MONETARY POLICY, INFLATION AND POVERTY IN
NIGERIA
BY
OMOLE OLUWASEUN EBENEZER
MATRICULATION NUMBER :
20162566
BEING A RESEARCH PROJECT TO BE SUBMITTED TO THE
DEPARTMENT OF BANKING AND FINANCE, COLLEGE OF
MANAGEMENT SCIENCE, FEDERAL UNIVERSITY OF
AGRICULTURE, ABEOKUTA
IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE AWARD OF THE
BACHELOR OF SCIENCE (B. Sc. Hons) DEGREE IN
BANKING AND FINANCE
SUPERVISOR: DR. ADEDEJI
SEPTEMBER, 2021
i
DECLARATION
I, Omole Oluwaseun Ebenezer with matriculation number 20162566 hereby declare that this
research “Monetary Poverty, Inflation and Poverty in Nigeria (1989-2019)” was undertaken by
me under the supervision of Dr. A. O. Adedeji, and that it has not been submitted anywhere for
any award of degree. Idea and views of this work are products of the research undertaken by me,
and the views of other researchers have been duly acknowledged.
_________________________
Omole, Oluwaseun Ebenezer
_____________________
Date
___________________
_________________
Omole, Oluwaseun Ebenezer
Date
ii
CERTIFICATION
This is to certify that this research work was carried out by OMOLE OLUWASEUN
EBENEZER with matriculation number of 20162566 under the supervisor of DR. A.O
ADEDEJI and has been approved for the award of Bachelor of Science in the Department of
Banking and Finance (B. Sc.)
in the Department of Banking and finance of the Federal
University of Agriculture Abeokuta, Ogun State, Nigeria.
_________________
________________
Dr A.O Adedeji
Date
(Project Supervisor)
____________________
_________________
Date
Dr. O.J. Oyetayo
(Head Of The Department)
iii
DEDICATION
This project is dedicated to Almighty God for his divine wisdom, grace, protection and provision
in the success of this project.
iv
ACKNNOWLEDGEMENTS
I would like to express my special gratitude to my supervisor D.r A.O. ADEDEJI for their able
guidance and support in completing my project.
I would also like to extend my gratitude to head of the Department Dr. Mrs O.J. OYETAYO for
her valuable guidance. I also acknowledge the great efforts of my wonderful lecturers in the
department of banking and finance
To my parent Mr and Mrs Omole, I thank you for your love, prayer, care, and financial support.
Special thank to my brother, Olasanmi Oladipupo and my sister, Olasanmi Itunu .
OMOLE OLUWASEUN EBENEZER
v
Abstract
Using time series data from 1989 to 2019, this study examined the impact of monetary policy,
inflation, and poverty in Nigeria. For the following variables, data was gathered from the Central
Bank of Nigeria statistical bulletin (CBN): money supply, inflation rate, poverty level, and per
capital income. The ARDL (auto regressive distributed lag model) was employed in this study.
The results of the ARDL model show that a broad money supply has a positive impact on
poverty reduction, whereas inflation has a negative impact, especially in the long run.With the
exception of inflation, the short term parsimonious results demonstrated that the broad money
supply and interest rate were inversely connected to poverty. Money supply granger causes
poverty reduction, meaning that money supply and interest rate have an impact on poverty. As a
result, the study advocated for an expansionary monetary policy as well as zero-interest
financing capable of encouraging investment in the economy's real sector while also halting the
inflationary trend linked to monetary policy.
vi
TABLE OF CONTENTS
Contents
Abstract ............................................................................................................................................ i
TABLE OF CONTENTS .............................................................................................................. vii
CHAPTER ONE ............................................................................................................................. 1
1.1
INTRODUCTION ............................................................................................................... 1
1.2
Statement of the Problem ..................................................................................................... 3
1.3
Research Questions .............................................................................................................. 4
1.4 Objective of the Study .............................................................................................................. 4
1.5
Research Hypothesis ............................................................................................................ 5
1.6 Justification of the Study .......................................................................................................... 5
1.7
Scope of the Study ............................................................................................................... 6
1.8
Organization of the Study .................................................................................................... 6
2.1 Introduction ............................................................................................................................... 7
2.2 Conceptual Review ................................................................................................................... 8
2.2.2 Inflation rate ....................................................................................................................... 9
2.2.3 Poverty ............................................................................................................................. 10
2.3
Theoretical Review ............................................................................................................ 13
2.3.1Classical and Neoclassical Theories of Poverty................................................................ 13
2.3.2 Marxian Theory of Poverty .............................................................................................. 14
2.3.3
The social Democratic Theory of Poverty .................................................................. 15
2.3.4
The social Darwinist Theory of Poverty..................................................................... 16
2.3.5
Situational Theory of Poverty..................................................................................... 17
2.3.6
Structural Theory of Poverty ...................................................................................... 18
2.3.7
The Membership Theory of Poverty .......................................................................... 18
2.3.8 Monetarist Theory of Inflation ......................................................................................... 19
2.3.9 Classical Theory of Inflation ............................................................................................ 20
2.3.10 the Phillips Curve ........................................................................................................... 20
2.4 Empirical Review.................................................................................................................... 21
vii
CHAPTER THREE ...................................................................................................................... 45
RESEARCH METHODOLOGY.................................................................................................. 45
3.1
Introduction ........................................................................................................................ 45
3.2
Theoretical Framework .................................................................................................... 45
3.3 Model Specification ................................................................................................................ 46
3.3.1 Vector Auto-Regression Model (Objective2) .................................................................. 47
3.4 Estimation Techniques ............................................................................................................ 50
3.5 Source and Measurements of the data .................................................................................... 50
3.5
Post Estimation Technique ................................................................................................ 52
3.7 Scope and Source of the Variables ......................................................................................... 55
CHAPTER FOUR ......................................................................................................................... 57
DATA ANALYSIS AND DISCUSSION OF FINDINGS ........................................................... 57
4.1 INTRODUCTION .................................................................................................................. 57
4.2 Trends in Poverty, Inflation rate, and Broad money supply ................................................... 57
4.2.1 Trend Analysis ................................................................................................................. 57
4.3 Descriptive Statistics ............................................................................................................... 64
4.4 Unit Root Test ......................................................................................................................... 66
4.5 Pre-Estimation......................................................................................................................... 68
4.6 TEST OF HYPOTHESIS ....................................................................................................... 74
4.7 Discussion of Findings ............................................................................................................ 74
CHAPTER FIVE .......................................................................................................................... 77
SUMMARY, CONCLUSION AND RECOMMENDATIONS ................................................... 77
5.1 Summary of the Study ............................................................................................................ 77
5.2 Major Findings of the Study ................................................................................................... 78
5.3 Conclusions of the Study ........................................................................................................ 78
5.4 Policy Recommendations........................................................................................................ 79
5.5 Suggestions for Further Study ................................................................................................ 80
APPENDIX .................................................................................................................................................... 81
viii
CHAPTER ONE
1.1
INTRODUCTION
Poverty and inflation are dreadful global phenomena that affect people at all levels and depths at
various times and stages of their lives. According to the National Bureau of Statistics, Nigeria is
Africa's most populated country, with a population of almost 200 million people.
Despite solid growth in Africa's second largest economy, poverty in Nigeria is growing, with
nearly a 100 million people living on less than $1.9 per day (Daniel, 2011). The proportion of
Nigerians living in absolute poverty, or those unable to afford the fundamental necessities of
food, housing, and clothes, increased to 60.9 percent in 2019 from 54.7 percent in 2004.
(National Bureau of Statistics, 2018). Despite the fact that Nigeria's economy is expected to
grow further, poverty is predicted to worsen as the wealth gap between rich and poor continues
to increase. It's no surprise that, despite the fact that Nigeria's economy has continued to grow,
the number of Nigerians living in poverty has continued to rise year after year, according to Kale
(2012).
According to the National Bureau of Statistics' newest report 2020, 116.719 million Nigerians
live in relative poverty out of a total population of 200 million. The term "relative poverty" refers
to a comparison of people's living standards in a specific society over a period of time. Inflation
has been a severe concern in Nigeria since the mid-1960s. In 1975, the Udoji committee, which
quadrupled the basic minimum salary in the public sector, signaled the peak of inflationary
pressures, resulting in massive strikes and turmoil in the private sector, where the Udoji
recommendations were not binding. Even when the oil boom collapsed in 1980, the persistent
1
overvaluation of the Naira generated major economic distortions in production and consumption,
as there was a high rate of import dependency, resulting in balance of payment deficits.
A low inflation rate results in lower nominal and real interest rates, lowering borrowing costs. In
a low-inflation economy, “households” will be encouraged to buy more durable products and
invest at a faster rate. This will improve economic growth by increasing productivity and mass
production of products and services. Low inflation is crucial for economic growth and, as a
result, poverty reduction. (2012) (Hossain, Ghosh, and Islam). High inflation reduces the amount
of available labor, resulting in lower productivity and, as a result, lower growth.
The central bank and monetary authorities are in charge of monetary policy. It is the process of
controlling the cost, value, and availability of money and credit in order to reach the intended
level of price, employment, output, and other economic objectives, according to the Central
Bank of Nigeria (CBN). Expansionary and contractionary monetary policies are used by
governments to regulate the economy during periods of inflation and recession. The role of the
monetary authorities is to harness mechanisms by establishing policies that promote economic
growth and stability.As a result, economic theory and a number of empirical studies support the
notion that monetary policy effects output, and therefore efficient monetary policy conduct is
critical (ori, Perovi, andimi, 2012). The majority of research that examine monetary policy
effects focus on policy measures against the whole economy, but this tends to obscure critical
nuances that could have resulted in more stable and target-oriented policy designs.
Excess money in circulation leads to inflation, which invariably leads to poverty. Monetary
authorities regulate money in circulation through contractionary or expansionary policies. The
proportion of food in relation to other commodities may differ between the rich and the poor.
Food consumes a large portion of the income of poor households. A rise in the price of vital food
2
items reduces their purchasing power, making these items unaffordable for them at the same
salary level. Food costs are erratic, resulting in famine and starvation, and hence contributing to
the overall inflation rate.As a result, the cost of everyday products is a major source of anxiety in
today's world. (Chaudhry et al., 2000)
It is important to recognize that monetary policy and inflation are not the same thing. To the best
of our knowledge, only Mbutor and Uba (2013) have looked into the relationship between
inflation and monetary policy in Nigeria, and found that increasing financial inclusion could
improve the effectiveness of monetary policy in Nigeria.However, when it comes to the
relationship between financial inclusion and poverty, Ajide (2015), Onaolapo (2015), and Chibba
(2009) believe that, given the current crisis, inclusive finance has a significant impact on poverty
reduction, and that the need to scale up financial inclusion efforts has never been more pressing.
These writers focused solely on the effects of inflation on poverty levels, ignoring the role of
monetary policy in the relationship
1.2
Statement of the Problem
The average Nigerian is a low-income individual. Nigeria is a country with great wealth in the
hands of a few and extreme/abject poverty on the doorsteps of many. The disparity between
Nigeria's economic metrics and macroeconomic variables and reality is concerning. People die as
a result of their inability to afford three square meals each day and basic public healthcare. As
bizarre as it may sound, this coexists with the affluent few's extravagant displays of riches.
Poverty in Nigeria is caused by a variety of factors. This includes, but is not limited to,
increasing disparities in resource distribution and a lack of an enabling environment. The high
rate of unemployment, on the other hand, is a defining feature of poverty in Nigeria. It is an
overstatement to say that unemployment equates to a loss of purchasing power. As a result,
3
fewer products and services are consumed. As a result, enterprises are forced to reduce
production output or seek other customers. In the long run, these cyclical trends have an impact
on economic growth. It is because of this fundamental knowledge that the celebration of
Nigeria's continuous GDP growth is so curious.Between 1970 and 2013, the rate of inflation
increased, resulting in various economic distortions, a situation in which the government of a
country intervenes in the economy through policies such as fiscal and monetary policies. Some
policies that resulted in economic distortions include the minimum wage, lump sum tax, taxation,
and government subsidies. Also, after the oil boom faded, the overvaluation of the Nigerian
currency contributed significantly to economic distortions in production and consumption,
resulting in a high rate of reliance on imported goods from other countries, i.e., more imports
than exports, resulting in a deficit balance of payment in the economy.
1.3
Research Questions
This study seeks to investigate the effect of monetary policy and inflation on poverty level in
Nigeria. To this end, this paper will provide answers to the following questions:
I. What is the trend of monetary policy, inflation and poverty in Nigeria?
ii. What is the direction of causation that exists among monetary policy, inflation and poverty
level?
iii. What effect does monetary policy and inflation have on poverty in Nigeria?
1.4 Objective of the Study
The broad objective of this study is to investigate the relationship that exists among monetary
policy, inflation and poverty in Nigeria, The specific objectives are to:
i. Examine the trend of monetary policy and inflation and poverty
4
ii. Determine the direction of causality among the money supply, inflation and poverty
iii. Analyze the effect of money supply and inflation on poverty
1.5
Research Hypothesis
HO1: Monetary policy and inflation have no significant impact on poverty level
HO2: There is a direction of causality that exists among monetary policy, inflation and
poverty
HO3: Monetary policy and inflation have no effects on poverty level
1.6 Justification of the Study
Aneffective monetary policy will effectively lower inflation, resulting in a reduction in the
country's poverty rate. This study appears to be not the first of its kind, although it has a broad
scope. Adigwe (2015), Aminu Umaru (2013), and Onwuteaka (2019) are some of the past
researches that looked at the effects of inflation and monetary policy on economic growth.
However, Ogbonna and Uma (2015) did research in the case of Nigeria, however their work
appears to focus more on monetary policy and inflation in Nigeria.As a result, this research
would provide a significant contribution to knowledge by highlighting a special focus on one sort
of monetary policy, namely interest rate reduction, as well as a comprehensive focus on the
impact of inflation on poverty reduction. The difficult component of this study is that while the
majority of these studies show monetary expansion as a growth booster and inflation as a growth
stumbling block, a few others have presented evidence to the reverse. Most monetary experts
have convincingly shown that inflation is solely a monetary phenomenon that occurs when the
rate of increase of the money supply exceeds the rate of growth of production in the economy.
5
According to Precious and Palesa (2014), the main goal of monetary policy is to guarantee that
money supply is at a dimension that is steady with the development focus of real income, to such
an extent that non-inflationary development will be certain. In view of this, the study will
consider some variables that will capture full effect of monetary policy and inflation rate in
poverty reduction in Nigeria.
1.7
Scope of the Study
The research study covers Nigeria using data of relevant variables from 1980-2019. It makes use
of both the descriptive and analytical method of analysis. The descriptive method is used by
appraising the results of the findings while the analytical method involves using both statistical
and econometrics tools.The study made use of majorly secondary data which were gotten from
journals, relevant textbooks, economic reviews as well as database such as Central Bank of
Nigeria database, World Development Indicator.
1.8
Organization of the Study
This research study is divided into five chapters. Chapter One consists of the background to the
study, problem statement, research questions, objectives of the study, research hypotheses,
justification of the study, scope and limitation of the study as well as the plan of the study.
Chapter Two reviews related literatures and theoretical framework. The literature review is
divided into the methodological review and empirical review. Chapter Three covers the
methodology of the research and model specification. Chapter Four explains the empirical results
gotten from the econometric analysis and the discussion of results. Chapter Five discusses and
summarizes the major findings of study, the inferred conclusions and relevant policy
recommendation.
6
CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
This study has attracted some basic conceptual and empirical discussions and methodological
review concerning issues surrounding monetary policy, inflation rate and poverty reduction in
Nigeria.
Poverty is difficult to define in a concise and widely acknowledged way, owing to the fact that it
impacts many elements of human life, including physical, moral, and psychological well-being.
As a result, many criteria have been employed to define poverty. The majority of studies viewed
poverty as a result of a lack of sufficient income to secure basic goods and services. Others see
poverty as a result of factors such as education, health, life expectancy, child mortality, and so
on. The poor were recognized by Blackwood and Lynch (1994) based on their consumption and
expenditure levels.Sen (1983) also associates poverty with entitlements, which he defines as "the
numerous bundles of goods and services over which one has command, taking into account the
means by which such goods are purchased (for example, money and coupons, etc.) and the
availability of the requisite goods." Others, on the other hand, define poverty as the inability to
meet “basic needs requirements” – (physical: food, healthcare, education, shelter, and so on, as
well as non-physical: participation, identity, and so on) for a meaningful life (World Bank,
2016). Attempts by economists and social scientists to conceptualize the issue of poverty abound
in the social science literature. Broadly Poverty can be defined in four ways: a lack of
fundamental needs/goods; a lack of or degraded access to productive resources; inefficient use of
common resources; and as a result of exclusion mechanisms (Olayemi, 2012). Poverty is defined
7
as a lack of access to basic necessities and goods. It is primarily economic or consumptionoriented. It uses consumption-based categories to explain the scope and depth of poverty, as well
as to determine who is and is not poor. As a result, the poor are defined as people who are unable
to purchase a certain basket of fundamental goods and services in a particular society.
Economists typically try to distinguish inflation from a one-time price increase or price increases
in a small set of economic commodities or services, according to Piana (2001). According to
studies in Nigeria, Fasanya (2013), monetary policy instruments have not played a significant
role in improving macro-economic variables in light of economic stability, high unemployment
rates in both rural and urban areas, poverty, inflation, and low living standards.
2.2 Conceptual Review
2.2.1 Monetary Policy
Monetary policy aids to long-term economic growth and financial stability by ensuring that
inflation is low and stable over time (Bernanke, 2011). The real economy is critical to the
country's overall growth. It provides numerous benefits to the country because it has been
determined to have the greatest influence on economic growth and job creation (Anyanwu,
2010). In many economies, the real sector's performance is used to judge the success of
macroeconomic policies. Monetary policy is a macroeconomic management technique that is
used to steer real-world outcomes in the desired direction.According to macroeconomic theory,
monetary policy should have an impact on the real economy through changing interest rates,
which would affect the cost of capital and investment in the productive sector. Monetary policy,
according to Mishkin (1996 and 2007), influences the economy through a range of channels,
including interest rates, credit and/or bank lending, asset values via exchange rates, equities and
8
housing prices. Because the channels via which shocks are communicated alter with
developments in both the global and domestic economies, investigations into the effect of
monetary policy on the economy have continued to generate significant scientific interest.The
foundation of the quantity theory of money was laid by Irving Fisher (Diamond, 2015. P. 49),
whose equation of exchange laid the groundwork for monetary policy. Money, according to him,
has no effect on economic statistics other than pricing. However, (Keynes, 1930 P. 90) and other
Cambridge economists claimed that money has an indirect effect on other economic variables by
altering the interest rate, which affects economic actors' investment and cash holding.. The role
of monetary policy in influencing the volume, cost, and direction of money supply has been
effectively articulated by (Friedman, 1968), whose position is that inflation is always and
everywhere a monetary phenomenon, while recognizing that an increase in money supply can
reduce unemployment but also create inflation in the short run, and thus monetary authorities
should increase the money supply.
2.2.2 Inflation rate
Inflation is defined as a sustained increase in the general price level of a broad range of products
and services in a country over a lengthy period of time.. Inflation has always been inextricably
related to money, as the adage goes: "inflation is too much money chasing too little
commodities." Inflation is defined as an economic scenario in which the rise in the money supply
exceeds the new production of goods and services in the same economy, according to Hamilton
(2001). Economists typically try to distinguish inflation from a one-time price increase or price
increases in a small set of economic commodities or services, according to Piana (2001).
Inflation is defined by Ojo (2000) and Melberg (1992) as a general and consistent increase in the
prices of goods and services in an economy.The percentage change in the price index is used to
9
calculate the inflation rate (consumer price index, wholesale price index, producer price index).
The consumer price index (CPI), for example, according to Essien (2002), assesses the price of a
representative basket of products and services purchased by the average consumer and is
generated on the basis of a periodic survey of consumer prices.
In the aftermath of the global financial crisis in 2008, Nigerian inflation accelerated. Inflation
remained high and stayed above the Central Bank of Nigeria's comfort zone.The argument over
the growth-inflation trade-off and the function of monetary policy has resurfaced, taking center
stage in recent policy discussions. The conventional view, driven mostly by the short run Phillips
Curve, was that a higher inflation tolerance could lead to faster growth, as opposed to the idea
that inflation represents a risk to growth above a certain point.
Monetary policy, for example, helps to keep inflation low and consistent over time, which
benefits to long-term economic growth and financial stability (Bernanke, 2011).Inflation that is
low and consistent improves the functioning of markets and makes them more efficient at
allocating resources. It also enables consumers and businesses to plan for the future without
being too concerned about unpredictably fluctuating price levels.
2.2.3 Poverty
Poverty can be defined in four ways: a lack of fundamental needs/goods, a lack of or degraded
access to productive resources, inefficient use of common resources, and as a result of exclusion
mechanisms (Olayemi, 2012). Poverty is defined as a lack of access to fundamental needs/goods
that is primarily economic in nature. It uses consumption-based categories to explain the amount
and depth of poverty, as well as to determine who is and is not poor. As a result, the poor are
defined as members of a society who are unable to purchase a certain basket of essential goods
and services.Poverty is explained by a lack of access to productive resources such as agricultural
10
land, physical capital, and financial assets. As a result, there is a severe lack of income,
unemployment, and malnutrition. In general, limited access to resources focuses attention on
poverty and limits an individual's ability to turn existing productive resources into a greater
quality of life (Sen, 1997). Inefficient utilization of communal resources might also result in
poverty. This could be due to a skewed policy environment, insufficient infrastructure, limited
access to technology, credit, and so on. All of these factors contribute to low productivity,
poverty, and a drop in economic growth.Finally, poverty can be caused by select groups
exploiting systemic mechanisms to keep "problem groups" out of economic development,
including the democratic process. The discomfort index, on the other hand, is a subjective
measure that combines unemployment and inflation rates. The discomfort index, sometimes
known as the misery index, is claimed to have little economic significance, but it is frequently
used by politicians to demonstrate the success of their programs (or the failure of their
opponents' policies) (Financial Dictionary). When looking at the discomfort index, the two
primary components will be examined (i.e., unemployment rate and inflation rate).The true
unemployment rate in Nigeria has been a source of contention. In 1992, the figure was given as
40 percent, while in 2010, it was given as 19.70 percent by people in government circles, which
contradicts the figures given by other independent sources. As a result, one can state
categorically that unemployment in Nigeria has been increasing. The millions of unemployed
Nigerians are a stark contrast to the country's much-touted economic boom.Various Government
Initiatives to Combat Poverty and Barriers Numerous policies and programs have been
established to satisfy the unique requirements of the poor as a result of the government's interest
for poverty alleviation. As a result of the continuous deterioration of living conditions in the
1980s, several poverty alleviation programs were established, including: 1986:Directorate of
11
Food, Roads, and Rural Infrastructure (DFRRI), 1993:Family Support Programme and the
Family Economic Advancement Programme, 2000:Poverty alleviation Programme (PAP),
2001:National Poverty Eradication Programme (NPAP).Programme to Reduce Poverty (PAP)
According to Ayaba (2012), PAP was implemented as a stopgap measure in early 2000 to
address the challenges of rising unemployment and criminality, particularly among youngsters.
Its ultimate goal was to improve Nigerians' well-being. The three main goals of PAP are as
follows:
(a) Lessen unemployment and hence increase effective demand in the economy;
(b) Increase the economy's productivities
(c) Significantly reduce the society's embarrassing crime wave. PAP's goals, in accordance with
the stated aims, include, among others, the following:
(a) Creating jobs for 200,000 people who are now unemployed.
b) Establishing a credit delivery system via which farmers can obtain credit.
c) By 2003, the adult literacy rate will have increased from 51% to 70%.
(d)Increasing healthcare delivery system from its percent 40 percent to 70% by year 2003.
(e)Embarking on training and settlement of at least 60% of tertiary institutions graduates.
(f)developing small and medium scale industries.National Poverty Eradication Programme
(NAPEP) Introduced early in 2001, NAPEP is the current programme which focuses on the
provision of “strategies for the eradication of absolute poverty in Nigeria”.
12
2.3
Theoretical Review
Several theoretical contributions have been made in the literature as regards monetary
policy, inflation and poverty. These theories are of relevance to this study as they serve as
bed rock to this research work. Nevertheless, no theory is sufficient in itself, an integrated
approach is needed.
2.3.1Classical and Neoclassical Theories of Poverty
Individuals, according to classical traditions, are mainly accountable for their own fate, choosing
to become poor in effect (e.g. by forming lone-parent families). The notion of 'poor subcultures'
argues that inadequacies may persist over time, due to a lack of adequate role models, and that
government assistance should be confined to altering individual skills and attitudes (i.e., the
laissez-faire tradition).
Neoclassical theories are more comprehensive and acknowledge causes of poverty that are
beyond the control of people.These include a lack of both social and private assets; market
failures that exclude the poor from credit markets and make certain irrational choices rational;
educational hurdles; immigrant status; poor health and advanced age; and employment barriers
for lone-parent families.
When comparing the classical and neoclassical approaches, the use of (quantifiable) monetary
units to quantify poverty and the readiness with which policy prescriptions can be implemented
are the primary differences.They also highlight the influence of incentives on individual
behaviour as well as the relationship between productivity and income.
Criticism of these approaches highlights their overemphasis on the individual (without, for
instance, taking into account links with the community) and the focus on purely material means
to eradicate poverty.
13
2.3.2 Marxian Theory of Poverty
This is a hypothesis that states that poverty is caused by the circumstances in which a poor
individual finds himself or herself. As a result, the poor individual is a victim of circumstances
caused by a variety of variables, one of which is the production system. According to Karl Marx,
the owners of means of production (capitalists)' entrepreneurial practices of moving away from
labor-intensive to capital-intensive means of production in order to raise production and
maximize profits leads to huge unemployment. In order to increase profitability, capital intensive
production pushes the capitalist to retrench workers. Massive unemployment results from
layoffs.Those who do not re-engineer end up as paupers at home, forming what Karl Marx refers
to as a reserve army of laborers (Harvey and Reed). These paupers eventually become destitute.
Continued austerity leads to an increase in the number of poor people in the economy, which
raises poverty levels in the long run.
A sequence of structural failures has resulted in an increase in the poor population. These
structural failures, according to Gordon et al. (1982:), include racial and gender discrimination,
as well as nepotism, which deny some groups of individuals access to jobs, education, and social
assistance.Albrecht and Milford (2001) add to this hypothesis by stating that substantial
economic restructuring leads to increased economic and social marginalization of a whole group
of individuals. Due to a lack of access to opportunities, such populations end up impoverished.
According to Marxist theory, poverty alleviation can be achieved through improving production
structures and providing more education and training to individuals who have been rendered
obsolete by technical advancements, allowing them to adapt to changes in their environment and
change their profession. Education also helps that retrenched people accept and adapt to change
(Winch, 1987).The idea also proposes for a government welfare program to assist individuals
14
who are unable to reengineer themselves through education in order to gain access to basic
necessities like food rations, health care, and subsidies (Coser, 1969; Harvey & Reed, 1992)
In order to alleviate poverty, Neo-Conservative theory of poverty recommends provision of
moral education to curb over-population. Moral education results in sexual restraints, delay in
marriage and practicing abstinence prior to marriage. Poverty can also be reduced through
improved production technology to ensure that production of goods and services satisfy demand
at affordable prices (Winch, 1987)
2.3.3 The social Democratic Theory of Poverty
This theory was developed based on events that occurred in the United Kingdom in the 1920s.
Poverty, according to the theory, is a class-based notion that arises through societal class fights
rather than from a lack of resources. Piero Sraffa, the proponent of this theory, stated that class
battles extended beyond production domains, and that so limiting poverty explanations to
production means, as Marx did, would limit the scope needed to comprehend poverty. Poverty is
influenced by the politics surrounding how products and services are created and distributed, just
as it is by the means of production used (Sraffa, 1926).The politics of commodities and service
distribution go a long way toward explaining why certain groups of society are poor. Poverty,
according to the Social Democratic theory, is both a class issue and a market-based factor.
Poverty alleviation necessitates distributive justice, which ensures that products and services
generated are dispersed equally so that all classes of society are fairly involved in their
enjoyment (Harvey & Reed, 1992). Sen (1981) comes to the conclusion that poverty is a result of
entitlement, noting that “starvation...is a function of entitlement, not of food availability.”
Entitlement is a legal claim on existing resources that is a result of a political process aimed at
improving market dynamics or correcting market faults.In times of economic downturn, market
15
forces fail as a result of capitalists' fear of taking entrepreneurial risks, slowing production and
causing unemployment, as John Maynard Keynes believed. When market forces fail, the poor get
poorer as a result of greater unemployment or retrenchment, as well as low-income levels. When
market forces fail, the government must intervene to assure sustained production and stable
employment (Sen, 1984).
Sen (1984) contends that when people's access to resources or income is limited, their own
capacities lead to absolute deprivation, but market forces are powerless to intervene. While
persons in positions of political power who are capable of producing should not be restricted
from doing so, the government must also ensure that all of what is produced is distributed fairly
in order to eradicate poverty.
This theory is also a proxy for Marxism, which links poverty to the production and distribution
of goods.
2.3.4 The social Darwinist Theory of Poverty
According to the social Darwinist theory of poverty, poverty is a self-inflicted condition that
evolves over time as a result of social evolution. The excellence or mediocrity of an individual
will inevitably induce that person to be impoverished or otherwise. As a result, poverty is "both a
final judgment and a purgative by which society selects the unfit." Poverty trends in any
community are the outcome of natural selection, and any man-made attempt to reverse the trend's
direction wreaks havoc on institutions' natural functioning (Harvey & Reed, 1992).Based on
studies in metropolitan areas, the Social Darwinist theory of poverty distinguishes two forms of
poverty. The first category is middle-class poverty, which is a reversible position brought on by
social and ecological changes in urban areas. Physical disabilities, old age, or a female-headed
home with dependent children are all signs of normal-class poverty. Because it focuses primarily
16
on income-based poverty, normal-class poverty is self-correcting. When a person reaches
adulthood, moves from one economic class to another, or is assimilated, it can be removed.Other
ways of eradication include education and training, as well as natural urban evolution (Harvey &
Reed, 1992). This hypothesis is largely irrelevant because it focuses on urban or pre-urban
poverty.
The second sort of poverty is lower-class poverty, which is produced by poor people's behaviors
or cultural practices and conventions. This type of poverty develops as a result of a way of life
that prioritizes labor, self-improvement, and family service (Harvey & Reed, 1992). Lower-class
poverty persists because educators and opinion leaders who are able to modify the normal-class
poverty avoid issues of culture and habits passed down from generation to generation. Lowly
class largely remains unchanged.Lower-class poverty therefore gradually evolves into a natural
process through which society eliminates the unfit members (Harvey & Reed, 1992).
This theory does apply given the fact that this study was based in the rural areas where the issue
of culture cannot be underestimated.
2.3.5 Situational Theory of Poverty
This idea explains why poor people are more likely to exhibit fatalism and desire for rapid
fulfillment. This behavior, according to Jones (1984: 248), is “a sensible response to deprived
conditions.... This behavior stems from the poor's opportunity structure rather than distinctive
cultural values.....” The impoverished, according to this interpretation, exhibit impulsive
behavior in order to maximize utility in order to ensure survival. The impoverished do not work
because they are despairing due to a lack of optimism and a lack of a direct correlation between
their efforts and the rewards they receive.The situation the poor find themselves in is one that
does not allow them to gain much from hard work and a long-term view of issues. Consider a
17
child in a slum trying to study. Lack of supportive factors and an enabling environment such as
school fees, career counselling, text books and the like will lead the child to give up and engage
in immediate gratification such as street vending, theft, commercial sex activities and the like in
order to satisfy immediate daily demands of food, shelter and clothing.
2.3.6 Structural Theory of Poverty
The structural theories, in which poverty is blamed on circumstances and institutions in the
social or economic systems, such as racism, sexism, and segregation barriers, rather than on the
individual, are the second major category of theories (Gordon, Edwards & Reich, 1982).
Poverty is thus caused by a lack of enough skills and work prospects to maintain acceptable
living standards or quality of life (Cobb 1992 Duncan 1992 and Maril 1988). Albrecht et al.
(2001) add to these theories by pointing out that large economic restructuring also leads to
greater economic and social marginalization of a whole group of individuals.Structural theories
absolve the poor from blame. Poverty is blamed on structural failures which include sexism,
racism, and bad governance, wretched state of infrastructural development, poor development
policies and even geographical placement.
2.3.7 The Membership Theory of Poverty
According to S. N. Durlauf of the University of Wisconsin's Department of Economics, the
importance of group memberships in shaping socioeconomic results should not be overlooked.
He ascribed membership-based theory to this particular perspective on the origins of poverty,
which he outlined in his work. The assumption behind this hypothesis is that during the course of
a person's life, the organizations to which he or she is connected have a significant impact on his
or her socioeconomic possibilities. Residential communities, schools, and businesses, for
example, are examples of endogenous groups, according to the author.The idea behind this
18
theory is that during the course of a person's life, the organizations to which he or she is
connected have a significant impact on his or her socioeconomic possibilities. Residential
communities, schools, and businesses, for example, are examples of endogenous groups,
according to the author. Other factors, such as ethnicity and gender, are exogenous. The
membership theory of poverty is based on the assumption that an individual's socioeconomic
result is determined by the composition of the many organizations in which he or she participates
over the course of his or her life.In theory, such groupings can be defined along a variety of
characteristics, including ethnicity, residential neighborhoods, schools, and workplaces. A
variety of conditions can cause these affiliations to have causal effects on individual outcomes.
Peer group effects, role model effects, social learning, and social complementarities are some of
these aspects.
2.3.8 Monetarist Theory of Inflation
The quantity theory of money was propounded by Milton Friedman (QTM). Money supply,
according to monetarists, is the most important predictor of a country's price level. When the
amount of money available in an economy changes, the price level changes directly and
proportionally. The quantity theory of money can be expressed as follows, using Irving Fisher's
equation for exchange:
MV= PQ.................................(1)
M= Money Supply in an economy
V= Velocity of Money in Circulation
Q= Volume of transactions
P= General Price Level
19
Inflation in an economy is caused by a change in the supply of money or the quantity of money
in circulation, which influences the price level but not the rate of development in the economy,
according to monetarists. They felt that the degree of inflation has a significant impact on
investments, exports, and capital accumulation, and thus on the long-term growth rate of an
economy. They focused on the long-run dynamics of an economy rather than the short-run
dynamics.
2.3.9 Classical Theory of Inflation
Adam smith is the father of the classical economist; he came up with a supply side model of
growth where he pointed out three important production factors, which are land, labour and
capital. He propounded a production function where he expressed output is a function of land,
capital and land that is:
Y=f (L, K, T).............................(2)
Y= Output K= Capital L= Labour T= Land
Adam Smith argued that savings leads to investment which leads to economic growth. He stated
that growth in output is as a result of investment growth, population increase, land and increase
in productivity generally. (Gokal and Hanif, 2004) stated that the relationship between inflation
and economic growth is negative by the reduction in firms profit level and saving through higher
wage costs. This theory was criticized, as it does not give any direct reason of inflation and the
tax effect on the level of profit and output.
2.3.10 the Phillips Curve
This theory was propounded by A.W Phillips in 1958. His theory focused on the relationship that
exists between inflation and unemployment. He estimated a curve known as the Phillips Curve,
20
this curve showed that there is an inverse relationship existing between wages and the rate of
unemployment using data from United Kingdom from 1862-1957. He argued that wages and
prices move in opposite direction thus showing that there is a relationship between prices and
unemployment. The backbone of the Phillips Curve is that empirically it shows that there is an
existing reliable correlation economically and statistically between inflation and unemployment,
(Umaru and Zubairu, 2012).
(Lucas R, 1973) argued that inflation is an important engine for economic growth, he stated that
low inflation conquered adamant nominal prices and wages while relative prices can be adjusted
to fit structural changes during production to aid modernization period. This to him speeds up
economic growth. (Romer D, 2001) is of the opinion that high rate of inflation leads to “Shoe
leather cost” i.e. inflation which is accompanied with extra effort by people to make them reduce
holding money and “Menu cost” that is, inflation that leads to change of prices more often, this
discourages investment and tax system in the long-run.
2.4 Empirical Review
Dauda and Makinde (2009) examined the nexus between financial sector development
and poverty reduction in Nigeria using annual time series from 1980 to 2010. The
evidences from both the VAR and impulse response showed that theindirect effect of
economic growth exerts the strongest influence on poverty reduction in the short run but
could be detrimental to the poor in the long run due to the adverse effect of income
inequality. The study concluded that the relationship between poverty and the financial
deepening proxied by broad money supply (M2) is negative and significant. Hence, the
McKinnonconduit effect is the likely main transmission channel through which the poor
21
benefit from the financial sector development in the long run. And also that credits to
private sector,
contrary to the general belief, have failed to cause a reduction in the incidence of poverty
in Nigeria.Nwigwe, Omonona and Okoruwa(2012)argued that while microfinance has
developed some innovative management and business strategies, its impact on poverty
reduction remains in doubt. With a view to understanding how Islamic microfinance as a
financial inclusion strategy can be applied in alleviating poverty and maintain sustainable
development in Nigeria, Onakoya and Onakoya (2014) analysed the principles of Islamic
finance and conceptualized its operational details to see the linkage between the real
economies and sustainable development. The study employed triangulation method,
which is the use of different data collection techniques within one study in order to
achieve more accurate results. They employed a descriptive research method by collecting
data from urban and rural areas; surveyquestionnaires coupled with semi structured
interviews were adopted. With the understanding that microfinance is a sub-division of
the financial sector, the survey conducted in Ogun State, a sub-national government of
Nigeria revealed that Islamic microfinance in concert with the right fiscal and monetary
policies framework, will contribute positively to poverty alleviation in Nigeria.
Jeanneney and Kpodar (2008) investigated how financial development helps to reduce
poverty directly through McKinnon conduit effect and indirectly through economic
growth. The results obtained with data for a sample of developing countries from 1966
through 2000, using the system GMM estimator, suggest that the poorbenefit from the
ability of the banking system to facilitate transactions and provide savingsopportunities
but to some extent fail to reap the benefit from greater availability of credit. Also that
22
financial development is accompanied by financial instability which isdetrimental to the
poor. Nevertheless, the benefits of financial development for the pooroutweigh the
cost.Inoue and Hamori (2010) examined empirically whether financial deepening has
contributed to poverty reduction using unbalanced panel data for 28 states and union
territories between 1973 and 2004. Using dynamic generalized method of moments
(GMM), they found that financial deepening and economic growth alleviate poverty,
while international openness and inflation rate have the opposite effect. Odhiambo (2010)
empirically analysed the causal relationship between financial development and poverty
alleviation in Zambia from 1969 to 2006. She examined the effect of three proxies for
financial development, namely: M2/GDP, private credit/GDP, and domestic money bank
assets; on per capita consumption, a proxy for poverty levels. Using a bivariate causality
test based on an Error Correction Model (ECM), she found that financial development
seems to cause poverty reduction when private credit and domestic money bank assets are
used, while the reverse causality is found when M2/GDP is used.
Badebo and Mohammed (2015) examined the effectiveness of monetary policy as an anti
–inflationary measure in Nigeria, using co-integration and error correction approach on
quarterly time series data covering 1980 Q1 to 2012 Q4.They found a long run
relationship between inflation and the vector of regressors employed. The study also
revealed that interest rate, exchange rate, money supply and oil price are the major causes
of inflation in Nigeria. Omeke and Ugwuanyi (2010) also carried out a study or better put
tested the relationship between money, inflation and output in Nigeria, employing cointegration and Granger-causality test analysis. The findings revealed no existence of a
co-integrating vector in the series used. However, Money supply was seen to Granger
23
causes both output and inflation. The result suggest that monetary stability can contribute
towards price stability in Nigerian economy since the variation in price level is mainly
caused by money supply and also conclude that inflation in Nigeria is too much extent a
monetary phenomenon.Danjuma et al (2012), in their study on the assessment of the
effectiveness of monetary policy in combating inflationary pressure on the Nigerian
economy, found that liquidity ratio and interest rate are the dominant monetary policy
tools that can be employed to combat inflation in Nigeria. Also, Emerenini and Eke(2014)
investigated the determinants of inflation in Nigeria using a monthly data from 2007 to
2014, using OLS method found that,exchange rate and money supply influenced inflation.
Apir (2015) studied the extent money supply can explain the inflationary phenomenon in
Nigeria and discovered that money supply is the main determinant of inflation in Nigeria
between 1999 and 2014.
Olorunfemi and Adeleke (2013) also examined the effect of money supply on inflation
and concluded that money supply and interest rate did influence inflation in
Nigeria.Williams and Adedeji (2004) examined price dynamics in the Dominican
Republic by exploring the joint effects of distortions in the money and traded-goods
markets on inflation, holding other potential influences constant. The study captured the
remarkable macroeconomic stability and growth for period 1991 to 2002. Using a
parsimonious and empirically stable error-correction model, the paper found that the
major determinants of inflation were changes in monetary aggregates, real output, foreign
inflation, and the exchange rate. However, there was an incomplete pass-through of
depreciation from the exchange rate to inflation.Mbongo et al (2014) examined the effects
of money supply on inflation in Tanzania. Using OLS, VAR and Error Correction Model,
24
they found that money supply and exchange rate to have a significant impact on inflation
in the short and long runs. Mubarik (2005) employed the Granger Causality test to analyse
the threshold level of inflation for Pakistan for the period between 1973 and 2000. Annual
data set was used. The result of the threshold model suggests that an inflation rate beyond
9-percent is harmful for the economic growth of Pakistan and inflation rate below the
estimated level of 9-percent is favorable for the economic growth. ErTugcu and Coban
(2014) investigated the short and long-run relationship between savings, inflation and
economic growth in Turkey. The Autoregressive Distributed lag (ARDL) Approach was
utilized for the analysis. The result showed that savings, inflation and economic growth
are co-integrated and either inflation or economic growth has positive impacts on the
savings in the Turkish economy.
Kisu et al (2012) examined the relative importance of monetary factors in driving
inflation in Malawi, using a stylized inflation model specification that included standard
monetary variables, the exchange rate and supply side factors. They came up with the
finding that money supply growth drives inflation with lags of about 3 to 6 months and
that the exchange rate adjustments play a relatively more significant role in feeding costpush(supply side factors) inflation.Mahmoud (2015) examined the link between consumer
price index (CPI) and Gross domestic product (GDP) in Mauritania. The result revealed a
positive and significant relationship between the GDP and CPI.
Khan et al. (2011) analyzed the relationship between financial sector development and
poverty for different countries. The study divided financial sector into four sectors;
Banking sector, Insurance companies, Stock market and Bond market, for the purpose of
estimating effect of financial sector development on poverty. For banking sector, the
25
following variables were used; central bank assets to GDP, deposits money banks assets
to GDP, bank deposits, concentration, overhead costs and net interest rate. For insurance
company, non-life insurance was used as the variable; to capture the effect of stock
market variable, stock market turnover ratio was used. For bond market, both market
capitalization to GDP and public bond market capitalization to GDP were used. The
results of the OLS estimation indicated that there is negative and highly significant
relation between poverty and central bank assets, coefficient of Deposits Money Bank
Fowowe and Abidoye (2012) concluded that measure of financial development does not
significantly
influence
poverty
in
Sub-Saharan
African
countries.
However,
macroeconomicvariables such as low inflation and trade openness can help reduce the
level of poverty after applying the Systems GMM estimatoron panel data for the
selectedSub-Saharan African countries.
Boukhatem (2015) provided an empirical assessment of the direct contribution of
financial development to poverty reduction in 67 low and middle-income countries over
the period 1986-2012. Using panel OLS and system GMM, the results showed the
important contribution of financial development to the reduction of poverty independently
of the econometric techniques used. The study also found that instability related to the
financial development would worsen the condition of poor population and would vanish
the positive effects of financial development. The study therefore suggested that pro-poor
public investment policy in low and middle-income countries are required in tandem with
financial development to ensure a reduction in the level of poverty. In (2017) analyzed the
relationship between inclusive finance and poverty alleviation in a panel of 86 countries
in Asian, African and Latin American countries from 2004 to 2013 by using generalized
26
method of moments. The results revealed that the impact of inclusive finance on poverty
alleviation (proxied by Gini coefficient) was different in Asia, Africa and Latin America.
For Asian countries, inclusive finance is negatively related to poverty alleviation which
implied that the development of inclusive finance can be used to narrow the income gap
between the rich and poor; for African and Latin American countries, there was a clear
inverted U-shaped relationship between the development of inclusive finance and poverty
alleviation, which indicated that the inclusive finance will first widen income gap, and
when the financial development reaches a high stage, it can also be used to narrow the
income gap and alleviate poverty. The study also found that export trade indicators in
Asian countries were not statistically significant in achieving poverty alleviation. Though,
the usage of natural resources had an impact on poverty alleviation in African countries,
but its effect was positive which implied that the usage of natural resources cannot raise
the standard of living or alleviate poverty in Africa. However, the industrial structures
alleviated poverty and narrowed income gap in Latin American countries. Therefore, the
study concluded that in order to narrow the income gap between the rich and the poor and
alleviate poverty, the first is to promote the development of inclusive finance, improve the
financial system, eliminate barriers and let middle and low income.
Literature Review on Monetary Policy, Inflation and Poverty in Nigeria
Literature Review on Monetary Policy, Inflation and Poverty in Nigeria
Author(s) and
Title
Scope
Methodology
27
Findings
Comment
Year
/Objective
Sani I
Threshold
1986-2009
Doguwa
relationship
Nigeria
1980
Garch
Inflation
The null
threshold is
hypothesis
between
statistically
cannot be
inflation and
significant
accepted
Inflation with
Methodology
economic
focus on long
growth has
run issue
economic
growth
Javier Andres
Inflation and
1989-2011
and Ignacio
economic
OECD
Hernando
growth
Panel
not conclusiv
Cordelia
Study joint
2009-2018
OLS
Money supply ARDL
Onyinyechi
and individual Nigeria
has negative
method of
2019
money supply
and
estimation
on economic
insignificant
can also be
growth
with
used.
economic
growth
Barr and
Money supply 1971-2010
Kantor
and economic
(1989)
Khayroollo
Panel ARDL
influence
long based on
activities in
economic
panel
South-Africa
growth
estimation
Inflation
ECM can also
Inflation and
South Africa
Money supply Scope was
1980-2010
OLS
28
Sattarov
economic
(2011)
growth
Finland
reduce
be used for
economic
the estimation
growth in
Finland
Chude Nkiru
Relationship
1987-2010
Patricia
between
Nigeria
(2016)
OLS
Positive and
Interest rate
significant
and inflation
money supply
between
rate are not
and economic
money supply
mostly used
growth
and economic
together.
growth.
Oluwagbenga
Impact of
1980-2018
Anthony and
inflation on
Nigeria
Dada
ARDL
Inflation and
More
exchange
variables can
economic
have
also be
Oluwabunmi
growth
significant
included.
(2020)
evidence from
negative
Nigeria
impact on
economic
growth
Literature Review on Monetary Policy, Inflation and Poverty in Nigeria (Cont)
Author(s) and
Title
Year
/Objective
Scope
Methodology
29
Findings
Comment
Akinola
Impacts of
1973-2013
Abdulgafar and
money
Nigeria
VECM
Positive
The results
money supply
was perfect
Olarinde Olaiya supply and
while
based on the
2019
inflation rate
analysis
inflation
was negative
Foluso A
Impact of
1987-2017
Akinsola and
inflation on
Nigeria
Nicholas M
2017
VAR
Negative
Analysis was
relationship
not
economic
between
compressive
growth
inflation and
economic
growth
Phiri Andrew et
Nonlinear
1970-1990
PANEL
Inflation has
Different
al 2016
impact of
South-Africa
negative
method of
inflation on
effect on
estimation
economic
economic
techniques
growth
growth
used
Positive and
Other method
significant
of estimation
Dingela
Dynamic
1980-2016
Siyasanga and
impact of
South-Africa
Khobai H
money
between
can also be
2017
supply on
money supply
used
economic
and economic
growth
growth
Mr TT
Overview of
ARDL
August 2013
No
30
Review the
Inconclusive
Mboweni(2003) the South –
South-Africa
Estimation
economy of
Africa
South-Africa
economy
as a whole
Monaheng et al
Inflation and
1980-2009
Panel ARDL
(2015)
economic
South-Africa
growth
Inflation has
Result from
had
the fixed
detrimental
effect was not
effect to
too good
economic
growth
Iwedi Marshal
To study the
1970-2014
(2016)
link between
Nigeria
VAR
Unidirectional ARDL
causality
method of
money
between
estimation
supply and
money supply
was
economic
and RGDP
applicable
growth
Literature Review on Monetary Policy, Inflation and Economic Poverty in Nigeria (Cont)
Author(s) and
Title
Year
/Objective
Nexhat and
Impact of
Scope
Methodology
Findings
Comment
1997-2019
OLS
Inflation has
Panel data
31
Esat Durguti
inflation on
2019
Kosovo
positive
supposed to be
economic
impact on
used for the
growth
economic
estimation
growth
Wiza
To show
Quarterly
Munyesa
relationship
2014
Panel ARDL
Inflation pose
Each variables
data used
a great effect
has different
between
1993-2011
on the growth
effects on
inflation and
(South-
of the
economic
economic
Africa)
economy
growth
It shows that
The analysis
inflation
was not
inflation and
affect
comprehensive
economic
economic
growth in
growth in
Nigeria
Nigeria
growth
Miftahu Idris
Relationship
1981-2016
2017
between
Nigeria
OLS
D Van
Monetary
2001-2017
ARDL
Long run and
Wyngaard
policy on
South-Africa
2019
inflation and
of money
economic
supply on
growth
economic
Abstract was
positive effect too much
growth
Astra Dimiti
The influence
1970-2016
ECM
32
Money
More variables
Amassoma et
of money
al 2018
Nigeria
supply does
can still be
supply on
not
included
inflation in
considerably
Nigeria
influence
inflation both
the short and
long run
Olorunfemi
Money
1970-2008
Sola
supply and
Nigeria
2013
VAR
Money
ARDL can
supply does
also be used
inflation in
not really
for the
Nigeria
influence
estimation
inflation
Asuquo
Money
1981-2015
OLS
Inflation,
Ekpeyong et
supply,
Nigeria
al
inflation and
and interest
2018
economic
rate had
growth in
positive effect
Nigeria
on growth
Reserve
labour force
Summary of Literature Review on Monetary Policy, Inflation and Poverty in Nigeria (Cont)
Author(s) and
Title
Year
/Objective
Scope
Methodology
33
Findings
Comment
Anyanwu
To investigate
1985-2000
Uchenna N and
the effect of
Nigeria
Alexanda O.U
money supply
affects each
on inflation
other
Bigben
Examine post
1986-2008
Chukwuma
SAP
Nigeria
Ogbonana
2015
VECM
VAR
Inflation and
Data life span
money supply
was too short
Exchange rate
Inflation and
is the most
interest rate
persistence of
reliable for
are not
inflation in
measuring
Nigeria
macro-
included
economic
policy
Ayad Hicham
Relationship
1970-2018
2020
between
Nigeria
VAR
There is a long
Focus
run
attention on
inflation rate,
relationship
Nigeria
money supply
among the
and economic
variables
economy
growth.
Ogenyi,A.O et
Determinants
1980-2010
ARCH AND
Money supply,
Only on
al 2019
of inflation
Nigeria
GARCH
Government
working
volatility in
expenditure
paper
Nigeria
and gross
domestic
product tends
to favor
inflation
34
Raymond,
Econometric
1980-2012
Adegoyega R
analysis of
Nigeria
2014
ARDL
Interest rate,
Inflation and
money supply
interest rate
effect of
were influence
were not used
changes in
by inflation
interest rate on
rate
together
inflation in
Nigeria
Feridun Mete
Forecasting
and Adebiyi
Micheal 2005
1980-2003
ECM
Exchange rate
No specific
inflation in
and domestic
comment on
developing
debt can be
the impact of
economies
used to
money supply
forecast
inflation
Mathias
Transmission
2000-2013
A.Cuba 2015
mechanism
quarterly data
VAR
money supply
Positive and
No issue on
significant
interest rate
transmission
Summary of Literature Review on Monetary Policy, Inflation and Poverty in Nigeria (Cont)
Author(s) and
Title /Objective
Scope
Methodology
Findings
Comment
Okoroafor
To determine
1990-2011
Ordinary
Human
Other
Micheal and
impact of
Nigeria
Least Square
development
variables can
Year
35
Nwaeze
poverty on
Chinweoke
2013
(OLS)
index is
still be
economic
statistically
included in
growth
significant to
this study
influence
economic
growth
Kwadwo
Impact of
19767-2015
Ordinary
Long run
The author
Ofori
money supply
Ghana
Least Square
effect of
could not get
Frimpon
on inflation
(OLS)
money supply actual
2017
on inflation
dependent
rate
variable for
this study
Nazima
Food inflation
1990-2015
Auto
There is a
Poverty index
Ellahi and
and poverty
Pakistan
regressive
link between
was not
Zaib Maroof
distributed
food inflation
consider in
2013
lag model
and poverty
this study
(ARDL)
rate
J.C
Inflation and
1998-2003
Error
Positive
This study
Vermeulen
unemployment
South Africa
Correction
relationship
too focus on
2017
in South Africa
Method
between
inflation and
(ECM)
inflation and
not poverty
employment
Iwan Azis
Macroeconomic E52-131
Panel Method Slower
36
Focus
2008
policy and
Japan
poverty
growth and
attention on
higher
Nigeria
inflation
economy
worsen
poverty
Kalie Pauw
Impact of
Paper 07/126
Panel ARDL
Poverty and
Unavailability
and Liberty
growth on
South Africa
inequality
of data
Nncube
redistribution of
have been a
2014
poverty and
major issue
inequality
on economic
growth
Adebayo
Financial
1986-2015
Ajisafe and
inclusion,
Nigeria
Solomon
Okunade
ARDL
Financial
Other method
inclusion has
of estimation
monetary
desirable
can also be
policy and
effect on
used for the
poverty level
poverty level
study
Literature Review on Money Supply, Inflation and Poverty in Nigeria (Cont)
Author(s) and
Title
Scope
Methodology
Findings
Year
/Objective
Dayo
Benedict O
Examine the
1970-2010
ECM
Money supply Limited to
relationship
Nigeria
has positive
37
Comment
some
and Kemi
between
relationship
Funlayo A
money
with capital
2013
supply,
accumulation
variables only
inflation and
capital
accumulation
Awogbemi
Causes and
1969-2009
Clement A
effects of
Nigeria
and Taiwo
Joseph K
ARDL
Money
No specific
supply, gross
method of
inflation in
domestic
estimation
Nigeria
product and
technique
inflation were
mentioned
2012
all positive in
the long run
Mbutor O
Inflation in
1970-2012
ECM
Inflation does
Limited
Mbutor
Nigeria: How
Nigeria
not really
variable used
2013
much is the
affect money
function of
supply as a
money?
result of
recession
Olalere,
Examine the
1980-2009
Sunday Shina
impact of
Nigeria
2015
VECM
Long run
ARDL
relationship
method of
money supply
between
estimation is
on inflation in
money supply
also
38
Nigeria
and inflation
applicable
Moses, K,
Monetary
Quarterly data OLS
Existence
CBN paper
Tule and S.O
growth and
1996Q1 And
relationship
work
odediran et al
inflation
2012Q4
between
2015
dynamics in
Nigeria
money
Nigeria
growth and
inflation
Oluremi
Money supply 1970-1989
Ogun and
mechanisms
Adeola
in Nigeria
Panel ARDL
Nigeria
Adenikinju
Money supply Variables
and inflation
used are not
depends on
specified
each other
1995
Ezeibekwe,
Monetary
1981-2018
Pane Data
Show that
Data
Obinna
policy and
Nigeria
investment
invariable
Franklin
domestic
depend on
2020
investment in
inflation rate
Nigeria
Literature Review on Monetary Policy, Inflation and Poverty in Nigeria (Cont)
Author(s) and Title
Scope
Methodology
Findings
Comment
Year
/Objective
Mukytah
Effect of
1981-2015
Vector Error
Long run
Causality
Galadima
money supply
Nigeria
Correction
relationship
among the
2017
on economic
Model
among the
variables also
39
growth in
(VECM)
Nigeria
variables,
carried out
money supply
and interest
rate are
positive
Gerald
Monetary
Working
Epstein and
policy and
paper No 113
James heintz
2006
Panel method
Financial
Attention are
sector
focused on
financial
improves
some area
sector reform
employment
only
for employ
and poverty
and poverty
Siyan Peter
To examine
1980-2014
Adegoriola
implication of
Nigeria
and Adewale
E.2016
VAR
There is a
Inflation and
relationship
poverty are
unemployment
between
the major
and inflation
inflation
focused in
on poverty
unemployment this study
and poverty
level
Tokunbo
Macro-
1960-2000
Error
Economic
This study is
Simbowale
economic
Nigeria
correction
growth in
not of recent
Osinubi
policies and
Method
Nigeria has
data span
2005
pro-poor
(ECM)
been slightly
used
growth in
pro-poor
40
Nigeria
Mohammed
Role of
1972-2008
Irfan and
inflation and
Pakistan
Amjad Ali
economic
negative effect the current
2011
growth in
in the short
situation in
explaining
but inflation
the country
prevalence of
has positive
poverty
effect
Aphu Elvis
Impact of
AGD2014-
2018
Ghana
2017
ARDL
Panel
Economic
The research
growth has
cannot give
Government
No specific
spending
role of
government
undermines
government
on poverty
economic
on poverty
eradication
growth by
action
displacing
private sector
activities
Literature Review on Monetary Policy, Inflation and Poverty in Nigeria (Cont)
Author(s) and
Title
Year
/Objective
Yakubu Musa
Scope
Methodology
Findings
Comment
To investigate 1970-2010
Auto
Government
Data period is
and Aminu
long run
regressive
revenue has
not of recent
Bello
effect of
distributed lag significant
2014
money supply
model
Nigeria
41
effect on
and
(ARDL)
money supply
Panel Data
There is
No specific
strong
method of
estimation
government
revenue
Amedeo
How much
1972-2002
Strano
can money
Ghana
2000
supply affect
indication
inflation rate
between
money supply
and inflation
Shahldur R
Effect of
1981-2008
Talukdar
inflation on
Texas
2012
Panel data
Relationship
Observation
between
for each
poverty in
inflation and
countries too
developing
poverty are
low
countries
negative and
insignificant
Dimiti
To investigate 1970-2016
Amassoma et
influence of
al 2018
ARDL
Nigeria
Money supply There are
does not
different
money supply
considerably
economic
on inflation
influence
condition
inflation in
long and short
run
Arne Bigsten
Policies for
1990
Series
42
MDG
Arrow
and Abebe
growth and
Shimeles
poverty
2006
reduction in
Africa
Zorobabel et
Eliminating
Abidjan 2015
Pane series
al 2015
extreme
can be
poverty in
reduced to
Africa
low
Panel
Poverty level
Gary Moser
Economic
IMF PAPER
Economic
and Toshihiro
growth and
International
growth not
2001
poverty
Development
necessarily
reduction in
influence
sub-Saharan
poverty in
Africa
Africa
Paper work
Voluminous
Source: Author’s Compilation 2021
Literature Review on Monetary Policy, Inflation and Economic Poverty in Nigeria (Cont)
Author(s) and
Title
Scope
Methodology
Findings
Comment
Year
/Objective
Javier Andres
Inflation and
1989-2011
Panel
Inflation with
Methodology
and Ignacio
economic
OECD
economic
focus on long
Hernando
growth
growth has
run issue
43
not conclusiv
Iwedi
To study the
1970-2014
Marshal
link between
Nigeria
(2016)
VAR
Unidirectional More
causality
variables can
money supply
between
still be
and economic
money supply
included in
growth
and RGDP
this study
Ikechukwu,
Nigeria
Industrial output,
Ordinary least-
The result shows
In appropriate
Nwokoye and
1980-2013
exchange rate,
square (OLS)
that foreign
method
Ebele
inflation,
method
direct
(2015)
population
investment,
growth and
population
foreign direct
growth and real
investment
exchange rate
are significant
determinant of
industrial output
Abdallah and
Ghana
Exchange rate,
Autoregressive
There exists
Ignore the
Abdul
1986-2013
manufacturing
distributed lag
short and long
major
output, interest
model
run relationship
(2016)
objective
rate and foreign
between
direct
exchange rate
investment
and
manufacturing
sector
performance.
Source: Author’s Compilation 2021
44
Based on the empirical review of different authors, their study has been based on two variables:
the impact of monetary policy on inflation rate, such as the study of Oseye(2011) who worked on
the impact of monetary policy on inflation rate, and the research work of Kisu et al (2012) who
examined the relative importance of monetary factors in driving inflation in Malawi. The goal of
this study is to fill a gap in the literature by merging the three variables of monetary policy,
inflation, and unemployment.
In Nigeria, Dauda and Makinde (2009) looked at the relationship between banking sector
development and poverty reduction. This study also aims to show the causation that exists
among the three variables. Also, to increase the data coverage up till 2019 CHAPTER THREE
RESEARCH METHODOLOGY
3.1
Introduction
This chapter consists of theoretical framework which provides the theoretical basis of the study
and the research methodology which illuminates the empirical investigation conducted. Also, to
assess the impact of monetary policy and inflation on poverty using appropriate econometric
technique, a model with dependent and explanatory variables to be estimated is specified, a priori
expectation of the parameters, technique of estimation and method of data analysis are all
treated.
3.2
Theoretical Framework
The theoretical framework of this study is Marxism theory of poverty. This is a form of
structural poverty school of thought that believes poverty is caused by vulnerable demographic
situations and difficult labor market conditions, rather than an individual's incompetence. The
works of German Karl Marx form the foundation of the Marxist school of thought, which
45
contends that inequality is an unavoidable part of a capitalist economy. The Marxists think that
poverty is an intended result of the class struggle between capitalists and labor. Modern
arrangements promote an imbalance of power and so provide inequality.
To preserve control over the means of production, capitalists actively exploit their laborers and
aim to prevent them from advancing socioeconomically as much as possible.
3.3 Model Specification
The model to be estimated in this study are stated as follows
POV= (M2, INFR, TROP)
WHERE:
POV= Poverty
M2= Broad Money Supply
INFR= Inflation rate
TROP= Trade Openness
The models are also specified in stochastic form:
POVi,t = α0 + α1M2i,t + α2INFRi,t + α3TROPi,t + µ
WHERE: µ is the error term
αi = parameters
Apriori Expectation:
α2, α4 < 0
α0, α1, α3, >0
46
3.3.1 Vector Auto-Regression Model (Objective2)
The Vector auto-regression (VAR) model is used for multivariable time series. The structure is
that each of the variables is a linear function of past lags of itself and past lags of other variables
and an error term. With the exclusion of restriction, these models don’t dichotomize variables
into ‘endogenous’ and ‘exogenous’ (Eduardo), thus a Vector auto-regression (VAR) model is
appropriate for establishing the dynamism of the relationship among public expenditure,
infrastructure and economic growth. VAR modeling was and is commonly used for forecasting
system of inter-related time series and for analyzing the dynamic impact of random disturbance
on the system variables (Dr Wahab, Lawal FCIB A, 2011). VAR in other words describes the
evolution of a set ‘k’ of variables (endogenous variables) over the same sample period (t=1….,
T) as a linear function of only their past values.
The mathematical representation of a VAR model is
yt  A1 yt 1  ...  ApYt  p  Bxt  t ....(3.00)
Where;
yt is a k vector of endogenous variables,
xt is a d vector of exogenous variables,
A1..., Ap and B are matrices of coefficients to be estimated
t is a vector of innovations that may be contemporaneously correlated but are uncorrelated with
their own lagged values and uncorrelated with all of the right-hand side variables.
47
In the estimation of a VAR model, this study specifies the model as thus assuming the series are
stationary at level and are cointegrated.
𝑞
𝑃𝑂𝑉𝑡
𝑞
𝑞
= 𝜋 + ∑ 𝜋1𝑖 𝑃𝑂𝑉𝑡−𝑖 + ∑ 𝜋2𝑖 𝑀2𝑡−𝑖 + ∑ 𝜋3𝑖 𝐼𝑁𝐹𝑅𝑡−𝑖 + ℇ 𝑡1
𝑂
𝑖=1
𝑖=1
𝑞
(3.01)
𝑖=1
𝑞
𝑞
𝑀2𝑡 = 𝜇𝑂 + ∑ 𝜇1𝑖 𝑃𝑂𝑉𝑡−𝑖 + ∑ 𝜇2𝑖 𝑀2 + ∑ 𝜇3𝑖 𝐼𝑁𝐹𝑅 + ℇ 𝑡2
𝑖=1
𝑖=1
𝑖=1
𝑞
𝐼𝑁𝐹𝑅𝑡
𝑞
𝑞
= 𝛼 + ∑ 𝛼1𝑖 𝑃𝑂𝑉𝑡−𝑖 + ∑ 𝛼2𝑖 𝑀2𝑡−𝑖 + ∑
𝑂
(3.02)
𝑖=1
𝑖=1
𝛼3𝑖 𝐼𝑁𝐹𝑅𝑡−𝑖 + ℇ𝑡3 (3.03)
𝑖=1
Where:
POV= Poverty
M2= Broad Mone y Supply
INFR=Inflation rate
If all the series are non-stationary i.e. I(1) but are cointegrated based on the necessary
cointegration tests. The study specifies the VECM model;
𝑞
𝑞
𝑞
∆𝑃𝑂𝑉𝑡 = 𝜋𝑂 + ∑ 𝜋1𝑖 ∆𝑃𝑂𝑉𝑡−𝑖 + ∑ 𝜋2𝑖 𝑀2 + ∑ 𝜋3𝑖 ∆𝐼𝑁𝐹𝑅𝑡−𝑖
𝑖=1
𝑖=1
𝑞
𝑞
𝑞
∆𝑀2𝑡 = µ𝑂 + ∑ µ1𝑖 ∆𝑃𝑂𝑉𝑡−𝑖 + ∑ µ2𝑖 𝑀2𝑡−𝑖 + ∑ µ3𝑖 𝐼𝑁𝐹𝑅
𝑖=1
𝑖=1
(3.04)
𝑖=1
𝑖=1
48
(3.05)
𝑞
𝑞
𝑞
𝐼𝑁𝑅𝑡 = 𝛼𝑂 + ∑ 𝛼1𝑖 ∆𝑃𝑂𝑉𝑡−𝑖 + ∑ 𝛼2𝑖 ∆𝑀2𝑡−𝑖 + ∑ 𝛼3𝑖 ∆𝐼𝑁𝐹𝑡−𝑖
𝑖=1
𝑖=1
(3.06)
𝑖=1
𝜋𝑖 , 𝜇𝑖 , 𝛼𝑖 , 𝛽𝑖 𝑎𝑛𝑑𝛿𝑖 𝑎𝑟𝑒𝑡ℎ𝑒𝑠ℎ𝑜𝑟𝑡𝑟𝑢𝑛𝑎𝑑𝑗𝑢𝑠𝑡𝑚𝑒𝑛𝑡𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡𝑠
However, if all the variables are co-integrated at I (1), the Vector Error Correction Model
(VECM) is most appropriate. The dynamism of VECM makes it enjoy superiority over the
previously considered models as it captures both short run and long run relationships among
variables. It also indicates the direction of causality which is a major weakness of the cointegration tests, such as Granger-causality test (Granger, 1988). The model is given as:
𝑞
∆𝑃𝑂𝑉𝑡 = 𝛼𝑂 + ∑𝑖=1 𝛼1𝑖 𝐼𝑁𝑅 + 𝑀2 + 𝛾3 𝐸𝐶𝑇𝑡−1 + 𝜀1𝑡
𝑞
𝑞
𝑞
𝑞
(3.7)
∆𝑀2𝑡 = 𝛼𝑂 + ∑𝑖=1 𝛼1𝑖 𝐼𝑁𝑅 + ∑𝑖=1 𝛼2𝑖 ∆𝑀2𝑡−𝑖 + 𝛾4 𝐸𝐶𝑇𝑡−1 + 𝜀2𝑡
(3.8)
∆𝐼𝑁𝑅𝑡 = 𝛼𝑂 + ∑𝑖=1 𝛼1𝑖 𝐼𝑁𝑅 + ∑𝑖=1 𝛼2𝑖 ∆𝑀2𝑡−𝑖 + 𝛾3 𝐸𝐶𝑇𝑡−1 + 𝜀3𝑡
(3.9)
𝜋𝑖 , 𝜇𝑖 , 𝛼𝑖 , 𝛽𝑖 𝑎𝑛𝑑 𝛿𝑖 𝑎𝑟𝑒 𝑡ℎ𝑒 𝑠ℎ𝑜𝑟𝑡 𝑟𝑢𝑛 𝑎𝑑𝑗𝑢𝑠𝑡𝑚𝑒𝑛𝑡 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡𝑠
ECT= Error Correction Term
Objective three is to show the effect
POV=f (M2, INF, TROP)
The ARDL model to capture the effect is therefore specified as follows:
𝑝
𝑃
𝑃
𝑃
𝑃𝑂𝑉𝑡 = 𝛼𝑂 + ∑ 𝛼1 𝑃𝑂𝑉𝑡−𝑖 + ∑ 𝛼2 𝑀2𝑡−𝑖 + ∑ 𝛼3 𝐼𝑁𝐹𝑅𝑡−𝑖 + ∑ 𝛼4 𝑇𝑅𝑂𝑃𝑡−𝑖 + 𝜀𝑡
𝑖=1
𝑖=0
𝑖=0
49
𝑖=0
3.4 Estimation Techniques
This study will adopt some pre-estimation test with a view to detect the appropriate econometrics
techniques to use.
3.5 Source and Measurements of the data
TABLE 2
Variables
Definition
Measurement
Not having access to basic PHC
essentials , or spend below 1.9$
per day considered to be poor
WDI
Poverty
Money supply
This is the measure of total money in
CBN
Naira /dollar
the circulation
Source
STATISTICAL
BULLETIN
Percentage
Inflation rate
Trade openness
CBN
Persistence increase in the prices
of goods and services
Ratio of total trade to GDP
STATISTICAL
BULLETIN
Index
WDI
Source: Author’s compilation 2021
The first objective of this study is to examine the trend of monetary policy, inflation and poverty
in Nigeria. This will be evaluated using descriptive method, specifically graph.
To evaluate the second and the third objectives, a series of pre estimation test will be carried out
on the series of the study. Such pre estimation tests include: cointegration test, unit root test etc.
Graphical analysis: This shows the trends in the variables under consideration, it shows the
diagrammatical representation of variables. Graphical analysis further provides information
about the movements as well as the description of the existence of structural breaks, trend
component, discontinuities in series.
50
Descriptive method: Descriptive method is coefficients that summarize a given data set, this
gives comprehensive information about the behavior and characteristics of the set of series under
consideration. This includes mean, median, standard deviation, kurtosis, skewness, Jarque-Bera
etc.
a. Formal pre -tests:
Formal preliminary tests are key in order to understand the stochastic processes that can cause
problems in estimation and results are the unit root test and cointegration test.
b.
Unit root test
This test is used to determine Stationarity of series. This test is to examine the stationarity of a
series. A stationary series implies a series has a constant mean, constant variance and a constant
co-variance. Hence, such series is predictable and hence stable over time. Unit root test is
adopted in this study to ensure that series are not stationary at I (2) second difference, the series
should be stationary at I(0), level and I(1), first difference. It examines the stability and reversion
of the mean, covariance and auto- covariance off the variables. In this study, the unit root test is
used to determine the order of integration - I(0) or I (1)- of the variables which helps with
choosing the appropriate and suitable method of estimation. The test statistic employed here is
the Augmented Dickey Fuller test statistic. The guiding hypothesis is stated below:
H0: The series is not stationary
H1: The series is stationary
Pr<5% reject null, accept otherwise.
c. Co-integration test
51
Ensuring the stationarity of series at level and first difference, co-integration test is used. Cointegration test is used to test for the long run relationship between variables. The result obtained
from the unit root test is used to conduct the co-integration technique. If the series are significant
at I(0) it means the variables are co-integrated, if otherwise that is if the series are significant at
I(1), the approach of Engle-Granger co-integration test is adopted. If the series are significant at
different orders i.e. level I(0) and difference I(1), we adopt Auto-regressive distributed lag bound
co-integration test.
3.5
Post Estimation Technique
The econometric model specified under this research work is a multiple linear regression model
which has one dependent variable and two independent variables. Under this section, the
econometric model will be estimated and the parameter estimates will be derived. The method/
technique to be used for estimation will be dependent on the outcome of the formal pre-test.
Autoregressive Distributed lag (ARDL) model is use for estimating both longrun and short run
when the series are of different order of integration. In this section, relevant diagnostics tests to
be examined under this section include:
a. Linearity Test
This test is commonly used to test for general misspecifications propounded by Ramsey (1969)
i.e. there is a linear relationship between the dependent variable (RGDP) and the independent
variables (tax and interest rate). This test is popularly known as Regressions Specification Error
Test (RESET). This test will thus be used to confirm if the model is mis-specified or not. This is
because a model not properly specified will generate insignificant estimates and also a nonsignificant model. The violation of this assumption may imply that the model under
52
consideration is non-linear or incorrectly specified. The test statistic to be used for this purpose is
the Ramsey Reset test.
b.
Normality test
This test is a residual diagnostic test, that is, test on the stochastic variable. This test will be used
to determine the normal distribution of the errors of the estimated regression model. This test is
important as it is one of the underlining assumptions of the least square technique. Hence the
violation of this assumption will generate unbiased estimates. Its violation may imply nonnormality of the error term. The test statistic to be used for this purpose is the Jarque-Bera test
statistics. The hypothesis is given as:
H0: The errors are normally distributed
H1: The errors are not normally distributed
Pr< 5% reject null i.e. not normally distributed, accept otherwise
c. Heteroskedasticity test
This test is to ascertain that the variance of the error term is constant overtime. It also tries to
explain further the independence of the error term with respect to the explanatory variables of the
regression model. This test is important to this research work as it is one of the assumptions of
the classical linear regression model. Hence, this test will be carried out to ensure that the
assumption of a constant variance is not violated. The violation of this assumption is an
indication of the presence of heteroscedasticity in the model. The test statistic which will be used
for this purpose is the Auto Regressive Lagrange Multiplier (ARCH-LM) test. The hypothesis is
stated as:
53
H0: No heteroskedasticity
H1: There is heteroskedasticity
Pr (F-stat) < 5% reject null, that is, there is heteroskedasticity. Accept if otherwise
d.
Serial Correlation test
This test is used to test for correlation among errors, that is, to ascertain whether the error terms
are independent of one another or not. This is also one of the major assumptions of the classical
linear regression model. The test statistic to be used for this purpose is the Breusch-Godfrey test.
The hypothesis is stated below as:
H0: No serial correlation
H1: There is serial correlation
Pr (F-stat) < 5% reject null, that is, there is serial correlation. Accept if otherwise
e.
Causality Test
Causality in econometrics is somewhat different to the concept in everyday use; it refers more to
the ability of one variable to predict (and therefore cause) the other. For instance, we have ∆𝑌and
k pair, it is possible to have that; (a) ∆𝑌𝑡 causes 𝐾𝑡, (b) 𝐾𝑡 causes ∆𝑌𝑡 , (c) there is a bi-directional
feedback (causality among the variables), and finally (d) the two variables are independent. The
problem is to find an appropriate procedure that allows us to test and statistically detect the cause
and effect relationship among the variables.
54
3.7 Scope and Source of the Variables
The variables used are: Economic Growth, foreign direct investment, public spending, and
openness in Nigeria from 1989 to 2019. The data are annual and sourced from WDI, CBN
statistical bulletin.
55
56
CHAPTER FOUR
DATA ANALYSIS AND DISCUSSION OF FINDINGS
4.1 INTRODUCTION
This chapter presents the results generated by the model specified in the previous chapter and
afterwards, examines critically and analyses the results empirically. The results to be presented
and analyzed include descriptive statistics and graphical analysis which belong to preliminary
analysis. It also presents the regression analysis and post estimation (diagnostics) results.
4.2 Trends in Poverty, Inflation rate, and Broad money supply
4.2.1 Trend Analysis
Graphical illustrations depict the movements, trend, fluctuation and discontinuities in the series.
Likewise, it provides a qualitative assessment of possible relationship among series. The figures
below show the graphical expressions of relevant variables. Figure 4.1 to 4.6 provided below
depict movements and trends in Poverty, Money supply, Inflation rate as well as Poverty and
inflation rate, Poverty and money supply and inflation rate and money supply.
57
TREND OF POVERTY REDUCTION IN PERCENTAGE
Poverty
90
80
70
60
50
40
30
20
1980
1985
1990
1995
2000
2005
2010
2015
Source: Author’s Compilation 2021
The trend above shows the movement of poverty reduction in Nigeria between the periods of
1980 to 2019, there is a fluctuation in the movement of the poverty reduction over the time. The
poverty level has not been stable there is reduction in poverty level between 2012 and 2013, it
also increases from 2015 to 2017 as it reach peak in 2018, the poverty level in 2018 was so high
as compare to the previous years. This increase in poverty level has made Nigeria to be the head
quarter of poverty in the world as about 80millions Nigerians are living in extreme poverty,
source from world development indicator (WDI).
58
M2
3.2E+13
2.8E+13
2.4E+13
2.0E+13
1.6E+13
1.2E+13
8.0E+12
4.0E+12
0.0E+00
1980
1985
1990
1995
2000
2005
2010
2015
Source: Author’s Compilation 2021
Figure 4.2 shown the trend of broad money supply of Nigeria from 1981to 2018. The trend
exhibits an upward movement over the time in consideration. There has been an increase in
money supplies which invariably help in industrial performance. The trend experienced constant
movements from 1981 to 1998 and continues to rise.
59
InflationRate
80
70
60
50
40
30
20
10
0
1985
1990
1995
2000
2005
2010
2015
Figure 4.4 Trend in inflation rate for Nigeria from 1981 to 2018
Source: Author’s Compilation 2021
Figure 4.3 shows the trend of inflation rate in Nigeria from a period of 1981 to 2019. The trend
depicted an oscillatory trend, meaning that there is an up and down movement in the trend.
During the period inflation rate was at peak in 1995 and record a lowest inflation rate in 2007.
Increase in inflation rate during this period may be as a result of increase in the volume of money
in the circulation which invariably leads to increase in the price of industrial products.
60
FIGURE4.4 TRENDS OF POVERTY AND BROAD MONEY SUPPLY IN NIGERIA FROM
1980-2019
3.2E+13
2.8E+13
2.4E+13
2.0E+13
1.6E+13
1.2E+13
8.0E+12
4.0E+12
0.0E+00
1980
1985
1990
1995
2000
POV
2005
2010
2015
M2
Source: Author’s Compilation 2021
The graphs above show the trends movement in poverty and money supply in Nigeria during the
periods of 1980 to 2019. From the trends it reveals that money supply rose above the money
supply, the poverty level was constant through the period. With the level of poverty there was
increase in money supply to suppress the level of poverty in the country that was why money
supply rose above the poverty level. Money supply was constant during the periods 1980 till
61
1998, and it continues to rise as from year 2000 upward and it continues in that movement till
2019.
FIGURE 4.5, TRENDS OF POVERTY AND INFLATION RATE IN NIGERIA FROM 19802019.
100
80
60
40
20
0
1980
1985
1990
1995
2000
POV
2005
2010
2015
INFR
Source: Author’s Compilation 2021
The trends above depict movements in poverty and inflation rate from 1980 to 2019. It revels
from the movements that poverty and inflation rate experienced sinusoidal movements which is
unstable movements. This unstable may be as a result of the situation of things in the economy.
Inflation cannot stop poverty also poverty cannot stop the inflation rate. Both inflation and
poverty affects economy as a whole.
62
FIGURE 4.6 TRENDS OF INFLATION RATE AND MONEY SUPPLY IN NIGERIA FROM
1980-2019
3.2E+13
2.8E+13
2.4E+13
2.0E+13
1.6E+13
1.2E+13
8.0E+12
4.0E+12
0.0E+00
1980
1985
1990
1995
2000
INFR
2005
2010
2015
M2
Source: Author’s Compilation 2021
Graph above is the trends that show movements in inflation rate and broad money supply in
Nigeria from 1980 to 2019. It shows from the trends that inflation rate was constant while money
63
supply rose above the inflation rate. There were upward movements in the movement of money
supply, this may be as a result of government plan to expand the economy through money
supply, and the money was used in a way that inflation was not raised.
4.3 Descriptive Statistics
Descriptive statistics show the qualities of the data used for estimation, the knowledge of which
assists in defining the appropriate methodology for estimation. The table below summarizes the
descriptive statistics:
Table below reveals some of the statistical properties of the selected variables for this study. The
variables are Poverty (POV), Inflation rate (INFR), Money supply (M2), per capital index (PCI)
and openness between 1980 and 2019.
Table 4.1
Mean
POV
56.61927
INFR
19.36212
M2
6.08E+12
PCI
108.1682
OPNS
13.03388
Median
55.86492
12.87658
8.68E+11
39.88000
12.9857
Maximum
88.29256
72.83550
3.04E+13
465.9600
22.81126
Minimum
22.77566
5.382224
1.52E+10
0.160000
3.029761
Std dev
18.16980
17.02816
9.05E+12
144.4240
5.349570
Skewness
-0.16257
1.757736
1.338501
1.181604
-0.016634
Kurtosis
2.246459
4.946601
3.392150
2.911657
2.422370
Jargue- Bera
1.094521
26.24018
11.59018
9.087910
0.543991
0.060631
39
0.761858
39
Probability
0.048533
0.000002
Observations
39
39
Source: Author’s Compilation 2021
0.003042
39
Table 4.1 .above reveals some of the statistical properties of the selected variables for this study.
The variables are Poverty (POV), Inflation rate (INFR), Money supply (M2), per capital index
64
(PCI) and Openness (OPN). Between 1980 and 2019, poverty averaged 56.61%, inflation rate
(INFR) averaged 19.36%, openness (OPN), 13.03% while broad money supply recorded an
average of ₦6.08E+12 billion, respectively. Between the periods considered, the highest values
of INF, POV, M2, PCI and OPN, 13.0%, ₦10816789, ₦46596000 billion, 12.87% and 6.4%
respectively. The lowest values however are 5.3%, 3.02%, 5.3%, 22.7% and 1.9% respectively.
The standard deviation values show the extent at which the observations are dispersed around
their respective means. The higher the standard deviation, the wider the dispersion. Skewness
measures the asymmetry of the distribution of the series around its mean. A positive skewness
means that the distribution has a long right tail and negative skewness implies that the
distribution has a long left tail.
The skewness of a normal distribution is zero. All variables are positively skewed except poverty
and openness which are negatively skewed. Kurtosis measures the peakedness or flatness of the
distribution of the series. For normal distribution the kurtosis is 3, but if it exceeds this value, the
distribution is assumed to be peaked (leptokurtic) relative to the normal, but if it is less than 3,
the distribution is flat (platykurtic) relative to the normal. POV,PCI and OPN are platykurtic
while INFR and M2 are leptokurtic.The two tests of Skewness and Kurtosis however, are not
individually sufficient in defining the distribution of the series, hence the need for Jarque-Bera
normality test. Since the Jarque-Bera test combines skewness and kurtosis properties, it provides
more comprehensive information. Following from the probability value of Jarque-Bera test, the
null hypothesis of normality is rejected for poverty, inflation and money supply and at 5%
critical level, but not rejected for PCI and OPN. However, at 1% critical level, we fail to reject
the null hypothesis of normality for POV, M2, and INFR.
65
4.4 Unit Root Test
The unit root test results reported in the tables below reveal that all the series are none- stationary
except one as the null hypothesis that the series are non-stationary cannot be rejected at any of
the chosen level of significance (1%, 5%, and 10%). This result implies that running a regression
analysis on these variables in their levels using the Ordinary Least Square technique could
generate spurious results as some of the fundamental least square assumptions have been
violated.
This study makes use of two different unit root tests, the Augmented Dickey-Fuller test (ADF)
and the Phillip-Perron (PP) tests. The ADF unit root test is in table 4.while the PP unit root test is
presented in table 5. From the ADF test result, all variables are non-stationary however after first
differencing; the non-stationary variables are rendered stationary.POV, PCI and M2
are
integrated of order one i.e. I(1) except for INF and OPN which are integrated of order zero I(0).
From the PP test result in table 5, all variables are non-stationary in their level form except for
poverty rate. All variables are integrated of order one i.e. I(1) except for poverty rate (POV).
The decision criteria for the hypothesis states that if computed probability value exceeds the
chosen significance level (in this case, 5%), we fail to reject the null hypothesis (that is we
accept H0), and if the computed probability value is less than the chosen level of significance, the
null hypothesis is rejected.
66
Table 4.2 Augmented Dickey-Fuller (ADF) Tests
VARIABLE
LEVEL
FIRST DIFFERENCE
I(d)
Intercept
Trend
None
Intercept
Trend
None
and
and
intercept
intercept
POV
0.252129 -3.20328 2.284641 -5.758*** -5.75*** -5.021** I(1)
M2
-2.885728 -2.74439 -0.24838 -2.57529
-2.72718 -2.594** I(1)
PCI
0.644242 -2.16345 1.853643 -5.125*** -5.348** -4.706** I(1)
INFR
-2.884748 -4.3856* -1.897** ………… ………… ………… I(0)
OPN
-1.020655 -2.692** 0.286502 ………… ………… ………… I(0)
*,**, *** represents 10%,5%,1% level of significance. Critical values are -3.626784, -4.234972,
-3.626784, -1.951000, -3.225334 and -3.540328 at 1%, 5% and 10% significance level
respectively.
Source: Author’s Compilation 2021
From the unit root results in Table (ADF) it shows that (POV,M2 and PCI) are stationary at
level meaning that null hypothesis cannot be rejected while (INFR and OPN) are all significant
at first difference, in favour of alternative hypothesis.
Table 4.3- Phillip-Perron (pp) Test
VARIABLES LEVEL
Intercept
FIRST DIFFERENCE
I(d)
Trend
None
Intercept
Trend
None
and
and
intercept
intercept
POV
0.296773 -3.20328 2.374896 -5.76884* -5.761** -5.0425* I(1)
M2
-2.450940 -2.20408 -0.10974 -6.73127* -6.8680* -6.7896* I(0)
PCI
1.270234 -1.79033 2.899179 -5.0076** -5.105** -4.7080* I(1)
INFR
-2.7563** -2.824** -1.7719* -9.44777* -10.340* -9.8312* I(0)
OPN
-5.4578* -8.457** -5.7312* ………… ………… ………… I(0)
*,**, *** represents 10%,5%,1% level of significance. Critical values are -3.626784, -4.234972,
-3.626784, -1.951000, -3.225334 and -3.540328 at 1%, 5% and 10% significance level
respectively.
Source: Author’s Compilation 2021
From the Phillip –Perron table only Poverty (POV) and per capital index (PCI) were stationary at
level, while Money supply (M2), Inflation rate (INFR) and Openness (OPN) were stationary at
first difference.
67
4.5 Pre-Estimation
Correlation Matrix
Table 4.4
INFR
POV
INFR
POV
1
-0.18180
M2
M2
0.743839
-0.331280 1
OPN
PCI
1
0.299926
1
1
OPN
0.718136 -0.354340 0.887469
PCI
0.411580 -0.98542 0.167360
Source: Author’s Computation 2021
From the results above there is a weak and negative correlation between Inflation rate and
poverty level. Also there is a strong correlation between money supply and openness, inflation
rate depicts a strong and strong correlation with poverty level. There is a weak and positive
correlation between money supply rate and per capital index while openness shows a positive but
weak correlation with poverty level.
Lag Length Selection for Dependent Variable (FPI)
Table 4.5
Lag
LogL
LR
FPE
AIC
SC
HQ
0
-150.8304
NA
269.6590
8.435021
8.479008
8.450374
1
-131.8386
35.87343
99.2525709
7.435476
7.523449
7.466181
2
-127.9846* 7.065608*
84.72685*
7.276922*
7.408882*
7.322980*
3
-127.8973
89.18138
7.327628
7.503575
7.389038
0.155189
Source: Author’s Compilation (2021)
*indicates lag order selected the criterion
68
LR: Sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error.
AIC: Akaike information criterion.
SC: Schwarz information.
HQ: Hanna-Quinn information criterion.
According to LR, FPE, and AIC the optimum lag length for FPI is 2 lag period. This implies that
in the ARDL equation, the optimal lag length for poverty in the equation is 2 using the AIC.
4.2.4 Cointegeration Test
Before proceeding to the estimation proper, there is need to determine if there is long-run
relationship among the variables.
The ARDL bounds cointegration test result is presented in the table 6.below
Following the mixed order of integration of the series as reported under the unit root section, the
ARDL Bounds cointegration test proves most appropriate to determine if there is long-run
relationship between food price inflation and the selected determinant variables. The test
provides two critical values to be checked against the F-statistic value in order to make
conclusion on either the rejection of the null hypothesis of no cointegration among the series, or
otherwise. If the F-statistic exceeds the upper critical bound, i.e. I(1) bound, at the chosen
significance level, then the null hypothesis is rejected. If the F-statistic gives a value below the
lower critical bound, i.e. I(0) bound, the null hypothesis is accepted. Inconclusive is the decision
if it fall between the I(0) and I(1) critical bounds. Table 4.1.6 presents the ARDL bounds test
results , the F-statistic value which is approximately 5.6 is greater than the I(1) critical bound
69
hence, there is long-run relationship among the variables. long run estimate Following this
cointegration test result, the study presents both the short run and of the ARDL technique.
Table: 4.6. Result of Bounds Cointegration Test
Test Statistic
Value
K
F-statistic
5.567220
4
Significance
I0 Bound
I1 Bound
10%
2.75
3.79
5%
3.12
4.25
2.5%
3.49
4.67
1%
3.93
5.23
Critical Value Bounds
Source: Author’s Compilation (2021)
70
4.3 Direction of Causality among POV, INFR, M2
Table: 4.7. VAR Causality Model
Pairwise Granger Causality Tests
Date: 01/02/10 Time: 18:38
Sample: 1980 2019
Lags: 2
Table 4.7
Null Hypothesis:
LM2 does not Granger Cause
LPOV
LPOV does not Granger Cause
LM2
INFR does not Granger Cause
LPOV
LPOV does not Granger Cause
INFR
INFR does not Granger Cause
LM2
LM2 does not Granger Cause INFR
Source: Author’s Computation 2021
Obs F-Statistic
Prob.
38
1.21254
0.3104
0.45759
0.6368
0.08250
0.9210
0.41471
0.6639
1.11948
4.17581
0.3385
0.0242
38
38
Results of the granger causality above shows that money supply does not cause poverty, also
inflation rate does not granger caused money supply but there was granger causality between
money supply and inflation rate.
Effect of Globalization, Institutional Quality on Poverty Reduction in Nigeria.
This subsection presents the result obtained from estimating the ARDL short run dynamic and
long-run models.
71
Table: 4.8.Short Run (Dynamic) and Long Run (Static) Model Result
Variable
Coefficient
Std. Error
T-Statistic
Prob.
D(INFR) 0.009378
0.049103
0.190996
0.8506
D(M2)
-0.358784
0.098612
-3.638339
0.0017
D(PCI)
-0.917523
0.431994
-2.123924
0.0470
D(OPN)
0.848671
0.392141
2.164197
0.0434
ECT(-1)
-1.216224
0.211233
-5.757746
0.0000
Source: Author’s Computation 2021
Result of the short run model shows that inflation rate (INFR), is positive but insignificant to
influence the level of poverty in Nigeria. Broad money supply was negative but significant to
influence the level of poverty rate; this means that a unit changes in broad money supply will
bring about a unit change in poverty. Also from the result per capital index was negative but
significant to change the level of poverty in Nigeria. Openness was positive and significant to
influence the level of poverty in Nigeria, this implies that if Nigeria trade with other countries
the level of poverty will reduce. A unit changes in the degree of openness will bring about a unit
change in the level of poverty in the country.
72
Table: 4.9 Long Run (Static) Model Result
VARIABLES
COEFFICIENT
STD.
T.STATISTICS
PROBABILITY
ERROR
INFR
-0.006630
0.057207
-0.115886
0.9090
M2
0.049855
0.029113
1.712451
0.1031
PCI
-0.537253
0.0992214
5.826165
0.0000
OPN
-0.754403
0.404924
-1.863072
0.0780
CONSTANT
11.310687
4.557790
2.481617
0.0226
Source: Author’s Computation 2021
R-square 0.917331
Fstatistic probability 14.05556(0.000000)
Adjusted R-square 0.852067
Durbin Watson statistsics 1.99000
Results from the long run shows that only per capital income (PCI) was significant in the long
run to influence the level of poverty, result shows that money supply was positive but
insignificant in the long run. Money supply was only significant in the short run.
The ARDL model has overall goodness of fit as the probability value of F-Statistic is less than
5% significance level, which informs rejection of the null hypothesis that model, has no
goodness of fit. Durbin-Watson statistic of 1.99 is close to 2. It is a sign of absence of serial
correlation. R-square of 0.852067 shows that 85.2% of total variation in unemployment is jointly
explained by the model.
73
4.6 TEST OF HYPOTHESIS
HYPOTHESIS I
There is no significant relationship between money supply and poverty, result from the ARDL
shows that money supply was significant in the short run, but insignificant in the long run. In this
case the null hypothesis can be rejected in favour of alternative hypothesis.
HYPOTHESISII
Second Hypothesis states that there is no granger causality among money supply, inflation rate
and poverty level. From the granger causality results it reveals that there was no causality
among money supply, inflation rate and poverty, causality only runs from money supply to
inflation rate. The null hypothesis cannot be rejected in this case.
HYPOTHESIS III
Hypothesis three states that there money supply and inflation rate do not affect poverty level.
Results reveal that both money supply and inflation rate have positive but insignificant effect on
poverty level.
4.7 Discussion of Findings
Result of the short run model shows that inflation rate (INFR), is positive but insignificant to
influence the level of poverty in Nigeria. Broad money supply was negative but significant to
influence the level of poverty rate; this means that a unit changes in broad money supply will
bring about a unit change in poverty. Also from the result per capital index was negative but
significant to change the level of poverty in Nigeria. Openness was positive and significant to
influence the level of poverty in Nigeria, this implies that if Nigeria trade with other countries
74
the level of poverty will reduce. A unit changes in the degree of openness will bring about a unit
change in the level of poverty in the country.
Also from the long run Results from the long run it shows that only per capital income (PCI) was
significant in the long run to influence the level of poverty, result shows that money supply was
positive but insignificant in the long run. Money supply was only significant in the short run.
From the granger causality result, there was no causality between money supply and inflation,
causality runs from money supply to inflation rate, meaning that increase in money supply will
increase the level of inflation in the country. Causality also runs from per capital index to
poverty level. If there is improve in per capital index it will reduce the level of poverty in the
country.
Post Estimation Diagnostic Tests
Sequel to the adoption of the ARDL technique, for any estimated model to be valid, there are
some certain assumptions that are needed to be verified. The table below shows the result of the
test on these assumptions
Table: 11. Diagnostic Test Result
S/N
TEST
Test statistic(Prob)
1.
Jarque-Bera Normality Test
1.059833(0.588654)
2.
Breusch-Godfrey Serial Correlation LM Test
0.000838(0.9992)
3.
Heteroskedasticity Test (Breusch- pagan -godfrey)
0.766702(0.0581)
4.
Linearity test (Ramsey RESET)
1.202067(0.3483)
Source: Author’s Compilation (2021)
75
4.4.1 Normality Test
The Jarque-Bera normality test shows if the residual of the estimated ARDL model comes from a
population that is normally distributed. As the probability value exceeds 5% significance level,
we fail to reject the null hypothesis that residuals are normally distributed.
4.4.2 Serial correlation Test
The LM test statistic seeks to investigate presence of serial correlation of the residuals. The null
hypothesis of no serial correlation is not rejected as probability value exceeds 5% significance
level. Hence residuals are not serially correlated.
4.4.3 Heteroskedasticity Test
The ARCH-LM test statistic, whose probability value exceeds 5% significance level, indicates
presence of homoscedasticity. Thus, the hypothesis of constant variance cannot be rejected.
4.4.4 Linearity Test
The Linearity test suggests that ARDL model is well specified, and it follows an appropriate
linear form, as the probability value exceeds the 5% critical level. Thus, the null hypothesis that
model is well specified cannot be rejected.
76
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Summary of the Study
This paper examined the effect of monetary policy and inflation on poverty in Nigeria. The
research was divided into five (5) chapters.
Chapter one comprised of background to the study, statement of the research problem, research
questions, research objectives, research hypothesis, and justification for the study, scope and
limitation of the study, organization of the study as well as definition of terms; wherein key
concepts of monetary policy and poverty were defined
Chapter two was divided into three major sections: The conceptual framework, empirical
framework and theoretical framework. Under the conceptual framework, reasons for increasing
or decreasing in banks deposit were itemise among others. The advantages, disadvantages and
effects of banks deposit were also elaborately explained. Thereafter was the empirical review in
which works of past authors concerning the subject matter in developed countries, developing
countries as well as in Nigeria were reviewed and critically appraised. The theoretical framework
comprised of theories such as the Mundell-Fleming model, monetarist theory among others.
Chapter three comprised of the research methodology which began with a restatement of the
hypothesis, model specification, estimation technique, Nature and sources of data, Limitation of
the methodology and a priori expectations
Chapter four was dedicated to data analysis and discussion of the findings made. First,
preliminary estimation analyses were carried out; including descriptive statistics, graphical
77
analysis, correlation analysis, unit root test as well as cointegration test. As the preliminary
estimations would suggest, the autoregressive distributed lag (ARDL) model was adopted for the
purpose of estimation. The results were interpreted and the research hypotheses were tested
accordingly. The findings made were also linked to previous research works to see the extent to
which findings were consistent.
Chapter five comprised of summary, conclusion and policy recommendations.
5.2 Major Findings of the Study
Having adopted the ECM-based ARDL for estimation, some major findings were made in this
study, which include the following:
i.
Only per capital income was significant in the long run to influence or change the
level of poverty in Nigeria
ii.
In the short run inflation rate was positive but insignificant while broad money supply
was negative but significant
iii.
In the long run broad money supply was positive but insignificant but was significant
in the short run only
iv.
From the granger causality test, there was causality between per capital index and
poverty but there was also causality between money supply and inflation
5.3 Conclusions of the Study
Based on the above findings made by the study, the following conclusions were surmised:
i.
Poverty is one of the most serious challenges faced by the developing countries like
Nigeria. Reducing poverty is one of the most important targets of the Millennium
78
Development Goals of the United Nations. However for reducing poverty, it is
imperative to understand the factors that cause poverty.
ii.
The long run relationship (cointegration) which exists among the variables implies
that these macroeconomic variables will adjust to long-run equilibrium
iii.
Per capital income is an important factor in curbing poverty as its significant in the
short and long run
iv.
Money supply is another factor that determines the level of poverty as it depicts a
positive impact on the poverty level.
5.4 Policy Recommendations
i.
This paper recommended that inflation should be targeted, in other words double-digit
inflation rate should be avoided. Although findings have revealed that inflation is of no
significant effect on growth pattern, it should however be noted that unnecessarily high
inflation rate could cause uncertainties to producers and investors, and could lead to
economic instability.
ii.
The study recommends that the federal government through the monetary authorities
should regulate monetary policy rate downwardly to encourage foreign and private
investment in the country. This is because it will in turn boost productivity and reduced
poverty level in Nigeria.
iii.
The government should channel its resources to the provision of basic amenities and
infrastructures such as power, good roads etc., such that the citizens can benefit and the
long-run effect being increased productivity and a higher standard of living.
iv.
The issue of corruption has to be tackled holistically in order to ensure that all efforts by
government towards eradicating poverty in Nigeria are achieved
79
5.5 Suggestions for Further Study
Findings and conclusions made in this study, however plausible they may seem, also may have
their own limitations hence further research could improve upon them. The author therefore
noted a few other ways through which findings could be improved:
i. By expanding the scope of study up to 2019 data, further research could provide more
information about the effect of monetary policy and inflation rate on poverty level in
Nigeria.
ii. Further research interests could also take asymmetry into consideration, since positive
shocks to a variable could have different impact on other macroeconomic variables. In
this wise, the non-linear ARDL technique was suggested
iii. Further studies could also increase the sample size by making use of high frequency
dataset, which could actually provide more valid results and might lead to better
conclusions.
80
APPENDIX
DATA
YEARS
POV
INFR
OPN
M2
PCI
IM
EX
GDP
1980 22.77566 20.81282 19.19614 1.44E+10 6.11E+10 19.19614 29.37517 6.42E+10
1981 25.16948
20.8128 9.581643 1.52E+10 5.95E+10 9.581643 8.590083 1.64E+11
1982
25.4806
7.69775 7.086126 1.67E+10 5.03E+10 7.086126 6.693707 1.43E+11
1983 28.70973
23.2123 4.511421 1.90E+10 3.48E+10 4.511421 5.533548 9.71E+10
1984 30.58066
17.8205 3.029761 2.12E+10 2.77E+10 3.029761 6.350781 7.35E+10
1985 26.35516
7.43535 3.295156 2.32E+10 2.79E+10 3.295156 7.096823 7.37E+10
1986 37.25208
5.71715 3.886755 2.36E+10 1.93E+10 3.886755
1987 50.22472
11.2903 6.647678 2.89E+10 2.17E+10 6.647678 12.84766 5.27E+10
1988 47.90396
54.5112 5.771463 3.84E+10 2.28E+10 5.771463 11.16915 4.96E+10
1989 52.02132
50.4667 8.931843 4.34E+10 2.21E+10 8.931843 25.25077
4.4E+10
1990 45.45457
7.3644 9.949967 5.76E+10 2.79E+10 9.949967 20.97477
5.4E+10
1991 46.57096
13.007 12.77474 7.91E+10 2.49E+10 12.77474 24.24687 4.91E+10
1992 44.44074
44.5888 14.25264 1.29E+11 2.64E+10 14.25264 23.97475 4.78E+10
1993 49.71078
57.1653 13.65321 1.98E+11 1.34E+10 13.65321 20.06654 2.78E+10
1994 56.84969
57.0317
1995 59.27368
72.8355 15.37256 3.19E+11 2.63E+10 15.37256 24.15582 4.41E+10
1996 55.86492
29.2683 17.23303
3.7E+11 3.28E+10 17.23303
1997 61.90011
8.52987 22.81126
4.3E+11 3.36E+10 22.81126 28.64975 5.45E+10
1998 64.67334
9.99638 21.13452 5.26E+11 2.92E+10 21.13452 18.14409 5.46E+10
1999 71.69717
6.61837
2000 74.26178
6.93329 12.97233 1.04E+12 4.02E+10 12.97233 36.02327 6.94E+10
2001 55.08743
18.8737 21.42954 1.31E+12
2002 57.78472
12.8766 16.79545 1.56E+12
5.3E+10 16.79545 23.23972 9.54E+10
2003 51.11944
14.0318 22.58358 1.77E+12
6E+10 22.58358 26.75138 1.05E+11
2004 50.97169
14.998 11.64207 2.13E+12
2005 50.00458
17.8635
2006 70.96248
8.23953 13.05043 3.56E+12 1.41E+11 13.05043 29.51613 2.36E+11
2007 52.75417
5.38222 18.10059 5.88E+12 1.55E+11 18.10059 21.23634 2.76E+11
2008 75.48682
11.578 15.12676 9.32E+12 1.93E+11 15.12676 25.67007 3.37E+11
2009 59.99067
11.5377 17.42837 1.09E+13 1.55E+11 17.42837 18.63034 2.92E+11
9.50999 2.67E+11 1.57E+10
13.1235
7E+11 3.44E+10
9.50999 13.54925 3.38E+10
23.0247 5.11E+10
13.1235 21.33433 5.94E+10
4E+10 21.42954 28.25096
7.8E+10 11.64207
12.0255 2.61E+12 9.89E+10
81
5.24909 5.48E+10
7.4E+10
20.2538 1.36E+11
12.0255 21.03396 1.76E+11
2010 76.88418
13.7202 17.66015 1.17E+13
2011 83.73758
10.8408 21.66102 1.32E+13 3.89E+11 21.66102 31.61694
2012 88.29256
12.217 12.98578 1.59E+13 4.39E+11 12.98578 31.54659 4.59E+11
2013 76.50933
8.47583 12.99895 1.74E+13 4.89E+11 12.99895 18.04991 5.15E+11
2014 79.13477
8.05738 12.45007 1.82E+13
2015 78.33146
9.01768 10.79023 1.87E+13 4.68E+11 10.79023
10.6567 4.95E+11
2016 70.96248
15.6969 11.50441 2.09E+13 3.96E+11 11.50441
9.21811 4.05E+11
2017 64.67334
3.5E+11 17.66015 25.66061 3.63E+11
4.1E+11
5.5E+11 12.45007 18.43513 5.68E+11
16.5 13.17604 2.59E+13 3.64E+11 13.17604 13.17156 3.76E+11
12.1 10.18645 3.04E+13 3.79E+11 1.02E+01 1.52E+01 3.97E+11
13.75 10.18906 3.05E+13 3.75E+11 10.18906 12.3471 3.93E+11
2018 88.29256
2019 89.3467
SOURCE: WORLD DEVELOPMENT INDICATOR AND CENTRAL BANK STATISTICAL
BULLETIN 2019.
Date: 01/02/10
Time: 18:09
Sample: 1980 2019
POV
PCI
OPN
M2
INFR
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
57.43745
56.35731
89.34670
22.77566
18.66692
-0.139352
2.224948
1.46E+11
5.17E+10
5.50E+11
1.34E+10
1.68E+11
1.109122
2.626532
12.91275
12.97906
22.81126
3.029761
5.316583
0.044860
2.423041
6.16E+12
8.68E+11
3.05E+13
1.44E+10
9.16E+12
1.403064
3.741637
19.22181
12.94178
72.83550
5.382224
16.83184
1.797076
5.105855
Jarque-Bera
Probability
1.130637
0.568179
8.433475
0.014747
0.568220
0.752684
14.04063
0.000894
28.92092
0.000001
Sum
Sum Sq. Dev.
2297.498
13589.70
5.82E+12
1.10E+24
516.5102
1102.376
2.46E+14
3.27E+27
768.8725
11049.13
Observations
40
40
40
40
40
82
Pairwise Granger Causality Tests
Date: 01/02/10 Time: 18:38
Sample: 1980 2019
Lags: 2
Null Hypothesis:
Obs
F-Statistic
Prob.
LM2 does not Granger Cause LPOV
LPOV does not Granger Cause LM2
38
1.21254
0.45759
0.3104
0.6368
INFR does not Granger Cause LPOV
LPOV does not Granger Cause INFR
38
0.08250
0.41471
0.9210
0.6639
INFR does not Granger Cause LM2
LM2 does not Granger Cause INFR
38
1.11948
4.17581
0.3385
0.0242
LPOV
LPCI
LOPN
LM2
INFR
LPOV
LPCI
LOPN
LM2
INFR
1.000000
0.584689
0.499548
0.855726
-0.122219
0.584689
1.000000
0.292637
0.848231
-0.454447
0.499548
0.292637
1.000000
0.523570
-0.038775
0.855726
0.848231
0.523570
1.000000
-0.303109
-0.122219
-0.454447
-0.038775
-0.303109
1.000000
ARDL Bounds Test
Date: 01/02/10 Time: 18:33
Sample: 1981 2019
Included observations: 39
Null Hypothesis: No long-run relationships exist
Test Statistic
Value
k
F-statistic
2.694342
4
I0 Bound
I1 Bound
Critical Value Bounds
Significance
83
10%
5%
2.5%
1%
2.45
2.86
3.25
3.74
3.52
4.01
4.49
5.06
Test Equation:
Dependent Variable: D(LPOV)
Method: Least Squares
Date: 01/02/10 Time: 18:33
Sample: 1981 2019
Included observations: 39
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LPCI(-1)
LOPN(-1)
LM2(-1)
INFR(-1)
LPOV(-1)
1.785005
-0.082117
-0.020380
0.088261
1.41E-05
-0.516297
0.835156
0.048400
0.058746
0.033324
0.001608
0.144284
2.137331
-1.696631
-0.346922
2.648567
0.008741
-3.578335
0.0401
0.0992
0.7309
0.0123
0.9931
0.0011
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.289891
0.182298
0.147358
0.716578
22.59958
2.694342
0.037822
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
0.035047
0.162959
-0.851261
-0.595328
-0.759434
2.229533
Dependent Variable: LPOV
Method: ARDL
Date: 01/02/10 Time: 18:32
Sample (adjusted): 1981 2019
Included observations: 39 after adjustments
Maximum dependent lags: 4 (Automatic selection)
Model selection method: Akaike info criterion (AIC)
Dynamic regressors (4 lags, automatic): LPCI LOPN LM2 INFR
Fixed regressors: C
Number of models evalulated: 2500
Selected Model: ARDL(1, 0, 0, 0, 0)
Note: final equation sample is larger than selection sample
Variable
Coefficient
Std. Error
t-Statistic
Prob.*
LPOV(-1)
LPCI
LOPN
0.524342
-0.058826
-0.031754
0.152304
0.054029
0.070558
3.442733
-1.088790
-0.450046
0.0016
0.2841
0.6556
84
LM2
INFR
C
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.079259
0.000636
1.289453
0.840439
0.816264
0.149900
0.741509
21.93267
34.76359
0.000000
0.035555
0.001644
0.862401
2.229212
0.386969
1.495191
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
0.0327
0.7013
0.1444
4.011804
0.349706
-0.817060
-0.561127
-0.725233
2.328085
*Note: p-values and any subsequent tests do not account for model
selection.
Pairwise Granger Causality Tests
Date: 01/02/10 Time: 18:38
Sample: 1980 2019
Lags: 2
Null Hypothesis:
Obs
F-Statistic
Prob.
LM2 does not Granger Cause LPOV
LPOV does not Granger Cause LM2
38
1.21254
0.45759
0.3104
0.6368
INFR does not Granger Cause LPOV
LPOV does not Granger Cause INFR
38
0.08250
0.41471
0.9210
0.6639
INFR does not Granger Cause LM2
LM2 does not Granger Cause INFR
38
1.11948
4.17581
0.3385
0.0242
85
7
Series: Residuals
Sample 1981 2019
Observations 39
6
5
4
3
2
1
0
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
Dependent Variable: LPOV
Method: ARDL
Date: 01/02/10 Time: 18:32
Sample (adjusted): 1981 2019
Included observations: 39 after adjustments
Maximum dependent lags: 4 (Automatic selection)
Model selection method: Akaike info criterion (AIC)
Dynamic regressors (4 lags, automatic): LPCI LOPN LM2 INFR
Fixed regressors: C
Number of models evalulated: 2500
Selected Model: ARDL(1, 0, 0, 0, 0)
Note: final equation sample is larger than selection sample
Variable
Coefficient
Std. Error
t-Statistic
Prob.*
LPOV(-1)
LPCI
LOPN
LM2
INFR
C
0.524342
-0.058826
-0.031754
0.079259
0.000636
1.289453
0.152304
0.054029
0.070558
0.035555
0.001644
0.862401
3.442733
-1.088790
-0.450046
2.229212
0.386969
1.495191
0.0016
0.2841
0.6556
0.0327
0.7013
0.1444
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.840439
0.816264
0.149900
0.741509
21.93267
34.76359
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
86
4.011804
0.349706
-0.817060
-0.561127
-0.725233
2.328085
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
4.26e-16
0.007807
0.274066
-0.284872
0.139690
-0.191580
2.326794
Jarque-Bera
Probability
0.975027
0.614152
*Note: p-values and any subsequent tests do not account for model
selection.
Breusch-Godfrey Serial Correlation LM Test:
F-statistic
Obs*R-squared
1.335433
3.093588
Prob. F(2,31)
Prob. Chi-Square(2)
0.2778
0.2129
Test Equation:
Dependent Variable: RESID
Method: ARDL
Date: 01/02/10 Time: 18:34
Sample: 1981 2019
Included observations: 39
Presample missing value lagged residuals set to zero.
Variable
Coefficient
Std. Error
t-Statistic
Prob.
LPOV(-1)
LPCI
LOPN
LM2
INFR
C
RESID(-1)
RESID(-2)
0.264307
0.036149
-0.000467
-0.046471
-0.000101
-0.677629
-0.405879
0.057992
0.356286
0.074871
0.070507
0.070298
0.001670
1.277828
0.414578
0.266949
0.741840
0.482809
-0.006627
-0.661061
-0.060547
-0.530297
-0.979018
0.217238
0.4638
0.6326
0.9948
0.5135
0.9521
0.5997
0.3352
0.8294
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.079323
-0.128572
0.148399
0.682691
23.54426
0.381552
0.906063
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
4.26E-16
0.139690
-0.797142
-0.455898
-0.674706
1.938992
Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic
Obs*R-squared
Scaled explained SS
1.466234
7.089198
3.367204
Prob. F(5,33)
Prob. Chi-Square(5)
Prob. Chi-Square(5)
Test Equation:
Dependent Variable: RESID^2
Method: Least Squares
87
0.2273
0.2141
0.6436
Date: 01/02/10 Time: 18:35
Sample: 1981 2019
Included observations: 39
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
LPOV(-1)
LPCI
LOPN
LM2
INFR
0.200206
-0.019869
-0.011289
-0.003775
0.007281
-0.000533
0.123901
0.021881
0.007762
0.010137
0.005108
0.000236
1.615862
-0.908029
-1.454316
-0.372377
1.425336
-2.255455
0.1156
0.3704
0.1553
0.7120
0.1635
0.0309
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.181774
0.057801
0.021536
0.015305
97.60204
1.466234
0.227331
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
88
0.019013
0.022187
-4.697540
-4.441608
-4.605714
1.864476
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