Introduction

Economic sustainability is closely tied to a government’s ability to effectively allocate its resources to critical sectors, such as education and health. For developing economies like Nigeria, recurrent expenditures on social services not only affect the well-being of the population but also contribute significantly to GDP growth (Igwe and Inyiama,2024). This study seeks to investigate the impact of recurrent expenditures on education, health, and other social services on Nigeria’s GDP, examining their potential to drive long-term sustainable growth.

Background

Over the past decades, the Nigerian government has committed significant resources to recurrent expenditures in education, health, and other community services. These sectors play a crucial role in fostering human capital development and improving the quality of life, which are key factors for sustainable economic development (Okezie and Asoluka, 2023). Despite these expenditures, Nigeria’s economic growth has remained uneven. This study aims to understand whether recurrent expenditures in these critical sectors contribute meaningfully to the country’s economic performance.

Problem Statement

Nigeria’s recurrent expenditures in social-community services, particularly education and health, have grown substantially over the years (Igwe and Inyiama,2024). However, the degree to which these expenditures translate into positive GDP outcomes remains unclear. This study seeks to determine the extent to which government spending on social services impacts Nigeria’s economic growth, focusing on its sustainability and effectiveness.

Objectives

  1. To assess the trend and patterns of recurrent expenditures on education, health, and other social services in Nigeria from 1981 to 2022.

  2. To examine the relationship between recurrent expenditures on social services and Nigeria’s GDP.

  3. To evaluate the impact of recurrent expenditures on education, health, and ‘other-spendings’ on Nigeria’s economic sustainability.

  4. To conduct a time-series analysis to estimate and visualize the impact of recurrent expenditures on GDP over time.

Conceptual Definition

Recurrent Expenditure: The regular, operational spending by the government on services like education and health, as opposed to capital expenditures, which involve investments in infrastructure (Okezie and Asoluka, 2023).

Social-Community Services: Public services such as education, healthcare, and other social welfare programs that are aimed at improving the general well-being of the population (Igwe and Inyiama,2024).

Gross Domestic Product (GDP): The total monetary value of all goods and services produced within a country, serving as a broad measure of national economic activity (Tsounis and Vlachvei, 2017).

Literature Review

Several studies have examined the relationship between government spending and economic growth, especially in developing economies. According to Adeyemi (2018), public expenditure on health and education significantly contributes to human capital development, which in turn drives GDP growth. Chukwu and Udochukwu (2019) found a positive relationship between government spending on social services and GDP growth, while Aigbokhan (2021) highlighted the inefficiency of resource allocation in achieving sustainable growth.

Elaigwu and Ali (2024) investigated the effects of government expenditure on Nigeria’s economic growth (2005-2022), employing an ex-post facto research design and multiple linear regression analysis. They found that both capital and recurrent expenditures positively impacted GDP per capita, emphasizing the need for increased and well-directed capital spending towards productive sectors. In contrast, Ogar and Ezeugwu (2022) focused on the impact of various government expenditures—administration, economic services, social services, and transfers—on Nigeria’s economic growth, concluding that only administrative expenditure had a significant positive relationship with growth, while expenditures on economic and social services were positive but insignificant. They recommended shifting focus towards capital expenditure and improving social services infrastructure. Similarly, Uremadu et al. (2019) analyzed government recurrent expenditures from 1999-2016, finding that administrative and public debt expenditures positively contributed to growth, while expenditures on pensions, the National Assembly, and transfers had insignificant impacts. Their study advocated for sustaining administrative and debt service spending while addressing leakages. A critical comparison of these studies reveals varying emphasis on the significance of different categories of expenditure, with mixed findings on the impact of social services and transfers, highlighting a persistent gap in understanding the optimal allocation of recurrent and capital expenditures for economic growth in Nigeria.

The literature remains inconclusive regarding the precise roles and impact of various categories of government expenditure, particularly social services in forms of education and health, on Nigeria’s economic growth. This study focuses on the key findings related to recurrent expenditures on education, health, and other community services, building on these studies to provide new insights into the Nigerian context.

Methodology

Data Analysis

The dataset covers the period from 1981 to 2022, including variables such as recurrent expenditures on education, health, other social services, and GDP. The data is publicly available from the NBS-CBN websites. The methodology of this study includes descriptive statistics calculated to determine the mean, minimum, and maximum values of recurrent expenditures on education, health, others, and GDP. Time-series plots are generated to visualize the trends of these expenditures alongside GDP over time (Sha and Taker, 2024). A normality assessment is conducted using the Shapiro-Wilk test, supported by histograms to assess the distribution of the variables. The variables are then standardized to ensure comparability and consistency in scale during analysis (Torres et al., 2024). A time-series analysis is performed using methods such as ARIMA or Vector Autoregression (VAR) to estimate the impact of recurrent expenditures on GDP (Mackarov, 2024). Diagnostic tests, including the Durbin-Watson test for autocorrelation, Breusch-Pagan test for heteroscedasticity, and Variance Inflation Factor (VIF) for multicollinearity, are applied to evaluate the reliability of the model (Xu, 2023). Finally, multiple regression models are run to estimate the individual effects of recurrent expenditures on education, health, and others on GDP, interpreting coefficients to determine the most significant drivers (Torres et al., 2024). R software is utilized for all statistical analyses, including descriptive statistics, regression, time-series analysis, and visualizations.

Trend Analysis

The time-series chart shows a significant increase in Nigeria’s recurrent expenditures on education and health from 1981 to 2022, particularly from the late 1990s onward. Education spending saw the most dramatic rise after 1999, while health expenditures also grew substantially, albeit at a slower rate. In contrast, GDP experienced a more gradual increase, highlighting a disparity between the rapid growth of government spending on social services and the relatively slower GDP growth. This suggests that while investments in education and health have surged, their impact on overall economic growth may require further analysis to assess their efficiency and effectiveness in boosting GDP.

# Time-series plot
ggplot(data, aes(x = YEAR)) +
  geom_line(aes(y = Education, color = "Education")) +
  geom_line(aes(y = Health, color = "Health")) +
  geom_line(aes(y = others, color = "others")) +
  geom_line(aes(y = GDP / 1000, color = "GDP (in thousands)")) +  # Scaling GDP for better visualization
  labs(title = "Time-Series of Recurrent Expenditures and GDP (1981-2022)",
       x = "Year", y = "Expenditures and GDP") +
  theme_minimal() +
  scale_color_manual(name = "Variables", values = c("Education" = "blue", "Health" = "green", "Others" = "red", "GDP (in Billions)" = "purple"))

The Shapiro-Wilk normality test results for Education (W = 0.77172, p = 1.165e-06), Health (W = 0.75247, p = 4.957e-07), Others (W = 0.71979, p = 1.267e-07), and GDP (W = 0.83747, p = 3.095e-05) all have p-values far below the typical significance level of 0.05. This indicates that the distributions of these variables significantly deviate from normality, meaning they are not normally distributed.

# Shapiro-Wilk test for normality
shapiro.test(data$Education)
## 
##  Shapiro-Wilk normality test
## 
## data:  data$Education
## W = 0.77172, p-value = 1.165e-06
shapiro.test(data$Health)
## 
##  Shapiro-Wilk normality test
## 
## data:  data$Health
## W = 0.75247, p-value = 4.957e-07
shapiro.test(data$others)
## 
##  Shapiro-Wilk normality test
## 
## data:  data$others
## W = 0.71979, p-value = 1.267e-07
shapiro.test(data$GDP)
## 
##  Shapiro-Wilk normality test
## 
## data:  data$GDP
## W = 0.83747, p-value = 3.095e-05
# Plot normality using Q-Q plots
qqPlot(data$Education, main="Q-Q Plot for Education")

## [1] 42 40
qqPlot(data$Health, main="Q-Q Plot for Health")

## [1] 42 40
qqPlot(data$others, main="Q-Q Plot for Others")

## [1] 42 40
qqPlot(data$GDP, main="Q-Q Plot for GDP")

## [1] 42 41
# Standardization (Z-scores)
data_std <- scale(data[, 2:5])
data_std <- as.data.frame(data_std)
data_std$YEAR <- data$YEAR
summary(data_std)
##    Education           Health            others             GDP         
##  Min.   :-0.7689   Min.   :-0.7334   Min.   :-0.6962   Min.   :-1.0809  
##  1st Qu.:-0.7575   1st Qu.:-0.7293   1st Qu.:-0.6920   1st Qu.:-0.8161  
##  Median :-0.4770   Median :-0.5174   Median :-0.6160   Median :-0.4741  
##  Mean   : 0.0000   Mean   : 0.0000   Mean   : 0.0000   Mean   : 0.0000  
##  3rd Qu.: 0.8189   3rd Qu.: 0.7033   3rd Qu.: 0.8290   3rd Qu.: 0.9943  
##  Max.   : 2.5824   Max.   : 2.5411   Max.   : 2.5091   Max.   : 1.7287  
##       YEAR     
##  Min.   :1981  
##  1st Qu.:1991  
##  Median :2002  
##  Mean   :2002  
##  3rd Qu.:2012  
##  Max.   :2022

The ARIMA(1,1,0) model for GDP with recurrent expenditures (Education, Health, and Others) as regressors shows that the autoregressive term (AR1) is significant at 0.6115. The drift term (1186.56) indicates a positive trend in GDP over time. Education has a negative coefficient (-7.44), while Health (11.04) and Others (7.98) have positive impacts on GDP, though Education is not statistically significant (p > 0.05). The model’s error measures, such as RMSE (1189.01) and MAE (901.39), suggest moderate forecasting accuracy. The residuals’ autocorrelation (ACF1 = -0.156) indicates low autocorrelation, suggesting the model performs reasonably well, though there’s room for improvement in fitting.

The ARIMA forecast plot shows that GDP is expected to continue its upward trajectory over time. The widening confidence intervals (shaded areas) indicate increasing uncertainty in the forecasts as time progresses, though the overall trend remains positive.

The GDP forecast shows a steady upward trend over the next 10 years, with increasing values from 73,451.36 to 84,747.81. However, the confidence intervals widen over time, indicating growing uncertainty in the predictions as the forecast horizon extends.

# Time series regression model (ARIMA)
#arima_model <- auto.arima(data$GDP, xreg = data[, 2:4])
# Convert xreg (Education, Health, Others) to a numeric matrix
xreg_matrix <- as.matrix(data[, 2:4])

# Run the ARIMA model with external regressors
arima_model <- auto.arima(data$GDP, xreg = xreg_matrix)

# Summary of the ARIMA model
summary(arima_model)
## Series: data$GDP 
## Regression with ARIMA(1,1,0) errors 
## 
## Coefficients:
##          ar1      drift  Education   Health  others
##       0.6115  1186.5635    -7.4374  11.0370  7.9831
## s.e.  0.1273   478.6042     6.7294   7.3041  5.0413
## 
## sigma^2 = 1649374:  log likelihood = -349.22
## AIC=710.44   AICc=712.91   BIC=720.72
## 
## Training set error measures:
##                    ME     RMSE      MAE        MPE     MAPE      MASE
## Training set 49.59675 1189.012 901.3884 -0.2650744 2.924369 0.5373369
##                    ACF1
## Training set -0.1556051
# Calculate the mean values of each regressor to create future estimates
future_xreg <- matrix(rep(colMeans(data[, 2:4]), 10), ncol = 3, byrow = TRUE)

# Forecast GDP for the next 10 periods using future regressors
forecasted_gdp <- forecast(arima_model, h = 10, xreg = future_xreg)

# Plot the forecast
plot(forecasted_gdp)

# Display forecast values
forecasted_gdp
##    Point Forecast    Lo 80    Hi 80    Lo 95     Hi 95
## 43       73451.36 71805.49 75097.24 70934.22  75968.51
## 44       74880.65 71759.10 78002.21 70106.65  79654.66
## 45       76215.65 71696.41 80734.90 69304.07  83127.24
## 46       77493.00 71687.40 83298.59 68614.10  86371.89
## 47       78735.07 71755.29 85714.85 68060.42  89409.73
## 48       79955.59 71903.23 88007.94 67640.57  92270.60
## 49       81162.91 72126.19 90199.63 67342.44  94983.39
## 50       82362.17 72416.37 92307.97 67151.38  97572.96
## 51       83556.50 72765.57 94347.43 67053.20 100059.80
## 52       84747.81 73166.17 96329.46 67035.21 102460.41
# Forecasting GDP based on the expenditures
#forecasted_gdp <- forecast(arima_model, h = 10, xreg = data[, 2:4])
#plot(forecasted_gdp)

The Ljung-Box test results show that the residuals from the ARIMA(1,1,0) model are not significantly different from white noise, as evidenced by the p-values (0.7006 and 0.2962) being greater than 0.05. This suggests that there is no significant autocorrelation in the residuals, indicating a good model fit. Additionally, the regression coefficients for education, health, and other recurrent expenditures indicate varying impacts on GDP, with education having the largest value (88.49), followed by health (72.58), and others (20.93).

The residual diagnostics from the ARIMA(1,1,0) model show that the residuals fluctuate around zero without a clear pattern, indicating no obvious serial correlation. The autocorrelation function (ACF) plot confirms that none of the lag values significantly exceed the confidence bounds, further suggesting that the model adequately captures the time-series behavior. The histogram of the residuals approximates a normal distribution, though with slight skewness, indicating that the residuals are mostly well-behaved. Overall, these diagnostics suggest that the model fits the data reasonably well.

# Diagnostics of the ARIMA model
checkresiduals(arima_model)

## 
##  Ljung-Box test
## 
## data:  Residuals from Regression with ARIMA(1,1,0) errors
## Q* = 4.666, df = 7, p-value = 0.7006
## 
## Model df: 1.   Total lags used: 8
# Perform Ljung-Box test for residual autocorrelation
Box.test(residuals(arima_model), type = "Ljung-Box")
## 
##  Box-Ljung test
## 
## data:  residuals(arima_model)
## X-squared = 1.0914, df = 1, p-value = 0.2962
# Heteroscedasticity Test
# Plot the residuals of the ARIMA model
plot(residuals(arima_model), main = "Residuals of ARIMA Model", ylab = "Residuals", type = "l")

# Multicollinearity Test using VIF
model_lm <- lm(GDP ~ Education + Health + others, data = data)
vif(model_lm)
## Education    Health    others 
##  88.48796  72.58479  20.92787

The linear regression results show that the model explains a significant portion of the variation in GDP, with an adjusted R-squared of 0.8708, indicating that about 87% of the variation in GDP is accounted for by the recurrent expenditures on education, health, and others. The overall model is statistically significant (F-statistic = 93.08, p < 2.2e-16). However, individually, only education shows a marginal effect on GDP (p = 0.0859), while health and other expenditures are not statistically significant (p > 0.05). The intercept is highly significant, suggesting a baseline GDP level independent of the variables included.

# Multiple regression
regression_model <- lm(GDP ~ Education + Health + others, data = data)

# Summary of the regression model
summary(regression_model)
## 
## Call:
## lm(formula = GDP ~ Education + Health + others, data = data)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -14454  -4899  -1969   5336  16853 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 23662.77    1523.03  15.537   <2e-16 ***
## Education      92.61      52.52   1.763   0.0859 .  
## Health        -31.14      74.66  -0.417   0.6789    
## others         28.57      35.15   0.813   0.4214    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7497 on 38 degrees of freedom
## Multiple R-squared:  0.8802, Adjusted R-squared:  0.8708 
## F-statistic: 93.08 on 3 and 38 DF,  p-value: < 2.2e-16

The plot illustrates the estimated effects of recurrent expenditures on education, health, and others on GDP, along with their respective confidence intervals. The confidence interval for education crosses zero, indicating some uncertainty about its positive impact on GDP, although its estimate appears large. Health has a wide confidence interval around zero, suggesting that its effect on GDP is uncertain and could be slightly negative or positive. The “others” category also shows a small estimate with a confidence interval crossing zero, implying a non-significant effect on GDP. Overall, these visual results align with the regression analysis, where education had a marginal effect, while health and other expenditures were not statistically significant.

# Visualize coefficients
summ(regression_model, confint = TRUE, digits = 3)
Observations 42
Dependent variable GDP
Type OLS linear regression
F(3,38) 93.078
R² 0.880
Adj. R² 0.871
Est. 2.5% 97.5% t val. p
(Intercept) 23662.771 20579.549 26745.993 15.537 0.000
Education 92.608 -13.710 198.925 1.763 0.086
Health -31.144 -182.294 120.006 -0.417 0.679
others 28.568 -42.587 99.723 0.813 0.421
Standard errors: OLS 
# Regression plot
plot_summs(regression_model, scale = TRUE)

Discussion of Findings:

The regression analysis and corresponding plot indicate the impact of recurrent expenditures on education, health, and other sectors on Nigeria’s GDP. The coefficient for education is positive and relatively large, suggesting that spending on education has the potential to positively impact GDP. However, the marginal statistical significance (p-value = 0.0859) and the wide confidence interval indicate some uncertainty in this finding. Investments in education likely contribute to long-term economic growth, but the current data implies this effect may not be immediately evident or consistently strong.

For health, the coefficient is negative, although statistically insignificant (p-value = 0.6789), indicating that health expenditures in this period may not have had a substantial direct effect on GDP. The broad confidence interval reinforces this uncertainty, and the negative estimate could reflect inefficiencies or delayed returns from health investments.

The “others” category, which may include a range of social services, showed a small and statistically insignificant impact on GDP (p-value = 0.4214). While its coefficient was positive, the lack of significance suggests that expenditures in these areas have not translated into measurable economic benefits over the studied period.

The overall model fits well, as evidenced by a high adjusted R-squared value of 0.8708, meaning that 87.08% of the variation in GDP is explained by the recurrent expenditures in education, health, and other areas. However, the lack of statistical significance in some variables suggests that the direct relationship between these expenditures and GDP is not straightforward.

Conclusion:

Recurrent government expenditures on education show a potential positive relationship with Nigeria’s GDP, although the effect is not strongly significant. Expenditures on health and other social services, however, do not appear to have a significant impact on GDP based on the current dataset. While government spending is essential for providing public goods and services, inefficiencies in resource allocation or delayed economic returns may explain the weak effects seen here.

Recommendations:

  1. Prioritize Efficient Allocation of Resources: The government should evaluate how funds are being utilized, especially in health and other sectors, to improve the efficiency of public spending. A more targeted and results-driven approach may increase the economic returns on these investments.

  2. Strengthen Education Funding: The findings suggest that education has a potentially positive effect on GDP. Therefore, increasing investment in quality education, along with policies that ensure long-term benefits, could enhance the overall economic growth of the country.

  3. Monitor and Reassess Health Sector Investments: Given the insignificance of health expenditures in impacting GDP, the government should explore why these investments are not yielding the expected economic outcomes. Addressing structural inefficiencies and enhancing healthcare infrastructure may improve the sector’s contribution to economic growth.

Broaden Research Scope

Future studies could explore a longer-term horizon and incorporate additional economic variables to better capture the effects of government expenditures on overall economic performance. This would provide more granular insights into which expenditures have the greatest economic returns.

REFERENCES

Adeyemi N. (2018). EFFECTS OF PUBLIC EXPENDITURE ON ECONOMIC GROWTH IN NIGERIA: A DISAGGREGATED TIME SERIES ANALYSIS. The International Journal of Management Sciences and Business Research, 2018, Vol. 1, Issue 7. (ISSN:2226-8235)

Chukwu Agwu Ejem and Udochukwu Godfrey Ogbonna. (2019). Pattern of Government Recurrent Expenditure and Economic Growth in Nigeria. INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH & DEVELOPMENT. DOI No. :10.24940/ijird/2019/v8/i10/OCT19012

ELAIGWU, Bernard Emmanuel1 and Ali Bako Khikando. (2024). Government Expenditure and Economic Growth in Nigeria (2005 - 2022). International Journal of Sustainable Development, . Volume 7, Issue 3. Pages 11-27. June, 2024 DOI: 6472-039024-732

Igwe, A.O., & Inyiama, O.I. (2024). Effect of Government Recurrent Expenditure on Economic Prosperity in Nigeria. European Journal of Accounting, Auditing and Finance Research, 12(1), 74-90. https://doi.org/10.1012345567

Mackarov, Igor. (2024). Time Series Analysis: yesterday, today, tomorrow.

Okezie A. Ihugba and Asoluka C. Njoku. (2017). Social and Community Services Government Expenditure and Nigeria’s Economic Growth. Springer International Publishing AG 2017

Shah, Dhawani & Thaker, Manishkumar. (2024). A Review of Time Series Forecasting Methods. INTERNATIONAL JOURNAL OF RESEARCH AND ANALYTICAL REVIEWS. 11. 749. 10.1729/Journal.38816.

Torres, M. J. F. Martinez-Ballesteros, A. Troncoso, F. Martinez-Alvarez. (2024). Advances in time series forecasting: innovative methods and applications[J]. AIMS Mathematics, 2024, 9(9): 24163-24165. doi: 10.3934/math.20241174

Tsounis N., Vlachvei A. (2017). Advances in Applied Economic Research. Springer Proceedings in Business and Economics. DOI 10.1007/978-3-319-48454-9_

Uremadu, S. , Orikara, P. and Uremadu, C. (2019) Testing Relationship between Government Current Expenditures and Economic Growth in Nigeria. Theoretical Economics Letters, 9, 584-604. doi: 10.4236/tel.2019.94041.

Xu Dan. (2023). Time series analysis as an emerging method for researching L2 affective variables. https://doi.org/10.1016/j.heliyon.2023.e16931