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ANYANWU, KELECHI CLARA
PG/M.SC/10/52414
Ogbonna Nkiru
Digitally Signed by: Content manager’s Name
DN : CN = Webmaster’s name
O= University of Nigeria, Nsukka
OU = Innovation Centre
FACULTY OF THE SOCIAL SCIENCES
DEPARTMENT OF ECONOMICS
GOVERNMENT LABOUR POLICY, GRADUATE
UNEMPLOYMENT AND LABOUR PRODUCTIVITY IN
GOVERNMENT LABOUR POLICY, GRADUATE
UNEMPLOYMENT AND LABOUR PRODUCTIVITY IN
NIGERIA
BY
ANYANWU, KELECHI CLARA
PG/M.SC/10/52414
AN M.Sc THESIS SUBMITTED TO THE DEPARTMENT OF
ECONOMICS, FACULTY OF THE SOCIAL SCIENCES
UNIVERSITY OF NIGERIA, NSUKKA
IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE
AWARD OF MASTER OF SCIENCE DEGREE IN ECONOMICS
SUPERVISOR: PROF IKPEZE, N. I.
SEPTEMBER 2014
i
TITLE PAGE
GOVERNMENT LABOUR POLICY, GRADUATE
UNEMPLOYMENT AND LABOUR PRODUCTIVITY IN
NIGERIA
ii
CERTIFICATION
This is to certify that ANYANWU KELECHI CLARA, a post-graduate student of
the Department of Economics, University of Nigeria, Nsukka, and whose
registration number is PG/M.Sc/10/52414 has satisfactorily completed the
requirements for the award of Master of Science (M.Sc) Degree in Economics.
PROF. N. I. IKPEZE PROF C. C. AGU
Supervisor Head of Department
iii
APPROVAL PAGE
This project has been read and approved as meeting the requirements for the award
of the Degree of Master of Science (M.Sc) of the Department of Economics,
University of Nigeria, Nsukka.
PROF. N. I. IKPEZE PROF C. C. AGU
Supervisor Head of Department
PROF. IGNATIUS, A. MADU
Dean, of Social Sciences External Examiner
iv
DEDICATION
This work is dedicated to the almighty God for his mercies, protection and grace
throughout this programme.
v
ACKNOWLEDGEMENT
I am elated to appreciate the efforts and contributions of all the staff of Economics
Department for their various roles towards the completion of this programme. My
deep appreciation goes to my lecturers: Professor Agu C.C, Professor Ona F, Rev
Fr Professor Ichoku H.E, Prof Chidebelu S.A.N.D; Dr Oleru J.O, Dr Nwosu
Emma, Dr Ukwueze Ezebilo, Dr Mrs Aneke, Dr Ifelumini (SPG), Dr Oduh
Moses,Mr Jude Chukwu, Dr Urama Nathaniel.
My special thanks to my former supervisor Prof Onyukwu E. Onyukwu and my de
jure supervisor Prof Ikpeze N.I for your guidance.
To those that have read and will read this work, thanks for their assistance.
I am greatly indebted and in no small measure to my parents, Mr and Mrs
A.A.Anyanwu, for their constant prayers and advise.
To my family; the Anyanwu’s, Okwulehie’s , Uneze’s, Chimazuru and Chikamara
for their encouragement, moral and financial support.
Finally, to my friends: Andrew, Nemerem, Fidel, Parson, Chika, Steve, Ugo,
Doris, Uche, Akudo, Nnamani, Kelechi, Pat, Casmir and Peter. Thanks for
everything; I really enjoyed your company.
vi
ABSTRACT
The study evaluates government labour/employment policy, graduate unemployment and
labour productivity in Nigeria. It spanned the period between 1987 and 2013. The
model was built on the combination of Keynesian framework and modern labour market
theory and the various channels through which labour policy can impact on graduate
unemployment and labour productivity in Nigeria. The study employed Ordinary least
square method of estimation, co-integration and error correction techniques in
estimating the model. Data were sourced from Central Bank of Nigeria annual statistical
bulletin/reports (various years) and National Bureau of statistics annual statistical
reports. The econometric software used for the study was E-views. The result from trend
analysis showed that labour productivity proxied by growth in Gross Domestic Product
(GDP) maintained steady and near horizontal movement while graduate unemployment
has been on the increase over the years. Co-integration result showed that there exists a
long-run linear relationship among the variables used for the study, hence adopting an
error correction model. The result clearly indicated that government employment policy
proxied in this study by the total number of graduates that have benefited (TNA) from
National Directorate of Employment (NDE) since its inception in 1986 have positive and
significant influence on labour productivity and negative significant influence on
graduate unemployment. The study also found insignificant relationships between
graduate unemployment, gross secondary school enrolment and total factor productivity
in Nigeria. The study went further to establish unidirectional causal link between
government labour policy, graduate unemployment and labour productivity in Nigeria.
The study recommends that holistic programme should be established to arrest high
trending unemployment in Nigeria. The programme should capture those who are
heavily underutilized and grossly underpaid, in order to have a fair representation of the
unemployment situation in the country. Government spending pattern should be checked,
since the rising government total spending has not translated to increase in labour
productivity.
vii
TABLE OF CONTENTS
Page
Title Page .. .. . i
Certification Page .. .. .. ii
Approval Page .. .. .. iii
Dedication .. .. .. iv
Acknowledgement .. .. .. v
Abstract vi
Table of Contents .. .. . vii
List of Tables .. .. . x
List of Figures xi
CHAPTER ONE: INTRODUCTION
1.1 Background to the Study ... .. 1
1.2 Statement of the Problem ... .. . 3
1.3 Objectives of the Study ... .. .. 5
1.4 Research Hypotheses 5 .
1.5 Policy Relevance of the study ... .. 6
1.6 Scope of the Study ... .. .. 6
CHAPTER TWO: LITERATURE REVIEW
2.1 Conceptual frame work ... . . 8
2.2 Theoretical Literature ... .. .. 10
viii
2.3 Empirical Literature ... .. .. 17
2.4 Literature Gap and Value Addition ... . . 22
CHAPTER THREE: METHODOLOGY
3.1 Theoretical Frame work ... .. .. 24
3.2 The Model 26
3.3 Model Specification 27
3.4 Estimation Procedure 30
CHAPTER FOUR: PRESENTATION OF RESULTS
4.1 Analysis of the Data Generating Process .. .. .. 34
4.1.1 Trend Analysis of the Core Variables .. .. 34
4.1.2 Unit Root and Co-integration Analyses .. .. 35
4.2 Presentation and Analysis of ECM Models .. .. 38
4.2.1 Endogeneity Test .. .. .. 38
4.2.2 Test for Validity of Instruments 40
4.2.3 The ECM Result for the Models 41
4.2.4 Total Factor Productivity Result 44
4.2.5 Granger Causality Result 46
4.3 Evaluation of Research Hypotheses 47
CHAPTER FIVE: SUMMARY AND CONCLUSION
5.1 Summary of Research Findings 48
5.2 Policy Recommendations ... .. .. 49
5.3 Conclusion ... .. .. 50
ix
5.4 Limitations of the Study ... .. .. 51
References ... .. .. 52
Appendix 57
x
LIST OF TABLES
Tab 1: Summary of Literature Reviewed 19
Tab 2: ADF and PP Unit Root Test Results for Individual Variables 36
Tab 3: Co-Integration Tests 37
Tab 4. Endogeneity Result for Graduate Unemployment (UG) Model 38
Tab 4.1: Endogeneity Result for Labour Productivity Model 39
Tab 4.2: ECM result for Graduate Unemployment (UG) Model 41
Tab 4.3: ECM Result for Labour Productivity Model 42
Tab 4.4: Result of the Total Factor Productivity Model 45
Tab 4.5: Pairwise Granger Causality Tests 46
xi
LIST OF FIGURES
Fig 1: NATIONAL UNEMPLOYMENT RATE ( 2000-2009) 19
Fig 2: TREND ANALYSIS OF THE CORE VARIABLE 35
1
CHAPTER ONE
INTRODUCTION
1.1 Background of the Study
There is no doubt that unemployment is one of the major challenges facing economies of
the world (developed and developing). This exerts more distorting impacts on the developing
economy. According to Ekpo (2008), a developing economy such as Nigeria’s is faced with
poor growth performance which manifests in the rising incidence of poverty, massive graduate
unemployment, skyrocketing inflation, worsening balance of payments disequilibrium,
monumental external debt burden, widening income disparity and growing fiscal imbalances,
which taken together can be said to constitute major causes of underdevelopment. In all, rising
graduate unemployment poses the most pervasive and devastative effect which threatens the
productivity of the Nigerian economy (Ake, 2010).
Graduate unemployment is defined as the unemployment among people with academic
degrees. It is a situation where tertiary institution graduates do not get jobs after going through
the academic ladder successfully. It is the greatest component of aggregate unemployment.
According to the International Labour Organization (1982), the unemployed are persons that are
available and willing to work but without work in the past 39 weeks. One is forced to ask how
many Nigerians are willing and available to work but are currently without job. On the other
hand, Frank and Bernard (2001) noted that the rising unemployment rate in a nation is too
significant to be ignored as it is necessary in assessing the level of economic activity in such
nations. Thus, besides real GDP, unemployment and growth in labour productivity remains
economic statistics that receives a great deal of attention from both economists and the general
public.
The unemployment rate is a sensitive indicator of the conditions of the labour market. When
the unemployment rate is low, jobs are secured and relatively easier to find. Low unemployment
is often associated with improving wages and working conditions as well as employers
competing to attract and retain workers.
In the recent past, Nigeria has experienced low labour demand and productivity, a sign
which is widely blamed on the failure of government policies and programmes over the years.
Specifically, since the mid 1980s, there has been an alarming increase in the rate of graduate
unemployment, low labour productivity and its attendant’s social and economic consequences.
Just like some other developing nations in Africa, the Nigerian government and policy makers
are increasingly finding it difficult to deal with graduate unemployment successfully.
2
The high rate of graduate unemployment in Nigeria according to Adeyeye et. al. (2012),
can be associated with lack of adequate provision for job creation in the development plans, the
ever expanding educational growth and the desperate desire on the part of youths to acquire
tertiary education irrespective of the social and economic reality. Consequently, a number of
skills acquired from these tertiary institutions appear dysfunctional and irrelevant since most of
the skills and knowledge acquired in tertiary institutions are kept redundant through
unemployment and sometimes skills are not fully utilized.
On the other hand, as the graduate population in Nigeria increases without being
absorbed in the active market, labour productivity does not increase at its full potentials, this
could be as a result of the failure of government to control this phenomenon over the years in
spite of numerous programmes and policies on this issue. Without denying the impact of other
factors, unemployment has exacerbated social ills and delinquent behavior among youths (most
of whom are graduates) especially armed robbery, political thuggery, advanced fee fraud and the
recent spate of terrorism (Boko Haram) in Nigeria, which has been on rampage for the past 4
years, forcing the government to be spending a lot of money on crime control. Recent statistics
released by the National Bureau of Statistics (NBS) showed that about 10 million Nigerians
were unemployed. In fact, this number swells by 120,000 graduates each year, which are
produced with little or no jobs waiting for them. (NBS 2009).
In a bid to address the menace of unemployment, various policies have been put in place
by the Federal Government of Nigeria. Notably, the Small and Medium Enterprise Development
Agency of Nigeria (SMEDAN) was established in 2003 to promote the development of Micro,
Small and Medium Enterprises (MSME) sector of the Nigerian economy. It is to source, process
and disseminate business information, develop policy, establish business support programmes,
build capacity and promote services, enhance MSME access to finance. Others are the Nigerian
Agricultural Co-operative and Rural Development Bank (NACRDB) Limited which is dedicated
to financing agriculture at both micro and macro levels. They are to provide affordable financial
and advisory services to the farm and non-farm enterprises of the Nigerian economy using well
trained and highly motivated staff, backed by appropriate technology. Those that were
established but later scrapped include: the Directorate for food Roads and Rural Infrastructures
otherwise known as (DFRRI), Mass Mobilization for Self Reliance and Economic
Reconstruction (MAMSER) and the National Agricultural Land Development Project
(NALDA). These were created by the Babangida regime but scrapped by the Abacha regime.
Among all, the most innovative of these programmes is the National Open
Apprenticeship Scheme (NOAS) introduced by the Federal Government in 1987. The NOAS is
3
an attempt to link education with training and labour demand. It was managed by the National
Directorate of Employment (NDE) under the Ministry of Labour and Productivity. The NDE is a
policy document that addresses the provision of graduate level of employment, the essence of
this is to know how government policy should be used effectively in the reduction of graduate
unemployment and enhance productivity in Nigeria. This was also meant to provide vocational
education and training to unemployed youth in over 100 occupations. The main objective of
these government programmes and policies is to provide means of livelihood to able but idle
army of unemployed youths, especially the graduates and give assurance to the private sector
through the services of the youths.
Unfortunately, the observable low labour productivity and high level of graduate
unemployment over the years in the country have shown that no meaningful progress has been
made by these programmes and policies. It is against this background that this study is designed
to examine the nexus between government policy, graduate unemployment and labour
productivity in Nigeria and its attendant consequences to the social and economic wellbeing of
the nation.
1.2 Statement of the Problem
Tackling graduate unemployment and low labour productivity in Nigeria has remained
twin problems facing the country for some decades now. The Nigerian total labour force is made
up of all persons aged 15-64 years excluding students, home keepers, retired persons and stay-at-
home to work or those that are not interested. Nigeria’s unemployment can best be defined as the
proportion of labour force that was available for work but did not work in the week preceding the
survey period for at least 39hours (Asoluka & Okezie, 2009). The 2009 official figures from the
Bureau of Statistics put the unemployed figure at 19.70 per cent, about 30 million. Though, this
figure still did not include more than 40 million other Nigerian youths (some graduates of various
degrees) captured in World Bank statistics in 2009. By implication, it means that if Nigeria’s
population is 167 million, according to World Bank figure, then 50 percent of Nigerians are without
employment (unemployed). Viewing this from the perspective of the recent events in the Middle
East where unemployment and poverty among others played a key role in the uprising, one can only
conclude that Nigeria’s unemployment poses a threat to not only productivity and output growth, but
its security and peaceful co-existence.
The rising trend of unemployment assumed a doomsday scenario in Nigeria a decade
after political independence. As noted by Akintoye (2008), between 1970 and 1980, national
unemployment rate rose from 4.3 to 6.4% and further rose to 7.1% in 1987. This, according to
Akintoye, is attributed to the economic depression which engulfed the nation from 1980,
4
resulting in massive closure of businesses and retrenchment of workers. This was followed by
the placement of embargo on recruitment which further worsened the unemployment situation.
The Structural Adjustment Programme (SAP) established in 1986 had on its part, a
positive effect on job creation leading to a sharp fall in unemployment rate from 7.1% in 1987 to
1.8% in 1995. Thereafter, unemployment figure hovered around 4% between 1996 and 2000.
One worrisome trend in the Nigeria labour market of recent has been the growing incidence of
unemployment among professionals such as accountants, engineers and other graduates from
universities and other tertiary institutions. Graduate unemployment accounted for 32% of the
total unemployed labour force between 1992 and 1997 (Akintoye,2008). This growing incidence
of graduate unemployment in the face of acute skill shortages presents a paradox which further
complicates the analysis of labour market distortions in Nigeria. (Dabalen et al., 2000).
Expectedly, unemployment reduction has remained the central focus of macroeconomic
goals in Nigeria. It is a continuing policy and responsibility of the federal government to use all
practical means to promote higher level of employment, production and purchasing power
(Essien & Atan, 2006). The most critical factor explaining the rising unemployment in the
country is the failure of government policies to consciously tackle graduate unemployment
among others. The need to avert the negative effects of unemployment and improve labour
productivity through effective government policies will make the tackling of unemployment
problems to feature prominently in the development objectives of the Nigerian government.
The primary cause of graduate unemployment and low productivity is the absence of an
appropriate and well articulated government policy to guide the strategies and programmes of
the various institutions operating in all sectors of the economy (Asoluka & Okezie, 2011).
However, efforts by different regimes in Nigeria such as Federal Ministry of Labour and
Productivity, the National Directorate of Employment (NDE), Small and Medium Scale
Enterprise (SMEs) and National Poverty Eradication Programme (NAPEP) in 2001 to generate
more employment and improve labour productivity to a large extent have not yielded the
required results (Essien & Atan, 2006).
Apart from representing a colossal waste of a country’s manpower resources, graduate
unemployment and low productivity generates welfare loss in terms of lower output thereby
leading to lower income and wellbeing (Ibrahim, 2006). In 2005 the unemployment rate as
recorded by the NDE was 11.9% in 2006, 14.6% in 2007, and 10.9% in 2008 while as at
December 2009 the Bureau of Statistics gave the unemployment rate as 19.7%. Predictably, this
has also been accompanied by a high rate of social vices in Nigeria.
5
In view of the unfolding reality coupled with the protracted debates in the literature, this
study is built to examine the nexus between government policy, graduate unemployment and
labour productivity in Nigeria. In doing this, the study will be guided by the following research
questions.
1.3 Research Questions
1. What is the nexus between government labour policy, graduate unemployment and
labour productivity in Nigeria?
2. What is the long-run impact of graduate unemployment on total factor productivity in
Nigeria?
3. What are the causality between government policy, graduate unemployment and labour
productivity in Nigeria?
Based on the above research questions the following objectives are drawn for this study.
1.4 Objective of the Study
The broad objective of this study is to examine government labour policy, graduate
unemployment and labour productivity in Nigeria. In view of this, the specific objectives of the
study are;
1. To determine the nexus between of government labour policy, graduate unemployment
and labour productivity in Nigeria.
2. To determine the long-run impact of graduate unemployment on total factor productivity
in Nigeria.
3. To determine the causality between government labour policy, graduate unemployment
and labour productivity in Nigeria.
1.5 Research Hypotheses
Based on the objectives of this study, the following research hypotheses are formulated:
1. Ho: Government policy on labour has no significant impact on the rate of graduate
unemployment and labour productivity in Nigeria.
2. Ho: There is no long-run relationship between graduate unemployment and total factor
productivity in Nigeria.
3. Ho: There is no causal link between government labour policy, graduate unemployment
and labour productivity in Nigeria.
6
1.6 Policy Relevance of the Study
The findings of this study will aid the government efforts in addressing the protracted
unemployment, especially graduate unemployment and low labour productivity in Nigeria.
Firstly, identification of the nexus between government labour policy, graduate unemployment
and labour productivity in Nigeria will assist policy-makers in formulating the right policies to
addressing protracted rising unemployment trend in Nigeria. Policy-makers will therefore find
the study very relevant for drawing policy issues in line with the challenges of ensuring an
improved labour productivity and reducing general unemployment rate, irrespective of gender
and level of education, in line with the development agenda of the present administration.
Secondly, investigating the long-run impact of graduate unemployment on total factor
productivity will be informative in predicting how total factor productivity will change if policy
makers are to change in reducing the rising trend of graduate unemployment status. Finally,
providing an insight towards understanding the causal relationship between graduate
unemployment, government labour policy and low productivity in the Nigerian economy, will
be an important tool in designing effective policy interventions that addresses graduate
unemployment and labour productivity issues and achieving the new policy framework (mass
employment for the youths).
1.7 Scope of the Study
This study is an empirical analysis of the nexus between government labour policy,
graduate unemployment and labour productivity with evidence from the Nigerian economy. It is
important to note in this study that the connection between government policy and
macroeconomic phenomenon such as unemployment is not well developed. In principle, a
computable general equilibrium model (CGE) should be developed; however, unemployment
sensitive CGE models have not been developed and face many problems (Fontana & Wood,
Lofgren et al., 2008). Hence these are beyond the scope of this study. Instead, this study
addresses three graduate unemployment issues that are thought to affect both the demand as
well as supply of labour in Nigeria. These are the interaction between employment rate, labour
demand and the number of graduates produced in the economy every year.
This study considered the effective government labour/employment policies instruments
targeted at graduate unemployment in National Directorate of Employment (NDE) scheme.
These include the start your Own Business (SYOB) under the small Scale Enterprise programme
(SSE), the sensitization of National Youth Service Corps (NYSC) on Entrepreneurship
development, the Graduate Attachment Programme (GAP) and the Solar Energy Training
7
Scheme (SETS) which was recently introduced. However, the study estimated the government
labour policy with the total number of persons that benefited from NDE graduate programmes
annually (TNA).
Total Factor Productivity (TFP) is a notion linked to the aggregate production function.
Hence total factor productivity was measured in this study as the ratio of output and weighted
input factors (to be specified in the methodology). Productivity, on the other hand, is a technical
concept which refers to a ratio of output to input and a measure of efficiency. (see Classical
Ricardian labour theory of value).
Finally, graduate unemployment is referred to as graduates who were available for work
and looking for jobs, but unable to find employment. Again, this study covers a reasonable range
of observations, ranging from 1987 to 2013 in order to have a clear picture of the nexus between
government labour policy, the rate of graduate unemployment and labour productivity growth in
Nigeria. This period was chosen due to the fact that government labour/employment policy
considered in this study is captured from the National Directorate of Employment which was
established on November 22, 1986 and its initial core programmes were formally launched on
30th January 1987.
8
CHAPTER TWO
LITERATURE REVIEW
2.1 Theoretical Literature
Theories of Unemployment
Scholars have propounded various theories relating to employment, underemployment
and unemployment. These include those of the Classical theory of unemployment, Innovations
theory of unemployment and Effective Demand theory of unemployment.
Classical Theory of Unemployment
The classical theory, as analyzed by Pigou (1933) and Solow (1981), argues that the
labour market consists of demand and supply of labour. Demand for labour is a derived demand,
obtained from the declining portion of the marginal product of labour. The demand curve is a
negative function of real wage in that if wages increase, the quantity demand for labour will
decline and the opposite is correct. The supply of labour is derived from worker's choice
whether to spend part of their time working or not working (leisure). Supply of hours worked is
a positive function of the real wage, because if the real wage rises, workers supply more hours of
work. In equilibrium, demand and supply of labour are intersected at a clearing point that
determines the equilibrium real wage rate and full employment.
Essentially, for Wicksell the cyclical unemployment was due to the wrong investment of
capital. Capital was invested in areas where rates of return were low. He concluded that public
works is the best measure to fight cyclical unemployment. After World War I, Wicksell thinks
that the boom and the rise in prices induced by the war would come to an end. Thus,
unemployment would rise. Workers would have to accept lower wages. He also thought that
government should provide financial support to the unemployed who could not find jobs. After
1921, Wicksell turns to Malthus. He thought that the causes of the unemployment are the surplus
people, shortage of capital brought about by the war, and the disorganized state of the monetary
system. For the third cause, after the war prices were falling and producers decided to produce
lower amounts of production because they knew they would receive lower prices for their
products. Thus, they let their money lie idle in banks and workers became unemployed. These
causes suggest that emigration became one of the important policies for solving the
unemployment problem.
Wage reduction is not a competent policy to increase employment. The increase in
wages is most likely due to increased labour productivity and wage reduction will reduce work
9
intensity and productivity. Wage reduction will not force some capital intensive firms to switch
to labour intensive techniques in the short run. Higher wages should stimulate the substitution
effect by employing more machines for labour. And this substitution will increase labour
productivity and employment in the long-run.
Hayek contends that unemployment is due “to a discrepancy between the distribution of
labour and industries, and the distribution of demand among their producers. This discrepancy is
caused by a distortion of the system of relative prices and wages.” In other words, the
unemployment is caused by “a deviation from the equilibrium prices and wages which would
establish them with a free market and stable money.” This is actually a mismatch between
demand and supply of labour, which is usually caused by expansionary monetary and fiscal
policies and powerful trade unions. These policies create economic dislocation and structural
changes in an economy which misdirect labour and other economic resources to other
alternatives. Unions are also able to set higher wages compared to market wages, which generate
unemployment, particularly in industries that become less profitable. In short, for Hayek the
unemployment problem is caused by resources being in the wrong places at the wrong time and
can be corrected if wages and prices are determined by the equilibrium of supply and demand.
(Nishhiyama and Leube 1984)
In line with Hayek theory of unemployment, Trehan (2001) provides an important
explanation of the search theory of unemployment. Firms search for the productive workers and
workers search for high-paying jobs, so both agents continue searching until matches are
reached at the point a worker will leave the unemployment pool. But if a worker realizes later on
that her productivity is worth higher wages and firms are paying high wages on the average, then
the worker’s reservation wage will increase. Consequently, the unemployment rate will start
rising gradually, indicating that a mismatch has occurred again.
Innovations Theory of Unemployment
Originally, this theory was developed by the German economist Von Mangoldt in 1855
in a book of entrepreneurial profits which connected profits to risk but this theory was refined in
2007 by Ekelund and Hebert. They provided several ways by which the entrepreneur can make
profits. These ways are (1) finding particular markets, (2) acquisition of productive agents, (3)
skillful combination of factors of production, (4) successful sales policy, and (5) innovations. It
is a well understood proposition that entrepreneurial profits will increase employment
(Mohammed 2010).
Schumpeter (1934) does not provide explicitly a theory of unemployment but his theory
of the business cycle does demonstrate clearly how unemployment can be reduced. Innovation
10
(see also Vecchi 1995) which creates more jobs relative to job destruction is the basic force
beyond the increases in employment and the decreases in unemployment. When entrepreneurs
innovate something new such as the production of a new product, the finding of a new market,
the finding of a new method of production, and the introduction of new technologies and a new
organization they increase investments to materialize those innovations. Domestic investment
expenditures will increase demand on economic resources and will increase their prices. Other
entrepreneurs will imitate the leaders by adopting the new innovations. Labor and materials will
be employed to produce the new items. Consequently, wages will be increasing and
unemployment will be declining, assuming that employment creation will outweigh employment
destruction due to the new innovations (see also Mortensen and Pissarides 1998 and Manuelli
2000).
According to Schumpeter (1934: 64), economic development generates changes in the
socio-economic environment, including the existing equilibrium. As he puts it, “Development is
spontaneous and discontinuous change in the channels of the flow, disturbance of equilibrium,
which forever alters and displaces the equilibrium state previously existing.” The essential
driving force for generating development is innovations introduced by the entrepreneurs whose
leadership becomes the triggering device for the discontinuous dynamic changes. Innovations
start by “the producer [not consumer] who as a rule initiates economic change and consumers
are educated by him if necessary” (Schumpeter 1934: 65).
The concept of innovation which creates changes according to Schumpeter (1934: 66)
covers the following five areas of development: “(1) the introduction of new good...or of a new
quality of a good. (2) The introduction of a new method of production, (3) The opening of a new
market,(4) The conquest of a new source of supply of raw materials, or manufactured goods,(5)
The carrying out of the new organization of any industry, like the creation of a monopoly
position or the breaking up of a monopoly position.” The new combinations are usually
embodied in new productive enterprises which start by utilizing the unemployed working
people, the unsold raw materials, the new technologies, and the unused productive capacity.
Effective Demand Theory of Unemployment
The level of aggregate demand will provide the necessary increases in total revenues. On
the other side, the cost of production has to decline. If revenue rises and cost declines, then the
reasonable level of profits can be found. There are various forces in Veblen’s work that reduce
the cost of production. Technology increases production and reduce the cost of inputs used in
the production process, and enterprises cut wages and increase productivity in order to cut cost
per unit of output. Better technology can reduce the prices of capital goods, and government can
11
cut taxes. Banks can reduce the interest rates as well. Administrative and insurance cost can be
declined in order to stimulate business enterprises. The decline in costs, given rising revenues,
will increase the profit level according to Veblen. Consequently, higher profits will force the
business enterprises to expand and employ more workers. Thus, employment will increase and
the rate of unemployment will decline.
Keynes (1936) considers unemployment as an involuntary phenomenon. He thinks that
employment is cyclical, generated by the deficiency of aggregate demand (Mohammed, 2010).
Capitalists hire workers and invest to produce output when the expectations about the economy
and profits are favorable. If expectations about the future are supported by reality, investments
and employment continue rising until equilibrium is reached. This equilibrium is attained by the
intersection of the aggregate demand and supply--the point of the effective demand—which may
be less than the full employment equilibrium. If expectations about the future of the economy
are not favorable, capitalists invest less and employ less number of workers. Hence, the
equilibrium is achieved where cyclical unemployment exists. This unemployment is due to the
deficiency of the aggregate demand, particularly investment expenditures.
Graduate Unemployment and Government Policies
According to Olufemi and Adebola (2011), both government and policy makers are
increasingly finding it difficult to deal successfully with graduate unemployment, part of the
reasons was blamed on lack of adequate provision for job creation in the development plans of
the governments, thereby rendering the skills acquired dysfunctional and irrelevant to them and
the entire economy. But still more and more graduates are being turned out from the various
universities every year.
The Government of Nigeria had attempted to curb graduate unemployment. Firstly,
through the National Directorate of Employment (NDE), this is one of the steps taken by the
Nigerian government to reduce the problem of unemployment in Nigeria which was established
in November 22, 1986. The objective of NDE was to promptly and effectively fight
unemployment by designing and implementing innovative programmes, which are directed
towards the provision of training opportunities through the guidance and management support
services to graduate farmers and small scale entrepreneurs. The objectives of NDE spanned
across the following programmes: (i) Youth employment and vocational skills development
programme, (ii) Special public works, (iii) Small scale industries and graduate employment
programme, and (iv) Agricultural development programme.
12
The aim of the agricultural programme is to generate employment for graduates, non-
graduates and school leavers in the agricultural sector, with emphasis on self employment in
agricultural production and marketing. The programme is monitored by a team of agricultural
professionals in the agricultural department of the directorate. Chinedum (2009) shares the same
views as in Akintoye (2008), inadequate funding and late release of funds from the federation
account among others have impaired the effectiveness of the NDE agricultural programmes.
Also the National Economic Empowerment and Development Strategy (NEEDS) was
introduced in March 2004, in order to confront the various macroeconomic imbalances, social
challenges and structural problems in the Nigerian Economy.
To achieve this goal, the major target of NEEDS is to engineer wealth creation,
employment generation and poverty reduction. Sequel to this, Adebayo and Ogunrinola (2006)
pointed out that for NEEDS to achieve its objectives there is need to design many integrated
programmes that can generate employment for women and youths to enhance growth and
development. In combating unemployment problem, this further point out the need to seek help
in the informal sector in order to drastically reduces unemployment. Hence, unemployment and
government policy, according to Walterskirchen (1999) has positive relationship with economic
growth. GDP and unemployment are both rising in the long run. Employment will only increase
when GDP is increasing faster than productivity, On the other hand, the greater the amount of
goods and services produced, the greater the labour required for production; because economic
growth and employment go hand in hand.
Labour Demand and Graduate Unemployment
The gap that exists between the demand for and supply of university graduates in the
Nigerian labour market is a serious issue in Nigeria, and this has manifested in the prevailing
high level of graduate unemployment. This has serious adverse social and economic
consequences on the Nigerian economy, some of which are declining quality of education,
inadequate funding, insufficient and outmoded learning materials, poorly trained staff, irrelevant
curricula and inadequate information on job vacancies for job seekers in the market. However,
there is need for the establishment of labour market information system, a legal framework for
labour market information management and improved funding of university education to
effectively harness the products of the Nigerian universities for sustainable economic
development (Bassey and Atan, 2012). The high rate of graduate unemployment persuaded
unemployed graduates to demand that government should provide them with job because the
supply for labour is greater than the demand for labour. The demand for labour is derived from
13
production and distribution activities in the goods and service sectors. As a result, its size and
shape are sensitive to what happens in the national economy (Dabalen et al., 2000). The demand
for labour in Nigerian economy has been poor and volatile at best. It is perhaps the most difficult
challenge getting or securing accurate information on labour demand while collecting labour
market information. Accordingly, Dabalen et al. (2000), the reason for this is that, hiring
decisions by firms are typically uncoordinated and in many cases unannounced.
Additional labour analysis problem in Nigeria has the fact that no systematic collection
of labour market information takes place. In many cases, information on labour demand are
obtained through secondary data such as manpower surveys, the labour market studies and direct
interviews with major employers. One way of examining the contribution of tertiary education
to the demand for labour is by taking a look at unemployment in Nigeria by the level of
education. From the Nigerian labour market, it is important to observe two (2) conditions. First,
school leavers with more than secondary education experience significantly lower
unemployment rate than those with secondary education or less. The difference is very sharp
when secondary school leavers are compared with post-secondary graduates. This suggests that
graduates of tertiary institutions stand better chances of obtaining employment compared to
those with only secondary education or less.
Fajana ( 2000) and Standing( 1983) opined that unemployment can be described as the
state of worklessness experienced by persons who are members of the labour force who
perceived themselves and are perceived by others as capable of working. Unemployed people
can be categorized into those who have never worked after graduation from the university and
those who have lost their jobs thereby seeking re-entry into labour market. However, most of the
previous studies on unemployment especially graduates unemployment in developing countries
(Falae, 1971; Bhalla,1973; Diejomaoh,1979; Bear and Herve, 1966; Bhagwati, 1973; Diejomaoh
and Orimolade, 1971) have tended to ignore the special case of the university graduates that are
first time job seekers.
According to William (1976) the meaning of work to paid employment is the result of
the development of capitalist productive relations. However, according to Fajana (2002) the
concept of work has partly shifted from productive effort itself to the predominant social
relationship. For instance, it is only in the sense of social relationship that a woman running a
house and bringing up children can be said not to be working (Hayes and Nutman, 1981; Iyoha,
1987).
Awogbenle and Iwuamadi (2010) noted that the framework of potential efforts and
strategies to boost employment and job creation for young people, entrepreneurship is
14
increasingly accepted as an important means and a valuable additional strategy to create jobs
and improve livelihoods and economic independence of young people. Regrettably, problems of
unemployment as experienced by the educated youths and even the uneducated but skilled
youths have become more pathetic in many developing economies, despite the neo-liberal
strategies in addressing the issue of enhancing human capital. The aim of this paper is therefore
to examine the constraints that impede young people in search of non-existing jobs and the
urgent need to orient people of these affected economies particularly Nigerians on imbibing self-
employment and entrepreneurship through vocational and entrepreneurial training programmes
as a short-term intervention mechanism.
2.2 Empirical Literature
A number of empirical literatures on the drivers of employment and labour demand exist
both in country and cross-countries. Shapiro and Stiglitz (1984) state that unemployment plays
the role of a macroeconomic ‘‘discipline device’’ in order to induce employees to intensify their
efforts in their job. They are based on the shirking models, where the firm, differentiating from
the unemployment salaries, increases the dismissal cost for the employee, thus inducing him to
intensify his effort.
Turunen (1998) presents disaggregated wage curve results by individual characteristics,
occupations, industries and regions in the United States, using a panel data set of young workers.
The results suggest that instead of a strong aggregate wage curve there are a number of different
wage curves over time and for different workers groups. The slope of the aggregate wage curve
varies over time, with the strongest wage curves appearing in the late 1980s. Wage curves exist
for most labour market groups: the wages of the least educated, those in relatively low-skill
occupations or service industries are most sensitive to changes in unemployment. Wages of
government workers and those in the mining industry increase with unemployment.
Brayton, Roberts and Williams (1999) tried to investigate if the Phillips curve is valid for
the 90's low inflation and low unemployment coexist. They use quarterly data from the period
1967 to 1998. They suggest that the Phillips curve cannot explain the low inflation during the
last years. The degree of capacity utilisation gives the best results for the last years, but this does
not appear in the total sample. They also provide evidence for a significant decrease in the
ΝΑΙRU after 1995. Finally, they present an (error-correction) adjustment model of prices in
conjunction with the long run trend unit labour cost.
Marcelino and Mizon (1999) examine the relationship between wages, prices,
productivity, inflation and unemployment. In their paper quarterly data have been used for the
15
period 1965(1) to 1993(1) for Italy, Poland and the UK. They apply a cointegrated VAR model
with regime shifts. They analyse the labour markets of these three countries and conclude that
there have been significant changes in the structures of the relationships between wages- prices
and unemployment - inflation for the period 1979/80. According to the qualitative results they
suggest that although there have been some important changes in the labour markets of these
three examined economies taking into account a greater degree of flexibility, there are no
common characteristics among them. Indeed, this is rationale if someone takes into
consideration the different starting points and policies followed in the three examined
economies.
Chletsos, Kollias, and Manolas (2000) investigated the relationship between
employment, growth rate, labour productivity and wages rate in the case of Greece for the period
1970-93. This period is divided into two sub-periods 1970-1980 and 1981- 1993. In the first
period they indicate that the employment level is positively related to the growth rate and wages
rates are negatively related to the labour productivity. The reverse result is observed in the
second period, which is characterized by the restructuring of the Greek economy.
Hsing (2001), based on the augmented Phillips curve and the autoregressive conditional
heteroscedasticity model, studied the impact of the union wage increases to non-union wages
and found that the growth of non-union wages is positively associated with the expected
inflation productivity growth and negatively correlated with the unemployment rate
Puhani (2002) estimated the changes in the Polish wage and unemployment structures
between the years 1994 and 1998 in order to identify the labour market characteristics associated
with increasing and decreasing relative demand as well as relative wage rigidities. The evidence
from his paper showed that the relative demand for workers with a low level of education has
decreased.
Broersma and Butter (2002) examined the influence of labour market flows on wage
formation and they applied the Johansen multivariate cointegration analysis for Netherlands.
The estimation results suggest the combination of the outflow from employment to
unemployment and the outflow of vacancies as indicators of labour market tightness, qualifying
for inclusion into the wages equation.
The need to ascertain the extent to which the theoretical views on graduate
unemployment in Nigeria are linked with some evidence leads us to the direction of examining
the empirical findings of different researchers on the study area. As recorded in Akintoye
(2008), graduate unemployment in Nigeria accounted for less than 1 percent of the unemployed,
in 1974, by 1984, the proportion rose to 4 percent for urban areas and 2.2 percent in the rural
16
areas. He noted further that, in 2005, Nigerian’s unemployment rate declined to 11.9 percent
from 14.8 in 2003. This decline was attributed to the various government efforts aimed at
addressing the problem through poverty alleviation programmes. This decline also pointed to an
increased number of people who got engaged in the informal sector activities. Unemployment
increased sharply from 14.9% in March 2008 to 19.7 in March 2009 (see Fig 1 below).
According to the NBS (2010), the overall unemployment rate was 19.7% but when
disaggregated by sector, gave 19.2% for urban and 19.8% for the rural respectively. Some states
in the country recorded higher the composite unemployment rate. Example, Bayelsa (38.4%),
Katsina (37.3%), Bauchi (37.2%), Akwa-Ibom (34.1%), Gombe (32.1%), Adamawa (29.4%),
Borno (27.7%), Kano (27.6%), Yobe (27.3%), Taraba (26.8%), Jigawa (26.5%), FCT (21.5%)
and Imo (20.8%) while some recorded lower than the composite unemployment rate. Example,
Plateau state (7.1%.) In 2005, Niger state recorded the lowest rate of 0.2 while Zamfara recorded
the highest rate of 51.1 when the rate of unemployment in the country was 11.9.
Figure 1
Source: Author’s Plot from NBS, (2010).
According, to Oyebade, (2003), Nigeria’s unemployment can be grouped into two
categories: (1) The older unemployed who lost their jobs through retrenchment, redundancy or
bankruptcy. (2) The younger unemployed, most of who have never tasted what it is to be
employed. According to statistics from the Manpower Board and the Federal Bureau of
Statistics, Nigeria has a youth population of 80 million, representing 60% of the total population
of the country. 64 million of them unemployed, while 1.6 million are under-employed. The 1990
- 2000 data on youth unemployment showed that the largest groups of the unemployed are the
17
secondary school graduates. There are also 40% unemployment rate among urban youth aged 20
- 24 and a 31% rate among those aged 15 - 19. Moreover, the educated unemployed tends to be
young males with few dependents. There are relatively few secondary school graduates and the
lower job expectations of primary- school graduates. There is no consistent trend of
unemployment rates in Nigeria.
Also, Sandra et al (2006) used a sample of 360 firms in Kano and its environs to examine
whether or not, in comparison to large firms, small firms are relatively better at creation of
employment opportunities. Their results were positive in that small firms were found to be
relatively better, and the conclusion they derived was that a policy that gives special preference
to small firms is justified.
An empirical investigation by Bakare (2011) showed that the rising nominal wages and
the accelerated growth of population which affected the supply side through a high and rapid
increase in labour force relative to the absorptive capacity of the economy appear to be the main
determinant of high unemployment in Nigeria. The econometric results suggested the need for
the government to embark on direct measures capable of creating jobs through industrialization
and mechanization of agriculture. Bakare also recommended that programmes of integrated rural
development and re-orientation of economic activity and social investments towards the rural
areas need to be embarked upon to create an appropriate rural -urban economic balance.
Biobele Richards (2007) in his study to identify the problems of recruitment in Nigerian
federal civil service and determine the extent of the utilization of job description and job
specification in the recruitment process, used stratified random sampling method to draw a
sample of 190 from five federal ministries, which was grouped into four major categories of
grade levels. The descriptive statistics in the study identified factors such as increasing pressures
for employment, utilization of informal sources of recruitment, long military era, federal
character principle, lack of independence of the service commission and delegation of
recruitment functions as the prevalent problems affecting recruitment in Nigerian federal civil
service. As results of these problems, job description and standard personnel requirements were
not adequately used in the recruitment process, especially at the lowest category of grade levels.
Based on the findings, it was recommended that the federal government should promulgate laws
that will protect employees in the private sector in order to reduce consistent pressures for
employment in the public sector.
Amupitan (2011) examined graduate unemployment and how the National Directorate of
Employment (NDE) has helped in curbing it in Kaduna state. Adopted statistical tables and
charts in the data analysis while the Average score method was used for the test of hypotheses
18
formulated. He discovered that inadequate awareness and poor funding of the activities of the
NDE in Kaduna State undermined its activity, and that skills acquisition is an effective tool in
reducing graduate unemployment. He recommended specific skills acquisition schemes as
elements that could empower unemployed graduates; that specific skills acquisition schemes
should be included in the curriculum of post-secondary schools. Organising symposia, seminars
and using the media were some of the recommendations made for improvement on the
awareness of the activities of the National Directorate of Employment.
Asoluka and Okezie (2011) examined the relationship between unemployment and
growth in Nigeria (1985-2009). One major findings of the study was that the economy grew by
55.5 percent between 1991 and 2006; and the population increased by 36.4 percent. All things
being equal, this should have resulted to a decrease in the rate of unemployment but rather,
unemployment increased by 74.8 percent. The study also found out that the average contribution
of the oil sector to the GDP between 1991 and 2006 was 30.5 percent while agriculture which
was the main source of gainful employment in the country contributed 36.7 percent just a
difference of 6.1 percent from that of oil that employed less than 10 percent of the labour force.
The study recommended that the agricultural sector as a medium of reducing unemployment in
Nigeria should be harnessed and advises that Government and all relevant stakeholders continue
in their quest towards reducing unemployment, as well as give their support in ensuring that the
agricultural sector is not downtrodden but embraced in this task.
Ekkehard Ernst (2011), made use of newly developed information on unemployment
dynamics, and adopted a matching model of the labour market to analyze the economic,
institutional and policy determinants of unemployment in- and outflows. He found standard
determinants to be significant with the expected sign. In particular adverse productivity shocks,
higher user costs of capital, stronger real wage growth, heavier tax burden, larger unionization
rate and more stringent employment protection legislation can be shown to depress
unemployment dynamics. Moreover, the impact of the degree of wage bargaining centralization
confirms the original Calmfors-Driffill insight also in the flow context. The paper also identifies
the impact of policy interventions through fiscal and labour market spending using a
macroeconomic, simultaneous equation set-up. The paper assess the relative contribution of
these policies in stimulating unemployment outflows and analyzes the effectiveness of different
policy instruments at different time horizons, stressing the importance of passive labour market
measures to stimulate unemployment outflows and to limit unemployment inflows.
Godwin and Johnson (2012) studied the existing gap between the demand for and supply
of labour in the Nigerian labour market. The study found out that serious distortions exist in the
19
market for university graduates in Nigeria, giving rise to unacceptable high level of graduate
unemployment. The phenomenon of rising graduate unemployment is bound to have serious
adverse social and economic consequences on the Nigerian economy. The study traces the
problem to the declining quality of education, resulting from inadequate funding, insufficient
and outmoded learning materials, poorly trained staff, irrelevant curricula and inadequate
information on job vacancies for job seekers in the market. They recommended the
establishment of labour market information system, a legal framework for labour market
information management and improved funding of university education to effectively harness
the products of the Nigerian universities for sustainable economic development.
Table 1: Summary of Literature Reviewed
Author / Date Location Nature of Data Method Research Findings
Shapiro & Stiglitz
I984
Greece Cross -Sectional OLS Unemployment occurs as a result of low salary income
Mertz et al 1995 Germany Cross-Sectional OLS Labour productivity directly affect unemployment rate
Gordon, 1997 United
Kingdom
Time-Series OLS Productivity growth increases unemployment rate and
vice-versa
Tunumen,1998 United
States
Panel OLS It indicated a strong aggregate wage curve in determining
unemployment.
Brayton et al,
1999
United
States
Quarterly Time
Series
OLS Adoption of an effective Philip curve measure
Marcelino and
mizon, 1999
Italy,
Poland
and Uk
Quarterly Time
Series
VAR Existence of significant changes in wage price-
unemployment structure
Chletsos et al
2000
Greece Time-Series ECM Employment level is positively related to growth rate but
wage rate is negatively related to labour productivity
Akintoye 2008 Nigeria Time-Series OLS Unemployment is caused by urban saturated job seekers
Dabalen et al 2000 Nigeria Survey data OLS Significant number of graduate employee are majorly the
public sector
Hsing 2001 Hong-
kong
Time-Series ARCH Growth of non-union wage rate negatively affects
productivity and unemployment.
Broersma and
Butter 2002
Netherland Time Series OLS Significance combination of outflow from employment to
unemployment to be included
Oyebade 2003 Nigeria Time Series OLS Human capital has a direct impact on youth employment
Ajibefun &
Daramola 2003
Nigeria Survey data OLS Evidence of a wide variation between unemployment and
economic growth.
Sandra et al 2006 Nigeria Cross Sectional OLS Good government labour policy significantly enhance
productivity
Biobele Richards
2007
Nigeria Time Series OLS Job description and standard personnel requirements were
not adequately used in the recruitment process
Bakare, 2011 Nigeria Time Series 2SLS government should embark on direct measures capable of
creating jobs through industrialization
Amupitan 2011 Nigeria Cross seasonal Descriptiv
e statistic
Suggested that skills acquisition schemes could empower
unemployed graduates
Asoluka & Okezie
2011
Nigeria Cross seasonal OLS Agricultural sector should be harnessed as a way of
reducing unemployment.
Godwin &
Johnson 2012
Nigeria Time Series OLS Sees declining quality of education and inadequate
information on job vacancies as the cause of
unemployment.
20
2.3 Conceptual Frame work
Concept of Unemployment
As noted by Bello (2003), from time immemorial, the subject of unemployment has
always been an issue of great concern to the economists, policy makers and economic managers
alike given the devastating effect of this phenomenon on individuals, the society and the
economy at large. The classical school of thought that provided the earliest thinking on
economic issues did not fail to give a central point of reflection on the undesirability of
unemployment. The Keynesian revolution of the 1930’s, which commandeered the explosive
attack on economic orthodoxy apparently, treated unemployment as a central issue of great
concern. Following the path of the predecessors, economists at all times and in all ages have
expressed various degrees of concern over the threat of the monster called unemployment.
Thus, the population of every economy is divided into two categories, the economically
active and the economically inactive. The economically active population (labour force) or
working population refers to the population that is willing and able to work, including those
actively engaged in the production of goods and services (employed) and those who are
unemployed. Whereas unemployed refers to people who are willing and capable to work but are
unable to find suitable paid employment. The next category to the economically inactive
population refers to people who are neither working nor looking for jobs. There seems to be a
consensus on the definition of unemployment.
The International Labour Organization (ILO) defined unemployment as the people who
are out of work, want a job, have actively sought for work in the previous four weeks and are
available to start work within the next fortnight; or out of work and have accepted a job that they
are waiting to start in the next fortnight (ILO, 2005). The unemployment definition can differ
from one country to another according to how it is measured and implemented. Unemployment
can also be described according to region, sex, educational level, age, and economic conditions.
Here our focus is on the classification of unemployment according to educational level (graduate
unemployment).
However, graduate unemployment is unemployment among people with academic
degrees. It is a situation where tertiary institution graduates do not get jobs after going through
the academic ladder successfully. One of the major causes of this is the mismatch between the
aspirations of graduates and employment opportunities available to them. According to Juan
Ramón (2011), graduate unemployment is an evidence of serious shortcomings in educational
system and labour market in developing economy, which explains the country's relative high
rate of youth unemployment and the imbalance between job supply and demand at the different
21
educational levels attained, which also complicates graduates access to the labour market and
has a negative impact on their professional career.
Confirming this, three out of ten graduates of higher education cannot find work, the
reason being that high education does not increase the chance of finding job; many graduates of
higher education who find work are not usually gainfully employed. They are forced to accept
marginal jobs that do not use their qualification, for instance, in sales, agriculture and manual
labour. Therefore, graduate unemployment requires coordinated action between education and
the labour market.
Concept of Labour Productivity
Productivity measures the relationship between the quantity and quality of goods and
services produced and the quantity of resources needed to produce them (i.e. factor inputs such
as labour, capital and technology) (Simbeye, 1992; Okojie 1995; Roberts and Tybout, 1997).
Mali (1978) defines it thus: "The measure of how resources are being brought together in
organizations and utilized for accomplishing a set of results. It is reaching the highest level of
performance with the least expenditure of resources". Productivity is viewed as the instrument
for continuous progress, and of constant improvement of activities. It is often seen as output per
unit of input. Hence, higher productivity connotes achieving the same volume of output with
less factor inputs or more volume of output with the same amount of factor inputs. Thus,
increased productivity could result from the reduction in the use of resources, reduction in cost,
use of better methods or improvement in factor capabilities, particularly labour. Two variants of
productivity measurements have been cited in the literature: total factor productivity (TFP),
otherwise known as multifactor productivity, and partial productivity.
Roberts and Tybout (1997) and Tybout (1992), assuming a neo-classical production
function at the sectoral or industry level, define total factor output to be a concave function of
the vector of inputs and time (a proxy for shift in technological innovation). To them, the
elasticity of output with respect to time is the total factor productivity. In a more general sense,
TFP = Total Output ie Weighted Average of all inputs. Critical among these factor inputs are
labour, capital, raw materials and purchase of spare parts, and other miscellaneous goods and
services that serve as inputs in the production process. In a more practical sense, these factor
inputs are reduced to the weighted average of labour and capital (Okojie, 1995; Roberts and
Tybout, 1997). The second variant, partial productivity (PP) is defined as:
PP = Total Output. That is, partial productivity is equated to total output.
22
Partial Input
The partial input could either be labour or capital. This can be measured at the national level,
sectoral level, industry or factory level. Existing studies on productivity measurement show a
predilection for productivity per labour input. Several reasons have been put forward for the
choice of labour as against other factors of production. According to Ilyin and Motyler (1986)
labour is the "means and end of production". Labour is the only factor that creates value,
influences its prices and those of other factors and sets the general level of productivity. Second,
it is the most easily quantified factor of production (Okpechi, 1991). Finally, given the low
technological base of developing countries' economies, the quest for improved managerial
capability and effectiveness should give the human factor appropriate recognition and attention.
While labour productivity seems to be the most convenient to use, it is however important to
note that this approach has an important limitation. It treats labour as being homogenous instead
of differentiating it according to age, sex, education, application of skills, aptitude, among
others. Nevertheless, this study applies productivity per worker as opposed to per capital or total
factor productivity.
Government Labour/Employment Policy
The government labour policy is the policies and programmes put in place by the
government over the years to reduce unemployment, especially graduate unemployment. Here in
this study we look at the policy design in National Directorate of Employment (NDE) that
targets graduates. Hence, the effective government labour policies instruments targeted at
graduate unemployment in National Directorate of Employment (NDE) scheme include the start
your Own Business (SYOB) under the small Scale Enterprise programme (SSE), the
sensitization of National Youth Service Corps (NYSC) on Entrepreneurship development, the
Graduate Attachment Programme (GAP) and the Solar Energy Training Scheme (SETS) which
was recently introduced. However, the study will estimate the government labour policy with the
total number of persons that benefited from NDE graduate programmes annually (TNA).
2.4 Literature Gap and Value Addition
There is no doubt that great deal of literature exists on unemployment and its determinants both
in Nigeria and across the world . The recent studies of unemployment and its drivers carried out in
Nigeria (see Sandra et al 2006, Biobele Richards 2007, Bakare, 2011, Asoluka & Okezie 2011
and Godwin & Johnson 2012) focus more on the causes and consequences of unemployment in
Nigeria. For instance, Asoluka & Okezie (2011) presented time series data of the rate of
unemployment and gross domestic product in Nigeria, for the period of 1985 to 2009 to show
23
the percentage changes in both unemployment and GDP without applying any quantitative
measures to ascertain the significant nature of these figures. They only analyzed the
consequences of the growing trend of unemployment on economic growth with some
recommendation. Godwin & Johnson (2012) that studied related subject to this study again
showed all the trend of unemployment and school enrolment rate in Nigeria. Most of these
studies analyzed unemployment rate based on the figure, no further empirical examination was
carried out and those that did so, used OLS technique to examine the significant impact of
unemployment rate on economic growth in Nigeria.
Therefore, this study will bridge this huge research vacuum by building on the
theoretical framework suggested by ‘Keynes and Modern theory of labour market’ to establish a
labour market productivity model of the nexus between government labour policy, graduate
unemployment and labour productivity in Nigeria. As a matter of fact, a study of intra-
relationship as in this study will pose serious, endogeneity problems, just as found in some other
studies carried out in other countries; hence this study will adopt various techniques to
checkmate this endogeneity and other estimation biasness that may result in a study of this kind,
in order to ascertain the true intra-connections in this phenomena.
24
CHAPTER THREE
METHODOLOGY
3.1 Theoretical Framework
The framework for analyzing the relationship between government policy, labour
productivity and unemployment is a complex issue. Starting with the classical economists view,
the framework for employment and output is a one-way relationship that goes from the input of
labour to output. In the classical model's steady state (conditions where the growth rate of capital
stock and output are equal) the rate of growth of labour force and technical progress ultimately
determine the growth rate of output.
The premise of the classical model therefore is that the growth rate of employment is
exogenous to the growth rate of output. In this framework, the supply of labour is positively
related to the level of real wage, while the demand exhibits a negative relationship with real
wage, but a positive relationship with productivity (Fashola, 1983; Todaro, 1990). As pointed
out by these authors, if there is some `involuntary' unemployment at or below the current real
wage, the real wage would fail to induce employers to take more labour until all involuntary
unemployment is eliminated. However, if increases in labour productivity translate to increased
wages and such increases induce the substitution of capital for labour the effect on
unemployment will be positive (Fajana, 1983; Krugman, 1994).
The policy implications of this have been viewed as misleading particularly, to
developing countries such as Nigeria (Todaro, 1990; Hussain and Nadol, 1997). Also as argued
by Hussain and Nadol (1997:3), the policy implication of the neoclassical approach to primary
commodities-producing countries like Nigeria is that, given the existence of says Law, whatever
that was produced is automatically sold irrespective of the characteristics of the goods produced
and the demand for them. Recent developments in the world market for primary commodities
have proved this to be wrong.
In contrast, Keynesian theory explains the determination of output or productivity and
aggregate demand. This approach sees demand for labour as a derived demand. Productivity
growth (a la Verdoorn's Law), should increase the demand for labour thereby reducing
unemployment. The Keynesian framework, as examined by Thirlwall (1979), Grill and Zanalda
(1995) and Hussain and Nadol (1997), postulates that increase in employment, capital stock and
technological changes are largely endogenous. Thus, the growth of employment is demand
determined and that the fundamental determinants of long term growth of output also influence
the growth of employment.
25
Contrary to the strong belief of the neoclassical that equilibrium wage rate, price, interest
rate and real cash balances guarantee the quality of national output and full-employment level,
the Keynesians strongly believe in the efficacy of aggregate demand. Rather than the workings
of the real wage, price, interest rate and real cash balances, what could guarantee the attainments
of full employment is additional government spending. The Keynesian prescription for reducing
unemployment is increase in aggregate total demand through direct increases in government
spending or policies that encourage more private investment. As argued by the Keynesians, as
long as there is unemployment and excess capacity in the economy, the supply of goods and
services will respond automatically to this higher demand. A new equilibrium will always be
established with higher income and lower level of unemployment.
However, the extension of the Keynesian model dominated development theorizing in
the 1950s and beyond. Such extensions could be found in Okun's Law and the Harrod-Domar
model. For instance, Arthur Okun developed the relationship between the actual and potential
output and between the actual and benchmark unemployment in an equation called the "Okun's
Law" (Dernburg, 1985). Thus:
( )*
**
Q QU U
Q
−= −
Where Q* is potential output, Q is actual output, U is the unemployment rate, U* is the
benchmark unemployment rate, and " is Okun's coefficient (Akinboyo, 1987). The implication
of Okun's coefficient is that a 1 percentage rise in unemployment causes the economy to lose
"percent of its output. Okun's Law clearly gives a direct relationship between output and
unemployment and indirectly between productivity and unemployment (a la Verdoorn Law).
In a similar vein, the neo-Keynesians, in their efforts to provide reasons why
employment growth lags behind growth of industrial output, came out with a typical variant of
the Harrod-Domar unemployment equation
[ ][ ]
( ) [ ][ ]
** *Y
Y NN
YY NN
− =
Thus, the essence of this equation is that the rate of output growth (Y) minus the rate of
growth in labour productivity (Y/N) approximately equals the rate of growth of employment
(N). The implication is that the gap between growth rate of output and the growth of labour
productivity accounts for the rate of labour absorption. As had been argued hypothetically by
Todaro (1990), if output is growing by 8 percent per year while employment is expanding by
only 3 percent, the difference is due to the rise in labour productivity, and vice versa. By
implication, rapid economic growth could generate lagging employment creation. This tends to
26
support Essenberg's (1996) argument that if the reduction in labour demand resulting from
productivity increases is more than compensated by overall increases in output, then both
productivity and employment can increase together. This is particularly so when higher
productivity leads to increased profit and higher rate of investment, which in turn results in
higher rate of growth.
In conclusion, the neo-classical approach posits that the rate of growth of employment
(unemployment) is exogenous to the rate of growth of output (productivity). In contrast, the
Keynesian argument is premised on the fact that it is the strength of demand that determines the
amount of resources utilized. As such, employment is demand determined and the rate of output
growth is itself an important determinant of the rate of growth of employment. Thus, output,
productivity and employment are determined endogenously.
3.2 The Model
The analytical ideas employed for this study is built on the combination of Keynesian
framework and modern labour market theory which provides at least three competing models to
explain equilibrium unemployment (Gordon, 1997). These models are; (i) the union models,
which view wages as being determined by a bargain between unions and firms; (ii) the search
models, where the wage is determined by a bargain between individual workers and firms; and
(iii) the efficiency wage models, where firms set wages above the competitive level to increase
workers efforts. Though some of the reasoning behind these models are different, there are
noticeable similarity among them: the first is that the equilibrium rate of unemployment is
determined by institutional settings, such as the size and power of unions, the bargaining system,
and by the generosity of the unemployment insurance system. Secondly, the equilibrium rate of
unemployment is independent of production and productivity growth.
On its own, Keynesian prescription for reducing unemployment is to increase the
aggregate total demand through direct increases in government spending policy that encourage
more private investment. As argued by the Keynesians, as long as there is unemployment and
excess capacity in the economy, the supply of goods and services will respond automatically to
this higher demand. A new equilibrium will always be established with higher income and lower
level of unemployment. Thus, while labour market institutions can potentially explain cross-
country differences today, they do not appear able to explain the general evolution of
unemployment over time” Nickell et al. (2005). Despite conventional wisdom, high
unemployment does not appear to be primarily the result of things like overgenerous benefits,
trade union power, taxes or wage inflexibilities. Nickell et al. (2005) provide, the most thorough
27
(econometric) and eclectic relationship of unemployment and government policy when he
pointed that rise in the unemployment rate, one-half can be attributed to institutional changes in
the labour market, such as the unemployment compensation payment system, the system of
wage determination, employment protection, labour taxes, and barriers to labour mobility, and
the other half to demand deficiency. Hence this idea of theoretical model will be invoked in this
study to explain the observed government policy, graduates unemployment and labour market
behavior in Nigeria.
Therefore, following the Keynesian framework, the three labour union models and
numerous literature reviewed, government policy can impact on graduate unemployment
through the following variables, government spending, output gap, money supply, interest rate
real wage rate, marginal product of labour, external debt management, education and training,
price level and lag of unemployment. Not all these variables will be included in this study due to
unavailability of some of the data. The theoretical relationship underpinning these variables will
be explained below after the specification.
3.3 Model Specification
Model 1: The relationship between Government Labour Policy, Graduate Unemployment
and Labour productivity
To analyze the significant relationship among government labour policy, graduate
unemployment and labour productivity, as in Objective 1 of this study, we made use of the
system of labour productivity model. The channels through which labour policy can impact on
graduate unemployment and labour productivity in Nigeria are presented in the following
equations:
Ug = f(TNA, INV, LP, GXP, Yg, GS) ------------------------------------------------- (1)
LP = f(TNA, INV, Ug, GXP, Yg, M2,IR) ---------------------------------------------- (2)
where
Ug = graduate unemployment rate.
TNA = government labour policy captured by the total number of persons that
benefited from NDE graduate programmes annually.
INV = investment rate proxied by gross fixed capital formation.
LP = labour productivity, proxied by GDP per person employed.
GXP = total government expenditure
Yg = output gap, (potential output ‘p’ minus actual output ‘y’).
GSE = gross secondary school enrolment
28
IR = real interest rate
M2 = broad money supply
The choice of the above listed indicators is in line with government labour policy
response variables implicated in the literature. According to Tejvan Pettinger (2011), there are
two main strategies for reducing unemployment - demand side policies to reduce demand-
deficient unemployment (unemployment caused by recession) and supply side policies to reduce
structural unemployment (the natural rate of unemployment).
However, these variables are justified below; let us first transform equations 1 & 2 to an
econometric format in the following equations.
1 2 3 4 5
6 1
t t t t t
t t
U g T N A I N V L P G X P Y g
G S E
α φ φ φ φ φ
φ ε
= + + + + +
+ + --------- 3
1 2 3 4 5
6 7 22
t t t t t
t t t
L P I N V I N V U g G X P Y g
M I R
α φ φ φ φ φ
φ φ ε
= + + + + +
+ + + ----------4
where
φI = the slope coefficients of the vector of explanatory variables defining the interaction
among government policy, graduate unemployment and labour productivity. However, a
simultaneous equation such as in 3 & 4 is bound to suffer problem of endogeneity, but this study
adopted Hausman Endogeneity method to take care of this during the estimation.
Theoretical Justification of the variables in Model 1
Government Expenditure: Fiscal policy can decrease unemployment by helping to
increase aggregate demand and the rate of economic growth. The government will need to
pursue expansionary fiscal policy; this involves cutting taxes and increasing government
spending. Lower taxes increase disposable income and therefore help to increase consumption,
leading to higher aggregate demand (AD).
Output Gap: With an increase in AD, there will be an increase in real GDP (as long as
there is spare capacity in the economy). If firms produce more, there will be an increase in
demand for workers and therefore lower demand-deficient unemployment. Also, with higher
aggregate demand and strong economic growth, fewer firms will go bankrupt meaning fewer job
losses. Keynes was a strong advocate of expansionary fiscal policy during a prolonged
recession. He argues that in a recession, resources (both capital and labour) are idle, therefore
the government should intervene and create additional demand to reduce unemployment.
Interest rate: Monetary policy would involve cutting interest rates. Lower rates
decrease the cost of borrowing and encourage people to spend and invest. This increases AD and
29
should also help to increase GDP and reduce demand deficient unemployment. Also lower
interest rates will reduce exchange rate and make exports more competitive. In some cases,
lower interest rates may be ineffective in boosting demand. In this case, Central Banks may
resort to Quantitative easing. This is an attempt to increase money supply and boost aggregate
demand.
On the other hand, the supply side policies deal with more micro-economic issues. They
do not aim to boost overall aggregate demand, but seek to overcome imperfections in the labour
market and reduce unemployment caused by supply side factors. Supply side unemployment
includes – Frictional, Structural and Classical (real wage).
Education and Training captured in this study by gross secondary school enrolment:
The aim is to give the long term unemployed new skills which enable them to find jobs in
developing industries, e.g. retrain unemployed steel workers to have basic I.T. skills which helps
them find work in service sector. – However, despite providing education and training schemes,
the unemployed may be unable or unwilling to learn new skills. At best it will take several years
to reduce unemployment.
Real wage rate: If unions are able to bargain for wages above the market clearing level,
they will cause real wage unemployment. In this case reducing influence of trades unions (or
reducing Minimum wages) will help solve this real wage unemployment. To the neo-classicists,
the supply side of the labour market is infinitely elastic at what they called “natural price” of
labour.
Improve Labour Market Flexibility: It is argued that a higher structural rate of
unemployment is due to restrictiveness of a labour market which discourages firms from
employing workers in the first place. For example, abolishing maximum working weeks and
making it easier to hire and fire workers may encourage more job creation. However, increased
labour market flexibility could cause a rise in temporary employment and greater job insecurity.
Model 2: Graduate unemployment and Total factor productivity
In order to estimate objective two of this study, this examines the impact of graduate
unemployment on Total Factor Productivity (TFP). This was measured in this study as the ratio
of output and weighted input factors.
t
i
YTFP
Weighted X
=
---------------------------------------------------------------- (5)
Where
Y = total output in the economy
Xi = aggregate input factors (weighted).
30
For simplicity, let ( X ′ ) be expressed as a vector of all the input factors, while (a) defines
a vector of factor input prices. TFP is then calculated as the ratio of output and weighted input
factors (weighted with corresponding input factors) as:
aY
TFPX
= ′
---------------------------------------------------------------------------- (6)
where
Superscript ‘a’ = a vector of factor input prices
Expressing equation (6) in an implicit and log form, the following equation is
formulated:
1 2 3 4
5 6 2
t t l t t t
t t t
T F P w M P U g G X
Y g I R
α φ φ φ φ
φ φ ε
= + + + +
+ + + --------------------- (7)
From equation (7) we can derive the concept of Total Factor Productivity Growth (TFPg)
by taking the log difference of TFP as:
( ) 100TFPg TFP= ∆ ∗ ------------------------------------------------------------------------------ (8)
Model 3: Causal relationship between Graduate unemployment and government labour
policy variables
We also investigated the causal relationships existing between government labour policy,
graduate unemployment and labour productivity; we therefore specify our model by adopting
the Granger Causality Tests. This model focuses on the relationship between two time series.
Granger (1969) defined the causality in terms of predictability, based on the fact that the effect
cannot come before the cause. The model is specified thus as;
1
1 1 1
n n m
t i t i j t j k t i t
i i i
U g X U g Xα β λ µ− − −
= = =
= + + +∑ ∑ ∑ ---------------- (9)
2
1 1 1
n n m
t i t i j t j k t i t
i i i
L P U g X Xλ β λ µ− − −= = =
= + + +∑ ∑ ∑ --------------- (10)
Where it is assumed that the disturbances µ1t, µ2t and µ3t are uncorrelated.
Equations 9 and 10 were used to capture objective three of the study which examines the causal
relationship existing between government labour policy, graduate unemployment and labour
productivity.
3.4 Estimation Procedure
This study employed the ordinary least square technique of estimation, co-integration
and error correction techniques in estimating the models of the study. Most economic time series
31
data are non-stationary (Gujarati 2004), hence correct and appropriate specifications of the time
series model requires that we determine whether such time series are stationary or not. In this
case, all variables of the study will be subjected into unit root test of stationarity to avoid a
spurious result (Granger 1969). Here, coefficient of determination (R2) together with the value
of Durbin-Watson will be determined. In the small way, the standard significance test
(traditionally measured by t-test), Overall significance of the model measured by the F-test and
the coefficient of the parameter of the variables used in determinining the conformity of apriori
expectation of the economic theories will be analyzed in the study.
Unit Root Tests
Many macroeconomic time series contain unit roots dominated by stochastic trends as
developed by Nelson and Plosser (1982). Unit roots are significant in examining the stationarity
of a time series because a non-stationary regressor invalidates many empirical results. The
presence of a stochastic trend is determined by testing the presence of unit roots in time series
data. In this study, unit root test was carried out using Augmented Dickey (1979) and Fuller
(1981). Augmented Dickey Fuller test (ADF) (1979) refers to the t-statistic of δ2 coefficient on
the following regression:
∆Xt = δ0 + δ1t + δ2Xt-1 + Σ αi∆Χt-i + µt…………………………………….... (11)
The ADF regression tests for the existence of unit root of Χt, namely in the logarithm of
all model variables at time t. The variable ∆Χt-i expresses the first differences with k lags and
final Ut is the variable that adjusts the errors of autocorrelation. The coefficients δ0, δ1, δ2, and αi
are being estimated. The null and the alternative hypothesis for the existence of unit root in
variable Xt is:
Ηο : δ2 = 0 (unit root) Ηε : δ2 < 0
Τhis study follows the suggestion of Engle and Yoo (1987) using the Akaike information
criterion (AIC) (1974), to determine the optimal specification of Equation (11). The appropriate
order of the model is determined by computing Equation (11) over a selected nexus of values of
the number of lags k and has been found that value of k at which the AIC attains its minimum.
The distribution of the ADF statistic is non-standard and the critical values tabulated by
Mackinnon (1991) are used.
The results of ADF test are compared with critical values at 1%, 5% and 10% (usually
the 5% conventional approach), which we have obtained from Mackinnon (1991) tables. The
results of absolute value of ADF statistic for the examined time series should exceed the critical
values, before the null hypothesis of a unit root is rejected. Taking first differences renders each
32
series stationary, with the ADF statistics in all cases being less than the critical value at the 1%,
5% and 10% level of significance.
Co-integration Test
After establishing the existence of unit root and their respective order of integration
identified then it will be necessary to evaluate the variables that have the same order of
integration or if their linear combination is integrated at level form. If the variables are
integrated of the same order then the presence of co-integration is established as well as their
linear combination (Enders, 1995). Equation below represents co-integration equation.
∆Yt=α1Yt-1+X1ᵠ + β1∆Yt-1 + β2∆Yt-1 + βp∆Yt-p +εi.......................................... (12)
where X is the optimal exogenous regressor which may consist of constant or a constant and
trend α, ᵠ and β are parameters to be estimated.
If there is the presence of co-integration in the model, equations 3&4 will be transformed into an
error correction model (ECM).
1 2 3 4
5 6 1 1
t t l t t t
t t t t
U g w M P L P G X
Y g G S E C M
α φ φ φ φ
φ φ ν−
∆ = + ∆ + ∆ + ∆ + ∆
+ ∆ + ∆ + ∆ + ------------ (13)
1 2 3 4
5 6 1 2
t t l t t t
t t t t
L P w M P U g G X
Y g I R E C M
α φ φ φ φ
φ φ ν−
∆ = + ∆ + ∆ + ∆ + ∆
+ ∆ + ∆ + + ------------ (14)
Equations 13& 14 stated above are ECM formulation which indicates the speed of adjustment of
variables that were in a disequilibrium state into equilibrium.
The error correction parameter indicated above shows how disequilibrium in the
explained variables are corrected into equilibrium in each periods but if it is statistically
significant, it implies that the disequilibrium is corrected at different periods but if otherwise
stated, then it is corrected at the same period. Where ∆=difference operator.
The ECM analysis simply provides the short-run dynamic adjustments of the
explanatory variables towards the Long-run equilibrium.
The significance levels of the F – statistics for the lagged variables and the t – statistics for
the coefficient δ of ECt-1 are used to test for Granger causality i.e. there are two channels of
causality (Granger 1988). These are called channel 1 and channel 2. If lagged values of a
variable (except the lagged value of the dependent variable) on the right hand side in equation 3
are jointly significant then this is channel 1. On the other hand, if the lagged value of the error
correction term is significant, then this is channel 2. The numbers in parentheses are the lag
lengths determined by using the Akaike criterion.
33
3.5 Justification of the Model
The analytical framework of the model is such that it incorporates the major features of an
econometric analysis in a systematic manner. For instance, in the model specifications,
provisions were made to ensure numerical accuracy and the stationarity of the variables used in
the study. Also, provisions were made for the elimination of co-integration by the application of
the error correction model (ECM) which helps in capturing the long-run behavioral pattern of
the economic variables under study.
3.6 Sources of Data
The data that will be used in the analysis are annual time series data, covering the period
1987 – 2013. It is a secondary data sourced from Central Bank of Nigeria annual statistical
bulletin/reports (various years) and National Bureau of Statistics annual statistical reports. If
these variables share a common stochastic trend and their first differences are stationary, then
they can be co integrated. E-view was the econometric software used in the analysis.
34
CHAPTER FOUR
PRESENTATION OF RESULTS
This section presents the results of the specified models. It starts with the results of data
generating process (trend analysis of the core variables, the unit root or stationarity test and the
co-integration statistics of the data series), the results of the main models and evaluation of the
research hypotheses of the study.
4.1 Analysis of the Data Generating Process
We start in this section with the presentation and analyses of the descriptive statistic,
trend analysis of the core variables, stationarity or unit root test and the co-integration test of the
data series. This will enable us to carry out some predetermine operations, if required, on the
variables, so as to have a more précised analysis and interpretation.
4.1.1 Trend Analysis of the Core Variables
The trend analysis of the core variables was conducted first to establish the trending
patterns of these variables, hence we present below the trending of graduate unemployment rate
(UG), government labour policy captured by the total number of persons that benefited from
NDE graduate programmes annually (TNA), and labour productivity (LP), proxied by GDP per
person employed and total factor productivity (TFP).
The results show that while the total number of persons that benefited from NDE
graduate programmes annually and labour productivity proxy, in this study, by the growth in
GDP per person employed maintained steady and near horizontal movement, graduate
unemployment rate has been on the increase over the years given other considerations (see Tab
4.1). This implies that these variables are trending, showing a typical sign of unit root. Also
observed in the trend analysis is that total factor productivity exhibits upwards trending as
graduate unemployment.
35
Figure 2: Trend Analysis of the core variable
0
20
40
60
80
100
1990 1995 2000 2005 2010
TNA UG LP
5.000E+10
5.000E+11
5.000E+12
5.000E+13
1990 1995 2000 2005 2010
TFP
Source: Author’s calculation
4.1.3 Unit Root and Co-integration Analysis
In an attempt to normalize our data from unit root problem, we test for the presence of
unit root in the variables and obtain their integrating order. If the dependent variable associated
to each model is found to be integrated of the same order with the explanatory variables
36
included in that model, the co-integration test will be carried out to ascertain their long-run
relationships.
Testing for Stationarity
This is necessary in order to ensure that the parameters are estimated using stationary
time series data. Also this study seeks to avert the occurrence of spurious results. To do this,
both the Augmented Dikky-Fuller (ADF) and Phillips-Perron (PP) unit root tests are carried out.
The essence of these tests is to verify the null hypothesis of unit root or non-stationary stochastic
process. To reject this, the ADF statistics must be more negative than the critical values and
significant. On the other hand, the Phillips-Perron test differs because it provides a more robust
test for serial correlation and time dependent heteroskedasticities of the stochastic process. The
results of ADF and PP test statistics for the levels and first differences of the annual time series
data for the period under investigation are presented in Table 2.
Table 2: ADF and PP Unit Root Test Results for Individual Variables
Source: Computed from the Output of E-views software.
The asterisk (*) denotes rejection of the unit root hypothesis at the 5%, while the asterisk
(**) denotes rejection of the unit root hypothesis at the 1% level respectively. The ADF statistics
were generated with a test for a random walk against stationary AR (1) with drift and trend at
VARIABLE ADF PP
LEVEL 1ST
Diff, Prob. Level 1st Diff. Prob.
GS -1.282402 -6.060380** 0.0000 -1.282402 -6.222578** 0.0000
GXP 2.510561 -4.245044** 0.0031 3.414083 -4.274490** 0.0029
INV 3.880293 3.512078 1.0000 5.075342 -3.412299** 0.0206
IR -4.524767** -6.217123** 0.0000 -4.523883** -18.09608** 0.0001
LP -9.186530** -2.049518* 0.2653 -4.846324** -1.705559 0.4159
M2 -0.331316 1.146277 0.9959 3.530804 -1.351276 0.5884
TFP 4.294228 0.924351 0.9940 4.672281 -3.305201** 0.0260
TNA -5.602038** -5.203217** 0.0003 -12.81987 -5.808393** 0.0001
Ug 1.020143 -5.222694** 0.0003 0.113228 -8.584703** 0.0000
Yg -2.016543* -5.176852** 0.0003 -2.217760* -5.171846** 0.0003
where,
Ug = graduate unemployment rate.
TNA = government labour policy captured by the total number of persons that benefited
from NDE graduate programmes annually.
INV = investment rate proxy by gross fixed capital formation.
LP = labour productivity, proxy by GDP per person employed.
GXP = total government expenditure
Yg = output gap, (potential output ‘p’ minus actual output ‘y’).
GS = gross secondary school enrolment
IR = real interest rate
M2 = broad money supply
Note: ** indicates significance at 5% and 1% level.
37
the maximum lag length of 9. While the PP test uses the automatic bandwidth selection
technique of Newey-West.
From the result in table 4.1, the ADF indicated that the dependent variables for the three
objectives of this study are integrated of order one (∆ = 1) along side with some of the
explanatory variables. The real interest rate, labour productivity, the government labour policy
captured by the total number of persons that benefited from NDE graduate programmes annually
and output gap show stationary at level (∆ = 0), in ADF as well as PP results. This shows a
prerequisite for the presence of long-run linear combination among them, and to avoid mistake
of analysis of a long-run relationship in short-run analysis, we conduct co-integration test for the
variables.
Results from Co-Integration Test
Given the unit root properties of the variables, we proceed to implement the Engle-Granger co-
integration procedure. The explanatory variables that have the same order of integration (∆ = 1)
with the dependent variable; we estimate their linear combination at their level form without the
intercept term and obtain their residual which is then subjected to co-integration test as shown
in Tab 3.below.
Table 3: Co-Integration Tests
Null Hypothesis: The Residual has a unit root
Model Critical Value T – Statistic Prob.
1% 5% 10%
Model 1 -3.737853 -2.991878 -2.635542 -4.538538 0.0016
Model 2 -3.737853 -2.991878 -2.635542 -4.035495 0.0051
*MacKinnon (1996) one-sided p-values. Source: EView output
From the table, since the t–statistic associated with two models (-4.38538 and -4.035495) are
respectively less than their1%, 5% and 10% critical values as shown in table 3 above. This
means that the residual is not stationary and hence there is long-run linear relationship or co-
integration among the variables. Consequently, the study adopts the Error Correction Model
which was specified in Chapter Three as co-integration was noted among the variables.
38
4.2 Presentation and Analysis of ECM Models
Based on the underlying properties of the models specified in this study, such as; linear
in parameters, random sampling of the exogenous variables, homoskedasticity (equal variance),
and finally, the no-serial correlation, we analysed the validity and endogenous test to avoid
spurious and biased results. We therefore, start by implementing the endogeneity properties
before presentation of the main models.
4.2.1 Endogeneity Test
This study adopted Hausman (1978) method of endogeneity test. He suggested the direct
comparing of the OLS and 2SLS estimate and determining whether the differences are
statistically significant. If any appreciable differences are found between the two methods, (OLS
and 2SLS), we conclude that the endogenous explanatory variable(s) must be endogenous.
Hypothesis
H0: δi = 0 (no problem of Edogeneity)
Against
H1: δi ≠ 0 (there is a serious Edogeneity problem)
Decision Rule: Reject H0 if tcal > ttab, and accept otherwise at 5% level of significance.
The endogeneity results for the two models are presented as:
Table 4. Endogeneity Result for Graduate Unemployment (UG) Model
Variable Coefficient Std Error t - value t - prob
C 190.7218 83.04028 2.296738 0.0332
TNA 2.591692 0.917797 2.823817 0.0108
INV -9.02E-12 3.86E-12 -2.338469 0.0304
LP -5.311456 2.222258 -2.390117 0.0274
GXP 2.74E-05 4.45E-06 6.162319 0.0000
YG -4.41E-06 1.42E-06 -3.105822 0.0058
GS -0.083457 0.407366 -0.204870 0.8399
Structured estimate
C 70.60203 55.46384 1.272938 0.2192
TNA 1.590465 0.593273 2.680831 0.0153
INV -1.08E-11 2.40E-12 -4.486972 0.0003
LP -2.408248 1.464044 -1.644929 0.1173
GXP 2.82E-05 2.75E-06 10.24496 0.0000
YG -4.76E-06 8.79E-07 -5.410996 0.0000
GS 0.280486 0.259429 1.081168 0.2939
RESID01 1.033048 1.24674 1.650245 0.1235
F – Stat. = 169.5667; P – value = 0.0000
39
Table 4.1: Endogeneity Result for Labour Productivity Model
Variable Coefficient Std Error t - value t - prob
TNA 1.158564 0.078943 14.67595 0.0000
INV -5.20E-13 4.79E-12 -0.108512 0.9147
UG -0.054811 0.188153 -0.291310 0.7740
GXP -9.96E-07 7.76E-06 -0.128418 0.8992
YG 9.96E-10 1.36E-06 0.000733 0.9994
M2 5.82E-13 2.09E-12 0.278870 0.7834
IR -0.018228 0.064248 -0.283707 0.7797
Structured estimate
C 2.957904 0.014095 209.8596 0.0000
TNA 0.022042 0.000291 75.85171 0.0000
INV -9.95E-15 9.28E-16 -10.72223 0.0000
UG -0.000988 3.59E-05 -27.47677 0.0000
GXP -1.93E-08 1.51E-09 -12.78671 0.0000
YG 9.24E-11 2.55E-10 0.362267 0.7216
M2 1.10E-14 4.37E-16 25.18405 0.0000
IR -0.000351 1.72E-05 -20.42850 0.0000
RESID02 0.019022 0.020389 1.84280 0.0923
F – Stat. = 7695.298; P – value = 0.0000
Source: EViews Output
The above estimated results as presented in Table 4 for the Graduate Unemployment
(UG) Model and Table 4.1 for the Labour Productivity (LP) model shows the estimates of
reduced form equation and their structured form estimate with the inclusion of the residual
estimates of the reduced form as one of the explanatory variable in the structured form equation.
A clear picture from the results shows that there are no significant differences between the two
estimates in each model as noted in Table 4 and 4.1 respectively.
However, judging from the hypothesis and its subsequent decision rule, the residual in
the structured form equation has no correlation with the residual in the reduced form equation.
This decision was arrived at following a 2-t Rule of Thumb; a variable is statistically significant
if its t-value is greater than 2 in absolute value at 5% level of significance. On the other hand, it
is statistically insignificant if its t-value is less than 2 in absolute value at any 5% level of
significance (Gujarati, 2004). This is because the residual variables in the structure estimates in
the two models are not statistically significant, and for this reason, we conclude that there are no
endogeneity problems in the models.
4.2.2 Test for Validity of Instruments
This work adopted a validity test designed by Danis Sargan as advised by Liviaton
(1963) cited in Gujarati (2004) who suggested a way to choose Instrumental Variable(s) (IV)
40
and the test for validity of such instruments called Dubbed SARG Test. This test can also be
done with the help of the F – statistic since we have a multiple instrumental variables such as
total government expenditure (GXP) and gross secondary school enrolment (GS) in the graduate
unemployment model (Tab 4), the broad money supply (M2) and real interest rate (IR) in labour
productivity model (Tab 4.1).
Hypothesis
H0: π1 = π2 = π3 = π4 = 0 (the instrumental variables chosen did not associate with the
endogenous explanatory variable in the model)
Against
H1: π1 ≠ π2 ≠ π3 ≠ π4 ≠ 0 (the instrumental variables chosen did associate with the endogenous
explanatory variable in the model)
Test statistic is given as;
SARG = (n – k)R2
Where n = 26 (the number of observations); and
k = 7 (the number of coefficient in the original regression equation).
This test follows a chi-square distribution with r degree of freedom, and
r = s – q = 3 – 1 = 2
where q = 3 (the lag length of explanatory variables correlated with error
s = 1 (the lag length of the instrumental variable).
Decision rule: reject H0 if chi-square calculated (χ*cal) is greater than chi-square tabulated (χ*
tab)
and accept otherwise.
SARG (χ*cal) = (26 – 7) x 0.127977 = 19*0.127977 = 2.43, and the chi-square tabulated (χ*
tab) =
0.103.
Comparing the two results (chi-square calculated and tabulated), we noticed that χ*cal =
2.43 > χ*tab = 0.103, and for this, we reject the null hypothesis and conclude that the
instrumental variables chosen did associate with the endogenous explanatory variable in the
model.
Also judging from the F – statistic from the result, the first model gave F – statistic value
of 169.5667 with the P – value of 0.0000, while the second model gave F – statistic value of
7695.298 with P – value of 0.0000 with 6 and 19 degrees of freedom and at 5% level of
significance. With high significant nature of these results, we can undoubtedly claim that the
ranked order condition for identifying an instrument or variable is satisfied.
Another way of interpreting these results is that for the first model, the instrumental
variables chosen for the graduate unemployment (UG) model, such as GXP and GS, are
41
uncorrelated with the error terms εt, but highly correlated with the endogenous explanatory
variable LP. Interpreting the second model results also means that the instrumental variables
chosen for the labour productivity model are uncorrelated with the error terms µt but highly
correlated with the endogenous explanatory variable, UG. The result clearly shows that the
stochastic error terms for the two models have zero expected values, which is without loss of
generality when the equation contained an intercept.
Having satisfied these two conditions of simultaneous specification such as equations 1,
2, 3 and 4 in Chapter Three above, we can now present the long-run and short-run estimation
results for the study.
4.2.3 The ECM Result for the Models
The empirical results from modeling the nexus between of government labour policy,
graduate unemployment and labour productivity in Nigeria are presented in Tab 4.2 and 4.3
below.
Table 4.2: ECM result for Graduate Unemployment (UG) Model
Variable Coefficient Std Error t - value t - prob
C 70.60203 55.46384 1.272938 0.2192
TNA -1.590465 0.593273 -2.680831 0.0153
INV -1.08E-11 2.40E-12 -4.486972 0.0003
LP -2.408248 1.464044 -1.644929 0.1173
GXP 2.82E-05 2.75E-06 10.24496 0.0000
YG -4.76E-06 8.79E-07 -5.410996 0.0000
GS 0.280486 0.259429 1.081168 0.2939
ECM-1 -0.953048 0.168674 -5.650245 0.0000
R-squared = 0.985062; Adjusted R-squared = 0.979253; F-statistic = 169.5667
Prob(F-statistic) = 0.000000; Durbin-Watson stat = 1.957611
Source: EViews Output
42
Table 4.3: ECM Result for Labour Productivity Model
Variable Coefficient Std Error t - value t - prob
C 0.958966 0.654852 1.464400 0.1613
LOG(TNA) 0.624447 0.130336 4.791044 0.0002
LOG(INV) 0.000558 0.013167 0.042383 0.9667
UG -0.001936 0.000524 -3.697218 0.0018
LOG(GXP) -0.021862 0.011237 -1.945471 0.0684
YG -2.10E-10 2.66E-09 -0.079003 0.9380
LOG(M2) 0.035870 0.017299 2.073592 0.0536
IR 6.71E-05 0.000181 0.371317 0.7150
ECM-1 -0.011471 0.004482 -2.559430 0.0203
R-squared = 0.960247; Adjusted R-squared = 0.941540; F-statistic = 51.33034
Prob(F-statistic) = 0.000000; Durbin-Watson stat = 1.898457
Source: EViews Output
Economic Interpretation of Results
In this context, the estimated parameters were subjected to test based on economic theory
so as to ascertain whether they are well behaved. In other words, the coefficients derived from
the models are checked to ascertain its conformation with ‘a priori’ expectation underlying each
variable.
The results in Table 4.2 and 4.3 above shows that government labour policy variable
captured by the total number of graduate that benefited (TNA) from National Directorate of
Employment (NDE) since its inception in 1986 have positive coefficients in both models. This
shows that the variables TNA conformed to the economic theory. Following the neoclassical
tradition, the theoretical connection between the government employment policy and
unemployment are in two sides. The one based on the “subsistence wage theory” (policy that
affect the supply side) and the “wage fund” theory (policy that affect the demand side).
On the supply side, the subsistence wage theory posits that every policy that naturally
multiplies the proportion of new individual into labour market will negatively affect
unemployment. This implies that there exists a strong connection between the government
policy and demand and supply of labour (Iniodu and Ukpong, 2001). The demand for labour
depends on the wage policy designed for the payment of wages, which consists of the revenue
necessary for its maintenance and the labour force necessary for full employment (Iniodu and
Ukpong, 2001).
43
Also the signs shown in the result is in line with the NDE mandate which was to design
and implement programmes to combat mass unemployment; to articulate policies aimed at
developing programmes with labour-intensive potentials; to obtain and maintain a data bank of
employment and vacancies in the country with a view to acting as a clearing house to link job
seekers with vacancies, and implement any other policies as may be laid down from time to time
by the Board established under Section 3 of its Enabling Act.
These results clearly prove that government employment policy has high influence over
employment condition of the economy. In the graduate unemployment model, a unit increase in
the number of graduates benefiting from NDE programmes will reduce graduate unemployment
and increase labour productivity by -1.59 and 0.624 units respectively. Following the 2-t rule of
thumb, a variable is statistically significant if the t-value of the variable is greater than 2 in
absolute term at a given 5% level of significance. Similarly, a variable is not statistically
significant if its t-value is less than 2 in absolute term at a given 5% level of significance.
Therefore, the government labour policy tagged in this study as TNA in both models were
statistically significant having their t – values as -2.680 and 4.7910 respectively.
Also investment variable proxy as gross fixed capital investment has the expected
negative sign of -1.08E-11 in the graduate unemployment model and positive sign of 0.000558
on the labour productivity model, conforming to ‘a priori’ expectation, though, with low
transmission effects compared with that of government labour policy variable. The investment
variable is statistically significant in the graduate unemployment model having t – value of -
4.486, but statistically insignificant in the labour productivity model with t – value of 0.042
respectively. By implication, this result shows that gross fixed capital formation has not
adequately induced labour productivity, probably due to high level of graduate unemployment in
the country.
On the coefficient of the total government expenditure variable, the theoretical expected
signs were not satisfied. The result shows that total government expenditure variable has
positive relationship with graduate unemployment and negatively related to labour productivity.
By implication, as the total government spending increases, the graduate unemployment also
increases. Though, this can be said to be the true picture of the Nigerian economy, for instance,
our raw data shows that as total government spending rose from N701, 059.40billion in 2000 to
N3, 536,624.90 trillion in 2010, graduate unemployment also rose from 40.3% in 2000 to
whopping rate of 88.3% in 2010. To further confirm the effect of total government spending on
graduate unemployment and labour productivity in Nigeria, the t –values of 10.244 and -1.945
44
associated with the variable in the two models show a robust statistical significance of the
variable.
Again, the influence of total government spending on graduate unemployment and
labour productivity show some high level misalignment between government spending and
stability of macroeconomic environment in the country. This implies that increasing government
total spending on the contrary distorts macroeconomic factors such as unemployment, real
interest rate and output gap does. However, output gap shows no significant effect on labour
productivity but significantly and negatively influences graduate unemployment.
The long-run error correction mechanism variable show significant correction of the
disequilibrium at lag one in the two models. It proves that 95% correction is made every one
year to position graduate unemployment to its equilibrium root. On labour productivity, a little
error (1.1%) is corrected to adjust it disequilibrium state. The two long-run factors in the models
has the right negative sign , showing that at every disequilibrium in the graduate unemployment
and labour productivity instruments, there is positive adjustment mechanism at every one year to
put them back to equilibrium.
Evaluating the fitness of the two models analyzed above, the coefficient of multiple
determinations R2 of 0.98 and 0.96 respectively suggests that 98% of the variation in the
graduate unemployment model was explained by the selected variables in that model, while 96%
variations in the labour productivity model was also explained by the factors included for that
estimation, a proof of goodness of fits. In the same vein, F – statistics of 169.56 and 51.33
respectively shows that the models are well specified and as a result maintains good fit.
Another interest measure of the precision of this analysis is the Durbin-Watson (DW)
statistic. A rule of thumb shows that when the DW statistic is less than R2 in a model, not
minding the significant level, such model is said to suffer from multicollinearity, positive first
order autocorrelation and spurious regression. Therefore, with the DW statistic being greater
than the R2 in this study, and with reasonable number of the significant factors, these models are
said to be free from multicollinearity, positive first order autocorrelation, estimation bias
emanating from wrong specification of model and spurious regression.
4.2.4 Total Factor Productivity Result
The estimated result show that factors, such as, gross fixed capital formation, used as
investment variables in the study, total government expenditure and output gap exact significant
and appreciable influence on the total factor productivity of labour in Nigeria (see table 8).
45
Table 4.4: Result of the Total Factor Productivity Model Variable Coefficient Std. Error t-Statistic Prob.
C 7.321950 1.309652 5.590758 0.0000
LOG(INV) 0.560979 0.108395 5.175312 0.0001
D(UG) 0.001635 0.004959 0.329673 0.7453
LOG(GXP) 0.563929 0.105412 5.349778 0.0000
D(YG) 1.37E-07 4.31E-08 3.178559 0.0049
LOG(GS) -0.240307 0.174899 -1.373977 0.1854
R-squared 0.994951 F-statistic 748.8309
Adjusted R-squared 0.993622 Prob(F-statistic) 0.000000
S.E. of regression 0.145763 Durbin-Watson stat 1.735370
Source: E-Views Output
The robust coefficients and significant levels of investment, government total spending
and output gap variables in the TFP model proved that they are the major factors affecting
labour productivity in Nigeria. As indicated in the result, a unit increase in gross fixed capital
formation (investment parameter) will have 56% increases in the total factor productivity of
labour in the economy, provided other factors are kept constant. Also the t-value of 5.17
confirms the factor that investment is a major driver of total factor productivity of labour in
Nigeria.
In a similar way, a unit increase in total government expenditure will increase labour
productivity of labour by 56% provided other factors are kept constant. This is in-line with
scholars view on total factor productivity and investment. According to Mali (1978) total
productivity is a measure of how resources are being brought together in organizations and
utilized for accomplishing a set of results. It is reaching the highest level of performance with
the least expenditure of resources". Productivity is viewed as the instrument for continuous
progress, and of constant improvement of activities. It is often seen as output per unit of input.
Hence, higher productivity connotes achieving the same volume of output with less factor inputs
or more volume of output with the same amount of factor inputs. Thus, increased productivity
could result from the reduction in the use of resources, reduction in cost, use of better methods
or improvement in factor capabilities, particularly labour.
Roberts and Tybout (1997) and Tybout (1992), assuming a neo-classical production
function at the sectoral or industry level, define total factor output to be a concave function of
the vector of inputs and time (a proxy for shift in technological innovation). To them, the
elasticity of output with respect to time is the total factor productivity. In a more general sense,
TFP = Total Output ie Weighted Average of all inputs. Critical among these factor inputs are
46
labour, capital, raw materials and purchase of spare parts, and other miscellaneous goods and
services that serve as inputs in the production process.
The output gap variables, though, have a significant effect on total factor productivity; its
coefficient is not robust when compared with investment and total government expenditure
influence. The result shows that a unit increase in the output gap will have 1.37E-07 influence
on total factor productivity, a less than 1% effect. Graduate unemployment and gross secondary
school enrolment variables exhibit some influence on total factor productivity, but their effects
are statistically not different from zero, meaning they have insignificant effect on total factor
productivity. This result confirmed the classical theory of unemployment that says the supply of
labor is derived from worker’s choice whether to spend part of time working or not working
(leisure). Supply of hours worked is a positive function of the real wage, because if the real
wage rises, workers supply more hours of work. In equilibrium, demand and supply of labor are
intersected at a clearing point that determines the equilibrium real wage rate and full
employment.
4.2.5 Granger Causality Result
On the Pairwise Granger Causality between government labour/employment policy,
graduate unemployment and labour productivity, the result shows that unilateral causal
relationship exist between government employment policy and graduate unemployment (see Tab
4.5).
Table 4.5: Pairwise Granger Causality Tests
Null Hypothesis: Obs F-Statistic Probability
UG does not Granger Cause TNA 24 4.07030 0.03379
TNA does not Granger Cause UG 0.20811 0.81394
LP does not Granger Cause TNA 24 63.4267 3.9E-09
TNA does not Granger Cause LP 5.90022 0.01016
LP does not Granger Cause UG 24 2.27625 0.12996
UG does not Granger Cause LP 5.42773 0.01366
Source: E-Views Output
Table 4.5 Granger Causality results shows that graduate unemployment granger cause
government labour/employment policy, but later does not granger cause the former. On the other
hand, a bilateral causal relationship exists between labour productivity and graduate
unemployment. Finally, the result in Table 4.5 above shows that unilateral relationship exists
47
between the labour productivity and graduate unemployment. It shows that where graduate
unemployment granger cause labour productivity, and labour productivity does not granger
cause graduate unemployment. This finding is also in-line with classical theory of
unemployment, where Wicksell, after 1921 World War I, argued that the causes of the
unemployment are the surplus people, shortage of capital brought about by the war, and the
disorganized state of the monetary system. For the third cause, after the war prices were falling
and producers decided to produce lower amounts of production because they knew they would
receive lower prices for their products. Thus, they let their money sit idle in banks and workers
became unemployed. These causes suggest that emigration became one of the important policies
for solving the unemployment problem.
4.3 Evaluation of Research Hypotheses
The three hypotheses of this study were evaluated based on the estimation results and the
findings drawn from it. Thus, the first hypothesis, which states that government
labour/employment policy has no significant impact on the rate of graduate unemployment and
labour productivity in Nigeria was rejected based on the results, and conclusion was drawn
that government policy on employment has significant negative impact on the rate of graduate
unemployment and positive significant impact on labour productivity in Nigeria. Also the
second hypothesis, which states that there is no relationship between graduate unemployment
and total factor productivity in Nigeria was as well rejected, having noticed that long-run
relationship exist between graduate unemployment and total factor productivity (see the result in
Table 4.5).
Finally, in the third hypothesis there is no causal link between government labour policy,
graduate unemployment and labour productivity in Nigeria was also rejected when we found
that graduate unemployment granger cause government labour/employment policy, but later
does not granger cause the former. Again, graduate unemployment granger cause labour
productivity, and labour productivity does not granger cause graduate unemployment.
48
CHAPTER FIVE
SUMMARY AND CONCLUSION
5.1 Summary of Research Findings
The study evaluated the effect of government labour/employment policy on graduate
unemployment and labour productivity in Nigeria. In order to avoid running spurious regression
emanating from misspecification and omitted variables bias, we carried out several preliminary
tests, aimed at implementing all the data processing techniques. Among them are the trend
analysis of the data series, unit root test using Augmented Dickey-Fuller and Phillip Perron
stationary test. The unit root result showed that the variables exhibit similar integrating order (∆
= 1). Also, co-integration test was conducted using Engle – Granger approach to ascertain the
long-run linear combination among the variables. In line with the rule guiding the Engle-
Granger Co-integration approach, the long-run error correction model was finally adopted for
the estimation.
In line with the estimated models and their results, a number of findings were made. It
found that government labour/employment policy variable captured by the total number of
graduates that have benefited (TNA) from National Directorate of Employment (NDE) since its
inception in 1986 have positive coefficients in both models. The TNA variable significantly
affects graduate unemployment and labour productivity in Nigeria. While the government
employment policy negatively influences graduate unemployment, it positively influences
labour productivity, and this is in conformity with economic theory. The study found that a unit
increase in the number of graduates benefiting from NDE programme will reduce graduate
unemployment and increase labour productivity by -1.59 and 0.624 units respectively.
Also found is that increasing government total spending negatively affect labour
productivity and positively affect graduate unemployment. By implication, it means that the
incremental spending by the government has no employment or labour productivity target in
Nigeria. However, output gap shows no significant effect on labour productivity but
significantly and negatively influences graduate unemployment.
The long-run error correction mechanism variable show significant correction of the
disequilibrium at lag one in the two models. It proves that 95% correction is made every one
year to position graduate unemployment to its equilibrium root. On labour productivity, a little
error (1.1%) is corrected to adjust its disequilibrium state. The two long-run factors in the
models has the right negative sign, showing that at every disequilibrium in the graduate
49
unemployment and labour productivity instruments, there is positive adjustment mechanism at
every one year to put them back to equilibrium.
Also found in the study is that a unit increase in gross fixed capital formation
(investment parameter) will have 56% increases in the total factor productivity of labour in the
economy, provided other factor is kept constant. Also the t-value of 5.17 confirms the factor that
investment is a major driver of total factor productivity of labour in Nigeria. A unit increase in
total government expenditure will increase labour productivity of labour by 56% provided other
factors are kept constant. This is in-line with scholars’ view on total factor productivity and
investment. A unit increase in the output gap will have 1.37E-07 influence on total factor
productivity, a less than 1% effect. Graduate unemployment and gross secondary school
enrolment variables exhibit some influence on total factor productivity, but their effects are
statistically not different from zero, meaning there have insignificant effect of total factor
productivity.
On Granger causality test, the study found that graduate unemployment granger cause
government labour/employment policy, but later does not granger causal the former. On the
other hand, a bilateral causal relationship exists between labour productivity and graduate
unemployment. Also unilateral relationship exists between the labour productivity and graduate
unemployment. It found that graduate unemployment granger cause labour productivity, and
labour productivity does not granger cause graduate unemployment.
Finally, the three hypotheses of the study were rejected and conclusion was drawn that
government policy on labour has significant impact on the rate of graduate unemployment and
labour productivity, there is long-run relationship between graduate unemployment and total
factor productivity, and there is causal link between government labour policy, graduate
unemployment and labour productivity in Nigeria.
5.2 Policy Recommendations
Based on the findings in this study, the following policy recommendations are proffered
for urgent consideration:
� Based on the findings in this study that government employment policy, though
significant to graduate unemployment, in reality has not reduced raging unemployment
figure among school graduates. We then recommend that there should be some
restructuring or reforms on the existing government labour/employment programmes,
such as National Directorate of Employment (NDE) in order to redirect efforts towards
achieving its mandate.
50
� Since our result shows that graduate unemployment causes government employment
policy but the later does not cause the former show a clear evidence of government
policy failure to tackling unemployment especially graduate unemployment, we
therefore recommend that new employment programs should be established with the sole
aim of tackling graduate unemployment. Since unemployment cuts across various
barriers (gender, class, age) and the National Directorate of Employment which is
saddled with combating unemployment cannot effectively manage graduate
unemployment as its resources (time, finance) are divided among other classes. Thus,
giving more attention to graduate unemployment by establishing a body with the
mandate of combating graduate unemployment will go a long way.
� Any holistic programme to arrest unemployment in Nigeria should also capture those
who are heavily underutilized, grossly underpaid, in order to have fair representation of
the unemployment situation in the country.
� Also the importance of skills acquisition cannot be ignored. Hence, we recommend that
learning of specific skills should be inculcated into the curriculum of schools, right from
primary to tertiary institutions in Nigeria and made compulsory for every student
irrespective of the course of study. Every student must be made to undertake any of the
following vocational courses like fashion designing, automobile repairs, traffic control,
animal husbandry, typesetting, catering, horticulture, swimming, memo writing, satellite
installation, wood work and even cooking should be included as compulsory electives
thereby creating the spirit of entrepreneurship, as practiced in some developed countries.
� The spending pattern of the government, whether state or federal government should be
checked, since the rising government total spending has not translated to increase in
labour productivity. If not checked, it is capable of distorting the macroeconomic growth
of the nation.
5.3 Conclusion
In this study the research evaluated the effect of government labour/employment policy
on graduate unemployment and labour productivity in Nigeria. Unemployment in the views of
the International Labour Organization (ILO) “occurs when a person is available and willing to
work but currently without work.” This situation especially the graduate unemployment which is
very much prevalent in Nigeria has become a menace to the society with its consequences on
political and socio-economic effects. These consequences cannot be over-emphasized as
51
unemployment especially graduate unemployment can lead to political instability and economic
quagmire.
Economically, graduate unemployment hinders the graduate from contributing their
acquired skills/knowledge to building a sound economic productivity. Although, government
have made several efforts by establishing some organizations and institutions saddled with the
task of tackling unemployment, such as National Directorate of Employment (NDE). On this
basis, the question that was evaluated in this study is the effects of this employment programme
on graduate unemployment and labour productivity in Nigeria. Among the findings of the study
is that government employment policy has significantly affected graduate unemployment and
labour productivity in Nigeria. While the government employment policy negatively influences
graduate unemployment, labour productivity is positively influenced. By implication, it implies
that as more graduates benefit from these programmes, graduate unemployment reduces and
labour productivity will increase. The study found that a unit increase in the number of graduate
benefiting from NDE programme will reduce graduate unemployment and increase labour
productivity by -1.59 and 0.624 units respectively.
Contrary to economic theory, the study found that increase in government total spending
negatively affect labour productivity and positively affect graduate unemployment. By
implication, it means that the incremental spending by the government has no employment or
labour productivity target in Nigeria. However, output gap shows no significant effect on labour
productivity but significantly and negatively influences graduate unemployment.
The long-run error correction mechanism variable show significant correction of the
disequilibrium at lag one in the two models. It proves that 95% correction is made every one
year to position graduate unemployment to its equilibrium value. We conclude that reducing
graduate unemployment requires clear political will by the government irrespective of party
affiliation, religion and ethnicity.
5.4 Limitation of the Study
The major limitations of this study are non-existence and non-availability of some
important data that are important for a study like this. For instance, variables such as the number
of employment opportunities created by the government annually and number of graduates
actually employed over the years in those jobs created, are needed in this type of study. Salary or
wage rate was needed also, but due to limitation of data, was not included. We then suggest for
further study, that research on this subject should try to include these variables to identify their
influence on graduate unemployment and labour productivity.
52
53
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APPENDIX
Endogenuous Estimates
Dependent Variable: LP
Method: Least Squares
Date: 06/11/14 Time: 17:54
Sample: 1987 2012
Included observations: 26
Variable Coefficient Std. Error t-Statistic Prob.
C 35.96825 0.943191 38.13466 0.0000
TNA 0.417975 0.021391 19.53969 0.0000
INV 5.81E-14 5.45E-13 0.106737 0.9162
UG -0.037714 0.021379 -1.764050 0.0947
GXP 4.86E-08 8.82E-07 0.055141 0.9566
YG 2.83E-09 1.54E-07 0.018318 0.9856
M2 8.79E-14 2.37E-13 0.370453 0.7154
IR 0.013081 0.007345 1.781063 0.0918
R-squared 0.960880 Mean dependent var 55.23846
Adjusted R-squared 0.945667 S.D. dependent var 2.346074
S.E. of regression 0.546858 Akaike info criterion 1.878403
Sum squared resid 5.382958 Schwarz criterion 2.265510
Log likelihood -16.41924 F-statistic 63.16056
Durbin-Watson stat 2.294514 Prob(F-statistic) 0.000000
Dependent Variable: LP
Method: Least Squares
Date: 06/04/14 Time: 12:40
Sample: 1987 2012
Included observations: 26
Variable Coefficient Std. Error t-Statistic Prob.
TNA 1.158564 0.078943 14.67595 0.0000
INV -5.20E-13 4.79E-12 -0.108512 0.9147
UG -0.054811 0.188153 -0.291310 0.7740
GXP -9.96E-07 7.76E-06 -0.128418 0.8992
YG 9.96E-10 1.36E-06 0.000733 0.9994
M2 5.82E-13 2.09E-12 0.278870 0.7834
IR -0.018228 0.064248 -0.283707 0.7797
R-squared -2.199686 Mean dependent var 55.23846
Adjusted R-squared -3.210113 S.D. dependent var 2.346074
S.E. of regression 4.813805 Akaike info criterion 6.205657
Sum squared resid 440.2817 Schwarz criterion 6.544375
Log likelihood -73.67354 Durbin-Watson stat 0.401641
59
Dependent Variable: LOG(LP)
Method: Least Squares
Date: 06/11/14 Time: 18:10
Sample: 1987 2012
Included observations: 26
Variable Coefficient Std. Error t-Statistic Prob.
C 2.957904 0.014095 209.8596 0.0000
TNA 0.022042 0.000291 75.85171 0.0000
INV -9.95E-15 9.28E-16 -10.72223 0.0000
UG -0.000988 3.59E-05 -27.47677 0.0000
GXP -1.93E-08 1.51E-09 -12.78671 0.0000
YG 9.24E-11 2.55E-10 0.362267 0.7216
M2 1.10E-14 4.37E-16 25.18405 0.0000
IR -0.000351 1.72E-05 -20.42850 0.0000
RESID02 0.019022 0.000389 48.84280 0.0000
R-squared 0.999724 Mean dependent var 4.010727
Adjusted R-squared 0.999594 S.D. dependent var 0.044846
S.E. of regression 0.000904 Akaike info criterion -10.91295
Sum squared resid 1.39E-05 Schwarz criterion -10.47746
Log likelihood 150.8684 F-statistic 7695.298
Durbin-Watson stat 1.304843 Prob(F-statistic) 0.000000
-.03
-.02
-.01
.00
.01
.02
1990 1995 2000 2005 2010
LOG(LP) Residuals
60
Estimated long-run labour productivity models
Dependent Variable: LOG(LP)
Method: Least Squares
Date: 06/04/14 Time: 13:36
Sample: 1987 2012
Included observations: 26
Variable Coefficient Std. Error t-Statistic Prob.
C 0.958966 0.654852 1.464400 0.1613
LOG(TNA) 0.624447 0.130336 4.791044 0.0002
LOG(INV) 0.000558 0.013167 0.042383 0.9667
UG -0.001936 0.000524 -3.697218 0.0018
LOG(GXP) -0.021862 0.011237 -1.945471 0.0684
YG -2.10E-10 2.66E-09 -0.079003 0.9380
LOG(M2) 0.035870 0.017299 2.073592 0.0536
IR 6.71E-05 0.000181 0.371317 0.7150
ECM-1 -0.011471 0.004482 -2.559430 0.0203
R-squared 0.960247 Mean dependent var 4.010727
Adjusted R-squared 0.941540 S.D. dependent var 0.044846
S.E. of regression 0.010843 Akaike info criterion -5.943157
Sum squared resid 0.001999 Schwarz criterion -5.507662
Log likelihood 86.26104 F-statistic 51.33034
Durbin-Watson stat 1.898457 Prob(F-statistic) 0.000000
Endogeneity Estimate for UG model
Dependent Variable: UG
Method: Least Squares
Date: 06/11/14 Time: 17:26
Sample: 1987 2012
Included observations: 26
Variable Coefficient Std. Error t-Statistic Prob.
C 190.7218 83.04028 2.296738 0.0332
TNA 2.591692 0.917797 2.823817 0.0108
INV -9.02E-12 3.86E-12 -2.338469 0.0304
LP -5.311456 2.222258 -2.390117 0.0274
GXP 2.74E-05 4.45E-06 6.162319 0.0000
YG -4.41E-06 1.42E-06 -3.105822 0.0058
GS -0.083457 0.407366 -0.204870 0.8399
R-squared 0.958567 Mean dependent var 46.63814
Adjusted R-squared 0.945483 S.D. dependent var 27.31800
S.E. of regression 6.378444 Akaike info criterion 6.768529
Sum squared resid 773.0063 Schwarz criterion 7.107248
Log likelihood -80.99088 F-statistic 73.26213
Durbin-Watson stat 1.631026 Prob(F-statistic) 0.000000