Introduction:

This study investigates the sustainability of the Nigerian government’s administrative recurrent expenditures in three critical classifications —general administration, defense, and internal security—and their impact on the country’s GDP from 1981 to 2022. Understanding how these expenses influence economic performance is crucial for formulating policies aimed at maintaining a balance between necessary public expenditure and sustainable economic growth.

Background

The recurrent expenditures of governments, particularly in developing nations, have long been a critical factor in influencing macroeconomic outcomes such as GDP growth. In Nigeria, the federal government’s recurrent administrative expenditures—allocated across sectors like general administration, defense, and internal security—are significant components of fiscal policy. These expenditures aim to ensure the smooth functioning of the state’s administrative and security apparatus. However, while recurrent spending can drive short-term economic activity, excessive recurrent expenditure, especially in non-productive sectors, can burden the economy by diverting funds from capital investment. The need to critically examine the sustainability and effectiveness of such spending is paramount, especially in light of Nigeria’s economic challenges, including rising debt levels, slow economic diversification, and fluctuating oil revenues. Given this context, understanding the impact of recurrent expenditure on GDP growth becomes crucial for formulating policies that balance security and administrative needs with sustainable economic development.

Problem Statement

Despite the substantial financial resources allocated to general administration, defense, and internal security by the Nigerian government, the effectiveness of these expenditures in promoting sustainable economic growth remains under explored. The recurrent nature of these expenditures may contribute to fiscal deficits, crowding out investments in capital projects that could spur long-term productivity. Moreover, with increasing pressure on public finances due to oil revenue volatility, it is essential to determine whether the current allocation of funds across these sectors is yielding tangible benefits in terms of GDP growth. This study seeks to rigorously assess the impact of administrative, defense, and internal security recurrent expenditures on Nigeria’s GDP, addressing the critical question of whether this spending pattern is sustainable or detrimental to long-term economic performance.

Research Questions:

  1. How have recurrent expenses on general administration, defense, and internal security evolved over the years?
  2. What is the correlation between these expenditures and Nigeria’s GDP?
  3. To what extent do these recurrent expenses contribute to the sustainability of Nigeria’s economic growth?

Methodology:

Data for this study was sourced from the National Bureau of Statistics (NBS) and the Central Bank of Nigeria (CBN). It includes values for recurrent expenditure on general administration, defense, internal security (Inter), and GDP between 1981 and 2022. The study employs econometric analysis using R programming language to investigate the relationships between these expenditures and GDP. The following statistical models will be applied:

  1. Descriptive statistics to observe trends over time.
  2. Correlation analysis to identify relationships between variables.
  3. Time series regression to assess the impact of recurrent expenditures on GDP.
  4. Stationarity tests and cointegration analysis (such as Augmented Dickey-Fuller test) to check for long-term relationships between the variables.
  5. Sustainability will be gauged through sensitivity analysis using predicted GDP growth with variations in expenditure. The study will employ visualizations such as line graphs for temporal trends, correlation heatmaps, and regression result tables to clearly present the analysis.

Data Analysis

Descriptive Statistics Overview:

General Administration spending has a mean value of ₦265.7 billion, with a substantial increase over time, reaching ₦992.24 billion in 2022. Defense expenditure averages ₦147.1 billion, with a maximum of ₦693.85 billion in 2022. Internal Security has an average spending of ₦171.45 billion, and it also showed substantial growth, peaking at ₦770.24 billion in 2022. GDP grew significantly from ₦19,549.56 billion in 1981 to ₦74,639.47 billion in 2022, with an average of ₦38,589.74 billion over the period.

Trend Analysis:

The visual clearly supports the insight from summary statistics above, it shows a sharp rise in Nigeria’s GDP from the early 2000s onward, which coincides with increases in General Administration, Defense, and Internal Security spending. While the expenses have also risen sharply in recent years, they remain proportionally small compared to GDP.

# Plotting the trends of recurrent expenditure and GDP over time
ggplot(data, aes(x = Years)) + 
  geom_line(aes(y = General_Administration, color = "General Administration"), size = 1) +
  geom_line(aes(y = Defense, color = "Defense"), size = 1) +
  geom_line(aes(y = Internal_Security, color = "Internal Security"), size = 1) +
  geom_line(aes(y = GDP / 1000, color = "GDP (in Billions)"), size = 1, linetype = "dashed") + 
  labs(title = "Trends in Recurrent Expenditure and GDP (1981-2022)", y = "Value (Billion Naira)", x = "Years") +
  scale_color_manual(values = c("blue", "green", "red", "purple")) +
  theme_minimal() +
  theme(legend.title = element_blank())

Correlation Analysis:

The correlation matrix reveals that there is a high positive correlation between General Administration spending and GDP (0.95), suggesting that as administrative expenses increase, so does the GDP. Defense spending also shows a strong correlation with GDP (0.91), though slightly lower compared to General Administration. Internal Security spending is similarly correlated with GDP (0.93), indicating that security-related recurrent expenses also move closely with GDP. Additionally, the expenditures (General Administration, Defense, and Internal Security) are highly correlated with each other, especially between Defense and Internal Security (0.99). These correlations suggest that recurrent expenses in these sectors may play a critical role in influencing GDP growth, though further analysis is needed to assess causal relationships.

High correlation values between independent variables, also known as multicollinearity, can be problematic in regression analysis because they inflate the standard errors of the coefficients, making it difficult to accurately assess the individual impact of each variable. This leads to unreliable and unstable coefficient estimates, potentially masking the true relationships between the variables and the dependent variable (GDP). In severe cases, multicollinearity can result in insignificant p-values for important predictors, despite a high overall model fit. To address this issue, it is essential to first diagnose the severity using concepts like a correlation matrix as observed and Variance Inflation Factor (VIF). If multicollinearity is confirmed (e.g., VIF > 5), corrective actions such as standardizing the variables or applying regularization techniques like Ridge regression should be implemented to produce more reliable and unbiased regression results.

# Install and load the car package if not already installed
if (!require(car)) install.packages("car")
library(car)

# Fit the model
model <- lm(GDP ~ General_Administration + Defense + Internal_Security, data = data)

# Calculate VIF
vif(model)
## General_Administration                Defense      Internal_Security 
##               11.85320               43.31808               58.39769
#If the VIF is greater than 5 or 10, it indicates severe multicollinearity.

Standardizing the variables can help mitigate multicollinearity, especially when dealing with variables measured on different scales.

FINDINGS

The regression analysis investigates the relationship between Nigeria’s GDP and the three administrative recurrent expenses: General Administration, Defense, and Internal Security, all scaled for interpretation. The model’s adjusted R-squared value of 0.8991 indicates that about 89.9% of the variation in GDP is explained by these three variables, demonstrating a strong fit.

Impact of General Administration on GDP: The coefficient for scaled General Administration is positive and statistically significant (Estimate = 14,905, p < 0.001), suggesting that increases in government expenditure on general administration positively influence GDP. This highlights that higher spending on administrative functions likely contributes to economic growth, aligning with the objective of understanding how this spending affects national output.

Impact of Defense on GDP: The coefficient for scaled Defense spending is negative (Estimate = -4,192) but statistically insignificant (p = 0.542), indicating that variations in defense spending do not have a reliable impact on GDP within this model. This implies that the relationship between defense expenditures and economic performance may not be strong enough to draw conclusive evidence, suggesting limited direct economic benefits from increased defense spending.

Impact of Internal Security on GDP: Internal Security spending shows a positive, albeit statistically insignificant effect (Estimate = 9,234, p = 0.250). Although this suggests that higher internal security spending might have a positive impact on GDP, the lack of significance means we cannot confidently assert its influence. The result indicates that while security could contribute to economic growth, more factors or better modeling may be required to fully capture its role.

## 
## Call:
## lm(formula = GDP ~ General_Administration_scaled + Defense_scaled + 
##     Internal_Security_scaled, data = data)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12678.9  -3689.4   -760.2   1891.8  18423.4 
## 
## Coefficients:
##                               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                      38590       1022  37.757  < 2e-16 ***
## General_Administration_scaled    14905       3562   4.185 0.000162 ***
## Defense_scaled                   -4192       6808  -0.616 0.541774    
## Internal_Security_scaled          9234       7905   1.168 0.250016    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6624 on 38 degrees of freedom
## Multiple R-squared:  0.9065, Adjusted R-squared:  0.8991 
## F-statistic: 122.8 on 3 and 38 DF,  p-value: < 2.2e-16

Regularization (Ridge Regression)

In the Ridge regression output, the coefficients represent the effect of each predictor variable on GDP, adjusted to account for multicollinearity by applying a penalty (regularization) to reduce coefficient magnitudes. The intercept (38,589.74) reflects the estimated GDP when all scaled variables are zero. The positive coefficient for General Administration (10,093.51) suggests that increasing administrative spending has a positive impact on GDP, though less pronounced than in the ordinary regression. Defense (3,490.87) and Internal Security (5,702.81) now show positive contributions, indicating that after regularization, these variables have a more balanced, albeit smaller, impact on GDP. Ridge regression stabilizes the coefficients, producing more reliable estimates in the presence of multicollinearity.

## 4 x 1 sparse Matrix of class "dgCMatrix"
##                                      s1
## (Intercept)                   38589.738
## General_Administration_scaled 10093.513
## Defense_scaled                 3490.871
## Internal_Security_scaled       5702.814
## 4 x 1 sparse Matrix of class "dgCMatrix"
##                                      s1
## (Intercept)                   38589.738
## General_Administration_scaled 10093.513
## Defense_scaled                 3490.871
## Internal_Security_scaled       5702.814

To conduct a time series regression to assess the impact of recurrent expenditures (General Administration, Defense, and Internal Security) on GDP, there is need to account for the time-based structure of the data.

# Convert the data to a time series object
data_ts <- ts(data[, c("GDP", "General_Administration", "Defense", "Internal_Security")], 
              start = 1981, frequency = 1)

Time series regression requires that the variables be stationary (constant mean and variance over time). Using the Augmented Dickey-Fuller (ADF) test to check stationarity. The Augmented Dickey-Fuller test results indicate that all variables—GDP, General Administration, Defense, and Internal Security—are non-stationary, as evidenced by high p-values and test statistics that do not support the null hypothesis of stationarity. To proceed with reliable time-series analysis, differencing is needed to transform these variables into stationary series, which may also allow for cointegration testing to examine any long-term relationships among them.

# ADF test for GDP
adf.test(data_ts[, "GDP"])
## 
##  Augmented Dickey-Fuller Test
## 
## data:  data_ts[, "GDP"]
## Dickey-Fuller = -1.743, Lag order = 3, p-value = 0.6749
## alternative hypothesis: stationary
# ADF test for General_Administration
adf.test(data_ts[, "General_Administration"])
## 
##  Augmented Dickey-Fuller Test
## 
## data:  data_ts[, "General_Administration"]
## Dickey-Fuller = -1.6334, Lag order = 3, p-value = 0.7181
## alternative hypothesis: stationary
# ADF test for Defense
adf.test(data_ts[, "Defense"])
## 
##  Augmented Dickey-Fuller Test
## 
## data:  data_ts[, "Defense"]
## Dickey-Fuller = 1.6075, Lag order = 3, p-value = 0.99
## alternative hypothesis: stationary
# ADF test for Internal_Security
adf.test(data_ts[, "Internal_Security"])
## 
##  Augmented Dickey-Fuller Test
## 
## data:  data_ts[, "Internal_Security"]
## Dickey-Fuller = 2.0975, Lag order = 3, p-value = 0.99
## alternative hypothesis: stationary

Given that the series are not stationary, there is need to difference them using the commands below.

# Differencing the variables if necessary
diff_GDP <- diff(data_ts[, "GDP"])
diff_GA <- diff(data_ts[, "General_Administration"])
diff_Defense <- diff(data_ts[, "Defense"])
diff_IS <- diff(data_ts[, "Internal_Security"])

Time Series Regression Model

The time series regression gave a similar results as the OLS multiple regression model. The time series regression indicates that General Administration has a significant positive impact on GDP, with an estimated coefficient of 48.15 (p < 0.001), suggesting that an increase in General Administration expenditure is associated with an increase in GDP. Defense and Internal Security, however, show no statistically significant relationship with GDP, with p-values of 0.54 and 0.25, respectively, indicating that their expenditures do not have a notable impact on GDP in this model. The residuals, as seen in the plot, exhibit some fluctuations over time, particularly after 2010, which may reflect economic volatility or structural shifts in expenditure impact on GDP.

# Time series regression with lagged terms to account for temporal effects
model <- dynlm(GDP ~ General_Administration + Defense + Internal_Security, data = data_ts)

# Summary of the model
summary(model)
## 
## Time series regression with "ts" data:
## Start = 1981, End = 2022
## 
## Call:
## dynlm(formula = GDP ~ General_Administration + Defense + Internal_Security, 
##     data = data_ts)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -12678.9  -3689.4   -760.2   1891.8  18423.4 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)            21874.27    1378.07  15.873  < 2e-16 ***
## General_Administration    48.15      11.50   4.185 0.000162 ***
## Defense                  -20.62      33.49  -0.616 0.541774    
## Internal_Security         40.57      34.73   1.168 0.250016    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 6624 on 38 degrees of freedom
## Multiple R-squared:  0.9065, Adjusted R-squared:  0.8991 
## F-statistic: 122.8 on 3 and 38 DF,  p-value: < 2.2e-16

Conclusion:

The results reveal that general administration expenditures have a positive and statistically significant effect on Nigeria’s GDP, showing the role of efficient public administration in economic performance. However, defense and internal security expenditures do not show significant impacts on GDP, which calls into question the effectiveness of these investments. Policymakers should, therefore, scrutinize these sectors to ensure that funds are allocated in ways that promote sustainable economic growth, rather than perpetuating unproductive expenditure. The insignificance of defense and internal security may reflect inefficiencies or complexities in how these expenditures translate into economic growth. Addressing these issues could provide insights into enhancing the effectiveness of public spending on these sectors.