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Volume 13 Issue 2, April -June 2025 

ISSN: 2836-9416 

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PUBLIC FINANCE INSTRUMENTS AND POVERTY 

ALLEVIATION IN NIGERIA 

 
Prof. Agbo, Elias Igwebuike and Ugwu Osmund Chinweoda, PhD. 

Department of Accounting and Finance, Faculty of Management and Social Sciences, Godfrey 

Okoye University, Ugwuomu-Nike, Emene, Enugu State, Nigeria. 
DOIU: https://doi.org/10.5281/zenodo.15309375 

 

Abstract: In low-income countries like Nigeria, governments use the instruments of public finance to 

carry out their crucial function of promoting the well-being of their residents which includes poverty 

alleviation. However, they often find deciding on how to achieve that objective difficult owing to some 

challenges. This study investigates the impact of public finance instruments on poverty reduction in 

Nigeria using ex post facto as the research plan strategy. Specifically, it examines the impacts of public 

revenue, public expenditure and public debt on poverty incidence in Nigeria for 1981 to2024.  

Unemployment, inflation and GDP growth rates are introduced in the study as control variables. 

Descriptive, correlation matrix and hierarchical regression are employed to analyze data. The findings 

indicate that while the impact of public revenue and public debt on the rate of poverty are positive and 

weak, the impact of public expenditure on poverty rate is both adverse and non-significant. Also, the 

findings show that the variables all move together toward the same direction during the study period. 

The implication is that there are strong interrelationships among the variables and that any alteration 

in one may have ripple effects across the others. Consequently, governments are advised to fine-tune 

their public finance instruments to invigorate the economy, reduce income imbalance and reduce 

poverty level significantly. 

Keywords: Public Finance, Public Revenue, Public Expenditure, Public Debt, Poverty Reduction, 

Nigeria.  

 

1.0 Introduction  

With properly-designed fiscal policies and spending, public finance is expected to reduce poverty 

incidence by fostering economic growth, improving entry to essential services and promoting inclusive 

development (Ejemezu&Ajala,2024). Although budgetary allocation appears to be the main platform 

for operationalizing pro-poor growth, it has proved to be among the most evasive challenges (Wilhelm 

& Fiestas, 2005). Indigence is a universal menace confronting several economies globally. For instance, 

United Nations, as reported in Ventura (2024), revealed that approximately 700 million persons were 

living on below $2.15 per day universally in 2024.This figure represented almost 10% of the world’s 

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American Research Journal of Economics, Finance and Management 

Volume 13 Issue 2, April -June 2025 

ISSN: 2836-9416 

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population (Ventura,2024). In Sub-Saharan Africa (SSA), the poverty level is high. In 2019, for 

instance, 40.9% of the region’s residents lived below US$1.90 per person per day (Jobarteh,2023). 

Poverty incidence in Nigeria is particularly worrisome.  No correlation exists between several policies 

the Nigerian government  which target poverty alleviation and the poverty level it recorded from year 

to year(Ejemezu &Ajala,2024).This situation has necessitated giving destitution serious and urgent 

government attention To alleviate poverty, the administration has been implementing a lot of schemes  

in different sectors of the Nigerian economy(Ejemezu &Ajala,2024) but its level has been rising; poverty  

has been  defiling every programme (Olasehinde & Adekoya, 2014).Since the past 42 years, the number 

of the indigent has been on the increase in Nigeria. National Bureau of Statistics (2020) reported that   

the pervasiveness of poverty  

sky-rocketed from 28.1 per cent during 1980 to46.3 percentin1985.Nigeria witnessed a decrease in the 

level to 42.7 percent in 1992 and an increase to 65.6 percent in 1996 and a reduction to 54.4 percent 

in2004.It move up again to 60.9 percent in 2010. Between 2020 and 2022, poverty level moved up 

from 46.4 percent to 62.9. In the year 2023, approximately 87million residents were already affected. 

With an HDI of 0.548 in 2022, Nigeria’s standing on the Human Development Index is not 

encouraging, as it held the 161st position among 189 countries (Adebayo, 2025). In spite of this urgly 

situation, the total public spending has continued   increasing (Apere, 2017). In 2017, for instance, it 

rose from 6456.70 Billion to 17,557.40 Billion, and then to N24,431.21 Billion in 2020, 2021, and 2022 

respectively (Central Bank of Nigeria,2022). 

Transforming those expenditures into considerable development has proven to  be  challenging 

throughout the years  as there have emerged troubling figures characterized by a persistent increase in 

poverty incidence as shown by high rate of unemployment and illiteracy. These have attracted global 

attention recently amid a shortfall in revenue mobilization to take care of the desired government 

expenditure (Nimvyap et al.(2023).  

The Nigerian government had attempted to better the lives of her population through through 

interventions in the   areas of education, health, economic empowerment of the and infrastructural 

development, using various schemes. However, the effects of all those interventions on the alleviation 

of penury in the country still remain questionable (Ajala & Adeyinka,2021).  

In 2023,the government arranged to spend N543 billion on servicing public debt out of the debt 

servicing cost of N592billion.Inspite of this amount of national debt outstanding, debt stock was to 

move up  to approximately N7 trillion ($45 billion) at the close of 2013 (Ozigbu, 2018). In 2024, the 

amount  national debt rose to N144,670 billion. The enormous public borrowing has been aimed at 

supporting the productive sectors of the economy to take Nigerians out of poverty. .However, just as is 

the case in several other low-income countries (Morseno-Dodson & Wodon, 2008), poverty incidence 

continues to increase in the country (Nimvyap et al.,2023).  

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It is undisputable that Nigeria has wealth in abundance. What remains an intractable question is the 

reason that these resources have not translated into national wealth (Kwode, 2024). What looks 

paradoxical is that the more revenues are assembled and spent, the poorer the Nigerians and Nigeria 

become (Obi, 2007).. 

1.1 The problem 

In spite of the situation highlighted above, the studies that seek to find the relationship between public 

finance instruments and poverty reduction were either executed outside Nigeria or too narrow in scope 

or methodology (Akpan & Orok, 2009). Further, majority of the researches on poverty alleviation have 

concentrated on broad macroeconomic policies without specific attention to the impact of public 

finance instruments on poverty alleviation while others have predominantly relied on theoretical 

analyses instead of robust empirical inquiries.  Even those studies that examined the impact of 

individual public finance instruments exclusively concluded with conflicting results.  

Consequently, a gap has been left in literature concerning how the instruments of public finance, taken 

together, affect poverty reduction in Nigeria. Therefore, the main objective of this work is to fill this 

opening by doing a robust empirical evaluation of the   contribution of public finance instruments in 

the fight against destitution in Nigeria. By so doing, the study provides concrete evidence and practical 

policy implications through appropriate econometric techniques. The data set for the period between 

1981 and 2023 facilitates the employment of updated information, thereby making it easier to carry out 

accurate and relevant analysis of the nexus between public finance instruments and poverty rates in 

Nigeria address potential concerns about omitted variables, the research   incorporates key  variables 

identified by literature  as important  causes of outcomes, namely economic growth, Inflation and 

unemployment rates.  

After reviewing some of the important concepts theories employed in section 2,the paper   dedicates 

Section 3 to methodology. Section 4 is for data analysis and discussion of findings, while Section 5 

concludes the paper.  

2.0. Literture review 

 2.1Conceptual review  

2.1.1 Poverty 

Poverty usually connected with abysmal income, absence of social, economic, cultural, and political 

entitlements and lack of access to  basic  necessities like food, shelter and clean water (Arora & Romijn, 

2012). It means not having the fundamental enablement to be part of human society effectively (Kuhe,et 

al.2016).  It refers to a complex and multidimensional phenomenon that affects persons and   societies 

in several ways (Covarrubias, 2023). While poverty is commonly estimated in financial terms,one 

should  remember that possessing insufficient money is just  an  indicator instead of the only  cause of 

poverty. Power dynamics, like denial of access to  basic needs, is capable of contributing to poverty. 

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(Arora &  Romijn, 2012). 

 2.1.2 Public finance  

This refers to an aspect of a discipline that deals with public revenue, expenditure and debt.  

 Creedy(1984) defines it aqs the management of a nation’s revenue, spending and borrowings through 

several public institutions and agencies. Public finance has some principles, namely, efficient resource 

allocation, even distribution of wealth and stability of the economy. It uses some instruments such as 

revenue, expenditure, debt and financial administration. 

 2.1.2.1.Public revenue 

Public revenue refers to the overall incomes that accrue to governments) from some sources.The means 

through which governments generate income  and reasons  for which revenue is required needed have 

differed considerably over time and from economy to economy. Generally, governments produce  

income from taxes, loans, grants and aids, licenses, savings, rents and rates, fees, fines, royalties and  

earnings from ventures. Of all the revenue sources available to government, tax is generally considered 

most important both indeveloping and developed countries(Agbo & Onuegbu, 2022). Public revenues 

are classified as capital and recurrent revenues. Capital revenue are irregular receipts employed by an 

administration to fund long- term and big capital projects. Recurrent revenue is  the name for the kind 

of revenue that government receives regularly such as taxes, licenses, fees and fines. States in Nigeria 

produce incomes from    PAYE, direct assessment, road taxes and other taxes and income from 

ministries, departments and agencies (MDAs)( Agbo,2024a).  

Federal government generates its own revenue from both oil and non-oil taxes.  Oil tax comes 

petroleum profit. Non-oil taxes come from company income tax(CIT), personal income tax  (PIT), Gas 

income, capital gain tax, stamp duty,value-added tax,etc.  

2.1.2.2 Public expenditure 

Public spending is the major policy tool through which an economy can directly control poverty 

incidence. It is a key  avenue through which government policies are made to affect development 

outcomes, particularly poverty levels. It is  multi-channeled. Public expenditure in Nigeria is broadly 

grouped into capital and recurrent expenditure, each with distinct implication for poverty alleviation. 

Capital expenditure refers to investments in infrastructure like roads, buildings and machinery. 

Recurrent expenditures include spending on social protection programs, personnel development and 

social services. 

2.1.2.3 Public debt 

Public authorities borrow money to carry out   their statutory obligations when the income at their 

disposal becomes below what they need to  spend. Public debt is therefore an important instrument 

that governments use to finance public expenditures and accelerate economic growth, especially when 

it is not feasible forthem to collect taxes or reduce  expenditure((Nimvyap et al.,2023). The sizes of 

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external debts are mainly determined by GDP, exchange rate, fiscal deficit, interbank rate, and terms 

of trade( Udoka & Anyingang ,2010),  

2.1.2.4 Public financial management  

This refers to the process by which governments acquire and dispose of financial resources  

(Abianga,2009).  The resources are properly managed and controlled through budgets    which are 

usually prepared annually or through developmental plans for some  specified period depending on  

government’s needs.  

2.2. Theoretical Framework  

This study is founded on the following theories: 

theories:  

2.2.1 Poverty theories  

a. Keynesian/neoliberal theory:.The proponents of this  theory lay  much emphasis on the responsibility 

of government to stabilize tha economy and  make  public goods available. They consider poverty as 

mainly involuntary and as caused  by the absence of  employment opportunities b. Marxian theory: This 

theory considers discrimination among classes and groups as mainly responsible for indigence. 

Consequently, it assigns an important role to public administration in regulating the market place.  

2.2.2 Public finance theories 

 Public finance theories explore how governments manage the finances available to them, 

encompassing areas like taxation, public spending and debt management. They aim to optimize these 

functions in the interest of the citizenry by ensuring efficient resource allocation, equitable income 

distribution and economic stability.  The primary theories of public finance center attention on how 

governments should raise and utilize funds to provide public services.  

2.3. Empirical Studies  

Yaru and Ohiaka (2022) investigated the  link between poverty incidence and income generated from 

indirect taxes for 29 selected SSA countries between 1990 and 2020.The results  gotten from the panel 

regression estimates indicated that GDP per capita has adverse significant impact on indigence within 

SSA. Markina (2022) evaluated the effect of  taxes on income and penury in  Ukraine, using both 

commitment to equity  (CEQ) and linear regression. CEQ was produced to determine how taxes and 

social expenditure influence destitution and inequality in different countries. Findings were that 

Ukraine's income tax overhaul should concentrate attention on transferring taxes from the rich to the 

destitute and preventing aggressive tax planning, instead altering tax rates and tax periods. Ikechukwu 

et al. (2021) employed CIT, PIT, PPIT and education tax     and education tax as direct tax variables 

during 1990to 2019 to estimate   the impact of direct taxes on the redistribution of income in Nigeria, 

using   annual data sourced from the FIRS and CBN Statistical Bulletin. The findings indicated that PIT 

and PPIT have   strong favorable impact on income redistribution in Nigeria, while CIT and education 

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tax both have weak adverse impact and help to decrease income inequality.  

Using   GDP, population, per capita income and   inflation as empirical variables, Ahmad and Awan 

(2021) examined the manner taxes influence indigence in Pakistan. The study analyzed time series data 

for 1998 through 2018 with correlation and regression methods .The results indicated that taxes and 

destitution have favorable connection. Multiple regression analysis indicated that while population and 

per capita income have positive impact on poverty, taxes, inflation, and GDP have adverse effect.  

Usman and Idoko (2021) evaluated the effect of taxation as an instrument for poverty alleviation in 

Nigeria from1990 to 2019.The research used ARDL to estimate the parameters. The results indicated 

that PPT,CIT and VAT have positive and strong link with the level of penury ,while CED and PIT have 

adverse and strong connection. Oduro (2001) cited in Kwode (2024)  carried out a study and concluded 

that public spending is capable of decreasing indigence by providing infrastructure and service to the 

indigent and putting in place the necessary conditions which will increase  the competence of the 

destitute to obtain assets, enabling  the provision of infrastructure and services  for the institutions that 

will decrease the  risks confronting the poor and reduce the impact of negative shocks through the 

provision of  buffers among others. In Indonesia, Birowo (2011) evaluated connection between public 

expenditure and poverty rate .Analyzing the data with OLS regression, the author noted that  budgetary 

increase and poverty are positively and slightly related connected. The study conducted by Megbowon 

et al.  (2020)  in Nigeria with ARDL analytical methods disclosed  that public expenditure  reduces 

indigence and that  long–run relationship that exists between public expenditure  and poverty rate for 

all tiers of governance in Nigeria.  Aladelusi and Isiaka (2023) sought to determine the extent that the 

destitute gain from public spending on education, agricultural sectors, health and the amount of public 

debt in Nigeria The ARDL method was employed for regression. The findings indicated that fiscal policy 

has a great effect in  decreasing poverty level and that long-run connections exists between them.  

Using  a setod empirical data and Ordinary Least Squares method, Nkamnebe(2023) evaluated the link 

between public spending and poverty alleviation for 2000 to 2022.Multidimensional Poverty Index 

(MPI) constituted  the dependent variable ,while public expenditure on education, health and 

infrastructure  became the explanatory variables. The study's primary findings disclosed that an upward 

movement in public spending on education has a significant adverse effect on poverty reduction, both 

in the short run and long run while government health spending has a strong adverse effect in the short 

run and no effect in the long run. Kwode (2024) examined the effect of public spending on poverty. The 

data employed were sourced from official publications of CBN and NBS and analyzed with regression 

method. The findings indicated that public spending has a positive link with poverty; it has non-

significant effect on poverty alleviation and adverse connection with poverty headcount ratio. Adebayo 

(2025) evaluated the impact of public spending on poverty in Nigeria from 1981 to2022, using VCM 

Model framework. The study analyzed the link between poverty level and public expenditure, GDP per 

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capita, Agricultural Credit Guarantee Scheme Fund and gross enrolment ratio in secondary education. 

With time series data and  cointegration   analysis,  a strong long-run relationship was found between 

government spending and poverty alleviiation. Bloj (2009) sought to find out the effect of budgeting 

process on  social policies and poverty reduction. The author found that the   recent tendency of 

developing nations to possess  results -oriented budgeting approach is in  order since this new approach  

is deemed to be directly linked with poverty alleviation  through the Medium Term Expenditure 

Framework.  

Using descriptive technique and   non-parametric statistics  on data series  covering 1980-2005, Akpan 

and Orok (2015) observed  that  budgetary provision for poverty reduction  program are dissatisfying 

and ineffective and that  actual release of even the allocations are significantly delayed- an issue  that 

has negatively affected the implementation poverty alleviation  programmes of government. Oyedele et 

al. (2013) employed co-integration and regression methods to examine the impact of external debt and 

debt servicing on poverty alleviation in Nigeria. Time series data on debt income ratio, debt service, 

degree of openness, growth of agricultural value added, per capita income, inflation rate and 

investment-income ratio for 1980-2010 were analyzed. Multiple regression results indicated that 

external debt and debt servicing cause poverty in Nigeria. Ekpo and Udo (2013) investigate the link 

between debt burden, growth and poverty alleviation in Nigeria  between 1970 and 2011.Elements of a 

failing state  comprising corruption, insecurity, and ethnic violence were included in the model  as 

explanatory variables ,while the dependent variable  (incidence of poverty) was measured by the ratio 

of public expenditure and social services and  income per capita. Findings disclosed that public debt is 

negatively linked to growth and poverty alleviation. 

Ozigbu (2018) evaluated the effect of public debt sustainability on the incidence of poverty in Nigeria. 

The study employed external debt stock and interest paid on external debt stock as explanatory 

variables and poverty rate as dependent variable. The outcome of Johansen-Juselius co-integration test 

disclosed that the series have long-run relationship. Nimvyap et al. (2023) analyzed the effect of public 

dept on poverty alleviation in Nigeria Secondary data covering 2000–2021) were employed in the 

research and analyzed using descriptive statistics, correlation matrix, and Error Correction Mechanism 

(ECM).The findings indicated that  external debt has positive and  significant effect on poverty 

reduction ,while domestic debt and debt servicing have adverse connection with poverty incidence in 

Nigeria  

Fatoba and Otonne (2024) explored the impact of fiscal policy  crashes on Nigeria's  iincome imbalance 

and  household poverty. The authors employed the impulse response function and variance 

decomposition methodology within the Bayesian Vector Autoregressive (BVAR) framework. The 

findings indicated that from the second year to the fifteenth year, a 1% change in tax income generates 

a reduced average effect of 0.036% on household poverty. Contrarily, household indigence level 

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increases with the changes in public spending.  

3.0 Methodology  

3.1 Research strategy 

Ex post facto research   plan   was  employed in the  study.  

3.2 Data and  Variables 

A paneldata series  covering 1981to 2023  were utilized. In the key variables, poverty reduction was  the 

dependent variable  while public revenue, public expenditure and public debt  constituted the 

explanatory variables. Economic growth rate, inflation rate and unemployment rate were the control 

variables. Poverty reduction was proxied by poverty headcount ratio. The data were analyzed using 

GenStat software.  

3.3Model Specification  

As was the case with Erin et al.(2020) and Erinand  Aribaba(2021),the study used  hierarchical 

regression model specified as follows:- 

Step 1: Baseline Model (Control Variables Only) Poverty Rate = β0 + β1(Unemployment Rate) +

β2(GDPGrowthRate) + β3(Infaltion Rate) + ϵi……. (1)  

Step 2:Full Model(Add Predictors) 

Poverty Rate = 0  + 1 ( Unemployment Rate )  + 2 (GDP Growth Rate) + 3 (Inflation Rate) + 

4 (Public Debt)  + 5  (Public  Revenue) + 6  ( Public Expenditure)  …….(2) 

Step 2: Full Model (Add Predictors) 

Model Evaluation 

 Compare R² from Step 1 and Step 2 to assess the additional variance explained by the financial 

predictors. 

 Inspect F- test change to check significance of added predictors. 

 Look at coefficients (β₄, β₅, β₆) to interpret individual effects of debt, revenue, and expenditure on 

poverty rate. 

4.0 Data analysis and interpretation 

 The empirical data obtained were shown in  tables, charts and graphs (see appendices1 to 9).Both 

parametric technique was used for data   analysis. Specifically, descriptive was done first as preliminary 

analysis and the inferential statistic that provided more detailed analysis.  

Descriptive statistics 

The descriptive statistics in Table 1 provide an overview of the central    tendency and spread of the 

major economic variables. The average values exhibit a positive financial balance, with public revenue 

(4470.312) exceeding public spending (3739.097). However, public debt (11798.395) is significantly 

high, raising concerns about fiscal sustainability.GDP growth rate modest at 3.180, but inflation rate  

notably high at 19.040, suggesting potential issues with price stability. In addition, the average 

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unemployment rate is 14.993 and poverty rate is 54.427 point, occasioning significant economic 

challenges which affect a big portion of the population. The values of skewness reveal important 

awareness into the spread of these variables. For total revenue, the skewness of -0.117 shows a  nearly 

symmetric distribution, suggesting balanced revenue sources. Contrarily, public spending indicates a 

high positive skew (6.923), indicating  that some  observations are significantly higher  than the  

average, which could reflect irregular spikes in spending. Similarly, the total national debt has a  

skewness of 17.515, implying that while most entities have lower debt levels,some holds extremely high 

amounts, contributing to overall financial instability.  

Finally, the data highlights pressing economic challenges, particularly high debt, unemployment rate 

and poverty rate. The values of skewness highlight disparities among data, indicating that averages may 

mask underlying inequalities, especially in  expenditure  and debt  distribution. Addressing these issues 

will be crucial for fostering economic solidity and improving the living conditions of the affected 

populations.  

Table 1: Descriptive Statistics 

Name Mean Median 
Observed 

min 

Observed 

max 

Standard 

deviation 

Excess 

kurtosis 
Skewness 

Public Revenue  4470.312 2575.100 10.500 18320.000 4714.094 -0.117 0.823 

Public 

Expenditure  
3739.079 1225.990 9.640 27500.000 5678.769 6.923 2.465 

Public Debt  11798.395 3818.470 13.520 144670.000 25686.472 17.515 3.978 

GDP Growth 

Rate  
3.180 3.400 -10.930 15.330 4.768 1.450 -0.464 

Inflation Rate  19.040 13.900 5.400 72.800 15.296 3.225 1.878 

Unemployment  14.993 11.900 1.800 56.100 14.587 1.302 1.432 

Poverty Rate  54.427 59.300 27.200 88.000 15.133 -0.449 0.057 

Correlation matrix 

The correlation matrix in table 2 provides insight into the linear connections between poverty and other 

variables in the study that are grouped into control variables and independent variables. Among the 

control variables, the link between poverty and GDP growth rate is 0.0506, with a p-value of 0.000. 

Although the correlation   is very weak and positive, the link is strong statistically, indicating a 

consistent but minimal association where slight increases in GDP growth rate correspond to slight 

increases in poverty. This could reflect growth patterns that do not convert to broad-based 

improvements in living standards.The link between poverty and inflation rate is negative (r = -0.237), 

meaning that  higher inflation may be associated with lower poverty levels. However, with a p-value of 

0.121, this connection is weak, suggesting the observed association may be as a result of random chance. 

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Similarly,  the correlation between poverty and unemployment is very weak and positive (r = 0.067) 

with a p-value of  0.667, indicating no statistically meaningful relationship. The implication is that, 

among the  data, alterations in unemployment  do not significantly influence poverty levels. Among the 

explanatory variables, public revenue indicates a weak positive connection with poverty  (r = 0.187, p = 

0.247), and public spending has a similarly weak positive link (r = 0.124, p = 0.427). Both of them are 

non-significant, implying that variations in public   fiscal activities are not directly connected with the 

alterations in poverty . Total national debt displays a negligible correlation with poverty (r = 0.010), yet 

it is significant (p-value = 0.000).In spite of the level of significance, the practical implication is  

minimal  because of the extremely weak strength of the  association. In summary, while GDP growth 

rate exhibits a statistically significant but positive correlation with poverty, all other variables, both 

control and independent, exhibit statistically non-significant relationships, underscoring the 

multifaceted nature of indigence  and its  determinants.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Table 2: Correlation Matrix 

  Public 
Revenu
e 

Public 
Expenditu
re 

Publi
c. 
Debt  

GDP 
Growt
h Rate 

Inflatio
n Rate 

Unemployme
nt 

Povert
y Rate 

Publicl 
Revenue 

Correlatio
n 

1       

Sig. (2-
tailed) 

       

Public 
Expenditure  

Correlatio
n 

.848 1      

Sig. (2-
tailed) 

.000       

Public Debt 

Correlatio
n 

.720 .937 1     

Sig. (2-
tailed) 

.000 .000      

GDP Growth 
Rate  

Correlatio
n 

.188 .019 -.006 1    

Sig. (2-
tailed) 

.223 .903 .969     

Inflation Rate  

Correlatio
n 

-.300 -.130 -.066 -.281 1   

Sig. (2-
tailed) 

.048 .405 .671 .064    

Unemployme
nt  

Correlatio
n 

.562 .569 .140 .053 -.322 1  

Sig. (2-
tailed) 

.000 .000 .366 .732 .033   

Poverty Rate 

Correlatio
n 

.187 .124 .010 .506 -.237 .067 1 

Sig. (2-
tailed) 

.247 .427 .947 .000 .121 .667  

Control variable: Unemployment, GDP Growth Rate, Inflation Rate. Dependent variable: Poverty Rate. 

Predictors: Total National Debt Outstanding, Total Revenue, Total Expenditure. *p-value < 0.05 

(Significant) 

Hierarchical Regression Analysis 

The results of the hierarchical regression analysis in table 3 provide valuable provide valuable insights 

into   how both control and explanatory variables affect poverty rate.  

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In the initial model that includes the control variables only, the R-squared is 0.269, indicating that 

approximately 26.9% of the variations in poverty rate is explained by these three variables. With an F-

statistic of 4.791 and a p-value of 0.006, which is below 0.05 threshold, the model is statistically 

significant- confirming that it has explanatory power. Among the control variables, GDP Growth rate 

has a positive and strong impact on poverty rate, with a coefficient of 1.512 and p-value of 0.002. This 

suggests that a unit increase in GDP Growth rate causes a rise in poverty rate by 1. 512.This is possibly 

as a result of the growth patterns that disproportionately benefit higher-income groups. In contrast, 

inflationrate has a negative statistically non-significant effect (coefficient = -0.108, p = 0.473), 

suggesting that inflation does not have a meaningful   effect on poverty in this model. Unemployment 

has a very small impact (coefficient = -0.003) and is also non- significant (p = 0.987), indicating no 

meaningful connection exists between it and poverty amidst other variables.  

In the second model, the independent variables (Public Revenue, public Expenditure, and National 

Debt) are added. This inclusion increases the R-squared to 0.304, meaning that the extended model 

explains 30.4% of the changes in poverty rate. The F-statistic is 2.617 with a p-value of 0.033, indicating 

the full model is statistically robust at 5% level. The R-squared change is 0.035, and this increase is 

significant, implying that adding the explanatory variables provides additional explanatory value. 

Public revenue has a positive and   non-significant impact on poverty rate (coefficient = 0.001, p = 

0.348). This indicates that a unit increase in public revenue will cause 0.001 rise in poverty level. This 

result agrees with the conclusion by Usman and Idoko (2021) who investigated the impact of tax 

revenue on poverty reduction in Nigeria from 1990 to 2019andfoundthat both PPT, CIT and VAT have 

positive link with poverty reduction. Public expenditure has an adverse and weak impact on poverty 

rate (coefficient = -0.002, p = 0.481). Even though this result conflicts with that   of Birowo (2011), it 

conforms with Oduro (2001), Megbowon et al. (2020) and Aladelusi and Isiaka (2023) and Kwode 

(2024) that all examined  the effect  of public expenditure on poverty rate in Nigeria and discovered 

that public spending decreases poverty incidence.. Public debt has positive but non-significant impact 

on poverty rate (coefficient = 0.000, p = 0.448).This result is in consonance with the conclusions in 

Oyedele et al.  (2013), OzigbuI2018) and Nimvyap et al.(2023) who, after investigating the influence of 

external debt on poverty reduction in Nigeria found that poverty rate  increases along with external 

debt in Nigeria. The results of the study completely show that while the  inclusion of  the public finance 

instruments  in the model improves the latter slightly, they do not have strong direct impacts on poverty 

when controlling for economic  factors like GDP Growth rate  inflation, and unemployment. Further, 

the analysis discloses that GDP growth rate  is the most influential  and strong predictor of poverty 

among the variables studied, even though it positive link with poverty  may seem counterintuitive. The 

inclusion public finance instruments (revenue, expenditure and debt) modestly improves the model’s 

explanatory power, but individually, they do not significantly affect the poverty rate.   

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Table 3: Result of Hierarchical Regression Analysis for Poverty Rate 

Predictors B R2 Δ R2 F-Stat 

Step 1     

Control variable  .269  4.791 (.006) 

GDP Growth Rate 1.512 (.002)    

Inflation Rate -.108 (.473)    

Unemployment -.003 (.987)    

Step 2     

Independent variable   .304 .035* 2.617 (.033) 

Public Revenue .001 (.348)    

Public Expenditure -.002 (.481)    

Public. Debt Outstanding .000 (.448)    

Control variable: Unemployment, GDP Growth Rate, Inflation Rate. Dependent variable: Poverty Rate. 

Predictors: Total National Debt Outstanding, Total Revenue, Total Expenditure. *p-value < 0.05 

(Significant) 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Table 4: Regression Coefficientsa 

Model Unstandardized 

Coefficients 

Standardiz

ed 

Coefficient

s 

T Sig. Collinearity 

Statistics 

B Std. Error Beta Toleran

ce 

VIF 

1 (Constant) 51.913 5.183  10.017 .000   

GDP Growth Rate 1.512 .452 .478 3.346 .002 .919 1.088 

Inflation Rate -.108 .149 -.109 -.724 .473 .821 1.219 

Unemployment -.003 .151 -.002 -.017 .987 .889 1.125 

2 (Constant) 50.662 5.420  9.347 .000   

GDP Growth Rate 1.348 .493 .426 2.735 .010 .798 1.254 

Inflation Rate -.101 .155 -.102 -.652 .518 .790 1.266 

Unemployment -.075 .226 -.072 -.333 .741 .410 2.442 

Public Revenue .001 .001 .382 .952 .348 .420 2.339 

Publicl Expenditure -.002 .003 -.631 -.713 .481 .925 3.588 

Public Debt  .000 .001 .481 .768 .448 .849 4.339 

a. Dependent Variable: Poverty Rate 

Diagnostic Plots 

 

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Fig 1: Diagnostic Plot  

 

Diagnostic plot 

The diagnostic plots as shown in fig 1 for the regression model, plus the standardized residuals plot, 

normal Q-Q plot, half-normal plot, and fitted values vs. residuals plot, show that the model assumptions 

are largely met. In the standardized residuals plot, a random scatter around zero suggests a good fit, 

while the normal Q-Q plot indicates that the residuals approximate a normal distribution, with any 

deviations being minor. The half-normal plot similarly indicates  that there are no significant outliers, 

and that the fitted  values vs. residuals plot reveals a consistent, random scatter around zero. This 

confirms the absence of heteroscedasticity. Overall, these findings suggest that the model is appropriate 

and robust, with only minor deviations from ideal conditions. Along with the diagnostic plot at fig 1,the 

line plots all confirm the robustness of the   regression.  

5.0 Conclusion and Recommendation  

The study evaluated the impact of public finance instruments on poverty reduction in Nigeria. Its 

specific aims were to establish the effect of public revenue, public expenditure and public debt on 

poverty rate for1981 to 2024.Unemployment, inflation and GDP growth rates were introduced in the 

model as control variables .The data-set were analyzed using correlation matrix and hierarchical 

regression model. Results show that none of the explanatory variables is statistically strong 

individually. The effects of public revenue and public debt on poverty incidence were found to be both 

positive and non-significant while public spending has adverse and weak effect on poverty rate. Further,  

the variables of the study were found to be all moving together in the same direction. The study    

recommends as follows:  

1.In order to better the general wellbeing of the   populace, public  revenue should  be wisely allocated 

to the  construction of high-quality infrastructure, namely,  schools, railroads, healthcare facilities and 

other commercial establishments throughout the states to help minimize the  disparity in income 

between the nation's wealthiest and least fortunate citizens. 2. There ought to be a more hoollistic and 

sustained policy interventions, particularly in addressing structural barriers to  poverty alleviation and 

improving the efficiency of public spending   in Nigeria’s socioeconomic development initiatives.  

3. Results -oriented budgeting approach ought to be adopted by the government as would create the 

required effect on penury and inequity.  

4. Governments should increase budgetary provisions or allocations to their poverty alleviation 

programmes and ensure sound management and efficient project implantation. In addition, it should 

encourage participation in the budget process.  

5. Government should review its policies on tax holidays and external borrowing and mobilize domestic 

savings efforts to tackle the nuisance of indigence in Nigeria.  

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6. Finally, since a strong interrelationship exists among the variables used in this study, government is 

advised to fine-tune their public finance instruments to allow for the   stimulation of the economy, 

reduce income imbalance and poverty level significantly.  

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APPENDIX 1  

ANNUAL HISTORICAL DATA  
Year Public 

 Revenue 

(N’Billion) 

Public 

Expenditure 

N’Billion) 

Public 

Debt  

(N’Billion) 

GDP 

Growth  

Rate 

Inflation 

Rate Unempt 

Poverty 

Rate 

      

1981 13.3 11.41 13.52 -6.80 20.90 3.90 27.20       

1982 11.4 11.92 23.83 -6.80 7.70 3.90 27.20       

1983 10.5 9.64 82.80 -10.93 23.20 3.90 27.20       

1984 11.3 9.93 40.48 -1.11 39.60 3.90 27.20       

1985 15.1 13.04 45.25 5.91 5.50 6.10 46.30       

1986 12.6 16.22 69.89 0.06 5.40 5.30 46.30       

1987 25.4 22.02 137.52 3.20 10.20 7.00 45.40       

1888 27.8 27.75 180.59 7.33 38.30 5.30 42.70       

1989 53.9 41.03 287.44 1.92 40.90 4.00 42.70       

1990 98.1 60.27 382.70 11.78 7.50 3.50 44.00       

1991 101.0 66.58 446.75 0.36 13.00 3.10 44.00       

1992 190.5 92.80 722.22 4.63 44.50 3.40 42.70       

1993 192.8 191.23 806.98 -2.04 57.20 2.70 42.70       

1994 201.9 160.89 1,056.69 -1.82 57.00 2.00 42.70       

1995 460.0 248.77 1,194.60 -0.08 72.80 1.80 60.00       

1996 523.6 337.22 1,036.70 4.19 29.30 3.80 65.60       

1997 582.8 428.22 1,097.70 2.93 8.50 3.20 74.00       

1998 463.6 487.11 1,193.85 2.58 10.00 3.20 74.00       

1999 949.2 947.69 3,312.18 0.58 6.60 8.20 74.00       

2000 1,906.2 701.05 3,995.63 5.01 6.90 13.10 88.00       

2001 2,231.6 1,018.00 4,192.66 5.92 18.90 13.60 88.00       

2002 1,731.8 1,018.18 5,098.88 15.33 12.90 12.60 65.70       

2003 2,575.1 1,225.99 5,808.01 7.35 14.00 14.80 65.00       

2004 3,920.5 1,504.20 6,260.60 9.25 15.00 13.40 54.40       

2005 5,547.5 1,919.70 4,220.58 6.44 17.90 11.90 65.70       

2006 5,965.1 2,038.00 2,204.72 6.06 8.50 12.30 65.70       

2007 5,727.5 2,450.90 2,608.03 6.59 5.40 12.70 59.30       

2008 7,866.6 3,240.82 2,844.06 6.76 15.10 14.90 59.30       

2009 4,844.6 3,452.99 3,818.47 8.04 13.90 19.70 59.30       

2010 7,303.7 4,194.58 5,241.66 9.13 11.80 21.40 64.90       

2011 11,116.8 4,712.06 6,519.69 5.31 10.30 23.90 64.90       

2012 10,654.7 4,605.30 7,564.44 4.21 12.00 27.40 68.20       

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American Research Journal of Economics, Finance and Management 

Volume 13 Issue 2, April -June 2025 

ISSN: 2836-9416 

Impact Factor: 6.41 

Journal Homepage: https://americaserial.com/Journals/index.php/ARJEFM, 

Email: contact@americaserial.com 

Official Journal of America Serial Publication 

 

 

American Research Journal of Economics, Finance and Management 
https://americaserial.com/Journals/index.php/ ARJEFM, Email: contact@americaserial.com 

21 | P a g e  

 

2013 9,759.8 5,185.32 8,505.71 5.49 8.00 24.70 67.00       

2014 10,068.9 4,587.39 9,535.53 6.22 8.00 25.10 46.00       

2015 6,912.5 4,988.86 10,948.51 2.79 9.60 29.20 40.75       

2016 5,616.4 5,858.56 14,537.12 -1.58 18.55 35.20 61.33       

2017 7,444.8 6,456.70 18,376.91 0.82 15.37 40.87 61.33       

2018 9,544.3 7,813.74 20,533.64 1.91 11.44 43.27 40.10       

2019 9,819.8 9,712.22 23,295.06 2.27 11.98 43.27 39.09       

2020 8,569.2 10,232.33 28,729.51 -1.92 15.75 56.10 39.10       

2021 10,345.0 12,164.15 35,097.79 3.40 15.63 56.10 63.00       

2022 12,586.53 14,946.25 40,912.61 3.10 21.34 5.30 62.90       

2023 12,370.0 19,808.44 91,477.86 2.74 28.92 5.40 62.90       

2024      18,320       

27.500.00 

144,670.00      3.40     12.48       5.30 47.00           

SOURCE: NBS, CBN AND WORLD BANK, CBN STATISTICAL BULLETIN 

 

 

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