THE NEXUS BETWEEN UNEMPLOYMENT AND OUTPUT GROWTH IN NIGERIA 1980-2011

THE NEXUS BETWEEN UNEMPLOYMENT AND OUTPUT GROWTH IN

NIGERIA (1980-2011)

BY

(OLIYIDE PAUL AZEEZ)10EA000787

A RESEARCH PROJECT SUBMITTED IN PARTIAL FULFILMENT

OF BACHELOR OF SCIENCE B.SC (HONS) DEGREE IN THE

DEPARMENT OF ECONOMICS, COLLEGE OF BUSINESS AND

SOCIAL SCIENCES.

LANDMARK UNIVERSITYOMU-ARAN

1

MAY, 2014

ABSTRACTThis paper critically examines the nexus between

unemployment and output growth in Nigeria from 1980 to 2011

using secondary time series data. The data were collected

from National Bureau of Statistics (NBS), IMF, World

Economic Outlook (WEO), Journals and CBN Statistical

Bulletin. The Johansen co-integration rank technique was

used to test for co-integration in an attempt to investigate

the long run relationship among variables and the Vector

Error Correction Model (VECM). Also, the variance

decomposition test was used to explain the percentage of

each of the explanatory variables used on the dependent

variable. The result of the Johansen co-integration test

showed an evidence of long run relationship among the

variables. The empirical findings of the VECM from the short

run model shows that a period lag GDPG, INF, FDI and two

periods lag GDPG, INF, and FDI significantly influence

current GDPG. Empirical findings from the variance

2

decomposition of GDPG indicate that in the first period GDPG

explains 100% of the total variation in itself and in the

10th period, GDP only explains 36.1% of the total variation

in itself. Further evidence revealed that the percentage of

unemployment to GDPG in the 10th period is 0.86%, inflation

explained 6.79% of the GDPG and foreign direct investment in

the 10th period explained 15.82% of the GDPG. Hence the

study recommended that, government should ensure that

available human resources are used as effective agents of

growth and modernization through general mobilization and

purposeful motivation. It also recommended that programs of

integrated vocational training and re-orientation of

economic activity towards self-employment and self-reliance

should be encouraged in order to minimize the unemployment

problem in Nigeria.

CHAPTER ONE

INTRODUCTION

1.1 BACKGROUND OF THE STUDY

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Nigeria’s unemployment is seen to have been a threat to its

development, security and peaceful co-existence of the

economy, being that Nigeria is made up of diverse entities

from different cultural and religious backgrounds most of

whom have shown differences in political, cultural, economic

and religious understanding and accommodation emanating from

concerns of abuse of power, resource allocation,

mismanagement, political discrimination, negligence and

corruption among others.

However, Tairu Bello (2003) from time immemorial argued that

the subject of unemployment has always been an issue of

great concern to the economists, policy makers, analyst and

economic managers alike; giving the devastating effect of

this happenings on individuals, the society and the economy

at large.

The examined facts on the performance of the Nigerian

economy in 1980 shows that inflation was 10 per cent while

the growth of GDP stood at 4.2 percent (higher

4

than the growth rate of the population). The rediscount rate

was 6 percent while that of unemployment was 6.4 per cent.

Also, other major findings of the study shows that the

economy grew by 55.5 percent between 1991 and 2006; and the

population increased by 36.4 percent. All things been equal,

this should have resulted to a decrease in the rate of

unemployment but rather, unemployment increased by 74.8

percent.

The study found out that the average contribution of the oil

sector to the GDP between 1991 and 2006 is 30.5 percent

while agriculture that is the main source of gainful

employment in the country contributed 36.7 percent just a

difference of 6.1 percent from that of oil that employs less

than 10 percent of the labour force. (Asoluka and Ihugba,

2011).

From 2000-2011, there was some attempt to manage the economy

better: inflation rate though double digits, was within the

5

accepted threshold for Nigeria, and though the economy grew

by almost 7 per cent, unemployment was consistently high

from 21.10 per cent in 2010 to 23.9 per cent in 2011 of

which the high rate of unemployment resulted in output loss,

indicating that Nigerian economy was producing below full

potential (NBS, 2011).

1.2 STATEMENT OF THE PROBLEM

According to the ILO (International Labour Organization),

over 900 million persons are living below the $2 a day

poverty line. It is estimated that 456 million workers

around the world are living in extreme poverty (below $1.25

a day).

The intensity of unemployment within the global economy can

be seen from the fact that unemployment rate is steadily

increasing each year in line with the increase in population

of about more than 160 million people.

However, between the ages 16 and 64, statistics showed that

Nigeria, compared to other countries of Africa has the

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largest segment of youth unemployment. In absolute terms; it

is estimated presently that about 22 million youths are

unemployed in Nigeria out of the 60 million people available

for work (NBS, 2011).

The study discovered that much of the ‘open’ unemployment

observed in Nigeria is due to structural factors such as the

nature of the educational system and its interface with the

labor market, technological change, permanent shifts in the

demand for goods and services and the skill content of the

labor force.

Therefore, the problem statement of the study is to look at

the following:

a)The causes of unemployment in Nigeria?

b)How has unemployment affected economic growth and

basically, the development of the country?

c)What are the effects or consequences of unemployment

in Nigeria?

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d)Have there been measures in the past towards reforming

unemployment in Nigeria? If there has been, how

effective have they been?

1.3 OBJECTIVES OF THE STUDYThe broad objective of the project is to examine the nexus

between unemployment and output growth in Nigeria.

The specific objectives of this study are to:

investigate the relationships that exist between

unemployment and output growth during the period under

review.

examine the casual relationship that exist between

unemployment and real GDP.

examine the major current causes of unemployment in

Nigeria, and its consequences on real GDP.

evaluates the various economic and manpower policies

and programmes that have been adopted by the Nigerian

government to tackle the nation’s chronic unemployment

problem.

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1.4 SIGNIFICANCE OF THE STUDYThe study tends to evaluate the relationship between

Unemployment and Output growth in Nigeria from 1980-2011. It

is believed that the research will be of great importance to

various policy makers, create jobs for job researchers,

analyst, economist, economic managers and the economy at

large that are willing to carry out further research on the

topic.

1.5 RESEARCH QUESTION

1. How has unemployment being a major problem in the

Nigerian economy?

2. How can unemployment be reduced and what are the

measures to be taken in order to have effective output

growth in the economy?

3.What are the causes of unemployment in Nigeria?

4.What are the effects or consequences of unemployment in

Nigeria?

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1.6 STATEMENT OF HYPOTHESIS

H0 Null - There is no significant relationship between

Unemployment and output growth.

H1 Alternative - There is significance relationship between

Unemployment and output growth.

1.7 SCOPE OF THE STUDY

The scope of this study covers the relationship between

unemployment and output growth in Nigeria from 1980-2011.

This reviewed year will give us 31 years impact to the

economy. The data used for this study is obtained mainly

from secondary sources which include Central Bank

Statistical Bulletins, Journals, etc. These data are mainly

restricted to the Nigerian economy.

1.8 ORGANIZATION OF THE STUDY

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The paper is organized as follows. Following the

introductory section in Chapter 1, Section 2 reviews the

literature. The methodology of the study is discussed in

Chapter 3. An econometric analysis of the relationship

between the persistent unemployment crises on economic

growth in Nigeria is considered in Chapter 4 and finally,

Chapter 5 presents the summary and conclusions of the paper.

1.9 LIMITATION OF STUDY

The research work was limited to secondary data provided by

the National Bureau of Statistics (NBS), IMF and CBN Statistical

Bulletin which might be inadequate to capture the reality of

the relationship between unemployment and output growth

because of the weak and unreliable data in Nigeria. Also the

research work was limited to financial constraints coupled

with the fact that other academic work were in progress.

1.10 OPERATIONAL DEFINITIONS OF TERMS

Unemployment: The unemployment rate can be defined as the

number of people actively looking for a job divided by the

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labour force. Changes in unemployment depend mostly on

inflows made up of non-employed people starting to look for

jobs, of employed people who lose their jobs and look for

new ones and of people who stop looking for employment.

Economic Growth: This is an increase in a country’s real

level of national output or an increase in the market value

of the goods and services produced by an economy over time.

It is conventionally measured as the percent rate of

increase in real gross domestic product, or real GDP.

Gross Domestic Product (GDP): This is the total market

value of the goods and services produced by a nation’s

economy during a specific period of time. It includes all

final goods and services. That is, those that are produced

by the economic resources located in that nation regardless

of their ownership and that are not resold in any form.

Inflation: This is a rise in the general price level of

goods and services over a period of time in an economy.

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Foreign Direct Investment: This are the net inflows of

investment to acquire a lasting management interest (10

percent or more of voting stock) in an enterprise operating

in an economy other than that of the investor. It is the sum

of equity capital, reinvestment of earnings, other long-term

capital, and short-term capital as shown in the balance of

payments. This series shows net inflows (new investment

inflows less disinvestment) in the reporting economy from

foreign investors. It stimulates domestic investment and

facilitates improvements in human capital and institutions

in the host countries.

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CHAPTER TWO

LITERATURE REVIEW

2.1 INTRODUCTIONThe literature discloses many studies on the relationship

among unemployment, real GDP per capita, inflation and

foreign direct investment in Nigeria at different time

periods. This part is designed to review the past studies

which demonstrate the relationship between unemployment;

real GDP per capita, inflation, economic growth.

2.2 LITERATURE REVIEW AND CONCEPTUAL FRAMEWORK

2.2.1 CONCEPT OF UNEMPLOYMENT

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Globally, unemployment is seen as a great problem to

economic development. In recent years, both the developed

and developing countries have witnessed this problem, though

the developed countries have been able to reduce the rate of

their unemployment. However, in developing countries,

especially in Africa, unemployment has been on a

continuously accelerating rise in the economy, culminating

in reduction of household income and living standards and

simultaneous rise in the level and incidence of poverty

(Kareem 2006).

Unemployment has also been categorized as one of the serious

impediments to economic progress. Apart from primarily

encouraging the development of youths who were denied of

legitimate means of livelihood to grow up in a culture that

encourages criminal behaviors; it represents an enormous

waste of a country's manpower resources, and generates

welfare loss in terms of lower output thereby leading to

lower income and poor well-being (Akinboyo, 1987; and

Raheem, 1993). Vandemoortele, (1991), Rama, (1998) Oladeji,

15

(1994) and Umo (1996) found that unemployment has serious

negative implications on the economies of Africa and Nigeria

in particularly. (Bakare 2012).

In 2000, the International Labour Organization (ILO)

defined the concept of unemployment as the proportion of

labour force that are available for work, but did not do

any work in the week preceding the survey period for at

least one hour. It is involuntary idleness of a person

willing to work at a prevailing rate but unable to find it.

Also, Balami (2006) conceptualized unemployment as a

situation where a worker or workers are involuntarily out

of work. This means that workers are willing and able to

work but could not find any work.

2.2.2 CONCEPTUAL CLASSIFICATION OF UNEMPLOYMENT

Many economics scholars, including Asoluka Njoku Okezie

and Ihugba(2011), Fajana (2000) and Alao (2005), Gbosi

(2005) and Awe (2013); identify the following

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classifications of unemployment, which are also experienced

in Nigeria:

1. FRICTIONAL UNEMPLOYMENT: This is a type of Unemployment

that occurs when an individual is out of his current

job and looking for another job. The time period of

shifting between two jobs is known as frictional

unemployment. This can be caused by lack of proper

information on the part of the employer and the

unemployed. The employable may remain unemployed on

account of shortage of raw materials, or break down of

machinery. Therefore, the better the economy is doing,

the lower this type of unemployment is likely to occur.

This arises demand-supply gap For example; someone

working in Kwara may leave his present job to Port-

Harcourt with the expectation of getting a higher paid

job. Unfortunately, when he got to Port-Harcourt, he

could not find a job. During this period without a job

and finding another, is being classified by labour

economists as a frictionally unemployed worker.

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2. SEASONAL UNEMPLOYMENT: this arises from seasonal

fluctuations or variations in demand for some products.

Elrenburg and Smith (1982) argued that the demand for

agricultural workers usually declines after the

planting season and remains low until the harvesting

season e.g. the sugar industry is seasonal in the sense

that the crushing of sugar-cane is done only in a

particular season.

Also, during festive periods like that of Christmas

celebrations there is an increased surge in the demand

for seasonal greeting cards, fireworks, hampers, gift

items, etc. which leads to an increase in the number of

sales personnel hired due to the high traffic of

demand.

However, once the Christmas celebration ends by early

January, demand for these commodities begin to drop

sharply, leading to a sharp fall in employment leading

to hired personnel being laid off, this is because they

18

were hired due to the rise in seasonal demand of such

commodities, however in the absence of the demand of

such product, their services become irrelevant.

3. STRUCTURAL UNEMPLOYMENT: This type of unemployment

occurs when there is a structural change in an industry

or in the economic activities of the country. It is

mostly found in the developing countries. This type of

unemployment is due to the deficiency of capital

resources in relation to their demand. It occurs when

there is a mismatch of skilled workers and occupational

vacancies in the labor market. As an economy develops

over time the type of industries may well change. This

may be because people's tastes for a particular job

have changed or it may be because technology has moved

on and the product or service is no longer in demand.

Also, a consequence of structural adjustment programmes

adopted by developing nations ranging from

privatization and commercialization of publicly owned

enterprises and deregulation policies.

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4. CYCLICAL UNEMPLOYMENT: Unemployment of this type is

caused by cyclical fluctuations in the economy. It is

normally associated with the phases of business cycle

especially the period of recession and depression. It

is also known as Keynesian unemployment. When there is

a downturn in an economy, the aggregate demand for

goods and services decreases and demand for labor

decreases. At the time of recession, unskilled and

surplus laborers become unemployed. In essence, during

the times of depression, business activity is at low

recede and unemployment increases. Some people are

thrown out of employment altogether and others are only

partially employed.

5. RESIDUAL UNEMPLOYMENT: This is caused by personal

factors such as physical or mental disability, poor

work attitudes, old age and inadequate training.

6. VOLUNTARY UNEMPLOYMENT: This is the type of unemployment

that people face because they choose to be unemployed.

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This occurs usually when individuals are bent on

getting particular types of jobs they consider more

rewarding than the existing ones. They remain

unemployed until they find what they consider lucrative

jobs.

7. DISGUISED UNEMPLOYMENT: this is an underemployment or

concealed unemployment. It is a situation that occurs

when labour is not optimally utilized. That is, labour

is underemployed (either not having enough paid work or

not doing work that makes full use of his skills and

abilities). It can be measured by the number of hours

worked per week. Most especially in Nigeria, the

official period of working time per week is forty hours

for which many workers fall short of due to non-

availability of work for them.

2.2.3 NATURE OF UNEMPLOYEMNT IN NIGERIA

Over the years, the percentage rate of unemployment in

Nigerian has been relatively high when compared with the

21

levels in the 1970’s and early 1980’s respectively. For

instance, in 1976, the unemployment rate was 4.3% and it

rose to 6.4% in 1980. By 1983, it dropped to 3.4% and by

1986 and 1987; it had mounted again to 5.6% and 6.2%

respectively (FOS, 1996).

The available data show that the Nigerian economy grew

relatively in the greater parts of the 1970s, with respect

to the oil boom of the 1970s and the excessive profits from

the oil boom encouraged wasteful expenditures in the public

sector dislodgment of the employment factor and also

distorted the revenue bases for policy planning. Asoluka

Njoku Okezie and Ihugba (2011).

Explicitly, the downturn of the economy led to the

implementation of stabilization quotas which included

restriction on exports, which caused import dependency of

most Nigerian manufacturing businesses, which in turn

resulted in Operation of many companies below their

installed capacity.

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The development led to the close down of many firms while

those that survived were forced to limit a large proportion

of their workforce thereby causing the Nigerian Government

to place an embargo on employment. These among many other

crises bring about the introduction of the structural

adjustment programme (SAP) in 1986 and the current economic

reforms under the regime of Gen., Ibrahim Babangida.

Consequently after the pre-SAP era, labour statistics

recorded that there was a decline in the rate of

unemployment. Evidence shows that unemployment rate fell

from 7.0% in 1987 to a minimal level of 3.5% in 1990 and

1992. Between 1994 and 1995 the unemployment rate fell

drastically below 2% but rose up again to 2.8% in 1996 (NBS

2012).

According to the National Bureau of Statistics, Nigerian

Unemployment Rate averaged from 2006 to 2011 is 14.6 Percent

reaching an all-time high of 23.9 Percent in December of

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2011 and a record low of 12.3 Percent in December of 2006.

By 2010, unemployment rate in Nigeria increased from 21.10

percent to 23.9 percent in 2011.

Data available suggest that unemployment rates vary by

urban-rural residence, sector, age, education and state of

residence of the labour force participants of which 17.1 per

cent of these are in the urban areas, while 25.6 per cent

are from rural areas. The study also shows that persons

between ages 0-14 years constituted 30.2 per cent; those

aged 15-64 (the economically or working class active

population) constituted 53.1 per cent, while those aged 65

years and above amounted to 16.7 per cent. (NBS)

According to the NBS, in 2011 the Labour Force stood at

67,256,090, of which 51,181,884 are employed while the

unemployed are 16,074,205. This provides a large pool of

potentials recruits by criminal gangs or terrorists for

various criminal activities be it crime, armed insurgency,

drug trafficking and vandalism.

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Furthermore, the table 1 in the appendix also shows that,

except for the 1987 rate which was slightly higher than that

of 1986, unemployment rate seem to be on the decline since

1986. The observed downward movement may be attributed

partly to the activities of the National Directorate of

Employment (NDE), Roads and Rural Infrastructure (DFRRI),

the Directorate of Food, the Peoples Bank and the Better

life for Rural Women.

Lawanson (2007) observed that unemployment rate is usually

highest among secondary school leavers, irrespective of

place of residence. For example, the Labour Force Survey of

June 1996 point out that unemployment rate was 52% among

secondary school leavers. Among non-educated people,

primary school leavers and post-secondary school graduates,

the rates were 8%, 32% and 8% respectively (FOS, 1996).

Notwithstanding, it is possible that the figures released by

the National Bureau of Statistics may not have captured all

25

the total picture of the unemployed in Nigeria, but it is

evident that unemployment rate has reached a very alarming

proportion in Nigeria.

2.2.4 CAUSES OF UNEMPLOYMENT

Various economic scholars like Tairu Bello (2003), Echebiri

(2005), Lawanson (2007), Njoku and Ihugba (2011), Okafor

(2011), Awe (2013) and Olubukola Adesina (2013) have been

able to pin point the causes of high rate of unemployment in

their various unemployment studies being experienced in the

country.

However, some of the fundamental factor that accounts for

the high rate of unemployment in Nigeria is as follows:

1. Poor Economic Growth Rate: Generally, the situation in

the country in the part of 1980’s, 1990’s and even in

this decade i.e. 21st century has been very hostile to

economic growth and development. The high level of

corruption, harsh economic policies, mismanagement of

public funds and the insecurity of the Nigerian

environment coupled with long – term absolute rule of

26

the military among other factors have dampened the

spirit of economic growth for a long time. Bello

(2013).

2. Adoption of Untimely Economic Policy Measures: this

is another crucial factor that has prompted

unemployment problem overtime (i.e.) the decease of the

small scale and cottage industries which operated in

both formal and informal sectors. Following the

introduction of the Structural Adjustment Program (SAP)

in September 1986 that ushered in deregulation,

liberalization and the devaluation program of the

domestic currency, many of the teething domestic firms

collapsed.

That resulted in the loss of many jobs and thereby

rendering many people unemployed. The negative effects

are still being felt in the country till today.

Although, these policies were designed to jump start

the growth of the economy, but given the structure of

the Nigerian economy, some of the policy packages

27

became out rightly inimical to the system due to wrong

timing.

3. Neglect of the Agricultural Sector: The agricultural

sector has been the leading provider of employment in

Nigeria especially in the 1960s and in the 1970s when

the sector provided employment for more than 65 percent

of the Nigerian population. However, unfortunately, in

the wake of oil discovery, the attention on this anchor

of the economy was gradually drawn away to the oil

sector where employment capacity is very low. The

resulting effect is the large number of job seekers who

have no place in the oil industry. Even with the

expansion of the industry, unemployment has continued

to grow at an alarming rate.

4. Imperfect Flow of Labour Market Information: Generally,

a market is a place where exchange takes place, where

demand and supply work themselves out. In every market,

there are always buyers and sellers. The labour market

is no exception. The labour market is not perfectly

28

competitive, as neoclassical economists contended.

There are imperfections in the labour market, which

eventually create the natural rate of unemployment. By

natural rate of unemployment, we mean that rate which

occurs because of imperfections and frictions in the

labour market. Labour market information is almost non-

existent in Nigeria. If it does, it is usually

unreliable and mis-leading (Gbosi, 2005).

5. Unstable Political Environment: It is important to

know that we cannot separate politics from economics. A

stable political environment usually enhances

macroeconomic stability. The political causes of

unemployment include political instability, ethnicity

problem, emigrants from neighbouring countries and the

political economy of unemployment.

6. Poor Enabling Environment: The poor economic

enabling environment that characterizes the economy

over the years has continued to pose serious challenges

to employment generation in Nigeria. This, coupled with29

poor security environment has continued to hamper

investment drives and thereby reducing the prospects of

employment generation. Many job seekers who would have

embarked on self - employment programs are unable to do

so because of the hostile production environment.

Others who make attempt are forced to wind up due to

absence of infrastructures and the overall heat of the

investment environment.

7. Wrong Impression About Technical And Vocational

Studies: The wrong impression of students about the

place of technical and vocational education also

accounts for the deteriorating state of unemployment in

Nigeria. There is an enduring societal biased attitude

against technical and vocational education. A large

number of job seekers lack practical skills that could

enhance self - employment. That is why rather than

providing jobs for others, the graduate unemployed

persons keep depending on the government and the non-

vibrant private sector for job offers. Bello (2003).

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8. Rapid Expansion of the Education System: There is a rapid

expansion of the educational system which leads to increase

in the supply of educated manpower above the corresponding

demand for them. This contributes to the problem of the

youth unemployment in Nigeria. According to the NBS (2011),

over 1.37 million students were enrolled in universities,

polytechnics and colleges of education in 2006 and another

1.98 million in 2007. Given that most courses are

completed in four or five years, many of these 3.2 million

students that enrolled in 2006 and 2007 entered the labor

force in 2010/2011. These do not include the number of

Nigerians of working age that dropped out at secondary

school level for various reasons and entered the job market

in the rural and urban areas out of the 21 million that were

enrolled in 2006 and 2007 (Olubukola S. Adesina, 2013).

2.2.5 CONSEQUENCES OF UNEMPLOYMENT

Bello (2003) argued that the consequences of unemployment in

Nigeria are very severe and threatening to the citizenry and

the economy as a whole. The unemployment episode has

31

continued to pose so many challenges to the survival of the

Nigerian nation. While some of these consequences bother

directly on the unemployed, others like epidemics are

limitless in effects. They include:

1. LOW GDP: It is an established economic reality that the

size of the workforce directly impacts a country’s GDP.

Not only does the work force produce manufactured goods

or services or agricultural produce in direct

proportion, but also brings in its wake increasing

purchasing power, which in turn, fuels economic growth.

Thus unemployment contributes to a reduction in the

potential which exists in spurring a country’s GDP.

2. ECONOMIC GROWTH: The adverse effect of high

unemployment on the domestic economy cannot be

quantified. The availability of abundant human resources

if utilized could serve as great catalyst to economic

growth but if otherwise, could exert negative influence

on the economy. The unutilized large quantum of human

resources in Nigeria due to non – availability of32

employment opportunities has continued to impede on the

prospect of growth in several ways. The resulting effect

of unemployment such as perpetration of violence and

general insecurity threatens economic growth and

development to a large extent. Therefore, rather than

being a source of growth stimulation, the army of the

unemployed remains a potential threat to the well – being

of the economy.

3. UNPRODUCTIVE LABOUR FORCE: The most direct impact on the

economy of an unproductive labour force is lost output in

terms of goods and services. With no income tax to

collect and the loss of receipts from indirect taxes such

as the value added tax, the government takes in less in

tax revenue.

4. SOCIAL CRIME AND VIOLENCE: With growing youth

unemployment, the divide between the rich and the poor

grows, resulting in social tensions which could affect

the entire fabric of a community, state and the entire

country (Niger-Delta).

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5. PSYCHOLOGICAL EFFECT: Young men and women, who have put

in a decade or two in schools and colleges, have dreams

and aspirations. These are dreams of securing satisfying

jobs following their years of struggle, meeting basic

necessities of life (food, clothing, shelter and

healthcare), graduating to a life of comfort and dignity

and, eventually, enjoying the luxuries of life. The

trauma of seeing their dreams shattered week after week,

month after month, can and does lead to deep

psychological scars that are very difficult to face at

such a young age. These can impact any individual’s self

-esteem and can lead to clinical depression.

6. DRUGS: Due to frustration as a result of not gainfully

employed, most people turn to drugs traffickers or

pushers.

7. FAMILY SUPPORT: In a country like Nigeria, where the

people retire when they are not tired and there is no

social security net, very often a family depends on a son

or a daughter graduating from school or college to take

34

up employment in order to support the entire family. When

that does not happen, the financial woes are unimaginably

sorrowful.

2.2.6 MEASURES TAKING TOWARDS RESOLVING UNEMPLOYMENT

PROBLEMS IN NIGERIA

According to Bello (2003), he gave some measures in resolving

the unemployment problems in Nigeria in one of his study on

unemployment; that realizing the grave implications of

unemployment for a developing economy, the Nigerian government

as far back as 1960s started developing measures and programs

to arrest the incidence of unemployment. As a consequence,

different administrations have instituted different programs

towards addressing the episode of unemployment.

1. THE ASHBY COMMISSION, NATIONAL MANPOWER BOARD (NMB) AND

UNEMPLOYMENT REDUCTION: An appraisal of Nigerian attempt

at systematic national development planning indicated

that “the inadequacy of administrative machinery to

provide the high level manpower implementation was a

35

contributory factor to the ineffectiveness of the

country’s first attempt at development planning.

As comprehensive national development planning became a

common phenomenon, Nigeria recognized the crucial role of

and the need for manpower development. As a result, the

Federal Government of Nigeria appointed the Ashby

Commission in 1959 to look into Nigeria’s needs in the

field of post certificate and higher education during the

two decades 1960 – 1986 (Damachi, 2001). And the

commission led to the establishment of the National

Manpower Board (NMB). The board was to be concerned with

finding solutions to the problem of manpower shortages,

manpower surpluses as indicated by unemployment and

underemployment (Damachi, 2001).

2. THE SECTORAL POLICIES: A number of sectoral policies

have been designed and implemented overtime to address

the challenging issues of unemployment in Nigeria. The

36

educational, industrial and agricultural sectors have

played leading roles in this respect.

I. EDUCATIONAL POLICY: Over the years, the educational

structure of the Nigerian labour force has

improved significantly. For instance, the National

Policy on Education of 1977 specifically made

provisions for the expansion in educational

facilities. The intention was to meet the

country’s manpower needs. (Gbosi, 2005)

At the turn of events in the educational sector in

1982, the sector commenced the implementation of the

6 - 3 - 3 - 4 system. This was designed towards

skill orientation with an underlying goal of meeting

the changing needs of the economy and of course

towards self - employment generation for the purpose

of unemployment reduction.

This policy was to give premium to skill acquisition

rather than the onetime emphasis on paper

qualification. This informed the establishment of a

number of vocational and technical institutions,

37

Universities of technology and agriculture.

Although, this policy design has laudable

objectives, the realization of its goals has however

been constrained by a number of factors which

include poor equipment, funding and poor disposition

of students towards vocational and skill acquisition

study areas. (Bello, 2003)

II. AGRICULTURAL POLICY: In agricultural sector, a number

of policy designs including substantial subsidy on

agricultural facilities have been implemented with

the underlying goal of growing the sector. This is

premised on the fact that the sector has the

potential for providing mass employment for the

citizenry. (Bello, 2003).

And in order to facilitate its access to credit,

commercial and merchant banks were directed to

lend to the agricultural sector not less than 8

percent of their total credit in 1980.

Furthermore, in order to enhance the banking

system’s lending, a total of N100 million

38

Agricultural Credit Guarantee Scheme Fund was

established by the Central Bank of Nigeria (CBN)

for agricultural purposes only. (Gbosi, 2005)

III. INDUSTRIAL POLICY: the policy objective of promoting

cottage, micro, small and medium scale industries

was geared towards the provision of mass employment

in the industrial sector. A number of financial

institutions which include the Nigerian Bank of

Commerce and Industry (NBCI); Nigeria Industrial

Development Bank (NIDB); the merger of which

produced the current Bank of Industry (BOI) were

established to finance the industrial sector.

( Bello, 2003)

3. NATIONAL DIRECTORATE OF EMPLOYMENT (NDE): one of

the measures taken by the Nigerian government to reduce

unemployment is the NDE in 1986 with the policy

objective of serving as a center for training for

skills acquisition for the school leavers; guidance and

39

counseling for the teeming population of the unemployed

graduates for the overall goal of mass employment

creation. Among all other unemployment combatant

programs, the NDE seems to have recorded more results in

the training of youths for skills acquisition,

employability or even self – employment. Through its

youth training program – the National Open Apprenticeship

Scheme (NOAS) – the establishment has turned out a good

number of school leavers from the labor market. (Bello,

2003).

4. SUBSIDY RE-INVESTMENT AND EMPOWERMENT PROGRAM

(SURE-P): The issue of Petroleum Subsidy is familiar

to Nigerians. Over the years, subsidy on Petroleum

Products (Diesel, Petrol and Kerosene) have been

subjected to progressive subsidy reduction as a matter

of socio-economic necessity, whereby the price of

Diesel (AGO) is completely deregulated to zero subsidy

level.

40

The potential impact of the discontinuation of the

subsidy regime on the poor can be mitigated through

properly targeted safety net programmes including

Public works and employment schemes in the area of

Infrastructure development projects which include;

(FERMA Preventive Roads Maintenance Programme, Niger

Delta Development Projects, Roads and bridges, Rail

transport projects, ICT, Petroleum/NNPC Projects)

maternal and child health, mass transit programmes and

vocational training and skill acquisition schemes.

(Federal Ministry of Finance, 2012).

2.2.7 UNEMPLOYMENT AND ECONOMIC GROWTH

According to Walterskirchen (1999) argued that there can be

no negative relationship between economic growth and

41

unemployment of which this was a wrong argument because GDP

and unemployment are both rising in the long run.

Unemployment however responds insignificantly to the changes

in the economy in the short run and economic growth lagged

by one year also remain insignificant in the short run and

the relationship of the economic growth lagged by one year

with employment has appeared to be negative. It is evident

that employment will only increase if GDP is rising faster

than productivity.

Other things being equal, the greater the amount of goods

and services produced, the greater the labor required for

production; because economic growth and employment go hand

in hand. But there is also the notion that higher

productivity could mean fewer jobs.

According to Calmfors and Holmlund (2000) there is often a

failure to distinguish between increases in output that are

due to higher capacity utilization and those that are due to

42

long-term growth. Labour-market reforms that lower wage

costs and thus increase employment will, of course, also

cause output to grow during the adjustment process.

The output increase will be reinforced with a lag by

increases in the capital stock, because investment will be

more profitable when the return to capital increases. This

leads to increases in labour productivity and to further

increases in labour demand. The adjustment process continues

until the return to capital has been restored to its

original level. The increase in the capital stock means that

labour-market reform will reduce real wages much less or not

at all in the long run than in the short run. (Bean, 1998).

According to Njoku and Ihugba (2011), he argued that there

is a hypothesis that regulation and taxation discourage the

operation of business and also reduce the demand for labor. It

may have an especially strong impact on smaller businesses,

since they have less access to capital markets than larger

enterprises.

43

According to Jhingan (2000) it is theoretically argued that

given a technique of partial capital intensity, the growth

rate of output and employment will be the same. But

technical changes, which may be due to education, improved

training and better management techniques, takes place over

time. This tends to increase labour productivity. If

labour productivity then increases, fewer workers are needed

to produce the same level of output, thus causing

unemployment in the economy.

Since the use of imported, expensive and inappropriate

capital intensive machines and equipment’s cannot be put to

full capacity in such countries due to lack of technical

personnel and infrastructural facilities like power, raw

materials, transport, etc., then the average cost of

production as a result of output cannot be maximized. Thus

such capital-intensive technique leads to prevalence of

unemployment in the economy. (Lawanson, 2007).

44

Nigerian unemployment rate increased by 69 percent from 6.1 in

1980 to 19.7 in 2009 while the economy grew by 72 percent from

201,036.3 in 1985 to 716,949.7 in 2009 (see the appendix table

2 ). What this means, is that, as unemployment was increasing,

the economy was equally growing, why? The rate of unemployment

can increase as a result of increase in the population of the

country because an increase in population signals an increase

in labor supply.

The contribution of inflation to GDP between 1981 and 1984

is 20.56 and 40.91 percent respectively which drastically

declined to 10.84 percent in 2011. The increase in

employment enhanced the purchasing power of the people in

the country and as result to it, consumption increases which

leads to raise aggregate demand and hence inflation in the

country. In Nigeria the percentage of foreign direct

investment to gross domestic product was 0.96 percent in

1986 but unfortunately, it increased to 3.4 percent in 1995

and 4.51 percent in 1996. However, as at 2006, the

percentage stood at 3.34 percent. In an attempt to attract

45

foreign capital, Nigeria’s investment policies have

witnessed significant changes since the introduction of

structural adjustment programmed (SAP) in 1986.

Host countries stand to derive a lot of benefits from

foreign direct investment. In spite of such benefits, Mishra

et’ al (2001) revealed that whereas foreign direct

investment has been associated with higher growth in some

countries, it has also been associated with higher incidence

of crises.

2.3 THE THEORETICAL FRAMEWORK

Aminu Umaru, Manu Donga. and Salihu Musa (2013), in their

study on unemployment gave a vivid theoretical approach to

unemployment and output growth in Nigeria.

2.3.1 CLASSICAL GROWTH THEORY

The classical economists like Adam Smith, David Ricardo and

Mill who were the exponents of the classical growth theory

assigned the rate of investment as the main factor for

46

fostering growth. Growth is a function of the share of

profits in the national income.

There exist a positive relationship between higher rates of

profit and higher rates of growth. Higher growth is achieved

via profits effective on the rate of investment. According

to the classical economists, the increased division of

labour and specialization made possible by increase in

growth rate of capital would result in increase in both

profits and wages.

However, it is argued that such increase may trigger off

income and population growth that may lead to diminishing

returns given that land is fixed. Classical models like

Ricardian growth model emphasized the limits to growth

imposed by the ultimate scarcity of land. The major short-

comings of this theory of growth is the failure to provide

for the possibility of the role of technical progress in the

growth process. Balami (2006).

47

2.3.2 THE HARROD-DOMAR GROWTH MODEL WITH UNEMPLOYMENT

Diminishing returns to the accumulation of capital (AK),

which plays a crucial role in limiting growth in the

neoclassical model, is an inevitable feature of an economy

in which the other determinants of aggregate output, namely

technology and the employment of labor, are both given.

However, there is a class of model in which one of these

other determinants is assumed to grow automatically in

proportion to capital, and in which the growth of this other

determinant counteracts the effects of diminishing returns,

thus allowing output to grow in proportion to capital. These

models are generally referred to as AK models, because they

result in a production function of the form Y = AK, with A

constant.

An early variant of the accumulated Capital (AK) model was

Harrod-Domar, which assumes that labour input grows

automatically in proportion to capital. To see how this

48

works, suppose first that the aggregate production function

has fixed technological coefficient

Y=f(L,K)=min(AK,BL)

Where A and B are the fixed coefficients. Under this

technology, producing a unit of output requires l/A units

of capital and I/B units of labor; if either falls short of

this minimum requirement there is no way to compensate by

substituting the other input. Surplus capital or supply of

capital is more or less than (B/A) times the exogenous

supply of labour. When AK <BL, capital is the limitation

factor. Firms will produce the amount Y = A K, and hire the

amount (1/ B) Y = (1/ B) A K < L of labor. With a fixed

saving rate, the capital stock will grow according to

Thus the growth rate of capital will be:

g=KK

=sA−δ

49

K−

=sAK−δK

Because output is strictly proportional to capital, g will

also be the rate of growth of output, and g - n will be the

growth rate of output per person. In the model as just

described, an increase in the saving propensity s will raise

the rate of growth g. If output per person is rising, then

the increase in growth will not be permanent, because with K

growing faster than L, eventually the binding constraint on

output will become the availability of labor rather than the

availability of capital; beyond that point there will be no

more possibility of growth in per capita output.

But if output per person is falling, the increase in growth

resulting from an increase in saving will be permanent In

this case, diminishing returns will never set in because the

faster growth of capital will be accompanied by a

permanently faster growth of labor input, which is made

possible by the fact that there is always a surplus of

unemployed labor in the economy.

50

2.3.3 THE NEW GROWTH THEORY (ROMER, 1990)

New Growth Theory is based on a view of the economy that

incorporates two important points. First, it views

technological progress as a product of economic activity.

Previous theories treated technology as a given, or a

product of non-market forces.

New Growth Theory is often called “endogenous” growth

theory, because it internalizes technology into a model of

how markets function. Second, New Growth Theory holds that

unlike physical objects, knowledge and technology are

characterized by increasing returns, and these increasing

returns drive the process of growth. Balami (2006).

This new theory addresses the fundamental questions about

what makes economies grow: Why is the world measurably

richer today than a century ago? Why have some nations grown

more than others? The essential point of New Growth Theory

is that knowledge drives growth.

51

Because ideas can be infinitely shared and reused, we can

accumulate them without limit. They are not subject to what

economists call “diminishing returns.” Instead, the

increasing returns to knowledge propel economic growth.

New Growth Theory helps us make sense of the on-going shift

from a resource-based economy to a knowledge-based economy.

It underscores the point that the economic processes which

create and diffuse new knowledge are critical to shaping the

growth of nations, communities and individual firms.

2.3.4 THE SOLOW GROWTH MODEL

This is an economic growth model in which the growth of

total GDP is explained by population increase, technical

progress, and investment. In this model there is full

employment, with an aggregate production showing constant

returns to scale.

In evaluating the process of economic growth Balami (2006),

Solow (2002) combined the supply and demand sides of the

52

economy together to generate economic growth. He argued that

economic growth can best be understood from neo-classical

point of view (supply side) which says Q = f (AKαL1-α).

Hence, the Solow model can also be referred to as the neo-

classical growth model. He assumed that savings is a linear

function of income, that capital does not depreciate so that

investment is simply the rate of increase of capital stock,

that savings is equal to investment, and that labour grows

at an exogenous constant proportion, the rate of growth or

level of technology is exogenously given.

2.4 REVIEW OF EMPIRICAL LITERATURE

Here the empirical literature on the relationship between

unemployment and economic growth were reviewed.

Obadan (2000) conducted a granger causality test to examine

the causal relationship between gross domestic investment

and economic growth in Nigeria for the period (1975 - 1996).

The result showed that the relationship between gross

53

domestic investment and economic growth is unidirectional,

running from investment to growth. Evidence from the

findings gives credibility to the crucial role of capital

formation in the growth process.

Zagler (2000) investigated the link between growth and

unemployment of U.K. for the period of 1982-1999. Structural

change played significant role in job creation and job

destruction of an economy. Fixed effects panel regression

method was used. The result showed a robust and negative

relation between unemployment and growth. Rapid growing

economies would face structural unemployment though for a

shorter period. Unemployment could be minimized through

efficient planning and improvement in human capital.

Bakare (2010) examines the determinants of the urban

unemployment in Nigeria. The variables for include level of

unemployment and demand for labour, supply of labour,

population, inflation, capacity utilization, gross capital

formation and nominal wage rate. Using time series secondary

54

data and parsimonious error correction mechanism, the study

found that the rising nominal wages and the accelerated

growth of population which affected the supply side through

a high and rapid increase in labour force relative to the

absorptive capacity of the economy appear to be the main

determinant of high unemployment in Nigeria.

Muhammad, et al., (2011) examines the role played by

unemployment on the making of the Nigerian Gross Domestic

Product (GDP) for a period of nine years (2000 - 2008).

Using the regression analysis, findings showed that

unemployment has an enormous effect (over 65 percent) on the

making of the Nigerian GDP and there exist an inverse

relationship between the model (unemployment) and the GDP

whereas increase in the model leads to decrease on the GDP

and vice versa.

Asoluka Njoku and Okezie Ihugba (2011) investigated the

relationship between unemployment and growth in Nigeria

(1985-2009). He identified that unemployment in Nigeria has

55

been on the increase which has resulted in increase in

social vices, human capacity under-utilization; increased

poverty amongst the citizenry, social alienation and weak

purchasing power among other negativity which has far

reaching consequences on the Nigerian economy.

Also Bakere (2012) conducted a study on stabilization

policy, unemployment crises and economic growth in Nigeria.

He employed OLS to determine the relationship for the period

of 29 years (1980-2008) and found that the nexus between

inflation, unemployment and economic growth in Nigeria were

negative.

Aminu and Anono (2012) conducted a study on the relationship

between unemployment and inflation. They used OLS, ADF for

unit root, Granger causality, Johansen cointegration, ARCH

and GARCH techniques. The study revealed negative

relationship between unemployment and inflation and no

causation between unemployment and inflation; though they

56

found that there is long-run relationship between the two

phenomena in Nigeria.

Aminu and Anono (2012) also investigated the effect of

inflation on economic growth and development in Nigeria.

They employed OLS, ADF and Granger causality and found that

there is a positive correlation between inflation and

economic growth in Nigeria, though the results revealed that

the coefficient of inflation is not statistically

significant, but is consistence with the theoretical

expectation, causation runs from GDP to inflation implying

that inflation does not Granger cause GDP but GDP does.

Garba (1997) conducted a study on foreign direct investment

and economic growth in Nigeria for the period (1970 - 1994).

His results showed that the coefficient of FDI was positive

with high values, indicating the sensitivity of FDI to GDP.

In his second model, the result obtained showed a good

measure of the elasticity of foreign direct investment with

respect to change in gross domestic product.

57

Andeyangtso (2006) in his study of the effects of foreign

direct investment on economic growth in Nigeria, the

empirical results show that there is a negative relationship

between the two variables.

Chang-Shuai Li and ZI-Juan Liu (2012) conducted a study on

the relationship among Chinese unemployment rate, economic

growth and inflation; they employed Granger causality test,

unit root, cointegration, VAR and VEC model. The study

revealed that unemployment impacted negatively on growth.

The study also revealed that there is causation between

unemployment and growth.

Rafindadi (2012) conducted a study on the relationship

between output and unemployment dynamics in Nigeria; he used

OLS and Threshold model and found a negative nonlinear

relationship between output and unemployment.

The Okun’s law also postulated the existence of a specific

empirical relationship between economic growth and the

58

change in the rate of unemployment. Several cross-country

studies on the convergence of the Okun’s Law Coefficient

(OLC) have gone underway. The importance of the Okun’s law

to public policy was captured by Perman and Tareva in this

statement: “The Okun’s law relationship has important

implications for macro policy as the size of the OLC is an

important indicator of the degree of interdependence of

output and labour movements around their long-run paths and

is regarded as a benchmark for policy-makers to measure the

cost of higher unemployment”.

59

CHAPTER THREE

METHODOLOGY

3.0 INTRODUCTIONThis chapter discusses the methodology used in the project;

the model specification, the research design, estimation

techniques, a priori expectations, and data sources.

3.1 MODEL SPECIFICATIONThe variables used in this study, had been used by the

classical and the neo-classical economists to measure

economic growth. Mankiw et al. (1992) found that human

capital played significant role in neoclassical model and

endogenous growth theory. Abbas and Foreman-Peck (2010)

elaborated the role of human capital in the economic growth

of Pakistan. There is a huge literature on investigating the

relationship between employment and growth as Dollar (1992),

Sachs and Warner (1995) and Vamvakidis (2002). This study

takes a lead from the above works with slight modications.

60

The first econometric model examines the short-run and

long-run relationship between employment and output growth

by applying the ordinary least Square (OLS) method. The

properties of the data are first investigated as it has been

established that most data contain trend. If these data are

stationary, then the OLS method is used without any fear of

spurousity. But if it is not stationary, a combination of

the variables are tested to see their cointegration.

The Johansen (1988) co-integration test is used to test for

the the possible cointegration. If this is cointegrated the

associated Error Correction Model (ECM) is used for the

estimation. Also, the Granger causality test is estimated

for the variables to determine the direction of causality

amongst the variables.

However, the essence of economic modeling is to represent

the phenomenon under investigation in such a way to enable

the researcher to attribute numerical values to the concept.

To determine the relationship between unemployment and

output we specify the model as:

61

Mathematical form GDPGRt=f(UNEMPt,INFLt,FDIt ) Statistical form GDPGRt=α0+α1UNEMPt+α2INFLt+α3FDIt ……………………3.1

Econometric form GDPGRt=α0+α1UNEMPt+α2INFLt+α3FDIt+μ1 … ……………..3.2UNEMPt = Unemployment rate at time tGDPGRt = Growth rate of gross domestic product at time tINFLt = Inflation rate at time tFDIt = Foreign Direct Investment rate at time tα0 = the interception of the model α1−α3 = the coefficient of the independent variables μt = error term at time t (stochastic term) that is used to

capture other variables, that are not included in the model.

It is expected to be purely random.

3.2 TECHNIQUES OF DATA ANALYSIS

The techniques of data analysis employed for this study is

the co-integration analysis and the Vector Error Correction

Mechanism (VECM). The model examined the time series

properties of various variables using Augmented Dickey-

Fuller (ADF) test. The Johansen co-integration rank

62

technique was used to test for co-integration among other

variables. Also the Vector Error Correction Mechanism (VECM)

and the Variance Decomposition was used to carry out the

percentage of the explanatory variables on the explained

variable in other to test for their causality. These

techniques of data analysis are discussed briefly as

follows:

3.2.1 UNIT ROOT TEST

The first step involves testing the order of integration of

the individual series under consideration and it is a very

important determinant of the stationarity of a time series.

A series Xt is said to be stationary if it has a constant

mean, finite variance, and the return to mean value

equilibrium when there is disequilibrium as well as zero

order of integration I(0) , which can also take the form of Xt

(0). This implies that the series Xt does not need to be

differenced as it is stationary at levels in the form in

which the data is presented. If the series is not

63

stationary, it means that it is time dependent and it has

finite variance and therefore, the series has to be

differenced first order to achieve stationary I(1). This

implies that the series is to be integrated of order one and

can be expressed as Xt I(1) .

However in general terms, if the series needs to be differenced

times in order to achieve I(0) , then the series is said to be

integrated of order ‘n’ which can be expressed as Xt I(n) . The

study made use of Augmented Dickey-Fuller (ADF) unit root

test to test the stationarity of the variables at their

levels and their first differences due to Dickey and Fuller

(1979, 1981).

Augmented Dickey-Fuller test bank on rejecting a null

hypothesis of unit root (the series are non-stationary) in

favor of the alternative hypothesis of stationarity. The

tests are conducted with and without a deterministic trend

(t) for each of the series. The general form of ADF test is

estimated by the following regression:

64

Δyt=α0+α1yt−1+∑n=1

nαΔyi+μt

Where:

Y is a time series, tis a linear time trend, Δ is the first

difference operator, α0 is a constant, n is the optimum

number of lags in the dependent variable and μ is the

random error term.

Δyt=α0+αyt−1+μt

3.2.2 COINTEGRATION TEST

The second step is the testing of the presence or otherwise

of cointegration between the series of the same order of

integration through forming a cointegration equation. The

basic idea behind cointegration is that if, in the long-run,

two or more series move closely together, even though the

series themselves are trended, the difference between them

is constant. It is possible to regard these series as

defining a long-run equilibrium relationship, as the

difference between them is stationary (Hall and Henry,

1989).

65

A lack of cointegration suggests that such variables have no

long-run relationship: in principal they can wander

subjectively far away from each other (Dickey et. al.,

1991). The original co-integration regression can be

specified as follows:

GDPGRt=α0+α1UNEMPt+α2INFLt+α3FDIt+et ……………………3.3

To incorporate the possibility of bi-directional causality,

the reverse specification of equation (3.1) is considered.

That is:Δet=λet+wt ……………………………………………………………..3.4

In order to provide a more definite answer to the non-

stationary in each time series, the Augmented Dickey-Fuller

(ADF) regression is estimated as shown in equation (3.2) for

a unit test root.

Thus, if λ equals zero, et is non-stationary, as a resultGDPGRt and UNEMPt , INFLt , FDIt , are not co-integrated. Inother words, if λ is significantly different from zero,GDPGRt and UNEMPt , INFLt , FDIt are found integratedindividually. If λ=0 , it is equivalent to a=1 forGDPGRt=α0Gt−1+et a unit root test.

66

The null hypothesis of no co-integration is accepted if the

estimated Augmented Dickey-Fuller (ADF) test statistics is

less negative than the critical values at 1% and 5% levels

and 5% levels and vice versa.

After showing the unit root test, co-integration test was

carried out among the variables. The presences of linear

combination on non-stationary variables that are made

stationary create necessary condition for co-integration

test. The co-integration presence therefore implies that a

stationary long run relationship existed among the series.

Hence, to really assess if the long run relationship exists

among the selected variables in this study, the analysis of

multivariate co-integrated system developed and expanded by

Johansen (1990, 1992 and 1994) was adopted.

The Johansen model proposed is as follows:

Yt=a1Yt−1+b2Xt−1........................................................................................3.4.1

67

The equation 3.2 can however be expressed in first

difference form to prevent spuriousness only when a variable

cannot be identified as I(0) or I(1) which now becomes;

GDPt=α0+∑i=1

nα1tGDPt−1+∑

i=1

nα2tUNEMPt−1+Et ………………………3.4.2

UNEMPt=β0+∑i=1

mβ1tGDPt−1+∑

i=1

nβ2tUNEMPt−1+Et …………………..3.4.3

3.2.3 ERROR CORRECTION MODEL

Giving that if GDPGRt and UNEMPt INFLt FDIt are found to be

co-integrated, an Error Correction Model (ECM) is required

to be included (Granger, 1988) in the following multivariate

auto regression:

GDPt=δ0et−1+∑i=1

nα1tΔGDPt−1+∑

i=1

nα2tΔUNEMPt−1+∑

i=1

nα3tΔINFL+∑

i=1

nα4tΔFDI+μt....3.5

Where t is a linear time trend, Δ is the first difference

operator, δ0 is a constant, et−1 error correction term, n is

the optimum number of lags in the dependent variable and μt

is the random error term.

68

If I(0)t is significantly different from zero, then the

dependent variable (GDPGRT) and the independent variables

(UNEMP, INFL and FDI) will have a long run relationship

which establishes co-movement among them.

Furthermore, the ECM reveals that the changes in GDPGRt are

not only a function of lagged changes in UNEMPt , INFLt and

FDIt but also on its own lagged changes. The ECM is very

pleasing due to its ability to induce flexibility by

combining the short run and long run dynamics in a unified

system (Henry and Richard 1983).

3.2.4 CAUSALITY TEST

After the testing of the cointegration relationship, we test

for causality between output growth and the estimator

variables: unemployment, inflation and foreign direct

investment in Nigeria. If the two variables are co-

integrated, it follows that there must be Granger causality

in at least one direction of which the Vector Error

Correction model (VECM) was used to test the causality in

the co-integrating equation.

69

A Vector Error Correction (VEC) model is a restricted Vector

Auto Regression Analysis (VAR) that has co-integration

restriction built into the specification, so that it is

designed for use with non-stationary series that are known

to be co-integrated in the equation. Its (VEC) specification

restricts long run behavior of the endogenous variables to

converge to their co-integrating relationships (error

correction) while allowing a wide range of short run

dynamics.

3.3 APRIORI EXPECTATION

The GDPGRT is expected to have an inverse relationship with

Unemployment and Inflation while the GDPGRT is expected to

have a positive relationship with Foreign Direct Investment

(FDI) .

These expected relationships can be represented symbolically

below:

Unemployment

70

The apriori expectation will be negative because output and

unemployment are inversely related. i.e.∂GDPt

∂UNEMPt=f'(UNEMP)<0

(Negative)

Inflation

The apriori expectation is negative because a change in

inflation will affect the output growth. i.e.∂GDPt∂INFt

=f'(INFL )<0 (Negative)

Foreign Direct Investment

The apriori expectation is positive because a change in FDI

will increase the output growth. i.e.

∂GDPt∂FDIt

=f'(FDI)>0

(Positive)

3.4 SOURCES OF DATA

The model was estimated using secondary data from a yearly

time series data for the period of thirty one years from1980

to 2011 sourced from the National Bureau of Statistics (NBS),

IMF, World Economic Outlook (WEO), Journals and CBN Statistical

Bulletin. The study utilized the yearly dataset on the

percentage rate of unemployment, percentage rate of real GDP

71

(constant basic prices), percentage rate of Inflation and

percentage rate of foreign direct investment.

CHAPTER FOUR

RESULTS AND DISCUSSION

4.1 INTRODUCTION

This chapter reports the results of empirical tests. The

results of the various empirical test conducted in the study

are presented, which include the Augmented Dickey-Fuller

(ADF) unit root test to determine the time series features

of the various variable, co-integration test, ECM and the

causality test. The variables include real gross domestic

product, unemployment, inflation and foreign direct

investment.

72

The chapter draws out the major interpretations from the

results and discusses the policy implication of the

findings.

4.2 EMPIRICAL ANALYSIS AND PRESENTATION OF RESULTS

4.2.1 Unit Root TestsS/No. Variabl

esADFStatistics with constantat levels

Criticalvalue at5%

ADFStatistics with constantat first difference

Criticalvalue at5%

Orderofintegration

1 UN -

0.25380

6

-2.960411

-6.586346

-2.963972

I(1)

2 GDPG -4.190425

-2.960411

I(0)

3 INF -4.928897

-2.960411

I(0)

4 FDI -4.048045

-2.960411

I(0)

Source: Eviews’ 7.1 computation (see appendix)

73

In the table above, Augmented Dickey Fuller (ADF) test of

unit roots was conducted for all the variables employed in

the study. The ADF statistics indicates that GDPG, INF and

FDI are stationary at level; whereas UN is stationary at

first difference.

4.2.2 Johasen Cointegration Test

Included observations: 30 after adjustmentsTrend assumption: Linear deterministic trendSeries: GDPG UN INF FDI Eigen Value Trace

Statisics

5%

Critical

Value

Hypothesiz

ed No. of

CE.

Prob*

 0.646866 58.97989  47.85613 None *  0.003

20.483546 27.75270  29.79707 At most 1 0.0845 0.231394 7.929615 15.49471 At most 2  0.473

00.001143  0.034306  3.841466 At most 3  0.853

0Trace test indicates 1 cointegrating equation at the 5% levelThe result of the Johansen cointegration test shows an

evidence of long run relationship among the variables. Trace

statistics established one cointegrating equation at 5%

level.

74

The normalized equation will therefore be presented thus;

GDPG=-0.598148UN+0.645638INF+0.513404FDI(0.16289) (0.645638 ) (0.58797)

From the normalized equation, it is being shown that INF and

FDI have a positive influence on GDPG, while UN have a

negative influence on GDPG. A unit increase in these

variables will bring about a reduction (decrease) in GDPG.

4.2.3 VECM Result

Long Run EquationGDPG=−12.20−0.52UN(−1)+0.72INF(−1)+1.77FDI(−1)

[−4.2956 ]*** [4.5492]*** [3.3850 ]***The coefficients of the long run VECM clearly show that the

variables of interest share a long run relationship. Using

the t-statistics it was established that UN, INF and FDI

significantly influence GDPG in the long run. In order to

tie the short-run behavior of the GDPG to its long-run

value, we shall express the relationship between GDPG and

the independent variables as an Error Correction Term (ECT).

Short Run ModelDependent Variable: GDPGVariables Coefficient S.E T-StatisticsC 0.7969 0.7445 (1.0704)D(GDPG(-1)) 0.3965 0.1523 (2.6030)***D(GDPG(-2)) 0.2932 0.1241 (2.3638)**D(UN(-1)) -0.1709 0.2607 (-0.6556)

75

D(UN(-2)) -0.4133 0.2636 (-1.5681)D(INF(-1)) 0.8786 0.1188 (7.3958)***D(INF(-2)) 0.4053 0.1007 (4.0267)***D(FDI(-1)) 1.1235 0.3953 (2.8424)***D(FDI(-2)) 0.1261 0.3841 (0.3282)ECT -0.9690 0.1964 (-4.9341)***

R-squared 0.8083

75 Akaike AIC

 5.734401

 Adj. Rsquared

 0.717605

 Schwarz SC

 6.205883

Note: (***), (**), (*) indicates 1%, 5% and 10% significance

level respectively

Empirical findings from the short run model shows that a

period lag GDPG, two periods lag GDPG, one period lag INF,

two periods lag INF and one period lag FDI significantly

influence current GDPG. The results indicate that a unit

change (increase) in a period lag GDPG will lead to 0.3965

unit change (increase) in current GDPG. Similarly, a unit

change (increase) in two period lag GDPG will cause 0.2932

unit change in the current GDPG.

In addition, a unit change (increase) in one period lag

inflation will increase GDPG by 0.8786 unit. A unit change

(increase) in two periods lag inflation will lead to 0.4053

unit change (increase) in GDPG. Also, a unit change

(increase) in a period lag FDI will cause current GDPG to

change (increase) by 1.1235 units.

The error correction term (ECT) is negative showing the

existence of disequilibrium. It indicated that 0.97% of the

76

disequilibrium in the previous period would be corrected in

the current period. The speed of adjustment of the model

from disequilibrium is very high.

4.2.4 VARIANCE DECOMPOSITION OF VARIABLES

Variance Decomposition of GDPGPeriod S.E. GDPG UN INF FDI

 1 3.724241

 100.0000

 0.000000

 0.000000

 0.000000

 2 4.647441

 78.10186

 6.048350

 11.11619

 4.733605

 3 5.733785

 60.30596

 5.430994

 15.52974

 18.73331

 4 7.800681

 34.69689

 19.59727

 30.37568

 15.33016

 5 8.562808

 35.81799

 19.58399

 26.57246

 18.02556

 6 9.002342

 37.37009

 19.35310

 24.05337

 19.22344

 7 9.427502

 37.54692

 20.23058

 22.94642

 19.27608

 8 10.04014

 36.37849

 21.14585

 22.61857

 19.85709

 9 10.55566

 36.08801

 21.46711

 21.61918

 20.82570

 10 10.99750

 36.08617

 21.84056

 20.73598

 21.33729

Empirical findings from the variance decomposition of GDPG

indicate that in the first period GDPG explains 100% of the

total variation in itself. Overtime, this percentage

77

decreases. In the 10th period, GDP only explains 36.1% of

the total variation in itself whereas other variables

namely; UN, INF and FDI explain 21.8%, 20.7% and 21.3% of

the total variation in GDPG respectively.

Variance Decomposition of UN Period S.E. GDPG UN INF FDI

 1 3.476346

 0.016026

 99.98397

 0.000000

 0.000000

 2 4.408070

 0.356220

 98.81158

 0.657818

 0.174386

 3 5.393291

 0.490970

 97.17495

 2.212391

 0.121687

 4 6.163743

 0.669296

 94.09347

 4.936313

 0.300919

 5 6.844226

 0.778694

 92.72750

 6.198169

 0.295638

 6 7.458899

 0.783354

 92.40410

 6.524452

 0.288092

 7 8.034237

 0.799926

 92.36336

 6.574548

 0.262166

 8 8.570871

 0.821636

 92.14408

 6.786856

 0.247431

 9 9.073459

 0.843972

 91.84993

 7.063693

 0.242405

 10 9.549973

 0.856453

 91.65848

 7.244756

 0.240309

The results of the variance decomposition of UN show that in

the first period, UN explains 99.98% of the total variation

in itself the rest is being explained by GDPG. Further

evidence reveal that in the 10th period variation a large

percentage of the variation in UN is being explained by

78

itself while the contribution of other variables namely

GDPG, INF and FDI are 0.86%, 7.24% and 0.24% respectively.

Variance Decomposition of INF

 Period S.E. GDPG UN

IN

F FDI

 1 8.009681

 3.610893

 0.043321

 96.34579

 0.000000

 2 8.821635

 6.005895

 0.858344

 92.41050

 0.725259

 3 9.578581

 8.507258

 6.101434

 82.41520

 2.976106

 4 11.15803

 6.407018

 4.938791

 86.27722

 2.376975

 5 12.52777

 6.199835

 3.993525

 87.05258

 2.754060

 6 13.20947

 6.736224

 3.671195

 86.07997

 3.512607

 7 13.82686

 6.846383

 3.422874

 86.26386

 3.466885

 8 14.64819

 6.719993

 3.050676

 86.79382

 3.435512

 9 15.38513

 6.748575

 2.769494

 86.85998

 3.621953

 10 16.03053

 6.792155

 2.564357

 86.90021

 3.743277

Empirical evidence from the variance decomposition of INF

suggests that in the first period INF explains 96.35% its of

the total variation. Other variables namely GDPG and UN

explain 3.61% and 0.04% respectively of the total variation

in INF. Overtime INF remains dominant in variation within

the variable. In the tenth period GDPG, UN and FDI explain

6.79%, 2.56% and 3.74% respectively of the total variation

in INF; the rest is being explained by INF.79

Variance Decomposition of FDI Period S.E. GDPG UN INF FDI

 1 1.872503

 14.79908

 0.052499

 1.743546

 83.40487

 2 2.101592

 12.43442

 2.301982

 16.41412

 68.84947

 3 2.442619

 15.75426

 1.873580

 27.12153

 55.25064

 4 2.775058

 15.25757

 1.540305

 36.95425

 46.24787

 5 3.020711

 15.28295

 1.336816

 41.18128

 42.19896

 6 3.194745

 15.43673

 1.663610

 42.03887

 40.86079

 7 3.395091

 15.67980

 1.683249

 43.46624

 39.17071

 8 3.603050

 15.69712

 1.596948

 45.50172

 37.20421

 9 3.785136

 15.73617

 1.561751

 47.01854

 35.68354

 10 3.953620

 15.82087

 1.568755

 47.94189

 34.66848

The result obtained from the variance decomposition of FDI

suggests that in the first period FDI explain 83.40% of its

total variation. Variables namely GDPG, UN and INF accounted

for 14.80%, 1.05% and 1.74% respectively of the total

variation in FDI. In the tenth period, GDPG, UN, INF

explains 15.82%, 1.57% and 47.94% respectively of the total

variation in FDI. The findings show that in the tenth

period, FDI accounted for 34.67% of its total variation.

80

81

CHAPTER FIVESUMMARY, CONCLUSION AND POLICY RECOMMENDATIONS

5.1 SUMMARY

The macroeconomic variable, unemployment is one of major

problems confronting Nigerian economy which has the

propensity to be among the complex economic and social

dimensions. Unemployment, which has a pivotal role to play

with an increase in its figures, will lead to economic

drawback and vice-versa. It should be borne in mind that the

study did not consider if the relationship between

unemployment and growth was negative or positive.

However, various studies reviewed in the literature have

come out with the result that high unemployment is and has

never been favourable to economic growth. Hence it will be

good to maintain the fact that the causality that runs from

unemployment to economic growth is an indication of

relationship showing that unemployment indeed has an impact

on growth. The inability of government to find a lasting

82

solution to this problem has affected the economic life,

economic activities and political system of the country as a

whole.

Therefore, this paper attempted to investigate the nexus

between unemployment and output growth in Nigeria through

the application of Augmented Dickey-fuller technique in

testing the unit root property of the time series and

cointegration test to test the long-run relationship between

unemployment, output growth, inflation and foreign direct

investment.

The results of the unit root revealed that all variables

except unemployment in the model are stationary at level and

unemployment at first difference, the cointegration test

shows that there is a long-run relationship between the

variables and Granger causality test of unidirectional

causality between unemployment, output growth, inflation and

foreign direct investment.

83

The findings of this study have been seen to show important

implication for economic development policy in Nigeria. For

any economy especially a developing economy like Nigeria,

there is a great and urgent need to vigorously tackle the

problem of unemployment.

Therefore, there is a need for strong institutional

collaboration for dealing with these multivariate

macroeconomic variables; unemployment, output growth,

inflation and foreign direct investment in Nigeria.

5.2 CONCLUSION

From the research carried out on the nexus between

unemployment output growth in Nigeria from 1980 – 2011, the

findings show that unemployment has significant influence on

the Gross Domestic Product, inflation rate and foreign

direct investment in Nigeria.

The situation of unemployment in Nigeria has been on the

increase which has resulted in increase in social vices,

84

increased poverty amongst the citizenry, social separation and

weak purchasing power, human capacity under-utilization among

other negativity. Although the Nigerian Government in previous

times had put in place policies and programmes which are meant

to combat this menace, but up till now these programmes have

not made much impact. It can be noted therefore that most of

the programs established in Nigeria during the course of the

fight against unemployment in the system could not achieve

their expected results. This is due to lack of adequate

commitment to the course of the programs, lack of

transparency; inadequate funding resulting mainly from

mismanagement, absence of enabling environment and among other

reasons. As a consequence therefore, rather than reducing

unemployment phenomenon, the country has witnessed an alarming

increase in unemployment incidence.

There is therefore the strong need for institutional

collaboration and improved co-ordination of policy measures

for dealing with unemployment. . While there are some

noticeable lapses, the overall policy direction for

85

employment promotion appears to be adequate, what is

required is the political will to pursue the policy measures

backed by adequate allocation of resources, adequate steps

to make the policies work as well as transparency in

programme implementation.

5.3 POLICY RECOMMENDATIONS

Based on the findings of this project, appropriate

recommendations can then be drawn given insight on how the

output growth can perform its role without necessarily

leading to low productivity due to unemployment.

However, based on the findings from this study, it shows

that there is a casual relationship between unemployment and

output growth in Nigeria, government should ensure that

available human resources are used as effective agents of

growth and modernization through general mobilization and

purposeful motivation. Special attention should be given to

the promotion of gender equality in human resource

development and in all employment practices. More also,

86

there must be need for direct policy to increase the

domestic output in order to stimulate employment. Since

more people will be employed as domestic output is

increased, increase in government public expenditure will

also stimulate aggregate demand thus producing consequent

effect of stimulating employment and enhancing both human

capital formation and optimum utilization of human

resources.

From this study also, it was impossible for aggregate demand

side of the economy be motivated without causing inflation

in an economy. Hence government has to employ policy mix so

as to put inflation under control if the gain that

government intends to achieve through the promotion of

economic growth is not to be eroded. The government should

provide enabling grounds for productive minority to operate

freely through subsidies, flexible credit facilities and

stable, sustainable electricity. If these machineries are put

in place the Nigerian economy will spring up and move along

the production possibility curve (frontier).

87

Also, the government‘s debt policy should be tilted towards

asset acquisition and capacity building both in the public

and private sectors that will serve as incentives to

productivity through the attraction of foreign direct

investment.

Government must ensure that profits made from FDI are re-

invested into the economy so as to increase the level of

production and create more room for employment. Government

at all levels should wage relentless wars against corruption

since it impedes economic growth because it reduces

investment by increasing the cost of doing business. It is

also important that more special and urgent attention must

be paid to internal security in order to give confidence to

investors since insecurity scares foreign as well as local

investors. There is also the need to put in place concrete

policies to engender a positive and competitive enabling

environment that would attract more foreign investors.

The other policy recommendations to reduce unemployment are:

88

Removal of socio- political impediments and discrimination to employment:

Job vacancies may be available but remained unfilled because

of prejudice against certain potential workers even if

qualified. Such prejudice includes religions, ethnics and

political bias.

This encourages mis-allocation of labor because the job

seeker with the “right” political, or religious or ethnic

connection gets the job whatever his or her qualification.

One way of removing non-economic impediment to employment is

by legislation aimed at preventing discrimination with a

strong political will to enforce such law.

1. Adoption of labour intensive strategy ofindustrialization:

Most manufacturing industries in developing countries are

capital intensive relying on imported technology that cannot

absorb reasonable number of local people. Indigenous

technological innovation that can encourage labor-intensive

industrialization should be encouraged. 

89

Labour market information system:

One of the major causes of unemployment is labour friction

brought about by lack of information concerning available

jobs. A labour market information system can generate all

job-related information to job seekers as well as employers

of labour. This should include existing vacancies and job

specifications, required qualifications, salaries and other

working conditions. Both private and public sector employers

should be induced to patronize the labour market information

system to reduce the duration of frictional unemployment for

more qualified job seekers.

Use of public works programme:

This is rooted in the Keynesian multiplier effect that the

creation of public work will boost aggregate demand thereby

stimulating investment and resulting to more employment.

Promotion of small and medium scale enterprises (Smes):

Small and medium scale enterprises have great potential for

employment creation. For SMEs to generate employment,

promotion of viable micro-credit schemes to overcome credit

90

problems of such enterprises are very essential. For SMEs to

generate employment, promotion of viable micro-credit

schemes to overcome credit problems of such enterprises is

very essential.

APPENDIX

Table 1: Unemployment Rates in Nigeria from 1980-2011

Year Rate

1980 5.3

1981 4.8

1982 5.4

1983 5.2

1984 6.2

1985 6.1

1986 5.3

1987 7

1988 5.1

1989 4.5

1990 3.5

91

1991 3.1

1992 3.5

1993 3.4

1994 3.2

1995 1.9

1996 2.8

1997 3.4

1998 3.5

1999 17.5

2000 13.1

2001 13.6

2002 12.6

2003 14.8

2004 13.4

2005 11.9

2006 12.3

2007 12.7

2008 14.9

2009 19.7

2010 21.1

2011 23.9Source: National Bureau of Statistics 2013

92

Table 2: Macroeconomic Indicators

Year% rate of Unemployment

% rate ofGDP

% rate of Inflation to GDP

% rate of FDI

1980 5.3 2.872 9.97 -1.151981 4.8 20.84 20.56 0.911982 5.4 -1.053 5.882 0.871983 5.2 -5.05 22.22 1.041984 6.2 -2.022 40.91 0.671985 6.1 8.323 3.226 1.711986 5.3 -8.754 6.25 0.961987 7 -10.75 11.773 2.61988 5.1 7.543 4.214 1.661989 4.5 6.467 9.02 7.91990 3.5 12.77 7.895 2.061991 3.1 -0.618 12.204 2.611992 3.5 0.434 4.575 2.741993 3.4 2.09 7.145 6.31994 3.2 0.91 7.427 8.281995 1.9 -0.307 2.732 3.841996 2.8 4.994 9.29 4.511997 3.4 2.802 10.67 4.251998 3.5 2.716 7.862 3.271999 17.5 0.474 6.618 2.892000 13.1 5.318 6.938 2.482001 13.6 8.164 18.871 2.482002 12.6 21.18 2.881 3.172003 14.8 10.34 4.031 2.962004 13.4 10.59 5.001 2.132005 11.9 5.393 7.86 4.442006 12.3 6.211 8.218 3.342007 12.7 6.972 5.413 3.642008 14.9 5.984 11.58 3.962009 19.7 6.96 12.54 5.072010 21.1 7.976 13.72 2.652011 23.9 7.19 10.84 3.62

93

Source: National Bureau of Statistics (NBS) (2010) (2011),

IMF, World Economic Outlook (WEO), CBN (2011)

UNIT ROOT TEST FOR UNEMPLOYMENTNull Hypothesis: UN has a unit rootExogenous: ConstantLag Length: 0 (Automatic - based on SIC, maxlag=7)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -0.253806  0.9209Test critical values: 1% level -3.661661

5% level -2.96041110% level -2.619160

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

Augmented Dickey-Fuller Test EquationDependent Variable: D(UN)Method: Least SquaresDate: 03/05/14 Time: 17:03Sample (adjusted): 1981 2011Included observations: 31 after adjustments

94

VariableCoefficien

t Std. Errort-Statistic Prob.

UN(-1) -0.025131 0.099018 -0.253806 0.8014C 0.811428 0.994514 0.815904 0.4212

R-squared 0.002216    Mean dependent var 0.600000Adjusted R-squared -0.032190    S.D. dependent var 2.977135

S.E. of regression 3.024672    Akaike info criterion 5.113824

Sum squared resid 265.3107    Schwarz criterion 5.206339

Log likelihood -77.26427    Hannan-Quinn criter. 5.143981

F-statistic 0.064417    Durbin-Watson stat 2.360609Prob(F-statistic) 0.801436

Null Hypothesis: D(UN) has a unit rootExogenous: ConstantLag Length: 0 (Automatic - based on SIC, maxlag=7)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -6.586346  0.0000Test critical values: 1% level -3.670170

5% level -2.96397210% level -2.621007

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

Augmented Dickey-Fuller Test EquationDependent Variable: D(UN,2)Method: Least SquaresDate: 03/05/14 Time: 17:05Sample (adjusted): 1982 2011Included observations: 30 after adjustments

VariableCoefficien

t Std. Errort-Statistic Prob.

D(UN(-1)) -1.222613 0.185628 -6.586346 0.0000C 0.753909 0.556080 1.355757 0.1860

R-squared 0.607733    Mean dependent var 0.110000Adjusted R-squared 0.593723    S.D. dependent var 4.704022

S.E. of regression 2.998336    Akaike info criterion 5.098332

Sum squared resid 251.7205    Schwarz criterion 5.191745Log likelihood -74.47498    Hannan-Quinn 5.128216

95

criter.F-statistic 43.37995    Durbin-Watson stat 1.952844Prob(F-statistic) 0.000000

UNIT ROOT TEST FOR GDPGNull Hypothesis: GDPG has a unit rootExogenous: ConstantLag Length: 0 (Automatic - based on SIC, maxlag=7)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -4.190425  0.0027Test critical values: 1% level -3.661661

5% level -2.96041110% level -2.619160

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

Augmented Dickey-Fuller Test EquationDependent Variable: D(GDPG)Method: Least SquaresDate: 03/05/14 Time: 17:07Sample (adjusted): 1981 2011Included observations: 31 after adjustments

VariableCoefficien

t Std. Errort-Statistic Prob.

GDPG(-1) -0.755642 0.180326 -4.190425 0.0002C 3.546237 1.473360 2.406904 0.0227

R-squared 0.377143    Mean dependent var 0.139290Adjusted R-squared 0.355665    S.D. dependent var 8.522767

S.E. of regression 6.841264    Akaike info criterion 6.746163

Sum squared resid 1357.284    Schwarz criterion 6.838678

Log likelihood -102.5655    Hannan-Quinn criter. 6.776321

F-statistic 17.55966    Durbin-Watson stat 1.722270Prob(F-statistic) 0.000238

96

UNIT ROOT TEST FOR INF

Null Hypothesis: INF has a unit rootExogenous: ConstantLag Length: 0 (Automatic - based on SIC, maxlag=7)

t-Statistic   Prob.*

Augmented Dickey-Fuller test statistic -4.928897  0.0004Test critical values: 1% level -3.661661

5% level -2.96041110% level -2.619160

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

Augmented Dickey-Fuller Test EquationDependent Variable: D(INF)Method: Least SquaresDate: 03/05/14 Time: 17:09Sample (adjusted): 1981 2011Included observations: 31 after adjustments

VariableCoefficien

t Std. Errort-Statistic Prob.

INF(-1) -0.911938 0.185019 -4.928897 0.0000C 9.073788 2.291427 3.959885 0.0004

R-squared 0.455849    Mean dependent var 0.028065Adjusted R-squared 0.437085    S.D. dependent var 10.18191

S.E. of regression 7.639251    Akaike info criterion 6.966817

Sum squared resid 1692.387    Schwarz criterion 7.059332

Log likelihood -105.9857    Hannan-Quinn criter. 6.996975

F-statistic 24.29402    Durbin-Watson stat 1.917905Prob(F-statistic) 0.000031

UNIT ROOT TEST FOR FDI

Null Hypothesis: FDI has a unit rootExogenous: ConstantLag Length: 0 (Automatic - based on SIC, maxlag=7)

t-Statistic   Prob.*

97

Augmented Dickey-Fuller test statistic -4.048045  0.0038Test critical values: 1% level -3.661661

5% level -2.96041110% level -2.619160

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

Augmented Dickey-Fuller Test EquationDependent Variable: D(FDI)Method: Least SquaresDate: 03/05/14 Time: 17:10Sample (adjusted): 1981 2011Included observations: 31 after adjustments

VariableCoefficien

t Std. Errort-Statistic Prob.

FDI(-1) -0.638064 0.157623 -4.048045 0.0004C 2.093585 0.571404 3.663931 0.0010

R-squared 0.361046    Mean dependent var 0.153871Adjusted R-squared 0.339013    S.D. dependent var 2.131752

S.E. of regression 1.733138    Akaike info criterion 4.000085

Sum squared resid 87.10922    Schwarz criterion 4.092600

Log likelihood -60.00132    Hannan-Quinn criter. 4.030243

F-statistic 16.38666    Durbin-Watson stat 2.249038Prob(F-statistic) 0.000351

JOHANSEN COINTEGRATION TEST

Date: 06/05/14 Time: 11:42Sample (adjusted): 1982 2011Included observations: 30 after adjustmentsTrend assumption: Linear deterministic trendSeries: GDPG UN INF FDILags interval (in first differences): 1 to 1

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05

No. of CE(s) Eigenvalue StatisticCriticalValue Prob.**

None *  0.646866  58.97989  47.85613  0.0032At most 1  0.483546  27.75270  29.79707  0.0845

98

At most 2  0.231394  7.929615  15.49471  0.4730At most 3  0.001143  0.034306  3.841466  0.8530

 Trace test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05

No. of CE(s) Eigenvalue StatisticCriticalValue Prob.**

None *  0.646866  31.22719  27.58434  0.0162At most 1  0.483546  19.82309  21.13162  0.0754At most 2  0.231394  7.895309  14.26460  0.3894At most 3  0.001143  0.034306  3.841466  0.8530

 Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

 Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):

GDPG UN INF FDI-0.187396  0.112091 -0.120990 -0.096210-0.066812  0.066095  0.156825  0.649621 0.076680 -0.015283 -0.093012  0.314038-0.022909 -0.186111 -0.025160  0.063325

 Unrestricted Adjustment Coefficients (alpha):

D(GDPG)  3.834774  2.528784 -0.943967  0.027853D(UN) -0.152031  0.293011  0.120249 -0.095195D(INF)  3.410269 -2.708914  2.940186 -0.016238D(FDI)  0.545790 -0.813592 -0.554982 -0.007095

1 Cointegrating Equation(s):

Loglikelihood -322.4691

Normalized cointegrating coefficients (standard error in parentheses)

GDPG UN INF FDI 1.000000 -0.598148  0.645638  0.513404

 (0.16289)  (0.17601)  (0.58797)

Adjustment coefficients (standard error in parentheses)

D(GDPG) -0.718622

99

 (0.19423)D(UN)  0.028490

 (0.10942)D(INF) -0.639071

 (0.29411)D(FDI) -0.102279

 (0.06522)

2 Cointegrating Equation(s):

Loglikelihood -312.5576

Normalized cointegrating coefficients (standard error in parentheses)

GDPG UN INF FDI 1.000000  0.000000  5.222773  16.16843

 (1.13665)  (3.81063) 0.000000  1.000000  7.652184  26.17252

 (1.80703)  (6.05811)

Adjustment coefficients (standard error in parentheses)

D(GDPG) -0.887575  0.596981 (0.17881)  (0.11696)

D(UN)  0.008913  0.002325 (0.11555)  (0.07558)

D(INF) -0.458083  0.203214 (0.29222)  (0.19113)

D(FDI) -0.047921  0.007404 (0.06084)  (0.03980)

3 Cointegrating Equation(s):

Loglikelihood -308.6099

Normalized cointegrating coefficients (standard error in parentheses)

GDPG UN INF FDI 1.000000  0.000000  0.000000  8.875925

 (3.34092) 0.000000  1.000000  0.000000  15.48785

 (5.09461) 0.000000  0.000000  1.000000  1.396291

 (0.76785)

Adjustment coefficients (standard error in parentheses)

D(GDPG) -0.959958  0.611408  0.020408 (0.18718)  (0.11502)  (0.19210)

D(UN)  0.018134  0.000487  0.053161 (0.12373)  (0.07603)  (0.12698)

D(INF) -0.232629  0.158280 -1.110906 (0.28583)  (0.17564)  (0.29335)

100

D(FDI) -0.090477  0.015885 -0.142007 (0.06057)  (0.03722)  (0.06216)

VECTOR ERROR CORRECTION Vector Error Correction Estimates Date: 03/05/14 Time: 17:20 Sample (adjusted): 1983 2011 Included observations: 29 after adjustments Standard errors in ( ) & t-statistics in [ ]

Cointegrating Eq: CointEq1

GDPG(-1)  1.000000

UN(-1) -0.516995 (0.12036)[-4.29558]

INF(-1)  0.722475 (0.15881)[ 4.54919]

FDI(-1)  1.769603 (0.52278)[ 3.38499]

C -12.19792

Error Correction: D(GDPG) D(UN) D(INF) D(FDI)

CointEq1 -0.969003  0.164620  0.232781 -0.220637 (0.19639)  (0.18332)  (0.42237)  (0.09874)[-4.93410] [ 0.89801] [ 0.55113] [-2.23448]

D(GDPG(-1))  0.396484 -0.089514 -0.391601  0.171614 (0.15232)  (0.14218)  (0.32759)  (0.07658)[ 2.60298] [-0.62958] [-1.19540] [ 2.24085]

D(GDPG(-2))  0.293231 -0.062139 -0.410197  0.037526 (0.12405)  (0.11579)  (0.26680)  (0.06237)[ 2.36378] [-0.53663] [-1.53748] [ 0.60164]

D(UN(-1)) -0.170898 -0.146728 -0.084355 -0.207170 (0.26069)  (0.24334)  (0.56067)  (0.13107)[-0.65555] [-0.60297] [-0.15045] [-1.58055]

D(UN(-2)) -0.413313  0.107254  0.914220 -0.151422 (0.26356)  (0.24602)  (0.56684)  (0.13252)[-1.56817] [ 0.43595] [ 1.61282] [-1.14266]

101

D(INF(-1))  0.878573 -0.076844 -0.750044  0.062048 (0.11879)  (0.11089)  (0.25549)  (0.05973)[ 7.39580] [-0.69300] [-2.93573] [ 1.03884]

D(INF(-2))  0.405319 -0.060033 -0.573406  0.023940 (0.10066)  (0.09396)  (0.21648)  (0.05061)[ 4.02668] [-0.63894] [-2.64872] [ 0.47303]

D(FDI(-1))  1.123472 -0.398955  0.027387 -0.409995 (0.39525)  (0.36894)  (0.85007)  (0.19873)[ 2.84241] [-1.08134] [ 0.03222] [-2.06309]

D(FDI(-2))  0.126053 -0.291596  0.500728 -0.150420 (0.38410)  (0.35854)  (0.82609)  (0.19312)[ 0.32817] [-0.81329] [ 0.60614] [-0.77888]

C  0.796926  0.709873 -0.586596  0.426671 (0.74450)  (0.69494)  (1.60119)  (0.37433)[ 1.07042] [ 1.02148] [-0.36635] [ 1.13984]

 R-squared  0.808375  0.132378  0.561803  0.497438 Adj. R-squared  0.717605 -0.278601  0.354236  0.259382 Sum sq. resids  263.5294  229.6146  1218.945  66.61911 S.E. equation  3.724241  3.476346  8.009681  1.872503 F-statistic  8.905760  0.322104  2.706607  2.089584 Log likelihood -73.14882 -71.15126 -95.35667 -53.20881 Akaike AIC  5.734401  5.596639  7.265977  4.359228 Schwarz SC  6.205883  6.068120  7.737459  4.830709 Mean dependent  0.284241  0.637931  0.170966  0.094828 S.D. dependent  7.008242  3.074366  9.967314  2.175833

 Determinant resid covariance (dof adj.)  30293.66 Determinant resid covariance  5581.798 Log likelihood -289.6922 Akaike information criterion  23.01326 Schwarz criterion  25.08778

VARIANCE DECOMPOSITION

 Variance

102

Decomposition ofGDPG: Period S.E. GDPG UN INF FDI

 1  3.724241  100.0000  0.000000  0.000000  0.000000 2  4.647441  78.10186  6.048350  11.11619  4.733605 3  5.733785  60.30596  5.430994  15.52974  18.73331 4  7.800681  34.69689  19.59727  30.37568  15.33016 5  8.562808  35.81799  19.58399  26.57246  18.02556 6  9.002342  37.37009  19.35310  24.05337  19.22344 7  9.427502  37.54692  20.23058  22.94642  19.27608 8  10.04014  36.37849  21.14585  22.61857  19.85709 9  10.55566  36.08801  21.46711  21.61918  20.82570 10  10.99750  36.08617  21.84056  20.73598  21.33729

 Variance

Decomposition ofUN:

 Period S.E. GDPG UN INF FDI

 1  3.476346  0.016026  99.98397  0.000000  0.000000 2  4.408070  0.356220  98.81158  0.657818  0.174386 3  5.393291  0.490970  97.17495  2.212391  0.121687 4  6.163743  0.669296  94.09347  4.936313  0.300919 5  6.844226  0.778694  92.72750  6.198169  0.295638 6  7.458899  0.783354  92.40410  6.524452  0.288092 7  8.034237  0.799926  92.36336  6.574548  0.262166 8  8.570871  0.821636  92.14408  6.786856  0.247431 9  9.073459  0.843972  91.84993  7.063693  0.242405 10  9.549973  0.856453  91.65848  7.244756  0.240309

 Variance

Decomposition ofINF:

 Period S.E. GDPG UN INF FDI

 1  8.009681  3.610893  0.043321  96.34579  0.000000 2  8.821635  6.005895  0.858344  92.41050  0.725259 3  9.578581  8.507258  6.101434  82.41520  2.976106 4  11.15803  6.407018  4.938791  86.27722  2.376975 5  12.52777  6.199835  3.993525  87.05258  2.754060 6  13.20947  6.736224  3.671195  86.07997  3.512607

103

 7  13.82686  6.846383  3.422874  86.26386  3.466885 8  14.64819  6.719993  3.050676  86.79382  3.435512 9  15.38513  6.748575  2.769494  86.85998  3.621953 10  16.03053  6.792155  2.564357  86.90021  3.743277

 Variance

Decomposition ofFDI:

 Period S.E. GDPG UN INF FDI

 1  1.872503  14.79908  0.052499  1.743546  83.40487 2  2.101592  12.43442  2.301982  16.41412  68.84947 3  2.442619  15.75426  1.873580  27.12153  55.25064 4  2.775058  15.25757  1.540305  36.95425  46.24787 5  3.020711  15.28295  1.336816  41.18128  42.19896 6  3.194745  15.43673  1.663610  42.03887  40.86079 7  3.395091  15.67980  1.683249  43.46624  39.17071 8  3.603050  15.69712  1.596948  45.50172  37.20421 9  3.785136  15.73617  1.561751  47.01854  35.68354 10  3.953620  15.82087  1.568755  47.94189  34.66848

 Cholesky

Ordering:GDPG

UN INFFDI

104

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