The Impact of Income Inequality and Poverty on Economic Growth in Nigeria

The Impact of Income Inequality and Poverty on Economic Growth in Nigeria

Edesiri Vivian Ukrakpo, Rockson. A. Itiveh

Department of Economics, Faculty of the Social Sciences, Delta State University, Abraka, Delta State, Nigeria.

DOI: https://dx.doi.org/10.47772/IJRISS.2025.909000670

Received: 29 September 2025; Accepted: 05 October 2025; Published: 25 October 2025

ABSTRACT

Income inequality and poverty are among the macroeconomic challenges facing the Nigerian economy, and it has been a concern for researchers and policymakers to find solutions to these challenges to ensure high economic growth. In light of the search for solutions to macroeconomic challenges facing Nigeria, the study investigated the impact of income inequality and poverty on economic growth. Annual data on gross domestic product growth rate, income inequality, poverty, population growth, inflation and foreign direct investment were sourced from the World Development Indicators (WDI) from 1980 to 2023. The study estimated a model of the dependent variable- gross domestic product growth rate on income inequality, poverty, population growth, inflation and foreign direct investment using the Autoregressive Distributed Lag (ARDL) Model and the Dynamic Ordinary Least Squares (DOLS) estimation techniques. The findings revealed that income inequality, poverty and the interactive term between income inequality and poverty had a negative and insignificant impact on economic growth in Nigeria. Based on the findings, the study recommended that the Nigerian government should design and implement viable economic policies towards income inequality and poverty reduction to promote economic growth in Nigeria.

Keywords: Income inequality, poverty, economic growth, ARDL, DOLS, Nigeria

INTRODUCTION

Income inequality and poverty are among the most pressing challenges facing global economies today (Smith,2023). These twin issues are intricately linked to economic growth and social stability, drawing significant attention from scholars, policymakers, and international organisations. The United Nations Sustainable Development Goals (SDGs), particularly Goals 1 and 10, explicitly recognised the eradication of poverty and reduction of income inequality as essential pathways to sustainable economic growth. (United Nations, 2015). Income inequality and poverty levels serve as key indicators of economic progress. Musa, Enaberue, and Magaji (2024) describe these issues as interconnected and mutually reinforcing challenges in Africa. Moreover, Krokeyi and Obayori (2020) highlight that rising poverty and income inequality significantly hinder growth and development in Nigeria’s economy.  They further explain that while poverty undermines individuals’ capacity to access basic needs such as food, housing, and education, income inequality disrupts economic growth by concentrating wealth in the hands of a few, thereby limiting broad-based development opportunities. Inequality remains a prominent issue in both advanced and emerging nations worldwide.

Overtime, successive Nigerian governments have implemented various poverty alleviation and income redistribution programmes to address these challenges. Poverty alleviation programmes including the National Directorate of Employment (NDE) (1986), Better Life Programme for Rural Dwellers (1987), the Family Economic Advancement Programme (1997), and the National Poverty Eradication Programme (NAPEP) (2011), were launched to improve living conditions. Recognising the negative impact of income disparity on growth, the Nigerian government has implemented various initiatives aimed at narrowing income gaps. Programmes such as the Agricultural Credit Guarantee Scheme (ACGS), Conditional Cash Transfer Programme (CCTP), Low-Cost Housing, Rural Electrification scheme (RES), N-Power, Family Support Scheme (FSP), and Subsidy Reinvestment Programme (Sure-P) represent efforts to alleviate inequality (Kolawole, 2021). More recently, programmes like the Youth Enterprise with Innovation in Nigeria (You win) and the Social Investment Programme (SIP) (2016) have aimed to promote entrepreneurship and provide social safety nets.  However, these programmes have largely failed to achieve their objectives due to widespread corruption, fiscal indiscipline, and weak institutional capacity.

The persistent levels of poverty and income inequality in Nigeria have significant implications for the country’s economic growth. Studies suggest that inequality can hinder growth by undermining social cohesion, reducing investment in human capital, and creating political instability. For instance, Gbosi (2012) argued that the unequal distribution of resources in Nigeria has limited the ability of many to contribute meaningfully to the economy. Similarly, Obayori, Udeorah, and Aborh (2018) emphasised that poverty leads to emotional and psychological despair, which in turn, reduces productivity and increases dependency.

Some empirical studies (Igbokwe Chinyere and Okeke Chidiebere , 2023; Yakubu Sule and Fatai Adekunle, 2023; Okonkwo Nkiru and Adebola Babatunde, 2022; Usman Yusuf and Ibrahim Abdullahi, 2022; Adebayo Femi and Abiola Grace, 2021; Bello Tunde and Obinna Chika, 2021; Adebayo Femi and Abiola Grace, 2021)  have investigated the impact of income inequality and poverty on economic growth, but the empirical results are mixed. The mixed results obtained from the previous studies warrants this study. This study is set to add to the discourse on the impact of income inequality and poverty on economic growth in Nigeria.

The objective of this study is to examine the impact of income inequality and poverty on the growth of the Nigerian economy. This study holds substantial significance for many stakeholders within and beyond Nigeria. This study focuses on examining the impact of income inequality and poverty on the economic growth of Nigeria, covering the period from 1980 to 2023. The choice of this timeframe is informed by the availability of comprehensive economic data and the significant structural changes in the Nigerian economy during these years, including policy reforms, economic crises, and poverty alleviation initiatives.

The remainder of the paper is organized as follows: Section 2 presents the literature review and sector 3 presents the research methodology. Section 4 present the results and discussion. While section 5 concludes and makes recommendations.

EMPIRICAL LITERATURE REVIEW

The relationship between income inequality, poverty, and economic growth has been a focus of numerous studies, offering critical insights into how these variables interact, particularly in developing countries like Nigeria. Recent empirical research has revealed the multifaceted impacts of inequality and poverty on economic development.

Igbokwe Chinyere and Okeke Chidiebere (2023) explored the interconnections among unemployment, income inequality, and poverty in Nigeria, using the generalised method of moments (GMM) estimation. Their research found that unemployment directly exacerbates income inequality, which in turn fuels the persistence of poverty and weakens economic growth. The study highlighted that the socio-economic consequences of this cycle disproportionately affect the most vulnerable populations, particularly women and youth. They recommended implementing targeted employment policies and expanding vocational training programmes as a means to break the cycle of poverty and inequality.

Yakubu Sule and Fatai Adekunle (2023) analysed the effects of globalisation on income inequality in Sub-Saharan Africa, with a specific focus on Nigeria. Their study found that while globalisation enhances economic opportunities for some, it exacerbates inequality by favouring skilled workers and urban centres. They recommended policy interventions to enhance rural inclusion and support skill development for low-income populations to bridge the inequality gap.

Okonkwo Nkiru and Adebola Babatunde (2022) examined the link between agricultural productivity and poverty alleviation in Nigeria. They demonstrated that while agriculture employs a significant portion of the population, poor access to credit and inputs limits productivity, perpetuating income disparities. The study advocated for policies that expand agricultural credit facilities and improve rural infrastructure.

Adewale Johnson and Musa Bello (2022) explored the relationship between income inequality and health outcomes in Nigeria. Their study highlighted that unequal income distribution restricts access to quality healthcare for low-income households, contributing to poor health outcomes and reduced labour productivity. The authors emphasised the need for universal healthcare schemes to address these disparities and enhance economic growth.

Usman Yusuf and Ibrahim Abdullahi (2022) utilised panel data to assess the impact of urbanisation on income inequality in Nigeria. Their findings revealed that while urbanisation generates economic opportunities, it also intensifies inequality by marginalising rural populations. The study proposed a balanced approach to urban development, ensuring that rural areas receive adequate investments in education, healthcare, and infrastructure.

Adeola Temitope and Evans James (2022) studied the structural determinants of income inequality in Sub-Saharan Africa. Their analysis identified weak institutional frameworks, corruption, and resource mismanagement as primary drivers of income disparities in Nigeria. The study underscored that high inequality levels limit opportunities for the majority of the population, thereby stifling economic growth. The authors recommended strengthening institutional governance to create an environment conducive to equitable economic development.

Udo Godwin and Ibrahim Ahmed (2022) utilised time-series data to assess the long-term effects of income inequality on Nigeria’s economic growth. Their findings indicated that inequality negatively impacts human capital development and investment levels, both of which are critical for sustainable growth. The study highlighted the importance of promoting equal access to education and healthcare to bridge the inequality gap and foster economic development.

Onifade Kunle, Fadeyi Oluwaseun, and Adebanjo Mojisola (2021) examined the dual effects of poverty and inequality on Nigeria’s economic growth. Their findings revealed that structural deficiencies in governance exacerbate both poverty and inequality, leading to slower economic growth. They recommended that the government prioritise inclusive governance and equitable allocation of resources to mitigate these issues.

Adebayo Femi and Abiola Grace (2021) assessed the effectiveness of income redistribution policies in Nigeria. Using micro-level data, they demonstrated that progressive taxation and social transfer programmes significantly reduce income inequality and improve overall economic growth. The study emphasised the importance of policy consistency and monitoring mechanisms to ensure effective implementation.

Bello Tunde and Obinna Chika (2021) explored the impact of income inequality on labor market participation in Nigeria. They found that high inequality discourages workforce engagement, particularly among lower-income groups, leading to decreased productivity. The authors recommended increasing investments in job creation programs targeted at marginalised populations.

Ajayi Samuel and Thomas Gregory (2021) conducted a comprehensive analysis of the relationship between income inequality, poverty, and political stability in Nigeria. They found that high levels of inequality and poverty fuel social unrest and weaken political institutions, creating an unfavourable environment for economic growth. They recommended strengthening social protection programs to reduce economic grievances and promote stability.

Akinyemi Folake and James Vincent (2021) investigated the impact of digital financial inclusion on income inequality in Nigeria. They found that access to digital financial services significantly reduces inequality by empowering low-income individuals with financial tools for savings and investments. The study recommended expanding mobile banking infrastructure in underserved areas to amplify these benefits.

Chioma Ifeanyi and Samuel Oyekan (2018) examined the link between inequality and crime rates in Nigeria. Using data from 2000–2017, they demonstrated that income inequality was a significant driver of increased crime, particularly in urban centres. The study recommended targeted interventions to provide social services and job opportunities for vulnerable groups.

Ibrahim Ahmed and Mohammed Yusuf (2018) analysed the role of government spending in reducing inequality in Nigeria. Their study revealed that while public spending on education and healthcare significantly reduced inequality, inefficient allocation and corruption hindered broader outcomes. They recommended reforms to improve the efficiency of public spending.

Amadi Chukwu and Ngozi Opara (2017) explored how microfinance institutions reduce poverty and inequality in Nigeria. Their findings showed that access to small loans and financial literacy programs helped low-income individuals increase their incomes and achieve economic stability.

RESEARCH METHODOLOGY

Model Specification          

This model is specified based on the specification of Igbokwe Chinere and Okeke Chidiebere (2023). The functional relationship of the model is expressed as:

Where:

• GDPt​: Gross Domestic Product at time t (proxy for economic growth).
• INEQt​: Income inequality, measured by the Gini coefficient at time t.
• POVTt: Poverty rate, measured as the percentage of the population below the poverty line at time t.
• INFt​: Inflation rate, measured as the annual percentage change in the Consumer Price Index at time t.
• FDIt​: Foreign Direct Investment a percentage of GDP at time t.
• POPGRt​: Population growth rate at time t.
• GI_POVT: The interactive term between income inequality and poverty.
• _β0: The intercept term.
• β₁, β₂,β₃,β₄,β₅: Coefficients to be estimated, reflecting the marginal effects of the respective independent variables on economic growth.
• ε _it: The error term, capturing unobserved factors that affect economic growth.

This model will be estimated using Autoregressive Distributed Lag (ARDL) model and the Dynamic Ordinary Least Squares (DOLS), which is suitable for determining the relationship between dependent and independent variables in a linear regression framework.

Estimation Technique

 This study employed Autoregressive Distributed Lag model (ARDL) and Dynamic Ordinary Least Squares (DOLS) techniques to estimate the impact of income inequality and poverty on growth of the Nigerian economy which are widely used statistical methods for estimating the relationships between a dependent variable and one or more independent variables given the ability to provide unbiased and reliable parameter estimates. The ARDL allows for mixed order of integration 1 (0) and I (1) which is common in macroeconomic data like income inequality, poverty and GDP growth, suitable for small sample sizes which fits the data set 1980-2023 and estimates both short run and long run relationships which is crucial for analysing the structural impact of income inequality and poverty on economic growth.  DOLS corrects for endogeneity and serial correlation by including leads and lags of differenced variables providing unbiased and reliable parameter estimates. Using both methods ensures the validity and reliability of the long run results.

Data Sources

The data for this study will be obtained from reputable and reliable secondary sources to ensure accuracy, consistency, and relevance. These data sources include:

1. World Bank Development Indicators (WDI): The World Bank provides comprehensive and standardized data on economic and social indicators for countries globally. This source will be used for obtaining data on Gross Domestic Product (GDP), Income Inequality (Gini coefficient), and Poverty Rate (percentage of the population below the poverty line).
2. Central Bank of Nigeria (CBN) Statistical Bulletins: The CBN statistical bulletins provide annual data on key macroeconomic indicators in Nigeria, including Foreign Direct Investment (FDI) and Inflation (INF).
3. National Bureau of Statistics (NBS): The NBS is the primary source of official statistics in Nigeria. Data on Population Growth (POPGR) was sourced from its reports.
4. International Monetary Fund (IMF) and African Development Bank (AFDB): The IMF and AFSDB provide macroeconomic data and reports on Nigeria’s economic performance and social indicators.
5. United Nations Development Programme (UNDP): The UNDP database offers valuable insights into poverty trends and human development indices, which are useful for capturing the broader dimensions of poverty beyond income levels.

RESULTS AND DISCUSSION

Descriptive Statistics

Descriptive statistics provide an overview of the central tendencies, variability, and distributional characteristics of the variables selected and utilised in the study. This section highlights the key statistical measures, including the mean, median, maximum, minimum, standard deviation, skewness, kurtosis, and the Jarque-Bera test for normality, as summarised in Table 4.1 below. This analysis was as well carried out in Ashakah and Wanogho (2021), Ashakah and Ogbebor (2020), Ogbebor and Ashakah (2021), Awogbemi (2022), Ashakah et al. (2025) and Mgbomene et al. 2025.

Table 4.1: Descriptive Statistics of Variables

Source: Authors’ Compilation 2025

The average growth rate of the Gross Domestic Product (GDPGR) over the study period is 3.07%. This implies that, on average, the Nigerian economy experienced moderate growth during this time with a maximum GDP growth rate of 15.32%, and the minimum value of -13.13% indicates periods of negative growth. The standard deviation of 5.20% signifies moderate variability. A skewness value of -0.86 indicates a negatively skewed distribution, suggesting that lower GDP growth rates are more frequent than higher ones. Additionally, the kurtosis value of 4.96 reflects a leptokurtic distribution, implying the existence of extreme values. The Jarque-Bera probability of 0.0019 suggests that GDP growth rates are not approximately normally distributed.

The mean value of income inequality (GINI INDEX) is 40.73182, reflecting the average magnitude of income disparity in Nigeria during the study period. The maximum GINI index recorded is 51.900, while the minimum is 35.100, reflecting considerable inequality levels.

The poverty rate (POVT) has a mean value of 35.07%, indicating that about 35 per cent of the Nigerian population lived in poverty on average during the study period. The maximum poverty rate is 45.20%, while the minimum is 25.10%, suggesting relatively lower than average poverty. The standard deviation of 6.96% supports this observation. The skewness value of -0.19 reflects a left-skewed distribution, indicating that poverty rates are more concentrated around higher values. The kurtosis value of 1.83 is close to the normal distribution benchmark of 3. However, the Jarque-Bera probability of 0.25 suggests the availability of normality.

The mean government capital expenditure (POPGR) is 2.63, highlighting the average level of population growth rate during the study’s duration. The highest value of 3.12 and the lowest of 2.09 indicate a rate less than GDPGR. The standard deviation of 0.23 indicates a low variability in this variable. A skewness value of -0.81 reveals a negatively skewed distribution, while a kurtosis value of 3.83 suggests a leptokurtic distribution, indicating the presence of extreme values. The Jarque-Bera probability of 0.05 points to a non-normal distribution of POPGR.

The average inflation rate during the study period is 18.87%, indicating high inflationary pressures. The maximum inflation rate is 72.83%, while the minimum is 5.39 %, reflecting significant fluctuations. The standard deviation of 16.15% highlights high variability. A skewness value of 1.90 suggests a nearly symmetric distribution, while the kurtosis value of 5.62 indicates a platykurtic distribution. The Jarque-Bera probability of 0.0000 confirms the abnormality in the rate of inflation. The mean value of foreign direct investment (FDI) is 1.17% of gross domestic product growth rate, reflecting a significant inflow of FDI. The maximum value of 4.28 and the minimum of -1.15 demonstrate a broad range. The standard deviation of 1.00 confirms high variability in this variable. The skewness value of 1.09 indicates a positively skewed distribution, while the kurtosis value of 3.37 reveals a leptokurtic distribution. The Jarque-Bera probability of 0.10 suggests that GREXP is not strictly normally distributed.

Correlation Coefficients

Pearson’s pairwise correlation coefficients between pairs of variables within the research are presented in Table 4.2. The coefficients indicate the extent or degree of association between the variable pairs.

Table 4.2. Matrix of Correlation Coefficients

Source: Author’s Computation

The correlations between GDPGR and the explanatory variables, income inequality (GINI), poverty (POVT), population growth rate (POPGR), inflation rate (INFL) and foreign direct investment (FDI) are analysed. The relationship between GDPGR and GINI is 0.1092, indicating about 11 per cent positive correlation between the two variables. This implies that income inequality positively affects economic growth. The correlation coefficient between GDPGR and POVT is 0.0646, indicating a about 7 per cent positive relationship between GDPGR and poverty. The positive correlation implies that poverty influences economic growth positively. The positive correlation found between economic growth and income inequality and poverty is contrary to theory and empirical evidence. This contrary result could be a result of measurement error in the data.  GDPGR has a moderate positive correlation (0.0580) with population growth. The estimated positive correlation between economic growth and population showed that population growth positively influences growth. The correlation coefficient between GDPGR and inflation rate (INFL) is -0.2094, indicating a moderate negative relationship. This implies that inflation impacts the growth of the economy negatively.  The correlation between GDPGR and FDI is 0.3187, indicating a moderate positive correlation. This shows that FDI has a positive impact on growth.

Unit Root Tests

The Augmented Dickey-Fuller (ADF) test was employed to assess the stationarity of the factors in the study. This test is essential in determining the integration order of each variable and the appropriate estimation technique. Stationarity is a necessary condition for the analysis of time series because regressions involving non-stationary variables can produce spurious results. The ADF test was performed on each variable at levels and first differences to establish their integration order as in Awogbemi (2022).

Table 4.3. ADF Unit Root Test Results

Source: Author’s Computation (2025)

The outcomes demonstrate that Gross Domestic Product Growth Rate (GDPGR), population growth rate (POPGR), inflation rate (INFL), and foreign direct investment (FDI) were stationary at level.  Income inequality and poverty were not stationary at level; they became stationary after the first difference. A variable is stationary if the probability is 5% or below. The probabilities of Gross Domestic Product Growth Rate (GDPGR), population growth rate (POPGR), inflation rate (INFL), and foreign direct investment (FDI) were less than 5% at the level test. The probabilities of income inequality and poverty were greater than 5% at the level test, but they turned out to be less than 5% after the first difference. Since no variable remained non-stationary after first differencing, second differencing was unnecessary. These outcomes are crucial for further econometric analysis, as they validate the Autoregressive Distributed Lag model (ARDL) usage.

Model Estimation Results

This section displays the results of model estimation, making use of the Autoregressive Distributed Lag (ARDL) model and the Dynamic Ordinary Least Squares (DOLS) techniques. The estimation aimed to determine the relationships over the short-run and long-run terms between the dependent variable, gross domestic product growth rate (GDPGR) and the explanatory variables, income inequality (GINI), poverty (POVT), population growth (POPGR), inflation rates (INFL), and foreign direct investment (FDI).

ARDL Model Estimation Results

Mixed orders of integration are supported by the ARDL model, which estimates both the short-run and long-run relationships. Table 4.4 presents the ARDL estimation results.

Table 4.4: ARDL Model Estimation Results

Dependent Variable: GDPGR

Sources: Author’s Computation (2025)

Test for Cointegration

The ARDL bound test determines whether a long-run relationship exists among the variables.

Table 4.5: ARDL Bound Test Results

Sources: Author’s Computation (2025)

The F-statistic value (2.771981) is lower than the upper bound critical value (3.920) at the 5% level, confirming the non-existence of a long-run relationship among the variables; this result did not require us to proceed to estimate the equation for error correction of the ARDL model.

Table 4.6: Dynamic OLS Estimation Results

Dependent Variable: GDPGR

Sources: Author’s Computation (2025)

Discussion of Estimation Results

The constant of the evaluated model came out as 68.43446 with a probability value of 0.0896. It means that economic growth would be about 68.43446 if all the variables incorporated in the model were assumed to have a zero value. The probability value (0.0896) of the constant is not statistically significant at the 5% level. The coefficient of the first lag of gross domestic product growth rate (GDPGR (-1)) was estimated at 0.251121 with a probability value of 0.0488. The result was statistically significant at the 5% level. The findings indicate that the first lag of GDPGR has the capacity to boost current-year growth.

The coefficient of income inequality, which was proxied by the GINI index, was estimated as -1.550887 with a probability value of 0.1843. Though the estimated coefficient of income inequality failed the statistical test at the 5% level of significance, the result revealed that a one-unit increase in income inequality would result in about a 1.550887-unit decrease in economic growth rate. The inference from this finding is that income inequality exerts insignificant negative effects on Nigeria’s economic growth rate. This finding agrees with the finding of Nwosa 2019.

The coefficient of poverty was estimated at about -1.194638 with a probability value of 0.2025. Also, the estimated coefficient of poverty failed the statistical test at the 5% significance level.  The finding revealed that poverty has an insignificant negative influence on economic growth in Nigeria, and policies targeted at poverty reduction may be capable of enhancing growth in Nigeria. The finding is opposite to the finding of Nnam and Inyang 2022. The difference in the findings could political instability and macroeconomic policy variations.

The coefficient of the interactive term between income inequality and poverty, estimated at about 0.038929 with a probability of 0.1311, showed a positive and insignificant impact on economic growth. The estimated result revealed that a unit increase in the interactive term would lead to an increase of about 0.0438929. The estimated sign of the interactive term did not comply with a priori expectations. The coefficient of the first lag of the interactive term was estimated at -0.004127 with a probability value of 0.1940. This coefficient of the first lag of the interactive term had the expected sign. It reflected that a one-unit increase in income inequality and poverty would lead to a negative effect of 0.004127 on economic growth in Nigeria. This study did not find a statistically significant impact of inequality and poverty in Nigeria during the period of the study. These findings are contrary to economic theory and this could be attributed to measurement error in the data on income inequality and poverty. Future research should investigate the channels through which income inequality and poverty might indirectly affect growth, such as through political instability or human capital degradation.

The coefficient of population growth was estimated at 17.71539 with a probability value of 0.0178. The estimated coefficient passed the significance test at the 5% level. The result showed that a per cent increase in population growth would result in about 17.71539 per cent increase in gross domestic product growth rate. The coefficient of the first lag of population growth was estimated as -22.82417 with a probability value of 0.0013. This result showed that population growth caused an unfavourable outcome on growth in the preceding year. The outcome indicates that population growth negatively impacts economic expansion in Nigeria.

The coefficient of inflation rate was estimated at about -0.083060 with a probability value of 0.0268. The result revealed that a one per cent increase in the inflation rate would lead to a decrease of about 0.083060 units in the rate of economic growth. The probability value of the estimated coefficient of inflation demonstrated that the estimate passed a statistical test of significance at the 5% level, which indicates a detrimental effect on Nigeria’s economic expansion. The result has policy implications as it can stimulate efficient inflation control measures to strengthen Nigeria’s economic performance.

The coefficient of FDI was estimated at 0.095974 with a probability value of 0.8961. This showed that current year FDI does not have a significant impact on economic growth in Nigeria during the period of the study. The coefficient of the first lag of FDI was estimated at 2.965469 with a probability value of 0.0000. This result revealed that the impact of FDI on growth was highly significant at the 1% level. The result showed that a one per cent increase in FDI has a lag effect of about 2.965469 per cent on GDPGR in Nigeria. The result implies that FDI inflows have the potential to enhance growth in Nigeria in the following year.

The coefficient of determination (R-square) was estimated at 0.710779, indicating that about 71% of the systematic variations in the target variable, gross domestic product growth rate (GDPGR), was explained by all the independent variables in the model. The F-statistic (7.864189)   and the associated p-value (0.0000) indicate that a significant relationship exists among the integrated variables in the model. The Durbin-Watson statistic of 1.531516 suggested the absence of autocorrelation in the regression model.  Also, the evidence from the panel dynamic ordinary least squares (DOLS) confirmed that income inequality and poverty had a negative and insignificant impact on growth. Overall, the results generated from the ARDL and Dynamic OLS confirmed that income inequality and poverty have insignificant adverse effects on growth in Nigeria over the period examined.

Econometric Tests

To ensure the reliability of the model estimates, diagnostic tests for serial correlation, heteroscedasticity, normality, and model stability were conducted. The Breusch-Godfrey Serial Correlation LM test revealed no indication of serial correlation. The Jarque-Bera normality test produced a probability value of 0.296113, showing that the residuals are normally distributed. The CUSUM stability test confirmed that the model was stable over the sample period, as the computed statistic remained within the critical bounds. These results affirm the robustness, stability, and reliability of the fitted models.

CONCLUSION

This study investigated the impact of poverty and income inequality on the growth of the Nigerian economy from 1980 to 2023. The DOLS and ARDL models’ results reveal that income inequality, poverty and the interaction between the two showed a negative but insignificant impact on economic growth in Nigeria. The findings are contrary to economic theory and a priori expectations. Economic policies targeted at income inequality and poverty reduction would help to boost economic growth and sustainable development, but such policies may depend less on these findings because they are at variance with economic theory and evidence from previous studies.

POLICY RECOMMENDATIONS

1. The study found a negative and insignificant impact of income inequality and poverty on economic growth during the period of the research. Based on the findings, the study proposed that further studies should employ alternative estimation techniques, such as Fully Modified Ordinary Least Squares (FMOLS), for comparison purposes.
2. The study revealed a negative and insignificant impact of income inequality and poverty on economic growth in Nigeria during the period of the research. Based on the findings, the study proposed that future studies should specify and estimate a non-linear model if different results are obtained.

REFERENCES

1. Adebayo, F., & Abiola, G. (2021). Income redistribution policies and their impact on inequality in Nigeria. International Journal of Policy Analysis, 29(3), 92–117.
2. Adeola, T., & Evans, J. (2022). Structural determinants of income inequality in Sub-Saharan Africa. African Economic Studies, 15(3), 134–156.
3. Adewale, J., & Musa, B. (2022). Income inequality and health outcomes in Nigeria. International Journal of Health Economics, 14(2), 56–73.
4. Ajayi, S., & Thomas, G. (2021). Income inequality, poverty, and political stability in Nigeria. Journal of African Governance Studies, 12(4), 98–117.
5. Akinyemi, F., & Vincent, J. (2021). Digital financial inclusion and income inequality: Evidence from Nigeria. Journal of Digital Economy Studies, 8(2), 45–63.
6. Ashakah et. al. (2025). External Debt, Debt Service and Economic Growth: Empirical Evidence from the ECOWAS Sub-Region. International Journal of Economics and Financial Issues, 15(1), 422-430.
7. Ashakah, O. F. & Wanogho, O. A.(2021). ECOWAS Economic Integration and Economic Growth: Empirical Evidence from Nigeria. Journal of Economics and Allied Research, 6(2), 1-11.
8. Ashakah, O. F. & Ogbebor,T.O.(2020). Foreign Direct Investment, Financial Development and Economic Growth in Key Emerging Markets. Journal of Economics and Allied Research, 5(1), 57-71.
9. Awogbemi, T. O. (2022). Health Expenditure and Economic Growth: The Nigerian Experience. Innovations, 69, 767-778.
10. Awogbemi, T. O.(2022). The impact of monetary policy on Nigeria’s economic growth. Innovation, 70, 1076-1084.
11. Bello, T., & Obinna, C. (2021). The implications of income inequality on labor market participation in Nigeria. African Labor Economics Review, 14(2), 78–95.
12. Central Bank of Nigeria (CBN). (2022). Financial Access Survey.
13. Gbosi, A. N. (2012). Contemporary Issues in Nigeria’s Public Finance and Development. Port Harcourt: NDBS Publishers.
14. Gbosi, N. G. (2012) Modern Labour Economics and Policy Analysis 2nd edition Pack Publishers, Ebonyi State, Nigeria. International Journal of Economics, Commerce and Management, United Kingdom Licensed under Creative Common Page 163
15. Igbokwe, C., & Okeke, C. (2023). The cyclical relationship between unemployment, poverty, and income inequality in Nigeria. Journal of Economic Policy Analysis, 18(1), 45–67.
16. International Monetary Fund. (2020). Fiscal Monitor: Policies for the recovery. Washington, DC: IMF. Kolawole, R. J. (2021). Evaluation of poverty alleviation programmes in Nigeria: The demand driven approach perspective. International Journal of Development and Management Review, 16(1), 161–177.
17. Krokeyi, W. S & Obayori, J. (2020). Income Distribution and Poverty Reduction in Nigeria. Journal of Management Science & Entrepreneurship, 20(7), 42-50.
18. Mgbomene, C., Ashakah, F. O., Metieh, F. C., Okoro, E. N., Dim, H. C., & Opara, F. N. (2025). Crude oil price volatility and the Nigerian economy. International Journal of Energy Economics and Policy, 15(3), 390–402. https://doi.org/10.32479/ijeep.18831
19. Musa, H. S., Enaberue, S., & Magaji, M. (2024). The Impact of Income Inequality on Economic Development in Africa. African Journal of Economics.
20. National Bureau of Statistics (NBS). (2023). Poverty and Inequality Report.
21. Nnam, C. P & Inyang, I. I. (2022). Economic Growth and Poverty in Nigeria. Lafia Journal of Economics and Management Science, 7(2), 202-219.
22. Nwosa, P. 1. (2029). Income Inequality and Economic Growth In Nigeria: Implication For Economic Development. Economica, 15(1), 107- 115.
23. Obayori, J. B., Udeorah, S. & Aborh, K. B. (2018). Human Capital Investment and Poverty Reduction in Nigeria.
24. Obayori, J. B., Udeorah, S. I., & Aborh, O. (2018). Social Implications of Poverty in Nigeria. Journal of Development Studies, 14(3), 101-118.
25. Ogbebor, T. O. & Ashakah, O. F. (2021). Access to Electricity and Economic Growth in Sub-Saharan Africa: Is There an Energy-Growth Nexus? Journal of Economics and Allied Research, 6(1), 103-119.
26. Okonkwo, N., & Adebola, B. (2022). Agricultural productivity and poverty alleviation in Nigeria. Journal of Development Studies, 19(3), 125–142.
27. Onifade, K., Fadeyi, O., & Adebanjo, M. (2021). Poverty, inequality, and economic growth: The Nigerian experience. Journal of Inclusive Economics, 13(4), 56–74.
28. Roser, M., & Hasell, J. (2022). Poverty: Share of the world population living in extreme poverty. Our World in Data. Retrieved from https://ourworldindata.org/poverty
29. Smith, J. (2023). The global challenge of income inequality and poverty.Global Economies Today.
30. Udo, G., & Ibrahim, A. (2022). Income inequality and its long-term effects on Nigeria’s economic growth. Journal of Development Economics, 19(2), 201–219.
31. World Bank. (2023). Global Poverty and Inequality Trends. Washington, DC: World Bank.
32. Yakubu, S., & Adekunle, F. (2023). Globalization and income inequality: Evidence from Sub-Saharan Africa. African Economic Journal, 17(1), 92–108.