Economics Project Topics

Relationship Between Population and Unemployment in Nigeria

Relationship Between Population and Unemployment in Nigeria

Relationship Between Population and Unemployment in Nigeria

Chapter One

Objectives of the Study

The specific objectives of this study, formulated in the past tense to reflect completed research, are as follows:

  1. To examine the historical trends of population growth in Nigeria.
  2. To assess the patterns and trends in unemployment rates over the past two decades.
  3. To analyze the causal relationship between population dynamics and unemployment in Nigeria.



Conceptual Review

Population Growth in Nigeria

The demographic landscape of Nigeria, marked by a population exceeding 200 million, has undergone significant changes, prompting a closer examination of historical trends in population growth (Aigbokhan, 2020). Over the past two decades, Nigeria has experienced a remarkable increase in its population size, necessitating a comprehensive overview to understand the trajectory and implications of this growth. Analysis of historical data provides insights into the patterns and magnitudes of population changes, laying the foundation for understanding the current demographic landscape (Ibrahim & Umar, 2018).

Several factors contribute to the unprecedented population growth in Nigeria. Economic factors, including poverty and income inequality, have been identified as key influencers (Ogbeide & Agu, 2021). High fertility rates, cultural norms, and inadequate access to family planning services also play pivotal roles (Aiyedogbon & Ohwofasa, 2022). Understanding these factors is essential for comprehending the drivers behind the demographic changes and for informing policies aimed at managing population growth (Chen & Li, 2021).

The impact of population growth extends beyond demographic statistics and profoundly influences various socio-economic sectors in Nigeria. The strain on education systems is evident, with an escalating demand for schools and teachers (Rodriguez & Garcia, 2023). Healthcare systems face challenges in providing adequate services to a rapidly growing population, impacting overall public health (World Bank, 2021). The labour market, as a socio-economic sector, bears the burden of accommodating the expanding workforce, contributing to the complexities of unemployment (Granville & Mallick, 2006).

Moreover, the relationship between population growth and socio-economic sectors is intricate and multifaceted. Increased population density in urban areas puts pressure on infrastructure and services, leading to challenges in housing, transportation, and sanitation (Kale, 2022). Agricultural practices, a significant economic driver, are affected as more hands compete for limited resources. As a result, understanding the impact of population growth on these sectors is crucial for devising holistic policies that address the interconnected challenges posed by demographic changes in Nigeria (Nurdiana et al., 2020).

In summary, the historical overview of population growth in Nigeria provides a foundational understanding of the demographic changes that have occurred over the past decades. Influential factors, ranging from economic conditions to cultural practices, contribute to this growth, necessitating a nuanced examination. The impact of population growth extends across various socio-economic sectors, posing challenges that require comprehensive and context-specific policy responses. By exploring these dimensions, this study contributes to the broader understanding of the interplay between population dynamics and the socio-economic landscape in Nigeria.

Unemployment Dynamics

Unemployment dynamics in Nigeria present a complex socio-economic challenge that demands a thorough exploration of definitions, measures, patterns, and contributing factors. Definitions and measures of unemployment serve as the foundation for understanding the magnitude and nature of this issue (Gujarati, 2019). Unemployment, typically defined as the state of being without a paid job while actively seeking employment, is measured through various indicators such as the unemployment rate, labour force participation rate, and employment-to-population ratio (Johnson & Brown, 2023).

Analyzing patterns and trends in unemployment rates is crucial for comprehending the dynamic nature of this issue over time. Nigeria has experienced fluctuations in unemployment rates over the past two decades, influenced by factors such as economic growth, government policies, and global economic conditions (Yelwa et al., 2021). A nuanced exploration of these patterns provides valuable insights into the temporal aspects of unemployment dynamics in the country (Osterling, 2021).

Several factors contribute to the persistent challenge of unemployment in Nigeria. Economic factors, including the overall health of the economy, industrial output, and GDP growth, play a significant role (Misini & Badivuku-Pantina, 2021). Education and skills development, or the lack thereof, also contribute to the unemployment scenario, as the mismatch between available skills and job requirements becomes evident (Aiyedogbon & Ohwofasa, 2022). Additionally, inflation has been identified as a factor influencing the unemployment rate, further complicating the relationship between economic variables and job opportunities (Rodriguez & Garcia, 2023).

Moreover, structural changes in the economy, technological advancements, and globalization contribute to shifts in the labour market, impacting employment opportunities (Granville & Mallick, 2006). Government policies and interventions play a pivotal role in shaping the unemployment landscape. Effective policies can stimulate job creation and economic growth, while inadequate or misdirected interventions may exacerbate the problem (Okoroafor & Nwaeze, 2023).





The methodology section of this study is designed to outline the research approach, data collection methods, and analytical techniques employed to investigate the relationship between population dynamics and unemployment in Nigeria. This chapter details the research design, population of the study, sampling techniques, data collection methods, data analysis procedures, model specification, validity and reliability measures, and ethical considerations.

Research Design

In this study, the research design plays a pivotal role in shaping the methodology employed to investigate the relationship between population dynamics and unemployment in Nigeria. A correlational survey research design is chosen for its ability to examine relationships between variables without manipulating them (Saunders et al., 2019). This design is particularly advantageous when exploring complex phenomena, such as the intricate connection between population dynamics and unemployment, allowing for a comprehensive and holistic understanding of the interplay between these factors.

The adoption of a correlational survey design is justified by its unique strengths in capturing the complexity of the relationship under investigation. Saunders et al. (2016) and Bell et al. (2019) emphasize the suitability of this approach for studies involving multifaceted dynamics. In the context of this research, the design is tailored to uncover patterns and trends within the Nigerian context, shedding light on the nuanced relationships between population dynamics and unemployment rates.

By utilizing a correlational survey design, the study aims to go beyond mere observations and delve into the identification of potential correlations between population dynamics and unemployment in Nigeria. Saunders et al. (2016) argue that this design allows for the exploration of associations, providing valuable insights into the underlying patterns and contributing factors. It enables the research to move beyond a surface-level understanding, fostering a nuanced comprehension of the dynamics influencing unemployment rates in Nigeria.

Furthermore, the correlational survey design is particularly relevant in this study due to its flexibility and adaptability to the complexities of social and economic phenomena (Bell et al., 2019). The diverse and interconnected nature of variables such as population growth, GDP growth, inflation, and poverty necessitates an approach that can accommodate these complexities. Saunders et al. (2019) highlight the correlational survey design’s capacity to navigate intricate relationships and capture the multifaceted nature of the variables involved.

 Population of the Study

The population of this study encompasses all macroeconomic variables relevant to understanding the relationship between population dynamics and unemployment in Nigeria. These variables include but are not limited to GDP growth rate, inflation rate, industrial output, and poverty levels (Goddard & Melville, 2020). Each of these variables plays a crucial role in shaping the employment landscape, and studying them collectively provides a comprehensive view of the macroeconomic environment.

Justifying the inclusion of these variables as the population of the study lies in their interconnectedness and their combined impact on unemployment rates. For instance, GDP growth is a key determinant of job creation, while inflation and poverty levels may influence the overall employment scenario (Charmaz, 2016; Johnson & Brown, 2023). By considering these variables collectively, the study aims to unravel the nuanced relationships that contribute to the unemployment dynamics in Nigeria.







Summary of Findings

Table 4.1 provides a comprehensive overview of the descriptive statistics for key macroeconomic variables—GDP growth rate (GDPGR), unemployment rate (UNEMPR), industrial output (INDOUTP), poverty levels (POVLEV), and population growth rate (POPGRA)—in Nigeria from 2010 to 2022. The mean GDP growth rate of 3.73% indicates a moderate pace of economic expansion, while the standard deviation of 2.24 suggests considerable variability over the study period. The unemployment rate, with a mean of 4.63%, reflects a relatively low and stable level throughout these years.

Examining the skewness and kurtosis values provides insights into the distribution characteristics. Positive skewness in GDPGR, UNEMPR, and POVLEV suggests a longer tail on the right side of the distribution, indicating potential outliers with higher values. Meanwhile, the negative skewness in INDOUTP and POPGRA implies a longer tail on the left side, hinting at potential outliers with lower values. The kurtosis values indicate that the distribution of GDPGR is less peaked than a normal distribution, while UNEMPR and POVLEV exhibit negative kurtosis, suggesting flatter distributions with potentially dispersed data points.

The presented findings shed light on the dynamics of these macroeconomic variables, offering valuable insights into the economic landscape of Nigeria. The stability in the mean unemployment rate suggests a certain resilience in the labour market, despite potential economic fluctuations. The moderate GDP growth rate underscores a steady, albeit not rapid, pace of economic expansion, a critical factor for a developing economy like Nigeria.

The observed skewness and kurtosis values highlight potential areas of interest for further investigation. Outliers in GDPGR, UNEMPR, INDOUTP, POVLEV, and POPGRA may indicate specific years or events that had a significant impact on these variables. Identifying and understanding these outliers could provide valuable context for policymakers and researchers seeking to comprehend the factors influencing economic dynamics in Nigeria.

It is crucial to recognize the limitations of relying solely on descriptive statistics. While they offer a snapshot of the central tendencies and distribution characteristics, they do not provide causal relationships or reveal the intricacies of interactions among variables. Further analysis, such as regression modelling or correlation studies, is necessary to delve deeper into the complex economic relationships at play.

Table 4.2, focusing on the Model Summary, provides crucial insights into the relationships among macroeconomic variables in the Nigerian context from 2010 to 2022. The coefficient of determination (R-square) at 0.772 indicates that approximately 77.2% of the variance in the unemployment rate (UNEMPR) can be explained by the predictor variables—population growth (POPGRA), industrial output (INDOUTP), poverty levels (POVLEV), and GDP growth rate (GDPGR). This substantial explanatory power suggests that the selected predictors collectively contribute significantly to understanding fluctuations in the unemployment rate.

The adjusted R-square, which considers the number of predictors in the model, is 0.658. This adjusted value is slightly lower than the R-square, implying that the inclusion of all predictors may not be equally contributing to explaining the variance in UNEMPR. The standard error of the estimate (2.01) represents the average variability between the observed and predicted values of the unemployment rate. A lower standard error indicates a more precise fit of the model, and in this case, it signifies relatively accurate predictions.

The Durbin-Watson statistic, at 1.002, is close to 2, suggesting that there is no significant autocorrelation in the residuals. This is crucial for the reliability of the regression model, as autocorrelation could lead to biased parameter estimates. The high value of the correlation coefficient (R = 0.879) indicates a strong positive relationship between the predicted and actual values of the unemployment rate.

Interpreting these findings provides a nuanced understanding of how the chosen predictors collectively contribute to explaining variations in the unemployment rate. The substantial R-square implies that a significant portion of the observed changes in unemployment can be attributed to the combined influence of population growth, industrial output, poverty levels, and GDP growth rate.

Despite the overall strength of the model, the slightly lower adjusted R-square suggests that there might be room for refinement. It raises questions about the relative importance of each predictor and whether all variables are equally contributing to explaining unemployment variations. Further investigation into the individual contributions of these predictors could enhance the model’s precision and explanatory power.

Table 4.3, featuring the regression coefficients estimates, provides crucial insights into the individual contributions of the predictor variables—population growth (POPGRA), industrial output (INDOUTP), poverty levels (POVLEV), and GDP growth rate (GDPGR)—to the unemployment rate (UNEMPR) in Nigeria from 2010 to 2022. The unstandardized coefficients (B) indicate the magnitude of the change in the dependent variable for a one-unit change in each predictor, while the standardized coefficients (Beta) offer a measure of the relative importance of each predictor in explaining variations in the unemployment rate.

The constant term (91.209) represents the estimated mean unemployment rate when all predictor variables are zero. This value, however, may not have a practical interpretation given that all predictor variables are continuous. It is essential to focus on the coefficients of the predictor variables.

The GDP growth rate (GDPGR) exhibits a positive and statistically significant relationship with unemployment (B = 1.375, p = .012). This suggests that holding other variables constant, an increase in GDP growth is associated with a higher unemployment rate. The positive sign aligns with economic intuition, indicating that a growing economy may attract more job seekers or experience changes in the labour market that contribute to higher unemployment.

On the other hand, industrial output (INDOUTP) displays a negative and statistically significant relationship with unemployment (B = -0.646, p = .018). This implies that ceteris paribus, higher industrial output is associated with a lower unemployment rate. Increased industrial activities often create more job opportunities, contributing to a reduction in unemployment.

Poverty levels (POVLEV) and population growth (POPGRA) do not exhibit statistically significant relationships with unemployment in this model. The coefficients for both variables are negative (B = -0.133 for POVLEV and -26.790 for POPGRA), suggesting a potential negative association with unemployment, but these relationships do not reach statistical significance at conventional levels.

Overall, the results from Table 4.3 emphasize the importance of GDP growth rate and industrial output in explaining variations in the unemployment rate in Nigeria. The positive relationship with GDP growth and the negative relationship with industrial output aligns with theoretical expectations, highlighting the complexities of the labor market dynamics in response to economic changes.

While poverty levels and population growth do not emerge as statistically significant predictors in this model, their coefficients still provide insights into their potential impact on unemployment. Further exploration of these variables and potential interactions with other factors may uncover more nuanced relationships, contributing to a comprehensive understanding of unemployment dynamics in Nigeria.

Table 4.4, presenting the residual statistics, offers crucial information about the model’s predictive accuracy and the distribution of the residuals. Residuals are the differences between the observed and predicted values of the dependent variable, and analyzing their characteristics helps evaluate the model’s performance.

The predicted values range from 1.5776 to 14.0276, with a mean of 5.4677, closely aligning with the mean of the actual unemployment rate (UNEMPR) from Table 4.1 (4.6346). This indicates that, on average, the model provides reasonably accurate predictions of the unemployment rate based on the selected predictor variables.

The minimum and maximum residuals are -3.30507 and 2.56240, respectively, with a mean residual of 0.00000. The standard deviation of the residuals (1.64170) reflects the extent to which individual predictions deviate from the mean prediction. The distribution of residuals, as indicated by the standard deviation and the range, suggests a relatively tight fit of the model, with most predictions clustered around the mean.

The standardized predicted values, ranging from -1.288 to 2.835, help assess the influence of each observation on the model. Standardized residuals, ranging from -1.644 to 1.274, measure the extent to which each observation’s predicted value deviates from its observed value in standard deviation units. These values provide insights into potential outliers and influential data points.

The standard error of the residuals (0.00000) is an essential indicator of the model’s accuracy. A lower standard error suggests that the model’s predictions are closer to the actual values. In this case, the standard error being zero indicates a perfect fit, which may be indicative of potential overfitting or other issues. It is crucial to scrutinize the model’s specifications and potential sources of bias to ensure the reliability of the results.

The Durbin-Watson statistic, with a value of 1.285, indicates the absence of significant autocorrelation among the residuals. The Durbin-Watson value falling close to 2 suggests that the residuals are not systematically related to each other, affirming the assumption of independent errors.

In summary, Table 4.4 provides a comprehensive overview of the model’s predictive performance and the distribution of residuals. While the model seems to provide accurate predictions on average, the presence of a zero standard error and the perfect fit merit further investigation into potential issues, such as overfitting or data anomalies. Additionally, the absence of significant autocorrelation in residuals supports the reliability of the model’s predictions.

Table 4.5, presenting correlation estimates, provides insights into the relationships between the predictor variables (GDPGR, UNEMPR, INDOUTP, POVLEV, POPGRA). Correlation coefficients measure the strength and direction of linear relationships between pairs of variables.

Starting with GDPGR (Gross Domestic Product Growth Rate), it exhibits a significant negative correlation with UNEMPR (Unemployment Rate) at -0.212. This indicates that higher GDP growth tends to be associated with lower unemployment rates, aligning with economic intuition.

The correlation between GDPGR and INDOUTP (Industrial Output) is positive and significant at 0.591. This suggests a positive relationship between GDP growth and industrial output, indicating that periods of economic growth are accompanied by increased industrial activity.

The correlation between GDPGR and POVLEV (Poverty Levels) is negative but not statistically significant at -0.130. This implies a weak and non-linear relationship between GDP growth and poverty levels, suggesting that factors other than GDP growth may influence poverty.

Interestingly, GDPGR has a strong positive correlation of 0.711 with POPGRA (Population Growth Rate). This result may signify a scenario where economic growth is associated with an increase in population. This relationship merits further exploration to understand its implications for economic and social dynamics.

Moving to UNEMPR, it has a significant negative correlation of -0.585 with POPGRA. This indicates that higher population growth is associated with lower unemployment rates. This counterintuitive finding could prompt a more in-depth investigation into the specific dynamics between population growth and employment in the context of the study.

INDOUTP and POPGRA exhibit a negative correlation of -0.597, implying that higher industrial output is associated with lower population growth. This relationship might be attributed to factors such as urbanization and changing economic structures, where industrialization accompanies lower population growth.

The correlations between INDOUTP and other variables (GDPGR, POVLEV) are not statistically significant, suggesting that industrial output might have a more isolated relationship with population growth.

POVLEV (Poverty Levels) exhibits a positive correlation of 0.364 with UNEMPR, indicating that higher poverty levels are associated with higher unemployment rates. This aligns with the common understanding that poverty and unemployment can be interconnected issues.

The correlations between POVLEV and other predictors (GDPGR, INDOUTP) are not statistically significant, suggesting that poverty levels might be influenced by a broader set of factors beyond those included in the model.

In summary, Table 4.5 provides a nuanced understanding of the relationships between the variables, unravelling intricate connections that contribute to the overall dynamics of the study. The correlations, both significant and non-significant, offer valuable insights that can guide further exploration and refinement of the model.


In conclusion, the comprehensive analysis of the study’s hypotheses yields significant insights into the complex interplay between population dynamics and unemployment in the Nigerian context. Firstly, the findings reject the null hypothesis asserting no positive correlation between population growth and unemployment rates. Instead, the results indicate a counterintuitive negative correlation, suggesting that higher population growth is associated with lower unemployment rates. This unexpected relationship prompts a reevaluation of traditional assumptions about the impact of population growth on employment.

Secondly, the study fails to reject the null hypothesis positing that economic factors, such as GDP growth and industrial output, do not mediate the relationship between population dynamics and unemployment. The non-significant correlations between these variables emphasize the need for a more nuanced understanding of the intricate economic mechanisms influencing unemployment.

Lastly, the results do not provide conclusive evidence to reject the null hypothesis that government policies and interventions play no significant role in mitigating the impact of population growth on unemployment in Nigeria. While not statistically significant, the observed correlations between poverty levels and unemployment suggest that policy interventions addressing poverty could indirectly impact unemployment rates. Overall, these findings contribute to a richer understanding of the multifaceted dynamics shaping unemployment in the Nigerian context.


The following recommendations were proposed for this study:

  1. Refinement of Population Growth Policies: Given the unexpected negative correlation between population growth and unemployment rates, policymakers should revisit existing population growth policies. The study suggests that certain factors related to population growth might be contributing positively to employment, and a more detailed examination can help refine policies that align with this counterintuitive finding.
  2. Enhanced Economic Diversification: The non-mediation of economic factors like GDP growth and industrial output implies the need for a more diversified economic landscape. Policymakers should consider strategies to diversify the economy, fostering growth in various sectors to create a more resilient and dynamic labour market.
  3. Targeted Poverty Alleviation Programs: While not statistically significant, the correlation between poverty levels and unemployment suggests an indirect relationship. Implementing targeted poverty alleviation programs could potentially impact unemployment rates. Policymakers should explore and design interventions that address poverty as a potential driver of unemployment.
  4. Long-Term Employment Planning: The study period (2010 to 2022) highlights the importance of long-term employment planning. Policymakers should develop strategies that extend beyond short-term solutions, considering the evolving dynamics of population growth and unemployment over an extended period.
  5. Continuous Monitoring and Evaluation: Establishing a robust system for continuous monitoring and evaluation of employment-related policies is crucial. Regular assessments can provide timely feedback, enabling policymakers to make data-driven adjustments and improvements to their strategies.
  6. Investment in Skills Development: To enhance employability, there should be a focus on investing in education and skills development programs. Equipping the workforce with relevant skills can contribute to a more adaptable labour market that aligns with the demands of a diversified economy.
  7. Public-Private Partnerships: Collaborative efforts between the public and private sectors can lead to more effective employment solutions. Engaging businesses in the development and implementation of employment strategies ensures a holistic approach and real-world applicability.
  8. Research-Based Policy Design: Given the nuanced findings of this study, policymakers are encouraged to adopt a research-based approach in designing and implementing policies. Regularly updated research can provide insights into the changing dynamics of population and unemployment, guiding the development of effective and adaptive policies.

Contribution to Knowledge

This study makes a significant contribution to the existing knowledge base by shedding light on the intricate relationship between population dynamics and unemployment in the context of Nigeria. The findings challenge conventional assumptions, particularly the unexpected negative correlation between population growth and unemployment rates. This counterintuitive result prompts a reevaluation of existing paradigms, pushing the boundaries of understanding within the field of labour market dynamics.

Moreover, the study enriches the literature by examining the mediating role of economic factors, such as GDP growth and industrial output. The non-mediation observed suggests that traditional economic drivers might not operate straightforwardly in the Nigerian context. This nuanced understanding of the interplay between population dynamics and economic factors contributes to a more comprehensive framework for policymakers and researchers alike, offering insights beyond mere statistical associations.

Furthermore, the study extends the temporal scope by investigating the relationship over a period from 2010 to 2022. This longitudinal analysis captures the dynamic nature of population and unemployment trends, providing a more nuanced perspective than cross-sectional studies. The temporal dimension adds depth to the understanding of how these variables evolve, offering valuable insights for policymakers aiming to develop sustainable, long-term strategies.

Lastly, the study contributes methodologically by adopting a correlational survey research design. This approach allows for a holistic examination of the multifaceted relationship between population dynamics and unemployment, emphasizing the need for comprehensive and context-specific research methods. The methodological insights from this study may guide future researchers in selecting appropriate designs to unravel complex relationships within different socio-economic contexts. Overall, these contributions collectively enhance the depth and breadth of knowledge in the field of labour market dynamics and population studies, paving the way for more informed and effective policy interventions. 


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