Poverty Reduction Strategies and Economic Growth

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Published on International Journal of Social, Politics & Humanities
Publication Date: May, 2020

OSUJI Anthony O. & NWACHUKWU Athanasius C.
Department of Social Sciences, Federal Polytechnic Nekede
Oweeri, Nigeria

Journal Full Text PDF: Poverty Reduction Strategies and Economic Growth.

This research work studied poverty reduction strategies and economic growth of Nigeria from 1980 to 2018. The major problem that inspired this research work is that inspite of the strong growth rate recorded in Nigeria; poverty has kept rising to the extent that nearly 100 million of her citizens live below the poverty line of $1.25 per day. The study sought to ascertain the extent to which the various efforts of the government towards tackling increasing poverty rate in Nigeria has helped to grow the economy. To achieve this major objective, two models were formulated; the first model used poverty rate, government expenditure on social services, human capital development, agricultural loans and agricultural labour input as the independent variables while growth rate of per capita income (GDP per capita) was the dependent variable. The second model used agricultural loans, agricultural labour input, human capital, government expenditure, discomfort index as the independent variables while poverty rate in Nigeria was the dependent variable. The Error Correction Model (ECM) was used to analyze the first model while the Autoregressive Distributed Lag (ARDL) model was used to analyze the second model. The findings showed that only human development index has a positive and significant long run impact on per capita GDP in Nigeria while poverty rate, government expenditure on social services and agricultural labour all increased per capita GDP in the long run. However, the short run analysis showed that all the poverty reduction indices have negative relationships with GDP per capita in the short run while only agricultural loans increased poverty rate in the short run. Consequently, the study concluded that government poverty reduction strategies moves especially in her expenditures on social services has not had the desired significant impact on the growth of the economy. The agriculture sector which is the major bridge through which poverty can be reduced among the populace still receives insignificant loans and low labour thereby plunging the economy into deep poverty. It was recommended that government should increase her expenditures on social services as well as in increasing her budgetary allocations in favor of the educational and health sectors, as the findings showed that investment in these sectors will reduce poverty and ultimately increase economic growth in Nigeria.

Keywords: Poverty reduction strategies, Economic growth, ARDL, ECM, Nigeria.

1. Introduction
In Nigeria, about two-third of the entire populace arerelatively poor, despite the country having vast potentials of wealth (Aliyu, 2002). The increase in Nigeria’s poverty index is not just low income per head, saving structure and growth rate, it comprises of an increase in inequality among individuals caused by inequality in basic needs and other infrastructural facilities. Anderson and Travis (2016) and Omoniyi (2018) found that Nigeria’s index on poverty has been on the increase in recent times despite the huge revenues accruing from oil exploration in the country.
Addae-Korankye (2014) defines poverty as obvious denial as per well-being, in a way that citizenslack the basic resources for living, and this consists of numerous angles, including income inadequacy and the failure to obtain basic amenities required for survival. Growth and development of the economy is seen as a powerful drive-in alleviating poverty. Jhingan (1985) refers development as a quantitative and continuous rise in a country’s per capital income complemented by extension in its labour force, consumption, capital and trade volume and welfare. This economic development or growth is due to advanced technology, optimum efficiency and ability in the use of resources and creation of material wealth.
Empirical studies show that high increased poverty can have a negative impact on overall, economic growth rate. They worsen social pressures, reduce the functioning of markets and badly impact the employability of the (exceptionally) deprived and disadvantaged (Chen and Ravallion, 2001).
The figure below presents a clear picture of Nigeria’s poverty profile and economic growth rate from 1980 to 2018.

Poverty profile and Economic Growth Rate, Nigeria (1980 – 2018)

Nigeria’s poverty became predominant since 1985 and was seen as an obstacle or limitation to economic growth due to the measurement of poverty on per capita income of $1 per day and $2 per day (Obadan & Odusola, 2001). The rate of increase in poverty in Nigeria was estimated from 37 percent to 66percent between the years 1980 to 1996 respectively. Between the years, 1996 to 1999, poverty level exceeded 70 percent of the Nigerian population (World Bank, 2010).
However, Nigeria’s unwavering drive towards poverty reduction led to increased growth in the economy far above the national poverty rate in 1995 with more than 100% rise in GDP. This was also recorded in the year 2010 when GDP exceeded the 100% mark but dropped drastically to 15% growth rate and further to 6.9% as at the end of 2018, as seen in the figure 1.1. One interesting fact about this is that all the while, poverty index for Nigeria has been hanging above the GDP growth rate for the majority of the years leading up to the year 2018.
Consequently, the World Bank ranking report of 2018 placed Nigeria at the bottom of Reducing Inequality Index (CRI) and the Human Capital Index (HCI) among the countries with very low quality of life. The United Nations (UN, 2015) set up the Sustainable Development Goals (SDGs) and the first goal is to eradicate poverty for all by the year 2030. However, Nigeria’s average 7.4% growth rate in Gross Domestic Product (GDP) has not significantly decreased her rising poverty profile (Omoniyi, 2018). According to the World Bank (2018) report recently, the poverty capital of the world is Nigeria having overtaken India as with the highest number of people in extreme poverty amounting to 86.9 million people representing almost 76 percent of the entire population.
In the face of these threatening poverty statistics for Nigeria, successive governments have outlined various poverty reduction strategies. Government has initiated various poverty reduction policies and programmes in order to eradicate poverty in Nigeria since 1980. The introduction of the Structural Adjustment Programme in1986 revealed more forceful policies and programmes to ease poverty and cushion the effects for the poor (Obadan, 2005). Some other programmes were sartorial interventions but their main aim was poverty reduction (Oshewolo, 2010). Landmark programmes on poverty eradication pursued in Nigeria include: National Economic Empowerment and Development Strategy (NEEDS),Guinea Worm Eradicating Programme (Health), National Poverty Eradication Programme (Poverty Alleviation), Operation Feed the Nation (Agricultural sector), Green Revolution (Agricultural sector), Free and Compulsory Primary Education (Education), National Directorate of Employment (Job Creation), Mass Transit Programme (Transport), Petroleum Trust Fund (Education, Health, Rural Development), Subsidy Reinvestment Programme (SURE-P), etc.
It is a well-known fact that poverty reduction leads to sustained growth in the economy. The 2018 Human Development index placed Nigeria among the 20 impoverished countries in the World using poverty indicators such as literacy level, access to safe water, nutrition, infant and maternal mortality, and the number of individuals on less than $1.25 a day. The World Bank report also showed that Nigeria was found to rank among the 20 poorest nations in the world below Kenya, Ghana and Zambia. This study is therefore, streamlined to examine poverty reduction strategies and poverty indices in Nigeria have affected the growth of the Nigerian economy with particular emphasis on the growth of income per capita in Nigeria for the period 1980 to 2018.

Nigeria’s Gross Domestic Product (GDP) rose by 69.7% from 2000-2010 (World Bank, 2010), and in 2014 it became updated as Africa’s largest economy due to her increased GDP from 1990 base year to 2010 base year (The Economist, 2014). However, Omoniyi (2018) noted that the level of poverty in Nigeria still needs more work. While the poverty index dropped from 31.1% to 21.8% from 1999-2016, indicating a slight decrease in the rate of poverty among impoverished Nigerians, the poverty headcount ratio using the $1.90 international poverty line fell by just 6.3% (World Bank, 2018). This indicates that the percentage of Nigerians that are poor has only reduced slightly, and because Nigeria’s population grew by 54.3% over the past twenty years; the absolute number of Nigerians below the international index on poverty has actually increased (Omoniyi, 2018).
The percentage of Nigerians who were absolutely poor rose from 54.7% in 2004 to 69.9% in 2010 (National Bureau of Statistics,2013). More so, the NBS (2013) reported that 112 million people lived in relative poverty while it put Nigeria’s population at 163 million. It is comparative with Uganda, which has only 28 million poor people; this is an indication that there are about four times as many people living in poverty in Nigeria as in Uganda. This shows that Nigeria has failed using all standards of poverty measurement including the relative poverty index. These figures are so despite the poverty reduction strategies of the government through social services, human capital development strides and the clamour for increased agricultural activities in the face of over-dependence on the oil sector.
The diverse poverty ratings in Nigeria exhibit variations on profiles; for example, total poverty hinges at 60.9%, 61.2% for $1.25 per day, 93.9% for the subjective rating while a recent research carried out by Harmonized National Living Standard placed the poverty profile at 69.0%. Additionally, Nigeria’s Gini coefficient was 0.268 in 1980, 0.295 in 1990, 0.430 in 2014, 0.490 in 2015 and 2016 and 0.834 in 2017and 2018 (UNDP, 2018). Similarly, the Human Development Index for Nigeria during the same period was 0.52 in 2014; it increased to 0.53 in 2015 and 2016 and remained at 0.532up to 2018 (UNDP 2018; World Bank 2018). These figures proved that income imbalance and human capital development rose in Nigeria during the period of study. This concisely indicates that there is a severe disconnect between poverty and growth because most of the population became poorer through exclusion.
The problem faced by poverty reduction strategies in Nigeria ranges from inefficient employment of common resources, weak policy environment, inadequate infrastructure, lack of access to improved technology, diverting of public funds to individual pockets. Other causes include the non-availability of credit instruments and exclusion of ‘problem groups’ from participating in the democratic process etc. Thus, it is still unsure whether it is weak institutions of poverty reduction, over-reliance on earnings from oil or just lack of adequate funding for poverty reduction programmes might have hindered economic growth in Nigeria.
Loans to the agricultural sector have been on a steady increase while agricultural labour has been declining over the years due to the over-reliance on the oil sector. Thus, we raise the question of whether the increased poverty level Nigeria is caused by the neglect of the agricultural sector or simply the low budgetary allocations or low credit granted to the agricultural sector. These questions remains to be answered and they constitute the numerous problems which this research work intends to solve.

The study will seek answers to the following research questions;
 How does government poverty reduction strategies (proxied by social services, human capital development, agricultural sector development) impact on Nigeria’s GDP per capita?
 What is the relationship between Agricultural sector loans, labour, human capital and poverty alleviation in Nigeria?

The study will be guided by the following null hypotheses:
H01: There is no significant relationship between government poverty reduction strategies
and per capita GDP in Nigeria.
H02: There is no significant relationship between Agricultural sector loans, labour, human
capital and poverty reduction in Nigeria.

Concept of Poverty
Poverty and the issues that go with it are very common and therefore are vital aspect of our everyday discourses. Quite a lot have been said about poverty and the strategies aimed at reducing it in Nigeria. The fact that poverty has a ripple consequence on human conditions, like physical and moral and psychological thinking, connotes that poverty has been conceptualized from different perspectives by different scholars. This makes it impossible to have a concise and acceptable definition that will be universally accepted. In conceptualizing poverty, the study will take at look at various definitions of poverty by various authors.
Ekpo (2002) sees poverty as a process of living where there is the existence of low income and consumption and one’s complete essence is not guaranteed with lack of education, poor standard of living and inability to discover one’s potentialities. Narayan (2000) asserts that poverty is a state of low income, inability to feed properly and lack of ability to meet with basic social and economic needs. Addae-Korankye (2014) defines poverty as consisting of several dimensions, including low income and the inability to possess basic goods and services required for survival also, pronounced deprivation and well-being. Kpakol (2014) defines poverty as the incapability of an individual(s) to garner the empowerment needed to in order to handle the challenges of the environment. In the foregoing, Asikhia (2016) suggested that people are impoverished when there is an inadequate capacity to control their environment. Hence, one becomes poor when his environment takes charge and dictates.
The World Bank (2010) defined poverty as surviving ona minimum of $1.90 per day. The World Bank sees extreme poverty as the living below $1.90 per day and moderate poverty as living less than $3.10 per day. Based on this definition, the World Bank sees poverty as hunger; lack of shelter, state of being sick and not being able to see a doctor (Karimu and Tasiu, 2017). Similarly, UNDP, (2018) defines poverty as a renunciation of choices and opportunities; a violation of human dignity and lack of basic capacity to participate effectively in the society.
The first dimension of the definition of Addae-Korankye (2014)shows that persons with physical challenged are deprived from formal education, inability to express themselves in public or attend social, economic or political functions. This is mostly experienced in the northern states of Nigeria; while other factors are mostly experienced in the rural areas and some in urban areas. Rural areas are denied of access to finance, factors of production, information, knowledge and so on, while the urban population does not have the necessary facilities to standard living due to overcrowding; the poor live in slums and individuals are denied of property, income assets and so on.
This definition of Narayan (2000) and Ekpo (2002) both sees poverty from the perspective of income inequality where income is low leading to low consumption. The World Bank takes the concept of poverty from the angle of a fixed income which is meant to be sufficient for an individual per day. The Bank sees $1.90 per day as the benchmark for daily living in developing countries mostly and grouped poverty into extreme and moderate. A more human approach to the definition of poverty is seen in the UNDP,(2018) concept of poverty which sees deprivation in any form substantiated by the incapacity to function effectively in the society as the core of poverty.
World Bank (2010) on its definition of poverty is chosen as the bedrock of this study because it captures the key factor in poverty incidence which is income inefficiency and inequality and inadequacy of basic amenities viz: food, shelter, healthcare and clothing etc. When an individual cannot afford these basic needs of life, his/her productive capacity diminishes and this cycle is likely to continue thus leading to low man-power and low output in the economy. Jelilov and Onder (2016) rightly observed that Nigeria’s Gross Domestic Product has been on the increase yet this has not led to a corresponding decrease in poverty incidence. They attributed this situation to a case of rising economy devoid of human capital development. Foster (1998), stated that often than not the scale used are:
a) The sensitively distributing equal poverty gap in order to measure up the squared proportionate poverty gap that reveals the harshness of poverty
b) The head count poverty index given by the percentage of the population that live in the households with a consumption per capita less than the poverty line;
c) Taking in to account how far the average poor person’s income is from the poverty line which reflects the depth of poverty through the poverty index line.
Recent studies by UNDP advocates the use of the Human Development Index (HDI).
According to UNDP (2009), HDI combine three mechanisms in the measurement of poverty:
a) Enhanced living standard determined by per capita income.
b) Longevity at birth (Life expectancy) and
c) Attainment of goof education
The first relates to survival-vulnerability at a relatively early age. Secondly, knowledge that is removed from reading and communication globally. Thirdly, a good standard of living with respect to general economic provisioning. Poverty has diverse indicators namely: Sickness, lack of education, starvation and malnutrition, dearth of income and productive resources enough to ensure sustainable livelihood, other basic services, rise in morbidity and mortality from illness, homelessness, unhealthy environment, social discrimination and exclusion, no participation in making decision and socio-cultural life (Obadan, 2005). The factors that cause poverty, according to Obadan (2005) include;
i). Structural causes that are perpetual and depends on a host of (exogenous) factors like limited resources, lack of skill, factors that are characteristic in the social and political set-up. The household headed by females, landless farmers, disables, orphans all are grouped into this category;
ii). Transitional problems are majorly due to adjustment of reforms structurally and fluctuations in local policies that may bring about price variations and rise in unemployment. Natural occurrences like wars, environmental depletion also foster transitory poverty.
Debating the concerns of poverty, Moore (2015) pinned three major effects:
1). Concerns for those affected. Inadequate feeding, dearth of medical care, absence of basic and job related education and marginalization within the labor market causes poverty which hitherto leads to psychological and physical misery;
2). Concern for economies of countries arising through the development of slums in cities, through the failure to use the available human resources and ecological problems aided by predatory exploitation in the agricultural sector; and
3). Concerns for the socio-political development of the countries affected especially corrupt elites.

Poverty Profile in Nigeria
World Bank reports (2018), estimated that at least 50% of the world – over three billion people, live on less than $2.50 (i.e. N900) a day. In Nigeria, the rate of poverty is high with various reports showing that two-third of the population live below the poverty line.

Poverty Profile (Nigeria, %)

Source: NBS Press briefing on Nigeria’s poverty profile 2018 Report

Nigeria’s extremely poor population rose from 6.2% in 1980 to 46.7% in 2018. The percentage of moderately poor population also increased to 45.5% in 2018 from previous years’ rates of 21.0%, 36.3% and 30.3% in 1980, 1996 and 2010 respectively. Furthermore, the non-poor population has been dropping since 1980 from 53.7% in 1985 to 30% as at 2018. However, the rate at which households drop below the poverty line is estimated to be higher than the rate at which many households exit the poverty line, based on the NBS report of 2018.
Nigeria has one of the world’s highest economic growth rates, averaging 7.4% (CBN, 2018) but over 80 million Nigerians representing 42.4% of the total population currently live below the poverty line of $1.90 per day (N680) according to the United Nations (UN, 2018).The World Bank (2018) report also showed that in February 2018, Nigeria overtook India as the country with the most people in extreme poverty. For the context, India’s population is 5 times bigger more than that of Nigeria. World Bank (2018) standards hold that living in extreme poverty is existing on less than $1.90 (N680) per day. This is because this set of people will be unable to meet even the minimal needs for survival.
In 2015, the United Nations set up the Sustainable Development Goals (SDGs) and the first of them was to “eradicate extreme poverty for all people everywhere by 2030”. However, in actualization of this in the global context, ninety individual needs to disembark from poverty every minute in order to wipe out poverty totally by 2030, and to attain this in Africa, fifty-seven people have to leave every minute; and in Nigeria, twelve people per minute. Nigeria currently has seven people diving into poverty every minute with the recent statistics released by the World Bank in 2018.
Nigeria’s population is moving more arithmetically than its economy (Eseme, 2016). Between 1990 and 2017, population growth in Nigeria rose by 81 percent. Nigeria is forecasted as the third most populous country in the world by 2050; only behind India and China. While the rate of poverty will be on drastic increase by 2020, the International Monetary Fund (IMF, 2018) projects Nigeria’s GDP to grow by only 0.8 percent in 2020. The National Bureau of Statistics reports that Nigeria’s 2018 record budget was running on a deficit, and this spill-over effect is also witnessed in the 2019 budget which will be mostly funded by much borrowing with government debts already on the rise (CBN, 2018).
Nigeria’s oil wealth recently experienced a dive due to oil price reduction globally. Since oil is the main revenue earner of Nigeria’s economy, it means that her oil-dependent GDP is being drastically affected too. The country’s economy has been hit hard by the recent recession with slow pace of recovery already witnessed in many sectors. The high rate of unemployment, corruption, lack of basic amenities, difficulty in doing business and millions living in poverty are some of the indicators of low standard of living in Nigeria which poses serious threat the overall growth and development of the Nigerian economy.

The Concept of Economic Growth
This is seen as a rise in the production of goods and services over a time frame (Amadeo, 2018). To be most accurate, Amadeo (2018) noted that to measure growth, inflation effect must be removed. Economic growth is profitable for businesses. Gross domestic product is the best mode of measuring economic growth. It takes cognizance of the general country’s output. They are seen as all produce for sale from businesses in the country. It doesn’t matter whether they are sold domestically or overseas.
Amedeo (2018) asserted that economic predictors monitor the growth of the economy in order to be in tune with the various stages of the business cycle within the economy at every point in time. The best phase is the “expansion” phase which reveals when the economy experiences steady growth in a sustainable fashion. Moreover, when growth rises to an unseeingly marginal stage it tends to overheat. This in turn launches an asset bubble. This results to too much money chasing too few goods and services, i.e. inflation kicks in. This is the “peak” phase in the business cycle.
Economic growth remains the strongest tool for reducing poverty and improving the quality of life in developing countries (DFID, 2018).Growth generates a multiplier effect on prosperity and opportunity. Enhanced incentives for parents to invest in their children’s education, by sending them to school hinge on strong growth and employment opportunities. This would lead to the emergence of a strong and growing group of entrepreneurs, which should generate pressure for improved governance. Strong economic growth therefore advances human development, which, in turn, promotes economic growth (DFID, 2018).
The DFID noted that the link between economic growth and human development operates through two channels. First, there is the ‘macro’ link whereby growth increases a country’s tax base and therefore makes it possible for the government to spend more on the key public services of health and education. The second channel between growth and human development is a ‘micro’ link, whereby growth raises the incomes of poor people and thereby increases their ability to pay for activities and goods that improve their health and education.

The Basic Needs Analysis Theory of Poverty (BNA Analysis)
According to the European Union Humanitarian Aid (EU human Aid, 2017), the BNA approach to poverty reduction, though not self-sufficient, but is meant to inform the response analysis process. It must be conducted together with other, complementary assessments focusing on the operational environment where the response is being planned. While those assessments provide information that is used to establish the operational feasibility of different response options, the BNA generates information around the priority groups and needs that the response should address, as well as around the most suitable types of interventions considering the objectives to be attained.
Suitability to the objectives and operational feasibility are two complementary dimensions against which response options will be compared. The BNA is the first building block of a three-phased process that includes the situation analysis (needs and operational environment), response analysis and response planning. The steps in BNA are designed to precede the implementation of the response and subsequent monitoring and evaluation of every poverty alleviation strategy thus:

Situation analysis, response option analysis and response planning
Source: European Union (EU) Humanitarian Aid (2017)

The steps involved are explained below:
Step 1:
Situation analysis: This step involves both a Basic Needs Analysis and an understanding of the Operational Environment, to provide all information required for a rigorous analysis of response options. The latter must consider both the suitability of different, possible types of interventions with respect to the objectives they aim at, and their operational feasibility. Basic Needs Analysis is the process designed to estimate or provide informed opinions about the affected populations, deficiencies in terms of their basic needs, the underlying causal mechanisms (underlying factors), and their humanitarian consequences (humanitarian outcomes). It entails a systematic set of procedures and the use of specific lines of inquiry undertaken for the purposes of setting current and forecasted priority needs (adapted from ACAPS 2014, Witkin & Altschuld, 2015).

Step 2:
Response analysis: Is the intermediary step between the analysis of needs and operational environment, and response planning. It is a structured process by which sectors, individually, define the strategic elements of the sector-specific response and conduct a comparative analysis of possible response options. It considers context, experience and lessons learnt and involves:
• The identification of objectives and targets groups. Targeting is the process by which areas and populations are selected to receive assistance. It includes mechanisms and criteria to define target groups, to identify members of the target populations, to ensure that assistance reaches the intended beneficiaries and meets their needs (Adapted from WFP 2006, Targeting in Emergencies).
• The identification and comparison of response options based on the primary and secondary information collected during situation analysis, context, experience and lessons learnt (step 1). The selection of sector-specific response options is informed by considerations of: (1) appropriateness (or suitability to the objective); and (2) acceptance, cost efficiency, technical, contextual feasibility, and risks for the targeted populations, the implementing agency and the context. Therefore, the preferred intervention(s) will simultaneously address the needs prioritised by the affected groups, whilst proving to be operationally feasible and able to minimize potential harmful side-effects (adapted FAO 2011).

Step 3:
Response Planning: Is the last stage and involves sectors getting together and planning their respective responses in light of other sectors’ plans. This is an inter-sector planning process, whereby sector-specific response options are reviewed to ensure inter-sector synergies, consistency and integration, and that multi-sector interventions – such as MPG programmes – are identified and jointly designed and sequenced. The outcome is an integrated inter-sector response plan, as opposed to a collation of sector plans.
This step will provide recommendations to plan programme, activities and practical arrangements for the response, including the sequencing and frequency of transfers (regardless of their nature), the type and amount of sector assistance to be provided, in light of other sectors’ assistance and the cumulative effect that this may have on recipients. If in-kind assistance, the sector will typically confirm the contents of the kit/package to be distributed, the frequency of the transfer, and the duration. If cash based interventions are selected during the response analysis as an appropriate response, stakeholders will have to discuss and decide on the most suitable type of cash transfer (if sector specific or multipurpose, and how to combine different CBI), the transfer value, and the most appropriate timing to deliver it. Finally, cross-sectoral themes such as protection and environmental issues will be analyzed and mitigation measures will be proposed, including by adjusting the response plan.
However, a major critique of this theory is its non-practicability in developing nations. The steps involved cannot be judiciously followed owing to the major hindering factor which the frequent changes in government coupled with the changes in policies and dropping of previous government’s policies in pursuit of new unrealistic ones. These hindrances make the basic needs analysis very moribund in application.

There is sizeable empirical evidence which links poverty reduction to development in Nigeria. However, most of the reviews examined growth and poverty while others looked at the reverse i.e. poverty reduction and economic development relationship.

Poverty and Economic Growth
Ijaiya, Bello and Ajayi (2011) ascertained the effect of economic growth on poverty reduction in Nigeria taking cognizance of a time subscript and a difference-in-difference estimator that shows this factor as a major player to changes in economic growth (Ijaiya et al, 2011). Data for poverty rate, initial reduction in poverty in Nigeria proxied by initial household consumption-expenditure, changes in poverty reduction in Nigeria proxied by changes in household consumption-expenditure, initial conditions of economic growth in Nigeria proxied by initial per capita income (US$) and changes in economic growth in Nigeria proxied by changes in per capita income were used in a multiple regression equation model. The result obtained indicated that the earlier level of economic growth is not disposed to poverty reduction, but a positive change in economic growth is prone to poverty reduction. They recommended an improvement and sustenance in the rate of economic growth in Nigeria from which poverty could be reduced. Measures, such as, huge investment in agriculture, infrastructural development, good governance and stable macroeconomic policies were also suggested by the researchers (Ijaiya et al., 2011).
Oloyede (2014) in his study sought to identify the causes of poverty and measures through which poverty can be minimized in the country. Analyzing the data using the time series approach through the ordinary least squares technique, the study covers the period of thirty years ranging from 1980-2010 to measure the incidence of poverty. The result showed the existence of overall significant effect of poverty reduction on economic development in Nigeria.
Oloyede (2014) noted in his study on economic growth and poverty reduction that despite various poverty reduction programs flag off by various past and present governments in Nigeria, it seems that this menace is very much alive. Therefore, his study aimed at finding out the causes of poverty and how it can be curbed or reduced to the barest minimum through policy recommendations. His study was for a period of 1980– 2010 using time series data obtained from secondary sources (CBN). The researcher applied the OLS technique to measure the relationship between incidence of poverty (dependent variable) and unemployment rate, per capita income, inflation rate, literacy rate, life expectancy and infant mortality rate (independent variables). His findings revealed the existence of overall significant effect of poverty reduction strategies on economic development in Nigeria. Particularly, infant mortality rate, life expectancy rate and per capita income had negative relationships with poverty incidence while literacy rate and inflation rate have positive but insignificant relationships with poverty index. He concluded that poverty eradication in Nigeria is a difficult and herculean task as efforts so far exerted by various past administration, has been relatively futile making poverty to have weighty consequences on the poor, the family and on the society. He advocated for Government policy on poverty alleviation to follow a multi- sectoral approach where all the stakeholders are given specific roles to play.
Tanimu and Saifullahi (2014) used bounds testing approach to cointegration and Granger causality test to determine the relationship between poverty, inequality and economic growth in Nigeria. Data from 2000 to 2012 on Real GDP, Poverty rate, population and literacy rate were used. Their result showed that there is a unidirectional causal relationship running from RGDP to poverty, which means that an increase in GDP in Nigeria caused high level of poverty. In addition, the result revealed that the RGDP Granger causes the literacy level without a feedback. The result further indicated that bidirectional causal relationship existed between literacy and poverty. They also indicated that population growth Granger causes literacy without feedback while unidirectional causality existed between poverty and population. They opined that demand-management policies aimed at reducing the gap between rich and poor should be vigorously pursued in order to minimize the rate of lingering inequality in the country and spur institutional change that will bring about betterment of people in the country (Tanimu and Saifullahi, 2014).
Oloniyi (2016) in his study of economic development and poverty reduction sought to identify the causes of poverty and measures through which poverty can be minimized in the country. Data on Poverty incidence (proxied by poverty rate), government agencies’ intervention expenditures and per capita GDP were used. Analyzing the data using the time series approach through the OLS technique, the study covered the period of thirty years ranging from 1980-2010 to measure poverty incidence. The result showed the existence of overall significant effect of poverty reduction on economic development in Nigeria (Omoniyi, 2018).
Gangas (2017) explored data on Real GDP, poverty index, unemployment rate, per capita index were used and analyzed using the multiple regression technique. He found that there is a positive connection between GDP per capita and the growth of GDP. Also, negative relationships exist between unemployment which is an indicator of poverty level in the country and GDP. He recommended that government should ensure that various poverty alleviation program that can reduce unemployment rate should be put in place to enhance increase in economic growth and reduction in unemployment rate.
Kahsu and Nagaraja (2017) carried out an empirical analysis of the connection between poverty and economic growth in Ethiopia using micro-panel data evidence from Amhara region of Ethiopia. Ethiopia is ranked as one of the poorest countries in the world, with a very low human-development ranking, or 174th out of 188 countries according to the UNDP’s human development report 2015. World Bank estimation recorded about 23 million of Ethiopians live in condition significantly below the basic poverty line and food security remains a major problem. The researchers estimated the fixed effects models (FEM) using panel data from four household income, consumption and expenditure (HICE) surveys conducted in the region between the period 1995/96 to 2010/11 by the central statistics authority (CSA) of Ethiopia. The FEM estimation results of their study indicated that growth plays a pivotal role on reducing poverty and a rise in inequality led to a rise in poverty. They also revealed that growth contributes far more towards reducing poverty, keeping inequality constant, than the latter does to increasing poverty, holding the former constant. Based on their findings, they recommended that the government should implement policies focusing on growth as well as redistributing income in favor of the poor and middle class households in all administrative zones of the region in Ethiopia.

Here, the study employs theoretical and empirical reviews of poverty reduction to formulate a model. For the purpose of empirical investigation, the models of Okorafor and Nwaeze (2013) and Omoniyi (2018) were adopted and modified to suit our purpose. The former modeled the relationship between poverty discomfort index, human capital development index and growth of the Nigerian economy; the Human development Index (HDI) captured poverty, Discomfort index captured unemployment plus inflation rates. The latter that is, Omoniyi (2018) used poverty index, mortality rate, unemployment,, life expectancy, school enrolment as poverty reduction indices with public debt as a control variable.
However, both models are modified by including government expenditure on social services and intervention schemes, agric loans and agric labouramong the explanatory variables and real GDP per capita as the dependent variable which estimates the relationship between poverty reduction rate and Nigeria’s economy. In addition, we estimate another model (model 2) which tries to find out the causal relationship between poverty rate and agric loans and labour, human capital development, government social services expenditure and discomfort index as control variables. Thus, the models are specified as follows:

That is to say that the coefficient of poverty rate is expected to have negative relationship with per capita GDP. The coefficient for government expenditure on social services, human capital development, agric loans and labour are expected to have positive and significant impact on real gross domestic product per capita. Also, the coefficients of agric loans, agric labour, human capital development, agric loan, social services expenditure and discomfort index are expected to have negative coefficients i.e. they decrease poverty rate in Nigeria.

The data on the variables used in the study is detailed in Appendix 1, marked, “Data set on Real GDP Per Capita, Government Expenditure on Social Services, Human Capital Development Index, Agricultural loans, Agricultural Labour Supply, Poverty Rate and Discomfort Index (1980 to 2018). Since the data are time series in nature, we carried out a test of stationarity using the Augmented Dickey Fuller Unit root test. The test is summarized as follows:

Unit Root Test
The ADF unit root test was conducted to ascertain whether the variables in the model are stationary. This is necessary as it helps to avoid spurious regression results. The summary of the test using E-views software is detailed in the table below:

Summary of Unit Root Test Results

Source: Researchers’ Computation using E-Views 9.0

The Table 4.1 above shows that the variables Per capita GDP (PCGDP), Human Capital Development Index (HCDI), Agric Loans (AGLOAN), Government Expenditure on Social Services (GXSOS) and Discomfort Index (DI) became stationary after first difference which implies that they are integrated of order one I~(1). Agric Labour Input (AGLAB) and Poverty Rate (POVR) are integrated of order zero (I~(0) as they were stationary at level form.

Lag Selection Criterion
Akaike Information Criteria
The selection made above is based on the AIC criteria on Eviews 9.0. The criteria were selected automatically by the software. After twenty (20) models automatically generated, The model selected a lag structure of ARDL (4,4,4,3,4,4) which were chosen based on the Akaike Information Criteria (AIC). This means that the maximum lag period for model 1 is 4 for the dependent variable Per capita GDP and the independent variables Poverty rate, government expenditure on social services, Agric loans and Agric labour. Only Human capital development index has 3 maximum lags based on the AIC criterion.

ARDL Bounds Test For Cointegration For Model 1
ARDL Bounds Cointegration Test Result For Model 1

Source: Authors computation with E-views package

From Table 4.3A above, the F-statistics for the model 1 is 4.41163 which is greater than the upper and lower I(1) bounds of 3.79 and 2.62 at 5% level of significance. Thus, we conclude that the variables are co-integrated. This implies that there is a long run relationship between Government poverty reduction strategies and per capita GDP growth in Nigeria. Again, from the result in Table 4.3B, using the Trace test of detecting co-integration test, it’s indicative that there is presence of one (1) Co-integrating equations in the model, hence there exist a long run relationship between agricultural sector loans, labor, human capita and poverty reduction in Nigeria. This implies that on the long run, the variables will converge.

Model Estimation
Short Run Estimation of the ARDL Model
The short run model was estimated and the result for both equations is shown below:

Short Run Estimates For Model One

Source: Extracted from E-views Output

Above indicates that the coefficient of the Error Correction Model ECM(-1) for model one is -0.4653 and is appropriately signed and significant. Also, the residual or coefficient of the ECM for the model two as shown in Table 4.4B is negative with value of -0.4352 and significant also. This shows that there is presence of short run significant relationship between poverty reduction indices and growth of per capital GDP in Nigeria and poverty reduction in Nigeria.

Long Run Estimation of the ARDL Model 1
Since the model one is estimated using the Autoregressive Distributed lag (ARDL) model, it is imperative to estimate the long run relationship as well as the short run coefficients as is indicative of any ARDL model. The long run relationship between poverty reduction indices and per capita GDP is summarized below:

Source: Extracted from E-views Output

The long run coefficients show that agricultural loan is negatively signed and has insignificant relationship with per capita GDP. Poverty rate, government expenditure on social services, human capital development index and agricultural labour are all positively signed with only human capital development index having significant impact on per capita GDP. This result and the other results presented above are further discussed below:

The result from the Unit root test showed that the variables per capita GDP ((PCGDP), Poverty rate (POVR), Government expenditure on social services (GXSOS), Human capital development index (HCDI), Agric loans (AGLOAN) and Agric labour (AGLAB) are integrated of order one and zero with none of the variables being integrated of order two. We therefore, applied the ARDL Bounds test approach to cointegration for model one and the Johansen cointegration test for model two. Both tests for cointegration stems from the fact that the variables of model one are of mixed order while the variables for model two are integrated of order one. The optimum lag length for the ARDL model using the Akaike information criteria was determined. The results showed that a long run relationship exists between the poverty reduction strategies and per capita GDP growth on one hand and on poverty reduction on the other hand.
The short run result for model one shows that the coefficient of error correction model ECM(-1) is -0.4653 and is appropriately signed implying that there is presence of short run, leading to the variables converging on the short run. The speed of adjustment suggests that about 46.53% of the previous period’s disequilibrium in per capital GDP is corrected every year by government poverty reduction strategies.
For model two, the short run results showed that 46% of previous period’s disequilibrium in poverty rate is being corrected every year by government poverty reduction strategies. Agricultural sector loan (AGLOAN) and Discomfort index (DI) have positive short run coefficient values of 13.7102 and 0.03179 indicating that there is a positive short run relationship between loan to agricultural sector, discomfort index and poverty rate in Nigeria. Agricultural sector labor (AGLAB), human capital development index (HCDI) and government social services expenditure (GXSOS) all have negative short run coefficients of -0.7601, -0.0238 and -9.1218 indicating that there is an inverse short run relationship between agricultural sector labor, human capital development index, government social services expenditure and poverty rate in the short run in Nigeria.
The long run coefficients for model one from table 4.4C showed that the coefficients of poverty rate (POVR), government social service expenditure (GXSOS), human capital development index (HCDI) and agricultural labour (AGLAB) are positive 0.0051, 0.1925, 0.0014 and 0.8653 respectively. This entails that the long run relationship between Poverty rate, government social service expenditure, human capital development index, Agric labour and per capita GDP are positive. Thus a unit increase in any of these variables increases per capita GDP in Nigeria in the long run. Only agricultural loan is negative with coefficient of -0.2958 hence indicating that a unit change in agricultural loan decreases per capita GDP in the long run.

Below shows the post estimation test results which comprised of the Coefficient of determination (R-squared and Adjusted R-squared), Heteroskedasticity test, Breusch-Godfrey serial correlation test, Durbin Watson test for autocorrelation and Normality test. These tests are necessary in order to ascertain the statistical robustness and predictive ability of the model. They are summarized as follows:

Source: Extracted from E-Views 9.0 output

The probability value of the Breusch-Godfrey Serial Correlation statistics (B-Q statistics) is greater than 0.05 in both models. Since the B-Q statistics are greater than 0.05, we therefore conclude that there is no presence of q order serial auto-correlation of stochastic errors terms in both models. Also the Durbin Watson statistics indicate no autocorrelation in the model since the XDW values tend towards 2 than to 0.
The primary reason to test for Heteroskedasticity after the model estimation is to detect violation of classical assumptions of least squares estimations. Since the calculated value of 26.23 for model one with probability value of 0.5602 is not significant, we conclude that the conditional variances of the error terms in model one are equal. For model two, we conclude that there is no heteroskedasticity in the model, entailing that the error term is homoskedastic.
From the table above, since the Jarque – Bera probability values (0.0743 and 0.9945) for models one and two are greater than 0.05 critical value, we conclude that the residuals of both models follows a normal distribution. The series are stable in the long run since their Cumulative Sum (CUSUM) lines are within the 5% critical value bounds.
The coefficient of multiple determinations also known as the adjusted Coefficient of determination (Adj. R2) from the bound test was employed to ascertain the goodness of fit of model one. Thus the (Adj. R2) showed that about 54.5% of the total variation in the model can be explained by the independent variables, leaving 45.51% to error term, this represent a fairly goodness of fit. For model two, the coefficient of multiple determinations showed that about 66.2% of the total variations in the model can be explained by the independent variables, leaving 53.81% to error term, this also represents a good fit.

Test of Hypothesis One
H01: β1, β2, β3, β4, β5 = 0 i.e. There is no significant relationship between government poverty reduction strategies and per capita GDP in Nigeria.
HA1: β1, β2, β3, β4, β5 ≠ 0 i.e. there is a significant relationship between government poverty reduction strategies and per capita GDP in Nigeria.
Decision Rule: If the prob value of the F-test is less than 0.05, reject H0, otherwise accept H0.
Conclusion: Since the probability value of the F-statistic in the ARDL result of Appendix 3, is less than 0.05 critical value (0.000014), we reject the null hypothesis H0 and conclude that there is a significant relationship between government poverty reduction strategies and per capita GDP in Nigeria.
Test of Hypothesis Two
H01: β1, β2, β3, β4, β5 = 0 i.e. there is no significant relationship between Agricultural sector loans, labour, human capital and poverty reduction in Nigeria.
HA1: β1, β2, β3, β4, β5 ≠ 0 i.e. there is a significant relationship between Agricultural sector loans, labour, human capital and poverty reduction in Nigeria.
Decision Rule: If the prob. (F-test) is less than 0.05, reject H0, otherwise accept H0
Conclusion: Since the value of Prob. (F-test) in the Error Correction Model result of Appendix 5, less than 0.05 critical value, we reject the null hypothesis (H0) and therefore conclude that there is a significant relationship between Agricultural sector loans, labour, human capital and poverty reduction in Nigeria.

The result of the analyses from both models revealed that
 The variables such as Per capita GDP, Human development index, Government expenditure on social services, agricultural sector loans, and Discomfort index had presence of Unit Root at first difference; While Poverty rate, and agricultural sector labor force possessed unit roots at level;
 The variables possessed both long and short-run relationships with per capita GDP and poverty rate in Nigeria;
 The ARDL result of Model 1, showed that only human development index has a positive and significant impact on per capita GDP in Nigeria, agricultural sector loan is inversely related to per capita GDP;
 Government expenditure on social services, poverty rate and agricultural sector labor have positive and insignificant long run relationship with Per capita GDP in Nigeria;
 The short run estimates revealed that Agricultural sector loan has a positive relationship with poverty rate, however, the Significance test showed that for the 38 years period (1980-2018) the research covered, only agricultural sector loan was positive but this was not found to be significant.
 Agricultural sector labor, human capital development index and government expenditure on social services and discomfort index on the other hand had an inverse short run relationship with poverty reduction in Nigeria. However, in the long run, government expenditure, human capital development and agric labour became positive but still not significant.
 The significant test revealed that the poverty reduction strategies variables did not significantly impact on growth of the economy in the short run, but in the long run, human capital development index became significant.
 The joint test showed that there was a significant relationship between agricultural loans, labor, human capital on poverty reduction in Nigeria; and government poverty reduction strategies were found to have a significant relationship with per capita GDP in Nigeria (economic growth).

The following recommendations are made from the findings of this research;
 The government should increase her expenditures on social services because the variable showed to be insignificant in the long run. In other words, to ensure long term growth of the economy, government should invest heavily in the provision of social services as this will significantly and drastically reduce poverty and enhance economic growth in Nigeria.
 Economic policy makers, planners and government should increase its budgetary expenditure in favor of the educational and health sector, as the findings showed that investment in these sectors will reduce poverty and ultimately increase economic growth in Nigeria.
 The financial sector, through directives from the monetary authorities, should devote more credit facilities to the agricultural sector of the country, as that has the capacity to reduce poverty in Nigeria, and increase the growth potentials of the economy.
 The use of discomfort index which is a summation of the inflation and unemployment rates showed positive effect on poverty rate in the short run in Nigeria. Thus, government should do well to tackle the menace of unemployment and inflation as its holds a positive correlation to poverty rate in Nigeria.
 To ensue steady growth of the economy and reduction in poverty rate, all the poverty reduction strategies used to model in this study should be included in the government economic enhancement road map. Strategies such as increased loans to the agric sector, increased services supply to the agric sector, increased expenditure on social services and reduction in inflation and unemployment rates should be vigorously pursued.

The research sought to examine poverty reduction strategies and economic growth of Nigeria, for a 38 year period from 1980 to 2018. Making use of time series data obtained from CBN statistical bulletin (2018), various publications of the World Bank National Accounts Data and various issues of National Bureau of Statistic (NBS) bulletin.
The research possessed two models, in which the ARDL and OLS estimation was used. The first model revealed that only Human capital development index will significantly impact on per capita GDP in Nigeria on the long run. While the second model revealed that Agricultural sector loan, Human capital development index, and government expenditure on social services have significantly impacted on poverty reduction in Nigeria. The co-integration and error correction test as well showed that there was a long and short-run relationship between poverty reduction strategies and the economic growth of Nigeria.
Therefore, the study concludes that government poverty reduction moves especially in her expenditures on social services has not had the desired significant impact on the growth of the economy. The agric sector which is the major bridge through which poverty can be reduced among the populace still receives insignificant loans and low labour thereby plunging the economy into deep poverty. Also, human capital development proxied by government expenditures on education and health have not led to a significant short term effect on poverty reduction as Nigeria’s poverty profile is still on the increase.

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