Fiscal Policy-Inflation Nexus: Evidence from Tunisia

1. Introduction

Inflation volatility, characterized by unpredictable and fluctuating inflation rates, poses significant challenges to economic stability and growth, especially in developing economies. It can undermine investment decisions, distort resource allocation, and erode consumers’ purchasing power. For policymakers, understanding the determinants of inflation and its volatility is paramount for formulating effective fiscal and monetary policies that can stabilize the economy [1].

Fiscal policy, encompassing government revenue and expenditure, plays a crucial role in shaping economic outcomes. The relationship between fiscal policy and inflation has been the subject of extensive theoretical and empirical research. Keynesian economics suggests that increased government spending can stimulate aggregate demand, potentially leading to higher inflation if the economy is near or at full capacity [2]. Conversely, reductions in government spending can dampen demand and reduce inflationary pressures.

Monetarist theories, on the other hand, emphasize the role of money supply in influencing inflation. According to these theories, inflation is primarily a monetary phenomenon, and fiscal deficits, if financed by monetary expansion, can lead to higher inflation [3]. The interplay between fiscal policy and monetary policy becomes critical in understanding inflation dynamics.

Empirical studies on the relationship between fiscal policy and inflation have produced mixed results. In developed countries, this relationship is often found to be weaker due to well-established monetary policy frameworks and institutional structures that mitigate the impact of fiscal deficits on inflation [4] In developing countries, however, the relationship tends to be stronger, influenced by factors such as limited access to international capital markets, lower credibility of monetary policy, and greater reliance on seigniorage financing [5].

Tunisia, a developing country in North Africa, has experienced varying inflation rates over the past decades, influenced by both domestic and external factors. The country’s economic structure, characterized by a significant public sector and reliance on agricultural and tourism revenues, makes it particularly susceptible to fiscal policy changes and external shocks. This study focuses on the impact of fiscal policy on inflation volatility in Tunisia, aiming to provide insights that can guide policymakers in managing inflationary pressures.

In Tunisia, the fiscal policy landscape has been marked by efforts to balance economic growth and fiscal sustainability. Government expenditure on social programs, subsidies, and public sector wages has been substantial, while efforts to increase revenue through taxation have faced challenges. The impact of these fiscal policy measures on inflation and its volatility remains a concern for policymakers. Understanding how different fiscal policy measures affect inflation can aid in the optimal allocation of resources. For instance, if government expenditure is found to significantly influence inflation, policymakers can adjust spending priorities to mitigate inflationary pressures.

This study contributes to the existing body of knowledge by providing a detailed and context-specific analysis of the Tunisian economy. It employs advanced econometric techniques to offer new insights into the fiscal policy-inflation nexus, which can be valuable for researchers and scholars in the field of economics. Reducing inflation volatility can enhance public confidence in the government’s economic management, leading to greater political and social stability, as citizens are more likely to support policies that lead to economic benefits. While focused on Tunisia, the study’s methodology and findings can be applicable to other developing economies with similar fiscal and monetary challenges. It provides a framework for analyzing the impact of fiscal policy on inflation, which can be adapted and applied in different contexts. By utilizing time series data and employing econometric techniques such as unit root tests, cointegration analysis, vector autoregression (VAR), and Granger causality tests, the study aims to uncover the dynamic relationships between key fiscal policy variables and inflation.

Inflation volatility remains a critical challenge for economic stability and growth, particularly in developing economies such as Tunisia. Despite various policy interventions, the Tunisian economy continues to experience significant fluctuations in inflation rates, which undermine economic performance and social welfare. The relationship between fiscal policy and inflation is complex and multifaceted, involving numerous factors such as government revenue, expenditure, money supply, balance of trade, and budget deficits. While previous studies have explored these relationships in general terms, there is a lack of comprehensive analysis focused specifically on the Tunisian context, especially using advanced econometric techniques. This study seeks to fill this gap by systematically investigating the impact of fiscal policy on inflation volatility in Tunisia, employing a robust quantitative approach to uncover the underlying dynamics and provide actionable insights for policymakers. The structure of this study is delineated as follows: Section 2 offers a review of pertinent literature. Section 3 includes the specification of the model and the data utilized to investigate the relationship between fiscal policy and inflation in Tunisia. Section 4 details the results, and Section 5 discusses the findings of the analysis. Finally, Section 6 summarizes the conclusions drawn and explores the possible implications for policymakers.

2. Literature Review

The relationship between fiscal policy and inflation has been extensively studied, with significant attention given to both theoretical and empirical perspectives. Recent research continues to explore this relationship, particularly in the context of developing economies where fiscal policy plays a crucial role in economic stability and growth.

Classical economic theories, such as those proposed by the Keynesian and Monetarist schools, provide a foundational understanding of the impact of fiscal policy on inflation. Keynesian economics posits that increased government spending can boost aggregate demand, leading to higher inflation if the economy is near or at full capacity [6]. Conversely, reductions in government spending can dampen demand and reduce inflationary pressures. However, monetarist theories, as articulated by Milton Friedman, emphasize the role of money supply in influencing inflation. According to these theories, inflation is primarily a monetary phenomenon, and fiscal deficits, if financed by monetary expansion, can lead to higher inflation [3]. The interplay between fiscal policy and monetary policy becomes critical in understanding inflation dynamics.

Empirical studies provide mixed results on the relationship between fiscal policy and inflation. In developed countries, the relationship is often found to be weaker due to well-established monetary policy frameworks and institutional structures that mitigate the impact of fiscal deficits on inflation [4]. In developing countries, however, the relationship tends to be stronger, influenced by factors such as limited access to international capital markets, lower credibility of monetary policy, and greater reliance on seigniorage financing. For instance, [7] examined the impact of fiscal policy on economic performance in a broad sample of countries, finding that fiscal consolidation is generally associated with lower inflation in the long run.

2.1. Government Revenue and Inflation

The relationship between government revenue and inflation is pivotal in understanding fiscal policy’s role in macroeconomic stability. Government revenue, primarily derived from taxes and other forms of public income, can influence inflation through its effects on fiscal balance and aggregate demand. Higher government revenue typically enables better fiscal balance, reducing the need for deficit financing, which can help mitigate inflationary pressures. This is particularly relevant in developing economies where fiscal imbalances often lead to inflationary financing methods, such as printing money.

Recent empirical studies offer nuanced insights into this relationship. [8] examined the impact of government revenue on inflation in Vietnam, finding that increased government revenue helps stabilize inflation by improving the fiscal position and reducing reliance on debt and monetary expansion. Their study suggests that efficient tax collection and enhanced revenue generation are crucial for controlling inflation. Additionally, [9] explored how fiscal consolidations, which often involve increasing government revenue through taxation, affect inflation. They found that successful fiscal consolidations can lead to lower inflation rates, particularly when public debt levels are high and inflation expectations are well-anchored. This underscores the importance of credible and sustainable fiscal policies in achieving price stability. Moreover, the composition of government revenue also matters. For example, indirect taxes such as Value Added Tax (VAT) can have immediate inflationary effects by directly increasing prices, while direct taxes like income tax might have a more muted impact on inflation through their effects on disposable income and aggregate demand.

In developing economies, efficient revenue generation is often challenged by Informal economic activities and administrative inefficiencies. Enhancing the efficiency of tax systems and broadening the tax base are critical strategies for increasing government revenue without exacerbating Inflation. Studies like that of [10] highlight the need for structural reforms in tax administration to ensure that increased revenue does not lead to Inflationary pressures.

2.2. Government Expenditure and Inflation

The relationship between government expenditure and inflation is a significant focus in economic research, particularly in the context of developing economies where fiscal policy plays a crucial role in macroeconomic stability. Government expenditure can influence inflation through various channels. According to Keynesian economics, increased government spending boosts aggregate demand, which can lead to higher inflation if the economy is operating near or at full capacity [6]. This is because higher demand can push up prices when supply constraints exist. Conversely, reducing government expenditure can decrease aggregate demand, thereby alleviating inflationary pressures.

Empirical evidence on this relationship is mixed, often varying by country and economic context. In developing economies, studies have shown that government spending tends to have a more pronounced impact on inflation. For instance, [11] investigated the effect of government spending on inflation in the West African Economic and Monetary Union (WAEMU) countries. They found that increased government expenditure significantly raised inflation, highlighting the direct impact of fiscal policy on price levels in these economies.

Furthermore, [12] analysed fiscal policy and its impact on the business cycle in OECD countries, concluding that counter-cyclical fiscal policies, including increased government spending during economic downturns, can help stabilize inflation. However, they also noted that the effectiveness of such policies depends on overall fiscal discipline and the ability to manage public finances sustainably.

[9] have also emphasized the role of public debt and inflation expectations in shaping the relationship between fiscal consolidations (which often involve cuts in government spending) and inflation. Their findings suggest that while fiscal consolidations can lead to lower inflation, the outcomes are significantly influenced by the levels of public debt and the credibility of fiscal policy. Similarly, [13] used time series data to explore the dynamic effects of fiscal policy shocks on inflation in South Africa, highlighting the significance of government expenditure in influencing inflationary pressures.

2.3. Budget Deficit and Inflation

A budget deficit occurs when a government’s expenditures exceed its revenues, often necessitating borrowing or money creation to finance the gap. This can lead to inflationary pressures if the deficit is monetized, increasing the money supply without a corresponding increase in goods and services.

Prior literature provides robust evidence on the connection between budget deficits and inflation. For instance, [14] conducted an extensive study across emerging markets and found a strong positive correlation between budget deficits and inflation. Their analysis indicates that larger deficits are associated with higher inflation rates, particularly in countries with limited access to international capital markets and weak fiscal institutions. This underscores the inflationary risk posed by fiscal imbalances in developing economies.

Similarly, [15] explored the dynamics of fiscal deficits and inflation in emerging markets, emphasizing the role of monetary policy. They found that when fiscal deficits are financed through money creation, it leads to significant inflationary pressures. Their study highlights the importance of coordination between fiscal and monetary policies to manage inflation effectively. Likewise, [16] examined the impact of fiscal deficits on inflation in various economies and found that the relationship is contingent on the credibility of fiscal and monetary authorities. In countries where fiscal discipline is weak and monetary policy lacks independence, budget deficits tend to have a more pronounced impact on inflation. This suggests that institutional quality plays a crucial role in mediating the effects of fiscal deficits on inflation. [17] utilized vector autoregression (VAR) models to analyse the relationship between fiscal policy variables, including budget deficits and inflation. Their findings indicate that budget deficits significantly contribute to inflationary pressures in Tunisia, emphasizing the need for fiscal consolidation and improved fiscal governance to achieve macroeconomic stability. Therefore, effective management of budget deficits, through both fiscal consolidation and robust fiscal institutions, is essential for controlling inflation, particularly in developing economies where fiscal imbalances are more likely to lead to inflationary outcomes. [18] employed cointegration and error correction models to analyse the relationship between fiscal deficits and inflation in Nigeria, finding evidence of a long-run equilibrium relationship between the variables. Similarly, [19] used cointegration techniques to study the impact of fiscal policy on inflation in developing countries, concluding that fiscal deficits have a significant long-run impact on inflation.

2.4. Money Supply and Inflation

The classical quantity theory of money, articulated by Milton Friedman, posits that inflation is “always and everywhere a monetary phenomenon,” suggesting that changes in the money supply have direct impacts on the price level when the velocity of money and real output are stable [3]. Recent empirical research continues to explore this relationship, particularly in the context of both developed and developing economies. [20] provided a comprehensive historical analysis, emphasizing that central bank credibility and inflation targeting are crucial in moderating the inflationary effects of changes in the money supply. Their study underscores the importance of effective monetary policy frameworks in controlling inflation. However, in developing economies, where monetary policy may be less effective, the relationship between money supply and inflation tends to be more pronounced. For instance, [21] examined the impact of money supply on inflation in Middle Eastern and North African (MENA) countries. They found that increases in money supply significantly contributed to inflationary pressures, highlighting the challenges these countries face in maintaining price stability amid rapid monetary expansion. Moreover, [22] explored the dynamics of money supply and inflation in India using time series econometric models. Their findings indicated a strong positive correlation between money supply growth and inflation, particularly in periods of high economic growth. This study demonstrated that in the absence of stringent monetary controls, increases in money supply could lead to substantial inflationary pressures. While [23] focused on inflation-targeting regimes and the role of money supply in monetary policy. They argued that even in regimes where inflation targeting is the primary focus, controlling the money supply remains crucial for achieving long-term price stability. Their empirical analysis showed that deviations from targeted money supply levels could lead to significant inflationary fluctuations.

2.5. Balance of Trade and Inflation

The balance of trade, representing the difference between a country’s exports and imports, plays a crucial role in determining its inflation dynamics. The relationship between the balance of trade and inflation is multifaceted, involving the interplay between external economic activities and domestic price levels. A trade deficit, where imports exceed exports, can lead to inflationary pressures by weakening the domestic currency, thereby increasing the cost of imported goods and services. [24] investigated the impact of trade openness on inflation in developing countries. Their study found that trade deficits tend to exacerbate inflationary pressures, as increased reliance on imports makes countries vulnerable to external price shocks and currency depreciation. The findings underscore the importance of maintaining a balanced trade position to mitigate inflation risks. Furthermore, [25] explored the balance of trade and inflation dynamics in Indonesia, an emerging market economy. Their analysis revealed that trade deficits contributed to higher inflation by increasing the cost of imported goods, particularly in periods of currency depreciation. The study highlighted the need for policies aimed at boosting exports and achieving a more favourable trade balance to control inflation. Additionally, [26] examined the long-term relationship between trade balance and inflation in Asian economies. Using cointegration and error correction models, they found that a persistent trade deficit leads to inflationary pressures over time, primarily due to the depreciation of the local currency and increased import prices. This long-term perspective emphasizes the structural aspects of trade and its implications for inflation. [27] concluded that while the short-term effects of trade deficits on inflation might be moderate, the long-term impacts are significant and substantial. This suggests that sustainable trade policies and efforts to improve trade balances are critical for long-term price stability.

In the context of Tunisia, the fiscal policy landscape has been marked by efforts to balance economic growth and fiscal sustainability. Previous research has shown that fiscal policy plays a significant role in economic performance. [28] investigated the relationship between fiscal deficits and inflation in Tunisia, finding that fiscal deficits financed through money creation contribute significantly to inflationary pressures. More recent studies have provided additional insights. For instance, [29] explored the effects of fiscal policy on inflation in Tunisia, emphasizing the importance of controlling government expenditure to manage inflationary pressures. The study highlighted the need for structural reforms to enhance fiscal discipline and improve the efficiency of public spending.

3. Model, Data, and Econometric Approach Specification

Inf = GoE + GoR + MSup + BoT + BD

(1)

Inf _it = α + GoE _it + GoR _it + MSup _it + BoT _it + α5 BD _it

(2)

Inf _it = α0 + α1 GoE _it + α2 GoR _it + α3 MSup _it + α4 BoT _it + α5 BD it + ε _it

(3)

After taking logarithm for equation (3)

Ln Inf _it = α0 + α1 Ln GoE _it + α2 Ln GoR _it + α3 Ln MSup _it + α4 Ln BoT_it + α5 Ln BD_it

(4)

Where Inf_it denotes for Inflation rate, acting as dependent variable. the independent variables include Government expenditure (GoE _it), Government revenue (GoR _it), Money Supply (MSup_it), Balance of Trade (BoT_it), Budget Deficit (BD_it). The error term ε_it is assumed to exhibit standard statistical properties.

Analysis of the Data

The study’s analysis was conducted using data obtained from the official Tunisia Central Bank. The variables utilized were clearly defined with data sourced from the World Bank. EViews 12 software was utilized for the analysis due to its flexibility and user-friendly features. This software effectively supported tasks such as data management, visualization, and analysis, leading to a thorough and efficient analytical process.

Variables Definition

Dependent Variable

1-

Inflation (Inf): represents to the consumer price index, which measures fluctuations in the expenses incurred by the typical consumer in obtaining a selection of goods and services, which may remain constant or vary at predetermined times, such as annually. The Laspeyres formula is commonly employed for this purpose. The data represent averages over specific time periods.

Independent Variables

1-

Government Expenditure (GoE): denote for the total amount of public spending by the government, which encompasses all levels of government, including central, state, and local authorities. This expenditure includes spending on goods and services that are used for public administration, defence, education, public order and safety, health, social protection, and environmental protection, as well as expenditures on infrastructure, such as roads, bridges, and other public works.

2-

Government Revenues (GoR): denote for the total income received by the government from various sources to finance its activities and obligations. GoR include tax revenues, non-tax revenues, and other revenues.

3-

Money Supply (Msup): denotes for the total amount of monetary assets available in an economy at a specific time. It includes physical currency, reserves held by commercial banks at the central bank, currency in circulation, demand deposits, savings accounts, time deposits, large time deposits, institutional money market funds, and other larger liquid assets.

4-

Balance of Trade (BoT): denotes for the difference between the value of a country’s exports and imports of goods and services over a specific period, typically measured on a quarterly or annual basis. Balance of trade categorized into trade surplus (the value of exports exceeds the value of imports), and trade deficit (the value of imports exceeds the value of exports).

5-

Budget Deficit (BD): denotes for current and capital revenue and official grants received (tax revenues from income tax, corporate tax, VAT, sales taxes, and non-tax revenues such as fees, fines, and profits from state-owned enterprises), less total expenditure (spending on infrastructure, education, healthcare, defence, social welfare programs, and public sector salaries) and lending minus repayments in current local currency.

Econometric Methodology

This study employs a range of econometric techniques to address the specific challenges associated with time series data, causality, and cointegration. The Autoregressive Distributed Lag (ARDL) model serves as a valuable tool in econometrics for estimating and analysing the long-term relationships among variables, particularly in the context of time series data analysis. This approach is often applied to model cointegration and the dynamic interactions between economic variables. Additionally, the specification of the ARDL model differs from traditional regression models by including lagged values of both dependent and independent variables, thereby capturing the temporal dynamics of the relationship. In ARDL models that feature cointegrated variables, an Error Correction Mechanism (ECM) is typically incorporated to address the short-term adjustment processes leading to long-term equilibrium.

The analytical framework may incorporate various econometric techniques, such as the dynamic ordinary least squares (DOLS) estimation method, the Johansen cointegration test, and the error correction model (ECM). These approaches are especially effective for examining the long-term relationships, short-term fluctuations, and causal linkages between the components of expenditure and the real gross domestic product (RGDP) in Saudi Arabia.

DOLS represents a statistical methodology employed for estimating parameters in dynamic regression models that integrate time series data, which may exhibit potential integration characteristics [30]. DOLS proves to be an effective instrument in the estimation of cointegrating relationships, particularly in economic datasets characterized by non-stationarity. Its utility lies in its ability to rectify potential biases stemming from dynamic variable interactions, thereby ensuring that the estimated parameters accurately capture genuine long-term relationships.

The general formula for a DOLS model can be represented as follows:

$$\mathrm{Y}\mathrm{t}=\mathsf{\alpha }+\mathsf{\beta }\mathrm{X}\mathrm{t}+{\sum }_{k=p}^q\mathsf{\gamma }\mathrm{k}\mathsf{\Delta }\mathrm{X}\mathrm{t}+\mathrm{k}+\mathsf{ϵ}\mathrm{t}$$

Where:

Yt: is the dependent variable at time t.

α: is the intercept term.

β: is the coefficient for the long-run relationship between Yt and Xt.

X_t: are the independent variables at time t.

ΔX_t+k: is the first difference of the independent variables, where k ranges from −p (lags) to q (leads).

γk: are the coefficients for the differenced independent variables, capturing the short-run dynamics.

ϵ_t: is the error term, assumed to be white noise.

The Johansen cointegration test is a statistical procedure used to determine the number of cointegrating relationships among a set of non-stationary time series variables. Developed by Søren Johansen in 1988, this test is particularly useful when dealing with multiple time series and aims to identify whether there exists a long-run equilibrium relationship among the variables. The starting point for the Johansen test is a VAR model of order k, which captures the dynamics of a set of n non-stationary time series variables:

X_t = A₁X_t−1+A₂X_t−2 + ⋯ + AkX_t−k + ϵ_t

Where:

Xt is an n×1 vector of non-stationary I(1) variables.

A1,A2,…,Ak are n×n matrices of coefficients.

ϵt is an n×1 vector of white noise error terms.

To test for cointegration, the VAR model is transformed into a Vector Error Correction Model (VECM), which can be expressed as:

$$\mathsf{\Delta }\mathrm{X}\mathrm{t}=\Pi Xt-1+{\sum }_{i=1}^{k-1}\mathsf{\Gamma }\mathrm{i}\mathsf{\Delta }\mathrm{X}\mathrm{t}-\mathrm{i}+\mathsf{ϵ}\mathrm{t}$$

Where:

ΔXt = Xt −Xt−1 represents the first difference of Xt.

Π=αβ′ is the impact matrix, which provides information about the long-term relationships among the variables. α is the matrix of adjustment coefficients, indicating the speed at which the system returns to equilibrium. β is the matrix of cointegrating vectors, representing the long-term equilibrium relationships.

Γi are matrices capturing short-term dynamics.

k is the number of lags included in the model.

The Johansen test provides two main statistics to test for the number of cointegrating vectors (r):

Trace Statistic=−Ti = r+1∑nln(1−λi)

The trace test examines the null hypothesis that there are at most r cointegrating vectors. The maximum eigenvalue test evaluates the null hypothesis that the number of cointegrating vectors is r against the alternative of r + 1.

Max Eigenvalue Statistic = −T ln(1−λ_r+1)

The ECM is based on the idea that even though time series variables may deviate from their long-run equilibrium in the short run, they tend to adjust to restore equilibrium over time. The ECM captures this adjustment process, quantifying how quickly the variables return to equilibrium following a short-term shock.

ΔY_t = α + β ΔX_t+λ (ECT_t−1) + ϵ_t

Where:

ΔYt =Yt−Yt−1 is the change in the dependent variable.

ΔXt=Xt−Xt−1 is the change in the independent variable.

α is the intercept term.

β is the short-run coefficient measuring the immediate impact of changes in Xt on Yt.

λ is the error correction coefficient, indicating the speed of adjustment back to the long-run equilibrium.

ϵt is the white noise error term.

ECTt−1 is the error correction term, which is the lagged residual from the long-run cointegration equation:

ECT_t−1 = Y_t−1−γ X_t−1

4. Results of the Analysis

4.1. Descriptive Statistics

Table 1 presents the descriptive statistics for variable of the study. The statistics include measures of central tendency, dispersion, shape of distribution, and other summary statistics based on 26 observations. The variables show varying levels of central tendency and dispersion. For instance, Ln GoR (government revenue) and Ln MSup (money supply) have higher average values compared to others. Moreover, the standard deviation indicates that Ln BoT (balance of trade) is the most volatile, whereas Ln GoE (government expenditure) is the least. Skewness and kurtosis values suggest that most variables are fairly symmetric with moderate tail weight, except for Ln BD (budget deficit), which shows significant kurtosis, indicating potential outliers. The Jarque-Bera test confirms normal distribution for most variables except for Ln BD.

4.2. The Augmented Dickey-Fuller (ADF)

ADF test is used to check for the presence of unit roots in time series data, which indicates whether the data is non-stationary. The null hypothesis for the ADF test is that the time series has a unit root (is non-stationary). As shown in Table 2, all the variables Ln Inf, Ln GoE, Ln MSup, Ln BoT, and Ln BD are non-stationary at their levels. However, all the variables become stationary after first differencing, as indicated by the significant t-values and low p-values.

These results suggest that most variables require first differencing to achieve stationarity, which is crucial for time series analysis to avoid spurious regression results.

4.3. Cointegration

Table 3 presented is the result of an Unrestricted Cointegration Rank Test (Trace), which is used to determine the number of cointegrating relationships in a multivariate time series dataset. The trace test suggests that there are at least two cointegrating equations at the 5% significance level. This indicates a long-run equilibrium relationship among the variables in the dataset. The presence of cointegrating relationships implies that despite short-term fluctuations, the variables move together in the long run.

4.4. VAR Result

The VAR Lag Order Selection Criteria table provides strong evidence that a VAR model with one lag is optimal for the given data. This conclusion is robust across multiple criteria (Log L, LR, FPE, AIC, SC, HQ), each indicating that the model fit improves significantly with one lag and that further lags do not provide additional benefits. This optimal lag length should be used in subsequent VAR modeling and analysis to ensure accurate and reliable results.

Table 4. VAR Lag Order Selection Criteria.

Lag	Log L	LR	FPE	AIC	SC	HQ
0	40.73	NA	3.95E-08	-2.86	-2.61	-2.79
1	173.53	201.8504*	7.44e-12*	-11.48219*	-10.01954*	-11.07652*

Table 4. VAR Lag Order Selection Criteria.

Lag	Log L	LR	FPE	AIC	SC	HQ
0	40.73	NA	3.95E-08	-2.86	-2.61	-2.79
1	173.53	201.8504*	7.44e-12*	-11.48219*	-10.01954*	-11.07652*

4.5. Correlation Result

The correlation matrix provides insight into the linear relationships between the variables, which can be useful for understanding the dynamics and interactions within the dataset. However, it is important to note that correlation does not imply causation, and further analysis (such as Granger causality) would be needed to Infer causality. The correlation matrix shows the strength and direction of the linear relationships between pairs of variables. There are very strong positive correlations between Ln GoR, Ln MSup, and Ln GoE, indicating that these variables tend to move together. as government revenue increases, money supply and government expenditure also increase. Ln Inf shows moderate positive correlations with Ln GOR, Ln MSup, Ln GoE, and Ln BoT, suggesting that Inflation is somewhat Influenced by these factors. Ln BD has negative correlations with all other variables, particularly with Ln MSup and Ln GoR, indicating that higher budget deficits are associated with lower values of these variables.

Table 5. Correlation Matrix.

	LN Inf	LN GOR	LN MSUP	LN GOE	LN BoT	LN BD
Ln Inf	1
Ln GOR	0.598	1
Ln MSUP	0.566	0.958	1
Ln GOE	0.558	0.975	0.918	1
Ln BoT	0.504	0.91	0.886	0.881	1
Ln BD	-0.422	-0.543	-0.707	-0.377	-0.533	1

Table 5. Correlation Matrix.

	LN Inf	LN GOR	LN MSUP	LN GOE	LN BoT	LN BD
Ln Inf	1
Ln GOR	0.598	1
Ln MSUP	0.566	0.958	1
Ln GOE	0.558	0.975	0.918	1
Ln BoT	0.504	0.91	0.886	0.881	1
Ln BD	-0.422	-0.543	-0.707	-0.377	-0.533	1

4.6. DOLS Estimation Results

Among the variables, Ln BD (Log of Budget Deficit) has a p-value of 0.05, indicating that it is marginally significant at the 5% level. This suggests that the budget deficit has a statistically significant positive impact on the inflation in Tunisia. The other variables (Ln MSup, Ln GoR, Ln GoE, and Ln BoT) have p-values greater than 0.05, indicating that they are not statistically significant. That is to say there is not enough evidence to suggest that these variables have a significant impact on the dependent variable within this model. The results suggest that, in this model, the budget deficit is the most Influential factor affecting the dependent variable (Ln Inf), with a positive relationship. However, the lack of significance for other variables suggests that their effects are not strong enough to be distinguished from zero in this analysis. The positive coefficient for the budget deficit indicates that an increase in the budget deficit is associated with an increase in Inflation.

Table 6. Dynamic Least Squares (DOLS) Estimation Results.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
Ln MSup	17.31	16.57	1.04	0.41
Ln GoR	5.57	7.53	0.74	0.48
Ln GoE	-42.02	41.7	-1.01	0.42
Ln BD	10.43	4.85	2.15	0.05
Ln BoT	0.48	0.57	0.84	0.43
C	68.22	47.09	0.83	0.43

Table 6. Dynamic Least Squares (DOLS) Estimation Results.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
Ln MSup	17.31	16.57	1.04	0.41
Ln GoR	5.57	7.53	0.74	0.48
Ln GoE	-42.02	41.7	-1.01	0.42
Ln BD	10.43	4.85	2.15	0.05
Ln BoT	0.48	0.57	0.84	0.43
C	68.22	47.09	0.83	0.43

4.7. Error Correction Model

ECM analysis suggests that both current and lagged values of certain variables significantly influence the dependent variable. Table 7 shows CointEq(-1) coefficient (-1.399114) and Prob. (0.0000), the highly significant and negative error correction term indicates a strong adjustment back to long-term equilibrium. A value of -1.399 suggests a rapid correction of disequilibrium at about 140% per period. This rapid adjustment implies that any short-term deviations from the long-term inflation path are quickly corrected, reflecting effective policy mechanisms or market forces in bringing inflation back to equilibrium.

4.7. Granger-Causality Test

The Granger Causality test results indicate whether one time series can predict another. The null hypothesis for each test states that the first variable does not Granger-cause the second variable. Based on Table 8, the findings can be summarised as bellow:

- Ln GoR Ln Inf. The p-value is 0.0692, which is slightly above the conventional 5% significance level. This suggests a marginal indication that Ln GoR might Granger-cause Ln Inf, but it is not statistically significant at the 5% level. Thus, we cannot conclusively reject the null hypothesis that Ln GoR does not Granger-cause Ln Inf.

- Ln MSup Ln Inf. The p-value is 0.1040, which is above 0.05. This indicates that Ln MSup does not significantly Granger-cause Ln Inf. Thus, we do not reject the null hypothesis.

- Ln GoE Ln Inf. The p-value is 0.0210, which is below 0.05. This indicates that Ln GoE significantly Granger-causes Ln Inf. Thus, we reject the null hypothesis and conclude that government expenditure can help predict future Inflation. This suggests that changes in government expenditure can be used to predict future changes in Inflation. Policymakers should consider the impact of government expenditure on Inflation when making fiscal decisions.

- Ln BoT Ln Inf. The p-value is 0.0748, which is above 0.05 but below 0.1. This suggests a marginal indication that Ln BoT might Granger-cause Ln Inf, but it is not statistically significant at the 5% level. Therefore, we cannot conclusively reject the null hypothesis that Ln BoT does not Granger-cause Ln Inf.

- Ln BD Ln Inf. The p-value is 0.9036, which is much higher than 0.05. This indicates that Ln BD does not Granger-cause Ln Inf. Therefore, we do not reject the null hypothesis.

In summary, Log of Government Expenditure significantly Granger-causes Log of Inflation. Moreover, no significant Granger-causality was found for the other variables in relation to Log Inflation, meaning that changes in these variables do not predict changes in Inflation and vice versa. These findings provide valuable insights into the dynamic relationships between Inflation and key economic variables in Tunisia, which can Inform both policy decisions and further academic research.

5. Discussion of the Findings

This study highlights the challenges inflation volatility poses to economic stability, particularly in developing economies like Tunisia. It underscores the importance of understanding the determinants of inflation and its volatility to formulate effective fiscal and monetary policies. The document sets the context by contrasting Keynesian and Monetarist views on fiscal policy’s role in influencing inflation.

The relationship between fiscal policy and inflation varies between developed and developing countries. Advanced economies typically possess well-defined monetary policy structures that help mitigate the effects of fiscal deficits on inflation rates. Conversely, in developing nations, the relationship between fiscal deficits and inflation is more pronounced, largely due to constraints such as restricted access to global capital markets.

The study employs several econometric models and tests to analyse the relationship between fiscal policy and inflation in Tunisia. For instance, (DOLS) model used to estimate the long-term relationships among variables such as government expenditure, revenue, money supply, balance of trade, and budget deficit. In Table 3, a positive coefficient of Money Supply (17.31) suggests that an increase in the money supply is associated with an increase in inflation. However, the relationship is not statistically significant at the conventional levels (p-value = 0.41), indicating that changes in money supply do not have a discernible long-term impact on inflation in this model. Although monetarist theories suggest a strong link between money supply and inflation, this result may imply that other factors or policy interventions in Tunisia mitigate the direct impact of money supply changes on inflation. This result aligns with [31]; [32]; [21]; [33]. Moreover, Government Revenue positive coefficient (5.57) but statistically insignificant coefficient (p-value = 0.48) suggests that government revenue increases may lead to higher inflation, although this effect is not significant in the long run. This result could indicate that while increasing government revenue (potentially through taxation) might contribute to inflationary pressures, other variables or external factors might dilute this effect in Tunisia. This result cope with [34]; [35]; [36]; [37]; [38]. Furthermore, Table 3 shows negative coefficient for Government Expenditure (-42.02), which indicates that an increase in government expenditure is associated with a decrease in inflation. However, this relationship is not statistically significant (p-value = 0.42). While Keynesian economics posits that increased government spending can lead to higher demand and inflation, the negative sign might suggest that in Tunisia, government expenditure could be focused on productive investments or subsidies that help reduce inflationary pressures. Yet, the insignificance implies this conclusion is tentative. Prior studies support these findings such [39]; [40]; [41]. Likewise, Budget Deficit coefficient (10.43) is positive and statistically significant at the 5% level (p-value = 0.05), indicating a robust relationship where an increase in the budget deficit is associated with higher inflation. The significant positive impact of budget deficits on inflation suggests that fiscal imbalances are a key driver of inflation in Tunisia. This highlights the importance of fiscal discipline and the need for policies aimed at reducing budget deficits to control inflation. This finding aligns with [42]; [43]; [44]. Besides, Balance of Trade coefficient is positive but statistically insignificant coefficient (0.48, p-value = 0.43) suggests a weak and non-significant relationship between balance of trade and inflation. While a trade deficit can theoretically lead to inflation by weakening the currency and increasing import costs, the lack of significance here may reflect other compensatory mechanisms in Tunisia’s trade or monetary policies that neutralize the inflationary impact. As [45]; and [46] found that while trade deficits can theoretically lead to inflation, the effect is often mitigated by monetary policy and exchange rate adjustments, resulting in a statistically insignificant relationship.

However, the lack of significance for money supply and government revenue implies that coordination between monetary and fiscal policies is crucial to manage inflation effectively without relying solely on changes in money supply or revenue adjustments. Although the negative coefficient for government expenditure is not significant, it suggests that well-targeted government spending might help mitigate inflationary pressures. Policymakers should consider focusing expenditures on sectors that enhance productivity and reduce costs. For instance, the insignificance of the balance of trade indicates that external economic factors or effective trade policies may help buffer the inflationary impact of trade imbalances. Policymakers might explore strengthening trade policies and enhancing export competitiveness.

On the other hand. Table 8 indicates the probability value of 0.07 is slightly above the conventional significance level of 0.05, indicating a marginal predictive relationship where government revenue might Granger-cause inflation. However, it is not statistically significant at the 5% level. While government revenue changes might influence future inflation rates, this relationship is not strong enough to be statistically significant. Policymakers should still consider monitoring revenue policies and their potential inflationary impacts, but the effect may be influenced by other variables or external factors. In addition to, changes in money supply do not significantly predict inflation in the short run, suggesting that monetary policy alone may not be a reliable tool for inflation prediction. Policymakers might focus on a broader set of indicators and variables to effectively manage inflation. Government expenditure is a key predictor of future inflation, highlighting its importance in fiscal policy planning. Tunisian policymakers should carefully consider the inflationary impacts of their spending decisions, ensuring that expenditures are directed towards productive and non-inflationary purposes. While variables like government revenue and balance of trade show marginal predictive relationships, they do not reach statistical significance impacts on inflation within the studied model, though they still play crucial roles in the broader economic context. This may indicate the need for a more comprehensive approach, considering other macroeconomic factors. The findings suggest that Tunisian policymakers need to focus on fiscal consolidation and efficient public spending to manage inflationary pressures. The study’s approach can also be applied to other developing economies facing similar fiscal and monetary challenges.

The differences between the DOLS and Granger causality results emphasize the need to differentiate between short-term and long-term dynamics when designing fiscal and monetary policies.

6. Conclusions

This study investigates the complex interplay between Inflation, Government Expenditure, Government Revenues, Money Supply, Balance of Trade, and Budget Deficit in Tunisia from 1998 to 2023. Utilizing a thorough analysis that incorporates various statistical techniques. The study has produced important findings that carry substantial implications for the country’s future development. The principal outcomes of our research are as follows:

1-

The study establishes a significant positive relationship between budget deficits and inflation in Tunisia. This finding underscores the critical role that fiscal imbalances play in driving inflationary pressures within the economy. The study highlights the importance of fiscal discipline, indicating that unchecked budget deficits can lead to sustained inflation, which can destabilize the economy and erode purchasing power.

2-

Although traditional monetarist theories suggest a strong link between money supply and inflation, this study finds that changes in money supply do not have a statistically significant long-term impact on inflation in Tunisia. This result implies that other factors, such as policy interventions or structural aspects of the Tunisian economy, might be mitigating the expected direct impact of money supply changes on inflation.

3-

The study finds a positive but statistically insignificant relationship between government revenue and inflation. This suggests that increases in government revenue, potentially through taxation, might not lead to substantial inflationary pressures. However, the influence of other economic factors may be diluting this effect in the Tunisian context, indicating that revenue generation alone may not be a direct inflation driver.

4-

While the negative coefficient for government expenditure suggests that increased spending could reduce inflationary pressures, this relationship is not statistically significant. This finding hints that government spending focused on productive investments and subsidies might have the potential to alleviate inflation, although further research is needed to establish this conclusively.

5-

The study identifies a weak and statistically insignificant relationship between the balance of trade and inflation. This suggests that other compensatory mechanisms within Tunisia’s trade policies or monetary framework might neutralize the inflationary impact of trade imbalances. As such, the balance of trade might not be a primary concern for inflation management in the Tunisian context.

6-

The findings highlight the need for Tunisian policymakers to prioritize fiscal consolidation and prudent budget management to stabilize prices. The lack of significant relationships for some variables indicates that effective management of inflation requires a coordinated approach between monetary and fiscal policies. Policymakers should focus on enhancing trade policies and export competitiveness to further buffer inflationary impacts.

7-

The study primarily centres on Tunisia, its methodology and findings are applicable to other developing economies facing similar fiscal and monetary challenges. The insights provided can guide policymakers in these contexts to better understand the relationship between fiscal policy and inflation, and to design effective strategies for economic stability.

Policy Implications

The results of this research carry significant fiscal policy implications for Tunisia, as outlined below:

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The significant impact of budget deficits on inflation underscores the need for fiscal consolidation and prudent budget management. Policymakers should prioritize reducing deficits to stabilize prices and maintain economic stability.

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The lack of significant relationships between money supply, government revenue, and inflation implies that a coordinated approach is necessary. Policymakers should ensure that monetary and fiscal policies work together to manage inflation effectively, rather than relying on isolated policy measures.

3-

Although the study did not find a statistically significant effect, the potential for government expenditure to mitigate inflationary pressures suggests that spending should be directed towards productive investments and sectors that enhance productivity and reduce costs. This approach can help alleviate inflation without exacerbating fiscal imbalances.

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Strengthening trade policies and enhancing export competitiveness can help buffer the inflationary impact of trade imbalances. Policymakers should focus on maintaining a balanced trade position to mitigate inflation risks associated with currency depreciation and import costs.

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Given the complex nature of inflation dynamics, policymakers should consider a broad set of economic indicators and variables when designing policies. This comprehensive approach can help in identifying and addressing the various factors contributing to inflation volatility.

6-

The study’s approach and findings can be applied to other developing economies facing similar fiscal and monetary challenges. The insights provided by this research can guide policymakers in other contexts to better understand the relationship between fiscal policy and inflation and to formulate more effective policies for economic stability.

Limitations and Future Research Avenues

The study on fiscal policy and its effect on inflation volatility in Tunisia may have several limitations, although they were not explicitly detailed in the document. Based on typical considerations in similar studies, the following limitations might be relevant:

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The study may not fully account for external factors, such as global economic conditions, political instability, or international trade dynamics, that can influence inflation volatility in Tunisia. These factors could introduce noise into the analysis and affect the interpretation of the results.

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The study period may not be long enough to capture the full range of economic cycles and structural changes in the Tunisian economy. Short-term analyses may not reflect long-term trends or the impact of policy changes over time.

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While the study’s methodology and findings can be applicable to other developing economies, the specific context of Tunisia might limit the generalizability of the results. Differences in economic structure, institutional frameworks, and policy environments may lead to different outcomes in other countries.

The findings presented in this article suggest several promising avenues for future research, which are detailed as follows:

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Investigate the impact of fiscal policy on inflation in specific sectors of the economy, such as agriculture, manufacturing, and services, to provide more detailed insights for policymakers.

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Consider the influence of external factors, such as global economic conditions, exchange rates, and international trade policies, on the relationship between fiscal policy and inflation volatility.

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Conduct comparative studies between Tunisia and other developing economies to understand the similarities and differences in how fiscal policy affects inflation, which can help tailor policy recommendations to specific contexts.

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Examine the implementation and effectiveness of various fiscal policies in controlling inflation and reducing volatility, taking into account the institutional and political contexts that influence policy outcomes.

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Investigate how the growth of the digital economy and technological advancements influence fiscal policy and inflation dynamics, particularly in terms of tax revenue collection and public spending efficiency.