THE IMPACT OF ICT INNOVATIONS ON UNEMPLOYMENT

RATE IN NIGERIA: AN ECONOMETRIC ANALYSIS

BY

UMAZAYI, THOMAS DAYO

Being a research project written and submitted to theDepartment of Economics, University of Nigeria, Nsukka, in

partial fulfillment of the requirements for the award of a Bachelorof science (B.Sc) Degree in Economics

AUGUST, 2013

ABSTRACT

This research work seeks to empirically measure the impact

of ICT innovations on unemployment rate in Nigeria from 1985 -

2011. This is important because most studies with respect to the

emergence of ICT innovations are centered on the impact of ICT

innovations on production, economic growth and/or employment. An

econometric model (Classical Linear Regression Model) was

formulated using the Ordinary Least Square (OLS) estimation

technique. Data collected from both the Central Bank of Nigeria

(CBN) and the National Bureau of Statistics (NBS), revealed that

within the period under review, ICT has a statistically

significant positive impact on unemployment rate in Nigeria while

recent ICT innovations worsened the unemployment situation

causing a structural break in unemployment rate since its

emergence. The study therefore recommends that for ICT

innovations to help the Nigerian unemployment situation, it must

be fully adopted basically through appropriate policy

implementation, increased public-private capital investment and

ICT human capital development.

CHAPTER ONE

1.1 Background of the study

The challenges confronting the Nigerian economy in the 21st

Century are diverse and enormous and they have kept the economy in

the most unacceptable state owing to the fact that it is a country

abundantly blessed and enormously endowed with both human and

natural resources but whose potentials remained largely untapped

and even mismanaged, especially, in the first four decades of its

independence. Ironically, an attempt to evaluate the country’s

economic achievements underscored the scope of its misfortunes

when compared with classical examples such as Indonesia and even

Malaysia. Hence, the quest for economic development in Nigeria was

aroused.

Nigeria, since the attainment of political independence in

1960, has undergone various fundamental structural changes. These

domestic structural shifts have however not resulted in any

significant and sustainable economic growth and development.

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; the outrageous profits from the oil boom encouraged

wasteful expenditures in the public sector, dislocation of the

employment factor and also distorted the revenue bases for policy

planning. This among many other crises resulted in the

introduction of the Structural Adjustment Programme (SAP) in 1986

and the current economic reforms. The core objective of the

economic structural reform is a total restructuring of the

Nigerian economy in the face of a massive population explosion.

However, these economic and financial structure put in place have

not yielded significant results.

While development may be seen as a gradual advancement or

growth through a series of progressive change, no single

definition incorporates all of the different strands of economic

development. Typically, economic development can be described in

terms of objectives. These are most commonly described as the

creation of jobs and wealth, and the improvement of quality of

life. Summarily, the main goal of economic development is

improving the economic well being of a community through efforts

that entail job creation, job retention, tax base enhancements and

quality of life. From the foregoing, we find that employment

and/or unemployment rate (which is a function of created jobs) is

a major factor in defining economic development. 

Since the advent of the new democratic dispensation, and more

fundamentally since 2003, efforts have been at top gear (at

Federal Government level) to reverse the trend of backwardness and

lay the foundation for Nigeria to realize its potentials and join

the first world economies. This is embodied in the Vision 20:2020

and other national crusades for socio-economic rebirths. Some of

these policy initiatives include ‘National Economic Empowerment

and Development Strategy’ (NEEDS) by President Obasanjo, Seven-

Point Agenda (by President Yar’adua) and National Transformation

Agenda (by President Goodluck E. Jonathan). The National Economic

Empowerment and Development Strategy (NEEDS), for instance, was

formulated with a focus on four key objectives (poverty reduction,

employment generation, wealth creation and value re-orientation). It

is also very conspicuous in the Yar’adua’s 7-point agenda (power

and energy, food security and agriculture, wealth creation and

employmen t , mass transportation, land reform, security and

qualitative and functional education) among others.

It is an established fact that unemployment rate is a very

significant factor to be considered in the quest for economic

development in Nigeria, it is therefore rational to evaluate the

factors affecting unemployment rate globally but with particular

reference to the Nigerian situation.

Akpang-Upkong (2010), in her book titled ‘Information and

Communication Technology in Nigeria: Prospects and Challenges for

Development’, stated that since the early 1980s, information and

communication technology (ICT) has permitted people to participate

in a world in which school, work, and other activities have been

increasingly enhanced by access to varied and developing

technologies which have helped people find, explore, analyze,

exchange, and present information; most importantly, without

discrimination when efficiently used. Although ICT is not new in

Nigeria and in the world at large but as noted by CALSS (2004),

Information tide has swept over the world since the 1990’s.

According to Ajayi, et al (1994), the development of

telecommunications in Nigeria began in 1886 when a cable

connection was established between Lagos and the colonial office

in London. By 1893, government offices in Lagos were provided with

telephone service, which was later extended to Ilorin and Jebba in

the hinterland. A slow but steady process of development in the

years that followed led to the gradual formation of the nucleus of

a national telecommunications network. In 1923, the first

commercial trunk telephone service between Itu and Calabar was

established. Between 1946 and 1952, a three-channel line carrier

system was commissioned between Lagos and Ibadan and was later

extended to Oshogbo, Kaduna, Kano, Benin, and Enugu; thus

connecting the colonial office in London with Lagos and the

commercial centers in the country with local authority offices.

The main transmission medium during the pre-independence era was

unshielded twisted pair. This evolved later from rural carrier

systems on high gauge lines to line carrier systems of twelve-

channel capacity. Small- to medium-capacity systems employing VHF

and UHF radio were introduced around 1955. In the early days, the

primitive coordinate pegboard switching system was used. This

progressed through manual switchboards of different sizes, shapes,

and capacities until Strowger exchanges were installed into the

national network between 1955 and 1960 along with 116 manual

exchanges. The installation of the Strowger exchanges marked the

beginning of automatic telephone switching in Nigeria.

With the attainment of independence in 1960, Nigeria

embarked on a periodic national development plan between 1960 and

1990. Telecommunications development was featured in each of

these plans, which were usually of five-year duration. The focus

of attention during this period was the expansion of the network

to meet the needs of the fledging commercial and industrial

sector of the economy through the installation of more telephone

lines and the expansion of trunk dialling facilities and the

reconstruction and rehabilitation of the telephone equipment and

other infrastructure damaged during the civil war. In the mid-

1990s, the Nigerian National Telecommunications Network was made

up of the following elements:

Telephone Services

Telex Services

Transmission Systems including microwaves, coaxial,

optical fibre and Domsat

International Services including International

Satellite System, Submarine Cable

Since its inception in Nigeria, a little over a century ago

(Ajayi et al, 1994), it has progressed through various stages of

development from the primitive communications equipment in the

colonial days to the enormous variety of technologies available

today. Consequently, ICT has been confronted with unprecedented

opportunities and challenges. ICT has turned into the sector which

shows the best syncretization of different technologies, and is of

the greatest growth potential and fastest growth rate in global

economy.

The development of ICT has not only quickened up the pace of

economic growth and the reshuffling of industrial structure as

well as facilitating a complete change of social life, it is also

affecting governments and enterprises in terms of management

models, which has become an important component in upgrading

national competition. Its products have already come into the

daily life of the general public, whose growth in production and

consumption has turned into a major engine in promoting economic

development, social progress and the improvement of daily life.

Observably then, the impact of the ICT industry on an economy

is impressive and could be responsible for creating new investment

and employment opportunities. In India for instance, the export of

computer software as of 1999 was already in excess of U.S. $2

billion and was set to become India's largest export industry

before the end of the first decade of the 21st Century (UNCTAD,

2002). Also, with the rise of knowledge economy and the wide

application of ICT, electronic and website technology are widely

used in the big (small office and home office mushroomed in big

cities) and medium-sized cities in China. In other words, working

at home is not strange any more. In particular, it is easy to

achieve ends by means of internet for those works like

advertisement, sales and intermediary. Therefore, those employment

forms like long distance employment, independent / individual

employment and family-based employment are enjoying great room for

development. ICT has brought about the changes in employment forms

and flexible forms of employment have developed quickly in China.

Statistics show that the self-employed and employees in the

private companies in urban China in 2003 were 42 million,

accounting for 17% of the total urban employees. A sample survey

made by the Chinese Ministry of Labour and Social Security in 2002

showed that, around 30% of the organized labours in the cities are

irregular staff, accounting for 24% of the total urban employees.

These two parts mean that flexible employees accounted for 42% of

the total employee in urban China. By the end of 2003, the urban

employees in China amounted to 256 million. Therefore, the

flexible employees were more than 100 million (CALSS, 2004).

It goes without saying that one of the impediments to the

socio-economic development of the Nigerian nation is the ever-

increasing rate of unemployment. Since the attainment of

independence in 1960, the optimistic predictions about the ability

of the modern industrial sector of the country to absorb the

increasing number of urban unemployed and rural underemployed

labour force have not been realised. One of the national

objectives of the first National Development Plan was to develop

as rapidly as possible, opportunities in education, health, and

other sectors for creation of more jobs. But unfortunately the

incidence of unemployment in the country has grown deeper and

become widespread cutting across all strata and geographical

entities.

Hence, an econometric analysis of the impact of ICT

innovations on the Nigerian unemployment rate has aroused a

considerable interest among economists and policy makers and that

serves as the subject of this research work.

1.2 Statement of Research Problem

Taking a general overview of economic development history

over the world, we may find out that technological innovations and

technology renovations are processes which continually breed and

produce new sectors as well as revamp and eliminate backward

sectors. Observing from the changing structure of the three major

industries in developed countries, we see that the primary

industry and the secondary industry have obviously witnessed a

decline in percentage, while the tertiary industry have

increasingly gone up. The development of the technical service

sector has surely increased the ratio of tertiary industries and

expedited economic development models, thus shifting from mainly

relying on material production to that of information generating

and service delivery. The changing of industrial structure will

result in the conversion of employment structure (CALSS, 2004).

Hence, the development of ICT will produce either of its dual

impacts on employment. For instance, in china (being one of Asia’s

Miracle Economies), the immediate consequence of such change is

that the number of employees in the tertiary sector rose sharply

while the demand of manual labourers and non-skilled workers

continually decreased. In the new European member states and the

candidate countries (EU-10), unemployment rate was very high at

14.3%; with the emergence and adoption of ICT, ‘the situation

resembles a vicious cycle for those out of the production process

(i.e. the unemployed) and a virtuous cycle for those within it

(i.e. the employed)’, (IPTS, 2004). Although, the unemployed are

beginning to use ICT, their number constitutes less than half the

number of employed users because work is an agent, if not the main

one, in acquiring ICT skills.

Therefore, on one hand, the unemployed increase, while on the

other hand, many posts remain vacant due to shortage of competent

job-seekers; on one hand, a war scrambling for high level

professionals spreads all over the world, on the other hand, lots

of employees have to leave their previous posts and join in the

team of social relief beneficiaries.

The real question that emanate from the foregoing discussion

is:

What is the impact of ICT on unemployment rate in

Nigeria?

Is there any structural change in unemployment rate as a

result of the adoption of ICT innovations in Nigeria?

1.3 Research Objectives

The general objective of this research work is to investigate

and evaluate the impact of ICT innovations on the unemployment

condition of Nigeria. More specifically, the study seeks to:

Analyse the long-run impact of ICT on Nigeria’sunemployment rate.

Test if there is any structural change in unemploymentrate as a result of the emergence of ICT innovations in Nigeria.

1.4 Scope of study

This research work focuses on the impact of ICT innovations

on only the unemployment rate of Nigeria; and for the sake of this

research work, it will focus on the telecommunication aspect of

ICT, due to the limitations of data on ICT components in Nigeria.

It covers period of 27 years i.e. some years before its emergence

in Nigeria till date (1985-2011); in other words, the start date

is before the period when policies where put in place at the

federal level to adopt ICT innovations. This is to help us test

for structural change.

1.5 Statement of working Hypothesis

For the essence of this research work, the following

hypothesis is employed in this study:

H0(1): ICT have no significant impact on unemployment rate in

Nigeria.

H0(2): ICT innovation has not caused any significant structural

change in unemployment rate in Nigeria.

1.6 Significance of Research

The outcome of this research work will be of immense

significant relevance to students, researchers and policy makers,

alike, for no other reason but for the fact that it is high time

we harnessed and began to appreciate the opportunities accorded us

with the emergence of ICT innovations in Nigeria via research and

policy making. More so, because most studies with respect to the

emergence of ICT innovations are centred on the impact of ICT

innovations on production, economic growth and/or employment, this

work will be significantly relevant because it is centred on

unemployment rate. It will also be of great importance to the

individuals, the governments and other researchers who might still

wish to conduct further research on the topic because it will also

add to the stock of existing knowledge on what an economy stands

to benefit (or loose) with the emergence of ICT innovations when

workable policies are on ground to adopt them. Finally, it will be

of special relevance to the entire Nigerian economy because it is

centred on ‘The Nigerian Economy’.

CHAPTER TWO

LITERATURE REVIEW2.1 Conceptual Framework

UNEMPLOYMENT RATE

DemandDeficient

Unemploymen

VoluntaryUnemploymen

t

FrictionalUnemploymen

ClassicalUnemployment

StructuralUnemploymen

ICT INNOVATIONS

PrivateInvestment on

ICT

GovernmentExpenditure on

ICT

ICT OUTPUT

The population of every economy is divided into two

categories: the economically active and the economically inactive.

The economically active population (labour force) or working

population refers to the population that is willing and able to

work, including those actively engaged in the production of goods

and services (employed) and those who are not (unemployed).

According to the National Bureau of Statistics (NBS), a

person is regarded as employed if he/she is engaged in the

production of goods and services, thereby contributing to the

gross domestic product, in a legitimate manner, which is a

component of the national accounts.

There is no precise definition of unemployment as various

countries adopt definitions to suit their local priorities. In

Britain for instance, the Department of Employment accepts as

unemployed any school-leaver who is not in paid employment but who

is available for work and is capable of working (Olajide, 1981).

The Census Bureau of the United States of America accepts Lloyd G.

Reynold’s definition of unemployment as “the difference between

the amounts of labour employed at current wage and working

conditions, and the amount of labour not hired at those levels”.

According to a United Nation’s definition, the unemployed consists

of all persons who, during the reference period, were not working

but who were seeking for work for pay or profit, including those

who never worked before. According to Tejvan (2009), unemployment

is broadly grouped into five (5) types:

Demand Deficient Unemployment occurs in a recession or period

of very low growth. If there is insufficient Aggregate Demand,

firms will cut back on output. If they cut back on output then

they will employ lesser workers. Firms will either cut back on

recruitment or lay off workers. The deeper the recession, the more

demand deficient unemployment there will be. This is often the

biggest cause of unemployment, especially in a downturn. This is

also known as cyclical unemployment – referring to how

unemployment increases during an economic downturn.

Structural Unemployment is unemployment due to inefficiencies

in the labour market. It may occur due to a mismatch of skills or

geographical location. For example structural unemployment could

be due to:

Occupational immobility. There may be skilled jobs

available, but many workers may not have the relevant skills.

Sometimes firms can struggle to recruit during periods of high

unemployment. This is due to the occupational immobility.

Geographical immobility. Jobs may be available in

London, but, unemployed workers may not be able to move there due

to difficulties in getting housing etc.

Technological change. If an economy goes through

technological change some industries will decline. This is likely

to lead to structural unemployment. For example, new technology

(nuclear power) could make coal mines close down leaving many coal

miners unemployed.

Real Wage Unemployment or Classical Unemployment occurs when

wages are artificially kept above the equilibrium. For example,

powerful trade unions or minimum wages could lead to wages above

the equilibrium leading to excess supply of labour (this assumes

labour markets are competitive).

Frictional Unemployment occurs when workers are in between

jobs e.g. school leavers take time to find work. There is always

likely to be some frictional unemployment in an economy as people

take time to find a job suited to their skills.

Voluntary Unemployment occurs when workers choose not to take

a job at the going wage rate. For example, if benefits offer a

similar take home page to wage – tax, the unemployed may feel

there is no incentive to take a job.

The next category, the economically inactive population

simply refers to those without work, who are not seeking for work

and/or are not available for work as well as those below or above

the working age. Examples include housewives, full-time students,

invalids, those below the legal age for work, old and retired

persons. While the percentage of the total number of persons

available for employment at any time is known as employment rate,

the natural rate of unemployment is the average rate of

unemployment around which the economy fluctuates. In a recession,

the actual unemployment rate rises above the natural rate while in

a boom, the actual unemployment rate falls below the natural rate.

ICT stands for information and communication technology and

is defined as a “diverse set of technological tools and resources

used to communicate, and to create, disseminate, store, and manage

information. These technologies include computers, the Internet,

broad casting technologies and telephone. Okogun et al (2012).”

The World Information Technology and Services Alliance (WITSA),

defines ICT as communicational equipment and software services

required to study, plan, support and manage information systems

based on computer soft as well as hard wares. According to the

United Nations Economic Commission for Africa (1999), ICTs cover

Internet service provisions, telecommunications and information

technology equipment and services, media and broadcasting,

libraries and documentation centres, commercial information

providers, network-based information services, and other related

information and communication activities. The Commission admits

the definition as being quite expansive. It is not uncommon to

find definitions of ICTs that are synonymous with those of

information technology (IT). Drew and Foster (1994) defined IT as

the group of technologies that is revolutionising the handling of

information. It is taken to embody a convergence of interest

between electronics, computing and communication. Chowdhury (2000)

posited that ICTs encompass technologies that can process

different kinds of information (audio, video, text, and data), and

facilitate different forms of communications among human agents,

and among information systems. Duncombe and Heeks (1999) simplify

the definition by describing ICT as an “electronic means of

capturing, processing, storing, and disseminating information”.

In scope therefore, Hamid et al (2012) stated simply that

Information and Communications Technology (ICT), includes a wide

range of hardware, software and supportive knowledge.

2.2 Theoretical LiteratureMany economists have come out with various opinions about

unemployment. Their economic literatures provide many explanations

for the unemployment problem. Some causes blame the economic

systems, and others blame the unemployed workers. Still, other

theories shift the problem to external sources and shocks, or

unpredictable events, and others argue that technology and labour

market institutions are the causes of the unemployment problem.

Other theories think the deficiency in aggregate spending and

innovations are the essential factors for explaining the problem

(Adil, 2011).This sub-chapter deals with the view of these

writers. On one end, is considered that of the classical idea

which originated from Say. The other extreme is that which

originated by Keynes and is known as the Keynesian model which

encompasses many dissenters.

2.2.1 The Classical Doctrine of Unemployment.

The classical macroeconomics was the dominant system of economic

thought during the one hundred and fifty years preceding the 1930s.

The foundation for Classical Macroeconomics lies in the quantity

theory of money, Say’s law and the notion of self-regulation markets.

Say’s law and the system of self-regulating markets led classical

economists to conclude that prolonged periods of unemployment were

impossible in a competitive market economy. This theory emphasize

that the price at which any good is sold represents the wages of

labour, the interest of the capitalist, the rent of the owners of

the land and fixed resources and the profit of entrepreneur. Say’s

idea was based on the relationship among production, income and

spending. He argued that the creation of products for the market

generated amount of income equal to the value of the product

produced. If business produced products worth N10, 000.00 they

also create income equal N10, 000.00. Since the value of their money

is the same as the worth of the products, this production process

had created the income necessary to buy the goods produced hence

no unemployment. In Say’s own reasoning, he said that people

offered their labour or the product of their labour to earn income

in which it is used in consumption spending. Production he said,

generated income which is spent on production. This idea brought

about the phrase ‘production creates its own demand’ which is

today known as Say’s Law. Using this simple but logical analysis,

the causes of high national level of unemployment existed; it was

because spending for business production was insufficient to cause

business to operate at a full employment level of production. Thus

insufficient spending was identified as the cause of unemployment.

To remedy high level of unemployment, aggregate demand had to be

increased. In this analysis, disruption may arise in between

production and prices resulting in over production in one industry

and under production in another industry. Generally there will be no

unemployment since they balance out each other. Also, with the

introduction of technology, which results in the automation of

job, this will result in the displacement of workers. However, the

automation will result in wide volume of goods being produced and

will result in falling prices as many of the goods will be

demanded. This will result in employment of workers to produce more

hence, unemployment cannot occur.

Although this idea was popular, there were economists who were

opposed to it. Ricardo opposed the theory of automatic re-

absorption of displaced workers. In his principles of Political

economy, he emphasized the opinion entertained by the labouring

class that the employment of machinery is frequently detrimental

to their interest and that workers lay-off as a result of

automation of jobs could be the situation. Marx also attacked the

Say’s. He said that technological advances continually served to

transform some parts of the working population into unemployment

because of the rapidly rising employment of machinery in

the production processes.

2.2.2 Modern Orthodox Theories.

This can be seen in the work of neo-classical economists like Alfred

Marshal and others. They used the equilibrium theory of economic

system as a focal point in their analysis. They said that any

disturbance in the economy is automatically adjusted through the

price mechanism. They maintained that unemployment in one sector of the

economy will be wiped out by over employment in the other sector of

the economy that at the global look at the economy there will be no

unemployment situation in the economy. The wage theory that says that

wages tend to keep to the level that will provide the work with

only a bare subsistence and that if wages for sometime rise above

this subsistence level, it inevitably leads to an increase in the

population. This increase in the population will increase

competition amongst workers for employment and it will cause the

wages paid to the worker to fall again. This ensures that there is

an equilibrating balance at all times as the increase in one

sector is checked by decrease in another sector. However, the

modern theories to some extent do not agree with this viewpoint.

The rigidities of the economic system tend to hinder these

theories from equilibrating sometimes. The trade union who prevent

reduction of wages, monopolist who tries to wipe out competitions,

immobility of labour and capital in the economy and market

imperfection due to lack of knowledge, are facts of economic life!

Finally, the argument for the trade union that they help to

consolidate the negotiating ability of the worker help the

economic system and also that labour and capital today are not

completely immobile, they are to some extent mobile in the long

run; this also help unemployment.

2.2.3 Hobson and Under-consumption Theory

Many modern economists have taken the view that unemployment

can and did exist in an economy; amongst these economists is Hobson who

propounded the doctrine of under consumption as the cause of

unemployment. The argument that there is tendency for lost of

purchasing power required to buy what has been produced is a

feature of under consumption theory. Hobson says that if income is

equally distributed amongst the populace there is the tendency for

the rich in that population to save a large proportion of their

own income because they are already supplied with the bare

necessities of life. This income saved will be invested in plant and

machinery and raw material to produce consumer goods. This will in

turn increase the output of consumer goods without corresponding

increase in the demand for them. This result in an over production

of the consumer goods, stock holding will be very high. In the next round

of production, the entrepreneur will be force to reduce

production .Reduction in production mean less people are taken in

for production and this lead to unemployment. If this is not checked in

the next round of production still less people will be employed as

a result of less saving that resulted from the former round of

production. This leads to chronic unemployment.

2.2.4 Keynes and the Under-investment Theory

Keynes stressed under investment as the main cause of

unemployment in an economy. According Keynes, the national income

has two components, that part saved and that part consumed

(Y=C+S). He emphasized that the entire amount saved must find its

way to investment; if this is not so, it will result in chronic

unemployment; however, the liquidity preference of individual who desire

to hold fund in the form of cash as impediment. The amount of

employment in an economy will depend on the volume of the national

income. In order to maintain a high level of employment as

previously, the amount of investment should be kept as high in the

present period. This has to be followed in some instance with the

interest rate low enough to encourage saving and investment.

Keynes said that even though it is possible for saving and

investment to be equal, a level high enough to achieve full

employment equilibrium is more likely to be reached at a lower

point and demand for labour will not be enough at that point to ensure

employment of those who desire job. Some later followers of Keynes

laid emphasis on interest rate as a method of inducing investment.

They said that the decision of businessmen is more influenced by

profit expectation and also by any cost reduction, which might be

obtained from technological invention. Finally, we should bear in mind

that investment in the Keynesian sense refers to business activities

that bring in new machines, new factories, capital equipment into

the economy and not the transfer of ownership which results from

the purchase of real property already inexistence for this does not

increase employment. In all these theories, the writers believe that

unemployment cannot exist and that if it did exist, it was only for

the time being. There are others who believe that unemployment is a must

and it must come, either in the short run or on the long run.

We should bear in mind that any action that government takes

to stimulate demand in one section will have repercussion

elsewhere; this is evident in the case of government pursuit of full

employment leading to inflation and adverse balance of payments.

2.3 EMPIRICAL LITERATURE

In recent times, there have been several empirical studies on this

subject; while some of these were carried out by foreign researchers with

respect to foreign economies, others were carried out by domestic

researchers with respect to the Nigerian situation. This section

therefore seeks to review the results of some empirical studies on the

impact of ICT on unemployment rate, even though some of these findings

are either questionable or have been questioned on a number of grounds.

2.3.1 Foreign Empirical Literature

UNCTAD (2011) has indicated that Information and communication

technology has role in the creation of employment and self-

employment opportunities. Impacts can be direct, through growth of

the ICT sector and ICT-using industries and indirect through

multiplier effects. In economies increasingly dependent on ICT,

individuals will benefit by having requisite ICT skills, thereby

enhancing their opportunities for employment. Arguably, ICT can

also lead to loss of employment as tasks are automated.

In respect of the ICT sector in low-income countries,

telecommunication services might offer the greatest opportunities

for employment creation (UNCTAD, 2010). Only a small number of

developing countries have a well-developed ICT sector. For those

that do, ICT manufacturing can be significant in employment terms,

sometimes involving the poor. In China, for example, the ICT

sector provides employment to about 26 million internal migrant

workers, with evidence that a large portion of their earnings is

remitted to poor rural and remote areas. Mobile telephony

penetration is increasing dramatically in developing countries

(ITU, 2010).

Broadband penetration can increase employment in at least

three ways (Katz, 2009). The first is the direct effect of jobs

created in order to develop broadband infrastructure, the second

is the indirect effects of employment creation in businesses that

sell goods or services to businesses involved in creating

broadband infrastructure and the third is induced effects in other

areas of the economy. The second two ways can be expressed,

through an input-output model, as multiplier effects. The

relationship between broadband diffusion and employment through

these mechanisms is a causal one, although the estimate of

employment growth relies on a number of assumptions. Data are

presented for Argentina and Chile comparing regional broadband

penetration and employment growth that show a moderately positive

linear relationship.

ESCWA (2009) examined the impact of telecentres on the

economic development of poor communities. Many of the impacts were

on employment opportunities. In Egypt, survey data from 2009

indicated positive impacts accruing to IT Club members, for

example, improving ICT skills and having better job opportunities.

In Jordan, a 2007 survey-based evaluation of the impact of the

Knowledge Stations Initiative on community development showed

positive impacts, affecting males and females almost equally, and

indirect employment opportunities through better access to

microloans. In the Syrian Arab Republic, cultural community

centres have trained a large number of people and appear to have

enhanced indirect employment opportunities.

The potential impacts of IT services and ICT-enabled services

on poverty reduction include employment and its multiplier

effects. Because workers in IT services and IT industries tend to

be relatively well educated, indirect employment may be the major

employment benefit for the poor (UNCTAD, 2010).According to the

World Bank (2009), women in India and the Philippines benefit

disproportionately from employment opportunities in IT services,

with women accounting for about 65 per cent of professional and

technical workers in the Philippines, and 30 per cent in India.

Both are higher participation rates than in other service

industries.

Evidence from six Latin American countries suggests that

Internet use by individuals is associated with increased earnings

(Navarro, 2010). Controlling for factors, such as education, that

relate to wealth before Internet adoption, the study found

significant differences between salaried and self-employed

workers. For the former, there was a large and statistically

significant positive return to Internet use on earnings for all

countries except Paraguay, where the difference was large but not

statistically significant. The earnings advantage ranged between

18 per cent in Mexico to 30 per cent in Brazil and Honduras.

Results showed a positive and statistically significant effect of

use only at work and this was always greater than the return to

use only at other places, including home. However, use at work as

well as other places displayed higher returns than use only at

work. For self-employed workers, results were similar, with

Internet users having higher earnings. Difficulties controlling

for pre-existing characteristics indicate that the results show an

upper bound on the impact of Internet use on earnings.

In examining the impact of Information and Communication

Technology on Employment in Selected Organization of Islamic

Conference (OIC) Countries, Hamid et al (2012) derived their

model from the Matteucci and Sterlachini (2003) model of study

and assumed constant elasticity substitution (CES) function of

production for this purpose. . The general form of adopted CES

production function along with two variables of labor force (L)

and investment (K) is considered: Q = A[(αL)-P + (βK)-P]1/P. Where Q

indicates production; L labour force; K capital investment;

parameter A technological alterations; α and β for measuring

labour force and capital investment reactions to technological

shocks and σ = 1/(1-P) indicates the substitution elasticity of

labour force and capital investment where the parameter p varies

between 0 and 1. In order to maximize the profit of the firm and

taking into account W as expenditures for labour force and P as

price of the product, the logarithmic labour force demand

function was produced: Log L = log Q – σlog(W/P) + (σ - 1)logA.

Further, since in perfect competitive market and assuming

constant return to scale it is possible to replace factors of

production ratio L and K with the ratio of their prices (W, P),

and also substituting Y and ICT instead of Q and A, so the second

equation was easily transformed into: L = L(K/L, Y, ICT). He

however noticed that on one hand interdependence of L to K/L is

basically accepted by the theories i.e. the rate of employing is

quite affected by per capita investment and on the other hand,

the impact of ICT on labour force quality is quite justified on

the basis of their reviewed literature. Therefore the research

was finally constructed on the following model: Log (L)it = α0 +

α1log(K/L)it + α2log(Y)it + α3log(ICT)it + μit. Where L indicates

employing level; K/L indicates per capita investment; Y indicates

domestic gross product; ICT indicates information and

communicational technology expenditures; α1, α2 and α3 show the

employment elasticity’s coefficients to per capita investment,

GDP and ICT expenditures respectively. Index i indicates cross

sections and index t indicates the time series in the panel data

model of analysis.

2.3.2 Domestic Empirical Literature

According to NBS (2011), analysis of employment data for the

past 5 years (2006-2011) shows that the rate of new entrants into

the labour market has not been uniform. The rate was on the

increase from 2007 to 2009 but declined significantly from 2009 to

2010. The rate increased again from 2010 to 2011. Within the five

year period there has been an average of about 1.8 million new

entrants into the active labour market per year. The variation and

in particular, rise of new entrants into the labour market since

2007 can be attributed to several issues.

Firstly, Nigeria has added over 24 new universities, 9

polytechnics, 9 colleges of education since 2006. Similarly, over

1.37 million students were enrolled in universities, polytechnics

and colleges of education in 2006 and another 1.98million in 2007.

Given that most courses are completed in 4 or 5 years, many of

these 3.2million students that enrolled in 2006 and 2007 entered

the labour force in 2010/2011. These highlights 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.

Additionally, NBS data reveals that women are getting married

later than they used to in the past. Accordingly a sizeable number

of these women that would have gotten married and stayed out of

the labour market by being housewives are entering the labour

market pending when they get married. At the same time, due to

positive gender empowerment policies and improvement in female

education, women aren’t only getting married later but also, are

increasingly becoming more insistent on financial independence and

consequently entering the labour market and demanding more jobs

than previously.

Furthermore, the Global economic crisis resulted in a lot of

job losses globally and accordingly many Nigerians previously in

the Diaspora have returned to Nigeria and joined the labour Market

especially from 2008 which represented the year with the highest

increase in new job seekers. The global crisis also affected the

growth of disposable income in some families prompting families

with previously just one working member being forced to send other

members of the family, for example, previously housewives into the

labour market to look for work to supplement household income.

There is also an increasing trend of disinterest by the

emerging younger generation in highly labour--‐intensive work such

as agriculture and factory work in preference for white collar

jobs, resulting in many preferring to remain in the labour market

rather than take up such jobs. The culminating effect was that

based on the definition of unemployment used, and owing to factors

largely outside the control of the Nigerian government, the result

of the survey showed that the national unemployment rate increased

to 23.9% in 2011 compared to 21.1% in 2010 and 19.7% in 2009. It

is conceivable that the unemployment rate may have been a lot

worse without many of the employment generating polices of

government which has helped to curtail the rise compared to other

countries in the world where rates have risen faster than Nigeria.

The rate is higher in the rural area (25.6%) than in the urban

area (17.1%).The result of the survey shows that persons aged 0

--‐ 14 years constituted 39.6%, those aged, between 15--‐64 (the

economically active population), constituted 56.3%, while those

aged 65 years and above constituted 4.2%. Analysis of employment

data for the past 5 years shows that the rate of new entrants into

the labour market has not been uniform in the past five years. The

rate was on the increase from 2007 to 2009 but declined

significantly from 2009 to 2010. The rate increased again from

2010 to 2011. Within

The five--‐year period there has been an average of about 1.8

million new entrants into the active labour market per year.

In order to catch up with more evolved economies, Nigeria

undertook several bold initiatives over the last decade, mainly to

enhance ICT diffusion in the country. In 1999, the total private

investment was more than N50million and while in 2008

N12,500million contributing 2.90% to the total gross domestic

product in 2008 (Okogun et al, 2012). According to Ndukwe (2004),

investment in the telecommunication sector ranks second only to

the oil industry. Of all the applications of ICTs, the use of

mobile phones is on the increase in most developing countries

while internet usage is considered to rank next to phone usage,

especially in Nigeria. Specifically, ICT has successfully aided

the following sectors of the Nigerian economy: the

Industrial/Manufacturing, Education, Transportation, Tourism,

Health, Banking, Commerce, Agriculture, Government Services,

Defence, Sports, and Rural Development. ICTs played vital roles in

the enumeration of the 2006 population census in Nigeria, and the

successful hosting of the 15th National Sports Festival, 2006. It

is expected that the Network Providers will soon devote their

assistance towards research in the higher institutions of learning

in Nigeria. According to Akwani (2005), the fastest growing

employer of labour in Nigeria today is the telecom industry

(Specifically, the wireless telephone sector that provides

services to individual customers using the GSM).

Okogun et al (2012) revealed that an anti-poverty measure

introduced through the use of ICT has been able to generate

substantial amount of employment through the use of mobile phone

by many Nigerian to sustain a living. There are many call centres

in villages and towns mostly operated by people between age

distributions of between 20-29 years (38%), mostly women with

secondary/post-secondary education in Nigeria. Some of these

people run shops for the sale of Global System of Mobile (GSM)

accessories as a major form of occupation as means of self-

employment as well as a means of sustaining livelihood. Past

studies have shown that over 2,000 persons are directly employed

by GSM operators and an estimated of 40,000 Nigerians are

benefiting from indirect employment generated by GSM operators in

Nigeria (Ndukwe, 2004). ICTs have also assisted in the area of

micro-credits finance and cooperatives. Farmers are now organizing

cooperatively to manage their access to market as an alternative

to being at the mercy of powerful buyers. Credits are now easily

made available to the poor for a better quality of life through

such social groups and ICTs. Through the use of ICTs such as the

GSM telephone, transaction costs of many Nigerian who are poor

have drastically been reduced. People make calls before travelling

and for business transaction. The technology has led to increase

service innovation, efficiency and productivity, (Okogun et al,

2012).

Oye et al (2011) studied the impact of ICT on unemployment

and the Nigerian GDP. They used the econometrics (simple linear

regression) method of analysis, collecting GDP data corresponding

to that of the unemployment for the period of nine years (2000 –

2008), and found that mobile phones, which is a proxy for ICT,

have significant socio-economic impact on the country where

unemployment has shown an enormous effect (over 65%) on the

making of the GDP for the years under study.

In studying the effect of ICT investment on economic growth,

Okogun, et al (2012) modified the Pesaran and Shin (1995, 1999),

Pesaran et al. (1996) and Pesaran (1997) autoregressive

distributed lag (ARDL) framework to model the economic value of

ICT investment in Nigeria over the period 1999 to 2009. This is

specified as G = α + β1PI + γIG + λNS + ε. Where G = Gross

Domestic Product (GDP); PI = private investment in

telecommunications; IG = ICT contribution to GDP; NS = Number of

subscribers; α, β1, γ, λ are the parameters while ε is the error

term.

Having reviewed quite a few literatures by theory and a lot

with empirical content, ICT has been shown to be a core variable

affecting unemployment rate. It is therefore the aim of this study

to measure the extent to which ICT has impacted the Nigerian

unemployment rate, which most studies have failed to carry out;

thereby, contribute to the existing literature in Nigeria.

CHAPTER THREE

RESEARCH METHODOLOGY

3.1 ANALYTICAL FRAMEWORK

This section looks at the methodologies and data sources

used in measurement of ICT impacts. Various analytical techniques

have been used to measure the economic impacts of ICT at the

macroeconomic, sectoral and microeconomic (firm) level. The main

techniques are econometric modelling using regression, growth

accounting and input-output analysis. Econometric techniques

estimate parameters of a production function using a regression

model. Growth accounting attributes growth in GDP to increases in

physical inputs, such as capital and labour, and advances or

improvements in production technology (ITU, 2006). It measures

multi-factor productivity growth residually (OECD, 2001). Input-

output matrices can be used to calculate the multiplier effects

of ICT.

The research technique employed in this study is the

econometric method. The choice of the method is informed by the

nature of the study, which is impact analysis. According to

Gujarati (2009), Econometrics literally means economic

measurement. He further said that it may be defined as the

quantitative analysis of actual economic phenomena based on the

concurrent development of theory and observation, related by

appropriate methods of inference. Hence, Econometric technique

enables us to obtain the estimated parameter(s) in order to draw

conclusion with regard to findings.

According to UNCTAD (2011), the usual objective of an ICT

impact analysis is to examine the relationship between ICT and

productivity, economic growth or employment. The analysis usually

includes other determinants such as labour, non-ICT capital and,

for firm-level studies, factors such as firm characteristics,

skills and innovation. Included in ICT are the ICT-producing

sectors, often split into manufacturing and services, and ICT

diffusion, measured by ICT investment and/or use.

3.2 SPECIFICATION OF MODEL

3.2.1 Functional form of the model

To capture both objectives of this research work, the

following functional model was used:

UR = f (POPLF, ICT, INF, GE, PINV) - - - - - - - - - - - - -

- - - - - - - - - - - - - - - - (1)

Where:

UR = Unemployment rate

POPLF = Population of labour force

ICT = ICT Output (i.e. ICT’s contribution to GDP)

INF = Inflation Rate

GE = Government Expenditure

PINV = Private Investment

N.B:- A dummy, to differentiate the time before the

emergence of ICT and the time after the emergence of ICT, was

introduced; and the scope is 1985 – 2011.

3.2.2 Econometric form of the model

URt = β1 + β2POPLF + β3ICTt + β4INFt + β5GEt + β6PINVt + β7Dίt + μt- - - - - - - - - - - (2)

Where:

β1 = the intercept

β2, β3, β4, β5, β6 & β7 are the slopes or parameters of their

respective variables

μt = Random or stochastic term

Where: Dί: 1 if after the emergence of ICT i.e. 1999-2011

0 if before the emergence of ICT i.e. 1985-1998

N.B: The annual data gotten from the various secondary data

sources were made bi-annual data (data-minning). This is so as to

increase the sample size for better econometric analysis.

3.2.3 EVALUATION OF APRIORI SIGNS AND MAGNITUDE

The choice of the variables for the model has been informed

by several previous literatures and the availability of data.

Several factors have been linked with the unemployment or

underemployment dilemma facing the country. Popular among them

is:

(a) Population of labour force (POPLF): the most critical

factor and most neglected is the high population growth rate in

the labour force. As indicated in a publication titled

“Productivity and Unemployment in Nigeria”, by Mike and Ayodele,

as at 1996, an annual average of about 2.8 million fresh

graduates enter the Nigerian labour market, with only about 10

per cent of them getting employment. And as the number

accelerates, there is an increasing surplus in the labour market

because of the limited Nigerian labour absorptive capacity.

Certainly, the situation is worse now as both the number of

secondary school and university graduates is increasing.

Therefore, an increase in the population of the labour force will

lead to increase in the unemployment rate of the economy. Thus

there is a positive relationship between unemployment rate and

the population of the labour force.

(b) ICT innovations (ICT): Says, which is one of the

classical economists, said that with the introduction of

technology there would be the automation of job and that will

result in the displacement of workers. He however went further to

state that the automation will result in wide volume of goods

being produced and will result in falling prices as many of the

goods will be demanded. This will result in employment of workers to

produce more; hence, unemployment cannot occur. But Ricardo and Marx, who

are both classical economists, opposed the theory of automatic re-

absorption of displaced workers stressing that technological

advances continually served to transform some parts of the

working population into unemployment because of the rapidly

rising employment of machinery in the production processes.

Schumpeter (1934) does not provide explicitly a theory of

unemployment but his theory of the business cycle does

demonstrate clearly how unemployment can be reduced. Innovation,

which creates more jobs relative to job destruction, is the basic

force beyond the increases in employment and the decreases in

unemployment. When entrepreneurs innovate something new such as

the production of a new product, the finding of a new market, the

finding of a new method of production, and the introduction of

new technologies and a new organization they increase investments

to materialize those innovations. Domestic investment

expenditures will increase demand on economic resources and will

increase their prices. Labour and materials will be employed to

produce the new items. Consequently, wages will be increasing and

unemployment will be declining, assuming that employment creation

will outweigh employment destruction due to the new innovations.

It is therefore true, both in theory and in practice, that

technological advancement/innovation could both result in

increased unemployment and mass employment; even though the

former comes before the latter (ceteris paribus). Hence, both

government expenditure on ICT (ICTGE) and private investment on

ICT (ICTPI), both of which measures the impact of ICT innovations

on unemployment rate, will have positive and negative

relationship with unemployment rate (as expected).

In respect of the ICT sector in low-income countries,

telecommunication services might offer the greatest opportunities

for employment creation (UNCTAD, 2010). Hence telecommunications

will serve as a proxy for ICT. More so, quality data for other

components of ICT in Nigeria are not available. Also, due to lack

of data on ICT innovations, data of investments on ICT were used

because according to Schumpeter (1934), when entrepreneurs

innovate something new, they increase investments to materialize

those innovations: investment has a positive linear relationship

with innovation. Both government expenditure on ICT (ICTGE) and

private investment on ICT (ICTPI) are inclusive in capturing total

investment on ICT in Nigeria; because the Nigerian

Telecommunication sector has been privatised. However, because of

the inavailabity of comprehensive data on both government

expenditure and private investment on ICT, ICT’s contribution to

GDP (i.e. ICT output) will be used. In their work, Okogun et al

(2012) found that as investment on ICT increase, its contribution

to GDP also increases. It should be noted however that ICT’s

contribution to GDP is a function of both government expenditure

on ICT and private investment on ICT.

(c) Inflation rate (INF): the Phillips curve examines the

relationship between unemployment rate and the rate of increase

in money wages. According to Jhingan (2007), Phillips derived the

empirical relationship that when unemployment is high, the rate

of increase in money wage rate is low; basing his analysis on

data for the United Kingdom. This is because workers are

reluctant to offer their services at less than the prevailing

rates when the demand for labour is low and unemployment is high

so that wage rates fall very slowly. Another factor, according to

him, which influences the inverse relationship between money wage

rate and unemployment, is the nature of business activity. In a

period of rising business activity when unemployment falls with

increasing demand for labour, the employer will bid up wages.

Conversely in a period of falling business activity when demand

for labour is decreasing and unemployment is rising, employers

will be reluctant to grant wage increases. They will rather

reduce wages. But workers and unions will also be reluctant to

accept wage cuts during such periods. Consequently, employers are

forced to dismiss workers, thereby leading to high rates of

unemployment. Thus when the labour market is depressed, a small

reduction in wages will lead to large increase in unemployment

(Jhingan, 2007). Note that ‘increase in wages’ here refers to

inflation; assuming that prices would change (increase) whenever

wages rose more rapidly than labour productivity.

(d) Government expenditure and Private Investment: these

both capture the total investments in an economy. From

government’s expenditure, individuals get their income and from

their income, they save; from their savings, they invest. From

the theoretical literature, Keynes stressed under investment as

the main cause of unemployment in an economy. Under investment

here could mean low government expenditure or low private

investment or both. According to Keynes, (national) income has

two components, that part saved and that part consumed (Y=C+S).

He emphasised that the entire amount saved must find its way into

investment; if this is not so, it will result to chronic unemployment.

In order to maintain a high level of employment as previously,

the amount of investment should be kept high in the present

period. Hence there is an expected negative relationship between

unemployment and both government expenditure and private

investment.

All these are summarized in the table below:

VARIABLES APRIORI SIGNS

POPLF +

ICT +/-

INF _

GE _

PINV _

3.3 EVALUATION TECHNIQUES

We employed first and second order tests to enable us

examine the economic implication of the models with respect to

apriori expectation. It is also to reveal if the estimated

parameters are theoretically meaningful and/or statistically

satisfactory (Madueme, 2010).

3.3.1 Statistical tests (First order)

They are as follows:

i. The student t-test: this is used to test for the

individual significance of the variables used in the model. That

is, to find out the significant influence of independent

variables on the dependent variable. This will be used to accept

or reject the research hypothesis.

ii. The F-test: this is used to test for the joint

significance of the variables used in the model. In other words,

it is used to test for the overall significance of the model.

iii. The co-efficient of determination (R2): this explains

the total amount of variations in the dependent variable (UR) as

a result of changes in the independent variables included in the

model.

3.3.2 Econometric tests (Second order)

i. Normality test: this will check if the residuals, which

is a proxy for stochastic error term, follows normal distribution

or not. The normality test that will be used in this study is the

Jarque-Bera (JB) test of normality.

ii. Autocorrelation test: the assumption is that errors

corresponding to different observations are uncorrelated. The aim

of this test is to determine if the errors corresponding to

different observations are serially correlated or not. It checks

the randomness of the residuals. The test statistics that is

adopted is the Durbin Watson test.

iii. Multicollinearity test: this helps to ascertain the

level of collinearity that exists among the independent variables

of the model. The reason is to check if there is collinearity

among the variables or not i.e. to check whether two or more

independent variables are exerting the same influence on the

dependent variable. The correlation matrix will be used for this

test.

iv. Unit root/Stationarity test: this is to investigate

whether the mean value and variance of the stochastic process are

constant over time (Madueme, 2010). The Augmented Dickey Fuller

(ADF) test statistics will be used for this test.

v. Co-integration test: this is to ascertain whether the

variables have a sustainable long-run relationship or are stable

over time, as a result of their different order of integration.

This test is done to avoid having a spurious regression (Madueme,

2010). The Augmented Dickey Fuller (ADF) test statistics will

still be used for this test by conducting a unit root test to the

residual series.

3.4 SOURCES OF DATA

Data were gathered from secondary sources after which

different statistical packages were used to extract relevant

information. They are secondary because they are published data.

They were collected from the Central Bank of Nigeria (CBN)

Statistical Bulletin and the National Bureau of Statistics (NBS).

They are also time series data from 1985 and 2011. The collected

data were mined (i.e. converted from their original annual series

to bi-annual series) to enable for a large sample size (i.e. 54

observations instead of 27) for better econometric analysis.

CHAPTER FOUR

PRESENTATION AND ANALYSIS OF RESULT

The results of the research carried out are presented and

analyzed in this chapter. These results would be subjected to

economic, statistical and econometric tests using E-views 3.1;

this is so as to ascertain whether the estimated parameters are

statistically satisfactory and/or theoretically meaningful.

4.1 Presentation of OLS Regression Result.

The result of the regression is presented in the table

below:

VARIABLES COEFFICIENTS STANDARD

ERRORS

T-

STATISTICS

PROBABILITY

CONSTANT -0.078250 1.317983 -0.059371 0.9529

POPLF 0.237878 0.087871 2.707120 0.0094

ICT 0.000760 0.000238 3.191982 0.0025

INF -0.017121 0.012376 -1.383360 0.1731

GE -0.002357 0.001208 -1.951585 0.0570

PINV -1.017900 5.005109 -3.503403 0.0010

D1 0.978969 0.486719 2.011361 0.0500

R2 = 0.749651DW = 1.532940Adjusted R2= 0.717692F-STAT = 23.45634*TABLE 4.1: Regression result(The full result is shown in the appendices)

4.2 Evaluation Based on Economic Criteria

This is conducted with the view that the a-priori

expectations from the economic theory would hold. From the

regression result, Unemployment Rate (UR) is the dependent

variable while Total Population of Labour Force (POPLF),

Information and Communication Technologies’ Output (ICT),

Inflation Rate (INF), Government Expenditure (GE), Private

Investment (PINV) and the Dummy Variable (D1) are the independent

variables.

According to the regression result, the coefficient of the

intercept (C) shows that if all other things remain the same

(ceteris paribus), Nigeria’s unemployment rate will continue to

fall by 0.078 bi-annually. It also showed a positive relationship

between Unemployment rate (UR) and the population of the labour

force; indicating that if the population of the labour force

increases by a million, unemployment rate will increase by 0.238.

As for inflation rate, it has a negative relationship with

unemployment rate; implying that a unit increase inflation rate

will make unemployment rate to fall by 0.017. Also as expected,

both Government expenditure and Private investment have negative

relationships with unemployment rate; while a billion naira

increase in government expenditure will decrease unemployment

rate by 0.002, a billion naira increase in private investment

will reduce unemployment rate by 0.018

The regression result has established a positive relationship

between ICT innovations and Unemployment rate in Nigeria. It

reveals that during the period under review, ICT output

contributed positively to the growth of unemployment rate in

Nigeria. The coefficient of ICT (0.000760) implies that as ICT

output increases by 1000(N’Millions), Unemployment Rate also

increases by 0.76. The coefficient of the dummy variable (D1)

shows that after the emergence and adoption of ICT innovations in

Nigeria (i.e. since 1999), the impact of (the same) ICT output on

unemployment rate increased to 0.98. N.B: - The data of POPLF and

ICT are in millions while GE and PINV are in billions.

4.3 Evaluation Based on Statistical Criteria (First Order Test)

The statistical or first order tests shall be conducted

taking into account the student t-statistic, F-statistic and the

R2 values.

The t – test

Using the 2-t rule of thumb, the t-statistics of the

variable under consideration is interpreted based on the

following: if it (i.e. the t-statistics value of the variable

under consideration) is less than/equal to -2 or it is greater

than/equal to 2, then it shows that the variable is statistically

significant; but if otherwise, it is not. Where:

tcal = β̂2–β2

se(β̂2) = (β̂2−β2 )√∑x2

i

σ̂ - - - - - - - - -- - - - - - - - - - (i)VARIABLES COEFFICIENTS T-

STATISTICS

CONCLUSION

CONSTANT -0.078250 -0.059371 INSIGNIFICAN

T

POPLF 0.237878 2.707120 SIGNIFICANT

ICT 0.000760 3.191982 SIGNIFICANT

INF -0.017121 -1.383360 INSIGNIFICAN

T

GE -0.002357 -1.951585 INSIGNIFICAN

T

PINV -0.017900 -3.503403 SIGNIFICANT

D1 0.978969 2.011361 SIGNIFICANT

*Table 4.3.1: T-test result.

From the result, the t-statistics of ICT (3.191982), which

is the variable of interest, is greater than 2; hence, it is

significant. Therefore, ICT (which is here captured by its

output) within the period under consideration is a factor that

has a statistically significant impact on Unemployment Rate in

Nigeria.

However, in order to give an answer to second research

question, the t-statistics of the dummy variable, introduced to

differentiate the periods before the emergence of ICT innovations

and the period after, is considered. If its t-statistics is

significant, then there is a structural break in unemployment

rate but if otherwise, there is no structural break.

From the regression result, the t-value of D1 (2.011361)

indicates its significance; hence, there is structural break in

unemployment rate as a result of the adoption of ICT innovations

in Nigeria. This implies that the impact of ICT on unemployment

rate after the adoption of recent ICT innovations is

statistically significantly different from its impact before the

adoption of recent ICT innovations. This further tells us that

ICT innovations have a statistically significant impact on

unemployment rate in Nigeria.

The F – Test

It is a test used to measure the overall significance of the

variables in the model. Under this test, the null hypothesis is

given as:

H0: the model is insignificant

H1: the model is significant

Decision Rule:

If Fcal > Fα (k-1, n-k), Reject H0; do not reject if otherwise

Where: Fcal = ESS/dfRSS/df =

ESS/ (k−1)RSS/ (n−k) - - - - - - - - - -

- - - - - - - - - - - - - - -(ii) ESS = Error Sum of Squares RSS = Residuals Sum of Squares And Fα (k-1, n-k) is the critical F-value at the chosen level of

significance (α) and (k-1) degrees of freedom (df) for the

numerator and (n-k) degrees of freedom (df) for the denominator;

k = number of parameters used in the regression;

n = number of observations; α = 0.05Where k = 6; k – 1 = 5

Where n = 52; 52 – 6 = 46Since F-statistics(23.45634) > Ftab(2.41), we reject H0 and

conclude that at 5% level of significance, the overall

significance of the parameters is statistically different from

zero implying a good fit.

R2

The coefficient of determination is defined as:

R2 = ESSTSS = 1 – RSSTSS - - - - - - - - - - -

- - - - - - - - - - - - (iii)

Adj R2 = 1 – RSS/ (n−k)TSS/(n−1)

= 1 – (1 – R2)n−1n−k - -

- - - - - - - (iv)It shows the variation impact of the explanatory variables

on the dependent variables. The coefficient of determination (R2)

from the regression result is given as 0.749651 while the

adjusted R2 is 0.717692. This implies that 74.97% of the total

variation in the unemployment rate of Nigeria is as a result of

the joint variation of both the population of labour force, ICT

output, inflation rate, government expenditure and private

investment.

Also, since the Durbin – Watson statistics (1.532940) is

greater than the R2 (0.749651), it further shows that the entire

regression model is statistically significant.

4.4 Evaluation Based on Econometric Criteria (Second Order Test).

The econometric tests are also performed based on the

assumptions of the Classical Linear Regression Model. The

following Second Order Tests will be conducted: test for

autocorrelation, multi-collinearity test, normality test, unit

root test and co-integration test.

Autocorrelation Test

This is to test whether errors corresponding to different

observations are uncorrelated. It checks the randomness of the

residuals. The Durbin–Watson test is adopted for this test.

Hence, we compare the established lower limit dL and the upper

limit dU of Durbin Watson based on 5% level of significance and

k-degrees of freedom.

Where: k = number of explanatory variables including the

constant.

Null Hypothesis Decision Rule Condition

No positive

autocorrelation

Reject 0 < d < dL

No positive

autocorrelation

No Decision dL < d < dU

No negative

autocorrelation

Reject 4 – dL < d < 4

No negative

autocorrelation

No Decision 4 – dU < d < 4 - dL

No autocorrelation,positive or negative

Do not reject dU < d < 4 - dU

*Table 4.4.1: Durbin-Watson d-test decision rules

Hypothesis testing:

(1) H0: ρ = 0 versus H1: ρ > 0. Reject H0 at 5% level if d < dU.That is, there is statistically significant positiveautocorrelation.

(2) H0: ρ = 0 versus H1: ρ < 0. Reject H0 at 5% level if theestimated (4 – d) < dU, that is, there is statisticallysignificant evidence of negative autocorrelation.

(3) H0: ρ = 0 versus H1: ρ ≠ 0. Reject H0 at 2α level if d < dU

or (4 - d) < dU, that is, there is statistically significantevidence of autocorrelation, positive or negative.

From the Durbin-Watson table, dL = 1.294, dU = 1.861, Where: dL = Durbin-Watson lower bound.

dU = Durbin-Watson upper bound.

Since d = 1.532940 from the regression result, we conclude thatthere is no decision as to whether there is autocorrelation ornot.

Multi-collinearity Test

This test is to check if there is perfect correlation

(collinearity) between independent variables. This will be

conducted using the correlation matrix. According to Gujarati

(2009), if the correlation coefficient between any pair of

regressors exceeds 0.8, then there is multi-collinearity between

the two variables.

The result of the correlation matrix is given below:

POPLF ICT INF GE PINV

POPLF 1.000000 0.785958 -

0.325338

0.909719 0.708011

ICT 0.785958 1.000000 -

0.270149

0.946388 0.984682

INF -

0.325338

-

0.270149

1.000000 -

0.353146

-

0.226260

GE 0.909719 0.946388 - 1.000000 0.889049

0.353146

PINV 0.708011 0.984682 -

0.226260

0.889049 1.000000

*Table 4.4.2: Correlation matrix

From the table above, there is the existence of collinearity

between GE and POPLF, GE and ICT, PINV and ICT, PINV and GE.

According to Blanchard in Gujarati (2009), multi-collinearity is

essentially a data deficiency problem and sometimes we have no

choice over the data we have available for empirical analysis.

Normality Test

This is carried out to test if the error term follows the

normal distribution. Under the null hypothesis that the residuals

are normally distributed, if the computed value of the JB-

statistics is greater than the tabulated value (chi-square

distribution with 2df at 5% level of significance), we reject H0.

Where:

JBcal = n[s26

+(k−3)2

24 ] - - - - - - - - - - -- - - - - - - - - - - - - - - - (v)

Where n = sample size, S = skewness coefficient, and k = kurtosis

coefficient.

Below is the graph of the distribution of the error term.

0

2

4

6

8

10

12

14

-2 -1 0 1 2

Series: ResidualsSam ple 1985:1 2011:2O bservations 54

M ean 2.35E-15M edian -0.033706M axim um 1.992149M inim um -2.185296Std. Dev. 0.720492Skewness -0.236934Kurtosis 5.890086

Jarque-Bera 19.29859Probability 0.000064

*Table 4.4.3: Error term distribution graph/ Jarque-Bera statistic

From the Jarque-Bera test conducted, the JB cal is found to

be 19.16124 while the tabulated result shows the following value:

5.99147. Hence, we conclude that the error terms of the variables

under consideration are not normally distributed.

Unit Root/Stationarity Tests

The test is conducted to ascertain the level of stationarity

existing between the variables under consideration; this is

because we are dealing with time series variables which are

generated through a stochastic process (i.e. a collection of

random variables ordered in time). For this purpose, the

Augmented Dickey-Fuller test is applied following the decision

rule stated as: if the absolute value of the Augmented Dickey-

Fuller (ADF) test is greater than the critical value at 5% level

of significance at level, 1st difference and 2nd difference we

conclude that the variables under consideration are stationary;

if otherwise, they are not.

The stationarity result is presented below:

VARIABLES ADF TEST

STATISTICS

CRITICAL

VALUE AT 5%

ORDER OF

INTEGRATION

ASSESMENT

POPLF -32.36084 -2.9178 I(1) STATIONARY

ICT 4.243519 -2.9167 I(0) STATIONARY

INF -7.071810 -2.9178 I(1) STATIONARY

GE -8.037277 -2.9178 I(1) STATIONARY

PINV 3.997374 -2.9167 I(0) STATIONARY

D1 -7.211103 -2.9178 I(1) STATIONARY

*Table 4.4.4: Unit root test for stationarity at levels(The full results are shown in the appendices)

From the table shown above, variables ICT and PINV are

stationary at order zero (0) while variables POPLF, INF, GE and

D1 are stationary at order one (1). As a result of their

different order of integration, we need to ascertain whether the

variables have a sustainable long run relationship or are stable

over time.

Co-integration Test

The co-integration test procedure is conducted to establish

a long run relationship between the variables under

consideration. According to Gujarati (2004), two variables are

said to be co-integrated if they have a long run or an

equilibrium relationship between them. To test for co-integration

among the variables, we will use the ADF test on the regression

residuals as proposed by Gujarati (2004). The ADF unit root test

on the residuals work with the same decision rule as unit root

test i.e. if the absolute value of the Augmented Dickey-Fuller

(ADF) test is greater than the critical value at 5% level of

significance at level [I(0)], we conclude that the variables

under consideration are co-integrated; if otherwise, they are

not. Below is the result of the co-integration test:

Variable ADF statistics Critical value (5%)

Residual term -5.654579 -2.9167

The ADF test statistics reported a result of -5.621353 which

is greater than the critical value at 5% (-2.9167) in absolute

terms. This means that the series are stationary at level. Thus,

although all the series are individually non-stationary, their

linear combination is stationary. Conclusively then, there is a

co-integrating relationship among variables i.e. there is long-

run equilibrium/relationship between the regressors and the

regressand. This means that the original regression is not

spurious. This further leads to the specification of the short-

run equation (model).

Short-run dynamics: The Error – Correction Model.

In this test, the error-correction mechanism is employed to

look at the short-run behavior of the dependent variable D(UR) in

relation to its explanatory variables D(POPLF), D(ICT), D(INF),

D(GE), D(PINV), D(D1) and ECM(-1). This equation incorporates the

short-run adjustment mechanism into the model. In the previous

test, it was evident that there is at least one co-integrating

relationship between the variables. Nevertheless, in the short-

run, there may be disequilibrium. Therefore, the error term

equation is employed to eliminate deviation from the long-run

equilibrium. Below is the result of the parsimony Error-

Correction Model:

VARIABLES COEFFICIENTS STANDARD

ERRORS

T-

STATISTICS

PROBABILITY

CONSTANT 0.018368 0.143404 0.128084 0.8987

D(POPLF) 0.152236 0.493701 0.308356 0.7592

D(ICT) 0.000896 0.000500 1.791906 0.0799

D(INF) -0.011832 0.017044 -0.694198 0.4911

D(GE) -0.0003220 0.001367 -2.355244 0.0229

D(PINV) -0.020016 0.011826 -1.692562 0.0975

D(D1) 1.008941 0.808291 1.248240 0.2184

ECM(-1) -0.768436 0.144663 -5.311903 0.0000

R2 = 0.463363DW = 1.735970Adjusted R2= 0.379886F-STAT = 5.550790Where (-1) is the one period lagged value of the residual from

the co-integration equation that ties the short-run behavior of

the UR to its long-run value.

From the above result, the coefficient of ECM in absolute

terms indicates that the Error-Correction model corrects 76.84%

of the deviation from the long-run equilibrium bi-annually. The

table also shows that the t-statistic of the ECM was significant.

However, since most of the estimated coefficients are not

statistically significant, we conclude that there is a strong

long-run relationship between the regressand and the regressors.

The estimate of (1-α) indicates the speed of adjustment in

eliminating deviation from the long-run equilibrium.

4.5 Evaluation of the Working Hypotheses

The results from the statistical tests conducted (especially

the R2 and the F-stat tests), indicates that the overall result

are significant in explaining variations in the dependent

variable (UR). Hence, inferences and conclusions drawn from the

model are both sound empirically and reliable for policy making.

This research work is based on the following hypothesis:

1. H0: ICT have no significant impact on unemployment rate inNigeria.

2. H0: ICT innovation has not caused any significant structuralchange on unemployment rate in Nigeria.From the regression result, it is obvious that in the long-

run, ICT have had and still has a positive significant impact on

the Nigerian unemployment rate (as all other things remain

equal). Also, using the introduced dummy variable to measure the

impact of the recent ICT innovations on unemployment rate in

Nigeria, we find that, the recent innovations in ICT worsened the

impact of ICT on Nigeria’s unemployment rate, as its contribution

to ICT’s impact is statistically significant. Hence, with

reference to the second null hypothesis, ICT innovations have

actually caused a significant structural change on unemployment

rate in Nigeria.

Consequently, we plan to increase unemployment rate as

policies are put in place to develop ‘what we call’ ICT

innovations in Nigeria either via increased government

expenditure on ICT or via increased private investment on ICT or

both. This, no doubt, will be a shocker to many. But is this

justifiable?

CHAPTER FIVE

POLICY RECOMMENDATIONS AND CONCLUSION

5.1 Justifications for the research findings

The result of this work seems to contradict most empirical

findings on this same subject; for instance, Akwani (2005), found

that the fastest growing employer of labour in Nigeria today is

the telecom industry (Specifically, the wireless telephone sector

that provides services to individual customers using the GSM);

Okogun et al (2012) revealed that an anti-poverty measure

introduced through the use of ICT has been able to generate

substantial amount of employment through the use of mobile phone

by many Nigerian to sustain a living. There are many call centers

in villages and towns mostly operated by people between age

distributions of between 20-29 years (38%), mostly women with

secondary/post-secondary education in Nigeria. Some of these

people run shops for the sale of Global System of Mobile (GSM)

accessories as a major form of occupation as means of self-

employment as well as a means of sustaining livelihood. Ndukwe

(2004) said that Past studies have shown that over 2,000 persons

are directly employed by GSM operators while he found that an

estimate of 40,000 Nigerians are benefiting from indirect

employment generated by GSM operators in Nigeria; Oye et al

(2011) studied the impact of ICT on unemployment for the period

of nine years (2000 – 2008), and found that mobile phones, which

is a proxy for ICT, have significant socio-economic impact on the

country where unemployment has shown an enormous effect (over

65%) on the making of the GDP for the years under study. All

these are just to mention but a few.

From their findings, we can deduce that the major, if not

the only, way ICT have created employment is through GSM

operation (i.e. call centers) and the sales of GSM accessories.

Although these researchers are right in stating that these

(‘. . . individual customers using the GSM . . .’, ‘. . . the use

of mobile phone by many Nigerian to sustain a living . . .’, ‘. .

. many call centers . . .’, ‘. . . the sale of GSM accessories. .

.’, ‘ . . . GSM operators. . .’, etc) have massively engaged many

unemployed Nigerians, it is quite clear however, that what they

mean by employment is pretty different from what employment

really is. The National Bureau of Statistics (NBS) regards a

person as employed if he/she is engaged in the production of

goods and services, thereby contributing to the gross domestic

product, in a legitimate manner, which is a component of the

national accounts. The Census Bureau of the United States of

America defines unemployment as the difference between the

amounts of labour employed at current wage and working

conditions, and the amount of labour not hired at those levels”.

Because GSM operators are not contributing to the Gross

Domestic Product and are not at the current wage level and/or

working conditions, they cannot and should not be referred to as

employed; they rather fall under the category of those who are

either frictionally unemployed or structurally unemployed or

technologically unemployed (which are all forms of unemployment).

Hence, the result of this research work is justified.

We can also deduce, from foreign empirical works within this

subject area, that the definition of ICT (as seen) in Nigeria is

not all-encompassing. In other words, Nigeria is yet to fully

harness the opportunities which are brought about by ICT. Hence,

it is not completely out of place if the Nigerian unemployment

situation is disadvantaged by the emergence of ICT innovations;

because Ricardo, in opposition to Say’s theory of automatic re-

absorption, emphasized that technological advances (employment of

machinery) are frequently detrimental to the interest of the

laboring class and that workers lay-off as a result of automation

of jobs could be the situation. In buttressing this opinion,

Schumpeter (1934) said that innovation, which creates more jobs

relative to job destruction, is the basic force behind the

increases in employment and the decreases in unemployment. As a

result of the extent to which ICT innovations have been adopted

in Nigeria, they create lesser jobs relative to job destruction.

This also justifies the outcome of this work.

It is worth noting, however, that the negative impact of ICT

innovations (positive linear relationship), as discovered by this

work, is basically on unemployment rate in Nigeria and not on any

other economic indicator.

5.2 Policy recommendations

It is not strange to find that innovations which many

economies are benefitting from, some economies are disadvantaged

by them. For instance, Roberts (2007) found that since the mid-

1990s, while the United States experienced an upward structural

shift in productivity growth because of ICT, Germany, Italy,

France, and Spain experienced a structural shift downward in

productivity growth. This is simply because of the varying kinds

of policies that are put in place which are either appropriate or

inappropriate for those economies.

It is economics to measure the impact of ICT innovations on

unemployment rate but it is not economics to state categorically

why or when ICT is failing; hence economics cannot professionally

proffer workable ways to fully benefit from adopting ICT

innovations. However, because we have reviewed some empirical

works which are somewhat related to this and have also come

across some more related research works in the course of this

work, we may rightly say that in Nigeria, the basic problem is

not policy but:

Inefficient implementation of already set policies

Deficient definition of ICT in Nigeria.

To the former, Nigeria already has some well set policies

crafted and enunciated under the National Policy for Information

Technology Development and the Telecommunications Act 2003. But

the question is how have we faired? Femi O. (2004) tried to

answer this question when he gave the informal review of NITDA’s

IT policy (which was intricately integrated into the Nigerian IT

development strategic goal paths) performance as seen in the

table below:

IT Policy Objective Performance ReviewTo ensure that Information Technology resources are readily available to promote efficient national development

Some Information technology resource has become available.However, it is largely driven by the private sector. 

To guarantee that the country benefits maximally, and contributes meaningfully by providing the global solutions to the challenges of the Information Age

ICT capacity is still inadequate to provide maximum benefit

To empower Nigerians to participate in software and IT

NITDA held some Open Source Software conference, but is

development yet to provide enabling mechanism for national participation

To encourage local production and manufacture of IT components in a competitive manner

4 Local manufactures were approved.  Some personal Computer assembly is now taking place in Nigeria. However, most are still imports.

To improve accessibility to public administration for all citizens, bringing transparencyto government processes

E-Government Conferences and National e-government Strategies LTD (NeGSt), but are still in developmental phases.

To establish and develop IT infrastructure and maximize itsuse nationwide

SAT-3 is underutilized.  Only used by Shell.

To improve judicial procedures and enhance the dispensation ofjustice

E-Judiciary Conference was held

To improve food production and food security

Efforts unknown

To promote tourism and Nigerianarts & culture

Effort unknown

To improve healthcare delivery systems nationwide

Effort unknown

To enhance planning mechanisms and forecasting for the development of local infrastructure

NIDTA is active member of the WSIS implementation, which is still at premature stages

To enhance the effectiveness ofenvironmental monitoring and control systems

Nigeria launched its first satellite in 2003, NigeriaSat-1.

Plans for a communications satellite are said to be underway 

To re-engineer and improve urban and rural development

Rural Internet Resource Centers were established in

schemes Bayelsa and Jigawa States.  Also 6 Mobile Internet Units were commissioned to travel the entire country

To empower children, women and the disabled by providing special programs for the acquisition of IT skills

Efforts unknown

  To empower the youth with IT skills and prepare them for global competitiveness

  There are currently 35 Diginet sites, each with 22 Computers.  Diginet is sponsored by the Education TaxFund.  A total of 60 sites is expected to complete by 2004

   To integrate IT into the mainstream of education and training

The National Universities Commission manages NUNET.  Only a dozen Universities use NUNET.  NUNET has nothing to do with NITDA

To create IT awareness and ensure universal access in order to promote IT diffusion in all sectors of our national life

NITDA frequently holds ITC seminar and Conferences.  E-judiciary, E-Nigeria to name afew

To create an enabling environment and facilitate private sector(national and multinational) investment in the IT sector

International partnerships with Microsoft Corporation, Oracle Corporation, Hewlett Packard, CISCO Systems, Accenture, Surrey Satellite Technology Ltd, to deliver E-government  and locally with Zinox, Omatek, Beta and UnitecComputers, Progenics Corporation, SystemSpec Ltd., Econet Wireless, Globacom, MTNNigeria and M-Tel.

To stimulate the private sectorto become the driving force for

Some core technologies within the Mobile Internet Unit used

IT creativity and enhanced productivity and competitiveness

private sector resources. 

To encourage government and private sector joint venture collaboration

Several Joint venture relationships exist between Nigerian Government and International Organizations, fewer exist between local Organizations

To enhance national security and law enforcement

E-Judiciary Conferences were held

To endeavour to bring the defense and law enforcement agencies in line with accepted best practices in the national interest

E-Judiciary Conferences were held

To promote legislation (Bills &Acts) for the protection of on-line, business transactions, privacy and security.

Nigerian IT Bill was not passed into Law

To establish new multi-faceted IT institutions as centers of excellence to ensure Nigeria's competitiveness in international markets

Still in premature planning stages

 To develop human capital with emphasis on creating and supporting a knowledge-based society

 Center for excellence are still in developmental phases

 To create Special Incentive Programs (SIPs) to induce investment in the IT sector

 Efforts Unknown

To generate additional foreign exchange earnings through expanded indigenous IT productsand services

NITDA's financial reports are not public 

To strengthen National identityand unity

Efforts Unknown

To build a mass pool of IT NITDA-ETF ICT Center of

literate manpower using the NYSC, NDE and other platforms as 'train the trainer" Scheme (TTT) for capacity building.

Excellence Program is aimed atenabling Nigeria to participate fully and activelyin the ICT Revolution through the development of a large pool of educated and qualifiedNigerian ICT professionals. However, not one single centerof excellence is ready.  This is still at the premature stages.

To set up Advisory standards for education, working practices and industry.

Efforts Unknown  

To establish appropriate institutional framework to achieve the goals stated above. 

NIDTA and auxiliary NITDA organizations were created

*Informal Performance Review of NITDA'S IT Policy by Femi O. (2004)

According to him, the above informal review is not alone, a

formal study conducted in 2003, by the Economist Intelligence

Unit IBM revealed that 'E-business in Nigeria faces serious

obstacles: inadequate telecoms infrastructure, unreliable power

supply and authorities who, by and large, lack the means to push

e-business forward".

To the later, feasibility studies reveal that the definition

of ICT in Nigeria is not all-encompassing as it is majorly

characterized by telephone services which are the least of the

components of ICT in Okogun’s list of ICT components. In defining

ICT, Daniel (1999), in an article presented to Industry Canada

stated that for a number of years, policy makers and analysts in

Canada and around the world have expressed an interest in

understanding and measuring the importance of the so-called "ICT

sector". In the absence of a standard definition for the ICT

sector, it has been very difficult to monitor its development, to

make international comparisons and to develop policies. In an

effort to stimulate discussions on this issue, Statistics Canada

and Industry Canada published a working document entitled

"Measuring the Global Information Infrastructure for a Global

Information Society" in June 1996. The main thrust of this

working document was to propose a definition for the ICT sector

in terms of the existing Canadian Standard Industrial

Classification (CSIC) and to present a statistical profile of the

Canadian ICT sector. The proposed definition was based on the

notion that the ICT sector should include industries "primarily

engaged in producing goods or services, or supplying

technologies, used to process, transmit or receive information".

After some deliberation, a standard list of industries was

adopted to describe the ICT sector and Industry Canada prepared

and published a statistical review of the ICT sector based on

this definition. This definition is as follows:

Industry groupings SIC Industry titles

Service Industries

4810 TelecommunicationBroadcastingIndustries

4820 TelecommunicationCarriers Industry

4830 OtherTelecommunication

Industries7720 Computer and Related

Services

Goods Industries

3340 Record Player, Radioand Television

Receiver Industry3350 Communication and

Other ElectronicEquipment Industries

3360 Office, Store andBusiness Machine

Industries3911 Indicating,

Recording andControlling

Instruments Industry3912 Other Instruments

and Related ProductsIndustry

At OECD, the Committee for Information, Computer and

Communications Policy (ICCP) established an Ad Hoc Statistical

Panel to address the issue of indicators for the information

society. More specifically, the Panel was to develop definitions

which would support the development of these indicators. The task

set at the first meeting of the Panel in 1997 was agreement on a

definition of the ICT sector, based on a list of industries drawn

from the third revision of the International Standard Industrial

Classification (ISIC. revision 3). This objective was realized at

the June 1998 meeting of the Panel and the resulting definition

was released by the OECD in September, 1998. This definition is

given in the table below:

Industrygroupings

ISIC Industry titles

Manufacturing

3000

Manufacture of office, accounting and computingmachinery

3130 Manufacture of insulated wire and cable

3210

Manufacture of electronic valves and tubes and other electronic components

3220

Manufacture of television and radio transmitters and apparatus for line telephony and line telegraphy apparatus, and associated goods

323 Manufacture of television and radio receivers,

Industrygroupings

ISIC Industry titles

0 sound or video recording or reproducing apparatus, and associated goods

3312

Manufacture of instruments and appliances for measuring, checking, testing, navigating and other purposes, except industrial process control equipment

3313

Manufacture of industrial process control equipment

Goods Related Services

5150 Wholesale of machinery, equipment and supplies

7123

Renting of office machinery and equipment (including computers)

Intangible Services

6420 Telecommunications

7200 Computer and related activities

According to Daniel (1999), though the OECD definition of

the ICT sector differs somewhat from the definition that was used

in Canada, the underlying principle is very similar. The main

differences between the OECD definition and the definition

previously used in Canada are:

The exclusion of radio and television broadcasting;

The inclusion of manufacturers of insulated wire and

cable;

The inclusion of wholesalers and lessors of ICT

equipment.

Even with telecommunication as the major actor in the

Nigerian ICT industry, Nigeria is still deficient in

telecommunication. The reason for this is not farfetched: the

scope of economic activities includes production, exchange,

distribution and consumption. But in Nigeria, economic

activities, with respect to telecommunications, majorly lies in

distribution and consumption (as found in the findings of

previous researchers in form of sales of GSM accessories, call

centers, etc.). In his article, published in one of the Nigerian

dailies, Chris (2011) also noted that Nigeria ICT profession and

industry have done well individually in ICT consumption but have

failed in Software Engineering Development, amongst others.

Roberts (2007), in his quest to know the impact of ICT on

European economies, found that Europe has benefited from the ICT

revolution less than some other nations, including the United

States basically because of their relatively less ICT investment

and their reluctance in making the process and organizational

changes that would allow them to achieve the full benefits of

ICT. He suggestively stated that European policymakers need to

follow five key principles if their nations are to fully benefit

from the worldwide digital revolution. These principles include:

focus on raising productivity through greater use of ICT; use of

tax incentives and tariff reductions to spur ICT investment;

actively encourage digital innovations and transformation of

economic sectors; encourage universal digital literacy and

digital technology adoption; and doing no harm to the digital

engine of growth.

From all of the foregoing, we may satisfactorily suggest

that:

NITDA’s IT policies be revisited, reviewed and

implemented to the later.

Public-private capital investment on ICT (all

encompassing) is necessary to enable for adequate ICT

infrastructure

The Importation of ICT equipments, facilities and

accessories be either stopped or discouraged while

local production and exportation of the afore-mentioned

be encouraged. Then as suggested by ICT - G22,

The Federal and State Governments must implement a

long-term ICT Human Capital Development

Policy. Education curriculum at all levels must be

revised to become more ICT centric, and an ICT Work-

Force assistance program should emerge to

retrain unemployed and underemployed graduates.  

Non-specialized foreign ICT expatriate should be

replaced with indigenous Nigerian or Nigerian

in Diaspora.

5.3 Summary and conclusion

From the analysis of data, we find that if all other things

are kept constant, unemployment rate in Nigeria will continually

fall by 0.078. It was also found that ICT has a statistically

significant positive impact on unemployment rate in Nigeria while

there is structural break in unemployment rate as a result of

recent ICT innovations; even though ICT innovations also have

positive impact on unemployment rate in Nigeria. The reason for

this, as found, is that Nigeria is yet to fully adopt ICT; we

have only, through the deficient policy implementation, been able

to adopt telecommunication which is only a component of ICT.

Conclusively therefore, for ICT to help the Nigerian

unemployment situation, as it is doing to some other economic

indicators, it must be fully adopted basically through

appropriate policy implementation, increased public-private

capital investment and ICT human capital development.

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