Exchange Rate Fluctuation and Industrial Output Growth in Nigeria
Research Project
1. 1
CHAPTER ONE
INTRODUCTION
1.1 BACKGROUND OF STUDY
Economic growth has been on the downside especially after the world oil market crash in the
1980’s in Nigeria. Nigeria has depended so much on oil and have refused to diversify and this has
caused so much problems in the economy such as higher exchange rate between naira and dollar,
unemployment, underutilization of installed capacity in the manufacturing sector, over-reliance on
imports, poor infrastructures and even the state of the agricultural sector which was once the
mainstay of the Nigerian economy before the oil boom is now in a severe state, even income and
standard of living has reduced drastically over the years. The Nigerian economy today is seen as
the largest economy in Africa due to its Gross Domestic Product but in terms of other economic
factors such as standard of living, cost of living, poverty level, social and economic infrastructure
etc, if compared with some African nations such as South Africa, it would be greatly displaced as
Nigeria even places 62nd in poorest countries in the world (Gross Domestic Product per capita
based on Purchasing Power Parity) as at 2015.
Manufacturing sector consists of mills, factories and plants which use heavy machinery in the
production of goods. The sector is divided into the following branches/sub-sectors: plastic sector,
food, beverages and tobacco sector, chemical sector, metal sector, telecommunication sector,
energy sector, textile sector, construction sector, transport sector, metal work sector etc.
The sector serves as a means of expanding export and reducing import substitution. It can also help
in generation of employment opportunities and earn foreign exchange for the country.
2. 2
Anyanwu (2000) noted that the problems faced by the sector can be solved by improving
infrastructures which have depreciated or become obsolete and also improving on availability and
affordability of goods and services in the nation.
The way forward is to implement policies that would enhance productivity in Nigeria as empirical
studies have shown that countries that were once like Nigeria solved their problems by enhancing
productivity. Based on this fact, it is essential that a study is carried out to evaluate the contribution
of the manufacturing sector (which is a sector based on productivity) to economic growth, after
which policies can be recommended to help improve the sector which would in turn improve
productivity and growth.
1.2 STATEMENT OF PROBLEM
In modern economies, the manufacturing sector plays a very vital role in fostering growth of the
economy and hence in the case of Nigeria, we can see why the economy has been suffering for a
long period due to the manufacturing sector’s low productivity.
In the words of Adeola (2005), manufacturing sector, a vital sector through history has been
neglected due to the discovery of oil and improper policy implementations.
Even Agricultural sector which ought to produce some raw materials needed by manufacturers has
also been neglected too. This has now also resulted in low exports as we do not even produce
enough to meet the local needs.
Empirical studies by Falokun (1995), have shown that Structural Adjustment Programme (SAP)
did not achieve much in solving the following problems but even caused more issues such as rising
inflation rates.
3. 3
The manufacturing sector performed really well between 1970’s and 1980’s with impressive
growth rates. After this period the negative effects of the collapse in the price of oil affected the
sector adversely after 1983 which led to the fall in its growth rate and profits. During this period
also, due to the effects of oil price fall, the government had to place some restrictions to imports
and this really affected manufacturers who were stranded due to unavailability of raw materials
and resources which they needed in their production process. As an aftereffect of this, capacity
utilization in Nigeria’s manufacturing sector fell definitely.
The problem of the manufacturing sector is majorly underperformance and low productivity
caused by other factors that affect it (internal and external factors), and this is also affecting
economic growth both directly and indirectly. Hence this study would help in proffering solutions
to help improve the manufacturing sector and enhance economic growth.
1.3 RESEARCH QUESTIONS
The following questions are examined in this research:
1. How has the manufacturing sector contributed to Nigerian economic growth?
2. At what level has the manufacturing sector performed?
3. What factors are limiting the performance of the manufacturing sector?
4. Which policies should be implemented to improve the manufacturing sector?
1.4 OBJECTIVES OF THE STUDY
The general objective of this research is to study the impact and relationship that exists between
manufacturing sector and economic growth.
The specific objectives include the following:
1. To know the level of contribution of the manufacturing sector to Nigeria’s economic growth.
2. To know the level of performance of the manufacturing sector.
4. 4
3. To know the limitations facing the manufacturing sector.
4. To know the right policies to implement so as to improve the manufacturing sector.
1.5 RESEARCH HYPOTHESIS
To determine the relationship that exists between manufacturing sector and economic growth in
Nigeria, the following hypothesis would be tested:
H0: Manufacturing sector in Nigeria does not positively impact on Nigeria’s economic growth.
H1: Manufacturing sector in Nigeria positively impact on Nigeria’s economic growth.
1.6 SIGNIFICANCE OF STUDY
This study is significant in the following ways:
1. Policies implemented by government with regards to manufacturing sector would be appraised
in this study.
2. The study will reveal various ways in which the manufacturing sector can help in solving
economic problems in Nigeria.
3. The study would reveal various ways in which the manufacturing sector can be improved by
reviewing the sector’s history to understand where things went wrong.
4. The role which the manufacturing sector plays in an economy and in fostering economic
growth would have been known.
1.7 SCOPE OF THE STUDY
This research is carried out to understand the impact of the manufacturing sector on economic
growth. The years covered in this research span from 1980 to 2014. Data needed for this research
include:
1. INDEX OF MANUFACTURING PRODUCTION (IMP)
2. CAPACITY UTILISATION OF MANUFACTURING SECTOR (CUM)
5. 5
3. EXCHANGE RATE (EXR)
4. GOVERNMENT CAPITAL EXPENDITURE (GCE)
5. GROSS DOMESTIC PRODUCT (GDP)
This would be gotten in the form of secondary data.
1.8 ORGANIZATION OF STUDY
The research is broken down into five chapters. Chapter one deals with the introduction to the
research work, its background, statement of the problem, hypothesis, research questions and reason
for carrying out the study. Chapter two entails the literature review which has to do with appraising
previews works related to this research work. Chapter three covers research methodology which
deals with source of data, research design, model specification. Chapter four covers the main
statistical and econometric analysis on the data to be studied. Chapter five has to do with summary
and policy recommendations.
6. 6
CHAPTER TWO
LITERATURE REVIEW
2.1 INTRODUCTION
Previous studies and research by notable academics that are relevant to this study will be reviewed
extensively so as to establish a theoretical basis for this research work in this chapter. This research
tries to experiment methods that have been used by other researchers and academics in other
countries and even in Nigeria to test for the impact of manufacturing sector on economic growth
and hence it is relevant to review such literature which would serve as a backbone or foundation
on which this study would be carried out.
2.2 CONCEPTUAL REVIEW
This is a review of existing literature of the basic concepts that are relevant or related to this study.
2.2.1 MANUFACTURING SECTOR
Manufacturing sector is a subsector of the Industrial sector and it includes organizations and
institutions engaged in the transformation of raw materials into useful goods or commodities. It is
also involved in the production and creation intermediate goods which are then used in other
sectors of the economy to produce other goods and services (Igwe et al, 2014).
Manufacturing sector accounts for a substantial part of any economy especially in developed
nations. It serves as a major employer of labour and generation of high incomes for countries
especially countries that engage largely in exports. Hence, a vibrant manufacturing sector would
improve export earnings of a country (Igwe et al, 2014).
According to Opaluwa et al (2010), the manufacturing sector plays a vital catalytic role in an
economy and its benefits leads to economic transformation.
7. 7
The manufacturing sector consists of mills, factories and plants which use heavy machinery in the
production of goods. The sector is divided into the following branches/sub-sectors: plastic sector,
food, beverages and tobacco sector, chemical sector, metal sector, telecommunication sector,
energy sector, textile sector, construction sector, transport sector, metal work sector etc. Although
the manufacturing sector is a very profitable and viable sector in an economy, not all industries in
the manufacturing sector actually have great benefit as some produce much social cost than social
benefits and hence bring about negative externalities. Worldwide, the manufacturing sector is said
to be the engine that drives sustainable economic growth and development as well as
transformation of all sectors of the economy. It serves as an instrument of wealth creation as well
as distribution of wealth. Empirical studies have shown that manufacturing sector contributes
tremendously to an economy’s output or Gross Domestic Product (GDP) especially in developed
countries. Studies have also shown that in many advanced countries today, the manufacturing
sector takes the lead position in terms of increasing productivity in the country and also improving
the export sector and it also serves as a major foreign exchange earner for such countries (Opaluwa
et al, 2010).
The manufacturing sector is a very reliable means of enhancing a country's productivity but if not
well managed and coordinated, it can prompt ecological issues like environmental contamination
and pollution. Even the produce from the manufacturing sector tends to be harmful to the
environment such as vehicles, air planes etc. Stringent environmental laws are usually used to
reduce this problem to the barest minimum cause it can't be totally eradicated (Obasan and
Adediran, 2010).
8. 8
Obasan and Adediran (2010) noted that between industrialization and agriculture, the industrial
sector offered much more opportunities for capital accumulation than the agricultural sector. It is
easier to accumulate capital from the industrial sector than the agricultural sector.
2.2.2 HISTORY OF MANUFACTURING IN NIGERIA
The manufacturing sector performed really well between 1970’s and 1980’s with impressive
growth rates. After this period the negative effects of the collapse in the price of oil affected the
sector adversely after 1983 which led to the fall in its growth rate and profits. During this period
also, due to the effects of oil price fall, the government had to place some restrictions to imports
and this really affected manufacturers who were stranded due to unavailability of raw materials
and resources which they needed in their production process. As an aftereffect of this, capacity
utilization in Nigerian manufacturing sector fell definitely (Dipak and Ata, 2003).
The share of the manufacturing sector in the total GDP of the country also clearly declined during
this era. In 1977 there was a 4% increase recorded in the manufacturing sector share in GDP and
this reached the level of 13% in 1981, but after that it declined to less than 10% in just a few years
(Dipak and Ata, 2003).
Before the 1970 oil boom, the manufacturing sector of Nigeria contributed only an approximate
value of 10% to the aggregate Gross Domestic Product of the nation. In 1959, the sector’s
contribution to output stood at 4.4%. As at 1973, it had moved up to 7% and in the late 1980’s
precisely 1988, it stood at 10%. From all these, it is clear that the oil price shock indirectly crippled
the Nigerian manufacturing sector. Even the Manufacturer’s Association of Nigeria reported a
negative growth trend between 1980 and 1989 (Dipak and Ata, 2003).
9. 9
Ku et al (2010) noted that even the Structural Adjustment Programme (SAP) introduced in Nigeria
around the 1980’s precisely 1985 couldn’t make notable improvements on the sector. After the
1980’s steps were taken to improve the manufacturing sector such as reduction of the import tariffs
on raw materials needed for manufacturers and replacement parts required by manufacturers.
Akinlo (1996) noted that even during the 1990 to 2000 period, productivity levels and growth of
the sector was really low and profit levels too was very poor.
It has also been observed that between 1990 and 2005, contribution of the manufacturing sector to
output has been low at 10% (Ku et al, 2010).
Malik et al (2004) observed that unskilled and underqualified personnel have for many years
flooded the manufacturing sector and even Ku et al (2010), further observed that in recent times,
the sector still has a lot of unskilled and underqualified personnel and this in turn affects the quality
and standard of locally produced goods.
Mazumdar and Mazaheri (2003) agreed to this by stating that it is due to the inability of
manufacturers to pay adequate wage that would attract the best qualified personnel and hence they
settle for unskilled labour. They also opined that investments should be made by manufacturers in
skilled labour so as to improve the sector and also inadvertently reduce poverty level.
In Nigeria, history has shown beyond reasonable doubt how a country can neglect such a profitable
and useful sector to the economy (manufacturing sector) and how it inadvertently affects other
sectors of a country’s economy. In recent times, it has been observed that only a few manufacturing
firms (about 10%) are performing at par. Many firms have faced adverse situations and at least
60% of firms are facing a shutdown scenario. Also, despite the prevailing crisis in the
10. 10
manufacturing sector, some businesses are still doing fine and are operating well (Mazumdar and
Mazaheri, 2003).
2.2.3 CAPACITY UTILIZATION OF THE MANUFACTURING SECTOR
Capacity utilization refers to the level of use of available capacity of any country or organization.
It is possible to maximize the use of available capacity as this ensures optimum productivity and
it is also possible to underutilize it too. In terms of the manufacturing sector, if a firm is using only
60% of its available capacity, it is very possible for the firm to raise its capacity utilization to 100%
without spending on construction of new factories (Simon-Oke and Awoyemi, 2010).
Capacity utilization shows the rate in terms of percentage of which a firm or an economy is
performing with regards to output levels. This means that for example, if a firm has the potential
of creating 100,000 canisters in a month given available capacity but it only produces 80,000
canisters, it is definitely not fully utilizing its capacity and the formula for calculating the capacity
utilization level or rate is:
𝒂𝒄𝒕𝒖𝒂𝒍 𝒐𝒖𝒕𝒑𝒖𝒕
𝒑𝒐𝒕𝒆𝒏𝒕𝒊𝒂𝒍 𝒐𝒖𝒕𝒑𝒖𝒕
× 𝟏𝟎𝟎
Hence, for the above example, the firm’s capacity utilization is 80% or 0.8 (Kalim, 1998).
In a study carried out by Simon-Oke and Awoyemi (2010), they stated that manufacturing was a
very good means of promoting productivity and a higher standard of living for any country. The
Nigerian Government has tried to implement policy measures that would aid industrial growth and
development but this can’t be effectively done if manufacturing capacity utilization is too low to
support such policies.
11. 11
Fabayo (1981) stated that capacity under-utilization occurs when an industry is not able to
maximize the use of its installed scale of plant on a consistent basis due to one reason or the other.
According to Simon-Oke and Awoyemi (2010), capacity utilization in the manufacturing sector
was as high as 78.7% in the 1970’s. It then fell to an alarming 43.8% in the 1980’s. Between the
years 2000 and 2005, the capacity utilization fluctuated between 34.6% and 52.78%.
Kalim (1998), stated that capacity utilization plays a crucial role in creating employment in the
industrial sector.
Based on an investigation carried out by Ukoha (2000), he discovered that exchange rate and
capital expenditure has a positive effect on capacity utilization. Hence, it is possible to improve
the manufacturing capacity utilization by improving exchange rate and increasing capacity
expenditure especially on manufacturing and also improving per capita real income. All these can
be achieved by implementing the right economic policies.
Kalim (1998) stated that Nigeria relies greatly on importation and has neglected her local reserves
and this has resulted in an indirect negative effect on manufacturing capacity utilization.
Based on studies carried out to assess manufacturing capacity utilization levels in Nigeria, it is
very obvious that the utilization rate has not been high enough to bring about any remarkable
impact on the performance level of the sector. Instead it seems to be one of the major challenges
limiting the sector.
2.2.4 ECONOMIC GROWTH AND ITS MEASUREMENT
Economic growth is a sustained increase in the output of a nation’s economy over a period of time.
It shows the overall productivity of a nation’s economy, that is the amount of goods and services
12. 12
that were produced in a given period. It can also be seen as an outward shift in a country’s
production possibility frontier (PPF) as a result of an increase in the country’s potential output. In
other words, the productive capacity of the nation’s economy has increased and thus represented
by an outward shift in the PPF (Uzoigwe, 2007).
For economic growth to occur, a lot has to happen in the economy. Government has to implement
policies that would improve productivity of the various sectors in the economy. In the
manufacturing sector, the capacity utilization has to be improved with enough investment to back
it up. Also policies should be implemented to discourage importation of goods that can be produced
within the economy (Felipe, 1998).
Economic growth is usually measured using Gross Domestic Product (GDP) which is the total
market value of goods and services produced within a country’s borders both by nationals and
non-nationals. Gross Domestic Product can be in nominal terms which means that it hasn’t been
adjusted for inflation. GDP that has been adjusted for inflation is called Real GDP. It is adjusted
by selecting a base year and using the prices (constant price) of that base year to calculate GDP.
Nominal GDP makes use of current prices of the given year to calculate GDP. GDP per capita is
also another means of showing or indicating economic growth. It takes into consideration the
population of a given country. It is the output per head or output per person in an economy. This
is calculated by dividing GDP by existing population (Adugna, 2014).
2.2.5 IMPACT OF MANUFACTURING SECTOR ON ECONOMIC GROWTH
Many theories have been postulated to explain reasons why economic growth occurs in an
economy. According to Nikolas Kaldor (1967), there exists a positive relationship between
manufacturing sector and economic growth and this definitely means that manufacturing sector
influences economic growth.
13. 13
Teshome (2014) noted in his work on Ethiopia that manufacturing sector in both developing and
developed nations can significantly contribute to economic growth.
For Pacheco-López and Thirlwall (2013), there exists a linkage between manufacturing sector and
economic growth and in our modern day, it is an established fact that a causal relationship exists
between economic growth and manufacturing. They also noted that if the manufacturing sector
grew at a fast rate, it would also drive economic growth too at a faster rate.
Szirmai (2009) also observed that since the industrial revolution, the manufacturing sector has
been an engine that fosters economic growth in developed countries.
Teshome (2014) also noted that a lazy economy could become a vibrant one if the right policies
were implemented for the manufacturing sector. Even Gregory (2006) discovered that over 200
years, most developed economies were able to achieve their development with proper use of their
manufacturing sector.
It is also a fact according to empirical findings and history that manufacturing sector fosters
economic growth through increasing returns. Manufacturing sector can bring about export
expansion which also earns foreign exchange from countries that are traded with, and exports help
economies to grow and improve balance of payment and balance of trade. It also generates
employment which then could raise level of income which also raises level of demand in the
economy and this increase in demand leads to higher productivity by firms to meet up with the
excess demand, all this forms a cycle of events that generally leads to economic growth. Most
economies that have experienced increases in their incomes can testify to this fact that indeed the
manufacturing sector fosters economic growth (Dipak and Ata, 2003).
14. 14
According to Opaluwa et al (2010), the manufacturing sector in advanced nations have been
effectively used to transform their economies into gigantic ones which export on a massive scale
to other nations and it serves as their leading sector in such economies.
Based on all what has been said, this research aims to study the manufacturing sector’s impact on
Nigerian economic growth over the given time period of 1980 to 2014 to know how significant it
has been.
2.2.6 PROBLEMS FACING THE NIGERIAN MANUFACTURING SECTOR
Nigerian manufacturing sector has been faced with so many challenges due to the state of the
Nigerian economy. The economy is not diversified and hence is majorly dependent on revenue
from oil sector. The manufacturing sector has been largely ignored despite all the advantages and
boost the economy can experience if the sector was highly functional. Also it has been historically
proven that the sector is very populated with unskilled labour (Dipak and Ata, 2003).
Another factor that also caused problems for the sector is that of energy which is an input needed
by firms and plants in the manufacturing sector in their production process. The energy production
in Nigeria especially that of electricity has been low for quite a while. Hence this has led to higher
cost of production for manufacturers and many have either gone out of business or lowered output
to reduce cost of production to a level conducive for their operation (Al Awad, 2010).
Exchange rate also tend to affect the sector as it creates an upward or downward pressure on export
prices depending on how it fluctuates. If export prices are going down, then that affects the income
generated from exporting products/goods produced within the country. Also, an increase in
exchange rate tends to make importation of some factor input needed by manufacturers more
15. 15
expensive for them to purchase. This also increases cost of production and indirectly reduces
output of the sector (Opaluwa et al, 2010).
Income in the economy is generally low and hence purchasing power of consumers is also low and
this has resulted in inadequate demand for manufacturing output which is quite discouraging to
manufacturers. Also, importation of goods that can be produced within the country also acts as a
problem for manufacturers as most Nigerians have this tendency to purchase more of foreign goods
compared to locally produced ones because they feel the quality would be better and superior to
locally produced goods and sometimes it’s usually cheaper (dumping, which is generally practiced
by advanced nations) than locally produced goods (Anyanwu, 2000).
According to Dipak and Ata (2003), due to global technological advancement, there is global
competition between countries producing goods for export and because Nigeria has a very poor
technological advancement rate, it fails to compete globally in terms of manufacturing output and
is thus outmatched by other countries. Also Nigerian firms in the bid to keep up with the latest
trend in technology tend to spend more on research and development which results into increased
cost of production. Other factors too according to them that affect the manufacturing sector badly
include interest rates which are usually high, unimplemented policies which could have improved
the manufacturing sector to some extent, high tariff rates, low demand by consumers etc.
Another thing that also affects the manufacturing sector is that which occurs when global demand
for manufacturing output is higher than global demand for natural resources. This would definitely
favor countries that are industrial nations as they would be able to increase their national income
by exporting their products. Other countries that deal with exportation of natural resource will
experience a downturn in their national incomes (Al Awad, 2010).
16. 16
2.3 EMPIRICAL REVIEW
Under this aspect of the study, a review of what other authors and researchers have studied both
in developed, developing and the Nigerian economy would be appraised. Their empirical findings
would be stated to further buttress on the subject matter of this study.
The value of the correlation between manufacturing capacity utilization and real output growth is
0.9. This was discovered in a research conducted by Corrado and Mattay (1997) in the United
States on the industrial sector of their economy.
Gajanan and Malhotra (2007) in their research on India, noted that capacity utilization varied
across industries in the Indian industrial sector and that these variations were as a result of
variations in demand for goods produced.
Khan and Wasif (2011) in Pakistan carried out a study on Pakistan to test the validity of Kaldor’s
law that deals with growth of an economy and manufacturing sector. They used data spanning
between the years 1964 to 2008 and they discovered that over that time period, Kaldor’s law was
significant to some extent and that economic growth was closely related with the manufacturing
sector’s growth. Also they discovered through the estimated results they had that developing the
industrial sector would also bring about economic growth. According to their findings, the
manufacturing sector in Pakistan had forward and backward linkages which is necessary for the
growth and development of their economy. In Pakistan, about half of their Gross Domestic Product
growth over the years analyzed was as a result of the growth of their manufacturing sector.
Teshome (2014) studied the impact of the manufacturing sector on Ethiopia’s economic growth
using Kaldor’s approach between the years 1980 to 2010. He discovered that the manufacturing
sector in Ethiopia had a slow growth rate. The urban center seems to be the main base or location
17. 17
of the manufacturing sector. He also discovered that if the manufacturing sector grew by one
percent, its resulted in the growth of the economy by forty-two percent, and the manufacturing
sector helped to increase the productivity of labour which aided economic growth.
Ku et al (2010) noted in their paper that in Nigeria, the contribution of the manufacturing sector to
Gross Domestic Product was very low and was in great need of reforms and that the government
didn’t accord the sector the level of importance it deserved and hence neglected the sector. It was
only of recent that the government recognized the sector’s important role in economic growth.
Felipe (1998) in his empirical study on the impact of the manufacturing sector on south-east Asian
development between the years 1967 to 1972 noted that the manufacturing sector is the most
efficient sector of the economies excluding the Indonesian economy. Also, he noted that the
externality that was produced by the manufacturing sector on other sectors of the economy was
much higher than what the other sectors produce on the manufacturing sector which shows that
the manufacturing sector to some extent contributed significantly to the development of other
sectors of the economy.
Rioba (2015) carried out a study on Eastern African development making use of Kaldor’s first law.
He noted that the contribution of the manufacturing sector to the GDP of the East African countries
has been very low and output is mostly constituted by primary products.
Simon-oke and awoyemi (2010) in their study on Nigerian manufacturing capacity utilization
discovered that there is still so much idle capacity in the manufacturing sector and this has
hampered industrial development. Over-reliance on foreign inputs and inadequate research is
really killing the manufacturing sector. They discovered that a positive long-run relationship exists
between manufacturing capacity utilization and industrial output.
18. 18
Uzoigwe (2007) in his study of Nigerian economic development, his findings showed that the
manufacturing sector along with the other key sectors he analyzed serve as a means by which
employment can be generated in the country hence reducing the problem of unemployment in the
country. It also serves as a means of increasing income in the nation and alleviating poverty. Also
a positive relationship exists between labour co-efficient and manufacturing output which suggests
that it is advisable to invest in human capital as this would definitely improve the productivity of
labour.
Al Awad (2010), in his study of Gulf Cooperation Council (GCC) region on the role of
manufacturing in promoting sustainable growth discovered that there existed a long-run
relationship between manufacturing and non-oil economic growth. In the short-run, the
manufacturing sector had no significant effect on non-oil economic growth in the GCC region.
2.4 THEORETICAL REVIEW
This is a review of theories and models of economic growth that were postulated by different
economists at different periods based on their understanding of how economies work. We have
various growth theories such as the endogenous growth theory, supply and demand model,
classical growth theory, Solow-Swan model etc. These theories further give a background to the
subject matter which is economic growth.
2.4.1 CLASSICAL GROWTH THEORY
This theory is based on the law of variable proportions. The theory of variable proportions holds
that if either capital or labour which are factor inputs is increased while holding other factor inputs
constant would lead to growth of output although the rate at which it would grow would be at a
diminishing rate until it finally gets to point zero. It is based on Thomas Malthus theory on
population.
19. 19
The classicalists believed that the rate at which population grew which was geometric and the rate
at which resources needed to maintain the economy grew which was arithmetic would lead to the
end of economic growth.
Economists that formed this theory believed that if real Gross Domestic Product has an equilibrium
level also called subsistence level. If it increased above this level, the population would also
increase and wipe out the excess income so as to bring it down to its original level (subsistence
level). Also if it happens that the real Gross Domestic Product falls below the subsistence level,
then people will die and this will lead to the real Gross Domestic Product rising back to its original
level.
A major criticism of this theory is that technology which is a fundamental factor is held constant.
Also economies of scale is not taken into consideration.
2.4.2 SUPPLY AND DEMAND MODEL
Under this theory, the assumption is that technology increases productivity without leading to
extreme higher costs. With technology, production process can be more efficient which would lead
to higher output and supply. Labour or capital need not increase and can be held constant. As a
result of the increase in supply of output, demand also tends to increase which leads to growth of
the economy (Rosenberg, 1982).
2.4.3 SOLOW-SWAN MODEL
This model of growth was the major model used in the 1950’s in analyzing economic growth. It
was developed by Robert Solow and Trevor Swan. They both developed their models individually
but due to the similarities of their model, it was merged together and called the Solow-Swan model.
It is an exogenous growth model. It makes use of capital accumulation, labour and technical
20. 20
progress in explaining the process of long-run economic growth. It is a neo-classical model of
growth and a form of advancement or improvement on the Harrod-Domar growth model. A few
assumptions of the model include:
1. Production of a single composite good
2. Diminishing returns to factor input
3. There exists a constant returns to scale
4. Flexibility in prices and wages
5. Labour is fully employed
6. Population growth rate is constant
7. Savings leads to investment
The basic idea of the model is that there is diminishing returns to both labour and capital. Savings
brings about capital accumulation but when the existing population grows or depreciation occurs,
the level of capital per worker falls. Economic growth ends when diminishing returns to capital
leads to a halt in capital per worker (making it constant) especially cause of technology isn’t
advancing. This stage is referred to as steady state. Economic growth can actually continue only if
new technology is introduced. The new technology must improve production process such that
more can be produced with less resources. A vital prediction of the model is that less developed
nations would grow faster than developed nations and even reach the level of the developed nations
if and only if they have same technology and rate of savings.
One criticism of this theory is that the source/sources of technological change isn’t stated.
21. 21
2.4.4 ENDOGENOUS GROWTH THEORY
It was developed by Paul Romer and Robert Lucas Jr. between the 1980’s and 1990’s. It was
generally an improvement on the Solow-Swan model. The usefulness of human capital was also
recognized in the model. Human capital refers to the skills and knowledge that has been deposited
in a human resource to make it more productive. Human capital also has increasing returns to scale
and also education and innovation improves human capital.
The theory generally states that economic growth is caused by endogenous factors i.e. economic
growth is created by factors that exist within the economic system. Human capital and innovation
should be the major focus of investment strategy for economic growth to occur. An economy that
is knowledge oriented would have positive effects that would lead to economic development.
2.4.5 SCHUMPETER’S THEORY OF GROWTH
It was formulated by Joseph Schumpeter in the 20th century. He sees growth as something that
occurs after innovation has taken place.
Innovation includes the following:
1. Introducing a new product
2. Introducing a new production process
3. Creating a new market
4. Finding a new source of raw materials
Innovation otherwise referred to as creative destruction by Schumpeter was a means in which
entrepreneurs came up with new products which they hoped would be able to steal the market and
give them some form of monopoly for a while till their own product becomes obsolete. The same
too applies for new technologies introduced by entrepreneurs in the bid to make older technologies
obsolete so as to capture the market. They get to enjoy some monopoly till their technology
22. 22
becomes obsolete. The process of making older technologies and products obsolete in the
introduction of new products and technologies is what is referred to as creative destruction. For
entrepreneurs to be effective, they need two key attributes:
1. technical know-how
2. funding
An entrepreneur is not necessarily the one who brings in the funds. There are people who can
decide to offer credit or invest in the ideas of the entrepreneurs, although according to the theory,
investment majorly comes from bank credits. According to the theory, the entrepreneurs are only
in charge of factor inputs. Entrepreneurs innovate in order to make profit from it. It is in the bid of
this making profit that economic growth occurs since profits are simply the excess income over
cost of production which is a surplus and these profits are only created when innovation occurs.
When a new innovation is introduced, other entrepreneurs get to learn it and as this happens, profit
reduces. Innovation can also be copied into other industries that are related to the one in which the
innovation was introduced. As entrepreneurs begin to repay bank loans, profits would fall and the
end result would be depression (Aghion and Howitt, 1990).
Schumpeter also believed along with Karl Marx that capitalism would cease to exist and would
then be replaced by socialism. This would happen because innovation would go obsolete, major
social unrests will occur due to the capitalists becoming weak and protecting themselves with the
military. People will generally discredit capitalism via any means possible. All these would lead
to the end of capitalism and socialism will come into being (Aghion and Howitt, 1990).
This theory was criticized in different ways, one of them being that in our modern day, it is totally
evident that innovation is the bedrock of advanced nations. Countries that are developed have
23. 23
enormous innovations in all sectors of their economy. Also, another criticism is that countries such
as USA and England that practice capitalism and are highly industrial have not moved into
socialism but have developed as an economy. It is therefore wrong to postulate that capitalism
would end and socialism would come in its place (Aghion and Howitt, 1990).
2.4.6 BIG PUSH THEORY
The theory was developed by Paul Rosenstein-Rodan in 1943. As the name suggests, it is a theory
that states that for growth to occur, large quantum of investments should be made in the economy
of developing nations as a bit by bit form of investment would not be sufficient to drive the
economy towards growth. Instead of such smaller investments to lead to economic growth, it
would only result in resource wastage. According to the theory, there is a minimum level of
investment that is necessary to drive a developing country towards economic growth. He likened
it to the minimum amount of force and speed needed by airplanes to take-off from the ground into
the air.
The theory states that there are three indivisibilities that justify the need for a big push and that
must be present for growth to occur:
1. Production function indivisibility: According to the theory, a large quantum of investment has
to be made in social overhead capital which comprises of investment in basic industries that
must be in excellent condition for the economy to experience real progress. Such industries
include transportation sector, energy sector, telecommunication etc. The social overhead
capital requires a minimum of 30% or 40% of a developing nations investment to foster growth.
2. Demand indivisibility: This basically means that investment made should span across all
industries in the economy so that there can be complementary demand for the output of the
different industries by workers in each industry.
24. 24
3. Supply of savings indivisibility: This indivisibility has to do with supply of savings which it
refers to as a must because savings leads to investment. Investment through foreign aid is not
sufficient enough to foster the growth and thus domestic savings must be large enough to
produce the amount of investment needed to foster economic growth.
One of the criticisms of this theory is that income in developing countries are quite low and hence
low income breeds low savings. Hence domestic savings would not be able to produce the quantum
of investment that could foster economic growth.
2.5 METHODOLOGICAL CONCEPTS
This is a brief description of the tools that would be used in analyzing the data on variables that
are used in this study. These tools are the main tools of analyses that would be used in chapter four
of this research work.
2.5.1 AUGMENTED DICKEY-FULLER TEST
This is a type of unit root test which is used to check if a given set of data is stationary or not. It is
used for complicated and longer time series data. The ADF test makes use of a negative number
and the more negative the number is, the stronger the rejection of the hypothesis that there is a unit
root test.
2.5.2 JOHANSEN COINTEGRATION TEST
It is a test used in testing for the cointegration of several times series data that are at level.
2.5.3 DURBIN WATSON TEST FOR AUTOCORRELATION
This is a test used to check if autocorrelation exists between values separated by a given time lag.
25. 25
2.5.4 ORDINARY LEAST SQUARES REGRESSION TECHNIQUE (OLS)
The ordinary least square regression is a tool used to estimate unknown parameters in a linear
regression model. It is one of the easiest methods of linear regression. It helps in giving a perfect
fit between a set of data and a given function by minimizing the sum of squared errors. The
regression model has both explained and explanatory variables. The explained variable is always
one in number while the explanatory variables can be more than one. Each explanatory variable
has a coefficient that shows the level of influence the variables have on the explained variable.
2.5.5 GRANGER CAUSALITY TEST
This a statistical hypothesis test used to check if a particular time series data can be used in
predicting another time series data. It was developed by Clive Granger. It is possible for one to
predict the future values of a time series data using previous value of another time series in
economics.
26. 26
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 RESEARCH DESIGN
Based on the major objective of this study which is to assess the impact of the manufacturing sector
on Nigeria’s economic growth, the ex-post facto and historical research designs were adopted. The
historical research design was used to assess the chronological performance of the manufacturing
sector while the ex-post facto research design was used to assess the causal relationship between
the variables used in the study.
3.2 SOURCES OF DATA
The data that will be made use of in this research work is secondary in nature. It will be obtained
from various sources such as Central Bank of Nigeria Statistical Bulletin and Statement of
Accounts and Annual Reports, World Bank Development Index and other relevant publications.
The data that was collected includes Gross Domestic Product (GDP in 2010 constant prices), Index
of Manufacturing Production (IMP), Capacity Utilization of Manufacturing sector (CUM),
Exchange Rate (EXR), Government Capital Expenditure (GCE).
3.3 DATA ANALYSIS TECHNIQUE
The Ordinary Least Square (OLS) technique is the data analysis technique that is employed in this
study. The E-views software is the statistical software that will be used to estimate the numerical
coefficients of the regression model. The variables used in the regression analysis are: Gross
Domestic Product being the dependent/explained variable while Index of Manufacturing
Production, Capacity Utilization of Manufacturing sector, Exchange Rate and Government Capital
Expenditure are the independent/explanatory variables.
27. 27
Also, the Granger causality test would be applied to determine the relationship between two
variables and its direction in the Nigeria Economy between 1980-2014.
3.4 THEORETICAL FRAMEWORK
KALDORIAN MODEL OF ECONOMIC GROWTH (KALDOR’S FIRST LAW):
These research work is aimed at studying the impact of manufacturing sector on Nigeria’s
economic growth and hence the Kaldorian model of economic growth (Kaldor’s first law) is what
is being used as the theoretical framework for this research. The Kaldorian model suggests that the
rate of growth in Gross Domestic product is positively linked with the growth rate of the
manufacturing sector. This was proposed by Nicholas Kaldor in 1967. It has been empirically
verified in many countries both developed and developing and thus it is appropriate to use this
theory as a basis to check the level of impact that the manufacturing sector has on economic growth
leaving non-manufacturing sector constant.
3.5 FORMULATION OF THE MODEL
This research study makes use of the Kaldorian model of economic growth as it’s theoretical
framework. The first law forms the basis or framework of this study which states that there is a
positive relationship/correlation between growth in manufacturing output and growth in economic
output (GDP) because manufacturing output is a part of GDP and there is also a causal relationship.
This is represented by:
Yi = ai + biMi
Yi represents growth in output/GDP while Mi represents growth in manufacturing output. Due to
the scope of this study, we are only focusing on the impact of manufacturing sector output on
Gross Domestic Product and we are assuming non-manufacturing sector’s contribution to be zero
because the effect of these non-manufacturing sectors is outside the scope of this study.
28. 28
The following variables are used as explanatory variables and proxies for manufacturing sector
output in showing the level of impact manufacturing sector has on economic growth: Index of
Manufacturing Production, Capacity Utilization of Manufacturing sector, Exchange Rate and
Government Capital Expenditure. Hence we obtain the following specification:
GDP = f (IMP, CUM, EXR, GCE)
The explanatory variables are represented by Xi:
GDP = f (X1, X2, X3, X4)
GDP = f (𝛽0 + 𝛽1 𝑋1 + 𝛽2 𝑋2 + 𝛽3 𝑋3 + 𝛽4 𝑋4 + U)
GDP = 𝛽0 + 𝛽1 𝑋1 + 𝛽2 𝑋2 + 𝛽3 𝑋3 + 𝛽4 𝑋4 + U
GDP = 𝛽0 + 𝛽1 𝐼𝑀𝑃 + 𝛽2CUM + 𝛽3EXR + 𝛽4GCE + U
Where:
GDP: GROSS DOMESTIC PRODUCT
IMP: INDEX OF MANUFACTURING PRODUCTION
CUM: CAPACITY UTILISATION OF MANUFACTURING SECTOR
EXR: EXCHANGE RATE
GCE: GOVERNMENT CAPITAL EXPENDITURE
3.6 RE-STATEMENT OF HYPOTHESIS
To determine the relationship that exists between manufacturing sector and economic growth in
Nigeria, the following hypothesis would be tested:
H0: Manufacturing sector in Nigeria does not positively impact on Nigeria’s economic growth
H1: Manufacturing sector in Nigeria positively impact on Nigeria’s economic growth
29. 29
3.7 STATEMENT OF A’PRIORI EXPECTATION
Based on macro-economic principles, the a’priori expectation which refers to the size and sign of
the parameters in economic relationship with regards to the variables under study show that:
𝛽0, 𝛽1, 𝛽2, 𝛽3, 𝛽4 are all > 0.
If the estimates turn out to have a different sign or size, then they should not be accepted.
3.8 DESCRIPTION OF VARIABLES
3.8.1 GROSS DOMESTIC PRODUCT
Gross Domestic Product (GDP) is the monetary valuation of all the final goods and services created
within a nation's boundaries or border in a particular time period both by its citizens and non-
citizens. GDP is calculated on an annual basis by default, and it can also be calculated on a
quarterly basis too.
3.8.2 INDEX OF MANUFACTURING PRODUCTION
This is an economic indicator used in manufacturing sector. It is used in measuring real output of
production activities in the sector. Fisher’s index is used in the computation of this index.
3.8.3 CAPACITY UTILIZATION OF MANUFACTURING SECTOR
Capacity utilization refers to the level of use of available capacity of any nation or organization. It
is conceivable to maximize the use of available capacity as this guarantees optimum productivity
and it is also possible to underutilize it as well. It is also seen as the relationship between actual
output produced by existing capacity and potential output that can be produced if existing capacity
is fully utilized.
30. 30
3.8.4 EXCHANGE RATE
This is the rate at which a nation’s currency exchanges for another nation’s currency. In this study,
we would be using the rate at which naira exchanged for a dollar over the specified period covered
by the study.
3.8.5 GOVERNMENT CAPITAL EXPENDITURE
This refers to the part of government total expenditure that is spent or invested into projects or
construction activities. It is used in the creation of assets or repayment of loan and it is a long-term
expenditure.
31. 31
CHAPTER FOUR
DATA ANALYSIS, PRESENTATION AND INTERPRETATION OF RESULTS
4.1 INTRODUCTION
The study modelled the data from manufacturing sector on the Nigerian economy by making use
of time-series data from 1980 - 2014 to analyze the impact of manufacturing sector on Nigeria’s
economic growth. The empirical analysis process is anchored on multiple regression model of the
perceived functional relationship between manufacturing sector and the Nigerian economy.
Numerical values of the model parameters are estimated via the ordinary least squares (OLS)
techniques facilitated by the application Econometric Views (E-Views) software for empirical
econometric analysis and Statistical Analysis System (SAS) for trend of all the variables. The
regression output includes other relevant statistics that enhance further analysis and evaluation.
Estimates of model coefficients are evaluated for isolated statistical significance based on P-value
and 0.05 level of significance. Explanatory power of the model, as a measure of goodness of fit
and confirmation of overall statistical significance, is determined from the coefficient of
determination (R-Square and adjusted R-Square). These statistics enhance insight into the extent
to which the index of manufacturing production (IMP), capacity utilization of manufacturing
sector (CUM), exchange rate (EXR), and government capital expenditure (GCE) influencing Gross
Domestic Product (GDP) in Nigeria within 1980 -2014.
4.2 DESCRIPTIVE STATISTICS
The descriptive analysis employed in this study is the summary of statistics table. This includes
the Mean, Median, Maximum, Minimum, Standard Deviation, Skewness, Kurtosis, Jarque-Bera,
etc.
32. 32
Table 4.1: Descriptive of The impact of manufacturing sector on Nigeria’s economic growth from 1980 -2014.
GDP
(Gross
Domestic
Product)
IMP
(Index of
Manufacturing
Production)
CUM
(Capacity Utilisation of
Manufacturing sector)
EXR
(Exchange Rate)
GCE
(Government Capital
Expenditure)
Mean 30.92485 48.74459 47.24651 66.40253 25.37570
Median 30.71181 38.26556 43.80000 21.88610 26.20161
Maximum 31.85020 105.3405 73.30000 163.5910 28.61724
Minimum 30.35512 8.549731 29.29355 0.544500 22.13428
Std. Dev. 0.466799 24.21248 11.61403 63.65893 2.030348
Skewness 0.756682 0.993179 0.252672 0.247626 -0.289426
Kurtosis 2.049841 2.977791 2.203877 1.233801 1.599649
Jarque-Bera 4.656564 5.754749 1.296728 4.906902 3.348410
Probability 0.097463 0.056282 0.522901 0.085996 0.187457
Sum 1082.370 1706.060 1653.628 2324.088 888.1494
Sum Sq. Dev. 7.408636 19932.30 4586.111 137783.6 140.1586
Observations 35 35 35 35 35
Source: Author compilation, 2016.
The characteristics of the distribution of the variables are presented in Table 4.1; Jarque-Bera is a
test statistic for testing whether the series is normally distributed. The test statistic measures the
difference of the Skewness (coefficient of symmetry) and the Kurtosis (coefficient of flatness) of
the series with those from the normal distribution.
The null hypothesis of normal distribution is accepted for GDP (Gross Domestic Product), IMP
(Index of Manufacturing Production), CUM (Capacity Utilization of Manufacturing sector), EXR
(Exchange Rate) and GCE (Government Capital Expenditure). Evidently, the statistic for Kurtosis
shows that Gross Domestic Product, Capacity Utilization of Manufacturing sector, Exchange Rate
and Government Capital Expenditure are and the statistic for Kurtosis shows that are Platykurtic,
which means it is simultaneously less peaked and has thinner tails. While Index of Manufacturing
Production can be said to be Mesokurtic, which means the distribution is peaked. Lastly, skewness
is a measure of asymmetry of the distribution of the series around its mean. The result of the
skewness shows that for Gross Domestic Product, Capacity Utilisation of Manufacturing sector,
Exchange Rate and Index of Manufacturing Production are positively skewed, implying that those
33. 33
variables have long right tails. Government Capital Expenditure is negatively skewed, implying
that Government Capital Expenditure variable have long left tails.
As presented in Table 4.1, Gross Domestic Product ranges from 30.35512 to 31.85020 with an
average value of 30.92485 and a standard deviation of 0.466799. The Index of Manufacturing
Production starts from 8.549731 to 105.3405 with an average value of 48.74459 and a standard
deviation of 24.21248. Capacity Utilisation of Manufacturing sector ranges from 29.29355
to 73.30000 with a mean of 47.24651 and a standard deviation of 11.61403. Exchange Rate ranges
from 0.544500 to 163.5910 having an average value of 66.40253 and a standard deviation of
63.65893. And Government Capital Expenditure ranges from 22.13428 to 28.61724 with a mean
of 25.37570 and a standard deviation of 2.030348.
4.3 TREND ANALYSES
Figure 4.1: The trend of Gross Domestic Product 1980 to 2014.
Source: Authors Computation; with underlying data from CBN (2014)
34. 34
Figure 4.1 shows the trend of Gross Domestic product, the proxy for the nation’s economy. It
shows that the Gross Domestic Product wasn’t all that good from 1981 until 1996. However, it
shows an encouraging upward movement to 2002 and had a noticeable rise in 2003. Figure 4.1
indicates a positive trend. This trend continues to increase and climax in the year 2014. This can
be attributed to the increase in the level of productivity in the Nigeria industry in recent years.
Figure 4.2: The trend of Exchange Rate 1980 to 2014.
Source: Authors Computation; with underlying data from CBN (2014)
Exchange rate is fairly low and stable in the pre-adjustment (SAP) era beginning from 1981 till
around 1992. This may be largely due to the decline in oil revenue arising from the oil glut
experienced in the early ‘80s. Figure 4.2 shows exchange rate had a steady upward movement
from 1992 till 1999. The trend experienced a sharp upward movement from year 1999 to 2014.
35. 35
Figure 4.3: The trend of Index of Manufacturing Production 1980 to 2014.
Source: Authors Computation; with underlying data from CBN (2014)
Figure 4.3 depicts the trend of index of manufacturing production. Taking a critical look at the
trend it is observed that there was a sharp rise manufacturing production in 1990. This is because
the base year used to calculate this index of manufacturing production is year 1990. It then falls
drastically and seen is to have a steady upward movement from 2002, the trend continues to
increase and climax in the year 2014.
36. 36
Figure 4.4: The trend of Capacity Utilization of Manufacturing Sector 1980 to 2014.
Source: Authors Computation; with underlying data from CBN (2014)
Figure 4.4 indicates that capacity utilization of manufacturing sector had a drastic fall in 1981
which continued till year 2000. Figure 4.4 shows capacity utilization of manufacturing sector had
a steady upward movement from 2000 till 2014. The trend continues to grow and reaches its climax
in the year 2014.
37. 37
Figure 4.5: The trend of Government Capital Expenditure 1980 to 2014.
Source: Authors Computation; with underlying data from CBN (2014)
The above trend in Figure 4.5 shows a stationary trend from 1980 to 1988. The trend later
experienced a zig zag upward movement from 1989 to 2013.
4.4 RESULTS OF STATIONARITY TESTS
Literature has established that most time series variables are not stationary. Therefore, using
nonstationary variables in the model might lead to spurious regression which cannot be used for
precise prediction. (Gujarati, 2003). Hence, our first step is to examine the characteristics of the
time series data used for estimation of the model to determine whether the variables have unit
roots, that is, whether it is stationary and the order of integration. The Augmented Dickey-Fuller
test is used for this purpose.
38. 38
Table 4.2: Augmented-Dickey Fuller (ADF) Test
Variable. ADF Value before
Differencing.
ADF Value After
Differencing
Critical Value Level of
Integration
GDP (Gross Domestic
Product)
-3.639407 -3.646342 -4.725420 I(1)
IMP (Index of Manufacturing
Production)
-3.639407 -3.646342 -7.253302 I(1)
CUM (Capacity Utilisation of
Manufacturing sector)
-3.646342 -3.271801 -3.724070 I(1)
EXR (Exchange Rate) -3.639407 -3.646342 -4.772588 I(1)
GCE (Government Capital
Expenditure)
-3.639407 -3.646342 -5.513706 I(1)
Source: Author compilation, 2016.
Table 4.2 reveals that Gross Domestic Product, Index of Manufacturing Production, Capacity
Utilisation of Manufacturing sector, Exchange Rate and Government Capital Expenditure series
have unit root. But Gross Domestic Product, Index of Manufacturing Production, Capacity
Utilisation of Manufacturing sector, Exchange Rate and Government Capital Expenditure were
found to be stationary at first difference. Their integration of the same order I(1) is an indication
that the variables could cointegrate in line with the opinion of Engle and Granger (1985). They
opined that when time series data are integrated of the same order, the data series tend to
cointegrate. This means that their short term characteristics are sustainable at the long term. They
listed the consequences of such cointegration to include that;
Time series data that are integrated of the same order I(1), share a stochastic component
and a long run equilibrium relationship.
Wide disparities from the zero line of equilibrium as a result of volatilities will be corrected
over a period of time.
ΔYt is believed to be responding to shocks to X under a state of cointegration over the short
and long term.
39. 39
However, after subjecting the time series data to unit root test, a new set of data series were
generated through the Augmented Dickey Fuller (ADF) procedure.
Table 4.2 shows that Gross Domestic Product, Index of Manufacturing Production, Capacity
Utilisation of Manufacturing sector, Exchange Rate and Government Capital Expenditure were
stationary at second difference. Thus, the Johansen-Juselius cointegration approach is applied to
examine the long run relationship among variables.
4.5 COINTEGRATION ANALYSIS RESULT
In determining the number of cointegrating vectors, trace test and maximum eigenvalue test using
the more recent critical values of MacKinnon et al. (1999) was applied. The assumption of no
deterministic trend and restricted constant was for all the variables. The choice was tested using
(AIC) and Schwartz Information Criterion (SIC). The result for both trace test and maximum
eigenvalue for unrestricted cointegration rank test are presented in Table 4.3 below.
Table 4.3: Summary of the Johansen-Juselius Cointegration Test Results
Trace test indicates 1 cointegrating eqn(s) atthe 0.05 level
* denotes rejection ofthe hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Max-eigenvalue test indicates 1 cointegrating eqn(s) atthe 0.05 level
* denotes rejection ofthe hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Source: Author compilation, 2016.
Cointegration test is to test whether there is long-run relationship between the dependent and
independent variables in the model. Therefore, by employing Johansen Cointegration test we make
use of the trace statistics and Max-Eigen respectively by comparing their values with the critical
values at 5% level. If the values of at least one of the Trace Statistics/MAX-Eigen are greater than
Hypothesized
No. of CE(s)
Eigen value Max-Eigen
value
Critical value
5 Percent
Hypothesized
No. of CE(s)
Trace statistic Critical Value
5 percent
None * 0.754772 46.38374 33.87687 None * 94.00794 69.81889
At most 1 0.477126 21.39771 27.58434 At most 1 47.62420 47.85613
At most 2 0.468856 20.87982 21.13162 At most 2 26.22649 29.79707
At most 3 0.149558 5.345962 14.26460 At most 3 5.346662 15.49471
At most 4 2.12E-05 0.000700 3.841466 At most 4 0.000700 3.841466
40. 40
the critical value, then we conclude that there is a long-run equilibrium relationship otherwise the
regression is not co-integrated.
From the above table, we can conclude that there is a long-run equilibrium relationship between
the dependent and the independent variables from the model since the Trace Statistics/MAX-Eigen
indicates more than one co-integrating equation at 5 percent level of significance. Thus, the
variables are co-integrated and therefore, a long-run relationship exists between economic growth
and the regressors used.
4.6 EFFECT OF MANUFACTURING SECTOR ON NIGERIA’S ECONOMIC
GROWTH
Table 4.4: Least squares result
DependentVariable:LOG(GDP)
Method: LeastSquares
Date: 04/13/16 Time:14:53
Sample:1980 2014
Included observations:35
Variable Coefficient Std. Error t-Statistic Prob.
C 27.56214 0.778033 35.42540 0.0000
IMP 0.005765 0.001284 4.490074 0.0001
CUM 0.010125 0.002689 3.765981 0.0007
EXR 0.001661 0.001044 1.591035 0.1221
LOG(GCE) 0.098244 0.030283 3.244201 0.0029
R-squared 0.927550 Mean dependentvar 30.92485
Adjusted R-squared 0.917890 S.D. dependentvar 0.466799
S.E. of regression 0.133760 Akaike info criterion -1.053973
Sum squared resid 0.536753 Schwarz criterion -0.831780
Log likelihood 23.44453 Hannan-Quinn criter. -0.977272
F-statistic 96.02009 Durbin-Watson stat 0.825780
Prob(F-statistic) 0.000000
Source: Author compilation, 2016.
41. 41
GDP = f (IMP, CUM, EXR, GCE)
Log(GDP) = β 0 + β1IMP + β2CUM + β 3EXR + β4Log(GCE) + μt
GDP = 27.56214+ 0.005765IMP + 0.010125CUM+ 0.001661EXR + 0.098244GCE
Std. Error= (0.778033) (0.001284) (0.002689) (0.001044) (0.030283)
t-Statistic= (35.42540) (4.490074) (3.765981) (1.591035) (3.244201)
Prob.= (0.0000) (0.0001) (0.0007) (0.1221) (0.0029)
The result explains 92.76% of the systematic variation in the influence of manufacturing sector on
Nigeria’s economic growth under the period of study as indicated by the value of the R-square.
And, the “good of fit” is satisfactory with an adjusted coefficient of determination which stood at
0.92%. The explanatory power of the independent variables is very high.
From the regression equation above, the value of the constant term is 27.56214. This simply
implies that if all the explanatory variables are held constant, the Gross Domestic Product is
27.56214. Thus, this is the autonomous value of the Gross Domestic Product.
The regression result show that the Index of Manufacturing Production is positive (0.005765) and
the relationship between Index of Manufacturing Production is statistically significant to Nigeria
economic growth. This shows that the variable is positively related to the growth of the economy,
which shows that the higher the manufacturing production, the better the Nigerian economy. The
positive sign of manufacturing production is a sign of a high level of production which will result
in positive economic growth, and shows a positive relationship between manufacturing production
and economic growth. The result also suggested that a unit increases in Capacity Utilisation of
Manufacturing sector would cause the Gross Domestic Product to rise by about 1.0125% and
capacity utilization of manufacturing is statistically significant to Gross Domestic Product.
42. 42
The result also suggested that, holding the effects of other variables constant, a unit increase in
Exchange Rate would cause Gross Domestic Product to rise by 0.001661 but not statistically
significant to Gross Domestic Product. Although the exchange rate is positive, the sign of the
exchange rate indicator could be negative or positive for economic growth to take place. This has
to do mainly with the state of the productive base of the economy, and their positions in the
international market. If a firm or an economy is already in the international market, the firm will
benefit from the upward movements of the exchange rate as against the domestic currency simply
because, the demand for their products will increase especially if the products in question are price
elastic. But, if a firm or nation is yet to be fully integrated into the international market, the cost
of entering the market when there is upward movement against the domestic currency might be
too high to bear, especially if the firm is import dependent.
Based on the outcome of our regression equation, a unit increases in Government Capital
Expenditure would cause the Gross Domestic Product to rise by about 9.8244%. And it is pertinent
to note that the relationship between government capital expenditure is statistically significant to
Nigeria economic growth.
TESTING OF HYPOTHESIS
To determine the relationship that exists between manufacturing sector and economic growth in
Nigeria, the following hypothesis would be tested:
H0: Manufacturing sector in Nigeria does not positively impact on Nigeria’s economic growth
H1: Manufacturing sector in Nigeria positively impact on Nigeria’s economic growth
43. 43
DECISION RULE
Reject the null hypothesis if the value of F calculated is greater than the value of F tabulated (F
cal>F tab), otherwise accept it. At 95% level of significance (α = 0.05), the F tabulated is given
as: F0.05, (5, 5) = 5.0503.
DECISION
Since F calculated = 96.02009 > F tabulated = 2.64. We reject the null hypothesis. In conclusion,
the results of the regression confirm with 95% confidence that the Manufacturing sector in Nigeria
positively impact on Nigeria’s economic growth.
4.7 CAUSALITY TEST
The robustness of the study was taken further using Granger causality bidirectional instrument to
know the direction of causation between the explained and explanatory variables. One of the
objectives of this study is to examine the relationship between manufacturing sector and economic
growth in Nigeria. In this study, granger causality test was applied to determine the relationship
between two variables and its direction in the Nigerian economy between 1980 and 2014.
44. 44
Table 4.5: Causality test result
Pairwise Granger CausalityTests
Date: 04/17/16 Time:07:16
Sample:1980 2014
Lags:2
Null Hypothesis: Obs F-Statistic Prob.
IMP does notGranger Cause GDP 33 0.11621 0.8907
GDP does notGranger Cause IMP 4.79910 0.0161
CUM does notGranger Cause GDP 33 1.73315 0.1952
GDP does notGranger Cause CUM 5.87285 0.0074
EXR does not Granger Cause GDP 33 6.01732 0.0067
GDP does notGranger Cause EXR 0.10667 0.8992
GCE does notGranger Cause GDP 33 0.56335 0.5756
GDP does notGranger Cause GCE 3.76009 0.0358
CUM does notGranger Cause IMP 33 2.55947 0.0953
IMP does notGranger Cause CUM 2.89081 0.0722
EXR does not Granger Cause IMP 33 2.14348 0.1361
IMP does notGranger Cause EXR 0.24241 0.7864
GCE does notGranger Cause IMP 33 3.15160 0.0583
IMP does notGranger Cause GCE 1.50254 0.2400
EXR does not Granger Cause CUM 33 11.6224 0.0002
CUM does notGranger Cause EXR 1.50968 0.2384
GCE does notGranger Cause CUM 33 5.96154 0.0070
CUM does notGranger Cause GCE 0.23836 0.7895
GCE does notGranger Cause EXR 33 1.39768 0.2639
EXR does not Granger Cause GCE 1.09249 0.3493
Source: Author compilation, 2016.
The causality test results suggest a bidirectional causation between the Gross Domestic Product
and index of manufacturing production. The F statistics is significant at 5 percent using a two-
tailed test; the critical value is 2.56 for (28, 5 degree of freedom). On the other hand, there is no
“reverse causation” from index of manufacturing production to Gross Domestic Product.
Furthermore, there is independence “no causation” between the Gross Domestic Product and
capacity utilizations of manufacturing sector, exchange rate to Gross Domestic Product, Gross
Domestic Product to government capital expenditure, government capital expenditure to index of
45. 45
manufacturing production, exchange rate to capacity utilization of manufacturing sector as well as
government capital expenditure and capacity utilization of manufacturing sector.
46. 46
CHAPTER FIVE
SUMMARY, CONCLUSION AND POLICY RECOMMENDATION
5.1 SUMMARY AND CONCLUSION
The goal of this study was to critically assess the impact of the Nigerian manufacturing sector on
the Nigerian economy. The results of this research shows that there is indeed a positive relationship
between growth of manufacturing sector and economic growth proving that Nikolas Kaldor’s first
law of economic growth holds in the Nigerian economy with respect to output of manufacturing
sector and Gross Domestic Product. The result showed that index of manufacturing production,
capacity utilization of manufacturing sector and government capital expenditure all have a positive
relationship and also are statistically significant to economic growth. Exchange rate also has a
positive relationship but isn’t statistically significant to economic growth.
The a’priori expectation of this research was also confirmed to be correct as all the regression co-
efficient had positive signs. Therefore, it is indeed correct to state that the manufacturing sector
serves as the engine that fosters economic growth in our modern times.
5.2 POLICY RECOMMENDATION
With the kind of result gotten from this research, it is pertinent for government to adopt sound
economic and industrial policies that would positively impact on the manufacturing sector and
help the sector to grow at an outstanding rate. Currently in the Nigeria, the mainstay of the
economy is the Oil sector. This sector used to generate near adequate income that was somewhat
enough to sustain the economy but due to the current ongoing crisis in the world market for oil,
the country seems to be having serious issues and thus, it’s high time the country starts looking at
47. 47
other viable options and means of sustaining the economy, one of which should be the
manufacturing sector.
The government should adopt policies that would improve upon their current efforts in advancing
the manufacturing sector. The government should begin to invest in the creation of more jobs in
the manufacturing sector so as to improve manufacturing sector output. The government can also
offer tax exemptions for new manufacturing outfits so they can grow at a faster rate and tax
holidays can be given to existing manufacturing firms so as to improve the output level of the
sector.
Another viable policy government can adopt is that of substituting importation of raw materials
needed by the manufacturing sector with locally sourced raw materials. Existing capacity in the
manufacturing sector should also be maximized so that the capacity utilization of the
manufacturing sector would increase as this also has an effect on the output of the sector.
The financial sector in Nigeria also has its own quota to contribute to the development of the
manufacturing sector. They would have to provide adequate credit facilities to those who want to
set up a new manufacturing outfit or those who want to expand their existing plant size.
Technical education in Nigeria should be improved and developed so that youths who can’t go to
a university or polytechnic would be encouraged to register and attend such schools. The status of
these technical institutions should be improved so that it can have a high standard just like that of
universities and polytechnics and so that its students can gain quality education, knowledge and
information. Doing all this will develop the country’s industrial base and thus lead to growth in
manufacturing output.
48. 48
Policies that would aid the participation of both private investors and foreign investors in the
manufacturing sector of Nigeria should be implemented. Private and foreign investors are usually
reluctant to invest in an economy when the chances of business failure are high or when the
government does little or nothing to encourage success of firms in the economy.
5.3 AREAS FOR FURTHER STUDIES
Having researched on the impact of manufacturing sector on economic growth in Nigeria, I suggest
other studies are carried out on productivity of the manufacturing sector, manufacturing sector
export and economic growth, impact of industrial policies on economic growth etc.
49. 49
REFERENCES
Adeola, F. A. (2005) Productivity performance in developing countries: Case study of Nigeria.
United Nations Industrial Development Organization (UNIDO) Report.
Adugna, T. (2014). Impacts of Manufacturing Sector on Economic Growth in Ethiopia: A
Kaldorian Approach. Journal of Business Economics and Management Sciences, 1(1), 1-
8.
Aghion, P., & Howitt, P. (1990). A model of growth through creative destruction. Cambridge,
MA: National Bureau of Economic Research.
Akinlo, E. A. (1996). Improving the performance of the Nigerian Manufacturing Sub-sector after
adjustment. The Nigerian journal of economic and social studies, p. 9.
Al Awad, M. (2010). The Role of Manufacturing in Promoting Sustainable Economic Growth in
the GCC. Institute for Social and Economic Research Working Paper. No. 4, pp. 1-23
Anyanwu, C. M. (2000). Productivity in the Nigerian manufacturing industry. Research
department: central bank of Nigeria, p. 124-129.
Böhringer, C., Moslener, U., Oberndorfer, U., & Ziegler, A. (2008). Clean and Productive?
Evidence from the German Manufacturing Industry. SSRN Electronic Journal SSRN
Journal.
Corrado, C., & Mattey, J. (1997). Capacity Utilization. Journal of Economic Perspectives, 11(1),
151-167. doi:10.1257/jep.11.1.151
50. 50
Dipak, M., and Ata, M. (2003). The African manufacturing firm, an analysis based on firm
studies in Sub-Saharan Africa. Taylor and Francis Ltd.
Fabayo, J. A. (1981). “Manufacturing and Industrial Utilization in a Developing Economy: A Case
of the Nigerian Cotton Textile Industry. The Journal of Business and Social Studies,Vol.
4, No. 22, pp. 53-63.
Falokun, G. O. (1995): “Sources of Industrial Growth in Nigeria”, Nigerian Institute of Social and
Economic Research (NISER) Pp. 12-13.
Felipe, J. (1998). The Role of the Manufacturing Sector in Southeast Asian Development: A Test
of Kaldor’s First Law. Journal of Post Keynesian Economics, 20(3), 463-485.
Gajanan, S., & Malhotra, D. (2007). Measures of capacity utilization and its determinants: A study
of Indian manufacturing. Applied Economics, 39(6), 765-776.
Gregory, C. (2006). Manufacturing Growth: What is the path to prosperity in Africa?, University
of California, Davis.
Gujarati, D. N. (2003). Basic econometrics. New York, NY: McGraw-Hill.
Igwe, N. N., Nwokedi, R. C., & Udeh, S. N. (2014). An Empirical Investigation into the Impact of
Change Management on Selected Manufacturing Firms in South East Nigeria. IJBA
International Journal of Business Administration, 5(1). doi:10.5430/ijba.v5n1p53
Kalim, R. K. (1998). Factors affecting capacity utilization in manufacturing sectorof pakistan: a
survey analysis. Pakistan Economic and Social Review, 36(2), 171–182. Retrieved from
http://www.jstor.org/stable/25825176
51. 51
Khan, K.S. and S. M. Wasif, 2011, Impact of manufacturing industry on economic growth in case
of Pakistan: A Kaldorian approach, Interdisciplinary Journal of Contemporary Research in
Business, 3(1): 1021-1037.
Ku, H. S., Mustapha, U., & Goh, S. (2010). Literature review of past and present performance of
the Nigerian manufacturing sector. Proceedings of the Institution of Mechanical
Engineers, Part B: Journal of Engineering Manufacture, 224(12), 1894-1904.
MacKinnon, J. G., Haug, A., & Michelis, L. (1999). "Numerical Distribution Functions of
Likelihood Ratio Tests for Cointegration", Journal of Applied Econometrics, Vol. 14, No.
5, pp. 563-577.
Malik,A., Teal,F., & Baptist,S.(2004) “The Performance of Nigeria Manufacturing Firms:
Report on the Nigerian Manufacturing Enterprise Survey” United Nations Industrial
Development Organization, Centre for the Study of African Economic, Department of
Economics, University of Oxford pps 2-33
Mazumdar, D. and Mazaheri, A. (2003). The African manufacturing firm: An analysis based
on firm, studies in Sub-Saharan Africa. Taylor and Francis Ltd.
Kaldor, N. (1967). Strategic Factors in Economic Development. New York: W.F. Humphrey
Press.
Obasan, K. A. & Adediran, O. A. (2010). The Role of Industrial Sector in the Economic
Development of Nigeria. Journal of Management and Society. Vol. 1, No 2, pp. 9-16.
Ogbu, O. (2012) Toward Inclusive Growth in Nigeria. The Brookings Institution’sGlobal
Economy and Development Policy Paper. No. 2012-03, June, pp. 17.
52. 52
Opaluwa, D., Umeh, J. C. & Ameh, A. A. (2010). The effect of exchange rate fluctuations on
the Nigerian manufacturing sector. African Journal of Business Management. Vol.
4, no. 14, pp. 2994-2998.
Pacheco-López P., & Thirlwall AP (2013). A New Interpretation of Kaldor’s First Growth Law
for Open Developing Economies, University of Kent School of Economics Discussion
Papers, KDPE.
Rioba, E. M. (2015). Manufacturing Industry and Economic Development in Eastern Africa:
A test of Kaldor’s first Law.
Rosenberg, N. (1982). Inside the black box: Technology and economics. Cambridge: Cambridge
University Press.
Simon-Oke, O., & Awoyemi, O. (2010). Manufacturing Capacity Utilization and Industrial
Development in Nigeria: An Assessment (1976 – 2005). African Research Review Afr.Res.
Rev., 4(2).
Szirmai, A. (2009). Industrialisation as an engine of growth in developing countries, United
Nations University - Maastricht Economic and social Research and training centre on
Innovation and Technology Maastricht, The Netherlands.
Teshome, A. (2014). Impacts of Manufacturing Sector on Economic Growth in Ethiopia: A
Kaldorian Approach. Journal of Business Economics and Management Sciences, Vol. 1(1),
1-8.
Ukoha, O. O. (2000). “Determinants of Manufacturing Capacity Utilization in Nigeria, 1970-
1998”. The Nigeria Journal of Economics and Social Studies, Vol. 42 (2): 121-130.
53. 53
Uzoigwe, C. D. (2007). Economic development in Nigeria through the agricultural, manufacturing
and mining sectors: an econometric approach.
55. 55
Where:
GDP: GROSS DOMESTIC PRODUCT AT 2010 CONSTANT PRICES
IMP: INDEX OF MANUFACTURING PRODUCTION
CUM: CAPACITY UTILIZATION OF MANUFACTURING SECTOR
EXR: EXCHANGE RATE
GCE: GOVERNMENT CAPITAL EXPENDITURE
