adviser to the President, Chief Olusegun Obasanjo. He was my first
supervisor and his cordial approach to my initial problems and explanations
to some of the complex aspect of this research gave me the confidence and
courage to complete it in spite all odds. I am indebted to the entire academic
staff of the Department of Economics whose reaction in the proposal, work
in progress, and final report of this research provided the guidance and
direction needed for the arrangement and preparations of this dissertation.
The researcher acknowledges with appreciation the encouragement and
iv
immense contributions of the various manufacturing industries that were
used for this work. They were indeed helpful in the supply of data used for
this research.
To my wife – Eunice, I remember and appreciate in a special manner
all her support. I am grateful to my mentors, Hon Aniagboso B.C and Mr
Mbah M.E who have always reminded me the need to have my doctorate
degree and have always supported me in prayers and morally. Chinyere
Ugwuanyi and Ify Okonkwo typed the entire manuscript in a very careful
manner. Their efforts are very much appreciated.
Finally, I am indebted to the Lord Almighty for granting me the
stamina to withstand the wears and tears I had to pass through before
completing this research. Nevertheless, all error of omission and commission
in this work are my sole responsibility.
Anumudu Charles Nnamdi
PG/Ph.D/2000/28777
v
TABLE OF CONTENTS
Page
Title Page ------------------------------------------------------------------------- i
Certification ----------------------------------------------------------------------ii
Dedication -----------------------------------------------------------------------iii
Acknowledgement ------------------------------------------------------------iv
Table of contents ------------------------------------------------------------- vi
Abstract ------------------------------------------------------------------------- viii
CHAPTER ONE: INTRODUCTION
1.1
Background of the Stud ----------------------------------- 1
1.2
Statement of Problem ------------------------------------- 8
1.3
Aims and Objectives of the Study --------------------- 11
1.4
Statement of working hypothesis ----------------------- 12
1.5
The Relevance of the Study ------------------------------13
CHAPTER TWO: CONCEPTUAL AND THEORETICAL ISSUES
2.1
Human Capital Development --------------------------------16
2.2
The Link Between Education and Human Capital
Development ----------------------------------------------------21
2.3
Stylized Facts on Human Capital Situation in Nigeria- 26
vi
CHAPTER THREE: REVIEW OF LITERATURE
3.1
Theoretical Literature------------------------------------ 33
3.2
Empirical literature----------------------------------------- 56
3.3
limitation of Previous Studies --------------------------- 74
CHAPTER FOUR: RESEARCH METHODOLOGY
4.1
Theoretical Framework------------------------------------ 76
4.2
Outline of the model specification--------------------------79
4.3
Method of Estimation and Evaluation--------------------- 80
4.4
Data Sources and Measurement -------------------------- 82
4.5
Population of the Study --------------------------------------- 83
4.6
Instrumentation ------------------------------------------------ 83
4.7
Sampling unit -------------------------------------------------- 84
4.8
Sampling method and validity of instruction ----------- 84
4.9
Reliability ------------------------------------------------------- 84
CHAPTER FIVE: ANALYSIS AND EVALUATION OF THE RESULTS
5.1
Interpretation of Research Findings --------------------- 86
5.2
Model Coefficient (parameters) -------------------------- 86
5.3
Test for Model Adequacy ---------------------------------- 92
5.4
Test for Normality ------------------------------------------- 92
5.5
Test for Multicollinearity ----------------------------------- 92
vii
5.6
Test for Autocorrelation ------------------------------- 93
5.7
Test for Hetroscedasity ------------------------------- 93
5.8
Test of Working Hypothesis -------------------------- 94
5.9
Further Analysis – Principal Component Analysis 95
5. 9.1 KMO And Bartlett’s Test--------------------------------- 96
5.9.2
Communalities --------------------------------------------99
5.9.3 The Rotated Component Matrix -----------------------100
5.9.4 The Component Score Coefficient Matrix ------------ 101
CHAPTER SIX; SUMMARY, POLICY IMPLICATION AND CONCLUSION
6.1 Summary ---------------------------------------------------- 103
6.2 Policy Implication ------------------------------------------ 104
6.3 Conclusion --------------------------------------------------- 107
Result and Data Attached
Appendix 1: Questionnaires --------------------------------------109
Appendix 2: List of Industries ------------------------------------ 115
Appendix 3: References --------------------------------------------129
viii
ABSTRACT
This work discusses the effect of human capital on labour productivity in
manufacturing industries in Enugu and Anambra States. The study applied the
ordinary least squares and the principal component Analysis in the estimation. The
evaluation results show that human capital has a positive effect on the sectoral labour
productivity level of the industry. Training, Education, Medicare and Research are
strongly correlated with productivity. By all econometric standards the statistical
evidence showed that the linear regression model was adequate and the Jargue Bera
Statistic confirmed the fact. The principal componenant analysis adopted in the
research revealed that Onitsha Aluminum manufacturing company has the highest
impact of human capital. In the kmo and Bartlett’s Test conducted, all variables except
Medicare extraction communalities are greater than 0.5, and component matrix shows
that Training of all the components of human capital has the highest impact on labour
productivity. Moreover, there are indications of under investment of human capital in
some manufacturing industries. There is need to improve upon the level of investment
and productivity of human capital so as to produce positive efficiency effect for
productivity growth in the manufacturing industries
ix
CHAPTER ONE
INTRODUCTION
1.1. BACKGROUND OF THE STUDY
It is now a generally accepted view that human capital plays a key
role in the development of any nation. In fact, the differences in the level of
socio – economic development across nations is attributed not so much to
natural resource endowment and the stock of physical capital but to the
quality and quantity of human capital. Human resource development tends
to improve the quality and productivity of labour, which in turn, leads to
economic growth. Besides, acting as an important vehicle of achieving
equitable income distribution, human capital is also a potent means of
addressing the problem of poverty. In the words of Nwaobi, (1996)
“human resources constitute the ultimate basis for the wealth of the
nations. Capital and natural resources are passive factors of production”.
Human beings are the active agents who accumulate capital, exploit
natural resources, build social, economic and political organizations and
carry forward national development. Clearly, a country, which is unable to
develop the skills and knowledge of its people and to utilize them effectively
in the national economy, will be unable to develop anything else.
Economists had long realized the importance of human resource
development
in
the
development
process.
For
instance,
besides
1
emphasizing the importance of education at various points in The Wealth of
Nations, Adams Smith specifically includes the acquired and useful abilities
of all the inhabitants or members of the society in his concept to fixed
capital. Alfred Marshal also emphasizes the importance of education as a
national investment and, in his view, “the most valuable of all capital is that
invested in human beings. In spite of this awareness, most early
economists still regard physical capital as the main component of a
country’s productive wealth; they still relegate natural and human capital to
the background. It took the effort of Schultz (1961a) and others to
rediscover the importance of human capital, which has in a more recent
effort to incorporate investment in education into the mainstream of
economic analysis.
In its very general form, human capital refers to the aggregate stock
of a nation’s population that can be drawn upon for present and future
production and distribution of goods and services. It comprises the
essential variables (i.e knowledge, skills and attitude) available within each
unit of a nation’s human resource stock. The United Nations Economic
Commission for Africa (UNECA: 1990) describes human capital as the
knowledge, skills, attitudes, physical and managerial effort required to
manipulate capital, technology, and land among other things to produce
goods and services for human consumption. In other words, human capital
is the totality of human potentials (knowledge, skills, attitude, energy and
2
technology), inherent within a nations human capital stock. This, if properly
developed and harnessed, would yield a high level of labour productivity.
Human capital can therefore be conceived as a developed skill, knowledge
and the capabilities of all the people of the society and which are needed in
the labour market for the production of goods and services. In economic
terms, it could be described as the accumulation of knowledge and its
effective investment in the development of an economy (Harbison and
Mayer 1964)
Generally, human capital is developed in several ways. The first is
through formal education, involving pre-primary, primary, secondary and
higher education. The second is “in–service or on the job” training, which is
a systematic or informal training programme in employing institutions in
adult education programme and through membership of various political,
social, religious and cultural groups. The third way is individual, selfdevelopment. This occurs when individuals seek to acquire greater
knowledge, skills or capacities through preparation on their own initiatives.
Human capital can also be developed through improvement in the health of
the working population by means of better medical and public health
programmes and improvement in nutrition, which jointly increase the
working capacity of people on a man-hour basis as well as over a working
life. The improvement in formal education, health and nutrition can be both
a cause of productivity growth and a result of it. Finally, human capital can
3
be developed through importation of educated manpower, mostly technical
expertise and consultants. Of the various ways of human capital
development, formal education seems to be the most veritable.
Corvers (1994) discussed the four effect of human capital on labour
productivity: the worker effect, the allocative effect, the diffusion effect, and
the research effect. The effects are based on the studies of Nelson and
Phelps (1966), Welch (1970), Ram (1980) and Pencavel (1991), inter alia.
The work argue that the first and second of these effects underpin the
relevance of human capital for the productivity level, whereas the latter two
effects underpin the relevance of human capital for productivity growth.
Welch (1970) has explained the first of these, the worker effect (or
productivity effect). He assumes that firms produce only one good with the
production factor education, and that other resources are given. The worker
effect refers to the positive marginal productivity of education with respect
to that particular good. Workers with a high level of education are assumed
to be more efficient in working with the resources at hand, (i.e. these
workers produce more physical output). In other words, education
increases the effective labour input. Therefore, a better educated labour
force shifts the production possibility curves outward. According to Welch
(1970; 43) the worker effect is presumably “related to the complexity of the
physical production process”. The more complex the production technique
is, the more is the ‘room’ left for the worker effect to improve the (technical)
4
efficiency of production. An increase in the proportion of intermediate or
highly skilled workers relative to low skilled workers increases the
productivity level of physical units. Productivity shows output per unit of
input employed. Increase in productivity comes about from increased
efficiency on the part of labour.
The allocative effect points to the greater (allocative) efficiency of
better educated workers in allocating all input factors to the production
process (including education itself) between the alternative uses. Welch
(1970) gives two examples of the allocative effect. If there is one fixed input
factor to produce two goods (or varieties), education may improve the total
revenues of firms by means of a better allocation of the input factor
between the alternative outputs. Although, the production process is
technically efficient because the firm produces on the production possibility
curve (expressed in physical units), workers have more knowledge of how
to maximize the marginal value product (expressed in money units) of the
input factor. Total revenues are maximized if the marginal value product of
the input factor is equalized for all goods. Another allocative effect is
present if in addition to education as an input factor two (or more) other
inputs are included in the production function. If just one good is produced
with two inputs, education may also help to select the efficient quantities of
inputs. In equilibrium the marginal value product of the inputs should equal
the price of the inputs. In fact, education seems to provide the skills to
5
make better decision based upon the available information (Ram,
1980:366). Education generally has the effect of lowering the marginal cost
of acquiring production related information and raising the marginal benefits
of such information. As a result of allocative effect, an increase in the
relative proportions of intermediate and highly skilled worker is expected to
lead to a higher productivity level in money units.
Third, the diffusion effect stresses that better educated workers are
more able to adapt to technological change and will introduce new product
techniques more quickly. Nelson and Phelps (1966) state that “educated
people make good innovators, so that education speeds the process of
technological diffusion” (Bartel and Lichtenberg, 1987). Moreover, Nelson
and Phelps (1966) stress the role of receiving, decoding and understanding
information in performing a job. In fact the diffusion effect can be regarded
as a special case of the allocation effect. A higher level of education
increases the ability to discriminate between more and less profitable
innovations and reduces the uncertainty about investment decisions with
regard to new processes and products. Therefore, education increases the
profitability of successful and early adoption of innovations. Higher
proportions of intermediate and highly skilled workers, relative to low skilled
workers, would be expected to lead to more rapid and successful adoption
of innovations and higher productivity growth.
6
Fourth, the research effect refers to the role of higher education as an
important input factor in research and development (R&D) activities.
Research and Development R&D in turn is a key factor for technological
progress and productivity growth e.g. the endogenous growth models in
Romer (1990) and Grossman and Helpman (1992). Since research and
development activities are very complex, a relatively large proportion of
intermediate and highly skilled workers is a prerequisite to increase
technological knowledge and achieve productivity growth.
This empirical analysis is applied to all the registered manufacturing
firms in Enugu and Anambra State. Since the labour productivity of a firm
is a measure of competitiveness, an increase in the employment shares of
intermediate and highly skilled workers may improve the competitive
position of industrial sector. If the employment shares of intermediate and
highly skilled labour are either too small or too large relative to the effect on
firms’ labour productivity, this may point to under investment or over
investment in human capital. The rest of the work is structured as follows:
chapter two provides some stylized fact on human capital situation in
Nigeria, and further traced the link between education and human capitals.
Chapter three reviews theoretical and empirical issues; while chapter four
outlines the analytical framework and the model as well as the method of
estimation and evaluation, Data sources and measurement. Chapter five
shows the evaluation and interpretation of research findings.
Further
7
explanations were made with the help of principal component analysis Kmo
and Bartlett’s test. Chapter six constitutes in detail the summary, policy
implication, and conclusion.
1.2. STATEMENT OF PROBLEM
Although, Human capital theory is not exactly watertight nor is causality
easy to establish, yet the impact of human resource development on the
industrial productivity is decidedly positive. It is really a matter of regret that
after over decades of experimenting in the art of industrialization, most of
our industries still remain lukewarm to the fundamental concepts of
industrial engineering technology. The level of technology has been very
low. It is a common observation that many of the capital equipment and
machinery used in the factories are obsolete and are of low yielding and
low efficiency capacity. In case of their breakdown, repairs are more
difficult because their models have since been discarded. The result is that
production is often disrupted in our factories.
The corpus of empirical
research unequivocally leans toward an affirmation of direct causation for
which the East Asian countries are recent examples. This consensus was
not forged from the beginning; it was inspired partly by disenchantment with
absolute growth oriented development strategies pursued in the fifties and
sixties which neglected or marginalized the social sector- education, health
and others, yet failed to deliver robust growth in industries or achieve
poverty reduction as well. The argument of those that may be termed the
8
“growth fundamentalist school” manifestly lost its force and was in urgent
need of revision. Thus attempts to placate growing social and political
discontent occasioned by deepening poverty led to the shuffling of relative
emphasis on purely growth oriented policies and concerns about social
conditions (World Development Report (WDR), 1995:36).
Partly, also, a body of solid empirical evidence confirming that
investment in human capital could spur productivity and accelerate
development instigated it. This, in effect is a repudiation of the mainstream
orthodoxy’s prescription of cutbacks in social programmes on the excuse
that they are a burden on the national budget. Needless to say that such
spending fosters social peace necessary for the economic apparatus to
function effectively. Moreover, it constitutes a direct affront on the economic
doctrine that holds income maximization as the supreme objective of
national economic policy and a measure of the wealth of nation. The
corollary: human resources -not capital, income or material resources are
the basis for the wealth of nations. Clearly the era of ignoring human
resource development is now passed; skating over the human resource
factor may not only imperil the growth process, it may ground it.
Undoubtedly, human beings are the active agents who accumulate capital,
exploit natural resources, build social, economic and political organizations
to
advance
productivity
in
industries
and
national
development.
9
Significantly though, the progress made has been less rapid to
markedly
attenuate Nigeria’s dependence on expatriates for the operation of many
vital functions. Particularly worrisome has been the deterioration in the
quality of educational service at all levels, especially at higher education
levels where persons are trained to take up leadership roles in science,
technology, management and business. Moreover, the expansion of human
capital stock has not been matched by a commensurate advancement in
physical capital. The net consequence has been paltry growth of
productivity, income and meager returns to education over the years.
The developments in Nigeria’s education system have attracted
considerable empirical scrutiny (Yusufu 2000). The mechanics of how
human capital influences productivity has however attracted modest
inquiry. The political rhetoric surrounding this issue is quite long, but
argumentation with scientific investigation especially for Enugu and
Anambra states is scarce. This is the motivation behind this research. The
research is also important for Nigeria that is faced with astronomical level
of unemployment paradoxically among the highly educated. Therefore, it is
pertinent to investigate how significant education is to industrial productivity
in the context of high unemployment among the well read. In view of this,
the critical research questions are: why has the productivity in industries
been fluctuating around very low levels of performance? Is increase in
10
productivity level of individual firms over the period attributable to firm
learning? What really determines the effectiveness of human capital in
labour productivity in industries? These questions constitute the focal
problem of this research.
1.3.
AIMS AND OBJECTIVES OF THE STUDY
The general objective of the study is to investigate the impact of human
capital on labour productivity in the industrial sector in Enugu and Anambra
states. This is in recognition of the key role that human capital plays in the
micro economy and the growing world–wide perception. Low human skill
especially in developing countries is a major factor in such increasingly
global problem of low productivity in industries. The specific objectives of
this research are:
1. To provide a quantitative evaluation of the impact of human capital on
labour productivity in Enugu and Anambra states. This will be applied to
selected industries in the states to help us make sound probability
statement on impact of human capital on industries in the two states.
2. To determine the statistical significance of the impact of human capital
on labour productivity
3. To determine the difference in the productivity level in the two states
4. To make recommendations that will enhance the contribution of human
capital towards labour productivity at both micro and macro levels of the
11
economy (ie to suggest ways of improving productivity through human
capital).
1.4. STATEMENT OF WORKING HYPOTHESES
The study will be guided by the following hypotheses. The hypotheses will
be verified with the use of data collected from the survey and inferences
made from the analysis of the data will form opinion or basis for the
acceptance or rejection of the hypothesis.
1. Ho: Human capital does not affect productivity in the manufacturing
sector in Enugu and Anambra States.
2. Ho: The impact of human capital on labour productivity is not
statistically Significant
3. Ho There is no difference in the productivity level in the two states.
12
1.5
THE RELEVANCE OF THE STUDY
The relevance and usefulness of this study is not in doubt.
Productivity involves embracing not only a necessary change for attaining
better results but also an ability to review situations whenever
circumstances demand. It involves the adoption of an analytical approach
in determining proper objectives and the best operational options for
achieving optimum results, using the best combinations of men and
material resources (or what is available of these) for the best possible
production of goods and services within a given time or period.
We must accept the general view held all over the would, that a
nations level of comfort and well being of its citizenry, bear close
relationship with the quality and quantity of what it produces for
consumption and for exchange. In our prevailing circumstances the need
for improved productivity in every facet of our industries takes on an urgent
significance. It becomes not just a method of ensuring a better quality of
life, but rather a necessity for our national survival. It is our belief that
productivity improvement in Enugu and Anambra State like in the
industrialized nations should.
-
pave the way for a reduction in consumption of imported
materials
-
enable us to develop import substitution manufactured
goods, by putting into optimum use many of our expensive
13
-
or sophisticated equipment.
-
Dispose us towards better unitization and maintenance of
plant and buildings with local development of several other
imports needed for running an efficient economy.
Our people are the most precious resource, and the aim of
development and productivity, is to improve their quality of life and
welfare.
Productivity – growth must of necessity enhance the realization of
other important national economic objectives such as attainment
of higher average real income or leisure time, even distribution of
income and employment opportunities.
important
national
non
–
economic
It also promotes other
objectives,
such
as
satisfaction, physical and social environment, national defense
and social justice.
If we truly inculcate and practice the
productivity culture, there would be an obvious improvement in our
quality of life.
Productivity improvement would not be at the
expense of our people; rather it would result in the reduction of
waste in its entire ramification.
This includes waste of time,
materials, equipment, capital, foreign exchange and above all
human efforts.
The two state governments are fully aware of its
responsibilities towards the citizens of the two states.
This
14
awareness underlies the quest for the optimum utilization of all
available human and material resources in securing for the people
of Enugu and Anambra State a better and higher standard of
living. We are also aware of the fact that the process of rapid
industrialization brings with it, deep structural changes with
specific implication of technological development or process, and
global interdependence. Our determined effort to expedite
development in all spheres of the economy implies giving greater
priority to increase in productivity in every sector of our industry.
15
CHAPTER TWO
CONCEPTUAL AND THEORETICAL ISSUES
2.1. HUMAN CAPITAL DEVELOPMENT
The strong growth performance of the economies of developed and
developing countries over recent decades has been a much-debated
matter. Among the generally agreed causal factors responsible for the
impressive performance of the economy of most of the developed countries
is an impressive commitment to human capital formation. Human capital
development is an integral part of capacity building development, which
encompasses both human and institutional capacity building. Human
capital development refers to the process by which a nation develops and
increases its human resource capabilities through the inculcation of the
relevant general and technical knowledge, skills and effectiveness to
realize set goals efficiently (Obadan and Adubi 1998). It is a process of
incremental acquisition of capacities. The government and private sector
have long recognized the importance of human capacity building. No
wonder it formed a prime point in the 1988 civil service reforms that were
abrogated in 1995. The task of human capital development is considered
key to Nigerian industrial productivity. To achieve human capacity building,
however, training is central. Local and international educational institutions,
16
such
as
universities,
polytechnics
and
management
development
institutions provide such training.
Human capacity development during the colonial era was tailored
towards general administration and maintenance of law and order. Today,
its scope has been broadened to capture the challenges in economic,
social and political spheres of the country. As such, greater emphasis is
placed on formal education and skills acquisition to achieve rapid
development. To this extent, the identified capacity deficiency in the system
is gradually being bridged. For instance, the country has witnessed
unprecedented growth in the number of tertiary institutions since her
political independence. If we discountenance the qualities of education
provided by these institutions, Nigeria can make bold to say that her level
of dependence on foreign institutions for the capacity building of its
workforce has seriously declined. Indeed, the manpower problem of Nigeria
today has taken a new dimension from capacity gap to management in the
area of good governance.
A well-developed human capital base of a nation is a sin-qua-non-to
productivity and, on this basis, some developing countries are far ahead of
others. The reason for this can be linked to the way and manner in which
human capital development is given preference in those nations. In other
words, a group of nations could be contemplated as equally rich or equally
poor, yet in terms of human capital development they are far apart from
17
each other. This research supports the argument that the direction of a
nation’s priorities and commitments, measured in terms of actual resources
devoted towards the education sector, leads to such differences in human
capital among different countries of the world. If a country gives high
priorities to education and health sectors, than it gives to physical capital,
she will be far ahead of other countries that give less priority to human
capital development. The impact of those investments are directly reflected
in terms of high literacy rates and markedly improved years of life
expectancy at birth, thus leading to higher per capita income and economic
development.
The distinction between human capital and physical infrastructure
investment lies in the gestation period. It needs to be underscored that,
while the physical infrastructure investment may ordinarily take a long time
to be completed, however, the impact period for human capital investment
could be longer if it is to forge good results. Not only that, while it may be
possible to abbreviate the gestation period of physical infrastructure
investment by appointing more resources through borrowing or foreign aid,
the same cannot be said of human capital because it will necessitate a
fixed number of years to shape a generation of educated and skilled labour
force. Another important distinction between physical investment and
human capital investment is that the former requires one time capital
expenditure while the latter requires investments on a family long basis.
18
This implies that the returns on the social sector investment takes a longer
term and, therefore, its impact on productivity and development should be
analyzed within a framework that has longer perspectives. It is an
established fact that a shift in the investment priority to social development
(ie education sector) would entail enduring positive impact on productivity.
The human capital theory emphasizes how education increases the
productivity and efficiency of workers by increasing the level of their
cognitive skills. Theodore Schultz, Garry Becker and Jacob Mincer
introduced the notion that people invest in education to increase their stock
of human capital. The proponents see human capital as the stock of
economically productive human capabilities, which can be formed by
combining innate abilities with investments in human beings (Babalola
2000). Examples of such investment include: expenditures on education,
on the job training, health and nutrition. Such expenditures increase future
productivity capacity at the expense of current consumption. However, the
stock of human capital increases in a period only when gross investment
exceeds depreciation with the passage of time, with intense use or with
lack of use.
The provision of education is seen as a productive investment in
human capital, an investment which the proponents of human capital
theory considers to be equally or even more equally worthwhile than that in
physical capital. In fact, contemporary knowledge in the united state
19
acknowledges that investment in human capital is three times better than
that in physical inputs. Human capital theorists have established that basic
literacy enhances the productivity of workers in low skill occupation. They
further state that an instruction that demands logical or analytical reasoning
or provides technical and specialized knowledge; increase the marginal
productivity of workers in high skill or professional positions. Moreover, the
greater the provision of schooling the greater the stock of human capital in
society and, consequently the greater the increase in national productivity.
Another interesting theory of education (modernization theory)
focuses on how education transforms an individual’s value, belief and
behaviour. Exposure to modernizing institutions, such as schools, factories
and mass media, inculcates modern values and attitudes. These attitudes
include openness to new idea, independence from traditional authority,
willingness and ability to plan and calculate future exigencies and a
growing sense of personal and social efficacy. According to modernization
theorists, these normative and attitudinal changes continue throughout the
life cycle, permanently altering an individual’s relationship to the social
structure. The greater the number of people exposed to modernizing
institution, the greater the level of individual modernity attained by the
society. Once a critical segment of the population changes in this way, the
pace of society’s modernization, industrial productivity and economic
development quickens. This, educational expansion through its effects on
20
individual values and benefits sets in motion the necessary building blocks
for a more productive work force and for sustained economic growth.
2.2. THE LINK BETWEEN EDUCATION AND HUMAN CAPITAL DEVELOPMENT
What exactly is the role of human capital and other social variables in
productivity and the development of an economy? In the traditional
neoclassical growth models developed by Robert solow and Trevor swan in
the 1950s, the output of an economy grows in response to larger inputs of
capital and labour (all physical inputs). Non – economic variables, such as
human capital or human health variables have no function in these models.
Furthermore, the economy under such a model conforms to the Law of
diminishing returns to scale.
With these assumptions, the neoclassical
growth models afford some implication to the economy; particularly that as
the capital stock increases, growth of the economy slows down and in
order to keep the economy growing it must capitalize from incessant
infusions of technological progress. It is well known that this type of
mechanism in the neoclassical growth model is neither inherent nor does it
strive to explain much. In economic lexicon, this simply means that the
technological progress is “exogenous” to the system. Yet, the reality is
quite contrary to that, particularly for the Nigerian economy, which kept
growing for well over three decades. This implies that technology is not the
only main driving force accountable for maintaining such high growth
21
performance in the developed economiecs, but that there are other factors,
which are outside the realms of neoclassical growth model.
Addressing the above issues, a new paradigm was developed in the
literature, commonly known as “endogenous growth model” by Paul Roman
(1986). This broadens the concept of capital to include human capital. The
new endogenous growth model argues that the Law of diminishing returns
to scale may not be true as in the case of developed economy. In simple
terms, endogenous growth model means that if a firm, which invests in
physical capital, also employs educated and skilled workers who are also
healthy, then not only will the labour be productive but it will also be
technologically more efficiently. This will lead to a so called “Hicks neutral”
shift in the production function and thus lead to increasing, rather than
decreasing, returns on investments. In other words, technology and human
capital are both “endogenous” to the system. There is no gainsaying that
the advent of “endogenous growth model” with human capital has certainly
enhanced the understanding of mysteries of rapid and long sustainable
high growth performances in industries of developed and developing
economies. It should be emphasized that for human capital to spawn a
perceptible impact in economic development, a nation needs to have a
minimum of at least 70 percent or more of literate population. This implies
that if an overwhelming large number of people in a country are literate,
even with simple basic education as being able to read newspapers, this
22
may open up the minds of the masses, possibly make them more
enlightened workers and perhaps institute some elements of discipline in
them which are basic prerequisites for a large organized production to run
efficiently and lead to rapid growth.
Education remains the only instrument through which the society can
be transformed. The extent to which a country invests in education, among
other social sectors, will determine the level and rate of its transformation.
Education does not only make use of physical materials but also human
capital so as to make up for the resources needed for social transformation.
As a stringent and unique factor in transition programme, education equips
human capital with the needed knowledge, skills and competencies, which
would make them functional, and contribute to the all round production in
industry and development.
In this research, three unique features of educational institutions that
may enhance their contribution to economic growth will be explored. These
are the content of education, public access to education and the closeness
of education to general industrial production and development.
1. Content of education: - The needs of the society in most cases
determine the contents of educational curriculum, which is transferred into
individual participants who have the opportunity to receive education.
Human capital is exposed to various ideas, knowledge, skills, and
attitudes that cut across all spheres of life through training and schooling.
23
The extent to which these knowledge, skills and attitude are digested and
applied into daily living determines the quality of human capital. A low
quality human capital will contribute little or nothing to labour productivity
and economic growth, while a high quality human capital will enhance
rapid national development when employed. The foregoing discussion
implies that the content of education determines the quality of human
capital, which in turn influences, the level of labour productivity and
economic growth.
2. Access to education by many people: - This is another characteristic of
educational institutions that enhances their contribution to human capital
development. If the vast majority of the population has access to education,
there will be a quicker development in labour productivity and economic
growth because the human capital resources will be large.
Access to
education is determined by the cost of education and the prevailing
educational policies. A considerable low – cost education will open access
to many people while more elusive educational policies will hinder easy
access to education. In another dimension, a positive cost benefit analysis
of educational system will attract more applications than an inversely
related cost benefit. When people realize a great benefit in investing in
education individuals and the public will show more interest in it.
3. The closeness of education to development: - This factor emphasizes
that the value attached to education as a factor of change and economic
24
development will determine the extent to which individuals, and the public
will invest in it. This means that when education becomes the prime factor
in the development of both human capital and infrastructure there will be a
“push and pull” scenario between education and other competing sectors of
the economy. But when an alternative factor is given preference over
education to speed up development, there will be contraction in enrolment
in schools and institution, most especially, when such alternative is
comparably cheaper. It must be emphasized that in its contribution to
development, education guarantees a healthy lifestyle, technological
advancement through capital accumulation especially through formal
schooling or informal skill development on the job, which makes the rate of
human capital accumulation, an increasing function of the overall
development process of a nation.
In the world development Report (1980) the World Bank expressed
renewed interest in human capital development. Drawing from the research
works of Hicks and Wheeler, Psacharopoulos (1984) reaffirms the
importance of education in promoting industrial sector and general
economic development. He emphasizes that the contribution is even
stronger if the complementary roles between education and other forms of
investment are taken into account. Moreover, relying on Schultz and
Denison’s method of measuring the contribution of education to industrial
25
sector, it was found that a substantial proportion of the rate of growth of
productivity in the industry resulted from investment in education.
Moreover, it will be incomplete to talk about the impact of tertiary
education on development without highlighting some economic and non –
economic benefits that education generates. Babalola (1995) views
education as a public good. Implied in this belief is the assumption that
education is expected to generate more benefits than individuals can gain
privately. Some of the economic benefits that education generates to the
society are direct financial returns, financial options hedging options non–
market returns, residence related benefits, employment related benefits
and social benefits.
2.3. STYLIZED FACTS ON HUMAN CAPITAL SITUATION IN NIGERIA
The Nigeria educational and training system has witnessed
phenomenal growth in the number of institutions (formal and informal) and
enrolment levels. Indeed, the surge has been very inspiring, even when
compared with best performing nations elsewhere in the world. Curiously,
this spectacular growth has not mitigated the growing demand for
education at all levels both for general education and acquisition of
technical as well as professional skills. In quantitative terms, primary school
enrollment rose from 2.9 million in 1960 to 13.0 million in 1990. Similarly,
secondary school enrolment soared from 1.9 million in 1980 to about 4.5
million in 1993 – a rise of about 223.6 percent over 14 years.
26
Simultaneously enrolment in federal universities alone climbed from 40,552
in 1976 to about 227,999 in 1993, an acceleration of about 562.2 percent.
In between 1980 and 1990, the enrolment in polytechnics grew from 42,381
to 84,948, representing a rise of about 200.4 per cent (Yesufu 2000).
This scenario coheres and is historically consistent with direction of
manpower policies and programmes of government, which stresses
expansion of educational / training facilities ostensibly to meet the
requirement of the economy for skilled or well-trained manpower. The
various facets of this strategy include increased enrolment, enlargement of
the number of training institutions
and broadening of
academic
programmes at the various levels of the educational system, and swelling
of graduate outturn by extension.
The expansion of educational facilities has persisted to date. For
instance in the universities, total student enrolment increased from 276,440
in 1995/96 to about 319,920 in the 1998/99 academic year, showing an
average annual increase of about 4.0 percent. Graduate outturn
correspondingly grew from about 49,950 to 61,750 over the same period,
representing an average annual increase of 5.9 per cent. The number of
polytechnics, their enrolment and outturn similarly grew between 1995/96
and 1998/99. Enrolment increased from 204,954 to 219,770 during the
period, representing an average annual increase of about 2.0 per cent. In
the case of the colleges of education, their number increased from 58 in
27
1995/96 to 61 in 1998/99 and total student enrolment from about 97,890 to
105,420 over the same period. This represented an average annual
increase of about 2.0 percent. Their graduate outturn also increased from
about 19,630 to 21,150 between 1995/96 and 1998/99 academic year
(Tables 1 and 2)
Table 2.1 Distribution of student Enrolment by Type of Institution and
Academic Year in Nigeria
No and type
of institution 1987/88 1988/89 1989/90 1990/91 1991/92 1992/93 1993/94 1994/95
University
30
Enrol.
158,758
Polyte
chnic
28
Enrol
68,625
College of
Education
Enrol.
48
61,890
30
32
32
164,005 172,911 195,759
28
28
32
32
222,974
237467
32
32
107,526
111,800
34
121,527
35
1995/96
1996/97
35
35
276,440
291,563
35
246,065
256,780
37
40
43
131,978
187,738
204,954
72,114
75,468
50
50
54
54
57
57
58
67,750
72,525
85,574
92,393
92,600
93,969
95,502
1997/98
35
46
210,306
61
97,890
319,914
45
207,198
58
35
304,390
43
1998/99
219,770
61
100,337
102,845
61
105,416
Source: National Manpower Board (Compiled with data from FME,
Secondary
Schools
5,991
5,868
6,001
5,860
6,009
5,959
6,074
6,429
NUC, NBTE, NCCE, and Tertiary Institution
Enro
6,429
6,470
6,490
2,944,781 2,684,274 2,901,499 3,123,277 3,600,620 4,032,083 4,451,329 4,844,991 4,680,339 4,923,717 5,071,4
Primary
School
33,796
34,904
35,433
35,446
36,610
38, 254
38,649
39,047
40,204
41,204
41,342
6, 596
5,274285
41,480
Enrol 14,208,966 14,066915 13,607,249 13,776,854 14,805,937 15,013,220 15,193,378 15,360,505 15,514,110 15,535,878 15,924,274 20000000
Source: National Manpower Board (Compiled with data from FME, NUC, NBTE,
NCCE, and Tertiary Institutions)
28
Table 2.2 Distribution of Graduate Outturn by Institution and Academic
Year in Nigeria (1987/88-1998/99)
No and type
of institution 1987/88 1988/89 1989/90 1990/91 1991/92
University
30
30
Enrol
37,286
38,367
Polyte
chnic
28
28
Enrol
25573
College of
Education
Enrol.
48
19,803
Secondary
Schools
5,991
Enro
490,297
Primary
School
Enrol
33,796
32
40,094
28
32
41,497
32
32
42,908
28,656
31,321
34,234
50
50
54
54
20,374
21,095
21,757
22,440
5,868
6,001
5,860
6,009
447,397
225,258
296,414
34,904
35,433
35,446
1,606,299 1,651,304
32
1,922,914
1996/97
1997/98
1998/99
35
35
35
35
35
48,219
37
37,418
36,610
1995/96
46,454
34
358,366
1994/95
35
44,624
32
27,450
1,494,873 1,513,460
1992/93 1993/94
52,823
40
40,898
54,853
43
57,980
61,749
45
46
43
43,965
47,130
57
58
58
61
21,295
18,850
19,158
19,637
20,128
20,631
21,147
5,959
6,074
6,429
6,429
6,470
6,490
6, 596
455,204
456,751
499,381
523,851
549,520
578,095
607,000
38,649
39,047
40,204
41,204
41,342
41,480
57
38, 254
50,900
54,718
58,275
61
61
2,138,157 2,129,712 2,131,085 2,142,112 2,154,718 2,163,337
2,000,000
Source: National Manpower Board (Compiled with data from FME, NUC, NBTE,
NCCE, and Tertiary Institutions)
29
Table2.3. Student Enrolment in Nigerian University (1999/2000)
Faculty of Study
1998/99
1998/99
(Actual)
(Actual)
1999/2000
(Estimated)
Number
%
Number
Sciences (pure & Applied)
50,546
15.8
52,799
Medicine & Related course
3,034
7.2
24,241
Pharmacy
5,119
1.6
5,645
Engineering /Technology
29,115
9.1
30,551
Environmental Studies
11,837
3.7
12,286
Agriculture
21,11
6.6
22,249
Veterinary medicine
2,899
0.9
2,657
Education (Science)
26,555
8.36
27,230
Subtotal
170,194
53.2
177,658
(Art & Humanities)
44,148
13.8
45,494
Social Sciences
38,390
12
39,849
Business/ Mgt studies
19,835
6.2
20,588
Education (Arts)
28,792
9
29,554
Law
18,555
5.8
18,928
Subtotal
149,720
46.8
154,412
Grand total
319,914
100
332,071
% Share
sciences
53.2
% Share
Arts
46.8
Art & Management studies
Source: National Manpower Board (Compiled with data from FME, NUC, NBTE,
NCCE, and Tertiary Institutions
30
Table2. 4. Graduate outturn from Nigeria university (1999/2000)
Faculty of Study
1998/99
(Actual)
Number
1998/99
(Actual)
%
1999/2000
(Estimated)
Number
Sciences (pure & Applied)
79666
12.9
84,689
Medicine & Related course
3,952
6.4
4,234
Pharmacy
926
1.5
1,042
Engineering /Technology
4,755
7.7
5,081
Environmental Studies
2,099
3.4
2,215
Agriculture
3,581
5.8
3,844
Veterinary medicine
371
0.6
391
Education (Science)
8,830
14.3
9,251
Subtotal
32,480
52.6
34,527
(Art & Humanities)
7,286
11.8
7,622
Social Sciences
8,151
13.2
8,534
Business/ Mgt studies
2,100
3.4
2,150
Education (Arts)
8,336
13.5
8,729
Law
3,396
5.5
3,583
Art & Management studies
Subtotal
29,269
47.4
30,618
Grand total
61,749
100
65,145
% Share
sciences
53.2
% Share
Arts
46.8
Source: National Manpower Board (compiled from data obtained from the universities
and NUC
Given an annual population growth rate of about 2.6 percent for the
Country, the growth of enrolment or demand for education could be
characterized as exemplary, of course, the importance and viability of
31
education and training institutions could be effectively determined by
reference also to the quantity of output vis-à-vis enrolments. Also important
are the types, number and quality of the manpower (trained human
resource) output and mixes in term of the professions and skills required or
needed for industrial productivity (Yusufu 2000). These pieces of
information are presented in table 1 to 4.
In general, it appears that the government is aware of the crucial
need to develop human capital in order to achieve economic and social
development. There have been mammoth developments in terms of the
institutional framework, physical structures and enrolments at all levels of
education and training. Indeed, until quite recently, the rate of enrolment
growth exceeded the rate of growth of population, which translates into
significant in government in national literacy and outturn of highly skilled
and professional manpower. But poor funding and decrepit infrastructure
and other learning facilities in many institutions are eroding these gains.
32
CHAPTER THREE
LITERATURE REVIEW
3.1. Theoretical Literature
Including human beings within the analytical framework of capital is by
no means a new idea. Many past economists (and non-economists) have
considered human beings and/or their skills as capital. Walras
(1939),
Kiker (1966) considered skills and acquired abilities of human beings as
human capital.
Adam Smith included in the category of fixed capital the skills and useful
abilities of human beings. The skill of a man, he said might be regarded as
a machine that yields a profit.
Fixed capital consists of the acquired and useful abilities of all
the inhabitants or members of the society. The acquisition of
such talents, by the maintenance of the acquirer during his
education, study or apprenticeship always cost a real
expense, which a capital fixed and realized as it were in his
person.
In the same vein J.B say (1767- 1832), asserted that since skill and
abilities are acquired at a cost and tend to increase workers productivity
they should be regarded as capital (Say, 1821: 92-94). In general however,
most of the well known names in the history of economic thought have
neither attempted an evaluation of human capital nor employ the concept
33
for any specific purpose, despite recognizing the importance of investment
in human beings as a factor that increase their productivity. In fact, the
concept of human capital was fairly prominent in economic thinking until
Marshall discarded the notion as being ‘unrealistic’ (Kiker 1966). Marshall’s
influence helps to explain why the typical view of economists up to
the1960s was that the demand for education was the demand for a type of
consumption good (Bowman 1990).
In the twentieth century, Walsh (1935), argued that the more advanced the
education (that is, the more vocational the purpose) the more profitable it
is, and hence the motive for undertaking it is economic gain. Although,
Walsh concluded that college training may be a form of capital formation,
he admitted that it is affected by important factors not all identical with
those that bear on other forms.
The concept of human capital was finally developed in the 1960s with
the emergence of human capital theory formalized by Schultz in (1961a,b)
and Becker (1962, 1964). The former analyzed educational expenditure as
a form of investment whereas; the later developed the theory of human
capital formation and analyzed the rate of return to investment in education
and training. Specifically, Shaffer and Schultz (1961a) in a critique of
human capital concepts cites the impossibility of separating investment and
consumption component of human capital: According to them, there are
other factors that explain why people invest in education beside the
34
eventual economic return that it may bring. Although Schultz (1961b),
recognized the importance of cultural contribution made -by education and
he argued that its economic contribution is even greater.
In his seminal work, Investment of Human Capital: a theoretical
analysis, Becker (1962) included in his concept of human capital activities
such as formal education and on-the job training (specific human capital).
For this author as well as for the majority of researchers who adopted the
human capital framework, education, skill and human capital are
interchangeable concepts. In particular, in the vast majority of human
capital studies “--education is the most important component of human
capital” Schultz (1993:17). There are, however, an increasing number of
the authors who point to the fact that formal education is only one way to
create skills. Howell and Wolf (1991), for instance questions the adequacy
of years of education as a measure of workplace skills. They argued that
most jobs acquire a multitude of different skills for adequate task
performance, ranging from physical abilities to cognitive skills and
interpersonal skills. Moreover, they consider that in some occupations,
educational attainment may not be a direct measure of job related to skills
per se but a devise used to screen for the ability to learn on the job
(Thurow, 1975) and for desirable social and personal characteristics
( Bowles and Gintis, 1976). Arrow
(1962) and Young (1992), among
others, have stressed the role of other forms of skills improvement, such as
35
learning by doing and on the job training. Taking a micro economic view,
i.e. plants as the unit of reference, one could consider along the lines Lall
and Wignaraja (1997) several ramifications of human capital concept;: Firm
stock of skills, (background and training of the entrepreneur or business
leader, the production manager, and other technically qualified personnel);
the structure of the labour force (by the quality and education); the
accumulation of human capital (increases in human capital stock by
training investment); and loses in human capital (exit of employees to set
up their own business or join other firms). More simply, and in accordance
with this view, the human capital concept can be divided into two main
components; skill development, referring mainly to industry – related
education and training (both formal and informal); and technological
capability formulation, which account, for the development of individual and
institutional skills and knowledge derived from technological effort (Lall,
1998).
It is worth noting, however, that ‘technological efforts’ and ‘education
and training’ are often commingled activities. In on the – job and ‘on line’
problem solving activities individuals consult with others, refer to texts, and
seek out instruction or guidance, Similarly in ‘off – line’ education,
individuals bring with them their practical experience, past and current, and
this experience interacts with the abstractions or examples that are offered
by the designer of the pedagogy. The individuals offering education or
36
training may not only be recounting theory and abstraction, but also
recounting experience and engaging collective problems solving with the
students.
In this vein, although one may be able to count’ inputs (e.g. hours ‘off
– line’ or costs) to education and training activities, the ‘separateness’ of
these activities to the accumulation of knowledge is far from obvious.
These human capital ramifications highlight the intricate connection that
exists between human capital and skills concepts.
Although
skills
and
human capital are treated in countless studies as synonymous concept e.g.
(Harris and Helfat 1997), more accurately they are distinct though
interrelated concept. Skills is itself a rather ambiguous term (Green et-al
1996). It can mean the ability to perform given task or to master various
techniques, or more broadly, it can refer to the range of behavioural
attribute such as reliability, ability to work without supervision, and stability
of employment. Thus, in a strict perspective, skill can be defined as the
required competence or needs of employment (Wu 1992), whereas in a
more comprehensive sense, skills may be identified as a complex “social
relation” (Naivelle 1956). Colardyn and Durand – Drouhin (1995) also
maintain that the notion of “Competence” in the sense of a “capacity to
accomplish concrete task” is now frequently associated with the ideas of
“skills”.
37
Several sociologists and other professionals, point to different
typologies of skills in order to account for the multiple dimensions of the
skill concept. A simplified version, based on Cezard’s (1979:18) proposal
distinguishes: “Job skill” – the qualities required by the particular
occupation, “Work skills” – workers professional knowledge, derived from
both formal education (internal or external to the school system) and
learning on the job and conventional skills “- classification of workers in an
occupational categories grid of a conventional form. These elements are
often intermingled in actual ‘grids’ or matrices so that an individual is
distinguished as a technician not only because of the content of his work
but also because of the nature of his education and training. Other
typologies such as that of Ashton et al (1997) identified three concepts of
skills: the stock of human capital acquired (Becker 1964, Stevens 1994),
the autonomy individuals enjoy at work (Braverman, 1974); and the tasks
people perform within their job and how effectively they carried this out
(Ash 1988; Primoff and Fine 1988).
Skill can be acquired through education and (formal) training but
practice makes perfect. Rosen (1986), points to the fact that most specific
job skills are learned from performing the work activities themselves. He
goes on to argue that there is no perfect substitute for apprenticeship and
for work experience itself. Learning potential is viewed as a by – product of
the work environment, tied to a specific work activity, but varying from
38
activity to activity and from job to job. In this vein, method of developing
skills include tangible investments such as investments in education and
formal training (In short, human capital) and intangible ones i.e. knowledge
and know – now. ‘General knowledge’ may be defined as the common
scientific, technological and cultural heritage and potential available to
every one (Aghion and Howitt 1998). This suggests that ‘know – how’ can
be generated by learning by doing (working on the job) as well as by
research. Skill, therefore appear as a chain concept linking human capital,
knowledge and technology – much “technology” being knowledge of certain
sorts of skilled workers and difference in technology may in reality be
difference in the availability of certain skills ( Wood and Von Tunzelmann
1996). They also suggest that the higher wages of skilled workers (relative
to unskilled workers) may derive from two sources, a return on investment
in human capital, and a rental for scarce know – how. The
operationalization of such interrelation however has struggled in the face of
enormous difficulties. This notwithstanding, those same interrelationship
can be useful to show that from the view- point of job performance there
may be situation of relationship between experience and training or
education or complementarily.
In Lall et al (1993) skills are explicitly related to technology. These
authors focus on the concept of technological capabilities and stress that
these capabilities are much more than a simple sum of education and
39
training of firms employees. In fact, they stress that capabilities are a form
of institutional knowledge that is made up of the combined skills of a firm’s
members accumulated overtime. In this context, investment in knowledge
suggests a natural externality (Romer 1986). For instance, the creation of
new knowledge by one firm is assumed to have a positive external effect
on the production possibilities of other firms because that knowledge
cannot be perfectly appropriated. In Lucas (1988) the specification
externalities take the form of public learning, which increases the stock of
human capital. In fact, Lucas includes in the “general skill level”, along with
an individual’s human capital effect on his /her own productivity, an external
effect translated into the aggregated human capital stock that contribute to
the productivity of all production factors.
In general, however, human capital as commonly defined (formal
education and training) conveys a view of human capital as being a private
good, whereas knowledge, as referred to above, tends to some degree to
be public and therefore and non – appropriable. A public good (or service)
is available to everyone in a particular catchment’s area, cannot be
withheld from non – payers and is ‘non rival’ that is one person’s
consumption does not diminish that of others. Therefore, the equivalence
between human capital and skills depends largely on the ‘tangibleness’ of
the respective components. Note, for instance, that human capital models
such as those presented by king and Kebelo (1987), Jones and Manuelli
40
(1988), Rebelo (1990), and Becker, Murphy and Tamura (1990) treat all
forms of intangible knowledge as being analogous to human skills that are
rivalries and excludable
(Romer 1990) Note that in a finite lifetime,
although an individuals human capital cannot grow without bounds, skills
acquired by the individual may be applied to an ever improving set of
production technologies in which case the value of human capital will
continue to rise through time.
Regardless of issue of tangibleness, it is undeniable that skills and
human capital are interrelated. For instance, Howell and Wolff (1991)
distinguish between direct measures of skills (cognitive, interactive and
motor skills), which appear to measure independent dimensions of job skills
and indirect measures of skills (educational attainment and earning
indicators), and conclude that educational attainment is highly correlated
with cognitive and interactive skills. Additionally, Lynch (1994) point out that
Cross functional competencies “the requisite new skills”, are not easy to
acquire formally, requiring instead a strong base of analytical, quantitative,
and verbal skills that formal higher education is more likely to convey.
Classical economists drew attention to the importance of education
as a form of national investment. For several classical authors (e.g. Smith,
Say and Senior) acquired skills and abilities were seen as increasing
worker productivity. Smith and his followers, however, accepted that
41
popular education, though socially important was largely unrelated to
success in the work place (Bowman 1990).
Research in the late 1950s and 1960s, which contributed the
foundations of human capital theory (Schultz 1961a,b, Becker 1962),
stimulated a new level of interest in the relationship between education and
the economy. These approaches were typically driven by the supply side of
economics, and by the neoclassical notion of equilibrium in which supply
(of education) will create its own demand. After such a promising
beginning, human capital theory has been seriously challenged since the
1970s by the appearance of the alternative theories (received in Teixeira
1999b). In middle-income countries where there was arguable considerable
difficulty in absorbing people into occupations who had spent substantial
time in university the growth of unemployment promoted increasing
skepticism about educational achievements and their economic benefits. In
this context, university education was counter – productive to achieving
some types of work discipline including authoritarian control of work content
and the acquisition of manual skills (for which experience is a better
teacher).
In economies that had by the 1970s fully plunged into deindustrialization, the value of university education in general literacy as well
as developing advanced and convergent social skills for interaction in the
workplace was much higher (Howell and Wolff 1991). The industrial
42
activities in these economies also often reflected a higher degree of
abstract problem- solving and knowledge acquisition (learning to learn) for
which university education (often regardless of subject) was of value.
Specifically, economist in the U.S were concerned from the 1970s not only
with issues such as de-skilling (Braverman, 1974; Kraft, 1977, Zimbalist,
1979) but continued to follow the agenda outlined by Schultz and Becker
for assessing the value of human capital, which led to the end of that
decade and throughout the 1980s to efforts to explain the excess returns
that appeared to accrue to individuals with higher education. The so –
called filtering and screening/ signaling theories (Arrow, 1973, Spence,
1973, Thurow, 1975) constituted an attempt to develop a theory to explain
these wage differentials and to address the persistent problem of racial and
gender wage differentials. According to Filtering theory (Arrow, 1973)
employers prefer workers with high levels of education because the
education system acts as a filter for individuals according to their innate
productivity, so education is a source of information not competencies.
Screening (Thurow,1975) or signaling (Spence, 1973), theories reject two
basic assumptions of human capital theory: perfect competition and deficits
of human capital (according to which human capital increases are always
absorbed by demand). They recognize the possibility of human capital
oversupply. In such conditions, there is competition for jobs not for wages.
Education is nothing more than a signal through which workers indicates to
43
employers their capabilities to take on certain jobs (Spence, 1973). At
present, government mainly treats education not as consumer good but as
a productive asset. Increasingly, all over the world, it is taken for granted
that educational achievement and economic success are closely linked (the
Economist, 1977). The conventional wisdom, therefore, is that more
education and training is assumed to lead automatically to improved
economic performance.
More over, there is a widely held belief that new ways of organizing
production are also putting a premium on education (Rodrigues and Lopes,
1997). It is argued that the capacity for a critical number of enterprises in a
given country to create a more efficient, industry post Taylorist work
organization is strongly influenced by education. Kovacs (1994) points out
that, in a less industrialized country, namely Portugal, the shortage of
skilled resources and the lack of capability for education and training
system to respond to firm shortage of skilled workers are the two main
obstacles to the development of the so called ‘anthropocentric production
systems’. Firms and other work organization seems to be changing from
chiefly production centered economic units to being learning centered
economic unit (Ferreira 1994). As a result there is a growing shift in the
emphasis from the focus on physical and financial capital to a focus on the
increasing importance of human capital and continuous learning for
sustaining competitive advantage.
44
In an era of human capital, what matters is not organizational form
(entrepreneurial or managerial) but organizational process learning and
transformation. The perceived status of more schooling in conjunction with
political pressures on the education system to expand in order to
accommodate all aspirants have tended to expand the number of educated
persons beyond the availability of appropriate jobs in the economic system.
This outcome may be influenced by the fact that even though the earnings
and employment opportunities for highly educated persons, such as
university graduates, may decline overtime, the earnings and employment
opportunities for less educated persons may deteriorate even more (Levin
1987).
Irrespective of the underlying causality, however, the production of
numerous graduates and post secondary trained individuals who are not
able to obtain appropriate employment presents an immense problem for
the formal educational sector of many countries (Whiston et al 1980). Two
decades ago Dore (1980), was already focusing on the problem of
“Educated
unemployment”
and
the
associated
“diploma
disease”.
According to this author, the Mismatch between job qualifications and
education levels and the quality of schooling, constitute challenges to the
argument that “the more education the better”. The problem of surplus of
job seekers over job available was also tackled by the Thurow (1975), who
developed the concept of the “labour queue”. According to this view,
45
employers prefer to hire people with more education at the prevailing wage
rate, either because they are (or are believed to be) more productive or
simply because employers prefer to associate with a better educated.
Thus, those who have received the remedial education, for example, are
unlikely to get first performance in the job competition model marginal
products, and hence earnings, are associated with the jobs not individuals.
Individuals are allocated to available jobs based on array of personal
characteristics, including education, that suggest to employers the cost of
training them in the skills necessary to perform the tasks associated with
their jobs. Thus, workers may possess more education and skills than their
jobs require (e.g. employers may be unable or unwilling to fully utilize the
education and skills of their workers). Rumberger (1987), found that, as
Thurow’s (1975) job competition model predicts, schooling is not rewarded
similarly in all occupations. Moreover, additional schooling beyond that
required for the job is not always rewarded. He concluded that additional
schooling is not completely unproductive; but simply the job constrains the
ability of workers to fully utilize the skills and capabilities they acquired in
school.
In this context, young people may demand education on the margin
specifically in order to stand a better chance of being hired for low-level
jobs. This reflects Fields (1972) “bumping” argument, which explain, the
rise in the private rate of return in the presence of an increase in supply of
46
education. Preferential hiring per education level, would lead to the general
up-grading of hiring standards and of the labour force in general, so long as
the education system produces more graduates than are needed to fill
skilled positions and some of them are willing to seek employment at lower
levels. The educated person move to the front of the queue for unskilled
jobs and is hired first at the unskilled wage rate “bumping” a less educated
person from a job. This lowers the probability of such an individual getting
an unskilled job and also lowers the present value of expected lifetime
income for the unskilled, whereas the expected lifetime income for a person
in the skilled labour market is unchanged. This results in a greater demand
for the education and even more political pressure. It is reasonable to
expect that in recessions, the first reactions of employers is to stop
recruiting new entrants (Bosanquet, 1987), However, what is puzzling is the
widening of educational wage differentials, which accompanies the
increasing number of educated workers in the labour force. The current
solutions offered to this puzzle are based on the argument of existence of a
corresponding (an event stronger) demand for educated labour derived
from capital skill complementarities (Griliches 1969, 1970) and the
technology skill interaction (Katz and Murphy) 1992, (Card and Limieux
1996), which is based on the argument that education becomes more
valuable in periods of rapid technological change (Nelson, 1964; Nelson
and Phelps,1966; Welch,1970). However, empirical evidence corroborating
47
these explanations is not convincing. An alternative explanation put forward
in Teixeria (2002) argues that the risk of fission (the event that a given plant
loses part or all its top educated and/or top skilled workers), which is likely
to undermine an establishments ‘survival capacity, leads payers to pay
increasing amount to the top educated or skilled workers (in spite of
increasing availability of these type of workers in the labour market) and
justifies the relative (inertia on the demand side of human capital. This
argument takes the view that the composition of human capital
accumulation is shaped by the demand, which according to the author is
more socially constructed than admitted in the economic literature.
Many studies identify human capital as a pre-condition for and often a
determinant of economic performance and international competitiveness
(e.g. Alderoft 1992). Human capital, in particular education is according to
some authors the source of economic growth through its development
impact in agriculture and industry (Schultz 1961) and an engine for
attracting other factors Benhabib and Spiegel (1994). In addition Lazonick
(1997) argues “skill Bases” form the foundation for people to engage in
collective and cumulative or (organizational learning which in turn is central
to the process of economics development. According to this author, the
foundation of Japan’s success in international competition was investment
in broad and deep skill bases to generate organizational learning. In
addition, some author argue that the process of industrial Deeping and
48
upgrading requires higher levels of skill, know-how and organization in
almost every function.
According to several authors, most of them human capital theoristhuman capital includes those activities for instance on the job training and
off the job training that are likely to increase the productivity of workers in
complex ways. (Woodhdill, 1987). A vast amount of studies on human
capital within the human capital theory framework implicitly assume that
individual productivity is reflected in earnings and thus earnings are often
used as a proxy for productivity (Berker (1964); Schult 1961 and Mincer,
1996). Increased education may enhance workers ability to acquire and
decode information about costs and productive characteristics of others
inputs (Welch 1970:42) education enhances productivity because it is
complementary to other inputs (such as capital) in the firm Griliches (1969)
or because it enables workers to adapt to technological change Nelson and
Phelps, (1966). In this sense, education besides providing a direct
improvement in productivity, also works as a source of information about
the individual’s ability to translate education into skills. We note also that
since human capital is a factor in producing additions to human capital
(Ben Porath, (1967), Becker, (1964), the disadvantage of an impoverished
early human capital stock accumulate over a lifetime.
Neoclassical principles are at the heart of human capital theory. The
wage (the price of labor) constitutes the mechanism of adjustment between
49
supply and demand. Optimizing behaviour motivate economic agents: profit
maximizations in the case of those who demand labour, and maximization
of utility /welfare in the case those who supply it. The quantity of labour
supplied is dictated by the rational (optimizing) choice of workers between
leisure and work. The quality of this labour is determined by past
investment in human capital. The match between demand and supply
occurs in perfectly competitive markets where in particular, perfect
information (about information and wages) exists and frees mobility.
Human capital theory, improving upon the neoclassical background,
which framed it, admits labour heterogeneity when it considers the
investment performed in human capital. This latter emerges as the
explanatory factor for wage differentiation between workers endowed with
distinct productivities. The competitive workings of the market ensure that
for equal work there is a corresponding equal wage. The interest of
employers in maximizing their profit leads them to employ all labour units
that, in marginal terms, lead to an output increase (evaluated in monetary
terms) higher than the cost increase. That is, new employees will be hired
up to the point where marginal productivity (decreasing) equals the wage
the only cost assumed to be supported by employers). From a marginal
productivity schedule the labour demand by the firm in a competitive
situation can be derived. According to human capital theory, firms have an
economic incentive to invest in human capital (Berker,1962). In particular,
50
firms invest in human capital in the expectation of higher future profits
derived from higher productivity level relative to the wage paid. In general,
employers pay educated workers more than uneducated ones throughout
their working life.
Psachaopoulos and Layard (1979) argued that the reason why
employers continue to prefer educated workers is that, not only does the
possession of an educational qualification indicate that an individual has
certain abilities, aptitute and attitude, but the educational process helps to
shape and develop those attributes. This incentive is bound only by the
existence of (eventual) diminishing returns to human capital, as any other
factor of production. Training for skills that are useful for other firms, but for
which there may not be a competitive labour market, and that an externality
may exist. When the labour markets for skills are imperfectly competitive,
firms may be able to increase the future supply of labour through training. It
should be noted that several authors referred to the complementarity
between the different component of human capital, namely, education and
training. In fact, formal education may prepare people to learn more quickly
the specific production skills taught by older workers in on the job training
(Foster 1987,). Hence, people with formal education also tend to receive
more on the job training (Bartel and Sicherman, (1995), Kremer and
Thompson (1998),
51
The clear direction of all the studies surveyed in this section point to
the rationality conveyed by human capital theory namely that of increasing
the quality of the firms labour force, in other words, the quantity of firms
human capital. Education and training are seen to improve performance in
an unproblematic manner by making people more productive workers.
Moreover, factors of production, in particular different types of workers,
may not be so easily substituted for each other as human capital theory
assumes.
Within the human capital theory frameworks it cannot be easily
conceived that in large swathes of seemingly still successful industrial
capitalism there are distinctly low limits on the demands placed on the
education and training system by employers, unless we resort to the belief
that these employers must be ill-informed or irrational. In this context, the
institutional context seems to be crucial in influencing the salience of the
skill formation system. It is not conclusive, however, that more education
and training could remove the institutional barriers. There may still be a
strong case as far as business is concerned for placing strict limits on the
amount spent on raising the skill levels of workforce. For instance, Finegold
and Soskice (1988:22) recognizing the strategic complementarities
between workers human capital investment and firm Research &
Development investment, claimed that the resulting multiple equilibrium
provided the theoretical rationalization for a low skills equilibrium that is a
52
self reinforcing
network return of societal and state institutions” which
interacts to stifle the demand for improvements in skill levels and is
consistent with a rational, optimizing behaviour. Similarly Teixeira (2002)
stresses the rationality of a low skill route in the accumulation pattern of
human capital at the level of the firm. Contrasting with Finegold and
Soskice (1988), however, behaviour is not optimizing but ‘satisfying’. In
concrete, following the evolutionary approach, it is assumed that firms
cannot maximize over the set of all conceivable alternatives due to the
complexity of problems involved. It is assumed explicitly that rational
behaviour of the firm is bounded.
The idea that the spread of new
technology for modern economic growth depended on learning potentials
and motivation that were linked to the development of formal schooling is
also stressed by Easterlin (1981:6).
The more schooling of appropriate content that a nation’s population
had, the easier it was to master the raw technological knowledge becoming
available. Moreover, substantial increases in formal schooling tend to be
accompanied by significant improvement in the incentive structure. Hence,
increased motivation often accompanied increased aptitudes for learning
the new technology.” An important aspect that comes up from the studies is
an increasing recognition that with new technology employers may need to
retain the skill of at least some workers (Bosworth et al 1992). In an
environment characterized by rapid technological change, several authors
53
emphasised the role of top educated and top skilled workers,. In particular
managers, University education is according to Gibbons and Johnston
(1974) crucial for “problem solvers” as it impacts a more general capability
to assess the adequacy of knowledge for the resolution of a problem and to
initiate a search to obtain further relevant information (“knowledge of
Knowledge”)
The more educated a manager is, the quicker he or she will be to
introduce new technique of production; additionally he or she is likely to
adopt productive innovation earlier because his or her ability to understand
and evaluate the information on new product and processes is higher.
Moreover, he or she tends to be quicker to adopt profitable new processes
and product, because the expected payoff from innovation, is likely to be
greater and the risk smaller. In other words such a manager is better able
to discriminate between promising and unpromising ideas and less likely to
make mistakes, Nelson and Phelps (1966).
In one of the first micro studies which related human capital and
technological issue Layard et al (1971) point out that in industries where
technical progress is rapid, firms lose their market, unless they innovate
and therefore they demand qualified personnel. The type of product
explains a good deal of the variation (23%) in the proportion of the labour
force having technical qualifications. The method of manufacture explains
15% of the variation whereas the ‘newness’ of the product explains 32%.
54
The same argument is stressed by Whiston et al (1980). According to these
authors, many highly trained and educated people may be needed to
change the design of products, processes and organizations in an
environment of rapid technological changes. In this context, the argument
goes, a shortage of skilled people (in particular, engineers and scientist)
can result in a failure to develop or delay in developing the planned product
and the production process by which they are to be made (Senker and
Brady, 1989). In the same line other authors ( Welch, 1970; Bartel and
Lichtenberg, 1987; Gill 1989; Booth and Snower, 1996) argue that in a
dynamic context, educated persons can take more advantage of available
technology and thus be more productive. Earlier Collins (1974)
demonstrated that educational requirement is highest in organizations with
a high rate of technological change.
In fact, high levels of education may interact with technological
progress in at least two levels Rebelo (1994); firstly, highly skilled
individuals, who have undergone long periods of formal schooling, are
responsible for the vast majority of innovations; secondly, the effective use
of new technologies often require; high levels of human capital. Pack
(1974) points out that lower efficiency in less developed countries in a
given industry would not necessary imply technical inferiority of older
equipment, rather such differential in efficiency could result from
55
organizational and motivational factors or human skill differential that are
unrelated to equipment characteristics.
3.2 EMPIRICAL LITERATURE
Since the rediscovery of the importance of human resource in
economic development by Schultz and others in the early 1960s, several
studies have emerged in an attempt to empirically determine the growthhuman capital linkage. Most of the studies are macroeconomic and often
seek to explain differences in economic growth rates across countries in
terms of levels and changes in education and human capital, among other
variables (Schultz 1999: 71). In these studies, a number of variables have
been used as proxy for human resource accumulation, thus signifying the
existence of measurement problem. These variables include primary and
secondary level and ratios, literacy rates, educational spending-teachers
ratios, stock of educational capital estimated using perpetual inventor
method, etc. While each of these proxies of human capital has its own
problem, the results of growth studies incorporating human capital yield
conflicting results. For cross sectional studies four categories of results are
easily identified.
The first consists of studies, which show a positive and significant
contribution of human capital to productivity growth. Among these studies
are Hicks (1980), Wheeler (1980), Weede (1983), Landan (1983, 1986),
56
World Bank (1995), Grammy and Assare (1996), Burnett et al (1995), Ojo
and Oshikoya (1995), Barro (1991). Barro’s (1991) study of 98 countries
between 1960 and 1985 used school enrolment rates as proxies for human
capital. His finding is that the growth rate of real per capita GDP is
positively related to initial human capital proxied by 1960 school enrolment
rate. For Romer (1990), human capital is the key input to the research
sector, which generate the new product, or ideas that underlie
technological progress. Thus countries with greater initial stocks of human
capital experience a more rapid rate of introduction of new goods and
thereby tend to grow faster. Mankiw, Romer and Weil (1992) used
augmented Solow Growth model with the product of secondary school
enrolment ratio and the proportion of the labour force of secondary school
age as a measure of flow of investment in human capital. Their results
show that investment in human capital substantially and significantly
influenced per capita -income growth. Even when primary school enrolment
was used as suggested by Romer (1995) and Klenow and Rodriquez-Clare
(1997), the results still show that human capital term is highly significant.
In their own study of East Asia, Burneth, Marble and Patrinos (1995)
indicate that massive investment in both primary and lower secondary
education significantly explained the development “miracle” experienced in
the region. Using varied forms of human capital investment such as school
enrolment, human development index and economic liberty index, Grammy
57
and Assane (1996) have found that human capital formation positively and
significantly contributed to labour productivity.
In their study of African countries, Ojo and Oshikoya (1995) found
literacy rate and average year of schooling to be positively related to per
capita output growth. Using other indices such as school enrolment, they
found that the signs of their coefficients were either wrong or statistically
insignificant. A significant departure from the cross sectional or crosscountry studies is that of Ncube (1999). Incorporating, human capital
variable (proxied by total enrolment) into the standard growth model, he
found a very strong long-run relationship between human capital
investment and economic growth in Zimbabwe.
The second group of studies found positive and significant
relationship between education and human capital. Studies in these
categories
include Benhabib and Spiegel
(1994), Spiegel (1994)
Jovanovich and others (1992), Islam (1995), Caselli and others (1996),
Hoeffler (1999), and Pritchett (2001). Benhabib and Spiegel (1994) used a
standard growth accounting framework that includes initial per capita
income and estimates of years of schooling from Kyriacou (1990) and
found a negative coefficient on growth of years of schooling. This negative
effect of educational growth was found by Spiegel (1994) to be robust to
the inclusion of a wide variety of ancillary variables (e.g dummies for SSA
and Latin America etc) and to the inclusion of samples. Using annual data
58
on a different set of capital stocks. Jovanovich, Lach and Levy (1992) found
negative coefficients on education for a non-OECD sample. Recent studies
based in panel data to allow for country specific effects such as Islam
(1995), Caselli, Esquivel and Lefort (1996) and Hoeffler (1999), consistently
found negative signs on schooling variables. Even Barro (1991) found a
negative impact of human capital on growth when student – teacher ratios
(showing equality of education) and adult literacy rates were used as
proxies for human capital.
Some cross – country studies have shown that the influence of
human capital is not uniform for all countries or group of countries. While a
positive relationship exists between human capital and growth in some
countries, in others the relationship is negative. Lau, Jamison and Luat
(1991) pooled data in 58 developing countries from 1960 through 1986 to
estimate an aggregate production function with average educational
attainment of the labour force as a proxy for human capital. Their finding is
that primary education has an estimated negative effect in Africa due to
ignorance, Middle East and North Africa, insignificant effects in South Asia
and Latin America, and positive and significant effect only in East Asia. For
Africa, they found Secondary education to have negative and significant
effect in Secondary education model. In models with both levels of
education, they found a negative and insignificant relationship for Primary
59
and Secondary education. Other studies in this category include
psacharopoulos (1985), Romer (1989), Diamond [1989].
The fourth category of studies found insignificant relationship between
human capital and economic growth. Behrman (1987) and Dasputa and
Weale (1992) for instance, have found that changes in adult literacy are not
significantly correlated with changes in output. World Bank (1995) also
reports the lack of partial correlation between growth and educational
expansion. In Pritchaett [2001], we find that cross – national data shows no
association between increases in human capital attributable to the rising
educational attainment of the labour force and the rate of growth of output
per worker. Specifically, he reports that the estimate of the impact of growth
in educational capital on growth per worker is negative and insignificant.
However, the association of education capital growth with conventional
measures of total factor productivity is large, strongly statistically significant
and negative. Although, the result varies across countries, the general
finding is that it has fallen below expectations because of perverse
institutional / governance environment, low marginal returns to education
and poor educational quality. Furthermore, Bills and Klenow (1996) argue
that the direction of causality runs from growth to human capital, not from
human capital to growth.
In Nigeria, there are few studies on the direct impact of human capital
on economic growth. Most studies, including Psachoropoulas (1985)
60
Akangbou (1983), Okedara (1978) and Mbanefoh (1980), concentrate on
finding the social and private returns to the different levels of education –
primary, secondary and university, using cross –sectional data. Odusola’s
(1998) study provides a significant departure from earlier studies for
Nigeria. He used real expenditure on education as a proxy for human
capital development (human capital investment). His finding is that real
human capital investments are positively related to growth, although the
relationship is weak. The study also finds a feedback mechanism between
human capital investment and the growth of per capita income.
Empirically, one can often find measures of education attainment that
can be used as proxies of human capital and skills Bates, (1990), Barro
and Lee (1993), (1996); Teixeira (1998), (1999a).
Romer (1989) and
Grossman and Helpman (1994) see human capital as the accumulation of
efforts devoted to schooling and training. The neglect of differences
between human capital (in particular, education) and skills (and between
the different concepts of skills), in spite of introducing some noise into the
analysis, may be empirically justified. The 1980’s were characterized by a
reversal in this critical attitude towards human capital theory. The screening
hypothesis appeared less applicable than the human capital theory, it
sought to replace, and failed to produce an empirically confirmed
alternative theory Blaug, (1976). A detailed study of workers in Kenya and
Tanzania (Knight and Sabot, (1990) using data
on ability, schooling,
61
skills and wages shows that by and large, the effect of schooling on wages
is not a result of signaling, but rather because schooling raises skills and
skills raise wages.
Therefore, since the late 1980’s, education (mainly at higher levels)
became once again increasingly associated with economic performance
issues. In particular, with the revival of research into Economic growth and
the emergence of the so-called endogenous growth theories, an important
role “the engine of growth” (Ehrlich 1990: 84) has been assigned to human
capital. The development of both the Lucas (1988) approach (inspired by
the work of Becker) and Nelson Phelps (1996) approach which assume
complementarities between education and research & development activity
coverage in a positive effect being attributed to educational attainment.
This positive effect was visible in terms of the productivity of workers; with
an important growth enhancing effect. The shift towards human capital
issues and performance was also a consequence of the growing concern
that the education system should be more responsive to expectations from
the economic system. One of the first attempts to rationalize the link of
causality between the economic (industry) and the education sector was
that of Field (1974), who studied education reform and manufacturing
development in mid-nineteenth century, Massachusetts. The shift of the
labour force out of predominantly agricultural or mining into manufacturing
products created a set of social tensions both within and outside the
62
workplace; given universal suffrage, these tensions in turn led to a
perceived need on the part of manufacturers and professionals for a
universal agency of socialization which would ensure a self disciplined,
differential, orderly, punctual and honest citizenry and a labour force which
would work well in manufacturing or bureaucratic units characterized by
administrative hierarchies, while in non-working hours it would go about its
business in an orderly fashion in an increasingly interdependent social
order.
At the level of firm or establishment, neither theoretical nor empirical
studies are as numerous as more aggregated studies. In terms of
economic performance most studies concentrate on the issues of economic
growth or rate of return analysis, whereas, in terms of technological
performance, the bulk of the recent empirical literature is focused on the
assessment of the hypothesis that technological change is based toward
human capital, and thus generates demands for such human capital. Katz
and Murphy (1992) found that the majority of employment shifts in the
industrial and occupational composition of employment toward relatively
skill-intensive sectors reflect shifts in relative labour demand occurring
within detailed sectors. These within sector shifts are likely to reflect skill
biased technological change David and Haltiwanger (1991), Krueger
(1991), Mincer (1991, 1995) Berman et al (1993), Machin et al (1996). At
the level of industry a greater incidence of training was found in industries
63
whose productivity growth (as a proxy of technological change) was fastest
(Lillard and Tan 1986, Bartel and Sicherman 1995). Bartel and Lichtenberg
(1987) reported that relatively more educated workers were employed in
those manufacturing industries where capital equipment was never and
Research & Development expenditures much more intensive. Similarly, a
greater utilization of educated workers and Steeper wage profiles were
observed in sectors with more rapid decade long productivity growth. Gill
(1989), Mincer (1993) found that a more rapid pace of technological
change in a sector generates a greater demand for education and training
of the sectoral workforce as evidenced by: the greater share of educated
workers and use of training, large educational wage differentials within
sectors with rapid productivity growth, larger mobility of educated, young
workers, steeper wage profile in progressive sectors and increase of
separation rates in the short term. Ben- Porath’s (1967) model assets that
more educated workers will train more, simply because human capital is an
input in the production of new human capital. Of the studies surveyed here
(concerning particularly the relation between human capital and economic
performance at firm level) several emphasize the fact that education and
skills may have particular effects at top levels of the firm. Firms hire new
managers and invest in both market and production information. Increased
education may enhance a manager’s ability to acquire and decode
information about costs Welch (1970) and to achieve and operate the best
64
factory organization (Fleming 1970). For pack (1972) managerial skill is in
fact the critical catalytic factor for productivity growth. More dramatically,
Eltis (1996) argues that weakness in management (i.e. the industry’s failure
to recruit those who had achieved the greatest success at the university
stage of their careers) explain the low profitability of UK manufacturing
firms. Focusing on entrepreneurs instead of managers, Fluitman and Ondin
(1991) found that, within a trade, those entrepreneurs who have attended
school for longer are more likely to be successful.
Putting all levels of skills, together, one of the earliest empirical
studies to relate human capital and firm performance, Benson and Lohnes
(1959), concluded that differences in intensity of employment of skilled
personal approach to be systematic and were related to the major process
and market of plants. More recent research shows that labour quality
contributes significantly to explaining inter-firm differences in productivity
(Griliches and Regev, (1995) and significantly impact on the companies
abilities to exploit increasingly returns and enhance the scale of their
operations
demonstrate
Majumdar,
that
(1998).
human
Similarly,
capital
is
an
Lynch
and
important
Black
(1995)
determinant
of
establishment productivity.
It is important to note, however that there are enormous gaps in the
knowledge concerning magnitude of any links between skill formation and
economic performance (Ashton and Green, 1996). Direct evidence
65
regarding the impact of education on productivity is not particularly
abundant, although, virtually all aggregate studies suggest that a positive
relation exists (Fallon, 1997). According to Maglen, (1990), most of the key
links between education and productivity have been assumed rather than
tested. In fact, much of the optimum about human capital’s contribution to
economic growth and development comes from microeconomic evidence,
which associates labour income increases with the improvement of formal
education (the easiest measurable human capital components) and training
Lynch (1989). As Rumberger (1987), reported except in the case of
agriculture, few empirical studies support the motion that education raises
individual productivity, contrasting with the positive view of human capital
theorist Berg, (1970), based on US evidence, concluded that education
generally does not raise the productivity of workers. An education
emphasis by managers in recruitment is justified according to this author,
by the fact that years of schooling are a good indication of the ability to get
along with others, and that more educated workers have greater potential
to be promoted to more responsible jobs. Moreover, it was found that
experience (an important component of human capital was associated with
higher earning but not with higher performance rating in the two firms they
studied. Also, Hotckiss (1993) found that secondary vocational training in
the US (1980) was not effective in raising the wage received. Some studies
even challenge the notion that earnings are directly and positively related to
66
productivity. For instance, Gottschalk (1978) found that wages are not
proportional to productivity either among or within occupations. Additionally,
it remains to be clarified how some forms of skill formation have much more
impact on the productivity of some worker depending on their situation
within the firm
The relevance of human capital to technological competence and
development seems to be universally accepted in the literature, though
empirically the evidence has produced mixed results. In specific sectors,
such as Banking, some evidence suggests little or no relation between
human capital and technological change. For instance, Groot and Grip
(1991), based on a sample of 100 banks in the Netherlands, (1980-1987),
found that the educational structure of commercial employers, managers
and boards seemed to be somewhat less influenced by technological
developments. Similarly, Levy and Murname (1996), concluded that
computerization had increased the bank’s demand for college graduates
but this increase had to do more with the increasing size of the financial
industry than on changing skill requirements within the bank. At the level of
industrial firms, Green et al (1996), found no relation between the
introduction of the technological change and establishments, human capital
accumulation (in effect, their training intensity by occupation). Additionally
Penn et al (1994), based on a 1985 UK survey, documented a tendency
towards a modest increase in the skill and responsibility of the largest block
67
of jobs; instead, they found that many newly created jobs called for few
skills.
According to Schultz (1961), human capital investments namely
expenditure in formal education and training, technically advanced
countries. In Benhabib and Spiegel’s (1994) model it was assumed that the
ability of a nation to adopt and implement new technology from abroad is a
function of its domestic human capital stock, human capital levels directly
affects aggregate factor productivity through two channels: one by
determining the capacity of nations to indurate technologies swatted to
domestic production Romer [1990], the other by influencing the speed of
technological catch up and diffusion Nelson and Phelps [1966]. This
relation between human capital and technological change is also stressed
by Van Zon and Muysken (1996: 44), who argue that “qualities may be of
overriding importance in the face of embodied technical charges, where the
use of new technologies may require workers to have a skill level which
offers enough slack (learning) capacity in order to master the new
production technologies forced upon us by increased international
competition using the stole of human capital (measured by a combination
of literacy and years of schooling) as proxy of social capability in a sample
of 80 countries (1960 – 1985), Hanson and Henrekson (1994) found a clear
effect of human capital on the capacity of assimilating technology from
68
abroad. They concluded that a higher level of capital facilities productivity
growth by technological diffusion from leaders as followers.
Formal education, largely though the provision of literacy, numeric
and general education is likely to generate a ‘basic ability to learn’ that is
vital in the innovation process Foster [1987] and may provide vicarious
experience of a broader world than the individual can personally encounter;
thus presenting to the mind alternatives of environment and of policy and
suggesting opportunities for progress, but also hazard against which
protection is acquired Hirshleifer (1966). Education constitutes, therefore a
source of information (Gibbons and Johnston (1974), which tends to be
highly relevant to ‘decode’ new technical information Lall et al (1993) and to
incorporate it into manufacturing process. Accordingly, the absorption of
new technology skill calls for skill and know-how development though
clearly. Empirically, some authors Barter and Litchtenberg (1987) Wozniak
(1987), Steedman and Wager, (1989), Senker and Senker (1994), Rios
Rull et al, (1996) have proved that the incentives to invest in technology
and particularly in research and development and human capital are
interdependent.
Using case study material Senker and Brady (1989) argued how important
and it is for firm to complement their processes of technological
development with appropriate human capital development strategies.
Similarly for Aoki (1986, 1988, 1990), the prerequisite for the functioning of
69
an integrated structure within the firm involve not only a technical
dimension, but particularly qualification and more precisely the learning and
adaptive capabilities of human resource. In a deeper analysis of the human
capital
technology
issue
Lall
and
Wignaraja
(1997)
found
that
technologically competent firms are larger, pay better, represent much
higher levels of education for the entrepreneur and production managers,
and employ more technical personnel. According to these authors, firms
have reached this large size because they are competent i.e. they invested
in technological capabilities development both earlier and to a greater
extent or more effectively than other firms.
Notwithstanding this, in many of the technologies there were economies of
specialization and size that meant that only large firms could reach efficient
levels of technological capabilities. Moreover, the existence of market
segmentation meant that only firms above a certain size were able to gain
access to the skills, information and credit needed to be competent Stigtitz,
(1989). The fact that competent firms pay better may indicate that they
employ workers with higher skills levels, give more training and then offer
higher wages to retains workers with higher skills levels give more training,
and then offer higher wages to retain workers.
As referred earlier, most of the studies that concentrate on human
capital related issue, namely those associated with human capital theory
implicitly assume that survival is not problematic. In fact, most of the
70
existing empirical studies, both those that use database analysis (eg Bartel
and Lichtenberg, (1987), Bartel (1989 1991), Michie and Sheehan (1998)
or case studies, (e.g. Blanch Flower and Burgess, (1996), Mason and
Wager, (1998) neglect the issue of survival, focusing their analysis, on firm
that are in business at the time of survey or study.
Firms, however fail at rates that are too high to support the contention
that survival is easy. A large proportion of firms do not survive as
identifiable units beyond their first few years, and only a small proportion
achieves significant growth. (Mansfield (1962), Mata and Portugal (1994),
Baldwin (1995). Combe (1965) argues that literacy and schooling raise
practically every variable that encourages the formation of organizations
and increase the staying power of new organizations. Along the same lines
Lall et all (1993) argue that the fact firms fail to grow and move into large
size groups may reflect lack of internal capacities to compete and grow and
or lack skills, information or vision on the part of entrepreneurs that would
allow them to seek the right input or adopt the right business strategy.
The matter of survival has been given less attention in the literature
on education and skills than it plays in industrial dynamics. These studies
nevertheless refer only in passing (or give only scant attention) to human
capital as a relevant variable for firm or establishment survival. The main
concerns of the studies of the demography of firms are concerned with the
relation between size and growth Simon and Bonimi (1958), Hymer and
71
Pashigian (1962), Mansfield [1962], Ghemawai and Nelebuff (1990),
Lieberman [1990], Dunne and Hughes (1994), the magnitude of job
creating and destruction flows Carneiro (1995), Baldwin et all 1998) or the
relative importance of industry and macroeconomic factors on firms survival
performance Mata (1993), Mata and Portugal (1994). Stiglitz (1989),
however point out that many firms fail to grow and get into larger size
groups due to the segmentation in factor markets –small firms find it more
costly than large firms to obtain the inputs, credit, skills or information they
need. Those studies that mention the human capital variable do so in a
rather marginal way and mostly in relation to the process of entry and
location of US plants, Carlton (1983) concluded that having a pool of
technical expertise in a region seems to matter only for the most technically
sophisticated
industry
encompassed
new
(communication).
entries
in
fabricated
Carlton’s
plastics,
(1983)
study
communication
transmitting equipment and electronic industries in the US between 1967
and 1971. Also, relative to entry process, storey (1986) restricting his
analysis to the cavity of Cleveland in England, found that individuals
working in large firms are unlikely to have the breath of knowledge of
otherwise comparable individuals working in a small firm and that their
opportunity cost (wage forgone) is likely to be higher too. Hamermesh
(1988), from another perspective found that additional years of schooling
by workers, ceteris paribus, reduce the probability of plant closure, whereas
72
tenure had only a small influence on that probability. Plants closures in
Hamermesh’s(1988), study are not effective exits but are proxied by the
number of ‘displaced’ workers. The sample encompasses 2,636 workers in
the US in the period 1977-1981.
Empirically, research on the link between human capital and survival
is scare. Those few studies, which focused explicitly on this link, were that
of Bates (1990) and more recently, that of Teixeira (2002). The first study
was based on a sample of 4,429 firm entrants between 1976 and 1982 in
the US; the author found that the likelihood of business discontinuance fell
sharply for the owner education groups having four years or five –plus
years of college, and that college education improves access to debt
capital; this offers an alternative explanation for the survival of small
business; human capital inputs, are in part the cause of financial capital
inputs, and the latter variables may be true predictors of firm survival. The
study of Teixeira (2002) relating plants performance with firms human
capital accumulation pattern, focus essentially on concept of firm fitness,
that is firms survival capacity; the estimated logistic model provides
statistical evidence that it is more profitable for a textile establishment, in
term of fitness or survival capacity to maintain inertia (characterized by
employment of no top educated or top skilled workers) than to hire an
individual with high levels of human capital.
73
The clear direction of all the studies respecting human capital and
firm performance pointed to the rationality conveyed by human capital
theory namely that of increasing the quantity of firm human capital. In this
context, it cannot be conceived that in large swathes of seemingly still
successful industrial capitalism, there are distinctly low limit on the
demands placed in the education and training systems by employers,
unless one resorts to the belief that these employer must be ill informed or
irrational. This exercise of reviewing existing literature permitted thus to
uncovers the little attention given by the majority of studies within human
capital theory upon the determinants of demands for human skills and how
those demands change. Moreover, it put forward the need and interest in
analyzing how the composition of human capital accumulation can be
shaped by demand and the role of social and institutional context may
influence it.
3.3 Limitations of Previous Studies
A lot of studies have been done in this area but the limitation that is
evident in the previous works reviewed is that no state level analysis has
ever been done. Most of the empirical and theoretical work reviewed did
not consider the general impact on the state level; hence this work is on a
state level analysis. In essence, most of the studies reviewed were
international and cross-country based. Unfortunately, the research and
74
Development as a variable in human capital is neglected in most of the
analysis. It is this gap that we intend to fill using Enugu and Anambra State
as a case study.
75
CHAPTER FOUR
RESEARCH METHODOLOGY
4.1 Theoretical Framework
This section provides an informal overview of the key features of the
model. We begin by applying the standard growth accounting framework as
adopted by Covers (1994) that has been used extensively for studying the
productivity of inputs such as capital, labour, energy and research and
development (R&D) (Berndt, 1991). We assume that the production
process of the firms in our sample can be represented by a production
function (F) that relates firm value – added (Q) to four inputs: ordinary
capital stocks (K), computer capital stocks (C), Labour (L) and, in some
cases, R & D (R).
In addition, we assume that the production function is affected by
time (t), and the industry (j) in which a firm (i) operates. Thus:


Qit  F K it , Lit , Cit , Rit , i, j, t    (1)
Following common practice, we assume that this relationship can be
approximated by a Cobb Douglas Production function and its variants. The
Cobb-Douglas functional form has the advantage that it is the simplest form
that enables calculation of the relevant quantities of interest without
introducing so many terms that the estimates are imprecise. More general
functional forms such as the transcendental logarithmic (translog) have
been utilized in research on the levels of computer investment and
productivity (Brynjolfsson and Hitt, 1995) with similar results. For most of
76
our analysis, we implement this function with three inputs: ordinary capital,
computer capital, and labour, written in levels or logarithms of levels
(Lower-case letters denote logarithms; firm and time subscripts on inputs
are omitted hereafter).


Q  A i, j, t K B1 LB 2 C B 3      (2a)
q  a (i, j , t )  B1 K  B2 L  B3    (2b)
The term a, often referred to as multifactor productivity, captures
differences in output across firms and overtime that are not accounted for
by capital or labour. This productivity framework is usually implemented in
time series or panel data setting by taking the time difference of each of the

factors, with x representing the time difference of x :





q  a  1 K   2 L  3 C      (3)
For each firm in each year, the output elasticities of non-computer inputs
(B1, B2) are set to equal their theoretical value. Under standard
assumptions (cost minimization, competitive output and input markets, and
factor quantities in long-run equilibrium), this equals the ratio of the cost of
the input to the value of output. Estimating these elasticities by averaging
factor input shares over the current and previous years, and rewriting the

equation as a function of multifactor productivity growth  a , where
 
subscripts refer to time period, and r,w,p are the real price of physical units
of capital, labour and output respectively, yields:
77
a  q  1 2 rt kt Pt Qt  rt 1 K t 1 Pt 1 Qt 1  K  12 wt lt Pt Qt  wt 1lt 1 Pt 1Qt 1 i  3 c ----------- (4)




The output elasticity of computer capital β3 could be calculated using a
formular similar to that for ordinary capital. Alternatively, multifactor
productivity growth can be first estimated excluding the contribution of
computers. Then this estimate can be used to estimate the computer
elasticity by regression after adding an error term, assumed to satisfy the
standard assumptions necessary for ordinary least squares to be unbiased
and efficient):




a1c    3 C   ----------------- (5)


Where: a1c  q  1 2

rt kt
 rt 1Kt 1 Pt 1 Qt 1  K  1 2 wt lt Pt Qt  wt 1lt 1 Pt 1Qt 1 i      (6)

Pt Qt
(Coefficients with hats ^ represent econometric estimates).
This approach, which was employed by Adams and Jaffe (1996) to study R
& D productivity provides unbiased estimates when all factors are in
competitive equilibrium. However, as shown by Berndt and Fuss (1986), it
may give biased estimates if a quasi-fixed factor, such as capital, is not in
equilibrium. In this case, the value of the service flows from that factor can
be adjusted to give accurate estimate of productivity growth. In particular,
Berndt and Fuss show that the expected ex post shadow rental price of
capital should replace the ex ante rental price in calculating inputs shares,
and that the expected shadow rental price of capital (zt) can be
approximated by multiplying the traditional Hall – Jorgenson ex ante rental
78
price by Tobin’s q (Ø), which is the market value of the firm divided by the
replacement cost of its physical capital stock.
Tobin’s q incorporates information on the expectations of investors
regarding the future input and output prices and thus the shadow price of
installed capital. We implement this approach by estimating equation (5)
using the expected shadow price of capital (zt = Qtrt) in place of the capital
rental price (rt), where Qt is a normalized value of Tobin’s q for each firm in
each year. In addition, the traditional growth accounting framework may
also attribute charges in market power and economies of scale to
productivity growth. Whether these gains are legitimately part of
productivity growth is a matter of interpretation.
4.2 OUTLINE OF MODEL SPECIFICATION
The standard methodology of growth studies begins with the neoclassical
(So/ow) (1957) production function of the form
Yt  AT F ( KT LT )                                      (1) wh
ere Yt is aggregate real industrial output, K is the capital stock, L is labour,
A is the efficiency factor and t is time dimension.
The emergence of endogenous growth theory and models
Romer
(1986) and Barror (1991) suggests that other endogenous factors such as
government policies (government spending and tax, trade policy, etc),
political stability, market distortion, human capital, etc, can affect
productivity. In other words, it is possible for productivity to occur without
79
exogenous factors such as changes in technology or population.
Accordingly, several studies (for example those reviewed by Renelt 1991
has attempted to integrate exogenous forces with endogenous factors in
explaining productivity across industries.
The model for this study follows Covers (1994) and Ncube (1999). In
general, the following formulation was employed.
Y=  0 +  1 T +  2 E +  3 M C +  4 R + V i ……………(2)
 1 ,  2 ,  3 ,  4 ,>0
where Y = Industrial output as a proxy for industrial productivity.
T= Expenditure on training
E= Expenditure on education
Mc=Expenditure on Medicare
R= Expenditure on research
Intuitively, all the four explanatory variables are expected to have positive
effects on the productivity level.
This is due to the intention of finding out
whether they contribute to the productivity process of industry or otherwise.
The  ‘s are coefficients to be estimated and their signs are expected to be
positive, Vi is of error term. (Pure white noise)
4.3 METHOD OF ESTIMATION/EVALUATION
Estimation was done by standard cross sectional econometric
technique like OLS and principal component analysis:- a multivariate
choice method. This approach develops a composite index by defining real
valued function over the relevant variables objectively.
Given a set of
explanatory variables, if we have to select the most important variable or a
80
limited numbers of variables from the set, principal component Analysis is
useful.
The principal of this method lies in the fact that when different
characteristics are observed about a set of events, the characteristics with
higher variation explains a higher proportion of the variation in the
dependent variable compared to a variable with lesser variation in it. It is a
tool used to construct a composite index in such a way that the weights
given maximizes the sum of the squares of correlation
Therefore, the
issue is one of finding weights to be given to each of the concerned
variables Weight to be given to each of the variables is determined on the
principle that the variation in the linear composite of these variables should
be the maximum. Once the weight to be given to each of these variables is
decided, we can focus on the important variables in order to reduce the
noise in the data. A set of assumptions has been used in our method of
construction of a composite index. These are: The condition of weak pareto rule demands that when a state
registers values of indicators uniformly higher than those of the other
- the former should have a higher ranking than the later ones;
 The condition of non-dictatorship implies that no single indicator
should be considered so significant as to determine the final ordering
all by itself;
81
 The condition of unrestricted domain implies that the method should
be capable of giving the final ranking for all possible data matrices;
 The final condition is that of independence from irrelevant
alternatives, which demands that while ranking too, the decision must
be guided by the values of the indicators for these units under study
alone and not by any other irrelevant phenomenon.
The application of Factor Analysis in this specific case has been accepted
in objective ranking of the regions. This method enables one to determine
a vector known as the first Principal Component or Factor, which is linearly
dependent on the variables, having the maximum sum of squared
correlation with the variables.
The weights given to the indicators are chosen in such a way so that the
Principle Components satisfy two conditions:
(a)
The numbers of Principal components are equal to the number
of indicators and are uncorrelated or orthogonal in nature.
(b)
The first Principal Component or P1 absorbs or accounts for the
maximum possible proportion of variation in the set of the
indicators.
This is the reason why it serves as the ideal
measure of composite index..
4.4 DATA SOURCES AND MEASUREMENT
From the model, it is clear that the needed data are industrial output,
expenditures on
training, education, Medicare, Research and
82
Development. We used the survey method to collect primary data from the
industries in both Enugu and Anambra State. The data were collected at a
particular point in time -2007, during which the data units were known and
made available. In summary, the sources of the data
were through:
(a) Annual industrial report and statement of account of the
Manufacturing industries
(b) Questionnaire method.
4.5 POPULATION OF THE STUDY
The population of 191 manufacturing industries used in this research
are the functional industries in Enugu and Anambra States as supplied by
the ministry of commerce and industry in the two states. They include both
the government and non-government industries.
4.6
INSTRUMENTATION
The questionnaire was the basic instrument used in the data collection; it
contains 28 questions that raised the data for the estimation of the model
and testing of the hypothesis.
The questions were made simple and
straight to the point. In the preamble, we introduced the researcher and the
purpose of the questionnaire. Then, we appealed to the respondents to cooperate with the researcher to enable him achieve his objective. The data
sought were sensitive information, which the firm, could not release
83
carelessly and with ease. A copy of the questionnaire is attached at the
appendix.
4.7
Sampling Unit
The sample unit refers to the person who would answer the questions
in the questionnaire? Because of the nature of data we sought we gave
the questionnaire to the managers, who directed them to the accountants
and other relevant officers of the industries of the engineering department,
etc.
4.8
Sampling Method and Validity of Instruction
The industries were scattered to all the local government areas in the
two states. We conducted a pilot survey to ensure that the survey is valid.
We use 25 manufacturing industries for the pilot survey. We discovered
that some of the industries were not functional or dead. In addition to the
written appeal we personally persuaded the managers to complete the
questionnaire. After distributing the questionnaire and intensive follow up
visits that lasted for 8 months we were able to receive well-completed
questionnaire, which we used for the analysis. Some of the industries
produce more than one output but all these were incorporated in our
analysis.
4. 9 Reliability:
On this the questionnaires that were pre-tested were matched with the
data required to estimate the model, which tests the hypotheses.
84
This indeed verified the applicability and reliability of the questionnaires
before it was formally distributed.
85
CHAPTER FIVE
ANALYSIS
5.0
AND
EVALUATION
OF
RESULTS
INTERPRETATION OF RESEARCH FINDINGS
In this section, we begin by presenting the outcome of the data
analysis. We also analyzed the result and tested the hypothesis with a
view to determining whether to accept or reject our hypothesis. The results
on KMO and Bartlet’s test and the Principal Component Analysis tested
were also shown
Table 5.1 Presentation of Regression Results
Variable
Coefficients
t-statistic
Prob.
0.94
Standard
Error
0.37
Training
2.53
0.01
Education
1.20
0.22
5.32
0.00
Medicare
1.36
0.91
1.49
0.14
Research
0.59
0.27
2.20
0.03
Constant
0.04
0.27
0.15
0.87
Dependent Variable; Output
R-Squared ( R 2 ): 0.58
F-Statistic 63.05
Durbin Watson: 1.64
Prob (F-Statistic) 0.00
5.2
MODEL COEFFICIENT (PARAMETERS) AND DISCUSSION OF RESULT.
The major focus of the research is to empirically determine the impact of
human capital on labour productivity in Enugu and Anambra States. The
significance of the regression model was tested at 0.05 levels. The Pvalues in the table [table5.1] were obtained from the sample used in the
86
analysis. A P-value less than the level of significance show that the
associated coefficient is significant. Otherwise, it is not. In table 5.1, all Pvalues associated with the variables in the regression model are less than
0.05, showing that they are all significant variables affecting productivity. In
this research labour productivity (proxied by output) was considered the
dependent variable in the regression model.
Table 5.1 shows that all
factors (explanatory variables) supposed to affect output exert a positive
and were statistically significant on the dependent variable, except
Medicare.. All also met “a priori” theoretically expectations.
Training, the proxy for human capital had a coefficient of 0.94. By
implication, therefore a unit increase (that is, a one million naira input in
training would lead to an increase in labour productivity of about 0.94
million naira, all other variables held constant. The P-value of 0.01
associated with this variable showed that the impact of this variable,
training was significant. In the authors viewpoint, training and productivity
are strongly correlated concepts considering that productivity increments
result from working more intelligently rather than harder. The reason here
is that the productivity of enterprises cannot increase without concomitant
training of the workers they employ.
Training is here envisaged in the
overall perspective of the organization, as an effort to educate individuals. It
is related to approaches of knowledge, management and organizational
learning wherein the concept of training is extended to the whole of the
87
organization.
It
provides
significant
contributions
to
productivity
improvement and at the same time reduce retention problems, if a career
development program is to work, it must be pragmatic, first, it has to be
developed and implemented within the environment of each individual
company. Second, it has to have sufficient flexibility to allow for the specific
career requirements of each individual company. Only the right type of
training can lead to increase productivity, Training is acquired at a cost and
is intended to increase workers productivity.
Education (a proxy for human capital,) with a coefficient of 1.20 indicated
that a one million-naira increase in capital expenditure would increase
Labour Productivity by 1.20 million naria. This was really expected, since
Education is a critical factor that should impact Labour Productivity.
Education creates the needed manpower with enhanced skills for
technological innovation and productivity growth. Workers with high level
of education are assumed to be more efficient in working with the
resources at hand, and these workers produce more physical out put. In
other words education increases the effective labour input from the hours
worked. Therefore, a better educated labour force shifts the production
possibility curves outward. An increase in the proportion of intermediate or
highly skilled workers relative to low skilled workers increases the
productivity level of physical units Productivity shows output per unit of
input implored.
Increase in productivity comes about from increased
88
efficiency on part of labour if there is one fixed input factor to produce two
goods or varieties, education may improve the total revenues of firms by
means of a better allocation of the input factors between the alternative
outputs.
In fact, education seems to provide the skill to make better
decision based upon the available information. Further implication of the
analysis is that better educated workers are more able to adapt to
technological change and will introduce new product technique more
quickly. Nelson and Helps (1966) stated that educated people make good
innovators so that education speeds up the process of technological
diffusion. Bartel and Lubtenbery (1987) in the same vein stressed the role
of education in decoding and understanding information in performing a
job.
A higher level of education increases the ability to discriminate
between more and less profitable innovations and reduces the uncertainly
about investment decisions with regard to new processes and products.
Therefore education increases the profitability of successful and early
adoption of innovations.
Higher proportions of intermediate and highly
skilled workers, relative to low skilled workers would be depicted to lead to
more rapid and successful adoption of innovations and higher productivity
growth. In other words, a low quality human capital will contribute little or
nothing to labour productivity while a high quality human capital will
enhance productivity.
89
Medicare also met “a priori” expectation, having a positive coefficient of
1.36. By way of interpretation, when there is a one million-naira increase in
the expenditure on Medicare, this would affect Labour Productivity
positively to the tune of 1.36 billion naria. The p-valve of 0.14 is evidence
that this impact was not significant. The link between Medicare and
productivity is not so tight as we thought,. Good health care is a kind of
motivation. It is closely interrelated to productivity according to the neoclassical economists. Successful integration of health care services results
in a management style and organizational form that supports the
accomplishments of productive work. It is very clear that most of our
employers and industries pay non chalant attitude at implementing
legislation that seem to favour workers whilst government sometimes feel
unconcerned about the breach of these legislation by employers.
This
shows why workers are not taking their jobs seriously as they seem to lose
interest on working. The framework identifies five major areas contributing
to performance and behavior; Personal effort, knowledge and skill, attitude
and values, environment, Health and sense of direction.
These areas
define the impact of training, personnel development management style;
project control, performance planning, and personal job costs to the worker
are placed in a unified concept. The framework provides a view of how to
make these factors work together to improve productivity; Economists
agree that in the long run productivity growth is the principal source of
90
improvements in living standards of workers. The link between productivity
growths and the standard of living of the average person is somewhat
looser in the short to medium run,
Research is another factor considered to impact on Productivity. This had
a parameter estimate of 0.59, indicating that a one million naira increase in
expenditure on Research add about 0.59 billion naira to the total labour
output. More so, this impact was significant, as the associated P-value of
0.03 tells. This is in line with economic principle
Research according to
Schultz (1962) stimulates a new level of interest in the relationship between
education and the industry. The research effort refers to the role of higher
education as an important input factor in research is a key factor for
technological progress and productivity growth.
Also Englander and
Gurney (1994) that activities are very complex; a relatively large proportion
of intermediate and highly skilled workers is a prerequisite to increase in
technological knowledge and achieve productive growth. One of the reason
why young or even pioneer industries are protected is to encourage
creatively and scientific breakthrough in our industries and increase
productivity, thereby providing job opportunities.
It is no doubt that
research enhances quality of production and can lead to the development
of better, more efficient and more productive workers. These approaches
were typically driven by the supply side of economics and by the
neoclassical notion of equilibrium in which supply of education will create
91
its own demand.
It is possible, that University education was counter
productive to achieving some types of work discipline including
authoritarian control of work content and the acquisition of manual skill (for
which experience is a better teacher).
5.3 TEST FOR MODEL ADEQUACY
According to theory, good R-squared values with a significant F-statistic
are sufficient indicators of the good fit a given model provides to a given
data set. In this analysis, the linear regression model produced an Rsquared value of 0.58 and an F-statistic of 63.05, which had a P-value of
0.00. By all econometric standards, these statistical pieces of evidence
showed that the linear regression model proposed for this research was
adequate.
5.4 TEST FOR NORMALITY
The data set used in this analysis was not normal as evidenced by the
Jarque Bera normality test. The Jarque Bera statistic of 11863.72, with a
probability value of 0.00 confirmed this fact.
However, according to
Gujarati (2003), when the number of observations is reasonably high, the
disturbance error term (ui) is normal. Thus this may not be a case to
discuss since we only had 191 observations in this analysis.
5.5
TEST FOR MULTICOLLINEARITY
Multicollinearity is one property of OLS that assumes that the
regressors are not correlated, as to distinctly attribute particular impacts on
92
the regressand to particular regressors. One
way to find out this is by
taking linear (simple) correlation of these regressors, and Gugarati (2003)
recommends if any of the correlation values is in excess of 0.8, then one
should suspect the presence of multicollinearity.
In this research, the
largest correlation value occurred between Training and Education, and it
was 0.48. By the above standard, therefore, these assumptions were
satisfied.
5.6
TEST FOR AUTOCORRELATION
The technique adopted here was the Durbin – Watson d-test. This test
gave us a value of 1.64 (which is approximately 2, to 1 decimal place) since
theoretically, a d-value of 2.0 implies zero autocorrelation. We may say with
some degree of confidence that autocorrelation was not a serious problem
in our model estimation. Thus
the disturbances did not have such a
serious serial correlation that may have undermined our results.
5.7 TEST FOR HETEROSCEDASTICITY
White’s test for heteroscedasity was adopted in this analysis to ascertain
if the disturbances had constant variance.
This was not the case as
evidenced by the F-statistic of 11.10, which had a P-value of 0.00. The
presence of heteroscdedasticity was a serious problem, because it
rendered the parameter estimates inefficient.
That is, the regression
coefficients no longer had minimum variance.
To correct this, White
Heteroscedasity – Consistent Standard Errors and Covariance was applied
93
while re-running the regression model.
Gugarati [2003] says that this
approach corrects such problem, by producing efficient parameter
estimates
5.8 TEST OF WORKING HYPOTHESES
In this research, it was hypothesized that human capital does not
affect productivity in the manufacturing industrial sector of Enugu and
Anambra States. And that where it did, the effect would not be significant.
This was verified by finding out statistically whether the parameter values of
the regressors were significantly
different from zero.
Thus, we
hypothesized:
H 0 : B1 = B 2 = B 3 = B 4 = 0
Vs
H 1 : At least one B 1 is not equal to Zero. Where B 1 through B 4 were
the regression coefficients of Training, Education, Medicare and Research,
respectively
TABLE 5.1 gives the following P-values for the various regressors:
Training:
0.01, Education: 0.00, Medicare 0.14, Research 0.03.
The
research hypothesis was tested on the 0.05 level of significance; It is clear
therefore that only Medicare was non-significant. Thus the null hypothesis
was rejected and the alternative upheld. In conclusion, human capital does
affect Labour Productivity in the industrial sector of Enugu and Anambra
State. And this impact is statistically significant as evidenced by training,
94
education and research out of the four repressors, which had P-values less
than 0.05. The t- statistic are commonly used in hypothesis tests to
determine the statistical significance of the parameters of econometric
models and are calculated as the ratio of the estimated parameter value to
its standard error.
5.9 FURTHER ANALYSIS – PRINCIPAL COMPONENT ANALYSIS (PCA)
The Primary objective of the principal component Analysis done in this
study was to determine the structure of the underlying variables accounting
for the impact on labour productivity.
These variables were Training,
Education, medicare and Research.
The principal component or factor analysis was also adopted in this
research to find out which components (firms) in the industrial sector of the
states under study had the greatest impact. We may not present the entire
tables generated using statistical package for Social Sciences (SPSS) in
this section, as they are so large. However, we have attached them in the
Appendix. Our analysis showed that all variables considered but medicare
had very significant impact on labour productivity. However, the strength of
the impact was highest with Training, Education and then Research.
Therefore, manufacturing industries in Anambra and Enugu States are
advised to devote more resources to these factors for better output.
95
5.9.1 KMO and Bartlett’s Test
The Kaiser – Mayer – Olkin Measure of sampling Adequacy measures the
proportion of Variance in the variables that are caused
by underlying
factors. The KMO coefficient of 0.67 is an indication that factor analysis
was
appropriate with the data set.
sphericity
Similarly, the Bartlett’s Test of
having a significance value of 0.00 (P< 0.00) supports the
Evidence by the KMO test. Thus the data set was suitable for structure
detection
KMO Measure of Sampling
Adequacy
0.669
103.959
6
0.00
Approx X 2
df
Sig.
Bartlett’s Test for
Sphericity
COMMUNALITIES
Initial
Extraction
Training
1.00
.611
Education
1.00
.553
Medicare
Research
1.00
1.00
.246
.532
96
The “Initial Communalities” measure the proportion of variance accounted
for in each of the four variables by the rest. Extraction Communalities” are
estimates of the variance I in each variable accounted for by the factors in
the factor solution.
For all variables except Medicare, the Extraction
Communalities are greater than 0.5, indicating that only Medicare may be
considered unsuitable for inclusion in further analysis.
Component Extraction
Initial Eigenvalues
Extraction
Loading
Sums
of
Squared
Total
%
of Cumulative %
variance
Total
%
of Cumulative
variance
%
1.943
48.570
48.570
1.946
48.570
Component
1
2
0.906
22.655
71.224
3
0.644
16.089
87.314
4
0.507
12.686
100.00
48.570
On this analysis it was specified that only components having eigenvalues
greater than 1 is extracted. Thus there is only one extracted component,
accounting for about 49% of the total variation in the variables, with a loss
of information of about 59%. The rotated sum of squared loadings was not
generated here because only one Component was extracted. The score
plot confirms the extraction of one factor.
The Steepest of this graph
corresponds to this component
97
98
5.9.2 COMMUNALITIES
Both for the initial and after-extraction, these communalities were 1, for all
the factors or components, that is, for all firms since communalities are an
estimate of the variance in each variable accounted for by all components,
these high communality values showed that the extracted components
represented the variables (firms) well.
Table 5.2: INITIAL EIGENVALUES/EXTRACTED SUMS OF SQUARED
LOADINGS
Component
Total
Percent of variance
Cumulative percent
1
178.590
93.502
93.502
2
4.436
2.322
95.825
3
4.222
2.210
98.034
4
3.752
1.965
100.000
The table specifies the variances of each of the extracted components .For
the initial solution, the first component accounted for 93.502 percent of the
variation in the original variables. And this variance was 178.590. In a
similar fashion, the second component accounted for 2.322 percent of the
total variability, while the first and second components accounted for
95.825 percent of the total variability kin the original variables. Overall, the
four components in this initial solution accounted 100.00 percent of all
variability in the original variable.
The rest were redundant, thus we
truncated the above table.
99
The extracted sums of squared loadings told us that four components were
extracted; the instruction was that every component with eigenvalue. > 1
should be extracted.
Table 5.3: ROTATED SUMS OF SQUARED LOADINGS
Component Total
Percent variance
Cumulative Percent
1
50.077
26.218
26.218
2
50.039
26.198
52.417
3
46.603
24.399
76.816
4
44.282
25.184
100.00
This table displays the extracted sums of Squared Loadings in an even
distribution. The varimax rotation maintains the cumulative percentage of
variation explained by the extracted components, which is now spread
more evenly over the components. With the significant changes in the
individual totals, the rotated component matrix will be easier to interpret
then the unrotated matrix.
5.9.3 THE ROTATED COMPONENT MATRIX
Please see appendix for the matrix. This tests which industry has the
highest productivity ratio.
The Rotated Component Matrix helped in
determining what the extracted components were.
Now, the first
component had the highest correlation coefficient of 0.888, and this was
associated with Onitsha Aluminum Manufacturing Company, Onitsha. It
should also he observed that this component was least correlated with
other components.
The second component was most highly correlated
100
with G.M.O. Rubber Products, Onitsha, having a correlation coefficient of
0.879. This was also least correlated with the rest of the components.
Furthermore, the third component was most correlated with Altra Industries
Ltd., Onitsha, with a correlation coefficient of 0.875. The forth and last
component had its highest correlation coefficient as 0.863, and this was
associated with Ejiamatu Group of Companies, Nnewi.
By inference, Onitsha Aluminum Manufacturing Company: G.M.O. Rubber
Products, Onitsha, Altra Industries Ltd; and Ejiamatu Group of Companies,
Nnewi, formed the hub of private sector industrial activities in this study.
5.9.4 THE COMPONENT SCORE COEFFICIENT MATRIX
This was obtained by multiplying each case’s original variable values by the
component’s score coefficients.
The resulting four component score
variables were representative of the 191 variables. They could also he
used in place of these 191 original variables (companies) for further
analysis, with zero loss of information. However, it is better to use the
scores of the saved components as against these variables themselves.
This is because the components were representative of all 191 original
variables, as they were not linearly correlated with each other.
6.3
COMPONENT MATRIX
Component
Training
.782
Education
.744
101
Medicare
.496
Research
.730
The above table presents the component matrix. The component that has
the highest coefficient is Training, with a value of 0.782. Thus Training has
the highest impact on labour productivity. Even though the other variables
were not extracted, education, having a correlation value of 0.744, is next
to Training in terms of contribution to productivity
This is followed by
research, and finally Medicare.
102
CHAPTER SIX
SUMMARY, POLICY IMPLICATION AND CONCLUSION
6.1
SUMMARY
In this work, we have undertaken an empirical investigation to
determine the impact of human capital on Labour Productivity in Enugu and
Anambra States. The results show that investment in human capital in the
form of education, training and research can lead increase in to productivity
in the manufacturing industries. It takes into account all the opportunities,
Strategies and Challenges that might face the process of productivity.
The research contributes to the idea that human capital development
remains the centerpiece and very central to the promotion of efficiency and
Productivity in the manufacturing sector of Enugu and Anambra State. As
a result, it encourages industries to evolve a process of expanding choices
and developing capabilities of the people in all economic, social and
cultural activities through which the skills, knowledge, productivity and
inventiveness are enhanced for a better and more meaningful life.
The problem remains however that the manpower mix of the tertiary
graduate outturn for instance often fails to reflect the true manpower needs
of the industries. The rising graduate unemployment is clearly a reflection
of the insensitivity of manpower producer to labour market requirement.
This calls for the establishment of a strong tripartite linkage between the
manpower planners, producers and employers for effective coordination
between the world of learning and the world of work.
103
6.2
POLICY IMPLICATION
The results have far reaching policy implications as they suggest that
the development of skills and knowledge coupled with their effective
utilization is important for the growth of manufacturing sector.
empirical results have provided some
The
important findings that could aid
policy formulation aimed at promoting efficiency and productivity growth in
the manufacturing sector and the economy as a whole.
However, the gains can be maximized if the right kind of education and
training is given to the human resources of the nation, and if they are fully
utilized in order to increase the productive capacity of the manufacturing
sector.
In order to accelerate productivity in our industries the government should
provide an enabling environment by ensuring macroeconomic stability that
will encourage increased
the private sector.
investment in human capital by individuals and
Other motivating factors like job opportunities,
enhanced wage structure and improved working conditions
will also
encourage increased investment in human capital by private individuals.
It is the pursuance of the state government determination to improve
the quality of life of the citizenry.
In a developing nation like ours,
productivity improvement becomes unrealities without the catalystic effects
of technology. Improvement in research contributes to cost reduction and to
innovations that lead to better ways of production. It also enhances the
104
quality of production as well as the safety of workers.
Research
development can also have a synergetic relationship with labour
productivity in that better research can lead to the development of better
efficient and more productive workers.
In solving the problem, some of the approaches of government, its
officials and agencies to matters affecting staff motivation and productivity
have to be realigned. The same will apply to employers of labour and
workers themselves. For example why should government or parastatals
contract services for which they have employed and trained staff who are
on payroll? . Why are employers in the private sector very slow or
nonchalant of implementing legislation that seem to favor workers whilst
the government sometimes feel unconcerned about the breach of these
legislations by employers? Why are workers not taking their jobs seriously
as they seem to loose interest on working?
The reason why government should protect and encourage young, old or
even pioneer industries is that apart from encouraging productivity, it will
also encourage creativity and scientific breakthrough by our scientists and
provide job opportunities for Nigerians. It is our belief that the two states
are blessed with enough human resources in almost all fields of education
and training but the major problem has been the management, the
motivation and allocation of these human resources in a manner that will
enhance and sustain productivity. Positive steps should be taken to support
105
the non-formal education and training. For instance mobile library services
should be extended to the industries so as to encourage and stimulate
private reading. This will augment the adult education programme that is
currently undertaken in many of the schools. This would help to raise the
literacy level in the whole society. The industries should participate in the
provision of this free and compulsory education scheme by inductively
contributing half of tuition fees while the government contributes the other
half. As a way of encouraging and stimulating good reading habit, each
primary and secondary school should be made to have a library, people
can be appealed to donate books to the library in the schools in their own
Local Community. We cannot deny the usefulness of libraries in stimulation
and dissemination of knowledge. Too, employers are the direct
beneficiaries of investment in production of knowledge and skills. To this
end, it is expected that they should be involved in the education and
training of people. They should contribute by sponsoring the training of their
employees. This has the advantage of ensuring that the skills developed
are those the respective employers actually need.
In
the case of Medicare, the present campaign against patronage of
quacks should be continued; medical centers should be provided within
easy reach of the staff. The mobile clinic system should be pursued. The
effort at immunization of children against deadly diseases should be
continued and pursued vigorously. Giving occasional talks to the Local
106
Communities and industries on the need for and importance of
immunization, good dietary habit and simulations would be of help. Very
often people argue that the manufacturing industries do not change, as
they tend to stick to their old ideas or ways of doing things because they
are said to be highly conservative. This is not very true; the problem is that
they are not sufficiently educated to appreciate the need for change. This is
why every effort should be made to raise the literacy rate in the entire
industries. Industries change their ideas when they see that the change is
going to be beneficial. So our industries must be educated to appreciate
the need for change. As industries that really invest in the education of its
people, especially the growing staff soon realizes that it has made a
worthwhile investment.
6.3
CONCLUSION
The study attempted to use a regression and Principal Component
Analysis (PCA) to study the impact of human capital on Labour Productivity
in manufacturing industries in Enugu and Anambra States.
The cross
sectional analysis was for 191 functioning industries in the two states. The
result of the study shows that training, education, and research do not only
have a positive impact on productivity but such impact is strong and
statistically significant. Their coefficients are statistically different from zero
at less than 5 percent level of significance. Medicare in the model has
positive but significantly insignificant effect on productivity
107
It is obvious to all personnel and industrial relations practitioners that
trainings and staff development is a sine quo non-for stimulating
productivity. A trained worker is more efficient, creative and productive
than an untrained worker. It should be noted from the result that there is a
significant sub sector specific effects of various types of capital expenditure
in productivity growth of the sector. There is need to improve upon the level
of investment so as to produce positive efficiency effect for productivity
growth in the manufacturing sector in Enugu Anambra States.
It is happily observed that both government of the two states and the
private sector have always taken staff training and development as an
important area of priority. A lot of money is spent annually in training staff
both locally and abroad. The component matrix showed that training
actually has the highest coefficient on productivity when compared with
other component of human capital. The extraction using rotated component
matrix also revealed that Onitsha Aluminum manufacturing industry has the
highest correlation coefficient among all the companies, followed by GMO
Rubber Production. Thus by inference and implication is an indication that
the hub of industrial activities in the two states lie on these industries. With
this, we have found literacy rate to be positively related to labour
productivity.
108
APPENDIX 1
THE QUESTIONNAIRE SCHEDULE
Department of Economics,
University of Nigeria,
Nsukka.
Dear Sir/Madam,
REQUEST FOR CO-OPERATION
This study is concerned with the impact of human capital or labour
productivity of your organization. The questions below have been designed
to find out the situation of things in your company principally from your point
of view as a manager in the organization. This questionnaire is meant to be
filled by all managers of management of this organization. This is in order
to balance the various opinions about the state of things in the
organization. It is pertinent to state that the success or failure of the attempt
will strongly depend on your readiness to give me your candid or true
opinion or answer to these questions. Although, the information being
sought from you is intended for academic purpose, it might nevertheless be
used to suggest ways and means for rectifying any observed problem.
Finally, I realize that the questions are many and very demanding, but
please, try to answer every one of them as soon as you can. I assure you
that any information you give will be treated strictly in confidence
Thank you.
Anumudu, Charles Nnamdi
109
Questions
1.
What is your sex
male (
2.
Name of industry of employment
)
female (
)
__________________________________
3.
Location of Industry ________________________________
4.
Type of formal education ( ) informal
5.
If formal, how many years of formal education have
you had altogether so far?
6.
What is your highest educational qualification?
(a) First School Leaving Certificate (
(b) Junior Secondary School (
)
)
(c) Senior Secondary School or G.C.E (
(d) First Degree or Above (
)
)
7.
When did you obtain your highest qualification? -----------------------
8.
How many hours do you work in a week? -------------------------------
9.
What is your rank?
(a) Manager
(b) Technician
(c) An Artisan
(d)
Others (specify)
10.
What is your basic salary in a month?
11.
Show the type of industry your enterprise belong (Tick) accordingly:
- Food and beverage
- Basic metal industry
- Textile wearing apparel and leather industry
- Wood and wood products including furniture
- Paper and paper products, printing and publishing
- Chemicals, petroleum, coal, rubber and plastic products
110
- Non-metallic mineral products. (Except petroleum and coal)
- Fabricated metal products, machinery and equipment
- Others (specify)
12. Complete the following by filling in the appropriate dash in the
space provided.
Labour employed
Number
Category
2006
in Number in 2007
Salary
Skilled labour
Semi-skilled labour
Unskilled labour
Total
13. Production
Product type
Quantity
Produced
2007
in Quantity
Produced
in
2007
Capital asset
Cost of
Machinery/
Machinery Employed in
Machinery
2006
Employed in 2007
Equipment
14.
How far is the management encouraging the search for and
acceptance of new ways and methods of doing work in this organization?
1. To a very great extent
2. To a great extent
3. To some extent
4. To a small extent
5. Is not at all
111
6. I don’t know.
15. Do you think that the workers themselves are ready to accept any new
way and
method of doing their work?
(1) Yes (2) No (3) I don’t know
16. Considering such daily events as:
1. Transportation problem
2. Dropping and picking up children at school
3. Electricity failure
4. Lack of raw materials
Which of these factors affect your industrial performance most?17. How
many hours in a day would you say workers in this organization actually
spend doing the real job for which they have been employed (specify the
actual number, please).
18. How many hours do the industry expect its workers to stay at their job
in a day?
19. How far do your workers like this closing hour to quit for another or
similar job elsewhere?
- To a great extent
- To a small extent
- To a considerable extent
- To some extent
- Not at all.
20. What is the level of competence or job experience of those workers
whose services directly or indirectly affect you or your performance in this
organization?
a. Very high
b. Fairly high
c. Neither high nor low
d. Fairly low
112
e. Very low
f. I don’t know.
21.
How adequate is your salary in this organization.
a. Very adequate
b. Just adequate
c. Inadequate
d. Neither adequate or inadequate
e. Very inadequate
22.
Possibly indicate whether all the members of your staff are in
agreement and have clear understandings of the mission or goals of
your organization and the means for achieving them?
a. They
are
very
much
in
agreement
and
have
good
understanding of the goals and the means.
b. They are somehow in agreement and have fair understanding
of the goals and the means.
c. They are neither in agreement nor have clear understanding of
the goals and the means
d. I don’t know.
23.
Do you think any of the following factors exists in your
organization?
Factors
Yes
No
I don’t know
Wife difference?
Ethnic difference?
Favoritism?
24.
To what extent do you think workers in your organization are worried
about them?
a. To a very large extent
b. To very small extent
113
c. To a considerable extent
d. To a small extent
e. Non of it at all
f. I don’t know
25.
Please indicate the channels of distribution of your product (tick as
appropriate)
a. Wholesale
b. Retailers
c. Direct to consumers
d. Commission agents
26. What are the expenditure on training for your staff for the year
ended 2007?
27. What are the expenditure on medical care for your staff for the year
ended 2007?
28. What are the expenditure on research and development for your
staff for the year ended 2007?
114
APPENDIX 2
MANUFACTURING INDUSTRIES IN ENUGU AND ANAMBRA STATES
S/No.
1.
Name and Address
Product Mix:
Premier Breweries Nig. Plc,
Niger Bridge Industrial Layout, Onitsha.
Beer.
2.
Nigeria Mineral Waters Industries Limited,
Soft drinks
3.
Foundary and Machine Tool
Industry Project (FOMTOP) Ozubulu.
Machine Tools
4.
Orient Aluminum, Awkuzu
Aluminum Profile
5.
General Cotton Mills Limited, Onitsha
Textiles
6.
Nigerian Starch Mills Industry Ihiala.
Industrial Starch,
dextrines, glues etc.
7.
International Enamelware Industries Ltd,
Onitsha.
Plastic & Metal wares
8.
Ikenga Hotels Limited, Awka
Hotel Services.
9.
United Biochemical Industries Limited,
Onitsha.
Drugs
10. S.M.O. and Company Limited,
Onitsha.
Galvanised Roofing SheetsToilet
Rolls, sanitary pads, Duplicating
Sheets, Polythene bag, Exercise
books.
11. G.O.D.M. Shoes Industry (Nig) Limited
Onitsha.
Canvas Shoes
12. G.M.O. Rubber Products
Onitsha.
Bicycle tyres,
Bicycle tubes.
13. Pokobros Foods Products Industries
Limited.
14. Dimex Inductries (Nig.) Onitsha.
15. Francomannyon Industries Limited,
Ojoto-Idemili L.G.A.
Livestock Food, Mezolina Vitarice.
Aluminium tools and Tea Spoons,
Soup Ladles, Wash han basin, Pots,
Flat Plates, Suckle Clips washers etc.
Pharmaceuticals
115
16. Nwa George and Sons Industries Ltd.
Onitsha.
Galvanishing and Roofing sheets
17. Estco Industries Ltd
Obosi-Idemili L.G.A.
Toilet Rolls, Paper Napkins Candles.
18. Vincen Standard Steel Industries
Onitsha
Pipe rolling Mill, Conduit Pipes,
Galvanised Pipes for Water. Coupling
angels.
19. Aroma Foundation Limited.
1 Eke Obo Square, Awka.
Eggs Chicks and Poultry Meat Animal
Feeds.
20. Goodwill Bread Industries, Awka
Bread.
21. Ano Plastic and Metal Industries Ltd,
Mile 4, Onitsha – Owerri Road.
Artificial Resins and Colorants
22. Onitsha Aluminium Manufactory Company
Akunna Njote Street, Woliwo, Onitsha.
Aluminium Products
23. Zeem International Ltd.
Nnewi.
Detergent Starch
24. Syndivel Plastic Industries Ltd, Nkpor.
Plastic Ceiling Board, Plastic Water
Pipes.
25. Eddison Nig Ltd. Nnewi.
Brake Disc, Pads and lining.
26. A.B. Expeller Gsroup Co. Limited,
Onitsha.
Vegetable Oil and cake
27. Ejiamatu Group of Companies,
Nnewi.
BAEJ Export Quality Drinks.
28. Evepon Industries Ltd.
Nkwelle – Ezunaka.
Cotton Buds.
29. Dueman Chemicals Ltd.
Umuoji – Idemili L.G. A.
Paint Colourants
30. Altra Industries Ltd.
Onitsha.
31. Silas Works Limited
7 Silas Work Road, Onitsha.
Footwear, Standing and Ceiling Fans
Radio Receiver.
Bread, Biscuits Doughnuts, Meat Pie,
etc.
116
32. Unataze Investment & Nig. Ltd
Bread, Cake, Biscuits, Buns, Fish
th
4 Mile, Onitsha – Enugu Road, Onitsha.
Roll etc.
33. Okechukwu Industries Ltd
41A/47 New Market Road, Onitsha
Assorted Garments
34. Peter .E. Venture (Nig.) Ltd.
13 Ilorin Street Box 788,
Fegge, Onitsha.
Hurricane Lamp, Burner, Aluminium
Cable Clips
35. Ugo Garments Ind. Ltd
17 Niger Street, Fegge, Onitsha
Polo – Shirts, Ghildren, Gents Wear.
Ladies Wear.
36. Centolight Industries (Nig) :Ltd
Iyaba-Umudim Nnewi.
Electrical Appliances, Auto-parts
Plastics suttans, Foot Wears, Floor.
37. Tempo Mills Ltd, Umunya
Onitsha.
Bran, Pellets.
38.
Chukwurah Agricultural Industries Ltd.
Agricultural Road Ind. Nnewi
Poultry Foods, Day Old Chicks.
39.
B.C. Complex W.A Ltd
25 Ridge Road G.R.A., Onitsha.
Plastic Products Fabrication of
Machinery etc.
40.
Cento International Co. Ltd.
Nnewi.
Plastics Products, Motor Battery
41.
Roadmaster Industries (Nig.) Ltd.
KM 6 ½ Onitsha/Owerri Road,
Onitsha.
Cars Tubes, Motorcycle Tyres,
Tyres, Car Tube, Galvinised Iron
Sheet.
42.
Okpoko Enterprises Ltd,
46 New Market Road,
Onitsha.
Adhesive Ruber Solution Wheel
Barrows Name and Office
Furniture.
43.
Rogers All Stars (Nig.) Limited
12 Okolo Street, Onitsha.
Record.
44.
Olympic Technical Works and
Foundry Ltd. Abagana.
All Farm Machines
45.
Conopy Industries Ltd
Ogweni Villege, Anocha L.G.A.
Canopy Soups, Target Soups
46.
Scalic Industrial Coy. (Nig.) Ltd.
Onitsha.
Candles
47.
Alpha paper Mill Ltd
Ogidi-Idemili.
Stationeries.
117
48.
Ekwulummili Industrial Coy
(Odilofele) Aluminium
Umudim Village Ekwulummili
Aluminium Cooking Utensils
Basin, Serving Tray.
49.
P.A Ibekwe Limited
35 Silas Works Road, Onitsha.
Wire Nails, Bed Springs, Door locks.
50.
SAM Industrial (Nig.) Ltd
Plot 1 Mabaoblu Close
Okpuno – Otolo Area Nnewi.
Sal Disinfectant, Germicide.
51.
Olympic Parkers Ltd.
Atani Road, Industrial Layout,
Onitsha.
Packaging of all Types and Plastic
Wares
52.
Adswitch Limited
Nnewi
Electrical Switch Gear and Fitting
Switch Panels and Feeder Pillars.
53.
Resources Improvement Manufacturing
Company Ltd, Nnewi.
54.
Geoelis-Cables Ltd.
Nkpor – Idemili L.G.
Electrical Cables, Except armoured
Cables.
55.
Rufuso (Nig.) Limited.
Awka.
Security Ink Golden Golden Gum.
Hare Creams, Oil Extraction from Palm
Fruits/Palm kernel and Groundnut.
Fatty Acid, Sludge, Palm
Kernel Cake and
Vegetable oil (life).
56.
Mununs West African Limited
30 Oguta Road, Onitsha.
Toilet Rools.
57.
Kates Beauty Products,
Niger Industrial Layout, Onitsha.
Maco Chenical Industries Limited,
Off United Primary School,
Odido-Nkpor, Idemili L.G.A.
Roll on Perfume, Cream
59.
Mazi D.N. Madu and Bros,
11 Port-Harcourt Road,
Head Bridge Market, Onitsha.
Metal Furniture
60.
Niger Wax Industrials Ltd.
21 Chinedu Street Nkposr/Obosi
Face Power, Body Cream,
Snow-white Cream.
61
Izundu Wire & Steel Industrial
Co. Ltd. 3 Atani Road, Head Bridge
Industrial Layout, Onitsha.
Flush Doors, Chain Link,
Bed Parts etc
58.
Chemicals.
118
62.
Dike Industries Limited
Km 53 Onitsha-Owerri Road
Ihiala.
Plastic Package,
Alcohol, Wine etc.
63.
Cutix Plc Okpuno – Otolo
Nnewi,
Electrical Cables.
PVC Compounds.
64.
Celac Industries (Nig.) Limited,
130 Awka Road, Onitsha.
Soap
65.
Intercontinental Feed Mills Limited
40-60 Industrial Layout, Obi-Otoo
Nnewichi Nnewi.
Animal feeds
66.
Alphac Odine Ind. Coy Limited
Nkpor – Umuoji Road.
Paint, Pigments Chemical
67.
Stena Mills Limited
Plots IN/17 & 18 Harbour Industrial
Layout, Atani Road.
Exercise Books, Typing sheets,
Memo Pad, Plastic files and
Jackets.
68.
PENNCO Chemical Industries Ltd.
Plot 58/59 Harbour Industrial Layout
Fegge-Onitsha.
69.
P.T. Ouochie Films Ind. Nig. Limited
Mile 4 Onitsha-Owerri Road Onitsha.
70.
Okolo Industries Ltd,
Mile 3 Onitsha – Owerri Road, Onitsha.
Maltrasses
Film Processing frames,
Identity Cards.
Palm Kernel Oil
71.
Ugochukwu Chemical/Ind. Ltd
29 Niger Street, Fegge, Onitsha.
Foam Manufacture
72.
Niger Wax Ind. & Co. Ltd.
Onitsha.
Snow White Creams
73.
Help From Above Bakery Equipment
Co (Nig) 16/18 Zik Avenue
Fegge Onitsha.
Gas Cookers,
Gas Ovens
74.
Amichi Industries Limited,
Amichi.
75.
Chainchord Industries Ltd.
Nkpor – Umugi Road, Nkpor.
76.
Pal Breweries Ltd, Oko.
Lager Beer
77.
Life Breweries Ltd. Onitsha.
Lager Beer
Mosquito Coil
Wheel Barrow, Iron Sponge
119
78.
Kingsize Pharmaceutical Ltd. Ogidi
Pharmaceuticals
79.
A Meg Afu Limited, Onitsha
67 Onitsha Farmem Road, Onitsha.
80.
Markson Chemical Ind. (W.A) Ltd
(A Division of Shirley Chem. Ind.)
Km 4 Nkpor-Uminigi Road, Nkpsor.
Cosmetics.
81.
Allied Steel Ind. Limited
Atani Road, Bridge Head Onitsha.
Iron Rods
82.
Alliance International Ltd.
No. 203 Awka Road, Onitsha
Galvanised Corrugated
83.
Agu Ofodile & Sons Ltd.
Mile 3 Onitsha/Owerri Road,
Onitsha.
Concrete Block Poles.
84.
Adtech Limited
Off Nnewi Nnobi Road, Onitsha.
Insulated Cables and Switch Gears
85.
Fairdeal Resources Ltd.
Plot 63 Nigerbridge Approach
Onitsha.
(Tricia) Body Cream Body Food
and Body Lotion (Tricia)
Lager Beer and Soft Drinks.
86.
City Biscuits Mfg. Limited
Km 5 Onitsha-Owerri Road.
Biscuits
87.
Dike Breweries Limited
Km. 43, Onitsha-Owerri Road Ihiala
88.
Fairdeal Breweries and Garment
Industries Ltd Plot 61 Nigerbridge
Approach Onitsha.
89.
Donseliz Limited, Onitsha.
Bread and Confectionaries.
90.
Confidence Rinlex Industrial Co. Ltd.
Awada Industrial Layout Onitsha.
Foam Products
91.
Atico Bread Industry, Abagana
Bread and Confectionaries
92.
Central Medical Sotfware
Nkwelle-Ezunaka.
Sysringes
93.
Dueman Chemical Ltd, Nkpor/
Umuoji Road, Idemili.
Natus Gin. Samco-Poundle
Anaezi Wine
Garment
Paints/Colourant
120
94.
Ediesoka Lnvestment Co. Ltd.
6 New Market Road, Onitsha.
Head Pans and Wire Fences
95.
Ezenwa Plastics Ind. Ltd.
45, Amobi Street, Onitsha.
Plastics
96.
Pioneer Art Gallery
84 Enugu Road, Awka.
Textile Prints/Arts Works.
97.
Hoes Industries Limited, Onitsha.
98.
Hite Okpe Ind. Ltd
20 Hottidge Street, Onitsha.
99.
Hodi Wire and Furniture Ind. Ltd.
No. 5 Okwuenu Street, Onitsha.
Tension Springs and Mosquito Nets
100.
G.L. Asun Tech. Merit
No.1 Chief Nwankwo Ekenye
Ezimma Street minimi, NawfiaNjikoka L.G.A.
Inner Doosr Opener 504,505 Nissan
Urvan, Honda accord, Tables Vices,
Bottle, Corking Machines, Walking
Sticks, (Netal) Scouts and Brigade Belt,
Tail Boar, Lock for Pick-up, Disabled
Walking Sticks, Shoe Protector.s.
Pot handles and Wheel Barrows
Shoes
101.
Niger Paper Ind. Limited
Niger Bridge Head, Onitsha.
Toilet Rolls
102.
Olympic Plastics Nig. Ltd.
Mile 3, Onitsha/Oweeri Road.
Plastics
103.
Ranent Industries, Nig Ltd.
Onitsha.
Tooth Pick and Fibre Reinforced
Plastics (FRP) Water Tank, Sanitary
Wares, Photo Frames.
104.
K.P. Beverages Ltd. Ogidi.
Spirits and Wine
105.
Bekks International Co. Ltd
Onitsha.
Biscuits and Confectionaries.
106.
Danapal Nigeria Limited, Agulu.
Bread and Plastics Wires.
107.
Emba Printing & Publishing Co. Ltd.
Onitsha.
Books.
108.
Godwin Okafor & Sons Ind. Ltd.
Isuofia.
Form Products.
109.
Phina Paints Ind. Ltd.
Industrial Layout, Awka.
Paints
121
110.
Ssvannah & Chemical Ind. Ltd.
Onitsha.
Foam Products.
111.
Zumba Paints Nig. Limited
Paints.
112.
Brollo Nigeria Ltd.
Plot in/62 Onitsha Harbour Layout
Steel Pipes
113.
Ekene Dili Chukwu (Steel Structures)
Ltd, Onitsha.
114.
Eddy Motor Nig. Limited.
Nkposr, Idemili L.G.A
Buses and Pick-up, Trucks
115.
Omatta Holdings – Limited,
Nnewi.
Fitters for all ranges of Trucks and
Cars Industrial Fitters for generators.
116.
Eagle Foods Ind. Ltd.
Umuchu-Aguata L.G.A.
Maize Flour, Garri, Industrial Starch
Tractors, Tanker and Tipper
Bodies
117.
Olympia Maize Milling Ind. Ltd.
Awka.
Maize Flour.
118.
Akudigwe & Co. Ind. 42 Adelabu
Street Surulere-Lagoss Factory
Umuoui Rd. Nkpo Idemili L.G.A
Onitsha.
Pretty Caty/Suzy
119
Jon Bosco Investments W.A. Ltd
Paper Converters – Toilet Rolls
35A New Market Road, Anambra
State Obosi – Tel: 946-217504,213740
120.
F & C Industry
3 Our Lady’s Road Nkpor-Agu.
121.
Samgoz & Brother Amawze-Oraukwu
Onitsha.
Insect Killer, Soap Cream
Soap.
122.
Godans Co. (Nig.) Ltd. Amaututu Village, Agulu Njikoka L.G.A.
Spade, Hand Fork, Cutlass
123.
Emickon Associates,
1 Nwandu Close, Kdeani Idemili
L.G.A. Nnewi.
Polythene Bags.
124.
Nelly .N. Continental Co. (Nig.)
37B All Saints Rd. Nkpor-Agu.
Polythene Bags.
125.
Frankmolly (Nig.) Enterprises,
Bean Flour, Salad Cream
122
174 Awka Rd. Onitsha.
126.
Gazasomer Industries (Nig.) Ltd.
Onitsha.
Toys and Lantern Burner
127.
Top hand Industry Akpakogwe
Nkwelle Ogidi.
Putty
128.
Celac Industries (Nig.) Ltd.
130 Awka Rd. Onitsha.
Bar Soap
129.
Roadmaster Malleable
fitting & Foundries Ltd., Plot
C6/Harbour Industrial Layout,
Onitsha.
Tube Fitting, Plumbing Materials
Spare Parts for Machinaries
130.
Uru Industries Limited
Nnewi.
Control cables for all models of
Motor Car, Lorries Buses, Trucks,
Agricultural and Industrial Machines.
These include Trottleaccelerator Cables,
Brakes Cables, Cluth Cables etc.
131.
Godwin-Kris Industries.
Auto-Rubber Parts for Motors and Motor
Cycles, Steering pads, Brake Rubbers,
Ensgine Seating, Suspension Rubber,
Damper Rubbers, Shock Absorber,
Stablizer Rubber etc, Motorcycle Tubes.
132.
Africana-Fep Publishers Ltd.
79 Awka Rd, Onitsha.
Book
133.
A.N. Ejeagwu and Sons Ltd
109 Upper Iweka Road Onitsha.
Toilet Rolls and Jumbo Reels
134.
Amanze Industries Ltd.
Km 5 Onitsha/Owerri Rd. Onitsha.
P.V.C. Pipes
135.
Auskoye Industries Ltd
Igwe Close Awada Obosi, Onitsha.
Umbrella and Rain Coats
136.
Bentraco Industries Ltd
23 Uga Street, Fegge, Onitsha.
Fibre, Rainforced Plastic Products
Allied Products.
137.
Confidence Minlex Ind. Co.
Awada Industrial Layout Onitsha.
Foam Product.
138.
Ebele Journey Cycle Ind. Ltd.
Idemili L.G.A. Awka-Etiti.
Bicycle, Tricycle
139.
A Bertos Ind. Lte.
Fusrniture
123
96 Awka Road, Onitsha
140.
Denson Paper Mills Ltd.
24 New Market Rd., Onitsha.
Paper Products
141.
Ekene Dili Chukwu (SS)
Onitsha.
Steel Structures
(Trailers, Tankers Refuge Bodies etc).
142.
Emba Printing & Publishing Co. Ltd
No. 191 Awka Road, Onitsha.
Text Books and other Books
143.
Ezenwanta Chemicals Ind.
10 Awada Layout, Onitsha.
Foam Products
144.
Eziuwaka Nig. Ltd.
3 Akunnia Njote Street, Woliwo
Layout, Onitsha.
Waterproof Products
145.
Ezemba Aluminium Manufacturing
Co. Ltd. Nkpor-Obosi Rd, Onitsha.
Aluminium Cooking Utensils
146.
Halce Industries Manufacturing Ltd.
4 Anambra Street Fegge, Onitsha.
Foam Mattresses, Cushions and
Pillows All Foam Products.
147.
John White Ind. Ltd
Nge/Inyabis Village Umudim Nnewi
Tubes & Tyres for Motors
Motocycles & Bicycles Fan Belts,
Ruber Hoses.
148.
Haluchi Agro Ind. Ltd. Awka
Industrial Layout, Awka.
Animal Feed, and Maize Grits and
Semolina.
149.
Keensway Nig. Ltd
17 New Market Road, Onitsha
Bread and Biscuits
150.
Kates Associated Products Ltd.
Plot In/76 Nigerbridge Head, Onitsha
Toiletries, Cosmetics Candles and
Plastics Containers.
151.
Kenechris Property & Agric Co. Ltd.
52 New Market Road, Onitsha
Water Pump
152.
Mocobros Allied Ind. Ltd.
Km 4 Onitsha/Owerri Road, Onitsha
Foam Products.
153.
Niger Paints Ltd.
No. 4 Sokoto Street, Onitsha
Paints, Wood Polish,
Adhesive, White Glue and Plastics
154.
155.
Niger Automotive Ind. Ltd, Onitsha
Nakpo Plastics Containers Ind. Ltd.
Plot In/76 Ind. Layout, Bridge Head,
Onitsha.
Brake Lining and Pads.
Containers & Toiletries Cosmetics
Pharmaceutical and Industrial
Products.
124
156.
Naz Industries Ltd
No. 1 Ikakwe Road, Nnewi
Motor Spare Parts.
157.
Oke Industrial Estate
Nanka Road Oko Aguata.
Aluminium Architectural Products
158.
OCE Filter Manufacturing Ind. Ltd.
Mile 12 New Onitsha Road, Akabaukwu
Uruagu, Nnewi.
159.
Pesaco Chemicals Ind. Ltd.
58/59 Harbour Ind. Layout, Onitsha
160.
Rozems Anems (Nig) Ltd
30 Uga Street, Onitsha.
Oil Filters
Polyester Foam
Products.
Industrial Hand Glover, Water
Hose.
161
R. O. Ozigbo & Co. Ltd
70 Port-Harcourt Road, Onitsha.
Staples, Hinges and Door Bolts &
Washers and Spare Parts.
162.
Savannah & Chemical Industries
Nig. Ltd Owerri Road, Onitsha.
Foam Products
163
Superb Industry Ltd.
5 Emodi Street, Onitsha.
Foam Products
164.
Sampson & Nabaebries Ind. Nig. Ltd
Plot 1 Close Okpune Otele, Nnewi
Sterilizing Solutions, Cosmetics
Bleaching Solutions.
165.
Show Light Farming & Food Processing
Mile 4 Onitsha/Owerri Road, Onitsha.
166.
Techneflex Company Ltd.
Wheat Flour
Km 5 Onitsha/Owerri Road, Onitsha. Wheat Offals.
167.
Ukawoods Enterprises Ltd, Onitsha.
Furniture
168.
Vincent Standard Steel Ind. Ltd.
Onitsha.
Steel Products
169.
Gasta Industries Nig. Ltd.
Plot 50 Odume Layout, Obosi
Foam Products.
170.
Sinsco Ind. Nig. Ltd
27 Nibe Street Behind Omagnba
Primary School,Omagba, Onitsha.
Plastics Packaging
171.
Syudicated Crown Cap Ltd.
27 Enugu Road, Onitsha
Crown Caps
Food Processing
125
172.
Maco Chemical Ind. Ltd.
21 Flanigan Street, Onitsha
Paints
173.
B.C. Ifegbo Associates Ltd,
25 New Market Road, Onitsha
Paper Converters
174.
L.L. Nwadike & Asso., Ltd
12 New Market Road, Onitsha.
Paper Converters
175.
Jacbon Ind. Ltd.
Central School Road, Nkpor.
Paint
176.
Iju Industries
39 Awka Road, Onitsha.
Auto Components, Pipes, Rubber
Foot Mats, Baloons, Surgical Hand
Gloves.
177.
Chriscord Ind. (Nig) Ltd.
Km 6 Onitsha/Owerri Road, Obosi
Idemili L.G.A.
Wheel Barrows, Iron Sponge, Paints
Cutteries.
178.
Fenok Ind. Limited.
Km 6 Onitsha/Owerri Road, Obosi
Idemili L.G.A.
Brake Pad, Brade Lining
Battery Clip and Wheel Balance
Weigh Clips.
179.
Silver Sam Paper Ind. Ltd
Mile 12 Onitsha Road, 172 Mebrus
Industry Ltd, Ozubulu, Nnewi L.G.A.
Paper Converter
Auto Mobile Exhaust System
180.
Nkwelle Oil Mills Ltd
Nkwelle-Ezumaka Oyi L.G.A.
Palm Oil
181.
G.O.D. Brothers Company Nig. Ltd.
No. 21 Godwin Kris Road, Nnewi.
National Group Rubber
Motor-cycle Tube.
182.
Hi-Tech Carpenters
80 Works Road, Awka.
Furniture
183.
Polyethylyne Bags
184.
Super Star Poly Co. Nig. Ltd.
43 Nkpor-Obosi Road, Onitsha.
Franklim Marble Ind. Ltd.
185.
C. N. Orajis Craft Centre (Nig) Ltd
Craft
186.
Lankac (Nig) Ltd.
Printing
No. 1 Ugwunabamkpa Road, Onitsha.
187.
Winas Industries Limited
Nkpor Umuoji Road, Nkpor.
Marble
Calcium Carbonate, Calcium Oxide,
Hydrate Lime, Marble Dust Kaolin,
P.O.P. Gypsum, Filspar, Supefalla,
126
Ceramics Tiles, Adhesives Wood
Preservatives & white Auto Destroyers.
188.
Asock Ind. Nig. Ltd.
1 Ezinifite Street, Bridge Head
Approach Woliwo, Onitsha.
Shovel, Head Pan, Wheel Barrows,
and Wood Preservatives.
189.
Finock Ind. Ltd.
Awka Industrial Layout Awka.
P. V. C. Pipes
190.
Afamlam Farms Ind. Ltd.
15 Uhuori Ukpor
Near Nnewi South L.G.A.
Animal Feed, Organic Fertilizers
P.K.O. Fish Pond and Allied
Products.
191.
Anilock Industrial Ltd
40 Oguta Road, Onitsha.
Metal Fabrication Works
192.
Anino International Plc.
95 Limca/Awka Road, Nkpor, Obosi
Office Pins, Paper Clips, Staple
Pins, Nails
193.
Becwise Industries Ltd.
5 Ihiala Street, Fegge Onitsha
South L.G.A.
Tooth Picks, Plastic Boot
194.
Bufambra Industries Ltd
Km 25, Nsugbe-Otuocha Express
Road Nsugbe.
Bufalo Shoe Dye
195.
Cepharm Products Ltd.
c/o CIC Ltd. Onitsha.
Hospital Esquipment
196.
Chukwuma Auto Products Ltd.
3 New Market Road, Nnewi.
Auto Spare Parts
197.
Cetlma Industral Company Ltd.
No. 1 Onwnu-Anatagu Street.
Off Oguta Road, Onitsha.
Pharmaceutical and Cosmetics
Plastics Products.
198.
Lamour Natural Spring Water Ltd,
Fessok Industry Estate, Adagbe
Village, Abagana.
Natural Spring Water
199
Sunrise Floor Mills Enugu
200
Adarice [Agro Based, ADANI
201
Niger Gas, Emene
202
Niger Steel ltd,Emene
127
203
Eastern Nigeria Estate
204
Anamco Enugu
205
Emenite Plc, Emene
206
TrippleSTAR, Emene
207
COSPAM, Nsukka
208
Ben Mac Cabel, EMENE
128
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