Post on 20-Jan-2023
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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