1. Introduction

Nigeria’s economic development depends on adequate investment in economic services, which includes sectors like agriculture, road construction, and transportation. This paper examines Nigeria’s recurrent expenditure in these sectors, investigating its relationship with GDP growth. By analyzing historical data, we aim to understand how government spending affects economic performance.

2. Problem Statement

Inadequate investment or misallocation in economic services could hinder Nigeria’s GDP growth. This study explores the alignment between recurrent expenditures in key economic services and overall GDP, using historical data to derive insights into patterns and correlations.

3. Objectives

  1. To analyze the historical patterns of Nigeria’s recurrent expenditure in agriculture, road, and transport sectors.
  2. To evaluate the relationship between these expenditures and GDP growth.
  3. To perform regression analysis to assess the strength of this relationship.

4. Purpose

Prior studies indicate that government expenditure on economic services can positively impact GDP by improving infrastructure, which in turn boosts economic productivity. However, excessive or poorly managed spending may have the opposite effect.

5. Methodology

The descriptive analysis, correlation matrix, time series, and regression analysis were carried out. The dataset spans 1981-2022 and includes expenditures in agriculture, road, transportation, and others, alongside GDP values. Standardization was performed on the variables for uniformity and to control for scale differences while correlations were explored to identify relationships between expenditures and GDP. The data analysis was done using R programming language and R-markdown software.

6. Data Analysis and Presentation

The descriptive statistics for the variables show that GDP has the highest maximum value of 74,639.47 billion, with a mean of 38,589.74 billion and a standard deviation of 20,854.23, indicating substantial variability. Expenditure on “Others” also shows significant variation, with a maximum of 435.04 billion, a mean of 61.53 billion, and a high standard deviation of 91.62 billion. “Road Construction” has a maximum of 218.47 billion, a mean of 52.63 billion, and a standard deviation of 69.99 billion, while “Transport and Communication” has a maximum of 90.03 billion and a standard deviation of 20.86 billion. “Agriculture” exhibits the lowest values, with a maximum of 81.87 billion and a mean of 22.15 billion. The median values for all variables indicate moderate central tendency, with “Others” showing the highest median of 10.17 billion. Overall, the data suggests that GDP and expenditure on “Others” are more volatile, while the other sectors exhibit more stable values.

# Summary statistics
# Calculate descriptive statistics without concatenated names
descriptive_table <- data %>%
  summarise(
    Min_Agriculture = min(Agriculture),
    Max_Agriculture = max(Agriculture),
    Mean_Agriculture = mean(Agriculture),
    Median_Agriculture = median(Agriculture),
    SD_Agriculture = sd(Agriculture),
    
    Min_Road_Construction = min(Road_Construction),
    Max_Road_Construction = max(Road_Construction),
    Mean_Road_Construction = mean(Road_Construction),
    Median_Road_Construction = median(Road_Construction),
    SD_Road_Construction = sd(Road_Construction),
    
    Min_Transport_Communication = min(Transport_Communication),
    Max_Transport_Communication = max(Transport_Communication),
    Mean_Transport_Communication = mean(Transport_Communication),
    Median_Transport_Communication = median(Transport_Communication),
    SD_Transport_Communication = sd(Transport_Communication),
    
    Min_Others = min(Others),
    Max_Others = max(Others),
    Mean_Others = mean(Others),
    Median_Others = median(Others),
    SD_Others = sd(Others),
    
    Min_GDP = min(GDP),
    Max_GDP = max(GDP),
    Mean_GDP = mean(GDP),
    Median_GDP = median(GDP),
    SD_GDP = sd(GDP)
  ) %>%
  pivot_longer(cols = everything(), names_to = "Statistic_Variable", values_to = "Value") %>%
  separate(Statistic_Variable, into = c("Statistic", "Variable"), sep = "_", extra = "merge") %>%
  pivot_wider(names_from = "Statistic", values_from = "Value")

# View the table
print(descriptive_table)
## # A tibble: 5 × 6
##   Variable                       Min     Max    Mean   Median      SD
##   <chr>                        <dbl>   <dbl>   <dbl>    <dbl>   <dbl>
## 1 Agriculture                 0.0128    81.9    22.2     8.77    25.9
## 2 Road_Construction           0.0948   218.     52.6    11.2     70.0
## 3 Transport_Communication     0.0318    90.0    17.2     8.94    20.9
## 4 Others                      0        435.     61.5    10.2     91.6
## 5 GDP                     16048.     74639.  38590.  28702.   20854.

The time series plots illustrate the trends of recurrent expenditures across five categories—Agriculture, GDP, Others, Road Construction, and Transport Communication—from 1980 to 2020. Agriculture and GDP expenditures show a gradual increase, with a notable sharp rise in GDP starting around 2000. “Others” and “Transport Communication” categories experience sporadic spikes, particularly after 2000, suggesting fluctuating investments. Road Construction shows a steady rise from around 1990, with a marked increase in the 2000s. Overall, these plots indicate rising expenditures over time across most categories, with GDP showing the most substantial growth trajectory.

# Transform data into long format for ggplot
data_long <- data %>%
  pivot_longer(cols = c(Agriculture, Road_Construction, Transport_Communication, Others),
               names_to = "Expenditure_Type", values_to = "Expenditure")

# Plot
ggplot(data_long, aes(x = YEAR)) +
  # Line plot for Expenditure Types
  geom_line(aes(y = Expenditure, color = Expenditure_Type), size = 1) +
  # Line plot for GDP on the secondary axis
  geom_line(aes(y = GDP / 1000), color = "black", size = 1, linetype = "dashed") +
  # Define primary y-axis for Expenditure
  scale_y_continuous(name = "Expenditure (in billions)",
                     sec.axis = sec_axis(~ . * 1000, name = "GDP (in billions)")) +
  # Define x-axis
  scale_x_continuous(name = "Year", breaks = seq(min(data$YEAR), max(data$YEAR), by = 5)) +
  # Customize the legend and theme
  labs(color = "Expenditure Type") +
  theme_minimal() +
  theme(
    axis.title.y.right = element_text(color = "black"),
    axis.text.y.right = element_text(color = "black")
  )

The correlation matrix graph displays the strength and direction of relationships between GDP and various expenditure categories (Agriculture, Road Construction, Transport Communication, and Others). High positive correlations exist between GDP and Road Construction (0.91) and between GDP and Agriculture (0.87), indicating that increased spending in these areas is strongly associated with GDP growth. Road Construction and Agriculture also show a strong positive correlation (0.92) with each other. Transport Communication and Others have moderate correlations with GDP (0.69 and 0.75, respectively), suggesting a weaker but still positive association with economic performance. The intensity of red color indicates the strength of these positive correlations.

Standardizing highly correlated variables is essential, particularly in studies like this one examining Nigeria’s recurrent expenditure on economic services, because it enables meaningful comparison across variables measured on different scales. In the case of this study, variables such as GDP, Agriculture, Road Construction, Transport, and Others vary significantly in magnitude. Standardization rescales these variables to a common range (usually with a mean of 0 and a standard deviation of 1), which prevents variables with larger scales from disproportionately influencing the analysis.

In highly correlated datasets, standardization also helps in reducing potential issues of multicollinearity in regression models, which can distort coefficient estimates, lead to unstable models, and reduce interpretability. For example, standardizing GDP alongside expenditure categories ensures that each variable contributes equally to the analysis, improving the robustness of correlation, regression, and time-series analyses.

This time series regression model indicates that GDP is significantly influenced by recurrent expenditure on certain economic services in Nigeria. The model has a high \(R^2\) value of 0.8788, suggesting that approximately 87.9% of the variability in GDP is explained by the independent variables (Agriculture, Road_Construction, Transport_Communication, and Others). Among these, Road_Construction and Others are significant predictors with p-values of \(5.65 \times 10^{-5}\) and 0.0174, respectively, indicating strong and moderate impacts on GDP. In contrast, Agriculture and Transport_Communication have higher p-values (0.3978 and 0.9678), showing they do not significantly contribute to GDP in this model. The residuals are reasonably well-distributed with a standard error of 7644, although there is some variation in the residuals’ spread, as indicated by a maximum residual of 17064. Overall, the model is statistically significant (p-value < 2.2e-16), and it highlights Road_Construction as a particularly impactful area of recurrent expenditure for GDP growth.

## 
## Call:
## tslm(formula = GDP ~ Agriculture + Road_Construction + Transport_Communication + 
##     Others, data = time_series_data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -10773  -5025  -1647   4703  17064 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             22977.436   1610.144  14.270  < 2e-16 ***
## Agriculture               100.293    117.236   0.855   0.3978    
## Road_Construction         194.266     42.715   4.548 5.65e-05 ***
## Transport_Communication    -3.787     93.195  -0.041   0.9678    
## Others                     52.526     21.098   2.490   0.0174 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7644 on 37 degrees of freedom
## Multiple R-squared:  0.8788, Adjusted R-squared:  0.8657 
## F-statistic: 67.04 on 4 and 37 DF,  p-value: < 2.2e-16

The OLS regression model results show that GDP is primarily influenced by expenditures on Road_Construction and Others, which are statistically significant with p-values of \(5.65 \times 10^{-5}\) and 0.0174, respectively. The intercept is highly significant, and the model achieves a strong fit with an \(R^2\) of 0.8788, meaning that 87.9% of the variance in GDP is explained by these variables. However, Agriculture and Transport_Communication are not significant contributors to GDP, as indicated by their higher p-values (0.3978 and 0.9678, respectively). The residuals are reasonably centered, with a standard error of 7644.

The OLS results align with the time series regression model, as both models identify Road_Construction and Others as significant predictors of GDP, while Agriculture and Transport_Communication remain non-significant. This consistency between the OLS and time series regression models strengthens the conclusion that recurrent expenditure on road construction and other services has a notable positive impact on GDP growth in Nigeria.

## 
## Call:
## lm(formula = GDP ~ Agriculture + Road_Construction + Transport_Communication + 
##     Others, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -10773  -5025  -1647   4703  17064 
## 
## Coefficients:
##                          Estimate Std. Error t value Pr(>|t|)    
## (Intercept)             22977.436   1610.144  14.270  < 2e-16 ***
## Agriculture               100.293    117.236   0.855   0.3978    
## Road_Construction         194.266     42.715   4.548 5.65e-05 ***
## Transport_Communication    -3.787     93.195  -0.041   0.9678    
## Others                     52.526     21.098   2.490   0.0174 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7644 on 37 degrees of freedom
## Multiple R-squared:  0.8788, Adjusted R-squared:  0.8657 
## F-statistic: 67.04 on 4 and 37 DF,  p-value: < 2.2e-16

Recommendations

Based on the findings that recurrent expenditures on road construction and other services significantly impact GDP, the following recommendations are suggested for the Nigerian government to boost economic growth:

  1. Increase Investment in Infrastructure Development: The government should prioritize and increase recurrent and capital expenditure on critical infrastructure, particularly road construction and maintenance. Improved road infrastructure enhances transportation efficiency, reduces business costs, and promotes regional trade and accessibility, directly contributing to GDP growth.

  2. Allocate More Funds to Key Economic Sectors: The “Others” category, which includes various economic services, has shown a positive impact on GDP. The government should further invest in sectors within this category, such as technology, energy, and telecommunications, which can provide high returns by fostering economic diversification, improving productivity, and driving innovation.

  3. Develop Agricultural Infrastructure and Value Chains: Although agriculture did not significantly impact GDP in this analysis, it remains a crucial sector for employment and food security. Targeted investments in agricultural value chains, such as processing facilities, storage, and export logistics, could enhance productivity, reduce waste, and increase agricultural exports, adding resilience and growth to the GDP.

  4. Strengthen Public-Private Partnerships in Transportation: Given the limited impact of Transport and Communication alone, the government should seek private-sector partnerships to improve transportation networks and communication infrastructure. This can help bridge the funding gap, improve service delivery, and leverage expertise from the private sector, contributing to broader economic gains.

  5. Implement Accountability and Monitoring for Recurrent Expenditures: To maximize the effectiveness of recurrent expenditures on economic services, the government should establish transparent monitoring frameworks and accountability measures. This would ensure that funds allocated to sectors such as road construction and other economic services are used effectively, minimizing waste and corruption and ensuring tangible development outcomes that support sustainable GDP growth.

These recommendations can support Nigeria in leveraging recurrent expenditure more effectively to stimulate economic growth, drive industrialization, and enhance the overall quality of life for its citizens.

7. Conclusion

In conclusion, this study has explored the relationship between Nigeria’s recurrent expenditure on economic services, including agriculture, road construction, transportation, and other sectors, and its impact on GDP. The findings highlight that expenditure on road construction and other key services plays a significant role in driving economic growth, while agriculture and transport, in this context, have a less direct impact. The strong correlation between these sectors and GDP underscores the importance of strategic investments in infrastructure and service delivery to foster economic development. Given the high explanatory power of the model, the Nigerian government is urged to prioritize investments in these critical areas, particularly infrastructure, to unlock the full potential of the economy. By adopting the recommended policy measures, Nigeria can boost its GDP, enhance productivity, and achieve sustainable economic growth, thereby improving the livelihoods of its citizens and securing a prosperous future.