Introduction

Nigeria’s economic growth has been shaped by various fiscal policies, with recurrent government expenditures playing a significant role. Recurrent expenditures, which include spending on administration, social services, economic services, and transfers, are critical to sustaining the country’s economic activities and public welfare. However, the sustainability of economic growth depends not just on spending levels but on how effectively these expenditures are allocated to stimulate long-term development. This study aims to examine the impact of Nigeria’s recurrent expenditures on GDP growth, identifying the key components that significantly influence economic performance while considering the sustainability of fiscal policies. By exploring these relationships, the study will offer actionable recommendations for optimizing recurrent spending, ensuring not only immediate economic growth but also long-term fiscal sustainability, social development, and economic resilience.

Nigeria, an economic powerhouse in Africa, has experienced fluctuations in its Gross Domestic Product (GDP) over the years. Understanding the factors that influence this vital economic indicator is crucial for sustainable growth and development. This publication analyzes the impact of recurrent expenditure on Nigeria’s GDP, drawing upon data from the Nigeria National Bureau of Statistics (NBS) spanning from 1981 to 2022.

Objectives:

Data and Methodology

The analysis utilizes data from the National Bureau of Statistics (NBS)[https://www.cbn.gov.ng/documents/statbulletin.asp], encompassing Nigeria’s recurrent expenditures and GDP from 1981 to 2022. The recurrent expenditures are categorized as:

The study employs various statistical techniques, including:

Data Exploration

The data obtained from the NBS, comprises of Nigeria’s recurrent expenditures and GDP (2010 base year) in Billion Naira from 1981 to 2022. The recurrent expenditures are broadly categorized by the NBS/CBN as administrative, social and community services, economic services and transfers.

This section conducts an analysis of both individual variables (univariate) and the relationships between the independent variables and the dependent variable, GDP (bivariate), it also includes visualizations like histograms, boxplots, correlation matrices, scatterplots, and time series plots.

Univariate Analysis

Exploring each of the variables as displayed below with histograms being continuous variable forms. All of the histograms appear to be positively skewed, indicating that majority of the data points are concentrated towards the left side of the histogram, for each distribution, the mean is greater than the median. Hence, each of the distributions are asymmetric, with a longer tail on the right. This is expected as budgets and national incomes usually are on annual increase, most years have lower values while a few years have higher values.

Exploratory Time Series Analysis

The time series charts show a general upward trend in GDP and all categories of recurrent expenditures (administrative, social and community, economic, and transfer) from 1980 to 2023, with sharper increases observed in recent years, particularly in transfer expenses. This suggests that rising government expenditures, especially in transfers, may be associated with the observed GDP growth over the period, potentially indicating a close relationship between fiscal spending and economic performance.

Autocorrelation Function (ACF) for GDP

The ACF plot for GDP shows high positive autocorrelation at early lags, indicating a strong relationship between GDP values in consecutive years, and a gradual decline at higher lags, suggesting a possible underlying trend. Most significant autocorrelations occur at lower lags, implying that GDP may be non-stationary and influenced by its past values. As a dependent variable in a model with recurrent expenditures (administrative, social and community, economic, and transfer) as independent variables, the trend in GDP suggests that it may require transformation (e.g., differencing) to achieve stationarity for accurate regression modeling. This trend implies that changes in expenditures over time are likely to significantly impact GDP, and careful attention to multicollinearity and time series structure will be necessary.

# Autocorrelation function (ACF) plot for GDP
data_ts <- ts(df$GDP, start = min(df$Years), frequency = 1)
acf(data_ts, main = "Autocorrelation of GDP")

Bivariate Exploratory Analysis

In this section, the variables are investigated in pairs, especially as they relate with GDP.

Correlation Test

For formal bivariate testing, the correlation tests were performed to quantify the relationships, relationships between 0.1 to 0.5 are considered weak, while 0.6 to 0.9 are considered strong, correlation value of 0 implies no-correlation, 1 implies perfect correlation. There relationships can be either positive (+) or negative (-). From the correlation matrix presented below, a strong relationship exist between the independent variables and GDP, as well as within the independent variables. These strong relationships might influence the models if not properly handled, causing a multicolinearity problem.

Bivariate Regression Plots

The figure presented below Visualizes the simple linear regression line for the relationships between the independent variables and GDP. While a high value is observed of the adjusted R-squared, the recurrent expense on transfers appear to have the least adjusted R-squared. The adjusted R-squared value indicates the degree at which the independent variable explains or is responsible for the variations in Nigeria’s GDP. While the values for other variables tends towards 100%, transfers exhibits the least, (66%) which is still over a guessing level of prediction.

NORMALITY TEST

The shapiro-wilk normality test is powerful for samples between 5-40. The outcome of the test below shows that the GDP distribution is significantly different from normality.

# Shapiro-Wilk test for normality for GDP column
shapiro.test(df$GDP)
## 
##  Shapiro-Wilk normality test
## 
## data:  df$GDP
## W = 0.83747, p-value = 3.095e-05

In support of the positively skewed histogram and the shapiro wilk test, the Q_Q chart displays the failure of the point observations to alin by the linear-diagonal line. As a result, normal transformations are applied to the variables.

# Q-Q plot for GDP column
qqnorm(df$GDP)
qqline(df$GDP, col = "red")

Standardize the variables

In order to get the variables to be analysis ready, they are standardized using the z-score.

FIT THE MULTIPLE REGRESION MODEL

# Fit the model with all predictors
model <- lm(GDP ~ Administration + Social_and_Community_Services + Economic_Services + Transfers, data = df)

# View the summary of the model
summary(model)
## 
## Call:
## lm(formula = GDP ~ Administration + Social_and_Community_Services + 
##     Economic_Services + Transfers, data = df)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -15504.4  -3469.5   -149.2   3063.7  13968.5 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                   21804.464   1272.144  17.140  < 2e-16 ***
## Administration                   28.843     13.496   2.137 0.039260 *  
## Social_and_Community_Services    13.713     16.341   0.839 0.406758    
## Economic_Services                 7.020     16.397   0.428 0.671045    
## Transfers                        -7.001      1.921  -3.645 0.000817 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 5943 on 37 degrees of freedom
## Multiple R-squared:  0.9267, Adjusted R-squared:  0.9188 
## F-statistic:   117 on 4 and 37 DF,  p-value: < 2.2e-16

Model Diagnostics QQ and W tests

The QQ plot displays majority of the residuals plotted along the linear diagonal line, this suggests a well fitted model. Formal approach of Shapiro-Wilk test which suggests that if the p-value > 0.05, the residuals are approximately normally distributed.As presented, the p-value is 0.2747, the residuals are approximately normal.

# Q-Q plot for residuals
qqnorm(model$residuals)
qqline(model$residuals, col = "red")

# Shapiro-Wilk normality test
shapiro.test(model$residuals)
## 
##  Shapiro-Wilk normality test
## 
## data:  model$residuals
## W = 0.96765, p-value = 0.2747
Homoscedasticity (Constant Variance)

Check if residuals have constant variance across fitted values using the Breusch-Pagan Test which posits that if the p-value > 0.05, it suggests homoscedasticity. From the presented outcome, no homoscedasticity is suggested.

# Plot residuals vs fitted values to check for homoscedasticity
plot(model$fitted.values, model$residuals)
abline(h = 0, col = "blue")

# Breusch-Pagan test for homoscedasticity
bptest(model)
## 
##  studentized Breusch-Pagan test
## 
## data:  model
## BP = 18.978, df = 4, p-value = 0.0007938

Summarized Findings

  • Univariate analysis reveals a wide distribution in administrative expenses, social and community service expenses, economic services expenses, transfers, and GDP from 1981 to 2022.
  • Correlation analysis indicates a strong positive correlation between GDP and all categories of recurrent expenditure, with administration expenses showing the highest correlation.
  • Regression analysis confirms that administration expenses, social and community services, and transfers have a statistically significant positive impact on GDP.
  • Model diagnostics suggest that the relationship between recurrent expenditure and GDP might be non-linear, necessitating further analysis with polynomial regression models.

The regression analysis reveals that recurrent expenditures significantly influence Nigeria’s GDP, but the impact varies across different components. Administrative expenses have a positive and statistically significant effect on GDP (p = 0.039), indicating that higher spending in this area contributes to economic growth. However, transfers exhibit a negative and significant effect (p = 0.0008), suggesting that excessive transfer spending may be detrimental to GDP. Social and community services, as well as economic services, show positive but statistically insignificant effects, implying that their current levels of spending may not substantially drive GDP growth.

Actionable Recommendations

  • Prioritize Administrative Efficiency: The strong link between administrative expenses and GDP highlights the importance of efficient government operations. Streamlining bureaucratic processes, leveraging technology, and promoting transparency can optimize administrative expenses and boost GDP growth in Nigeria. Given its positive contribution to GDP, administrative spending should be sustained but carefully managed to ensure efficient allocation of resources without leading to fiscal imbalances.

  • Strategic Investment in Social and Community Services: While the impact of social and community services on GDP is statistically significant, further analysis is needed to identify specific areas within this category that yield the highest returns. Targeted investments in education, healthcare, and social safety nets can enhance human capital and drive economic growth in NIgeria. Although currently insignificant, increasing and effectively managing investments in social and community services, as well as economic services, could drive future GDP growth. Prioritizing projects with long-term economic benefits will enhance sustainability.

  • Optimize Economic Services Expenditure: The study suggests a statistically significant relationship between economic services expenses and GDP. Nigerian policymakers should prioritize investments in infrastructure development, particularly in sectors like power, transportation, and communication, to attract investments into the country and foster economic diversification.

  • Transparency and Accountability in Transfers: Transfers, while crucial for social welfare, need to be managed transparently and efficiently to avoid leakages and maximize their impact on GDP. Implementing robust monitoring and evaluation mechanisms can ensure that transfers reach the intended beneficiaries and contribute to economic growth. The negative impact of transfers calls for a reevaluation of these expenses. Policymakers should consider restructuring transfer programs to ensure they are more targeted, reducing inefficiencies and reallocating funds to more productive sectors.

  • Focus on Fiscal Sustainability: To ensure long-term economic growth, Nigeria needs to adopt a fiscal policy framework that balances recurrent expenditure with investment in sustainable projects. This involves curbing wasteful spending while promoting economic sectors that foster resilience and development.

Conclusion

Recurrent expenditure plays a crucial role in influencing Nigeria’s GDP. By prioritizing administrative efficiency, making strategic investments in social and community services, optimizing economic services expenditure, and ensuring transparency in transfers, policymakers can effectively leverage recurrent expenditure as a catalyst for sustainable economic growth and development in Nigeria.

Contact the Author Organization: Quiver Analytics

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