Pub impact of high population on nigerian economy

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IMPACT OF HIGH POPULATION ON
NIGERIAN ECONOMY
By
OKWUOSA ONYEKA NNAMDI

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ABSRACT
The time is not riper now in our country than this strategic topic The Impact
of High Population on the Nigerian Economy is undertook. It was
discovered using the ordinary least square method of regression with the aid
of E views statistical package, tested on five different times series data
covering thirty-five years (1980-2014), that the impact of Nigeria’s high
population is not negative on the economy after all. There exist, as revealed
from the findings, a positive relationship and high significance of total
population level of Nigeria with economic growth. In addition, human
capital showed no significant impact on economic growth. Attainable
demographic policies and revitalization of human capital development were
recommended to, not only boost economic productive activities, but further
enhance economic growth and development in Nigeria.
Keywords: Population, economic growth, human capital, demography.

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CHAPTER ONE
INTRODUCTION
1.1 Background of the Study
In the early twenty-first century, the world population had fluctuated around
6 billion, in which developing countries contributed to 80% of the total
figure and mostly occur in Asian countries Pham, T. N and Tran, H. H
(2011). The fact is, population growth and the economy always have a close
relationship. Over periods, the arguments about positive and negative effects
of population on economic growth and development are still complicated
problems for most of the economists. One of these economists is Thomas R.
Malthus who stated in his model in 1826, that the population level can
reduce the output per capita because population increases at a geometrical
rate while production rises at an arithmetic rate so that output growth rate
cannot keep the same pace. Another famous economist is Robert M. Solow
(1956) who unlike Malthus, focused on the term, ‘population growth rate’
instead of the ‘population level’. He stated that an increase in the population
growth rate can decline the capital per worker as well as the steady-state

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output per worker. As a result, higher population growth can be detriment to
productivity and thus, economic growth.
Moreover, the Nigerian economy over the years has been marred by periodic
booms and bursts as reflected in her unsteady and unsustainable economic
growth rates, which is not disconnected from her incessant political / ethnic
tensions and instabilities, as well as population and macroeconomic
mismanagement. Notwithstanding, Nigeria has remained an oil rich country,
earning an estimated $2.2 million a day in oil revenue and the 12th largest
oil producing nation in the world (World Bank, 2014). However, the
atmosphere of economic and demographic mismanagement, instability and
political tension has kept the country from achieving its potentials.
The World Bank Country Director for Nigeria using World Bank statistics
stated that poverty per capita in Nigeria is at 62.6%, 50% of Nigeria’s 170
million population is unemployed and that at least 71% of Nigerian youths
are unemployed bringing unemployment rate to 23.9% as at august 23, 2014.
Although, there has been a recent review using a new calculation
methodology placing Nigeria’s unemployment rate at 6.4% of the nation’s

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72 million labour force population (Kale, Y. 2014). Furthermore, the World
Bank also in 2014, ranked the country third poorest following India and
China with first and second respectively. The agency showed that Nigeria
has a Human Poverty Index of 33.1%, with 7% of the world’s 1.2 billion
poor persons as Nigerians. The World Bank also stated that more than 58
million of the population of Nigeria is rated ‘poor’ according to standard
definitions. These discouraging indicators in the light of the fact that Nigeria
is oil rich and the 26th
largest economy in the world after re-basing, are
grossly paradoxical and a clear case of what mainstream economists term
“resource curse” with corruption and lack of adequate human capital
development & empowerment most glaring.
Furthermore, it may be interesting to note that Nigeria’s population level is
at 177 million people as at 2014 and having a growth rate of about 2% per
annum. With this, it is strikingly revealing that we record birth rates of at
least 3.2 million per year, two hundred and sixty-six thousand, six hundred
and sixty seven (266,667) births per month, eight thousand eight hundred
and eighty-nine (8,889) births per day, three hundred and seventy (370) birth
per hour and six (6) births per minute (Author’s calculation).

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However, there are also some optimist views that have stated that population
growth can make a positive impact on economic growth. An example is
Ahlburg, D. (1998) who believed that larger population can lead to
‘technology-pushed’ and ‘demand-pulled’ advantages. This is to say, that
higher population growth can increase the needs for goods and boost the
technological development. Therefore, it can increase the labour
productivity, income per capita and living conditions all other things being
equal. Also stating prima facie, if we focus on massive human capital
development, empowerment and industrialization, then our already high
population (which we can do little or nothing to reduce especially in the
short run) will begin to yield more and more positive impacts on the
economy. This is the underlying theme of this research work.
This research work therefore, focuses on analyzing the impact of population
growth on the economy of Nigeria which is among Africa and Asian
developing countries portrayed as one of the most critical situations in the
world.

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1.2 Statement of the Problem
Over the years, high population rate, which have obvious negative impacts
on any nation’s economy, have starred grimly at the face of our country
Nigeria. Hitherto, writers have emphasized the negative impacts of high
population on economic growth which include: cancellation of average
output of the economy by high population; low and stagnant average
income; pressure on: agricultural land, food, employment creation, urban
housing, space, standard of living, access to quality education, health
facilities and other infrastructure; scarcities; economic hardship;
malnutrition and high death rate. This provoked high death rate will in turn,
balance-off the high population. This shows that there exists an inherent
reverse mechanism in the long run. Unfortunately as Lord Keynes stated in
1923, ‘The long run is a misguide to current affairs. In the long run, we are
all dead’.
Nevertheless, there are also far reaching implicit and explicit positive
impacts of high population rate on the economy which have been relegated
to the background. They include among others: unprecedented opportunity

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for economic and social development through innovations. This will
motivate, human progress, economies of scale or a greater output per unit of
input made possible by larger market and by a larger and more specialized
labour force, pressure on increased family or community size causing people
to work harder and motivating individuals and organizations to develop &
adopt innovations or improved method of production (Metras & Weeks,
1994 in Mokgadi, R. L 2004, “Consequences of High Population Growth in
Developing Countries: A case of South Africa)”. However, there are certain
problems to be answered such as, ‘Is population growth beneficial or
detrimental to economic growth?’
1.3 Research Questions
Based on the objectives of this study clearly stated in section 1.4 below, the
following research questions have been generated and expected to be
answered at the end of this work.
i. Is there any impact of human capital development on economic
growth in Nigeria?

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ii. What is the nature of relationship between population growth rate in
Nigeria and economic growth?
1.4 Objectives of the Study
The main objective of the study is to evaluate the impact of population
growth on Nigerian Economy. Specifically, the study aim to:
i. Evaluate the impact of human capital development on the economic
growth of Nigeria.
ii. Determine the relationship between population growth rate and
economic growth of Nigeria
1.5 Research Hypothesis
H0: There is no significance in relationship between population and
economic growth.
1.6 Significance of the Study
This study is intended to be very beneficial to first, our valued policy makers
and of course, individuals with some quest for knowledge especially in the
field of Economics, Political Science and other disciplines close with the

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efficacy to effect change in our polity. With the availability of a reliable time
series data which has posed a big problem for past researchers, and focus on
the unpopular view of demographic trend, policy makers are now better
equipped to channel robust policies towards making our vast population
advantageous for economic growth and development.

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CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
This chapter examines the relevant theories and works done by different
authors on the subject matter under discourse. Afterwards, attempts are
made to find out the existing gap regardless of the efforts so far, and
opportunity to fill in the discovered gap or missing link is maximized and
presented in the following order: Theoretical literature, empirical literature
review and justification for the study.
2.2 Theoretical Literature Review.
2.2.1 Conceptual Framework.
The concept of population and economic growth is one of the oldest in
economic literature. “A population is the total number of persons at a
specified time, living in a particular geographic area or country or in a well
delimited part of a country” (United Nations, 2008). According to Okafor
(2004), population is a critical factor in the development plans of any

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civilized society. For effective planning for the development of developing
countries, it is necessary to have an actual count of the population i.e. in
form of an accurate census. This will enable government to know how many
people to whom they should distribute amenities and social services.
According to Udabah (2002), it is a central problem of economic
development. If the population of a nation expands as fast as national
income, per capita income will not increase. When population expands
rapidly, a country may by great effort raise the quantity of capital only to
find that a corresponding rise in population has occurred making the net
effect of its “growth policy” maintained at the original low standard of
living. Much of the problem of developing nations like that of Nigeria is due
to population growth. Most developing nations have made appreciable gains
in income like Nigeria do in exporting crude, but most of the gains have
been eaten up (literally) by the increasing population. Moreover, the early
Roman Christians and Islamic writers were largely in favour of population
growth without showing concern for the need to balance the number of
people with available resources. This attitude was apparently influenced by
high mortality, which characterized the period.

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Economic growth and development is also a very vital concept in this
research topic. In an attempt to explain the concept, Kuznets, (1973) defined
a country’s economic growth as the long term rise in her capacity to supply
increasingly diverse economic goods to its population. This growing
capacity is based on advancing technology, institutional and ideological
advancements that it demands. According to Answers.com 5th
June 2015 (an
internet source), economic growth is defined as an increase in the capacity of
an economy to produce goods and services, compared from one period of
time to another. Economic growth can be measured in nominal terms, which
include inflation, or in real terms, which are adjusted for inflation. Economic
growth occurs whenever people take resources and rearrange them in ways
that are more valuable (Concise Encyclopedia of Economics).
Thom Hartmann, (1993) defined the concept of economic growth as the
growth in the total output of an economy without reference to inflation or
deflation, or total population. In his views he stated that this is the definition
that nations typically use and is reported in the news, which tends to inflate
(speaking of inflation) economic growth figures, since population usually
increases and prices usually increase due to inflation. However, better

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measures of true economic growth can be calculated, which take into
account inflation or deflation, as well as per capita measures which take the
total population into account. The only true measure of economic growth is
both per capita and inflation/deflation adjusted GDP. To further dissect the
concept of economic growth and development, a tabular presentation is
shown below with different sub sections for better clarity.
Table 2.1: Highlights of Economic Growth vs. Economic Development
Economic Growth Economic Development
Definition Economic growth is defined
as the increase in the value
of goods and services
produced by every sector of
the economy.
Economic development is
defined as the increase in the
economic wealth and overall
well being (health, education
& income) of the citizens.
Scope It is concerned with small
changes in the economy.
It is concerned with whole
changes in the economy.
Implication It refers to an increase in the
real output of goods and
services in the country like
increase in income, savings
and investment.
It implies changes in
income, savings and
investment along with
progressive changes in
socio-economic structure of

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the country (institutional and
technological changes).
Utilization Economic growth relates to
optimum utilization and
development of under-
utilized resources of
developed countries.
Economic development
relates to the utilization and
development of unused
resources in underdeveloped
countries.
Growth Growth refers to steady,
general and gradual increase
in the rate of savings, output
and investment.
Development relates to a
stationary state to a higher
level of equilibrium.
Direction Economic growth relates to
problems of developed
countries.
Economic development
relates to problems of
developing countries.
Effect Brings quantitative changes
in the economy.
Brings quantitative and
qualitative changes in the
economy.
Source: Amakom, (2010).
2.2.2 Review of Basic Theories
Most world thinkers or philosophers have in recent times been attracted by
the nature of the relationship between population growth and the socio
economic system of a given geographical zone. This attraction gave rise to

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the postulation of so many theories of population. According to Kelley, A. C
(1986), there are two broad theories of population growth namely; Micro
and Macro Analytical Theories. The former, stresses the role of individuals
as it relates to fertility, survival life span etc while the latter is concerned
with societal evaluations as it relates to patterns of fertility, growth,
mortality et cetera. [Although, some authors may classify them into the
Pessimistic Theorist (or The Malthusian theory), the Optimistic Theorist (or
Marxist theorist) and the Liberal theorist]
Under the Microeconomic Population Theory is the Declining Mortality
Theory of Population Growth which is based on two arguments: 1. Need for
fewer children to be born to ensure desirable family size. Parents do not
need to keep up with large family size. 2. Declining mortality actually
imposes hardship on families who will have to spend to keep up a very large
offspring.
Also is the Social Status Theory of population Growth where people who
tend to seek high social status, control their number of children to the least
minimum. This theory states that there is widespread desire to rise to a high

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social status but higher families inhibit social status mobility, thus the
control of their family size.
Furthermore, under the Macroeconomic Population Growth Theories is the
popular Malthus Demographic Theory propounded by Rev. Thomas Malthus
in 1798 in his book entitled, “First Essay on Population” which was based
on two propositions – that population would grow at a geometric rate (i.e. 1,
2, 4, 8, . .) mainly due to a lack of conscious restraints on fertility, while
Food would grow at an arithmetic rate (i.e. 1, 2, 3, 4, . .) basically due to
diminishing returns to increasingly scarce land. In the short run, this will
result to food shortages, starvation, and death. In the long run, therefore,
population size would be held in check by food availability and mortality.
Population pressures would constrain income per capita to a low level of
subsistence - a “Malthusian trap,” as it has been termed. These images
caused economics, unfairly, to be dubbed the “dismal science.”
Nevertheless, Malthus theory is not without a flaw. Though fortunately,
Malthus' predictions were not sustained by the preponderance of experience
over the next two centuries. Couples did not breed without restraint, but
rather by consciously managing fertility in response to changing conditions.

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Food was not unduly constrained by land availability. Instead, technology
blossomed and food expanded apace in the very geographic regions where
Malthus focused his empirical studies. Ironically, food surpluses turned out
to be a “problem” confronting many nations, and governments implemented
policies designed to curtail farm production.
The Anti- Malthusian Theory of Population Growth Rate is the reverse or
opposite of the Malthusian Demographic Theory which refuses to see any
negative impact of high population rate but considers it as a sign of
prosperity. According to the modern American Economist Simon Julian, the
ultimate resource of economic growth is people who are skilled and spirited.
People who will exert their will and imagination for their benefit and for
others are needed (Dyson, 1996).
In discussing the Economic Growth Theories, the origin of modern growth
theory lies in the work of Robert Solow and dates back to 1956 in Solow’s
article “A contribution to the theory of economic growth” (Solow, 1956).
Modern growth theory is still widely used in economic theory although the
modeled processes sometimes seem to be too simplistic (Foxon, et al., 2013)

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and are based on critical assumptions. According to the neoclassical growth
model, output (understood as GDP) grows due to increases in the inputs,
physical capital, labour and productivity used to produce it. According to
Solow’s Model of Growth, traditionally, the economic output of a country is
seen as a function of capital and labor inputs, combined with technological
change (Solow, 1956). The standard production function used shows that
economic output (Y) is a function of the sum of labor, capital inputs and the
level of technological progress. The model is built around a standard CRS
production function, with given levels of capital and labor. Also, growth
only occurs through the expansion of knowledge, i.e. we have technological
progress. The economy eventually reaches its equilibrium of the balanced
growth path where output, capital and labor are growing at a constant rate. In
Solow model, the growth rate is completely determined by advances in
knowledge or the technological progress.
In the Schumpeter’s Growth Theory, growth process involves three principle
elements namely: innovation, entrepreneur and the bank credit. The first
element ‘innovation’ can take on any form of the following five types
namely; i) Producing a new good or new quality of goods. 2) Using a new

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method of production. 3) Finding a new market. 4) Locating a new source of
supply. 5) Finally, reorganization of an industry, such as a monopoly. The
second element which is ‘Entrepreneur’ of the Schumpeterian type is one
that has the qualities of leadership in being a pioneer in breaking new
grounds. The entrepreneur is one that does not go by rational calculations
(since these are not possible in this perception of development) but is an
innovating and dynamic type of individual who enjoys finding challenges
and doing something new. The final element ‘Bank Credit’ besides
innovations and the innovating entrepreneur is another essential element of
the Schumpeterian model. The availability of credit, gives to the
entrepreneur, the freedom needed to undertake risks of investments
connected with innovations. Without bank credit, the entrepreneur would
have to depend upon the routine saving associated with abstinence from
consumption.
Apart from the major elements, there are two basic concepts associated with
Schumpeter. One of them is the Creative Destruction Concept. Schumpeter
is prominent for his theories about the vital importance of the entrepreneur
in business, emphasizing the entrepreneur’s role in stimulating investment

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and innovation, thereby causing creative destruction. This creative
destruction occurs when innovation makes old ideas and technologies
obsolete. This process has been called the Schumpeter Mark I regime. He
further emphasized that it is necessary in other to absorb and to retain the
growth consequences on account of the innovational activities of the
entrepreneurs. The second concept is the Creative Accumulation Concept. In
Capitalism, Socialism and Democracy, Schumpeter focuses on innovative
activities by large and established firms. He describes how large firms
outperform their smaller counterparts in the innovation and appropriation
process through a strong positive feedback loop from innovation to
increased R&D activities. This process of creative accumulation is the main
characteristic of what has been called the Schumpeter Mark II regime. He
describes how the innovating entrepreneur challenges incumbent firms by
introducing new inventions that make current technologies and products
obsolete.

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2.2.3 Other Related Theoretical Issues
Positive and Negative Effects of Population on Economic Growth
One of the positive effects of population on economic growth is ‘the
Economies of Scale’ phenomenon of population growth: Despite of the
Malthus’ theory of diminishing return when it comes to scarce resource like
food and water, some of optimistic population growth economists like
Kuznets (1956), Boserup (1965) and Simon (1981), believed that population
growth can really help the nation economy to turn from ineffective economy
into ‘economies of scale’ state. According to Kendrick (1977), economies of
scale are an important factor to increase the productivity (increase in output
per unit of labor) of one nation. A country, which has a rapid population
growth, can suffer many burdens, such as capital dilution, shortage of
necessity resources and the casualty could lead the whole population to
poverty, famine and starvation. However, there are three arguments
supported for the idea that population growth can boost the country economy
by “economies of scale” phenomenon.

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Firstly, a nation which has a rapid population growth rate means that its
population size will develop with a quicker rate. The bigger the population
size, the larger the market size becomes. In order to meet the product
demand of the large-size market, bigger and more effective as well as longer
performance period manufacturing plants are required to develop (Simon
1994). Therefore, the producing cost and setup cost per one output have
tendency to reduce.
Secondly, the large-scale of population not only have a large size market but
also possess an impressive number of labors. Because of the availability of
labor force, it is possible for firms to divide their labor into particular
division of labor to do specific tasks. According to Adam Smith, “division of
labor has caused a greater increase in production than any other factor and
this diversification is greatest for nations with more industry. Moreover,
through specialization, working skill of labor force is likely to improve more
quickly with learning-by-doing since a large size of population demands a
tremendous number of products. As a result, the average time spending for
producing one unit of output have tendency to decrease more quickly than in
smaller market-size. Correlating with saving producing time, the cost per

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one product is also deducted and firm is more efficient through
specialization.
Finally, the rapid population growth rate could cause a positive effect on
communication and transportation. Transportation plays an important role in
economic development. A good transportation system can help reduce
transportation cost and travel time. Along with high population growth rate,
the increase in population density is inevitable. A dense population is likely
to pressure the government to develop more in transportation system such as
railroad, highways and road. Take China as an example, according to United
Nations Population Division, in 1985, its population density was 110
people/km2 and the total amount of railroad was 52,000 km while in 2010,
the total length of railroad is 91,000 km (increase 75%) and its population
density is 141 people/km2 (increase 28%). Transportation improvement is
surely a general trend for every economic development, but it is not deniable
to state that the population density has a strong impact on number of
construction of transportation. As Julian L. Simon stated in “The Ultimate
Resource”, “population growth clearly leads to an improved transportation
system, which in turn stimulates economic development”.

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Acceleration of technological progress: The Industrial Revolution started at
the beginning of 18th century and ended at the end of 19th century. This is
the period when Malthusian population growth model was broken down and
technology proved its own importance to economic growth. In Cobb-
Douglas model, y = Akαh1-α; where y is output per worker, A is
productivity and h is human capital per worker; technological progress,
which increase the value of parameter A, eventually lead to the higher output
per worker with the same number of input. According to early neoclassical
model of Solow (1956), the role of technological change is crucial and he
emphasized that it is even more important than the accumulation of capital.
There are some theories supported for positive effect of population growth
on technological growth, two most well known theories belonged to Boserup
and Simon (1981). Among the optimistic economists in population growth
field, Boserup is quite famous as an Anti Malthusian Economist. In her
theory, she argued that when the population faces a critical event like
shortage of food or other necessity goods, people would find a way to
overcome the situation by increasing workforces, using new method of
producing or inventing new machines, tools, etc. In Simon-Steinmann

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Economic growth model, Simon also shows the idea that the greater the total
population, the greater the level of technological growth which eventually
lead to yield in greater per capita income.
A country, which has a higher population growth rate, implies that there is a
rapid increase in school-age population. Instead of investing in other
essential industrials to increase the overall capital accumulation, the
government has to spend more public spending in schooling and educational
facilities. The pressure created by massy number of school-age population
also retards the general education level of the nation. However, in long run,
huge investment in education in present can result in the accumulation of
human capital, which is a special stock of competence, knowledge,
personalities as well as the ability to produce economic value. Human
capital has two effects on economic development. First, human capital can
be used as a productive factor like other capitals like machine, vehicles etc.
Second, human capital can directly contribute to the development of new
technology which affects productivity positively. Hence, greater population
growth tends to raise the level of technology growth.

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The population growth enlarges the size of labor force, so, the average wage
rate, therefore, is pushed down. In developing countries, low wage rate is
considered an important factor in the progress of industrialization and
modernization, which are closely related to the wealth of the nation.
Moreover, instead of spending a huge amount of money to pay the labor,
firm can invest more in R&D sector, which finally result in the sufficient
development of new technology that leads to higher productivity. Hence, the
growth of population is likely to help firms to have a better chance in
competing with other foreign rival firms.
On the other side, the negative effects include, ‘Capital dilution’: The first
problem caused by population growth is capital dilution. In Asian
Developing countries, the total population is going up dramatically. For
example, according to United Nations Population Division, in 1965, India
had the total population around 497 thousands while in 2010, the total
population of India is approximately 1,214 million (increased 1.44%).
Assume that the amount of capital in a country is constant, an increase in
population will lead to a decrease in capital per worker (since adding more

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workers can lower the amount of capital at each worker’s disposal). In
economics, this situation is called capital dilution.
Standard of living: Population growth also leads to higher total consumption
demand for goods and services. If supply lower than demand, the goods will
become scarce. Due to high demands and shortage of resources, the prices of
the goods will increase. The raise in price, however, declines the demand for
goods, this decrease in demand is caused by the inadequate income per
capita, which implies that people cannot afford to buy necessary goods and
services required to survive. Consequently, this leads to starvation, poverty,
disease as well as a decrease in economic growth.
Age structure: The demography divides population into three categorizes,
which are: young age population (0-14 ages), working age population (15 -
64 ages) and old age population (over 65 ages). Amongst these three
categorizes, young age and old age population can negatively affect on the
output per capita for two reasons. First, population in the ages of below 14
and over 65 belong to the group in which most people are not or stop
working. In case they have no ability to work, the proportion of population

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participating in productive works will be reduced, which leads to a decline in
the total output per capita.
Let us take a practical example in China. Because of the “one child policy”
per household, the fertility rate in Chinese declines, which is automatically
means that older population will take a larger portion than in the past. Thus,
Chinese population is promptly aging. We can see that along with the
decrease in fertility, the ratio of the working-age (15-64) to non-working-age
population go up irregularly starting in the late 1970s. It reaches its peak in
2010 and is having a tendency to go down due to the increment of elder
population. For example, from 1995 to 2000, the old age population growth
rate in China raises from 6.01% to 6.79% while in contrast, GDP per capita
growth rate decreased critically from 9.7% to 7.6%. Second, the savings rate
is different depending on ages. Working-age people save the most since they
can draw money from their salary. While in case of the elder and the
younger, because of not working, they have no or little income (although
they sometimes receive subsidy from government or family support), so they
have no ability to save. If a country has a high percentage of elder and
younger people, the savings rate per capita will go down. According to the

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Solow model, fewer saving available for investment can lead to a decline in
steady state output per worker as well as bring detriment to the economy.
2.3 Empirical Literature Review
The impact of population on the process of economic growth is one of the
oldest topics in the literature on economics spanning from 1798. The
evaluation of this subject matter has varied over time, ranging from the
highly pessimistic to the outright optimistic. A systematic review of the
major studies in this literature represents a useful way to organize a survey
of the consequences of demographic change. Such an approach places the
population debates in perspective, and it infuses a healthy dose of caution in
appraising current debates.
In 1798, Reverend Thomas Malthus with his two propositions which is the
first ever essay on population, postulated that population would grow at a
geometric rate due mainly to a lack of conscious restraints on fertility, and
food would grow at an arithmetic rate due substantially to diminishing
returns to increasingly scarce land. As years went by, it became clear that the
Malthusian ideas regarding population-economic linkages were incomplete,

31
and richer analytical and empirical foundations were needed. The urgency
for such a framework was made apparent by demographic events. By the
mid 20th century, it was recognized that the simultaneous occurrence of
declining mortality and exceptionally high and sustained fertility in scores of
developing countries was resulting in high population growth rates. A
concern emerged that these rates could not be sustained over long periods of
time. While, as in the past, fertility would predictably decline (the
Demographic Transition), still it was unclear whether such a decline would
be soon or rapid enough to avoid potentially deleterious effects on welfare,
economic progress, and the environment. Thus, while the “Malthusian
Problem” reappeared, approaches to assessing population consequences
assumed quite different tacks. It was time for a fresh reassessment.
Expanded Elaborations began in the 1950s, 1960s and 1970s.
The United Nations in 1953 undertook a critical study which underlies one
the major themes of this study – the positive impact of high population on
economic growth, “Determinants and Consequences of Population Trend”,
which provided a major balanced economic demographic interaction studies.
It was found out that the impact of population on some economic growth

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factors were judged to be positive due to economies due to scale and
organization, on some other economic growth factors, negative due to
diminishing returns, and on some neutral technology and social progress.
That is to say that impact (whether positive, neutral or negative) was
dependent on varying factors.
Attention of researchers began to focus on Asia having a clear high
population rate. Ansley J. Cole and Edgar Hover (1958) in their renowned
book “Population Growth and Economic Development in Low Income
Countries” based on an experiment conducted in India using simulation
results of mathematical model calibrated by India data, found out that
India’s development will be significantly enhanced by a decrease in their
population rate. This study drew scholarly attention since it focused attention
on physical capital as key to economic development, other than land as
focused by Malthus.
National Academy of Sciences (1971) in their study, “Rapid Population
Growth: Consequences and Policy Implications” found out, though
alarming, and listed twenty five different negative impact of population

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growth with no single positive impact. Nevertheless, with careful reading,
important insights assisting in illuminating the flow and ebb of population
assessments are revealed.
The United Nations in 1973 (i.e. after twenty years) updated its early
assessment of 1953. This revision arrived at a less eclectic, and a somewhat
more pessimistic (but by no means alarmist) evaluation of the various
impacts of population growth. This is particularly true of anticipated
difficulties of feeding the expanding populations (reverting to traditional
Malthusianism), and of pressures on capital formation (reverting to the
concerns of Coale and Hoover 1958). Furthermore, Simon Kuznets in 1973
made a contribution derivable from the United Nations 1973 study and had a
finding based on simple correlations; though they found out net negative
impact of population growth on per capita output but was not obvious in the
data. While his work was based on longer-run assessments, and while they
were appropriately qualified, they were important to conditioning the
bottom-line UN assessment. Moreover, given the strong priors of
demographers and policy makers, that the negative impacts of population
growth on development were large; the inability to easily “confirm” this

34
hypothesis through simple, albeit inconclusive, correlations more than any
other factor, kept the population debate alive during the ensuing decades.
The 1980s researches on population impact on growth were referred to as
the revisionists (i.e. a break away from the traditional arguments that
previously structured the population debate). So in 1981, Julian L. Simon
made a publication he titled, “The Ultimate Resource” which challenged
most negative views of population on economic growth by different
prevailing authors. First, it concluded that population growth was likely to
exert a positive net impact on economic development in many Third World
countries in the intermediate run; a startling assertion that attracted extensive
attention. Second, it illustrated that the outcome of population impacts on the
economy are likely to hinge both on the time dimension of the assessments,
and whether feedbacks are included in the analysis.
Allen C. Kelley's (1988) survey for the Journal of Economic Literature
concludes that, “Economic growth would have been more rapid in an
environment of slower population growth, although in a number of countries
the impact was probably negligible and in some it may have been positive”

35
(p. 1715). Adverse impacts are most likely to occur where 1) water and
arable land are scarce, 2) property rights are poorly defined and 3)
government policies are ineffective and biased against labor.
Revisionists continue to contend that strong, modern institutions can soften
the impact of population growth’s negative effects on economic
productivity. Population growth appears most detrimental and most difficult
to surmount in the poorest, least- developed countries, where modern
institutions have yet to realize their potential to organize society and
economies. Nicholas Eberstadt (2011) expresses this conclusion, “population
growth is clearly a form of social change; nations and governments that cope
poorly with change are unlikely to deal adeptly with the disequilibria that
more rapid rates of population growth necessarily bring”. Finally, it is
noteworthy to state that in more recent times, what has been labelled a
‘Revisionism Revised’ has emerged (Birdsall et al. 2001; Sinding 2009).
That Revisionism Revised is well founded; indeed if anything, it is believed
that they seem to be understating the power of their case.

36
From the 1990s and beyond, researches on this subject matter were referred
to as the ‘New Paradigms’. While most of the 1990s was preoccupied with
digesting the revisionist results of the 1980s, population research did
advance in several areas. First, the findings from “simple correlations”
between the rate of population and per capita economic growth appeared to
have changed. While a general lack of correlation was the widely obtained
statistical result for the 1960s and 1970s, in the 1980s the correlation turned
negative (see Kelley and Schmidt 1994).
On the one hand, most analysts agreed that such simple correlations are
difficult to interpret, plagued as they are by failure to adequately account for
reverse causation, excessive reliance on cross-section data, sensitivity to the
selection of countries, the complexity of demographic linkages that are
poorly modeled, spurious correlation, econometric pitfalls, and data of
dubious quality. On the other hand, the previous finding of no correlation for
the 1960s and 1970s in the face of strongly held priors of a negative
correlation literally kept the population debate alive. Now, a change in this
relationship from one of no-correlation to one of a negative correlation for
the 1980s required an explanation. New questions appeared: what accounts

37
for the changed correlations; are the new results robust; are they
quantitatively important?
The ability to address these issues coincided with the emergence in the
1990s of empirical “convergence” models of economic growth. Pioneered
by Robert Barro (1997), these empirical paradigms distinguish between
factors (economic, political, social, institutional and geographic) that
determine each country's long-run level of per capita output, and the shorter-
to-intermediate-run transition of countries to this longer-run state. These
models lent themselves to investigating the impacts of demography since
they exposed both short- and long-run impacts.
Efforts to model demography using the new convergence models have
varied notably. Barro (1997), for example, looked primarily on the longer-
run impacts of demography, and found that reductions in the total fertility
rate increased the potential for economic growth. In yet an earlier study,
Kelley and Schmidt (1995), building on the Barro core variables,
distinguished between several alternative demographic influences on the
economy's potential output in the long-run, (e.g., the impacts of population

38
size and density), and timing of demographic impacts (e.g., the timing of
reductions in birth and death rates) which influence both the short and long
run.
Bloom and Williamson (1998), also building on Barro’s empirical
framework (although with different core variables highlighting policy and
geography), modified the demographic modeling to break out an accounting
reckoning of age compositional impacts. While explicit modeling of longer-
run demographic impacts is absent in their framework, their clean
accounting framework clearly exposes the impacts of changing age
structures, driven by changes in fertility and mortality. These are
quantitatively important impacts on the transition to long-run output per
capita. Their results focused on East Asia where declines in fertility were
rapid and shorter-run transition effects are predictably large.
Kelley and Schmidt (2000) compared the above (and other) modeling efforts
in a single empirical investigation, and came up with a somewhat surprising
result: demography accounts for around 20% of changes in output per capita
growth from 1960-1995 across a wide collection of countries. While for

39
several reasons they consider their findings qualified, it is interesting that
these findings are broadly consistent with those of the 1980s.
The impact of Population looked likely adverse over the period 1960-1995;
this impact varies from decade to decade; components of demographic
change exert both positive and negative impacts; these impacts vary notably
from place to place; and, as a determining variable of long-run economic
prosperity, population’s impact is notable, but not remarkable. In the shorter-
to-intermediate run, during periods of “transition” (both demographic, and
economic), population's impact can be elevated or diminished, depending on
the pace of demographic change and especially on the country's specific
institutions (government policy, efficacy of markets, definition of property
rights).
In less developed economies, relatively rapid population growth almost
always resulted in a fall in the standard of living due to the rather severe
limits to the technical progress in agriculture or to the fixed supply of land,
as pointed out by Malthus (1798). This prompts Clark (2007) to state that
income levels before the nineteenth century could not escape the Malthusian

40
equilibrium due to the very low rate of technological advance in all
economies. However, according to the ‘neutralist’ or ‘revisionist’ view, high
population growth rates in developing countries since the middle of the
twentieth century have had little effect on per capita GDP growth (see, for
instance, Kuznets (1967), Kelley (1988), and Kelley and McGreevey
(1994)). Simon, (1981& 1989) would go as far as suggesting that population
growth may have had a positive impact on per capita GDP growth in the
long run through improvement of productivity through the contribution of
new ideas and the learning-by-doing resulting from increased production
volume. Nevertheless, the current consensus is that, as more data become
available, rapid population growth has exerted a significant negative effect
on economic growth in developing countries (see, for example Birdsall and
Sinding (2001), Barro and Sala-i-Martin (2004), Sachs (2008), and Headey
and Hodge (2009)).
Further research by economists Allen Kelley and Robert Schmidt indicates
that during the 1980s population growth, on average, acted as a brake on
economic growth as measured by the growth rate of per capita gross
domestic product, or GDP. Results of this extensive analysis suggest that the

41
relationship between population growth and depressed economic
performance is strongest among the poorest nations of the developing world,
and that the effect on this group extends back through the 1960s and 1970s.
The growth of gross domestic product can be constrained by high
dependency ratios, which result when rapid population growth produces
large proportions of children and youth relative to the labor force.
Among other western countries, attention of researchers has been on Asia
since the early fifties. A recent study by Fumitaka, F. and Qaiser, M (2010)
on Pakistan, detected a long-run co integrating relationship between
population growth (POP) and economic growth (GDP). Also, a
unidirectional long-run causality from Population to GDP was in evidence.
In other words, Pakistan’s population expansion Granger-caused the nation’s
economic development. These findings indicate that Pakistan represents a
textbook example of the population-driven development where the
population expansion induces economic development. Interestingly,
Pakistan with a population of about 190 million is a neighbouring country to
the largest populated country in the world- India; perhaps the positive effects
of India is rubbing off on them.

42
The Population Reference Bureau 2014 - a United States international
development agency that informs people around the world about population,
health, and the environment, and empowers them to use that information to
advance the well-being of current and future generations – in their recent
research, found out that the Worldwide population in 2014 is 7.2 billion
people; 6 billion live in less developed countries and 1.2 billion in more
developed countries. The average total fertility rate worldwide is 2.5%
which ranges from 1.1 children per woman in Taiwan to 7.6 in Niger. Global
infant mortality rate is at 38 per 1000; which declined from 80 infant deaths
per 1,000 live births in 1970 to 38 per 1,000 live births in 2014.
Furthermore, 53% of the world’s population lives in urban areas. Nigeria is
the 7th
most populous nation in the world with 177 million people (as at
2014), China, India, United States, Indonesia, Brazil and Pakistan are first to
sixth respectively. From their projection, Nigeria will be the 3rd
most
populous nation by 2050 as China will overtake India, followed by Nigeria
before United States in ranking as first to fourth respectively. Finally,
shocking is the population clock that shows world total birth per year, day
and minutes as 143,341,000; 392,714 and 273 respectively as death rate is

43
56,759,000; 155,505, and 108. Below is a summary of empirical literature in
tabular form.
Table 2.2: Summary of Empirical Literature
Author(s) Year Location
of Study
Topic Variables
of the
Model
Method of
Analysis
Findings
United
Nations
1953 New
York
City
“The positive
impact of high
population on
economic
growth,
“Determinants
and
Consequences
of Population
Trend”
None Qualitative
or
observational
research
Method via
Survey
It was found
out that the
impact of
population on
some economic
growth factors
was judged to
be positive due
to economies
due to scale
and
organization.
Ansley J.
Cole and
Edgar
Hover
1958 India/
New
Jersey
“Population
Growth and
Economic
Development
in Low
Income
Countries”
Fertility
rate, total
population,
national
Income.
Simulation
results of
mathematical
model
calibrated by
India data
Found out that
India’s
development
will be
significantly
improved by
reductions in
their
population rate
National
Academy
of
Sciences
1971 Washinto
n D.C
“Rapid
Population
Growth:
Consequences
and Policy
Birth rate,
fertility
rate,
poverty
rate,
Quantitative
Multiple
Regression
Analysis
Found out,
though
alarming,
twenty five
different

44
Implications” family
income
level.
negative
impact of
population
growth with no
single positive
impact.
The
United
Nations
1973 Geneva “Reassessment
of the positive
impact of high
population on
economic
growth,
“Determinants
and
Consequences
of Population
Trend”
Population
level,
standard of
living,
gross
domestic
product.
A more
robust
regression
analysis
This revision
arrived at a less
eclectic, and a
somewhat
more
pessimistic
(but by no
means
alarmist)
evaluation of
the various
impacts of
population
growth.
Simon
Kuznets
1973 United
States
“Modern
Economic
Growth:
Findings and
Reflection”.
Labour
force,
population,
productivit
y of
labour,
Experimental
Research
Method
Simple
correlation
He found out
net negative
impact of
population
growth on per
capita output.
Julian L.
Simon
1981 United
States
“The Ultimate
Resource”
Price of
raw
materials
(copper),
wages,
inflation.
Quantitative
Method of
research by
Generalized
Least Square
It concluded
that population
growth was
likely to exert a
positive net
impact on
economic
development in
many Third
World

45
countries in the
intermediate
run
Jess
Benhabib
& Spiegel
+
Pritchett
&
Summers
1994
and
1996
New
York
“Role of
Human
Capital on
Economic
Growth”
Physical
capital
stocks,
human
capital
stocks,
income,
population
literacy
rate,
Regression
using
Ordinary
Least square.
(Cob-
Douglas
aggregate
production
function
model+ cross
country data)
Concluded that
the positive
link from
education
attainment to
output growth
is, at best,
weak.
Barro 1997 Harvard
USA
“Myopia and
Inconsistency
in the Neo
classical
Growth
Model”
Panel
Data:
drop-out
rate,
family
income,
education
of parents.
Quantitative
Research
Method
using
Ordinary
Least Square
Reductions in
the total
fertility rate
increased the
potential for
economic
growth.
Bloom
&William
son
1998 Cambrid
ge
“Demographic
Transition and
Economic
Miracles in
Emerging
Asia”
Mortality
rate,
fertility
rate,
labour
force
Ordinary
Least Square
Population
growth has a
purely
transitional
effect on
economic
growth
Acemoglu,
Daron.
1998 Massach
usetts
“Changes in
Unemploymen
t and Wage
Inequality: An
Alternative
Theory and
Some
Wage
inequality,
demand
for skills,
job
compositio
n
Quasi
experimental
The direct
consequence of
random
matching is
that the
expected rate
of return on

46
Evidence”. human capital
is increasing in
the expected
amount of
physical capital
with which a
worker will be
provided.
Kelley
and
Schmidt
2000 USA “Population
Change and
Economic
Development”
Population
growth,
population
age
structure,
birth and
death rate
Generalized
least square
regression
Given the right
conditions,
fertility will
decline in
Asian countries
with
remarkable
speed.
Gustav, R.
&
Stewart,
F.
2001 United
Kingdom
“Dynamic
Link Between
the Economy
and Human
Development”
.
Infant
Mortality
Shortfall
reduction,
GDPper
capita, gini
coefficient,
public
expenditur
e on health
and
education.
Ordinary
Least Square
method of
Regression
Achievements
in human
capital
development
themselves,
can make a
critical
contribution to
economic
growth.
Fumitaka,
F. and
Qaiser, M
2010 Pakistan “Is Population
Growth
Beneficial or
Detrimental to
Economic
Development?
A New
Evidence from
Gross
Domestic
Product,
Population
growth.
Regression
Analysis
Pakistan’s
population
expansion
Granger-
caused the
nation’s
economic
development.

47
Pakistan”
The
Populatio
n
Reference
Bureau
2014 United
States of
America
“2014 World
Population
Data Sheet”.
Birth rate,
death rate,
population
rate.
Survey Nigeria is 7th
most populace
nation in the
world after
India, China,
USA et cetera
Source: Author’s Compilation.
2.4 Summary of Literature Review
From the foregoing, attempts have been made to first, define the major
concepts of the research topic. The various theories surrounding the work
were x-rayed from population to the economic growth theories. The findings
from multiple researches of different authors were rigorously examined in
the empirical literature showing the expectations and the obtainable from
various works as it relates to the research topic under discourse. Researches
spanning from organizations to individuals were summarized in tabular
forms showing clearer details. In a more technical language, this chapter has
provided an overview of economic theories and empirical studies on the
relationship between population and growth. The theoretical literature has
placed emphasis on population activities in the context of growth theories
has been outlined and brief overviews and main findings of relevant and

48
related empirical studies have been presented. Factors that account for the
positive impact of population on economic growth are the economies of
scale, technological acceleration etc whereas for the negative impact include
capital dilution, age structure, standard of living.
Furthermore, the surveyed empirical results reveal that the effect of
population growth on per capita GDP growth is either way positive
especially in recent times and negative in the seventies. This is because of
the conscious efforts to curtail birth rate by Governments in developing
countries to stimulate growth. China provides a clear example by suddenly
introducing a collection of highly coercive methods to reduce the total
fertility rate from about 5.8 to 2.2 births per woman between 1970 and 1980
which is paying them at the time being. This adverse population growth
began when too much concentration was earlier given to reducing mortality
rate causing an imbalance (although there was hope for a decline in that
prevailing fertility rate). The pre revisionists of the seventies experienced
more negative impact of population but since researchers could not prove it
using a simple correlation, debate continued until the revisionist, revised

49
revisionist and new paradigm of the 1990s where the anti-Malthusians
(optimist) school of thought starting gaining grounds.
In conclusion by way of contribution, this study will add value to current
literature because a more concise regression analysis using better suitable
data is used to fully portray to a large extent the impact high population has
on economic growth. This will make our policy makers in Nigeria posses
another good tool to encourage the efforts of doing what is necessary in
making our high population become positive to economic growth.
2.5 Justification for the Study
This research work is very vital particularly now in Nigeria when our
population growth has been ever increasing and seem to be deterrent to the
growth and development expectations of the economy. This work taking a
rather considered unpopular stand is of the view that this high population
which we can do little or nothing to correct especially in the short run, can
contribute beneficially to boosting significantly our economic growth and
development, if we encourage (by way of private and public sector
contributions and enabling economic and business environment) massive

50
citizenry capacity building and entrepreneurial strands. Unlike most works
that have given more than sufficient considerations to verifying the negative
impact of high population on growth, this research work will in no doubt add
to the rare optimist view of the positive impacts of high population on
economic growth (this time around) in Nigeria.
Although, the research direction of this work is not considered a virgin
course, it is significantly justified because a more reliable data not available
to previous researches is currently available and posses a more efficient
result devoid of heteroskedasticity for policy maker’s consumption.

51
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 Introduction
Unlike laboratory scientists, economists cannot conduct controlled
experiments. Their work relies on surveys involving standard economic
statistics and on expectations from the theories of their discipline. Using
these, economists try to identify patterns over time and through
comparisons, shape their conclusions. This study will therefore employ the
correlation or regression method of analysis using secondary data which will
be interpreted using the classical linear regression model by use of ordinary
least square with the aid of Economic views (E-views) statistical software.
Our regression result will form the basis for our final conclusion based on
our findings.
3.2 Theoretical Framework
The underlying theory to give credence and backbone to this research work
shall be the Simon Julian’s “Anti-Malthusian Theory”. This is so

52
considering the drive and aim which this research work focuses. High
population growth is loaded with potentials of turning the Nigerian’s
economy around if we focus on boosting the resourcefulness of our populace
through skills and financial empowerment. As the American Economist
Simon Julian postulated, “The ultimate resource of economic growth is
people who are skilled and spirited. People who will exert their will and
imagination for their benefit and for others are needed (Dyson 1996)”.
According to this theory, more people contribute to increase in the stock of
knowledge through competition among them. Division of labour and
economies of scale happens if there is increase in population growth. Thus
population growth increases growth and development. As was earlier
discovered, this theory is the reverse or opposite of the Malthusian
Demographic Theory - based on the dreadful negative effects of high
population on growth (scarcity of food, et cetera) as postulated by Rev.
Malthus - that refuses to see any negative impact of high population rate but
considers it as a sign of prosperity.

53
3.3 Model Specification
This research work examines the research hypotheses, ‘Significance in
relationship between population and human capital on economic growth’.
Models will be justifiably specified, to fully accommodate the necessary
verifications. For the model, the dependent or regressand or explanatory
variable will be real gross domestic per capita which is justifiable as the best
proxy for economic growth as used by a wide range of authors. On the side
of the independent or explained variables or regressors: population growth,
literacy rate, human development index and human capital which are good
indicators of any population will measure for the model. Since the both
major variables (TPOP and HC) measure on the same dependent variable
GDPpc, there will be no need for a separate model specification. We will
therefore proceed to specify the justified models using the statistical tool
thus:
GDPpc = α0 + β1TPOP + β2LITR + β3HDI + β4HC + µi
…………………………………………………………….…………Eqn 1

54
Where:
GDPpc is the Gross Domestic Product Per capita(head)
TPOP is the Total Population
LITR is the Literacy Rate
HDI is the Human Development Index
HC is the Human Capital
α0 is the intercept (value of GDPpc when TPOP, LITR, HDI, HC is zero)
β1 is the slope (magnitude of change of GDPpc by a unit change in TPOP
etc)
µi is the stochastic disturbance or error term.
However, because we intend to standardize all the variables – dependent and
independent – (since they have different rates: some in percentage, nominal
value et cetera) and interpret the resulting slope coefficients as elasticity, the
modified form of the equation above is rewritten in natural logarithm form
and becomes thus:
LnGDPpc = α0 + β1LnPOPG + β2LnLITR + β3LnHDI + β4LnHC + µi
………………………………………………………………......…Eqn 2

55
3.4 Estimation Technique and Procedure
The correlation or regression technique of analysis is adopted in this
research work. The secondary data used, will be estimated using the classical
linear regression model via the ordinary least square method using
Economic views (E-views) statistical software. The complete analysis shall
follow this procedure:
Unit Root or Stationarity Test
First, our time series data gathered from different sources will be subjected
to a stationarity test to contain the spuriousity (possible falsification or
errors) of the data using the Augmented Dickey Fuller Test (ADF Statistics).
A series is said to be stationary if its mean and variance are constant over
time and the value of covariance between two time periods depends only on
the distance or lag between the two time periods and not on the actual time
at which one covariance is computed Gujarati (1995). The study uses the
Augmented Dickey Fuller (ADF) test to determine the optimal length in the
dependent variable. This is done to ensure that there is no serial correlation
in the residuals. The ADF test addresses a shortcoming of the Dickey Fuller

56
test of not considering the possibility of autocorrelation in the error term by
adding a lagged difference term, and therefore corrects for high-order serial
correlation. When the data are found stationary (either at level, first
difference or second difference), we now proceed to the next step which is
the co integration test.
Co integration Test
The necessary condition for a co-integration test, is that the data tested is at
least stationary at level. This is because if the series are stationary at level, a
standard regression could be carried out, as there is no risk of spurious
regressions. The co integration test simple ascertains the variables that
possess ample long run relationship with the dependent variable.
It is important to note that the two approaches above, are simply the pre tests
(ascertaining the fitness of the variables for the model) before the data is
subjected to the regression proper (producing the ordinary least square
regression results).

57
Regression Results
The regression results are finally obtained by few statistical procedural
manipulations with the aid of the statistical software E views. The results are
then interpreted but before final conclusions, are subjected to a post test to
test for serial correlation and heteroskedasticity – statistical issues that affect
the efficiency and reliability of the results. After this is undertaken, we can
now safely conclude that our results are statistically sound for verification of
our hypotheses without bias.
3.5 Evaluation of Estimates
3.5.1 The Econometric A priori Expectation
This shows whether each independent variable in the equation is comparable
with the postulations of economic theory; that is, if the sign and size of the
parameters of economic relationships follow with the expectation of the
economic theory. We will represent them in a simple table below for both
model 1 and 2 differently.

58
Table 3.1: Table of A priori Expectation
REGRESSAND REGRESSOR RELATIONSHIP
GDPpc TPOP +/-
GDPpc LITR +
GDPpc HDI +
GDPpc HC +
Any parameter estimates with a positive sign (+) indicates that the
independent variable in question has a direct or positive relationship with the
dependent variable. This means that if that particular independent variable
increases, the dependent variable will increase too. Thus, they move in the
same direction. However, a negative sign (-) implies an inverse or negative
relationship meaning that if the independent variable increases, the
dependent variable will decrease, and vice versa. Thus, they move in
opposite directions.
3.5.2 Statistical Criterion: First Order Test
The aim of this test is to evaluate the statistical reliability of the estimated
parameters of the model. Most widely known and commonly used is, the

59
Co-efficient of determination (R2
) and the Adjusted Co-efficient of
determination ), F-statistic, and the t-statistic.
Co-efficient of Determination ) and Adjusted
The square of the coefficient of determination R2
or the measure of goodness
of fit is used to judge the explanatory power of the explanatory variables on
the dependent variables. The R2
denotes the percentage of variations in the
dependent variable accounted for by the variations in the independent
variables. Thus, the higher the R2
, the more the model is able to explain the
changes in the dependent variable. However, if R2
equals one, it implies that
there is 100% explanation of the variation in the dependent variable by the
independent variable and this indicates a perfect fit of regression line. While
where R2
equals zero. It indicates that the explanatory variables could not
explain any of the changes in the dependent variable. Therefore, the higher
and closer the R2
is to 1, the better the model fits the data.
Owing to the defect of the R- squared, tending to increase in value as more
variables are added to the model, the Adjusted R- squared was formulated to
contain this porosity.

60
The F-test
The F-statistics tests for the overall significance of any regression model. It
is used to test whether or not, there is a significant impact between the
dependent and the independent variables. In the regression equation, if
calculated F is greater than the table F table value, then there is a significant
impact between the dependent and the independent variables in the
regression equation. While if the calculated F is smaller or less than the table
F, there is no significant impact between the dependent and the independent
variable.
The t-statistic
The t-statistic determines the statistical significance of each variable
coefficient. Here, the absolute t-value of each coefficient is compared with
1.96 and if greater than 1.96, such variable possessing the coefficient is
accepted as statistically significant and fit to be used for inferences and
possibly for forecasting.
3.5.3 Econometric criterion: Second Order Test
The second order test aims at investigating whether the assumption of
econometric method employed are satisfied or not in any particular case.

61
They determine the reliability of statistic criteria and also establish whether
the estimates have desirable properties of unbiasedness, and consistency. It
also tests validity of non-auto correlation disturbances. The Durbin-Watson
(D-W) statistic is widely known and used for the test.
Test for Auto – Correlation (DW)
This Durbin – Watson (DW) is appropriate for the test of first order
autocorrelation and it has the following criteria.
(a) If d* is approximately equal 2(d* = 2) we accept that there is no
autocorrelation in the function.
(b) If d* = 0, there exist perfect positive auto-correlation. Furthermore, if
O<d*< 2, that is if d* is less than two but greater than zero, it denotes that
there is some degree of positive autocorrelation, which is stronger, the closer
d*is to zero.
(c) If d* is equal to 4(d*=4) there exist a perfect negative auto-correlation,
while if d* is less than four but greater than two (2 < d* < 4), it mean that
there exist some degree of negative autocorrelation, which is stronger the
higher the value of d*.

62
3.6 Test of Research Hypotheses
Before we state our statistical yardstick for the Test of Hypotheses, let us
recall our working hypothesis:
Hypothesis
H0: There is no significance in relationship between population and
economic growth.
The above stated hypothesis will be tested at 0.05 level of significance. The
probability at which the t-value of the major variables (TPOP and HC) is
significant will be compared with the chosen level of significance (0.05).
The Hypotheses tested is:
H0: β1 = β2 = β3 =…. β5 = 0 (No Significance in relationship)
H1: β1 ≠ β2 ≠ β3 ≠…. β5 ≠ 0 (Significance in relationship)
Decision Rule: Reject H0 if p<0.05 and accept H1. But if p>0.05, reject H1
and accept H0 all at α = 5%.
3.7 Data Type and Sources
Data used in this research work are basically secondary and sourced from
various sources which include: Global Entrepreneurship monitor data set,

63
World Bank Group Entrepreneurial Survey (WBGES), OECD’s Self
Employment Attitude Research, Central Bank of Nigeria Statistical Bulletin,
The United Nations Development Programme (UNDP) Human
Development Report, EIM’s COMPENDIA data base (Comparative
Entrepreneurship Data for International Analysis), World Bank World
Development Report/ Indicators and the internet sources.

64
CHAPTER FOUR
DATA PRESENTATION, ANALYSES AND DISCUSSION OF
FINDINGS
4.1 Introduction
The set of data provided for this research work cannot be meaningful
without the analysis and interpretation of results obtained. Data analysis
which entails breaking down the information provided into smaller pieces to
further enhance the understanding of the study was undertaken using the
regression method of analysis. The researcher used E views 3.1 software
package to run the ordinary least square (OLS) for models specified in
chapter three.

65
4.2 Data Presentation
4.2.1 Regression Results
Table 4.1: Presentation of Regression Results
White Heteroskedasticity-Consistent Standard Errors &
Covariance
Variable Coefficient Std. Error t-Statistic Prob.
C 0.650826 0.795878 0.817746 0.4199
LNTPOP 0.233189 0.100260 2.325840 0.0270
LNLITR 0.166366 0.545455 0.305004 0.7625
LNHDI 2.739641 1.490131 1.838523 0.0759
LNHC 0.054899 0.032883 1.669507 0.1054
4.2.2 Statement of the Regression Equations
From the regression results above, a specification of the mathematical
equation is thus:
LnGDPpc = F (LnTPOP, LnLITR, LnHDI, LnHC)
LnGDPpc = 0.65 + 0.23LnTPOP + 0.17LnLITR + 2.74LnHDI + 0.05LnHC + µi
*2.33 *0.31 *1.84 *1.67
*= t-statistic

66
4.3 Data Analysis
4.3.1 Stationarity Test
Time series data were used for the regression which is known for its defect
of porosity, thus a test called a unit root test using Augumented Dicky Fuller
is used to ascertain the stationality of the data. A series is said to be
stationary if its mean and variance are constant over time. The study uses the
Augmented Dickey Fuller (ADF) test to determine the optimal length in the
dependent variable. This is done to ensure that there is no serial correlation
in the residuals. The ADF test addresses a shortcoming of the Dickey Fuller
test of not considering the possibility of autocorrelation in the error term by
adding a lagged difference term, and therefore corrects for high-order serial
correlation. The author calls the unit root test and co integration tests pre
tests since they are first ascertained before the actual regression results are
produced.

67
Table 4.2: Summary of Unit Root Test
VARIABLE ADF STATISTICS CRITICAL
VALUE
ORDER OF
INTEGRATION
LnGDPpc -7.6897 1% 1(0)
LnTPOP -9.1167 1% 1(2)
LnLITR -5.5531 1% 1(1)
LnHDI -5.5825 1% 1(1)
LnHC -6.1897 1% 1(1)
The decision rule for stationarity test is that the Augumented Dicky fuller
Statistics (ADF Stat) is greater than the critical value @1% significance
level. From the above LnGDPpc is stationary at level thus denoted by the
symbol 1(0). LnTPOP is stationary at second difference denoted by 1(2)
while LnLITR, LnHDI and LnHC are all stationary at first difference and
denoted by 1(1). After the unit root test is satisfactory, the data are now fit
for co integration.
4.3.2 Co integration Test
The necessary condition for co integration is that the variables must be at
least non stationary at level. The co integration simply shows the variables
that have ample long term relationship with the dependent variable.

68
Table 4.3: Presentation of the Co integration Report
Series: LNGDPPC LNHC LNHDI LNLITR LNTPOP
Lags interval: 1 to 1
Likelihood 5 Percent 1 Percent Hypothesiz
ed
Eigenvalue Ratio Critical
Value
Critical
Value
No. of
CE(s)
0.980880 210.2880 68.52 76.07 None **
0.682849 79.70708 47.21 54.46 At most 1 **
0.586961 41.81061 29.68 35.65 At most 2 **
0.241060 12.63158 15.41 20.04 At most 3
0.101423 3.529113 3.76 6.65 At most 4
Test indicates 3 co integrating equations at 5% level of significance
It can be seen from the above that there three co integrating variables (i.e.
variables that have an ample long term relationship with dependent variable
Gross Domestic Product per capita). These variables are literacy rate, human
development index and human capital. In other words, more than population
level, literacy rate, human development index and human capital, have a
long term effect on the Gross Domestic Product per capita of Nigeria.
4.3.5 Test for Serial Correlation and Heteroskedasticity
An efficient Linear Classical Model, should posses equal variance and error
terms but Contrary to the law - or better still assumptions - of Linear

69
Classical Model in econometrics exist the problems of serial correlation and
heteroskedasticity. The decision rule for testing for Serial correlation and
heteroskedasticity (that affects the efficiency of a model) using E views is
that the probability of the observed R-squared is either greater than or less
than 0.05. When P(Obs* Rsquared) > 0.05, there is no serial correlation in
the model and vice versa.
Table 4.4: Serial Correlation Test
Breusch-Godfrey Serial Correlation LM Test:
F-statistic 2.484897
Probability
0.101528
Obs*R-squared 5.275823
Probability
0.071510
Table 4.5: Herteroskedasticity Test
White Heteroskedasticity Test:
F-statistic 6.246320
Probability
0.000158
Obs*R-squared 23.02168
Probability
0.003337
From the above, there is no serial correlation in the model because the
probability of the observed R-squared (0.071510) is greater than 0.05. On
the other hand, there exist herteroskedasticity in the model owing to the fact
that the probability of the observed R-squared (0.003337) is less than 0.05.

70
This is corrected using the Heteroskedasticity consistent standard error and
covariance test. The author calls these two tests above post tests because
they are carried out on the regression results before the final authentic and
reliable ordinary Least Square results are acceptable as BLUE- Best Linear
Unbiased Estimate for statistical interpretations and inference.
4.4 Evaluation of Research Hypotheses
4.4.1 A priori Expectation.
There is obviously what theory has said about the expected relationships
between the explanatory and explained variables. This is examined in this
sub section and represented in a table of conformity.
Table 4.6: Summary of Economic A priori Expectations
VARIABLE EXPECTED
SIGN
OBTAINED
SIGN
REMARKS
LnTPOP +/- + Conform
LnLITR + + Conform
LnHDI + + Conform
LnHC + + Conform
All of the used variables conformed to theory. Total population was
expected from review of literature to have either a positive or negative

71
relationship with gross domestic product per capita. That is to say that an
increase in population can either increase or decrease the GDPpc. There will
be an increase in GPDpc if there are sound human capital, fertility policies
among others on ground to manage the growth in population otherwise it
will have very significant negative effects on GDPpc. Increase in literacy
rate even with common sense will increase GDPpc since the populace are
rightly educated to contribute meaningfully to productivity. This increase in
productive economic activity is what we call economic growth. When
divided by the total population, we get the economic growth per capita. The
same applies for human development index and human capital.
4.4.2 Statistical Criteria
Simply put, the statistical criteria tend to evaluate the statistical reliability of
the estimated parameters of the models.
Coefficient of Determination (R- squared)
The R- squared measures the “goodness of fit” of a model. This is done by
measuring the extent of variability of the dependent variable by changes in
the independents. Judging from the regression results in table 4.1 above, by

72
91%, changes in the independent variables (population growth, literacy rate,
human development index and human capital) affect the state of the
dependent variable - gross domestic product per capita. In other words,
population growth, literacy rate, human development index and human
capital account for 91% of what affects gross domestic product per capita.
Adjusted Coefficient of Determination (Adjusted R- squared)
Owing to the defect of the R- squared, tending to increase in value as more
variables are added to the model, the Adjusted R- squared was formulated to
contain this porosity. So, by 90%, the Adjusted R- squared confirms the
claims of the R- squared.
The F- Statistic
The overall significance of the model is tested using the F- statistic.
F0.05 (k-1, d.f)
Where k – 1 = 5 – 1
= 4
(N/B: k is the number of parameters- TPOP, LITR, etc)
Degree of freedom (d.f) = n – k

73
Where n (number of observations) = 35
and k (number of parameters) = 5
Thus, d.f = 35 – 5
= 30
Therefore,
F0.05 (4, 30) = 2.69 (checking 4 under 30 from the F0.05 distribution table)
Comparing with the F cal:
F-statistic (calculated) = 79.4 (from the regression results in table 4.2)
Since the F-calculated is greater than F-table, we reject H0 and accept H1 that
the model has goodness of fit and is statistically different from zero. In other
words, there is significant impact between the dependent and independent
variables in the model.
T-statistic
This unlike the F-statistic compares the individual significance of the model.
Here, we compare the estimated or calculated t-statistic with the tabulated t-
statistic.
t α/2 (d.f)
t α/2 = t 0.05/2 = t 0.025 (two-tailed test).

74
Degree of freedom (d.f) = n – k
= 35– 5
= 30
So, we have: t0.025 30
We now check 0.025 under 30 in the table of t distribution; this gives us
1.960 as our tabular t-statistic.
We can now use the yard stick of 1.960 to evaluate or compare each
independent variable for all models, to ascertain its significance. If
calculated t (gotten from the regression result) is greater than the tabular t (t
distribution table) then the relationship between the two variables are
significant, but if the other way, it is insignificant.
NB: Some researchers may choose to compare the individual t-statistic
obtained with ±1.96 to determine significance or insignificance. If t-stat >
±1.96 the independent variable is significant to the dependent variable and
vice versa other things being equal.

75
Table 4.7: Summary of the t- statistic
VARIABLES CALCULATED
T STATISTIC
TABULAR T
STATISTIC
CONCLUSION
LnTPOP 2.3258 1.960 Significant
LnLITR 0.3050 1.960 Insignificant
LnHDI 1.8385 1.960 Insignificant
LnHC 1.6695 1.960 Insignificant
As revealed from the table above, in Nigeria only population plays a very
significant role in affecting the level of gross domestic product per capita.
Literacy rate (ages above 15 both male and female in schools getting
educated), human development index (a measure of the level of health,
education and income of the populace) and human capital (resourcefulness
of the populace) play very insignificant roles. This fact can be because our
literacy rate, human development index and human capital are significantly
low in Nigeria. Until we strive to increase them by sound policies, sound
implementations and curtail of systemic corruption our positive look of
gross domestic product per capita is not in view.

76
4.4.3 Econometric Criteria
The essence of the econometric criteria is to investigate whether the
assumptions of the econometric method employed are satisfied or not in any
particular case. They determine the reliability of the Statistical criteria and
also establish whether the estimates have the desirable properties of
unbiasedness and consistency. It also tests the validity of non-
autocorrelation disturbances.
The Durbin-Watson Statistic
In testing for autocorrelation in the model, the Durbin-Watson statistic is
used. From the regression result, the Durbin-Watson statistic is 2.04. This
implies that there is no autocorrelation since d* is approximately equal to
two. It tends towards two more than it tends towards zero. Therefore, the
variables in the model are not auto correlated.
4.4.4 Test of Hypothesis
H0: No significance in relationship between population and economic
growth

77
Conclusion
In answering the research question, since the probability at which the t-value
of Total Population (TPOP) is significant, is less than the chosen level of
significance (i.e. 0.0270 < 0.05), we reject H0 and accept H1 that the model
has goodness of fit and is statistically different from zero. In other words,
there is significance in relationship between Nigeria’s high population and
economic growth. Furthermore, human capital has a t value of 1.6659 which
is greater than 0.05 and thus reveals that there is no significance in
relationship between human capital development and economic growth in
Nigeria. In other words there is no impact of human capital on the economic
growth of Nigeria. This is very glaring as there is wide spread illiteracy rate
in Nigeria making contributions to economic growth almost insignificant.
This is contrary to empirical literature and we may push the unforeseen
reasons to structural rigidities and a matter of another research work.
4.5 Discussion of Findings
We have seen from the foregoing that there is significance in relationship
between Nigeria’s high population and economic growth but insignificant
for human capital. Taking a closer look at the regression result we will

78
discover that at the point where Nigeria’s total population, literacy rate,
human development index and human capital were all at zero, gross
domestic product per capita was at 0.65. This is referred to as the intercept
interpretation in more technical economic language. Furthermore there exist
a positive relationship between Nigeria’s total population, literacy rate,
human development index and human capital with total market value of all
product produced per head within the economy (referred to as the GDPpc).
In other words, an increase in either TPOP, LITR, HDI or HC of Nigeria
will bring about a corresponding increase in our GDPpc and vice versa
ceteris paribus.
It is revealing from our study that within the period of 1980 to 2014, a 1%
increase in total population brought about a 0.23% increase in gross
domestic product per capita in Nigeria. From the findings, we can
comfortably say that an increase in literacy rate by 1% will increase gross
domestic product by 0.16%. Interestingly, if Nigeria puts in more efforts by
way of more strategic and developmental policy formulation and more
importantly, religious implementation to improve its HDI (health, education
and income status of its populace), it will fetch us a whopping 273%

79
increase in gross domestic product per capita. This is not farfetched as a very
healthy, educated and comfortable populace will drive the economy in no
small way. Finally, increase in human capital by 1% will lead to a 0.05%
increase to the economy. This reveals that human capital plays a very
minimal role for economic growth in the Nigeria.
Spectacularly, this study reveals that the population of Nigeria (which we all
know is high) plays a very significant role in booming the economic growth
of Nigeria. This veracity is supported with the fact that it is the only
independent variable exceeding ±1.96 in its t statistics of 2.33 showing a
high level of significance. Recall that this research work among other things
strongly stands with the ‘Anti Malthusian’ theory which is the theoretical
framework upon which this study is based that high population plays a
positive role in economic growth. It is therefore ‘veracity vindicated’. Policy
makers therefore should borrow a leaf of strength from this research to focus
more (unlike before) on the strengths of our already high population to drive
our needed growth and thus development.
Recall that the variables of this study were standardized to enable for
standard rate and elasticity interpretation. From our results, total population,

80
literacy rate, and human capital all have a coefficient of 0.233, 0.166 and
0.055 which is less than unity. This implies that these independent variables
are inelastic to the dependent variable. In other words, an increase in the
value of total population, literacy rate, and human capital will bring about a
‘less than proportionate’ increase in gross domestic product per capita the
economy of Nigeria. This case is reverse with human development index
with coefficient 2.740 showing an elastic case – increasing HDI in Nigeria
will bring about a more than proportionate increase in gross domestic
product per capita. This serves as a clue to policy makers to understand that
any effort to improve the nation’s HDI have a positive crowding out effect
on the overall economy other things being equal.

81
CHAPTER FIVE
SUMMARY OF FINDINGS, CONCLUSION AND
RECOMMENDATIONS
5.1 Introduction
From the foregoing in the previous chapter, results of the regression have
been carefully x rayed beginning with the a priori to the econometric
criterion. This chapter among other things closes the curtain in a nut shell
the whole efforts of the preceding chapters of this long rigorous work, makes
some vital recommendations and suggests areas for further study.
5.2 Summary of Findings
The research hypothesis test which verifies the research objective has clearly
shown significance in relationship for population and insignificance in
relationship for human capital on economic growth (as proxied by GDP per
capita) in Nigeria. The ordinary least square regression further show that a
positive relationship exist - with degrees of variability - between total
population, literacy rate, human development index, human capital and gross
domestic product of Nigeria with population standing out as the most
significant factor that affects the economy of Nigeria.

82
Interestingly, the veracity of the Anti Malthusian theorists (optimists) was
vindicated that high population has a positive impact on the economic
growth of a country which underlines the theoretical framework of this
research work. Furthermore, the elasticicity interpretation revealed that
development policies that focus on building the human development indices
have a very high tendency to grow the gross domestic product per capita
which is a sound measure of economic growth.
5.3 Conclusion
From various literatures, the impact of population growth on per capita GDP
growth can either be negative or positive. From the first attempted research
of the United nations in 1953, the impact of population on growth was
dependent on factors (positive due to economies of scale and organization,
negative due to diminishing returns and even neutral due to technology and
social progress). There was no correlation in the sixties until the revisionists
of the eighties and new paradigms of the nineties. But in conclusion there
exist more positive relationships in countries with sound demographic
policies and institution. Our research work shows a positive significant
relationship between population and economic growth and clear

83
insignificance for human capital. Furthermore, Governments in developing
countries can influence population growth in order to stimulate growth.
China provides a clear example by suddenly introducing a collection of
highly coercive methods to reduce the total fertility rate from about 5.8 to
2.2 births per woman between 1970 and 1980. Today they are the second
world largest economy with the second largest population.
Population especially if massively educated (i.e. increase in human capital)
is a big asset to the development of that country (provided sound institutions
are in place) because cheap labour will produce cost effective product. The
already made market will encourage turnover and specialization of labour.
All these efficiencies will in turn make that same country an efficient
producer and exporter of her commodities. What else defines economic
growth than this? With a sustained industrialization and favourable balance
of trade that trickles down to the large populace, economic development is
already incubated. This therefore leads us to a safe conclusion that the
positive impact of population on economic growth of a country cannot be
relegated to the background.

84
5.4 Recommendations
Having come this far, this research work will be grossly incomplete without
some policy recommendations geared at improving and sustaining the
necessary nitty- gritties for deriving an optimum economic growth from the
largest black nation in the world. These recommendations are:
 Revitalize Human Capital Development: In many developing
countries, poor Marginal Physical Productivity of Labour (MPPL) has
been the lag seriously behind the poor economic growth rates
considering their poorly skilled labour forces. And it is often
financially and politically difficult for governments – because of
excessive greed and corruption – to invest in human assets at the
levels needed to build workable institutions and healthy, literate labor
forces. Yet, it is these human assets that have not just lowered
production costs relative to the developed countries but have also
attracted foreign investment to the “miracle” countries of East Asia as
well as to several in Latin America. Furthermore, transforming
demographic opportunity to economic growth is an institutional task.
Revisionists have long maintained that rapid population growth and

85
high fertility have had their greatest negative repercussions when
national institutions and human capital development have been
ineffectual, particularly in the poorest countries of the developing
world. For example, efforts to put up a well-developed educational
system and easy access to funds for beneficial entrepreneurship
ventures in Nigeria will make population contribute more to economic
growth. It worked for countries in East and Southeast Asia.
 Population Policies to Halt Further Excessive Population Growth:
when population continue to grow excessively without check, positive
contributions to economic growth become frustrated. This research
work is of a strong view that Nigeria law makers should put in efforts
to curtail excessive fertility. This can be done by passing a ‘four child
policy law’ that limits birth rate to not more than four per woman in
her life time. This policy will even ensure further, that parents give
birth to children they can adequately train and readdress the culture of
‘as much as I can bear syndrome’. It is noteworthy to state quickly,
that a very wide discussion group be involved in this law process
because of the diverse cultural, ethnic and religious ideologies of

86
Nigeria. This will certainly be a very rewarding action for Nigeria in
the long run.
5.5 Agenda for Further Research
 More effective ways to control excessive population growth: In as
much as high population can be beneficial especially when sound
policies and institutions are in place, it is not to forget that excessive
population rampant in third world countries lacking the necessary
skills to handle it, will only constitute cog in the wheel of progress for
their economic growth. Furthermore, talk about population decline in
a few rich countries has deflected discussion from the fact that the
global population is still rising rapidly, with many developing
countries seeing explosive population growth. The population of
Uganda, five million in 1950 and 25 million today, is expected to
reach 127 million by 2050; Pakistan, 38 million at independence in
1947, could reach 290 million by 2050. If fertility rates do not decline
in those countries, not only their population but the global population
will continue to grow rapidly despite stabilization in the rich
developed world.

87
However, various countries have adopted some policies to curtail
unfavourable population growth but a closer look will reveal
weakness and counter-productivity. Example China’s one child policy
when deeply dissected will make you understand that this one child
policy will in the long run adversely affect labour productivity. This is
because the less children are born, the more elderly dominate in the
long run than able bodied individuals thus affecting adversely,
economic activity and subsequently growth. The need for better
options therefore is pertinent.

88
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