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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,

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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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