IMPACT OF AGRICULTURAL IMPORTS ON UNEMPLOYMENT IN

NIGERIA

BY

AFOLAYAN, TOLULOPE COMFORT

17/77JA127

BEING A RESEARCH PROJECT SUBMITTED TO THE DEPARTMENT

OF ECONOMICS, FACULTY OF SOCIAL SCIENCES, UNIVERSITY OF
ILORIN, ILORIN, IN PARTIAL FULFILMENT OF THE
REQUIREMENTS FOR THE AWARD OF BACHELOR OF SCIENCE
(B.Sc. HONS) DEGREE IN ECONOMICS

June, 2021

1
 DECLARATION

I, AFOLAYAN, Tolulope Comfort, with Matriculation Number, 17/77JA127, hereby declare

that this research project was undertaken by me and I take full responsibility for the gap(s)
herein.

.................................................... ....................................................

Signature Date

2
 CERTIFICATION

This is to certify that this research project has been read and approved, having met the minimum
standard in partial fulfillment of the requirements for the award of Bachelor of Science (B.Sc.
Hons) degree in Economics, Faculty of Social Sciences, University of Ilorin, Ilorin.

__________________________ ____________________
MR. J. A. SANNI Signature & Date
(Project Supervisor)

___________________________ ____________________
DR. M. A. YARU Signature & Date
(Head of Department)

___________________________ ____________________
PROF. I. P. IFABIYI Signature & Date
(Dean, Faculty of Social Science)

___________________________ ____________________
External Examiner Signature & Date

3
 DEDICATION

To every Nigerian youth thriving against all odds in an environment that clearly states the
opposite. We are so inspiring to see!

4
 ACKNOWLEDGEMENT
The success and final outcome of this project required a lot of guidance and assistance from
many people and I am extremely fortunate to have got this all along the completion of my project
work. Whatever I have done is only due to such guidance and assistance and I would not fail to
thank them. First and foremost, I'm grateful to the Almighty God for enabling me to persevere
through this process and ensuring this comes to pass.

My sincere appreciation goes to Mr. J. A. Sanni, my project supervisor for his relentless support,
intellectual advice, and excellent supervisory role despite his very tight schedule. The successful
completion of this project would have not been possible without his nurturing. I extend my
gratitude to all my lecturers in the department for impacting both academic and moral knowledge
in me.

I owe a great debt of gratitude to my parents, for the moral and financial support, encouragement
and prayers received during the course of this project. It all mattered greatly. I also appreciate my
friends and mentors for their support even till this moment since the past four years. Although, it
would be impossible to list all your names, I do not take for granted the shoulders you all have
given me to stand upon.

Bless your hearts.

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TABLE OF CONTENT Page

Front Page 1

Declaration 2

Certification 3

Dedication 4

Acknowledgement 5

Table of Content 6

Abstract 9

References

Appendix

CHAPTER ONE: INTRODUCTION

1.1 Background of the Study 10

1.2 Statement of Problem 13

1.3 Research Questions 15

1.4 Objectives of the Study 15

1.5 Scope of the Study 15

1.6 Statement of Hypothesis 15

1.7 Significance of the Study 16

1.8 Organization of the Study 17

CHAPTER TWO: REVIEW OF RELATED LITERATURE

2.0 Introduction 18

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2.1 Conceptual Issues 18

2.2 Theoretical Review 22

2.3 Empirical Review 23

2.4 Research Gaps 27

CHAPTER THREE: RESEARCH METHODOLOGY

3.0 Introduction 28

3.1 Theoretical Frameworks 28

3.2 Model Specification 30

3.3 Nature and Sources of Data 32

3.4 Estimation Technique 32

3.5 Model Evaluation Criterion 33

CHAPTER FOUR: DATA ANALYSIS AND INTPRETATION

4.0 Introduction 34

4.1 Descriptive Analysis 34

4.2 Regression Results 38

4.3 Interpretation of Results 40

4.4 Residual Diagnostic Test 43

4.5 Discussion of Results and Policy Implication 44

CHAPTER FIVE: COCLUSIONS AND RECOMMENDATIONS

5.0 Introduction 46

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5.1 Summary of the Study 46

5.2 Conclusion 47

5.3 Recommendations 47

References

Appendix

8
 ABSTRACT

Unemployment in Nigeria is continually increasing at a rapid rate and one of the measures
popularly prescribed to the country key stakeholders to this phenomenon especially in the face of
declining oil revenue, is to focus attention on the agricultural sector. Agriculture has been a
significant sector in the Nigerian economy and is still a major sector despite the emergence of
oil. Fundamentally, with about 25% contribution to GDP in 2018, it still provides job
opportunities for the vibrant population, improves poverty levels in rural areas and contributes
to economic growth. Among the challenges faced by the sector are high level of food
importation, neglect due to oil revenue, low capital investment from both the government and
private sources, unfavorable trade policies and infrastructural inadequacies. The effects of these
challenges on the sector negatively impacts labor input which ultimately affects agricultural
sector output. This motivated the objectives of this research work which is to establish the impact
of agricultural imports on unemployment. The specific objectives of the study were to examine
the impact of agricultural imports on unemployment rate in Nigeria and to show the relationship
between domestic agricultural productivity and unemployment levels. From the review of related
literature, it has shown that agricultural productivity has a positive impact on employment in
Nigeria.

This was done with the use of both descriptive and inferential analysis. The descriptive analysis
used include graphical representation, measures of central tendency, measures of dispersion and
correlation matrix. These were used to determine the relationship and growth pattern among the
variables used for this study. The inferential analysis used was the Fully Modified Ordinary
Least Square (FMOLS), while testing for stationarity and long-run relationship using Augmented
Dickey-Fuller test for unit root and ARDL Bound test respectively.

Key words: Unemployment, Agriculture, Agricultural Imports, Food Imports, Nigeria.

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 CHAPTER 1

INTRODUCTION

1.1 Background of the study

The unemployment problem is not novel to nations of the world, as it is a global phenomenon.
From developed worlds like France, the United States and the United Kingdom to developing
countries like Nigeria, governments constantly introduce policies and programs to ensure that the
rate of unemployment is reduced as much as possible and, in some instances, kept at a constant
rate. This is mostly done by the creation of more jobs through diversification of the economy,
increased public expenditure directed towards expansion of existing industries, among a few
other solutions, particular to that economy. However, unemployment rates are very much likely
to increase in recent times due to economic contagions. The issue of unemployment and
employability has risen to the top of the political agenda. Businesses and citizens alike are
experiencing increasing levels of unemployment as the challenging economic climates continue
to hit hard. As such, it has become imperative for governments, welfare agencies, support
organizations, and companies themselves to come up with a plan to arrest this alarming upward
trend in unemployment (Capgemini et Oracle, 2013). In other words, there is an increasing need
for jobs and responsible bodies must rise to this need. A majority of the 3.3 billion people
employed throughout the world, though, are working under poor working conditions that do not
guarantee them a decent living. Over the past 30 years, there has been a great decline in working
poverty in advanced and high-income countries. But the situation remains serious in low-income
and middle-income countries. One-quarter of those employed there do not earn enough to escape
extreme or moderate poverty; due majorly to the fact that the economies are not as robust as that
of developed countries hence, too fragile to absorb the increasing need for jobs (ILO,2019).

In Africa, the situation is not without some inflammatory effect. With high levels of
unemployment and vulnerable employment on the rise, influenced by an outrageous population
growth rate, the future of work in this region faces a tremendous challenge in terms of job
creation and sustainability. Unemployment appears to be on a downward trend in Northern
Africa, but labor market distress remains pervasive, particularly among women and youth.
Northern Africa still exhibits the highest unemployment rate globally, at 12.1 per cent in 2015,

10
however this is an improvement from 12.5 per cent a year earlier and marks the first decrease
since 2011. The situation is particularly endemic in Sub-Saharan Africa where over 70 per cent
of workers are in vulnerable employment against the global average of 46.3 per cent. These are
workers that have limited access to social protection schemes and are often confronted by low
and highly volatile earnings. Moreover, the informal economy in the region contributes 50-80
per cent of GDP; 60-80 per cent of employment; and 90 per cent of new jobs. What more, 9/10
workers in both rural and urban areas are estimated to hold only informal jobs. The share of
informality varies across the region: informal employment is lower in southern Africa, where it
ranges from 32.7 per cent in South Africa to 43.9 per cent in Namibia. In other sub-Saharan
African countries, the percentage exceeds the 50 per cent and reaches as high per cent in the
United Republic of Tanzania, 89.2 per cent in Madagascar and 93.5 per cent in Uganda and 65
per cent in Nigeria (ILO, 2017).

In West Africa i.e Nigeria in particular, the incidence of unemployment is alarming, and is
arguably the most fundamental challenge that the country faces. Researches have clearly shown
that unemployment was existent in the 1980s, but the available reports from various local and
international bodies, and the glaring evidence of joblessness in these times are clear indications
that there was no time in Nigeria’s checkered history where unemployment is as serious as now.
One cannot conclude that the government at one level or the other has not done anything at one
time or the other, to reduce unemployment in Nigeria. For instance, the creation of National
Directorate of Employment (NDE) and its skills acquisition programs, NAPEP, PAP, the SURE-
P, YOUWIN, just to mention a few, are some of the various intervention mechanism aimed at
ensuring economic growth that is rich with job creation opportunities. Over the period of 2005 to
2013, the federal government claimed a strong GDP growth rate of 6 to 6.5% which is apparent
paradox as this was not consistent with the reality of the rising and almost fluctuating
unemployment rate annually from 11.9% in 2005 to 19.7% in 2009, 16.7% in 2013 and 17.1% in
2014. As at 2018, GDP growth rate has slowed down to 1.92% with unemployment percentage
staggering at 23%.

Amongst other recommendations to the unemployment problem in status quo and all of
government efforts, the option of returning to the agriculture sector is a reoccurring one. The
potential of the agricultural sector is one that is prominently backed up by evidence of its

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contribution to the ‘pre-oil boom’ economic era in the 1960’s. The Nigeria agriculture sector is
seen as a necessary sector in creating a framework for the nation’s economic growth. This was
the view in the 1960s and in line with the fact that agriculture was the dominant contributor to
the Nigerian economy at the time. However, since the 1970s onwards, agriculture sector’s
contribution to the Nigerian economy has declined on account of the oil boom of the 1970s
which resulted in the neglect of the sector. Such neglect of the sector is further illustrated by the
fact that over the past 20 years, statistics on Nigeria has shown that value added per capita in
agriculture in the country was less than one percent per annum. This highlights concern for the
Nigeria economy as the government seeks to use agriculture at present to bring about
improvements in the fortunes of the economy through provision of employment opportunities
(Enilolobo et al, 2018). In Nigeria, the contribution of the agricultural sector to the growth of the
domestic economy was relatively significant prior to early 1970s; and however, as the oil sector
emerges as the major export earner of the economy, the agricultural sector’s contribution to the
growth of the economy declined from 60% in the earlier 1970s to 40%, 30% and less than 26%
between 2000 and 2007. In 2018, agriculture’s contribution Nigeria’s GDP staggered at 21.2%
from 28.68% and 22.93% in 2016 and 2017, signaling a persistent drop in agricultural output.
Export crops like cocoa, cotton, groundnut, rubber, palm oil and palm kernel that initially
contributed up to 65% and 75% of the foreign exchange earnings and which was the main source
of revenue of the government through export product, suddenly declined its contribution to total
real gross domestic product due to agricultural sector neglect, as oil sector emerged in the
economy consequently resulting in a decline in employment security for the larger populace
(Eze, 2017). It is undeniable that agricultural sector has the potential capacity to absorb the
increasing demand for jobs and provide a sustainable means of living to all sects of demographic
distribution- the aged, educated and non-educated, the working class, and even the ocean number
of graduates that complete tertiary education in Nigeria every year with very little prospects of
finding employment. With all of this in perspective, we can discern a potential comparative
advantage that Nigeria possesses in shifting the posture of the unemployment narrative.

However, a major hinderance to these possibilities is the menace that the obsessive culture of
importation has caused. The failure to improve domestic productivity has positioned the country
to importing almost everything that is being consumed- down to toothpicks. Evidently, the value
of non-oil imports trade grew from a mean value of N36.55 billion; representing 96.8% of total

12
import into Nigeria within the duration 1970-1979, to N118.36 billion; representing 93.4% of
aggregate import trade during duration 1980-1989, N3.48 trillion in the period 1990-1999;
representing 79.9% of aggregate import trade and N19.33 trillion; representing 82.0% of
aggregate imports trade for the duration 2000-2008. The value of the country’s imports of goods
and services (BoP, current US$) was $85.35 billion as of 2014 (Nteegah et Mansi, 2016). The
latest value at $71.6 billion as of 2018, (equivalent of N26 trillion at then exchange rate of N365
per $1) a 40% increase from the previous year. This is such an alarming progression for an
economy abundant in resources- manpower and material, importing so much at the expense of
jobs and good living standards for the average individual. Also putting into perspective, a CBN
report claims that Nigerians expend a higher per cent of their income- about 56% on average on
food. While these figures are majorly reflective of the aggregate import history of Nigeria, they
do not fail to show how much is being heavily invested on imported goods and services, costs of
which could have been utilized as capital for the revitalization of domestic agricultural
production and put jobs in the hands of the ever-booming unemployed populace.

To this extent, this study seeks to measure the impact that agricultural imports has on
unemployment rates in Nigeria through a time series analysis and to interrogate into the potential
effects that increasing agricultural output will have on unemployment levels among other
objectives.

1.2 Statement of the Research Problem

This study addresses the problem of widespread importation of food and agricultural products in
its relation to its impacts on unemployment rates in Nigeria. The research problems can be
highlighted in two phases, there is an alarming unemployment situation in Nigeria and
simultaneously, there is an underutilization of resources- land and manpower resources which
has resulted in an overdependence on foreign products for survival.

Unemployment is a persistent challenge in Nigeria which has over the years not been effectively
addressed. Besides the attendant social, economic, political, and psychological consequences that
come with being unemployed, it contributes to low GDP and leads to increase in crime and
violence, psychological effect, adverse effect on health and political instability. This is not a
recent challenge in Nigeria as the national unemployment rate rose from 4.3 per cent in 1970 to
6.4 per cent in 1980. This fluctuated around 6.0 per cent until 1987 when it rose to 7.0 per cent

13
and now stands at 23%. Programs such as the structural adjustment program was introduced in
1986 to tackle unemployment challenge and it was only mildly effective as unemployment rate
declined from 7.0 per cent in 1987, to as low as 1.9 per cent in 1995 after which it rose to 2.8 per
cent in 1996 and has been growing worse since. Unemployment rate in Nigeria is presently
growing at the rate of 16 per cent annually with the youth impacted the most and accounting for
three times the general unemployment (Doreo, 2013, Adekola et al, 2016). National
unemployment rate staggers at 23 per cent, underemployment rate at 20.2 per cent which refers
percentage of people working for incommensurate pay due surplus supply and youth
unemployment rate is an outrageous 55.4 per cent (NBS, 2018).

With the pending situation, there is a glaring need for an employment window that encompasses
and engages all classes of the Nigerian society- the rich and the poor, the aged and the youth, the
working class and unemployed. And to the extent that the solution to unemployment is the
creation of more jobs, the agricultural sector has a proven track record of being able to provide
these jobs to a readily available market both home and abroad. Rather, the agricultural sector is
being underutilized through the neglect by government, when they over-concentrate on the oil
sector for national income, and also by the citizens when they tag agriculture ‘a dirty man’s
terrain’, most especially the youth. The implications of this, is a reduced support for local
farmers and agricultural infrastructure which ultimately results in reduction in agricultural
productivity and output which is evident in the data over the years. Reduced output results in a
deficiency of agricultural supply which in turn means a heavy dependence on imports for
survival which eventually results in unemployment for local farmers and agricultural sector
stakeholders. Agriculture in most developing nations serve as a major sector that is vital for
economic growth and provides employment to a large segment of the populace. Nigeria as a
developing country is yet to utilize this sector’s abundance judiciously to aid growth and
exportation.

Perhaps, we can conclude that efforts of the government towards tackling the issue of
unemployment have obviously not been rigorous enough to solve the problem. Whilst, some
economists believe that the problem is the overconcentration on the oil and gas sector and that
maximizing the ever-abounding comparative advantage of the agricultural sector will bring about
automatic widespread solution to the problem equivalent to the population level, others believe

14
the problem is as a result of institutional problems such as high levels of corruption,
marginalization of the informal sectors and lack of progressive policy changes. The contention of
this study is not to juxtapose the correctness of this claims but rather, to evaluate the potential
harms of agricultural importation on the existing unemployment problem and re-evaluate the
potential gains of increasing domestic productivity to accelerate exports.

1.3 Research Questions

The research work seeks to answer the following questions;

1. What is the impact of agricultural imports on unemployment rate in Nigeria?

2. What is the effect of an increase in domestic agricultural productivity on unemployment rate?

1.4 Objectives of the Study

The broad objective of this study, is to examine the impact of agricultural imports on
unemployment in Nigeria. The specific objectives however are to;

1. Show the impact of agricultural imports on unemployment rate in Nigeria

2. Show the relationship between domestic agricultural productivity and unemployment
levels.
3. Evaluate the impact of the agricultural sector on rural-urban socio-economic
development.
4. Measure the sufficiency of the domestic agricultural sector in absorbing increasing need
for jobs.

1.5 Scope of the Study

The study will focus on the assessment of the impact of Agricultural Import on Unemployment
rate in Nigeria within the period, 1981-2018 based on data availability, using data from the CBN
statistical bulletin, NBS and the National Accounts data of the World Bank.

1.6 Statement of Hypothesis

H1; There is a relationship between agricultural imports and unemployment rate

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H0; There is no relationship between agricultural imports and unemployment rate

H0; There is a positive impact of agricultural imports on unemployment rate in Nigeria

H1; There is negative impact of agricultural imports on unemployment rate in Nigeria

1.7 Significance of Study

Quite a number of literatures have been published on the topic of the agriculture sector and its
impact on the economic welfare of developing nations. Various contributions have also been
made to the subject matter of especially in the light of the unemployment situation in Nigeria.
Hence, the foremost significance of this research is to contribute to existing works on the subject
matter. So, in this regard, this study hopes to expands knowledge on the subject matter
particularly in relation to Nigeria.

Secondly and very importantly, this study will be significant for individual recommendations in a
way that it influences individual action plans based on the findings and results. Macro-economic
variables such as balance of trade- imports and exports, unemployment, the aggregate
agricultural sector and economic productivity are largely influenced by micro-economic
behaviors such as individual preferences and decisions e.g whether to increase import demand on
food and farm produce for a new restaurant, patronize local farmers or grow a personal farmland.
This study, through thorough data analysis and findings, is important in influencing those
decisions for individual to make informed choices based on the results for positive socio-
economic change.

Also, on the grand scale, the study aims to enhance information for policy making. The need for
well-informed socio-economic decisions and policy formulation for the problems that currently
exists in Nigeria, is most pressing now than ever. This study will interrogate the challenges and
hopefully provide insights into the possible solutions to the upsurge of unemployment through
the agricultural sector and through a thorough time series analysis as regards to the relationships
of both phenomena.

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1.8 Organization of the study

The impact of agricultural import on unemployment in Nigeria is examined in five chapters to

which this study is divided. The first chapter contains the background of the study, the statement
of the problem, research questions, objectives of the study, the scope of the study, significance of
the study. The second chapter comprises of the conceptual framework of the study, empirical
review of related literatures and the theoretical framework of the study. The third chapter consist
of the methodology used which include method of analysis, model specification and estimation
technique. The fourth chapter contains presentation of data, analysis and interpretation of results
obtained from data collected. The fifth chapter includes the summary of the findings of the
research work, conclusions and recommendations

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 CHAPTER 2
LITERATURE REVIEW
2.0 Introduction
This chapter discusses the conceptual issues around agricultural imports and unemployment,
the theoretical and empirical review of literature relevant to the study as well as the summary
review of literature and gaps. It goes further to assert why this research is imperative.

2.1 Conceptual Issues

2.1.1 Agriculture
Agriculture is the most comprehensive word used to denote the many ways in which crop
plants and domestic animals sustain the global human population by providing food, and other
products. The English agriculture was derived from the Latin word “ager” which implies field
and “colo” which means cultivate, the combination of both words means field or land tillage.
But the word the word has come to subsume a very wide spectrum of activities that are integral
to agriculture and have their own descriptive term such as cultivation, domestication,
horticulture, arboriculture and vegeculture, as well as form of livestock management (Harris &
Fuller, 2014).

Agriculture is the art and science of growing plants and raising animals for food, other human
needs, or economic gain. The Oxford English Dictionary (2019) defines the term as the
science and art of cultivating the soil, including the allied pursuit of gathering crops and
rearing livestock.

In Nigeria, the improvement in agriculture has been moderate despite different agricultural
approaches. In fact, different programs are been presented and executed by the government to
enhance the circumstance ever since 1970’s. These programs included encouraging of
mechanized large-scale farming by both federal and state government. National Acceleration
of Food Production (NAFP), River Basin Development Authority (RBDA), Operation Feed
the Nation (OFN), Structural Adjustment Program (SAP) and so on are parts of the programs
introduced. However, SAP was being introduced to serve as economic liberator for the
country. Likewise, the government introduced Agriculture Credit Scheme (ACS) to carter for

18
the financial aspect (Omotor,Orubu and Inoni. 2019).

2.1.2 Agricultural Import

This implies the importation of agricultural products to meet the shortage in supply as a result
of the lag between local agricultural output supply and demand for agricultural products.
Nigeria relies on import to meet its food and agricultural product needs which are mostly
wheat, rice, poultry, fish and food services, consumer-oriented foods, etc. worth about $10
billion annually. Europe, Asia, the United States, South America and South Africa are major
sources of Agricultural Imports.

A number of agricultural initiatives by the Nigerian Federal government includes program

such as the Anchor Borrowers Program (ABP) to diversify her economy away from oil. The
Federal government also launched the “Green Imperative” agricultural Plan in January 2020 to
accelerate mechanization in agricultural sector. In August 2019, the government restricted
food imports by closing its land borders with neighboring countries which significantly
reduced illegal cross-border trade of agricultural items but led to spike in food prices. In 2019,
U.S. food and agricultural export to Nigeria reached $595.5 million (up 93% compared to
2018). Wheat account for 79% of Nigeria’s total imports of U.S.-origin food and agricultural
products. Nigeria also imports U.S-origin soybeans, intermediate food products (especially
vegetable oils and animal fats), consumer-oriented food products (mostly condiment and
sauces, processed vegetables, wine, prepared food, dairy products, non-beverage ethanol)
(International Trade Administration, 2020).

The concept of agricultural imports is broad and should be classified in cases of analysis in
other to avoid misclassification of goods and misleading statistics. The standard international
trade classification (SITC) is a global trade standard for classifying goods involved in trade
and is used by the majority of the world. Agricultural products are classified majorly under
section 0 and 2 which comprises a wide variety of input and output agricultural items from
basic food items such as wheat, rice and milk, to inputs such as raw materials (seeds, fertilizers
etc.) and machinery (tractors, harvesters etc.)

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2.1.3 Concept of Unemployment
Gbosi (1997) defined unemployment as a situation in which people who are willing to work at
the prevailing wage rate are unable to find jobs. In recent times, the definition of
unemployment by the International Labor Organization states that “the unemployed is a
member of the economically active population, who are without work but available for and
seeking for work, including people who have lost their jobs and those who have voluntarily
left work (World Bank, 2015). The application of this definition across countries has been
faulted, especially for the purpose of comparison and policy formulation, as countries
characteristics are not the same in their commitment to resolving unemployment problems.
More so, the preponderance of housewives who possess the ability and willingness to work
and the definition of the age bracket stand as limitations to the definition by ILO (Douglason
et al., 2006).

Unemployment as a macroeconomic and social phenomenon occurs due to the inability of

eligible workforce to get appropriate jobs. Imoisi, Amba, and Okon (2017) explained that the
unemployment rate as one of the fundamental measures of economic growth and development
has become a crucial issue in both developing and developed economies. Okun (1962) further
explained that, theoretically, there is an inverse relationship between unemployment rate and
economic growth. Raifiu (2017) observed that in Nigeria in the last few decades there had
been tremendous growth in the economy, most especially with regard to the nation’s gross
domestic product and export trade performance. Despite these achievements, the nation is still
confronting many socioeconomic problems of which unemployment is a critical one.

The analysis from agricultural point of view suggests that people who have been majorly
affected by unemployment are a great proportion of underprivileged Nigerians in rural areas
where agriculture is primarily subsistence, land is not easily accessible or not fully utilizable,
credit facilities are lacking and improved production methods are not practiced. In recent times
however, the situation has been compounded by the increasing unemployment of professionals
such as accountants, engineers, among others. According to a 1974 survey, reported by Ademu
(2006) graduate unemployment 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 areas.

20
Graduate unemployment, (Akintoye, 2008) accounted about 32% of the unemployed labor
force between 1992 and 1997. It is impressive to note here that, in 2003, Nigerian’s
unemployment rate declined substantially to 2.3 percent. This decline was attributed to the
various government efforts aimed at addressing the problem through poverty alleviation
programs and an increased number of people who got engaged in agricultural activities. It is
worrisome to note that the current unemployment rate according to National Bureau of
Statistics (NBS, 2021) stood at 33.33%.

2.1.4 Types of Unemployment

Unemployment comes from different causes. In general, it can be manifested into five types:
frictional, cyclical, voluntary, structural and institutional.
1) Frictional unemployment refers to the period between job transitions. Here, people are
regarded as unemployed while they are attempting to find a new job.
2) Cyclical Unemployment occurs during recessions of economic cycle. As a matter of
fact, it is not surprising that during economic recessions, the demand for goods and
services falls. Employers may respond by reducing the labors. When supply of labors is
greater than the demand, unemployment results. However, it is believed that such
unemployment will disappear when the economy recovers.
3) Under the economists’ view, people tend to participate freely in workforce.
Unemployment is usually regarded as involuntary. However, there are scenarios that
people choose not to work. Voluntary unemployment describes such phenomenon. It is
functionally another type of frictional unemployment. It happens when people are not
able to find employment that matches their expectations.
4) Structural unemployment occurs when the skills, experience, and education of workers
do not match job openings. Structural unemployment is a form of frictional
unemployment, but it usually lasts longer. It may encourage voluntary unemployment.
5) Institutional unemployment explains how interference in the labor market can create
unemployment. The government is the most common instigator of institutional
unemployment. Governments can set taxes, create price floors or price ceilings, and
indirectly support other factors of institutional unemployment such as labor unions

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2.2 Theoretical Review
Economic theories over the years have tried to explain the relationship between level of output
and unemployment. Some of these economic theories will be considered under this theoretical
review.

2.2.1 The Keynesian Theory of Unemployment

Keynes (1936) considers unemployment as an involuntary phenomenon. He thinks that
employment is cyclical, generated by the deficiency of aggregate demand. 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 a smaller 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. He argues further
that involuntary unemployment is explained by insufficiency of effective demand, instability
of exchange rates, and international mobility of finances which create uncertainty that weakens
entrepreneurial confidence to make investments to reduce unemployment. Similarly, other
Keynesians argue that the unemployment is due to the contractionary nature of the monetary
policy which creates deficiency in aggregate demand. Other Keynesians think that the
unexpected increase in price level, or a higher rate of inflation, will reduce the real wage and
increase demand for labor. That is, the rate of unemployment will decline similar to the old
proposition of Phillips curve suggesting there is a tradeoff between the rate of unemployment
and the rate of inflation.

According to this theory, to increase employment, there must be expansionary monetary and
fiscal policy which will increase money in circulation, which is expected to increase aggregate
demand and in turn lead to increase in production which will later lead to increase in
employment, output and income. This theory is relevant to this study because Keynes

22
advocated for state intervention in an economy and for state to successfully and effectively
intervene in the economy, there is the need for quality institutions.

2.2.2 Classical Theory of Unemployment

Pigou (1933), McDonald and Solow (1981) examined the classical theory of unemployment
and made a case that the labor market comprises of the demand for and supply of labor.
Demand for labor is a derived demand, gotten from the falling off of the marginal product of
labor. The demand curve is an inverse relationship of the real wage in the sense that if real
wages increase, the quantity demanded for labor will fall and vice versa. The supply of labor is
gotten from employee's decision whether to spend part of their time working or not working.
Supply of labor has a direct relationship with the real wage, because if the real wage increases,
employees supply more labor hours. At equilibrium, the demand for and supply of labor
intersects at a point that determines the equilibrium real wage rate as well as full employment.
The classicalists were of the view that involuntary unemployment was a short-term occurrence
stemming from a discrepancy between the wage level and the price level. Unemployment was
the outcome of excessive high real wages. The classicalists opined that occasionally wages
would decrease and there would be no unemployment except for frictional unemployment
which is caused by time delay between leaving one job and starting another. This school of
thought proposes that urban unemployment problem can be traced to the fault of employees
and the numerous trade unions power. They believed strongly in market forces. Thus, insisting
that urban unemployment is caused by inadequate supply of labor of more than the capacity of
the economy. As a result, the classicalist school contended that demand for excessive high
wages of workers without a corresponding productivity increase makes the product expensive
in that way discouraging competitiveness amongst indigenous industries and foreign
industries. The impact of these trends is sales reduction, which inevitably leads to mass
employee’s retrenchment resulting to unemployment. Therefore, this theory is relevant to this
study.

2.3 Empirical Review

Quite a few numbers of literatures have examined the impact of agricultural imports and
unemployment, while several of them examined agriculture and other macroeconomic variables.

23
Here are some conclusions made by various authors who have examined this relationship. A few
of these conclusions are hereby discussed in this section.

Raymond (2002), investigated the impact of agricultural financing and unemployment rate in
Nigeria. He examined this impact using time series data collected from the Central Bank of
Nigeria (CBN) and the World Bank database from 1981 to 2018. The study used Johansen, Error
correction model (ECM), and Granger causality analytical techniques. The study concluded that
there is an inverse long run positive relationship between agricultural financing and
unemployment rate in Nigeria. The study recommends among others that government policy on
agricultural credit should place more emphasis on strengthening banks’ commitment.

Oyetade, Shir & Razak (2015) examined the relationship between macroeconomic factors and
agricultural sector in Nigeria. The study employed annual data which span across 1981 - 2013
and were sourced from Central Bank of Nigeria (CBN), National Bureau of Statistics (NBS),
and World Bank. The method employed in analyzing the data are unit root test, granger
causality and regression analysis. The study established the existence of long run relationship
between agriculture output and specific macroeconomic variables such as; inflation,
unemployment, interest rate, exchange rate, food import and commercial agricultural loan).
The study recommends adequate financing of the sector for improvement in agricultural output
to be obtained and sustain simultaneously.

Manggoel, Damiyal, Damar & Da’ar (2012) explored agriculture as a mitigating factor to
unemployment in Nigeria in the period 1981- 2011. The methodology employed includes
Augmented Dickey Fuller, Engle Granger cointegration and ARDL regression. This study
concluded that unemployment and agricultural output as a long run relationship. The analysis
from agricultural point of view suggests that people who have been majorly affected by
unemployment are a great proportion of underprivileged Nigerians in rural areas where
agriculture is primarily subsistence, land is not easily accessible or not fully utilizable, credit
facilities are lacking and improved production methods are not practiced. The literature
suggested that the problem of unemployment in Nigeria can be mitigated by giving agriculture
an adequate attention.

24
Nahagan & Vera (2016), in their research work, explained the impact of agricultural export
and economic growth in Nigeria from 1980 - 2012.This paper investigates the impact of
agricultural exports on economic growth in Nigeria using OLS regression, Granger causality,
Impulse Response Function and Variance Decomposition approaches. Both the OLS
regression and Granger causality results support the hypothesis that agricultural exports- led
economic growth in Nigeria. The results, however, show an inverse relationship between the
agricultural degree of openness and economic growth in the country. Impulse Response
Function results fluctuate and reveal an upward and downward shocks from agricultural export
to economic growth in the country. The Variance Decomposition results also show that a
shock to agricultural exports can contribute to the fluctuation in the variance of economic
growth in the long run. The study concluded that agricultural export does not lead to economic
growth in Nigeria and for Nigeria to experience a favorable trade balance in agricultural trade,
domestic processing industries should be encouraged while imports of agricultural
commodities that the country could process cheaply should be discouraged. Undoubtedly, this
measure could drastically reduce the country’s over reliance on food imports and increase the
rate of agricultural production for self-sufficiency, exports and its contribution to the economic
growth in the country.

Oluwafemi, Saidi & Onyeka (2019), investigated the nexus between agriculture and
unemployment in Nigeria from 1981 – 2016. This study made a modest contribution to the
debates by empirically analyzing the link between agriculture and unemployment in Nigeria,
using time series data from 1981 to 2016, obtained from the Central Bank of Nigeria’s
Statistical Bulletin and World Bank World Development Index. The data were analyzed using
ADF (Augmented Dickey Fuller Test) unit root test, Autoregressive distributed lag, Bounds
test of cointegration, Autoregressive distributed lag error correction model estimation and
Granger causality. The results of ADF unit root test revealed variables were at different orders
of integration, the ARDL bounds test revealed cointegration between variables, and the
Autoregressive distributed lag error correction model estimation revealed that change in
agriculture output in the current period is negative and significant for current unemployment.
The speed of adjustment to equilibrium is 74.10% within a year when the variables wander

25
away from their equilibrium values. Based on the result of granger causality, the paper
concludes that a two- way causality exist between agricultural output and unemployment in
Nigeria. Therefore, the policy implication of these findings is that any reduction in total
agricultural output would have a negative repercussion on unemployment in Nigeria.

Ayinde (2011) examined the effect of agricultural growth on unemployment and poverty in
Nigeria over the period of 1980 to 2011. Data for the study was obtained from NBS, Central
Bank of Nigeria, IMF publications and United Nations publications. ARIMA model, Granger
Causality approach and Co-integration techniques of data analysis were used to analyze the
data. The results from the Granger Causality test showed there is a unidirectional causation
from poverty to agricultural growth change, unidirectional causation from poverty to change in
unemployment and unidirectional causation from change in agricultural growth to
unemployment rate meaning that agricultural growth and unemployment in Nigeria is
dependent on poverty. Unemployment rate depend on agricultural growth during the time
frame. Bernard and Adenuga (2017), employed the Error Correction and the Granger Causality
test to analyze the contribution of agricultural sector alongside other explanatory variables
such as GDP, foreign private capital, federal government expenditure on employment
generation in Nigeria. The study revealed that there is a positive relationship between
agricultural output and employment generation in Nigeria. The authors supported the
Keynesian view that increase in aggregate supply will increase employment generation of a
country.

Ayinde (2008) examined agricultural growth and unemployment in Nigeria. The study
employed t-test, Duncan Multiple Range test, Granger Causality test and regression analysis.
The t-test was used to establish whether there exists significant difference in the
unemployment rates of rural and urban areas. The Granger Causality test was used to examine
the dimension and the linkage between agriculture and unemployment. The results revealed
that unemployment rate is generally higher in the urban areas which may be as a result of rural
urban migration and various organizations laying off their members of staff for them to
become more computerized and mechanized. The Granger Causality test showed that there is
unidirectional causation between agricultural growth and national unemployment and between

26
urban unemployment and agricultural growth. He recommended that for unemployment rate in
Nigeria to be curbed, there must be a huge intervention in agricultural production and its
sustainability in order to not let the macroeconomic problem persist and recommending
policies to alleviate poverty should focus on increasing agricultural growth.

Olanrewaju (2104) employed the chi-square statistical method of data analysis to establish if
a relationship exists between youth participation in agriculture and unemployment, using
primary data. The findings show that youth are ready to practice agriculture in the absence of
the scarce white-collar jobs if government can provide enabling environment by funding and
developing the agriculture sector. The results gave the same outcome validating that
agricultural development/funding has positive effect on youth participation and thereby
reducing unemployment.

Dawson (2005) examines the contribution of agricultural exports to economic growth in less
developed countries. The results show significant structural differences in economic growth
between low, lower-middle, and upper-income countries. The findings further indicate that
investment in the agricultural export has an effect on economic growth in those countries.
Arguably, proactive measures or policies should be promoted for agricultural exports and
growth in countries across the globe.

2.4 Gaps in Literature

Many of the reviewed studies focused on the examination of the effect of agricultural output
on the growth of the Nigerian economy, unemployment and impact of agricultural financing
on agricultural output and as a result there are very few studies that have investigated the
relationship agricultural imports and unemployment in Nigeria. Thus, the exact relationship
between agricultural imports and unemployment rate in Nigeria remains ambiguous. On this
note, it has become imperative to establish a study which will assess the impact of agricultural
imports over the years on the rate of unemployment in Nigeria.

27
 CHAPTER THREE

RESEARCH METHODOLOGY

3.0 Introduction

This chapter explain the methodology for this study with information on construction and
specification of model. It equally discusses the nature and sources of data, variables in the model,
method of estimation of data, evaluation techniques and statistical tools employed in this study.

3.1 Theoretical Framework

Theory of Marginal Productivity of Labor

In this classical theory of factor pricing (labor in this case), the marginal revenue product is
MRP which is the multiplication of marginal revenue (MR) by the marginal product of labor
(MPL), or productivity. Mathematically, it is MRPL = (MR) (MPL). And the profit-
maximizing firm will hire workers until MRPL = W, where W is the given wage rate. It is
assumed that the production function is of the form where output (Q) depends on two
resources Labor (L) and all other resources combined as O, and is subject to a constant return
to scale, where the sum of the exponents of L and O is equal to one. It is also assumed that the
production function is affected by the technological level A such that,
𝑄 = 𝐴𝐿𝑎𝑂𝑏 … 1
Differentiate the production function partially with respect to labor yields
𝜕𝑄
𝜕𝐿
= 𝑎𝐴𝐿𝑎 − 1𝑂𝑏 … 2

Use the marginal product of labor in the MRPL equation to obtain

MRPL = MR x MP = MR x (aALa − 1Ob) … 3
because,
𝑀𝑃𝐿 = (𝑎𝐴𝐿𝑎 − 1𝑂𝑏)
The MRPL should equal to the real wage rate W/P, where P is the price of the product. The
MRPL is equal to the value of the marginal product of labor if MR = P under perfect
competition. Under imperfect competition, the MRPL is smaller than the value of the marginal
product, indicating the existence of labor exploitation. In any event, solving for L, we obtain
the employment level

28
 L = aPQ/W . . .4
where PQ represents the gross domestic product, or GDP. If the numerator and the
denominator of the above equation are divided by L, one can obtain
L = aPQ/L/W/L = a
a = (average product of labor)/(average wage)
This equation states that if labor productivity (or the average product of labor) increases,
assuming W is constant, the demand for labor, L, will rise, and the unemployment rate will
decline. And this shift (or increase) in the demand for labor can occur, for example, if
investment or capital formation increases. This is because if labor productivity increases
relative to wages, the employer or the producer will increase the firm’s rate of profit by hiring
more workers (L). The previous analysis was adopted by Arthur Lewis (1954). Lewis develops
what was called the Lewis model in which he assumes that if there was a surplus of labor and a
given demand for labor, then the wage rate is fixed. Lewis points out that under this condition
capitalists do make a certain level of profit. The capitalists will reinvest part of the profits in
new capitals. This investment will raise labor productivity. Hence, the demand for labor will
increase, and these new employed workers can come from low productivity sectors or the rural
areas. This increase in employment will provide more profits for the capitalists, and more
profits will increase investment, employment, and income. In short, demand for labor will shift
to the right when labor productivity rises, indicating an increase in employment and income.
Clearly, the introduction of new innovative marketing techniques will increase the demand for
the product and consequently will increase demand for labor.

Moreover, if productivity increases due to a greater utilization of capital goods, new

technological advances, and better quality of labor (due to education, training, and health),
then the demand for labor (or employment) will increase. In other words, successful
innovations will increase productivity and employment (Schumpeter 1934). In addition, if the
prices of capital goods decline, the quantity demanded for these goods will rise. Consequently,
output will increase, so will the employment of labor. If resources are complemented, the
employment of more capital in the production process will increase the demand for labor or
employment. In fact, technological change or growth will be equal to the growth rate of output
minus the growth rate of labor productivity. If productivity increases significantly, it will

29
increase the growth rate of the gross domestic product (GDP) with larger increases than
productivity, which forces employers at that point to hire more workers to accommodate
expected demand. Wages will rise but if labor productivity increases at a rate faster than the
increase in wages, then the rates of inflation and unemployment will decline.

3.2 Model Specification

There are many factors that influence unemployment aside from agricultural imports which is
proxied by food imports, agricultural raw materials imports and agricultural machinery under
three models. This study takes cognizance of the following control variables; population,
government expenditure, trade openness, inflation and gross domestic product (GDP) growth
rate. For simplicity purpose, the model is built on the assumption that a linear relationship exists
between the dependent variable and the explanatory variables. The postulates functional form of
the model;

𝑈𝑁𝐸𝑀𝑃 = 𝐹 (𝐴𝐺𝑅𝐼𝑀𝑃, 𝑃𝑂𝑃𝐺𝑅, 𝐺𝐸𝑋𝑃, 𝑇𝑅𝐷𝑂𝑃𝐸𝑁, 𝐼𝑁𝐹, 𝐺𝐷𝑃𝐺𝑅) … (1)

Where;

UNEMP = Unemployment Rate

AGRIMP = Agricultural Imports (– proxied by food imports)

POPGR = Population Growth Rate

GEXP = Government Expenditure

TRDOPEN = Trade Openness

INF = Inflation

GDPGR = Gross Domestic Product Growth Rate

By description, unemployment rate (UNEMP), the dependent variable in this model, denotes the
annual value of the rate of unemployment.

The exogenous (control)variables in the model and the rationales for selection are explained
thus;

30
Agricultural Imports: This refers to the total value of aggregated agricultural products imported
into the economy. Due to the broadness of this concept, agricultural imports is proxy to food
imports to derive a specific perspective of the impacts of agricultural imports to the
unemployment rate in the economy in terms of food importation. Through the analysis of the
model that comprises food imports as a proxy to agricultural imports, we can derive the direction
of unemployment rate as a result of the importation of food products as classified under section 0
of the Standard International Trade Classification (SITC).

Population rate: The growth rate of the total number of occupants of the state over the year of
study.

Government Expenditure: The level of government spending in the agriculture sector over the
years of study, is to determine the direction of unemployment rate in line with movements in
expenditure.

Trade Openness: The degree of the economy’s openness to trade, to determine the exposure of
domestic products to international economies, as well as from these economies.

Inflation Rate: The annual percentage rise in general price level in the economy for the period
covered by this study, to determine the responsiveness of unemployment levels to inflation over
the years of study.

Gross Domestic Product (GDP): The growth rate of per capita income on annual basis over the
selected period, to quantify the size of the economy and the response of unemployment to growth
or decline in GDP over the years.

Transforming the model to its structural form to encompass the constant term (β0) and error term
(µ) to take care of those variables that are not included in the model;

𝑈𝑁𝐸𝑀𝑃𝑡 = 𝛽0 + 𝛽1 AGRIMP𝑡 + β2 POPGR 𝑡 + β3 lnGEXP𝑡 + β4 TRDOPEN𝑡 + β5 INF𝑡

+ β6 GDPGR 𝑡 + 𝜇𝑡 … . (2)

Where; AGRIMP = f (FOOD IMPORTS), the model therefore becomes;

UNEMP 𝑡 = 𝛽0 + β1 FOODIMP𝑡−1 + β2 POPGR 𝑡 + β3 GDPGR 𝑡 + β4 lnGEXP𝑡 + β5 INF𝑡

+ β6 TRDOPEN𝑡 + 𝜇𝑡

31
t = time period (1981– 2019)

t-1 = previous year value

UNEMP = Unemployment rate

AGRIMP = log of Agricultural Imports

FOODIMP = Food Imports

POPGR = Population growth Rate

lnGEXP = log of Government Expenditure

GDP = GDP growth rate

INF = Inflation rate

β0 = intercept parameter

β1 – β6 = Coefficient of Regressors

µ = Error term

3.3 Nature and Sources of Data

This research work incorporates time series data spread from 1981-2019 and the criterion for
selection of this period relies solely on the availability of data. In order to estimate the model in
this study, data is required for the following variables; annual value of unemployment rate,
agricultural products food imports, degree of economic openness, gross domestic product (GDP)
per capita and population rate. The sources of these data are secondary and are obtained from the
Central Bank of Nigeria (CBN) Statistical Bulletin, National Bureau of Statistics (NBS) and
World Bank.

3.4 Estimation Techniques

The study will adopt a descriptive and empirical analysis. The descriptive analysis includes the
use of graph, tables and percentage where necessary and the empirical involves Ordinary Least
Square (OLS) regression technique.

32
When carrying out a research which involves time series analysis, the nature of trends of the
variables in use must be initially tested. Due to the nature of the data employed in this study, it is
expedient to apply Augmented Dickey Fuller (ADF) test statistics to evaluate the stationarity or
unit root properties of the time series. The OLS test statistics of R2, t-test and F-test will be used
to estimate the marginal influence of the individual predictor variable on the dependent variable
as well as identifying the significance of the individual predictor variables, holding all other
variables constant. Durbin Watson (DW) test may also be equally carried out to test for serial
correlation. First Difference Autoregressive Distributive Lag (ARDL) model shall be used to
cater for the influence of past value of both the dependent and the independent variable on the
present value the dependent variable. This study shall utilize EViews computer software.

3.5 Evaluation Criteria

To evaluate the results, this study employs the economic or a-priori criteria and statistical
criteria.

Economic or A-priori criteria: this depicts the expectation about the estimated parameters based
on theory or previous studies. Here the sign and magnitude of the coefficient of the variables will
be used to make an informed decision. The a-priori expectation of this study is that; β1, β2, β3,
β4, β5, β6> 0.

Statistical Criteria; these tests are used to determine the statistical reliability of the estimates and
are usually referred to as the First Order Tests. The most widely used first order tests are
covariance, correlation, standard error, f-statistic and t-statistic (Ijaiya, 2013). The criterion for
the test of reliability of the parameters in this study is p-value. The decision rule is that p-value
must be less than the significant value, if the null hypothesis is to be rejected.

33
 CHAPTER FOUR
PRESENTATION AND ANALYSIS OF RESULTS
4.0 Introduction
This chapter focuses on the presentation, analysis, interpretation of the regression results
obtained and other tests carried out in this study. It begins with the test for trends in both
dependent and independent variables as a prerequisite for time series analysis. In addition, the
chapter presents the Augmented Dickey Fuller unit root test conducted to test for stationary in
variables, ARDL Bound test to test for the long relationship among the variables and the
regression result using E-views as well as the interpretations and evaluations of the result.
4.1 Descriptive Analysis
This section describes the descriptive analysis adopted for this study. The summary statistics
evaluates the data using representation of trends, certain measurements of central tendency and
dispersion, (that is, the mean, and the standard deviation). Below is the graphical representation
of the variables’ trends: unemployment, food imports, population growth rate, GDP growth rate,
government expenditure, inflation and trade openness.
LUNEMP LFOOD_IMP LGEXP
3.5 3.6 10

3.0 3.4
8
3.2
2.5
3.0
2.0 6
2.8
1.5
2.6
4
1.0 2.4

0.5 2.2 2
1985 1990 1995 2000 2005 2010 2015 1985 1990 1995 2000 2005 2010 2015 1985 1990 1995 2000 2005 2010 2015

LGDPGR LTRDOPEN LPOPGR

11.2 4.0 1.00

10.8 3.6 0.98

10.4 3.2 0.96

10.0 2.8 0.94

9.6 2.4 0.92

9.2 2.0 0.90

1985 1990 1995 2000 2005 2010 2015 1985 1990 1995 2000 2005 2010 2015 1985 1990 1995 2000 2005 2010 2015

LINF
4.4

4.0

3.6

3.2

2.8

2.4

2.0

1.6
1985 1990 1995 2000 2005 2010 2015

34
 Table 4.1: Summary of Descriptive Statistics of Variables
The table below indicates that a total of 39 data sample units for the period 1981-2019 were used
for each of the variables in the model. From table, the average (mean) Log of Unemployment
(LUNEMP) which shows the percentage of the labor force without jobs, for the period 1981 to
2019 is 2.156802 percentage points. The overall output for the same duration is 3.299534 with a
minimum output of 0.641854 with a dispersion rate (standard deviation) of 0.748665 percentage
points.

The Log of food import (LFOOD_IMP) which shows the percentage of food in total imports, has
a maximum output of 3.420003 percentage points with a minimum rate of 2.280216per cent. The
average during the sampled duration was 2.803769 per cent and the dispersion rate was
0.188100percentage points.

LFOOD_
LUNEMP IMP LGDPGR LGEXP LINF LPOPGR LTRDOPEN
Mean
2.156802 2.803769 10.29219 6.122492 2.679167 0.948136 3.376316
Median
2.476538 2.830204 10.07274 6.854027 2.530116 0.949992 3.527063
Maximum
3.299534 3.420003 11.17588 9.181410 4.288204 0.996891 3.975523
Minimum
0.641854 2.280216 9.530920 2.265558 1.684176 0.911795 2.212206
Std. Dev.
0.748665 0.259020 0.572482 2.284722 0.692793 0.025894 0.493685
Skewness
-0.292124 -0.169916 0.298237 -0.415261 0.869334 0.079076 -1.104833
Kurtosis
1.656848 3.035842 1.575399 1.757839 2.867816 1.732228 3.219769
Jarque-Bera
3.486280 0.189751 3.876065 3.628187 4.940708 2.652421 8.012754
Probability
0.174970 0.909486 0.143987 0.162986 0.084555 0.265481 0.018199
Sum
84.11528 109.3470 401.3954 238.7772 104.4875 36.97731 131.6763
Sum Sq. Dev.
21.29894 2.549473 12.45395 198.3582 18.23856 0.025479 9.261549
Observations 39 39 39 39 39 39 39
Source: Author’s Computation 2021(E-views 9.0).
The Log of GDP growth rate (LGDPGR) data the indicates the rate of change in GDP over the
period of study. The maximum output recorded is11.17588percentage points, with a minimum of

35
9.530920per cent. It is averaged at 10.29219 percent over the time period and countries with a
dispersion of 0.572482percentage points.

The Log of Government Expenditure, represents the total of capital and recurrent government
spending over the study period and from the above table, was highest at 9.181410percent, with a
minimum of2.265558per cent. The average spending during the sampled duration was
6.122492percent and 2.284722per cent rate of dispersion.

The Log of Inflation, which estimates the rate of fluctuations in consumer prices, is at its
maximum at 4.288204percentage points with a minimum of 1.684176, with the average
as2.679167 and the dispersion rate at0.692793 percentage points.

The Log of Population rate, which indicates the progression of changes in the size of inhabitants
of the country, shows the maximum level at 0.996891 percentage points and 0.911795
percentage point at the minimum, with an average mean of0.948136. The rate of dispersion
(standard deviation) is low at0.025894percentage points.

The Log of Trade Openness, measured by the percentage of trade in total GDP within the study
period, has a maximum output at 3.975523 percentage points, a minimum at 2.212206
percentage points and average mean of 3.376316 percentage points. The standard deviation is
measured at 0.493685 percentage points. The above descriptive statistical results being for the
year 1981-2019.

36
 The Correlation Matrix

Table 4.2: The Correlation Matrix

LUNEMP LFOOD_IMP LGEXP LGDPGR LTRDOPEN LPOPGR LINF

LUNEMP 1.000000 0.051334 0.743502 0.791928 0.228355 0.474076 0.485078

0.051334
LFOOD_IMP (0.7563) 1.000000 -0.099749 -0.185729 -0.001263 -0.196384 -0.166501
0.743502
LGEXP (0.0000) -0.099749 1.000000 0.940495 0.655219 0.249713 -0.257897
0.791928
LGDPGR (0.0000) -0.185729 0.940495 1.000000 0.442810 0.488716 -0.297215
0.228355
LTRDOPEN (0.1620) -0.001263 0.655219 0.442810 1.000000 -0.103261 0.002654
0.474076
LPOPGR (0.0023) -0.196384 0.249713 0.488716 -0.103261 1.000000 -0.276333
0.485078
LINF (0.0017) -0.166501 -0.257897 -0.297215 0.002654 -0.276333 1.000000
Source: Author’s Computation 2021(E-views 9.0)
Table 4.2, shows the summary statistic of the relationship between one variable and the other.
The table indicates, the coefficient of correlation between the Log of Unemployment (LUNEMP)
and Food Import (LFOOD_IMP) is 0.051334. This indicates a weak direct relationship between
unemployment and agricultural imports as proxied by food imports. A correlation coefficient, in
this case, is interpreted as strong if it is greater than 0.5 (equivalent to 50 per cent) and weak if it
is not. The correlation coefficient does not only calculate the strength of the relationship; it also
measures the direction of the relationship (positive/direct or negative/indirect).

From the table, there is a strong direct relationship between government expenditure (LGEXP)
and unemployment (LUNEMP), with 0.743502 correlation coefficient, while the correlation
coefficient for the log of GDP growth rate (LGDPGR) is 0.791928, signaling a strong direct
relationship with unemployment. It can also be shown that there is a weak direct association
between trade openness and unemployment as indicated by the coefficient of the Log of Trade
Openness (LTRDOPEN) at 0.228355. The coefficient of Log of Population Growth Rate,
0.474076, indicates a fairly correlated relationship between population growth and
37
unemployment and the rate of inflation is also fairly correlated with unemployment as indicated
by the coefficient of the Log of Inflation (LINF), 0.485078. The Log of Unemployment
(UNEMP), is 100% correlated with itself. This, therefore, explains the diagonal 1.0000 observed
from the correlation matrix tables above.

From the statistical expectations established in chapter three, in testing for reliability the p-value
of the coefficients must be less than 0.05 to be considered significant at 5%. Therefore, from the
values in the table above, the result of food imports (0.7563) is not significant as 5% level as it is
greater than 0.05, same as the coefficient of the Log of Trade Openness (LTRDOPEN).
However, the Log of Government Expenditure (LGEXP), Government Expenditure (GDPGR),
Population Growth Rate (POPGR) and Inflation (LINF) have statistically significant coefficient
results as shown in the table above.

4.2 Regression Results and Interpretation

The results of the unit root test, co-integration test and estimated model are summarized
in this segment.

4.2.1 Unit Root Test

The root unit test is performed to verify whether the variables are stationary. This is done to
prevent the result of spurious regression. If the variable is stationary, the current values are not
determined by past values. The unit root test used in this analysis is the Augmented Dickey-
Fuller test.

38
Table 4.3: Augmented Dickey-Fuller Unit Root

Level ADF First Difference Probability Level of

Variables
t-statistics ADF t-statistics values integration

(D)LUNEMP -6.112933* 0.0001 I(1)

LFOOD_IMP -3.754398** 0.0305 I(0)
(D)LGEXP -7.708211* 0.0000 I(1)
(D)LGDPGR -3.351691* 0.0074 I(1)
(D)LTRDOPEN -7.370701* 0.0000 I(1)
LPOPGR -4.693421* 0.0039 I(0)
LINF -4.436434* 0.0059 I(0)
(*), (**) and (***) denotes significant at 1%, 5% and 10% respectively.
Source: Author’s Computation, 2021 (E-views 9.0).
Note: the significant value used by the researcher for this analysis is 5 per cent of the highest
occurrence in the table above. The Mackinnon values set the essential values for the
determination of the integration order. The values of the Mackinnon and the ADF test statistics
are compared and decisions are made either to reject or to accept the null hypothesis. By taking
the values of each variable in absolute terms, it can be shown that LUNEMP, LGEXP, LGEPGR,
and LTRDOPEN are stationary at the first difference and are therefore considered to be I(1)
series while LFOOD_IMP, LPOPGR, and LINF are stationary at the level and are considered to
be I(0) series.

This result implies that using Fully Modified Ordinary Least Square (FMOLS) method to
estimate the parameters directly will lead to spurious regression results since there are non-
stationary (at level) series in the model. This necessitated a test of co-integration to check if at all
there is a long-run relationship among the variables used in the model. Since the variables are
combinations of I (0) and I (1) series, the ARDL bound test is the most suitable for ascertaining
whether a long-run relationship exists among the variables.

4.2.2 Cointegration / long-run relationship test

39
 The ARDL bound test was carried out to ascertain whether a long-run relationship exists
among the variables that have been observed to be stationary at level and first difference. The
ARDL bound test result was interpreted by comparing the F-statistics value with the critical
values (lower and upper bound) and the decision will be based on the null hypothesis which
states: No long-run relationships exist. If the F-statistics value is less than the lower bound value
(I0 bound) at 5% significance, then do not reject the null hypothesis and conclude that no long-
run relationship exists. However, if the F-statistics value is greater than the upper bound value
(I1 bound) at 5% significance level, then there exists a long-run relationship. But if the F-
statistics falls in-between the lower bound and upper bound values at 5% significance level, then
the result is inconclusive.
The ARDL bound test result is summarized below:
Table 4.4 ARDL Bounds Test Results
Null Hypothesis: No long-run relationships exist

Test Statistic Value K

F-statistic 5.493149 6

Critical Value Bounds

Significance I(0) Bound I(1) Bound

10% 2.12 3.23

5% 2.45 3.61
2.5% 2.75 3.99
1% 3.15 4.43
Source: Author’s Computation, 2021 (E-views 9.0)
From the results above, the F-statistics value of 5.493149 exceeds 3.61 -the upper bound (I1
Bound) value of at 5% significance level resulting in the rejection of the null hypothesis. The
conclusion, therefore, is that there exists a long-run relationship between the variables.
Since the ARDL bound test result revealed that a long-run relationship exists among the
variables, we’ll proceed to the estimation of the parameters using a co-integration regression of
Fully Modified Ordinary Least Square (FMOLS) technique because the pre-test results have
shown that the variables are non-stationary and are co-integrated.

40
4.2.3 Presentation of the Estimated Model (Cointegration Regression)
This empirical result presented in the table 4.2.3 show the estimated parameters, variable
coefficient, standard error, t-statistics, and probability value. The result obtained from the
estimation technique (FMOLS) is presented below:
Table 4.5 Fully Modified Least Squares (FMOLS) Regression result
Dependent Variable: LUNEMP
Method: Fully Modified Least Squares (FMOLS)

Variable Coefficient Std. Error t-Statistic Prob.

LFOOD_IMP 0.019586 0.298076 0.065708 0.0948

LGDPGR -1.033104 0.809251 -1.276617 0.2112
LGEXP 0.533138 0.211580 2.519798 0.0171
LPOPGR 7.697275 5.278274 1.458294 0.0155
LINF 0.281662 0.115665 2.435157 0.0208
LTRDOPEN -0.768990 0.283795 -2.709670 0.0109
C 5.645902 5.608257 1.006712 0.3219

R-squared 0.742205 Mean dependent var 2.170174

Adjusted R-squared 0.692309 S.D. dependent var 0.753979
S.E. of regression 0.418232 Sum squared residual 5.422452
Long-run variance 0.191342
*At 10% level of significance
Source: Author’s Computation, 2021(E-views 9.0)

4.3 INTERPRETATION OF RESULTS.

4.3.1 Coefficients

Food Imports (LFOOD_IMP): The Log of Food Imports (LFOOD_IMP) has a coefficient of
0.019586, implying that there is a positive relationship between food imports and
unemployment. A percentage increase in food imports index will result to an increase in
unemployment by about 0.019586 percentage points. The probability value (0.0948) is less than
the chosen level of significance 0.1 or 10%, indicating a statistically significant relationship.
Therefore, food imports represent a key influence on unemployment level.

41
GDP Growth Rate (LGDPGR): The coefficient of -1.033104 shows a negative relationship
between the rate of GDP growth and unemployment and a probability value (0.2112) greater
than the chosen level of significance 0.10 or 10%, showing that the relationship is insignificant.

Government Expenditure (GEXP): The coefficient of LGEXP indicates that a percentage

increase in government expenditure increases unemployment by 0.533138 percent. The
probability value (0.0171) is less than the chosen level of significance 0.10 or 10%, which
confirms the statistical significance of the relationship. Theoretically, an increase in government
expenditure is intended to reduce unemployment through the implementation of projects and
programs which is however not the same in the case of Nigeria as shown by the analysis.

Population Growth Rate (LPOPGR): the coefficient of LPOPGR is shows a positive

relationship between rate of population growth and unemployment. A percent increase in
population growth will cause an increase in unemployment by 7.697275 percentage points. This
relationship is statistically significant, as its probability value probability value (0.0155) is less
than the chosen level of significance 0.10 or 10%.

Inflation Rate (LINF): the coefficient of LINF exhibits a positive relationship between inflation
and unemployment. A percent increase in inflation rate will cause an increase in unemployment
rate by 0.281662 percent. This is theoretically consistent as the current economic situation and
the most recent recession experienced in Nigeria which was accompanied by the high inflation
rate (14% at at 2020), recorded several jobs losses that influence the growing unemployment
rate. The relationship is also statistically significant as the probability value 0.0208 is less than
the significance level (0.0208 < 0.1).

Trade Openness (LTRDOPEN): The result shows a negative coefficient of -0.768990 but it is
statistically significant at 0.0109 percentage points. This implies that, a unit increase in the trade
quantity index reduces unemployment rate by 0.768990 percent. The result also indicates that the
relationship is statistically significant with a probability value of 0.0109 which is less than 0.1 or
10%. In this light, the rate of trade openness as proxy by the percentage of trade in GDP, is
considered to be a key variable that affects the unemployment rate in Nigeria.

4.3.2 Statistical Criteria (First order test)

Coefficient of Multiple Determinants (R2)

42
The R-Squared (R2) which measures the overall goodness of fit of the entire regression shows a
value of 0.742205 (0.742205%, approximately 74%). This indicates that the independent
variables (LFOOD_IMP, LPOPGR, LGDPGR, LGEXP, LINF, and LTRDOPEN) account for
about 74% of the variations in the dependent variable (LUNEMP).

Table 4.6: T – Statistics Result

Variables Probability value Remarks

LFOOD_IMP Significant
0.0948
LGDPGR Not significant
0.2112
LGEXP Significant
0.0171
LPOPGR Significant
0.0155
LINF` Significant
0.0208
LTRDOPEN Significant
0.0109

The result of the t-statistics also shows that, Log of Food Imports (LFOOD_IMP), Government
Expenditure (LGEXP), Population Growth Rate (LPOPGR), Inflation (LINF) and Trade
Openness (LTRDOPEN) is significant in the model, while the Log of GDP Growth Rate
(LGDPGR) is not significant in the model at all levels of significance (1%, 5% and 10%). This
shows that five out of the six variables are significant enough to affect unemployment rate.

43
4.3.2 Economic Criteria

The table below shows each variable and their conformity to a-priori expectations;

Table 4.7: Variables and conformity to prior expectation

Variables Parameters Expected Observed Remarks

LFOOD_IMP β1 Positive Positive Conform
LPOPGR β2 Positive Positive Conform
LGDPGR β3 Negative Negative Conform
LGEXP β4 Negative Positive Non conform

LINF β5 Positive Positive Conform

LTRDOPEN β6 Negative Negative Conform

Source: Author’s computation

From the above table, it can be deduced that five out of the six variables conformed to the a-
priori expectation except Government Expenditure (LGEXP). From the a-priori expectation
criteria, this model can be judged to be a good model as it conforms to what was expected to a
reasonable extent.

4.4 Residual Diagnostic Test

Various residual diagnostics tests were carried out to assess the validity of the model. These
include: testing if the residuals are normally distributed, serially correlated, presence of
heteroskedasticity and testing for specification errors. The result for these tests is presented in
table 4.8 below.

44
Table 4.8: Residual Diagnostic Test Results

Statistics Probability
Jarque-Bera Normality Test 0.6153255 0.1657
Breush-Godfrey serial Correlation LM Test 0.006123 0.9382
Heteroskedasticity Test: Breush Pagan Godfrey 2.371299 0.4800
Ramsey RESET 8.396057 0.0720

Source: Author’s computation using E-view 9.0 (2021)

To test if the model is normally distributed or not the Jarque –Bera Normality Test was
conducted and the result showed a P-value of 0.1657 which is greater than 5% level of
significance thereby implying that the model is normally distributed. The Breush Godfrey Serial
Correlation LM Test results have a p-value of 0.9382 which is greater than 5% also signifies the
absence of serial correlation and likewise, the Breush-Godfrey Hetereskodasticity reveals an
absence of heteroskedasticity in the model given its associated p-value of 0.4800 which is greater
than 5%. The Ramsey Test shows an evidence that, the functional form of the model is properly
specified given an associated P-value of 0.0720 which is greater than 5% significance level.

In summary, the residual diagnostic tests result revealed that, the residual is not serially
correlated, there is absence of heteroskedasticity, absence of specification error shows that, the
model is normally distributed. Since the error term is well-behaved, inferences made from the
estimated model can said to be valid.

4.5 Discussion and Economic implication of Result

From the analysis, the variables used are good, as they are all stationary either at level or
first difference. Following the steps in estimating the time-series data, the result of the model
shows that, five of the six variables (Food imports, Population rate, Government expenditure,
Inflation and Trade openness) are significant in determining the rate of unemployment except for
GDP growth rate. Food imports, government expenditure, population growth rate and inflation,
have a positive or direct relationship to the dependent variable (unemployment), whereas GDP
growth rate and Trade openness have an inverse relationship to the dependent variable.
Coefficients of food imports, GDP growth rate, population rate, inflation and trade openness are
consistent with a priori expectations, except for government expenditure.

45
 There is need for reduce the increasing rate of food importation in Nigeria and scale up
the domestic production of Nigeria’s agricultural needs which would require human resources
inputs, thereby decreasing unemployment rate. The effects of inflation (INF) show that there is a
positive relationship between the inflation rate and the rate of unemployment. Neither a very
high inflation rate, nor a too low (deflation) rate, is good for the economy, but inflation rate when
appropriated moderated, tends to increase the price/cost valuation of goods and services
generated in the economy, which as a result increases aggregate demand and ultimately increases
wage rate. Population rate is seen to have a relationship with unemployment rate and the rate of
GDP growth has a negative relationship with unemployment, which indicates that national
output is not a real depiction of the per capital output. Trade openness is seen to have a direct
relationship with unemployment, which might be indicative of the sufficiency of governmental
trade policy in sustaining the performance of the employment levels. The outcome of R-squared
(74%) has shown that, the model is fits, as the value of R-squared is strong. Variables are jointly
significant based on the outcome of the F-statistics.

Conclusively, for the 1981-2019 reporting period, agricultural imports as proxied by food
imports had an effect on the rate of unemployment in Nigeria. In addition to food imports,
population rate, GDP growth rate, inflation rate and trade openness were also influential in the
determination of the rate of unemployment in Nigeria as indicated by the results of the statistical
analysis.

46
 CHAPTER FIVE

SUMMARY, CONCLUSION AND RECOMMENDATIONS

5.0 Introduction

This chapter displayed the summary, conclusion and recommendations of the whole study.

5.1 Summary

Unemployment in Nigeria is continually increasing at a rapid rate and one of the measures
popularly prescribed to the country key stakeholders to this phenomenon especially in the face of
declining oil revenue, is to focus attention on the agricultural sector. Agriculture has been a
significant sector in the Nigerian economy and is still a major sector despite the emergence of
oil. Fundamentally, with about 25% contribution to GDP in 2018, it still provides job
opportunities for the vibrant population, improves poverty levels in rural areas and contributes to
economic growth. Among the challenges faced by the sector are high level of food importation,
neglect due to oil revenue, low capital investment from both the government and private sources,
unfavorable trade policies and infrastructural inadequacies. The effects of these challenges on the
sector negatively impacts labor input which ultimately affects agricultural sector output. This
motivated the objectives of this research work which is to establish the impact of agricultural
imports on unemployment. The specific objectives of the study were the impact of agricultural
imports on unemployment rate in Nigeria and to show the relationship between domestic
agricultural productivity and unemployment levels. From the review of related literature, it has
shown that agricultural productivity has a positive impact on employment in Nigeria.

This was done with the use of both descriptive and inferential analysis. The descriptive analysis
used include graphical representation, measures of central tendency, measures of dispersion and
correlation matrix. These were used to determine the relationship and growth pattern among the
variables used for this study. The inferential analysis used was the Fully Modified Ordinary
Least Square (FMOLS), while testing for stationarity and long-run relationship using Augmented
Dickey-Fuller test for unit root and ARDL Bound test respectively and it was revealed that
agricultural imports has a statistically significant role and influence on the rate of unemployment
in Nigeria as discovered in the various literatures reviewed.

47
5.2. Conclusion

The agriculture sector plays an important role in Nigeria, especially in the area of employment
creation, reduction of poverty, diversification, as well as the improved standard of living and
economic growth of the Nigerian economy. The rate of food imports is high, the population rate
is increasingly influencing the unemployment levels, government expenditure in the agricultural
sector is inadequate and trade policies are unfavorable to domestic agricultural producers. This,
therefore, discourages labor participation as well as domestic participants in the sector e,g
farmers, thereby increasing the rate of unemployment. Hence, the need for proactive strategies to
reverse this trend.

The thrust of this study was to investigate empirically, the impact of agricultural imports on
unemployment in Nigeria, using contemporary economic techniques of bounds testing procedure
within the framework of ARDL modeling. The result shows a unique long run cointegration
relationship between the index of unemployment and agricultural imports control variables. The
estimated trend of unemployment in the long run and short run showed mixed results with
respect to the control variables. But it is certain that all the variables specified in the models
(food imports, government expenditure, population growth rate, GDP growth rate, trade
openness and inflation rate) have significant impact on unemployment in Nigeria either in a
positive way or negative way.

It was also found out that besides the impact of foo importation, the poor population
management system in Nigeria is also main influence on unemployment rate in the country.

5.3 Recommendations

Based on the summary and the conclusions above the following is recommended.

The government should improve access to financing in the agricultural sector. Financial
initiatives and programs such as grants and cheap loans can be offered to domestic farmers other
sector participants. These investments could reduce unemployment in the country, provide
sufficient resources for the optimum output of the sector as well as promote the food self-
sufficiency of the country.

48
Also, policies should be formulated to stimulate the exportation of food products. Policy makers
should ensure that the policy of trade openness offers opportunities required for the improvement
of the agriculture sector. Policies such as import quotas, high tariffs on products that Nigeria has
comparative advantage as well as subsidies export costs for participants in the domestic
agricultural sector.

Also, advanced and new technologies must be generated and developed to enhance industrial
value-added growth especially in the agriculture sector which should engender economic growth
that will impact employment levels positively.

All levels of government must be collaborative in approaching agriculture as a major sources of

job creation and make combined efforts to increase participation in the sector. The three tiers of
government in all 36 states can organize bi-annual capacity building programs throughout the
country, with focus in the youth especially. The training can also target the objective of changing
the perception of the youth towards agriculture by showing them the other lucrative paths in the
sector. Also, with the ridiculously large size of unused arable land that Nigeria possesses, the
government can consider luring investors into the various states across the country.

At the macroeconomic level, there is no overstating the fact that the challenges are enormous.
Clearly, trade policy can only do well when the appropriate supportive macroeconomic policies
are effective. In particular, the exchange rate stability has remained a recurrent decimal. The
need to stabilize the rates of inflation and rein in interest rates as part of fiscal policy and stable
monetary policy can only continue to be re-emphasized. This has been coupled with the
imperatives of infrastructure development. It is expected that the government will live up to its
promise of committing funds (occasioned by the recent reprieve from the huge external debt
burden) into the social sectors, to address the problems of poverty.

Policy specifically aimed at increasing production output should be implemented, such as

increased sustainable subsidies, government expenditure and combined tax cuts. This will have a
positive influence on economic units and could improve the economy in the long run.

In terms of inflation, while it is a necessary evil, it should be limited a little (not a harsh
monetary policy) and not a very high degree (rate) of taxation. A moderate level of inflation is

49
required to control economic growth; hence the financial authorities need to support the
government’s effort with appropriate economics policies and programs.

The process of industrialization and increased development will be hindered when lives, assets,
facilities and properties are at risks, such as the issues of Boko Haram, Herdsman, Bandits,
kidnappings and ethnic militant groups. Rapid industrialization and increased agriculture
production can only be accomplished and maintained in Nigeria if there is a stable business and
socio-economic climate, and thus national security needs to be strengthened and strengthened to
curb these social disruptions.

There is need for the government to put in place urgent and efficient population management
structures, if the menace of unemployment is to be solved. The high population growth rate
influences social vices such as kidnapping, robbery and terrorism due to lack of means of living.
So, till employment levels are stabilized, it is recommended that the rate of population is
controlled.

The Nigerian government should shun short-term solutions and focus arrangements beyond the
sole focus on the oil and gas sector. Nigeria’s economy can only be temporarily driven using
high commodity prices vehicle for driving a sustainable growth through diversified economic
mechanism.

50
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54
 APPENDIX

1.Date of all variables

YEAR UNEMP FOOD-IMP AGRIC-RMI POPGR GEXP TRDOPEN INF GDPGR

1981 5.2 15.54433074 0.521181869 2.709843 11.41 18.17172618 20.81282291 15,258.00

1982 4.3 19.46234053 1.065915736 2.599832 11.92 13.77983316 7.697747247 14,985.08

1983 6.4 21.5548761 1.133859499 2.534264 9.64 10.04496861 23.21233155 13,849.73

1984 6.2 21.28781475 1.542705839 2.52888 9.93 9.380541231 17.82053329 13,779.26

1985 6.1 17.95692129 1.994721397 2.562422 13.04 10.39197861 7.435344828 14,953.91

1986 5.3 15.7696387 1.199246494 2.602904 16.22 9.135845723 5.717151454 15,237.99

1987 7.0 10.85984398 1.63214752 2.625517 22.02 19.49533511 11.29032258 15,263.93

1988 5.1 16.46855468 1.592205312 2.630947 27.75 16.94060969 54.51122478 16,215.37

1989 4.5 15.26373966 1.604580181 2.612566 41.03 34.18261725 50.46668812 17,294.68

1990 3.5 14.59044426 1.507044877 2.57931 60.27 30.92474008 7.364400306 19,305.63

1991 3.1 14.29564565 1.583994472 2.545949 66.58 37.02160486 13.0069731 19,199.06

1992 3.5 15.15459606 1.571956211 2.521578 92.80 38.22738831 44.58884272 19,620.19

1993 3.4 14.82610641 1.566893935 2.503347 191.23 33.71975493 57.16525283 19,927.99

1994 3.2 14.71669809 1.557472374 2.493414 160.89 23.05923645 57.03170891 19,979.12

1995 1.9 14.74826155 1.570079248 2.489914 248.77 39.52837841 72.8355023 20,353.20

1996 2.8 17.51441946 1.161734366 2.488917 337.22 40.25772925 29.26829268 21,177.92

1997 3.4 18.38688963 0.959312365 2.488785 428.22 51.46101079 8.529874214 21,789.10

1998 3.5 19.59734175 0.900316112 2.491319 487.11 39.27860747 9.996378124 22,332.87

55
 1999 17.5 27.02321964 1.456164836 2.496357 947.69 34.45783118 6.618373395 22,449.41

2000 18.1 19.91769604 0.924607628 2.503847 701.05 48.99559947 6.933292156 23,688.28

2001 13.7 21.70472748 2.613243678 2.511617 1,018.00 49.68050029 18.87364621 25,267.54

2002 12.2 19.57764009 1.024340528 2.521515 1,018.18 40.03516859 12.8765792 28,957.71

2003 14.8 15.49647911 0.60488156 2.537255 1,225.99 49.33496486 14.03178361 31,709.45

2004 11.8 19.17413568 1.291768349 2.559662 1,426.20 31.89587044 14.99803382 35,020.55

2005 11.9 18.98824559 1.383558529 2.585689 1,822.10 33.05946007 17.86349337 37,474.95

2006 12.3 17.95533875 0.666261097 2.610844 1,938.00 42.5665658 8.22522152 39,995.50

2007 12.7 20.05801237 0.874917599 2.632173 2,450.90 39.33693151 5.388007969 42,922.41

2008 14.7 9.778790198 1.189652104 2.649864 3,240.82 40.79683535 11.58107517 46,012.52

2009 19.7 11.82807694 0.961601004 2.662917 3,452.99 36.05871041 12.55496039 49,856.10

2010 21.1 10.25156707 0.784061927 2.671443 4,194.58 43.32075684 13.72020184 54,612.26

2011 15.8 30.56950419 4.212581548 2.677884 4,712.06 53.27795833 10.84002754 57,511.04

2012 16.2 22.70974303 0.758004241 2.680914 4,605.39 44.53236805 12.21778174 59,929.89

2013 16.7 17.8293897 2.806849385 2.6769 5,185.32 31.04885996 8.475827285 63,218.72

2014 17.1 17.02716499 0.676908499 2.665019 4,587.39 30.88519372 8.062485824 67,152.79

2015 17.6 16.9489162 0.770747233 2.647419 4,988.86 21.33265187 9.009387183 69,023.93

2016 18.0 12.85060101 0.818804152 2.627703 5,858.56 20.72251888 15.67534055 67,931.24

2017 18.5 16.3157578 0.908826599 2.607676 6,456.70 26.347599 16.52353998 68,490.98

2018 23.1 10.93179312 0.791532284 2.586546 7,813.74 33.00783349 12.09473155 69,799.94

2019 27.1 9.915774702 0.757917961 2.564842 9,714.84 34.02387783 11.39679497 71,387.83

Sources: Central Bank of Nigeria, Nigerian Bureau of Statistics, World Bank.

56
2. Augmented Dickey-Fuller
i) Unemployment
Null Hypothesis: D(LUNEMP) has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic - based on SIC, maxlag=9)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -6.112933 0.0001

Test critical values: 1% level -4.226815
5% level -3.536601
10% level -3.200320

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(LUNEMP,2)
Method: Least Squares
Date: 06/09/21 Time: 13:28
Sample (adjusted): 1983 2019
Included observations: 37 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

D(LUNEMP(-1)) -1.043006 0.170623 -6.112933 0.0000

C 0.011775 0.117694 0.100049 0.9209
@TREND("1981") 0.001986 0.005207 0.381325 0.7053

R-squared 0.523665 Mean dependent var 0.009453

Adjusted R-squared 0.495645 S.D. dependent var 0.474711
S.E. of regression 0.337130 Akaike info criterion 0.740909
Sum squared resid 3.864328 Schwarz criterion 0.871524
Log likelihood -10.70682 Hannan-Quinn criter. 0.786957
F-statistic 18.68915 Durbin-Watson stat 1.930268
Prob(F-statistic) 0.000003

ii) Food import

Null Hypothesis: LFOOD_IMP has a unit root

Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic - based on SIC, maxlag=9)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -3.754398 0.0305

Test critical values: 1% level -4.219126
5% level -3.533083
10% level -3.198312

*MacKinnon (1996) one-sided p-values.

57
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(LFOOD_IMP)
Method: Least Squares
Date: 06/09/21 Time: 13:32
Sample (adjusted): 1982 2019
Included observations: 38 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

LFOOD_IMP(-1) -0.614585 0.163697 -3.754398 0.0006

C 1.788445 0.473960 3.773412 0.0006
@TREND("1981") -0.003532 0.003659 -0.965398 0.3410

R-squared 0.293498 Mean dependent var -0.011831

Adjusted R-squared 0.253127 S.D. dependent var 0.285274
S.E. of regression 0.246539 Akaike info criterion 0.113063
Sum squared resid 2.127351 Schwarz criterion 0.242346
Log likelihood 0.851803 Hannan-Quinn criter. 0.159061
F-statistic 7.269936 Durbin-Watson stat 1.960310
Prob(F-statistic) 0.002288

iii) Government Expenditure

Null Hypothesis: D(LGEXP) has a unit root

Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic - based on SIC, maxlag=9)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -7.708211 0.0000

Test critical values: 1% level -4.226815
5% level -3.536601
10% level -3.200320

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(LGEXP,2)
Method: Least Squares
Date: 06/09/21 Time: 13:33
Sample (adjusted): 1983 2019
Included observations: 37 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

D(LGEXP(-1)) -1.262795 0.163825 -7.708211 0.0000

C 0.313958 0.081101 3.871182 0.0005
@TREND("1981") -0.004321 0.003176 -1.360449 0.1826

R-squared 0.636109 Mean dependent var 0.004705

Adjusted R-squared 0.614704 S.D. dependent var 0.328103
S.E. of regression 0.203661 Akaike info criterion -0.267115
Sum squared resid 1.410245 Schwarz criterion -0.136500
Log likelihood 7.941630 Hannan-Quinn criter. -0.221067

58
F-statistic 29.71730 Durbin-Watson stat 1.885577
Prob(F-statistic) 0.000000

iv) GDP Growth Rate

Null Hypothesis: D(LGDPGR) has a unit root

Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic - based on SIC, maxlag=9)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -3.351691 0.0074

Test critical values: 1% level -4.226815
5% level -3.536601
10% level -3.200320

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(LGDPGR,2)
Method: Least Squares
Date: 06/09/21 Time: 13:34
Sample (adjusted): 1983 2019
Included observations: 37 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

D(LGDPGR(-1)) -0.492987 0.147086 -3.351691 0.0020

C 0.017240 0.012803 1.346584 0.1870
@TREND("1981") 0.000206 0.000576 0.357538 0.7229

R-squared 0.254827 Mean dependent var 0.001096

Adjusted R-squared 0.210993 S.D. dependent var 0.040323
S.E. of regression 0.035817 Akaike info criterion -3.743182
Sum squared resid 0.043617 Schwarz criterion -3.612567
Log likelihood 72.24886 Hannan-Quinn criter. -3.697134
F-statistic 5.813496 Durbin-Watson stat 1.941298
Prob(F-statistic) 0.006736

v) Population Growth Rate

Null Hypothesis: LPOPGR has a unit root

Exogenous: Constant, Linear Trend
Lag Length: 8 (Automatic - based on SIC, maxlag=9)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -4.693421 0.0039

Test critical values: 1% level -4.296729
5% level -3.568379
10% level -3.218382

59
*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(LPOPGR)
Method: Least Squares
Date: 06/09/21 Time: 13:37
Sample (adjusted): 1990 2019
Included observations: 30 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

LPOPGR(-1) -0.078318 0.016687 -4.693421 0.0002

D(LPOPGR(-1)) 1.735703 0.149919 11.57757 0.0000
D(LPOPGR(-2)) -1.461716 0.259766 -5.627049 0.0000
D(LPOPGR(-3)) 0.863658 0.204137 4.230775 0.0005
D(LPOPGR(-4)) -0.041955 0.153734 -0.272908 0.7879
D(LPOPGR(-5)) -0.311706 0.151041 -2.063709 0.0530
D(LPOPGR(-6)) 0.691737 0.159606 4.334018 0.0004
D(LPOPGR(-7)) -0.574867 0.145336 -3.955439 0.0008
D(LPOPGR(-8)) 0.290048 0.073255 3.959427 0.0008
C 0.073370 0.015418 4.758703 0.0001
@TREND("1981") 2.36E-05 2.12E-05 1.113602 0.2793

R-squared 0.996631 Mean dependent var -0.000615

Adjusted R-squared 0.994857 S.D. dependent var 0.006713
S.E. of regression 0.000481 Akaike info criterion -12.16329
Sum squared resid 4.40E-06 Schwarz criterion -11.64952
Log likelihood 193.4493 Hannan-Quinn criter. -11.99893
F-statistic 562.0230 Durbin-Watson stat 1.898719
Prob(F-statistic) 0.000000

vi) Inflation

Null Hypothesis: LINF has a unit root

Exogenous: Constant, Linear Trend
Lag Length: 1 (Automatic - based on SIC, maxlag=9)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -4.436434 0.0059

Test critical values: 1% level -4.226815
5% level -3.536601
10% level -3.200320

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(LINF)
Method: Least Squares
Date: 06/09/21 Time: 13:39
Sample (adjusted): 1983 2019
Included observations: 37 after adjustments

60
 Variable Coefficient Std. Error t-Statistic Prob.

LINF(-1) -0.700260 0.157843 -4.436434 0.0001

D(LINF(-1)) 0.358341 0.155191 2.309028 0.0273
C 2.206272 0.515555 4.279414 0.0002
@TREND("1981") -0.015819 0.009006 -1.756635 0.0883

R-squared 0.376077 Mean dependent var 0.010606

Adjusted R-squared 0.319356 S.D. dependent var 0.676624
S.E. of regression 0.558223 Akaike info criterion 1.773688
Sum squared resid 10.28321 Schwarz criterion 1.947841
Log likelihood -28.81323 Hannan-Quinn criter. 1.835085
F-statistic 6.630374 Durbin-Watson stat 1.669815
Prob(F-statistic) 0.001252

vii) Trade openness

Null Hypothesis: D(LTRDOPEN) has a unit root

Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic - based on SIC, maxlag=9)

t-Statistic Prob.*

Augmented Dickey-Fuller test statistic -7.370701 0.0000

Test critical values: 1% level -4.226815
5% level -3.536601
10% level -3.200320

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(LTRDOPEN,2)
Method: Least Squares
Date: 06/09/21 Time: 13:40
Sample (adjusted): 1983 2019
Included observations: 37 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

D(LTRDOPEN(-1)) -1.210603 0.164245 -7.370701 0.0000

C 0.090956 0.097909 0.928990 0.3594
@TREND("1981") -0.003157 0.004313 -0.731939 0.4692

R-squared 0.615440 Mean dependent var 0.008297

Adjusted R-squared 0.592819 S.D. dependent var 0.438144
S.E. of regression 0.279583 Akaike info criterion 0.366569
Sum squared resid 2.657665 Schwarz criterion 0.497184
Log likelihood -3.781532 Hannan-Quinn criter. 0.412617
F-statistic 27.20634 Durbin-Watson stat 1.988746
Prob(F-statistic) 0.000000

3. Auto-regressive Distributive Lag

61
Dependent Variable: LUNEMP
Method: ARDL
Date: 06/09/21 Time: 13:54
Sample (adjusted): 1982 2019
Included observations: 38 after adjustments
Maximum dependent lags: 1 (Automatic selection)
Model selection method: Akaike info criterion (AIC)
Dynamic regressors (1 lag, automatic): LFOOD_IMP LPOPGR LGEXP
LGDPGR LINF LTRDOPEN
Fixed regressors: C
Number of models evalulated: 64
Selected Model: ARDL(1, 0, 0, 0, 1, 0, 0)

Variable Coefficient Std. Error t-Statistic Prob.*

LUNEMP(-1) 0.768608 0.119968 6.406769 0.0000

LFOOD_IMP -0.114060 0.187072 -0.609714 0.5468
LPOPGR 4.403517 3.534966 1.245703 0.2228
LGEXP 0.402030 0.134572 2.987469 0.0057
LGDPGR -3.788908 1.677710 -2.258380 0.0316
LGDPGR(-1) 2.558631 1.440554 1.776144 0.0862
LINF -0.167886 0.072691 -2.309583 0.0282
LTRDOPEN -0.352364 0.187678 -1.877490 0.0705
C 8.615417 3.621351 2.379061 0.0242

R-squared 0.898883 Mean dependent var 2.170174

Adjusted R-squared 0.870988 S.D. dependent var 0.753979
S.E. of regression 0.270816 Akaike info criterion 0.428639
Sum squared resid 2.126896 Schwarz criterion 0.816488
Log likelihood 0.855865 Hannan-Quinn criter. 0.566633
F-statistic 32.22444 Durbin-Watson stat 2.364656
Prob(F-statistic) 0.000000

*Note: p-values and any subsequent tests do not account for model
selection.

4. Bounds Test

ARDL Bounds Test

Date: 06/09/21 Time: 14:13
Sample: 1983 2019
Included observations: 37
Null Hypothesis: No long-run relationships exist

Test Statistic Value k

F-statistic 5.493149 6

Critical Value Bounds

Significance I0 Bound I1 Bound

10% 2.12 3.23

5% 2.45 3.61

62
2.5% 2.75 3.99
1% 3.15 4.43

Test Equation:
Dependent Variable: D(LUNEMP,2)
Method: Least Squares
Date: 06/09/21 Time: 14:13
Sample: 1983 2019
Included observations: 37

Variable Coefficient Std. Error t-Statistic Prob.

C 0.110957 0.111590 0.994321 0.3283

D(LFOOD_IMP(-1)) 0.018738 0.206953 0.090544 0.9285
D(LPOPGR(-1)) 3.256697 7.088676 0.459423 0.6494
D(LGEXP(-1)) 0.210861 0.296721 0.710637 0.4830
D(LGDPGR(-1)) -2.041127 1.904734 -1.071607 0.2927
D(LINF(-1)) -0.061746 0.090571 -0.681743 0.5008
D(LTRDOPEN(-1)) -0.303796 0.218820 -1.388334 0.1756
D(LUNEMP(-1)) -1.155240 0.196274 -5.885867 0.0000

R-squared 0.570065 Mean dependent var 0.009453

Adjusted R-squared 0.466287 S.D. dependent var 0.474711
S.E. of regression 0.346803 Akaike info criterion 0.908692
Sum squared resid 3.487901 Schwarz criterion 1.256998
Log likelihood -8.810797 Hannan-Quinn criter. 1.031486
F-statistic 5.493149 Durbin-Watson stat 1.984997
Prob(F-statistic) 0.000428

5. Fully modified Ordinary least Squared method (FMOLS)

Dependent Variable: LUNEMP
Method: Fully Modified Least Squares (FMOLS)
Date: 06/09/21 Time: 16:25
Sample (adjusted): 1982 2019
Included observations: 38 after adjustments
Cointegrating equation deterministics: C
Long-run covariance estimate (Bartlett kernel, Newey-West fixed bandwidth
= 4.0000)

Variable Coefficient Std. Error t-Statistic Prob.

LFOOD_IMP 0.019586 0.298076 -0.065708 0.0948

LGDPGR -1.033104 0.809251 -1.276617 0.2112
LGEXP 0.533138 0.211580 2.519798 0.0171
LPOPGR 7.697275 5.278274 1.458294 0.0155
LINF 0.281662 0.115665 2.435157 0.0208
LTRDOPEN -0.768990 0.283795 -2.709670 0.0109
C 5.645902 5.608257 1.006712 0.3219

R-squared 0.742205 Mean dependent var 2.170174

Adjusted R-squared 0.692309 S.D. dependent var 0.753979
S.E. of regression 0.418232 Sum squared residual 5.422452
Long-run variance 0.191342

63
6. RESIDUAL DIAGNOSTICS
HETEROSKADASTICITY
Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic 2.371299 Prob. F(7,29) 0.0480

Obs*R-squared 13.46883 Prob. Chi-Square(7) 0.0615
Scaled explained SS 31.29920 Prob. Chi-Square(7) 0.0001

Test Equation:
Dependent Variable: RESID^2
Method: Least Squares
Date: 06/11/21 Time: 14:04
Sample: 1983 2019
Included observations: 37

Variable Coefficient Std. Error t-Statistic Prob.

C 0.070251 0.066195 1.061276 0.2973

D(LUNEMP(-1)) 0.104722 0.111827 0.936470 0.3568
D(LFOOD_IMP) 0.116882 0.122151 0.956866 0.3465
D(LPOPGR) 5.189139 4.995954 1.038668 0.3075
D(LGDPGR) -2.098224 1.044082 -2.009636 0.0539
D(LGEXP) 0.581017 0.181505 3.201114 0.0033
D(LINF) -0.051707 0.054251 -0.953110 0.3484
D(LTRDOPEN) -0.144427 0.129124 -1.118509 0.2725

R-squared 0.364022 Mean dependent var 0.083095

Adjusted R-squared 0.210511 S.D. dependent var 0.231710
S.E. of regression 0.205882 Akaike info criterion -0.134215
Sum squared resid 1.229236 Schwarz criterion 0.214091
Log likelihood 10.48299 Hannan-Quinn criter. -0.011421
F-statistic 2.371299 Durbin-Watson stat 1.893289
Prob(F-statistic) 0.047952

SERIAL CORRELATION
Breusch-Godfrey Serial Correlation LM Test:

F-statistic 0.006123 Prob. F(1,28) 0.9382

Obs*R-squared 0.008090 Prob. Chi-Square(1) 0.9283

Test Equation:
Dependent Variable: RESID
Method: ARDL
Date: 06/11/21 Time: 14:07
Sample: 1983 2019
Included observations: 37
Presample missing value lagged residuals set to zero.

Variable Coefficient Std. Error t-Statistic Prob.

64
 D(LUNEMP(-1)) 0.031330 0.438961 0.071372 0.9436
D(LFOOD_IMP) 0.002563 0.199291 0.012862 0.9898
D(LPOPGR) -0.278676 8.793530 -0.031691 0.9749
D(LGDPGR) 0.083160 1.988135 0.041828 0.9669
D(LGEXP) 0.001578 0.292796 0.005391 0.9957
D(LINF) 0.001734 0.090077 0.019255 0.9848
D(LTRDOPEN) -0.002353 0.209966 -0.011205 0.9911
C -0.005123 0.125039 -0.040972 0.9676
RESID(-1) -0.038709 0.494676 -0.078251 0.9382

R-squared 0.000219 Mean dependent var 7.31E-18

Adjusted R-squared -0.285433 S.D. dependent var 0.292238
S.E. of regression 0.331331 Akaike info criterion 0.836376
Sum squared resid 3.073847 Schwarz criterion 1.228220
Log likelihood -6.472948 Hannan-Quinn criter. 0.974519
F-statistic 0.000765 Durbin-Watson stat 1.976813
Prob(F-statistic) 1.000000

STABILITY - RAMSEY RESET TEST

Ramsey RESET Test
Equation: UNTITLED
Specification: D(LUNEMP) D(LUNEMP(-1)) D(LFOOD_IMP) D(LPOPGR)
D(LGDPGR) D(LGEXP) D(LINF) D(LTRDOPEN) C
Omitted Variables: Squares of fitted values

Value df Probability
t-statistic 2.897595 28 0.0072
F-statistic 8.396057 (1, 28) 0.0072

F-test summary:
Sum of Sq. df Mean Squares
Test SSR 0.709248 1 0.709248
Restricted SSR 3.074519 29 0.106018
Unrestricted SSR 2.365271 28 0.084474

Unrestricted Test Equation:

Dependent Variable: D(LUNEMP)
Method: ARDL
Date: 06/11/21 Time: 14:09
Sample: 1983 2019
Included observations: 37
Maximum dependent lags: 1 (Automatic selection)
Model selection method: Akaike info criterion (AIC)
Dynamic regressors (1 lag, automatic):
Fixed regressors: C

Variable Coefficient Std. Error t-Statistic Prob.*

D(LUNEMP(-1)) -0.095385 0.164865 -0.578564 0.5675

D(LFOOD_IMP) -0.131473 0.172766 -0.760987 0.4530
D(LPOPGR) 0.298217 7.305901 0.040819 0.9677
D(LGDPGR) -1.554111 1.651113 -0.941250 0.3546
D(LGEXP) -0.117229 0.310719 -0.377281 0.7088

65
 D(LINF) -0.037648 0.080268 -0.469032 0.6427
D(LTRDOPEN) 0.024804 0.198115 0.125201 0.9013
C 0.012519 0.103269 0.121231 0.9044
FITTED^2 5.134699 1.772056 2.897595 0.0072

R-squared 0.391412 Mean dependent var 0.049755

Adjusted R-squared 0.217529 S.D. dependent var 0.328570
S.E. of regression 0.290644 Akaike info criterion 0.574338
Sum squared resid 2.365271 Schwarz criterion 0.966183
Log likelihood -1.625259 Hannan-Quinn criter. 0.712482
F-statistic 2.251015 Durbin-Watson stat 2.053134
Prob(F-statistic) 0.053685

*Note: p-values and any subsequent tests do not account for model
selection.

66