Financial Policies and Corporate Income Tax Administration in Nigeria

Abstract

Corporate taxation assumes a pivotal role in all economies, as it constitutes a substantial source of revenue for governmental agencies tasked with fulfilling social obligations. Nonetheless, modifications in financial policies and the unpredictability of macroeconomic factors result in a significant decline in this vital revenue source for the government. This study examines the financial determinants influencing corporate tax revenue in Nigeria from 1990 to 2022. In this analysis, the broad money supply, access to credit by the private sector, borrowing costs, and exchange rates are utilized as independent variables, while corporate tax revenue serves as the dependent variable. Data pertinent to this investigation on corporate income tax are sourced from the Federal Inland Revenue Service, whereas information regarding the broad money supply and credit extended to the private sector is acquired from the Central Bank of Nigeria. Additionally, statistical data on interest and exchange rates are gathered from the World Bank. This investigation applies autoregressive distributed lag and error correction models, acknowledging the existence of a long-term relationship within the series. The significant findings indicate that the broad money supply positively and significantly affects corporate income tax in the short run, but this effect diminishes to a positively insignificant level in the long run. Additionally, the interest rate is shown to have a significant harmful effect on corporate tax income in the short run, while it becomes negatively insignificant over the long term. Other financial policy factors do not significantly account for changes in corporate income tax. This study suggests the formulation of financial policies that are advantageous to corporate organizations, particularly through the reduction in borrowing costs, to facilitate business growth and enhance the government’s ability to collect substantial corporate tax revenue. The originality of this research is apparent in its utilization of financial policy instruments to illustrate the effectiveness of financial guidelines on corporate tax receipts and to argue for particular amendments that are essential when these guidelines prove detrimental to business activities.

1. Introduction

The taxation of corporations plays a vital role in establishing an effective tax system, serving as a primary source of revenue essential for the fiscal stability of a nation while also influencing business financial operations (Andrejovská, 2019). Nigeria’s tax framework has shown susceptibility to disruptions stemming from the country’s financial policies and broader macroeconomic changes. While corporate income tax is fundamental to the prosperity of an emerging economy, various financial policies and economic factors affecting government revenue have remained ongoing challenges. Most of the trade and industry variables that influence company revenues, assets, and investments have a direct impact on corporate tax revenue. All of these components interact, especially with the financial and macroeconomic statistics of the country. According to Shimamoto (2023), the process of acquiring financial resources consistently poses significant challenges. Examining these elements is critical for regulators because it demonstrates the monetary factors that influence earnings from taxes and the future trajectory of such impacts in relation to business organizations. A mix of financial and other elements determines an economy’s corporate revenue threshold, reflecting the country’s continually shifting financial circumstances. Tax policies, both domestic and international, play a crucial role in shaping the tax revenues of corporations. The fundamental tax rate, as determined by the legislation of each nation, is arguably the most significant and accessible instrument of fiscal and monetary policies.

Corporate tax, or Companies Income Tax (CIT), is a tax imposed on the profits earned by corporations in Nigeria. It is regulated under the Companies Income Tax Act (CITA) and is overseen by the Federal Inland Revenue Service (FIRS). The taxable profits are determined for the financial year by subtracting allowable costs and any reliefs as defined in the CITA (FIRS, 2024). Corporate tax is the compulsory levy on the taxable income of companies operating in Nigeria. The collection of taxes and the relevant tax regulations are problematic to implement in countries that are both wealthy and poor. Hence, raising tax income is a top priority for governments. Authorities may improve revenues by considering the determinants of tax revenue (Gamze, 2019). Numerous works in the academic community highlight various factors affecting tax income (Castro & Camarillo, 2014; Piancastelli & Thirlwall, 2020; Method, 2018). The influence of financial policies and rates of tax on tax collections in various nations or country groups within periods have been investigated (Andrejovská, 2019; Andrejovská & Glova, 2023; Cozmei, 2015; Karpowicz, 2018; Tahlova & Banociova, 2019). Due to many constraints, the link between financial regulation and corporation taxes has garnered very little attention in the existing literature. The fundamental challenge is a lack of information, such as irregular corporation tax rate adjustments. The central aim of this investigation is to assess the impact of financial policy instruments on the improvement of business taxation in a developing context, particularly in Nigeria.

This study aims to achieve the following specific objectives:

• Determine the influence of broad money supply on corporate tax revenue in Nigeria;
• Examine the effect of private sector credit on corporate income tax;
• Assess the impact of the Central Bank’s interest rate on corporate taxation;
• Evaluate the degree to which exchange rate fluctuations affect business taxation in Nigeria.

In pursuit of the specified research objectives, the following questions have been articulated:

• Does the broad money supply have an effect on corporate tax revenue in Nigeria?
• To what extent does bank credit to the private sector enhance the income tax revenues of companies?
• How does the interest rate influence the income tax collected from corporations in Nigeria?
• What impact do fluctuations in the exchange rate have on corporate tax receipts in Nigeria?

This study is arranged in a systematic manner. Immediately after Section 1, Section 2 delivers an in-depth review of the relevant literature. Section 3 describes the methodology adopted for the research, followed by an analysis of the results in Section 4. Section 5 articulates the principal conclusions derived from this study and discusses the policy implications of the results.

2. Overview of the Literature

2.1. Conceptual Review

The progression of corporate taxation has become one of the most important legislative actions, perceived as a key contributor to government income that stimulates economic growth and development. The taxation of corporate income involves multiple facets, including tax rates, incentives, regulatory structures, and international tax agreements (Filipa et al., 2024). The phenomenon of globalization has added complexity to the corporate taxation environment, as companies seek to broaden their operations internationally to capitalize on growth prospects and enhance cost efficiencies (Filipa et al., 2024). Besides its revenue-generating capabilities, taxation acts as a vital fiscal instrument for resolving societal, economic, and political matters (Sani et al., 2024). Additionally, when the central bank implements interest rate changes, continuous violations of the inflation target can significantly impact the economy’s path if the tax structure is not adjusted for inflation (Gavin et al., 2007). According to Amadi et al. (2024), ensuring the stability of the banking system is crucial for minimizing variations in interest rates. The implementation of sound monetary policy can profoundly affect the economic financing landscape by managing the availability and costs of credit (Omankhanlen et al., 2021). An optimally formulated monetary approach is aimed at increasing state revenue via tax receipts from both businesses and individuals, especially in emerging countries (Omodero, 2024). According to Nguyen and Than (2020), if inflation passes a specific threshold, consumption, the worth of finances, investments, and output would fall, resulting in a detrimental effect on income taxes and the total expansion of the economy. However, corporate taxation has a strong link with the tax rate since an increased tax rate raises companies’ income tax, and the resultant effect is seen to be an adverse relationship, as proven by (Clausing, 2007; Basheer et al., 2019) corroborate that corporate income tax is deemed a major source of financing since it is gathered from businesses; nevertheless, in many impoverished nations, reduced rates of taxation have prevented them from implementing substantial growth initiatives (Basheer et al., 2019).

2.2. Notional Basis

2.2.1. Keynes Monetary Strategy Hypothesis

The theory underpinning this study is Keynes’s monetary policy hypothesis. Keynes (1973) argued in favor of a low interest rate policy. He maintained that during recessionary periods, monetary policies must address liquidity concerns in the financial services sector and promote corporate financing by ensuring that small-, micro-, and medium-sized enterprises can access necessary financial support (Bašić & Ćurić, 2021; Boiko et al., 2022; Lebedeva & Shkuropadska, 2023). As liquidity increases, interest rates fall. Lower borrowing costs will stimulate investments as capital performs optimally. Keynesians claim that a broad-based monetary stimulus reduces interest rates by raising loanable funds in the banking sector. It is believed that lower interest rates stimulate investment and interest-sensitive consumer spending, boosting government revenue (Antonio, 2019). The Keynesian hypothesis challenged the assumption that revenue and commodity prices are directly related (Keynes, 1973). According to Keynes, financial regulation has a considerable influence on how the economy runs. According to this viewpoint, changes in the quantity of cash in circulation can have long-term effects on interest rates, exchange rates, inflation, and general consumer demand, as well as job creation, productivity, and profits (Antonio, 2019). This study focuses on how monetary instruments affect the amount of corporate tax income that the government may collect from businesses. As a result, this theory is quite significant. A purposeful attempt by the country’s monetary authority to cut borrowing costs and exchange rates will increase commercial activity and enable expansion for enterprises to generate more revenue that will yield greater corporation taxes for the government.

2.2.2. Companies Income Tax Law and Its Evolution over Time

The tax imposed on corporate entities, commonly known as companies’ income tax, is a direct tax levied by jurisdictions on the income or capital of companies operating within Nigeria. This tax is specifically charged on a company’s earnings, with a standard rate of 30% applied to the profits realized in the fiscal year that follows the assessment year. The Companies Income Tax Act of 2007 provides the legal basis for this tax, which is administered by the Federal Inland Revenue Service. It is significant to highlight that the framework for corporate income tax was established in 1979 with the introduction of the Companies Income Tax Act (CITA), which has its origins in the Income Tax Management Act of 1961. The initial legislation governing corporate income tax in Nigeria was the Companies Income Tax Ordinance, which was enacted in 1939. Thus, more significant changes happened in 2020 as a result of the enactment and adoption of the Finance Act 2020. Currently, only big enterprises with a turnover of NGN 100 million or more pay corporate income tax (CIT) on earnings at the rate of 30%, whilst medium-sized companies with a turnover of NGN 25 million but less than NGN 100 million pay CIT at the rate of 20%. Only small enterprises with a turnover of NGN 25 million or less pay zero-rate CIT but are required to file all tax reports at the end of each fiscal year.

2.3. An Examination of Past Works

Andrejovská and Glova (2023) conducted a study examining how particular economic variables influence corporate tax revenues. Utilizing a panel regression methodology, the research analyzed data from 27 EU Member States over the period from 2004 to 2020. The findings revealed that the effective tax rate has a substantial impact on corporate tax collections. In a separate study, Gökpınar (2023) explored the relationship between financial and macroeconomic indicators and tax revenue in Turkey. The research evidence demonstrated a co-integration connection between revenues from taxes and all indices. Based on the long-term estimations of coefficients, the overall money supply (M2), manufacturing production measure, deposit rate of interest, and trade statistics all had an advantageous effect on the income tax threshold, but the rate of unemployment and the currency exchange rate had detrimental effects. Additionally, the data indicated that wide money (M2) had the largest beneficial impact on tax collections. The research by Raouf (2022) focused on assessing the effects of financial participation on tax revenues in EMEA states. The data indicated a nonlinear link between monetary inclusion and income from taxes. The investigation also found that being financially included is a key generator of tax income.

Ihuarulam et al. (2021) conducted a statistical analysis to investigate the correlation between tax revenue and various macroeconomic variables. Their study utilized panel data from six ECOWAS countries, examining factors such as tax income, GDP, inflation, unemployment, trade openness, and currency exchange rates over the period from 2005 to 2019. The results revealed a significant association between rising prices and tax revenue collections. Similarly, Saptono and Mahmud (2021) analyzed the macroeconomic factors influencing tax revenue in six Southeast Asian countries from 2008 to 2019. Their findings demonstrated that per capita income, production levels, and trade liberalization positively and significantly affected overall tax revenue. Conversely, this study found that price increases were an unnecessary factor since it had no meaningful influence on the tax indicators of achievement. Helcmanovská and Andrejovská (2021) evaluated the significance of earnings from taxes and rates of taxes in the overall picture of tax rivalry in the European Union. To help achieve the aim, tax rates were analyzed and compared with company tax collections. The aim was achieved by applying multiple regression analysis. This study found no significant correlation between constitutional and average effective rates of taxation and corporation tax receipts.

Nguyen and Than (2020) used statistics from 1999 to 2016 to analyze factors influencing corporate tax collection in Vietnam. This study found that corporate income tax revenue was significantly positively impacted by GDP at present prices and corporate income tax liabilities. The corruption perceptions index and tax rate had a beneficial and insignificant influence, while yearly inflation rates had an undesirable and inconsequential effect. Piancastelli and Thirlwall (2020) conducted an analysis of the factors influencing tax revenue across 59 industrialized and emerging economies from 1996 to 2015. Their methodology involved regressing actual tax collections against various determinants of income tax, including broad money supply. The findings revealed that the service sector and trade volume exert the most significant influence on tax revenue relative to other variables. Both factors were shown to have an encouraging and considerable influence on tax revenue.

Method (2018) sought information on prospective taxing variables that impacted the improvement of tax proceeds from 1999 to 2016. This study assessed the constructed theory to determine if prospective tax predictors drive tax revenue achievement in Tanzania. A framework with four elements, monetary, institutional, organizational, and financial considerations, found that possible tax predictors had a considerable impression on tax receipts generation. Mining shares and loans had helpful and economically significant benefits, but the efficiency of government had an undesirable and substantial impact on tax income. Additional factors, such as production, buildings, transit, amenities, the enforcement of law, regulatory efficacy, and the prevention of nepotism, all had beneficial impacts on tax revenue, whereas foreign direct investment had a minor negative impact.

According to Kobyagda (2019), structural variables dictate tax weight, and the tax capacity is underutilized in the majority of UEMOA nations that are members. In a comparable manner, a great deal of governments’ budgetary initiatives failed to mobilize resources. The findings revealed that West African Economic and Monetary Union nations may meet or exceed the UEMOA’s statutory taxation rate of 20%, assuming measures to adequately tax those in the informal economy are accomplished.

Giving to Tahlova and Banociova’s (2019) empirical research, corporate income tax revenues are influenced by both tax components related to the corporate tax arrangement and non-tax variables such as fluctuating and global variables. The findings of the research suggest that corporate income tax revenues are influenced by a combination of tax legislation and non-tax factors. Gamze (2019) identified a positive relationship between financial inclusion and tax revenues, highlighting the significance of financial participation in shaping tax collection levels. Additionally, Basheer et al. (2019) examined the impact of monetary and economic factors on the tax revenues of Bahrain and Oman during the period from 1990 to 2010. The investigation’s outcomes highlighted a prominent association between tax revenues and a range of economic and financial indicators, including the expansion of gross domestic product, the capital-to-assets ratio of financial institutions, credit risk premiums, net foreign direct investment inflows, and the cash surplus or deficit recorded throughout the research period.

In their comprehensive analysis, Terefe and Teera (2018) identified several factors that positively affected the tax revenues of East African nations over the study period, including per capita GDP, foreign aid, trade openness, the share of agriculture, business engagement, and the proportion of services provided. In contrast, they noted that urbanization, the official currency exchange rate, and inflation had detrimental effects on the region’s tax revenue relative to the GDP. Income taxes and migration had a poor influence on the present period’s tax collections, but two-period latent urbanization and official currency exchange rates had a favorable contribution. Hence, the investigation’s strong findings imply that tax income rises under a macroeconomic climate that is not volatile. The correlational research conducted by Kawano and Slemrod (2016) examined the relationship between corporate tax rates and tax revenues in OECD countries from 1980 to 2004, revealing that higher implied tax rates were associated with increased corporate profits.

The study conducted by Cozmei (2015) sought to explore the potential contradiction between the corporate tax rate and the corporate tax-to-GDP ratio in the European Union, revealing that lower corporate tax rates did not result in a decrease in the corporate tax-to-GDP ratio, a pattern recognized by numerous scholars. A panel dataset from EU countries was utilized to evaluate the impact of corporate tax competitiveness on the cultural reactions of businesses. The data do not show that decreasing the strain on corporation tax rates does not result in a decline in business incomes over the years. Castro and Camarillo (2014) utilized both stationary and dynamic panel data methodologies to examine the effects of financial, institutional, cultural, and social factors on tax revenue across 34 nations that are members of the Organization for Economic Cooperation and Development from 2001 to 2011. Their findings indicated that GDP per capita, the manufacturing sector, and individual rights positively influenced tax collection, while the agricultural sector and the level of foreign direct investment were also significant, particularly in high-income countries. Muibi and Sinbo (2013) analyzed the macroeconomic elements that drive tax income in Nigeria. The key conclusion of the empirical research was that tax revenue responded considerably to fluctuations in levels of income, the currency exchange rate, and price level.

Clausing (2007) examined the differences in business income tax collection levels compared to GDP within OECD countries from 1979 to 2002. This study detailed these variations by analyzing the statutory tax rate, the scope of the taxable base, corporate profitability, and the commercial sector’s contribution to GDP. The results demonstrated a positive relationship between tax rates and corporate earnings, indicating that a corporate income tax rate of 33% would be ideal for maximizing revenue across the sample. Moreover, this optimal tax rate was found to decrease as economies became smaller and increasingly connected to the global economy.

3. Materials and Methods

The main objective of this research is to analyze the impact of financial policies on corporate taxation. This investigation utilizes annual data spanning from 1990 to 2022. The subsequent section outlines the linear model employed in the time series econometric analysis.

$${lnCIT}_t={\beta }_0+{\beta }_{1}ln{M2\_M3}_t+{\beta }_{2}ln{INT}_t+{\beta }_3{lnCPS}_t+{\beta }_4{lnEXG}_t+{\epsilon }_t$$

(1)

In this context, “ε” represents the error term, “t” denotes the time dimension, encompassing annual data from 1990 to 2022, and “ln” refers to the natural logarithm. The dependent variable, which is the logged corporate income tax, is abbreviated as lnCIT. The independent variables include the logged broad money supply, abbreviated as lnM2_M3; the logged interest rate, abbreviated as lnINT; the logged bank credit to private sector, abbreviated as lnCPS; and the logged exchange rate, abbreviated as lnEXG. In accordance with the framework established by Dickey and Fuller (1979), the research applied the Augmented Dickey–Fuller (ADF) unit root test to evaluate the stability of the datasets. The results indicated that all series were stationary at the first difference, with the exception of lnM2_M3, which attained stationarity at the level.

Based on the autoregressive distributed lag (ARDL) model proposed by Pesaran et al. (2001), when the unit root is at order 1 and 0, ARDL is the best technique for the analysis. However, the co-integration test should be carried out to establish the existence of a long-run relationship or not. When compared to other co-integration strategies, the ARDL model has some advantages that are often discussed in the literature. First off, a strictly integrated order of variables is not necessary for the ARDL approach. Second, the model offers more accurate estimation results, particularly for the attributes of small samples. Third, the ARDL model is a useful tool since it takes the consequences of endogenous independent variables into account. The ARDL model can be stated as follows using the baseline model in Equation (2) as a starting point:

$$\begin{gathered} {\Delta lnCIT}_t={\sigma }_0+\sum _{i=0}^p{\alpha }_{1i}{\Delta lnCIT}_{t-i}+\sum _{i=0}^p{\alpha }_{2i}{\Delta lnM2\_M3}_{t-i}+\sum _{i=0}^p{\alpha }_{3i}{\Delta lnINT}_{t-i}+\sum _{i=0}^p{\alpha }_{4i}{\Delta lnCPS}_{t-i}+\sum _{i=0}^p{\alpha }_{5i}{\Delta lnEXG}_{t-i} \\ +{\delta }_1{lnCIT}_{t-i}+{\delta }_2{lnM2\_M3}_{t-i}+{\delta }_3{lnINT}_{t-i}+{\delta }_4{lnCPS}_{t-i}+{\delta }_5{lnEXG}_{t-i}+{\mu }_t \end{gathered}$$

(2)

where CIT, M2_M3, INT, CPS, and EXG remain as previously defined. Δ is the difference operator, and ε refers to the residual term. Similarly, $\alpha$ denotes the drift, $t-1$ denotes the lag lengths, ${\alpha }_1-{\alpha }_5$ are coefficients to be estimated, $ln$ denotes natural logarithms, and ${\mu }_t$ is the error term.

Equation (3) is used as the starting point for the ordinary least squares (OLS) technique before the bound test is carried out to test for the long-term equilibrium relationship between the variables. The alternative hypothesis that there is a long-term association between the variables is used to test the null hypothesis that there is no co-integration between the variables. The following is how the null hypothesis, which states that there is no long-term relationship, is articulated:

$$H_0: {\alpha }_1={\alpha }_2={\alpha }_3={\alpha }_4={\alpha }_5=0$$

In this case, the alternative hypothesis would be as follows:

$$H_1: {\delta }_1\ne {\delta }_2\ne {\delta }_3\ne {\delta }_4\ne {\delta }_5\ne 0$$

where ${\alpha }_1, {\alpha }_2, {\alpha }_3, {\alpha }_4, \mathrm{a}\mathrm{n}\mathrm{d} {\alpha }_5$ remain as defined earlier. Last but not least, the ARDL methodology used AIC to choose the best model and the appropriate length for the lag level.

Specification of the long-run and short-run ARDL approaches is crucial since this study’s goal is to comprehend both short- and long-run dynamics of the impact of taxation on price variance in Nigeria. Thus, Equation (3) represents the model’s long run:

$${lnCIT}_t={\alpha }_0+\sum _{i=1}^{v_1}{\alpha }_1{lnCIT}_{t-i}+\sum _{i=0}^{v_2}{\alpha }_2{lnM2\_M3}_{t-i}+\sum _{i=0}^{v_3}{\alpha }_3{lnINT}_{t-i}+\sum _{i=0}^{v_4}{\alpha }_4{lnCPS}_{t-i}+\sum _{i=0}^{v_5}{\alpha }_5{lnEXG}_{t-i}+{\mu }_t$$

(3)

The unrestricted ARDL of the error correction model is estimated, as shown in Equation (4), to predict the short-run parameters of the model when the long-run equilibrium exists:

$${lnCIT}_t={\alpha }_0+\sum _{i=1}^{v_1}{\alpha }_1\Delta {lnCIT}_{t-i}+\sum _{i=0}^{v_2}{\alpha }_2\Delta {lnM2\_M3}_{t-i}+\sum _{i=0}^{v_3}{\alpha }_3\Delta {lnINT}_{t-i}+\sum _{i=0}^{v_4}{\alpha }_4{\Delta lnCPS}_{t-i}+\sum _{i=0}^{v_5}{\alpha }_5\Delta {lnEXG}_{t-i}+{\theta ECM}_{t-i}+{\mu }_t$$

(4)

Because this study controlled for GDP, inflation, and informal sector, which directly affect tax revenue collection in Nigeria, Equation (5) is stated as follows:

$$\begin{gathered} {lnCIT}_t={\alpha }_0+\sum _{i=1}^{v_1}{\alpha }_1\Delta {lnCIT}_{t-i}+\sum _{i=0}^{v_2}{\alpha }_2\Delta {lnM2\_M3}_{t-i}+\sum _{i=0}^{v_3}{\alpha }_3\Delta {lnINT}_{t-i}+\sum _{i=0}^{v_4}{\alpha }_4{\Delta lnCPS}_{t-i}+\sum _{i=0}^{v_5}{\alpha }_5\Delta {lnEXG}_{t-i} \\ +\sum _{i=0}^{v_6}{\alpha }_6\Delta {lnGDP}_{t-i}+\sum _{i=0}^{v_7}{\alpha }_7\Delta {lnINF}_{t-i}+\sum _{i=0}^{v_8}{\alpha }_8\Delta {lnNFS}_{t-i}+{\theta ECM}_{t-i}+{\mu }_t \end{gathered}$$

(5)

where θ is the system’s rate of adjustment, and ECM stands for the stochastic error term.

4. Results

This section provides the data analysis findings. The results contain descriptive statistics used to evaluate the normal distribution of data and a correlation matrix that validates the degree of link among the variables. There is also a unit root test for dataset stationarity and a bound test for determining if a long-run connection exists or not. We also have results for VAR lag order selection criteria, long-run ARDL estimation, short-run ARDL estimation with ECM, and diagnostic tests that corroborate the model’s dependability in this study.

Table 1. Description of the variables.

Variable	Notation	Measurement	Source
Corporate tax reveue	LNCIT	Total corporate tax collected in billions of Naira but converted to its natural logarithm	Federal Inland Revenue Service
Credit to private sector	LNCPS	Bank credit facility to the private sector collected in billions of local currency but converted to the natural log form	Central Bank of Nigeria
Broad money supply	LNM2_M3	Money in circulation collected in billions of national currency but converted to its natural logarithm form	Central Bank of Nigeria
Interest rate	LNINT	Collected in percentages	World Bank
Exchange rate	LNEXG	Collected in percentages	World Bank
Control variables:
Gross Domestic Product	LNGDP	Collected in billions of dollars from 1990–2023	World Bank
Inflation rate	LNINF	Price level changes in goods and services	World Bank
Informal Sector Operation	LNNFS	Informal sector output (% of official GDP)	World Bank

Source: Authors’ data information, 2024.

provides details of the variables’ measurement and sources, while

Table 2. Descriptive statistics.

	CIT	M2_M3	INT	CPS	EXG
Mean	5.114	8.003	2.910	7.684	4.534
Median	5.508	8.242	2.879	7.736	4.861
Maximum	7.882	10.79	3.455	10.57	6.054
Minimum	1.096	3.859	2.441	3.513	2.079
Std. Dev.	2.011	2.098	0.205	2.208	1.128
Skewness	−0.469	−0.363	0.215	−0.341	−0.718
Kurtosis	1.976	1.838	3.724	1.781	2.326
Jarque–Bera	2.651	2.581	0.974	2.684	3.461
Probability	0.266	0.275	0.614	0.261	0.177
Sum	168.8	264.1	96.03	253.6	149.6
Sum Sq. Dev.	129.4	140.9	1.344	156.1	40.71
Observations	33	33	33	33	33
Correlation Matrix
	CIT	M2_M3	INT	CPS	EXG
LNCIT	1.000
LNM2_M3	0.697	1.000
LNINT	−0.780	−0.781	1.000
LNCPS	0.696	0.698	−0.779	1.000
LNEXG	0.644	0.644	−0.671	0.633	1.000

Authors’ calculation, 2024.

displays the descriptive statistics and correlation analysis for this investigation. The goal of descriptive statistics is to determine the normality of the dataset’s distribution, which is accomplished by calculating the Kurtosis and, most significantly, the Jarque–Bera probability values. Table 2 shows that the Kurtosis is within acceptable limits, and the Jarque–Bera p-values are more than the 5% significant threshold, indicating that the datasets employed in this investigation have normal distributions. For the correlation analysis, LNM2_M3 (wide money supply) exhibits a substantial positive association with CIT (corporate income tax), indicating that business organizations need enough money in circulation to satisfy government corporate tax requirements. The interest rate, which is the cost of borrowing, has a high negative association with CIT and money supply. It indicates that the country’s monetary authorities should work to cut borrowing costs so that businesses may obtain funds for operations. Credit to the private sector and the exchange rate have a strong positive association with CIT but a strong negative relationship with interest rates. The inference is that the money available to the private sector is too expensive, given the high borrowing costs.

In light of the numerous macroeconomic variables that influence tax revenue collection in Nigeria, this research incorporates an analysis of GDP, inflation rates, and the activities of the informal sector, which collectively affect the government’s tax revenue generation. The results are presented in Appendix A. The correlational analysis in Table 2 and

Table A2. Descriptive statistics and correlational analysis.

	LNCIT	LNM2_M3	LNCPS	LNINT	LNEXG	LNGDP	LNINF	LNNFS
Mean	5.202	8.093	7.778	2.902	4.581	5.325	2.676	4.026
Median	5.657	8.392	7.972	2.873	4.869	5.551	2.565	4.029
Maximum	8.116	11.06	10.87	3.455	6.127	6.353	4.290	4.071
Minimum	1.096	3.859	3.513	2.441	2.079	3.952	1.609	3.914
Std.Dev.	2.046	2.132	2.243	0.207	1.144	0.813	0.633	0.033
Skewness	−0.466	−0.373	−0.355	0.251	−0.709	−0.463	0.975	−1.261
Kurtosis	2.001	1.856	1.800	3.582	2.358	1.703	3.558	5.073
Jarque–Bera	2.643	2.642	2.752	0.836	3.438	3.596	5.829	15.10
Probability	0.267	0.266	0.252	0.658	0.179	0.166	0.054	0.525
Sum	176.8	275.1	264.4	98.67	155.8	181.1	90.98	136.8
SumSq.Dev.	138.1	149.9	165.9	1.415	43.17	21.80	13.24	0.036
Observations	34	34	34	34	34	34	34	34
Correlation Matrix
	LNCIT	LNM2_M3	LNCPS	LNINT	LNEXG	LNGDP	LNINF	LNNFS
LNCIT	1.000
LNM2_M3	0.697	1.000
LNCPS	0.696	0.598	1.000
LNINT	−0.492	−0.493	−0.791	1.000
LNEXG	0.647	0.548	0.537	−0.688	1.000
LNGDP	0.494	0.491	0.900	−0.427	0.718	1.000
LNINF	−0.294	−0.276	−0.278	0.274	−0.275	−0.278	1.000
LNNFS	0.057	0.041	0.035	−0.083	0.165	−0.053	0.316	1.000

indicate that money supply, credit to private sector operations, and exchange correlate strongly and positively with corporate income tax, while the interest rate is adversely related with corporate tax, money supply, and private sector credit.

Table 3. Unit root test.

	ADF-STAT	Critical Value @ 5%	p-Value	Order of Integration	Remarks
LNCIT	−4.883	−2.960	0.000	I(1)	Stationary
LNM2_M3	−4.491	−2.957	0.001	I(0)	Stationary
LNINT	−5.656	−2.960	0.000	I(1)	Stationary
LNCPS	−4.125	−2.960	0.003	I(1)	Stationary
LNEXG	−5.238	−2.619	0.000	I(1)	Stationary

Authors’ calculation, 2024.

and Table A2 show the values at which each variable becomes unchanging. This test is required since the variables may not remain constant over time and can produce erroneous regression results if not properly examined. As a result, after the degrees of stationary patterns have been determined, the unit root test will allow us to select the appropriate econometric instrument. According to Pesaran et al. (2001), when I(1) and I(0) series come together, the application of ARDL is critical in estimating the long-run connection. Thus, the stationarity level of all the series in Table 3 indicates that LNM2_M3 is stationary at order zero, whereas the others are stable at first difference. As a result, this study uses the ARDL estimate approach proposed by (Pesaran et al., 2001). In Table A2, the inflation rate is also stationary at level while the informal sector output and GDP became stationary at first difference.

However, the unit root test used to choose the ARDL estimate approach in Table 3 and

Table A1. Unit root result.

Variables	ADF-Statistic	Critical Value @ 5%	p-Value	Order of Integration
LNCIT	−5.068	−2.957	0.000	1(1)
LNM2_M4	−4.132	−2.954	0.003	1(0)
LNCPS	−4.207	−2.957	0.002	1(1)
LNINT	−5.739	−2.960	0.000	1(1)
LNEXG	−5.322	−2.957	0.000	1(1)
LNGDP	−4.891	−2.957	0.000	1(1)
LNINF	−3.039	−2.957	0.000	1(0)
LNNFS	−4.981	−2.957	0.000	1(1)

is insufficient to confirm a long- and short-run connection. As a result, a bound test is required to determine if the dependent and independent variables have a long or short-term relationship. The rule is that if the F-statistic is higher than the lower and upper bound critical values at the 5% level of significance, there is a long-run relationship; if it is lower, we reject the null hypothesis of a long-run relationship. According to the bound test in

Table 4. Bound test.

Critical Value Bounds	Lower Limit Bound I(0)	Upper Limit Bound I(1)
10%	2.45	3.52
5%	2.86	4.01
2.5%	3.25	4.49
1%	3.74	5.06

Note: F-statistic value = 5.06; K = 4. Authors’ calculation, 2024.

, the F-statistics of 5.06 exceed the lower limit of 2.86 and the upper bound of 4.01 at a critical value of 5%. In

Table A4. Bound test.

Critical Value Bounds	Lower Limit Bound I(0)	Upper Limit Bound I(1)
10%	2.03	3.13
5%	2.32	3.50
2.5%	2.60	3.84
1%	2.96	4.26

Note: F-statistic = 4.59; K = 7. Authors’ calculation, 2025.

, the bound test result shows the F-statistic to be 4.57, and it is higher than both the lower and upper limit bounds. This proves that a long relationship is in existence among the series applied in this study.

The ARDL bound test examines if a long- or short-term association occurs in the series. If the F-statistic exceeds the upper and lower boundaries at 5% significance, we reject the null hypothesis of no long-run co-integration; otherwise, we do not reject the null hypothesis. Table 4 displays the findings of the ARDL bound test, which was used to ascertain the presence of a long-term connection. As a consequence, the F-statistic value of (5.06) surpasses the critical value of both the lower (2.86) and upper bounds (4.01), as established by Pesaran et al. (2001). Similarly, as we controlled for inflation, informal sector and GDP in Table A4, the result shows F-statistic value of 4.59 which is greater than the upper and lower bounds limit of 2.32 and 3.50 respectively. In this regard, the null assumption, which claims that there is no long-run link between the variables studied, is rejected since one has been established.

Table 5. VAR lag order selection criteria.

Lag	LogL	LR	FPE	AIC	SC	HQ
0	18.24	NA *	0.024	−0.883	−0.649	−0.808
1	20.46	3.550	0.022 *	−0.964 *	−0.684 *	−0.874 *
2	20.50	0.057	0.024	−0.900	−0.573	−0.795
3	20.50	0.002	0.026	−0.833	−0.459	−0.714

* indicates the lag length selected by all the criteria. Source: Authors’ calculation, 2024.

shows the results of the most optimal lags from the ARDL constraint assessment. Validating the appropriate latency produces a more trustworthy output while eliminating serial association and ensuring an impartial outcome. As shown in Table 5 and

Table A5. VAR lag order selection criteria.

Lag	LogL	LR	FPE	AIC	SC	HQ
0	22.68	NA	0.023	−0.947	−0.577	−0.826
1	22.87	0.272 *	0.024 *	−0.895 *	−0.478 *	−0.759 *
2	24.10	1.665	0.024	−0.909	−0.447	−0.759
3	24.88	1.007	0.025	−0.896	−0.387	−0.729

* indicates the lag length selected by all the criteria. Source: Authors’ calculation, 2025.

, the most acceptable lag is one, which is supported by every single parameter. Despite the fact that lag 1 is picked by all other criteria, the AIC option takes precedence over all other choices in the event of lag order choice variations by all the parameters.

The long-run ARDL estimate in

Table 6. Long-run ARDL estimation dependent variable: LNCIT.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
LNCIT(−1)	0.450	0.169	2.656	0.013 **
LNM2_M3(−1)	0.362	0.303	1.197	0.242
LNINT(−1)	−0.478	0.224	−2.137	0.042 ***
LNCPS(−1)	0.023	0.252	0.091	0.928
LNEXG(−1)	0.134	0.089	1.490	0.148
C	0.732	0.893	0.819	0.420

Notes: R-Squared = 0.79; Adjusted R-Squared = 0.69; S.E. of regression = 0.14; F-statistic = 1135; Prob. (F-statistic) = 0.000; Durbin–Watson = 1.89; AIC = −0.918; SC = −0.643. ***, ** confirms significant level at 5% and 1%, respectively. Authors’ computation, 2024.

shows that the broad money supply has a negligible positive (t-statistic = 1.197; p-value = 0.242) influence on corporate income tax. The long-run estimation in

Table A6. Long-run estimation.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
LNCIT(−1)	0.416	0.247	1.679	0.106
LNM2_M3(−1)	0.371	0.384	0.966	0.343
LNINT(−1)	−0.451	0.248	−1.817	0.082 ***
LNCPS(−1)	0.015	0.279	0.053	0.958
LNEXG(−1)	0.165	0.288	0.575	0.571
LNGDP(−1)	0.061	0.263	0.231	0.819
LNINF(−1)	0.015	0.052	0.278	0.783
LNNFS(−1)	0.603	1.151	0.524	0.605
C	−2.111	4.647	−0.454	0.654
R-squared	0.595	Mean dependent var		5.327
Adjusted R-squared	0.494	S.D. dependent var		1.942
S.E. of regression	0.144	Akaike info criterion		−0.812
F-statistic	725.7	Durbin–Watson stat		1.827
Prob(F-statistic)	0.000

*** indicates significance level @ 10%. Authors’ estimation, 2025.

also establishes this result (t-statistic = 0.966; p-value = 0.343). Thus, the result is consistent with the findings of (Piancastelli & Thirlwall, 2020), who discovered that a wide money supply has little effect on tax collection in 59 countries, both developed and developing. Credit to the private sector likewise had a negligible positive influence on CIT in the long run (t-statistic = 0.091; p-value = 0.928) and the short run. This finding is supported by Basheer et al. (2019) and Method (2018). Similarly, currency fluctuations have an intangible influence on CIT in both the long and short run; the evidence is confirmed by (Terefe & Teera, 2018) in their long-term research of East African countries. The interest rate or borrowing cost has a substantial negative impact on CIT in the long run (t-statistic = −2.137; p-value = 0.042) but has no effect in the short run. Gökpınar, (2023) findings contradict this conclusion. Similarly, the interest rate impacts negatively and significantly on CIT in the long run, as shown in Table A6, but all the control variables examined appear positively insignificant.

This study looked at the short-term performance of financial variables as displayed in

Table 7. Short-run ARDL estimation with ECM dependent variable: D(LNCIT).

Variable	Coefficient	Std. Error	t-Statistic	Prob.
C	−0.0318	0.057	−0.552	0.586
D(LNCIT(−1))	0.478	0.237	2.019	0.055 ***
D(LNM2_M3(−1))	0.608	0.308	1.972	0.060 **
D(LNINT(−1))	−0.321	0.237	−1.356	0.188
D(LNCPS(−1))	0.009	0.209	0.043	0.966
D(LNEXG(−1))	−0.003	0.103	−0.031	0.975
ECM(−1)	−1.059	0.284	−3.733	0.001

Notes: R-Squared = 0.55; Adjusted R-Squared = 0.44; S.E. of regression = 0.14; F-statistic = 4.97; Prob. (F-statistic) = 0.002; Durbin–Watson = 2.1; AIC = −0.946; SC = −0.623. ***, ** confirms significant level at 5% and 10%, respectively. Authors’ computation, 2024.

and

Table A7. Short-run estimation.

Variable	Coefficient	Std. Error	t-Statistic	Prob.
D(LNCIT(−1))	0.456	0.261	1.749	0.094
D(LNM2_M3(−1))	0.753	0.342	2.205	0.038 **
D(LNINT(−1))	−0.259	0.245	−1.056	0.302
D(LNCPS(−1))	−0.042	0.224	−0.190	0.851
D(LNEXG(−1))	−0.017	0.321	−0.053	0.957
D(LNGDP(−1))	−0.007	0.283	−0.027	0.978
D(LNINF(−1))	0.013	0.056	0.246	0.807
D(LNNFS(−1))	−0.306	1.544	−0.198	0.844
ECM(−1)	−1.102	0.317	−3.468	0.002 *
C	−0.044	0.067	−0.658	0.516
R-squared	0.572	Mean dependent var		0.212
Adjusted R-squared	0.397	S.D. dependent var		0.180
S.E. of regression	0.139	Akaike info criterion		−0.846
F-statistic	3.271	Durbin–Watson stat		2.133
Prob(F-statistic)	0.011

**, * indicate significance level at 5% and 1%, respectively. Authors’ estimation, 2025.

, and the findings in Table 7 suggest that broad money supply has a substantial and positive (t-statistic = 1.972; p-value = 0.060) effect on CIT in the short run at the 10% level. In Table A7, broad money significantly impacts CIT at a 5% materiality level. This conclusion is confirmed by the studies of Gökpınar (2023) and Gamze (2019). All other parameters are deemed irrelevant, as is the case in (Muibi & Sinbo, 2013; Nguyen & Than, 2020). The ECM coefficient indicated in Table 7 is shown to be negative and significant at the 1% level. This demonstrates a slow convergence rate, with the model pushing itself towards equilibrium by 106% every year. The consequence is that any disequilibrium caused by disruptions from the year preceding must be adjusted at 106% speed in order to come back to the equilibrium point in the current year. However, Table A7 indicates a 110% speed of adjustment to stability in the present year.

The model’s stability was assessed using the CUSUM test in Figure 1 and recursive coefficient tests in Figure 2. CUSUM tests are commonly used in data analysis to identify significant modifications in regression equations. Fundamental alterations are significant adjustments in the regression model’s coefficients that might render the model inaccurate and distort projections and estimates. The CUSUM check in Figure 1 has a greater likelihood when an error occurs in the regression model’s baseline. But the recursive coefficients test in Figure 2 is more powerful when the fundamental breach incorporates a gradient coefficient or a combination of the error term and coefficients. Thus, the presence of the blue line in the middle of the red dotted lines without touching the 5% borders shows that the model is stable. Figure A1 and Figure A2 confirm these results.

In addition, to ensure robust and accurate outcomes, this research performed diagnostic procedures on the ARDL framework.

Table 8. Post-estimation tests.

Type			F-Statistic	p-Value
Ramsey RESET Test			1.299	0.266
Breusch-Godfrey Serial Correlation LM Test			2.349	0.119
Heteroskedasticity Test: Breusch–Pagan–Godfrey			0.550	0.765
Test of Multicollinearity Using Variance Inflation Factors
Variable	Coefficient Variance	Uncentered VIF	Centered VIF
D(LNCIT(−1))	0.056	6.820	2.892
D(LNM2_M3(−1))	0.095	9.931	2.466
D(LNINT(−1))	0.056	1.714	1.653
D(LNCPS(−1))	0.044	6.026	2.422
D(LNEXG(−1))	0.011	1.572	1.290
ECM(−1)	0.081	2.193	2.192

Source: Authors’ computation, 2024.

clearly shows that the model that was estimated passes all the diagnostic procedures, indicating that it is an accurate representation of the data and meets the criteria for normality, heterogeneity, correlation between events, and incorrect definition. Also, we establish that the results are free from multicollinearity, serial correlation, and heteroskedasticity, as shown in Table 7 and

Table A8. Diagnostic tests for reliability and robustness.

Test Type	F-Statistic	p-Value
Ramsey RESET Test	0.156	0.697
Breusch-Godfrey Serial Correlation LM Test	0.762	0.479
Heteroskedasticity Test: Breusch–Pagan–Godfrey	0.396	0.924

.

5. Findings

This study examines the response of corporate income tax to financial policy modification in Nigeria from 1990 to 2022 using ARDL and ECM approaches. However, when we controlled for GDP, inflation rate, and informal sector operations, this study was extended to 2023. The ECM(−1) has a −3.73 minus t-statistic, a 0.00 p-value, and a statistically significant negative coefficient of −1.06. It states that if the individual elements deviate from the right placement benchmark by 1% in the short term, they will return to symmetry by 106% every year. This outcome confirms the presence of a long-term relationship between the variables. As a result, the swiftness of return to equilibrium in the event of an imbalance in corporate taxation producing activities is around 106% on an annual basis. This is a lot, and it requires more extensive beneficial changes to monetary policies that govern economic operations that generate corporation taxes. A key aspect of this research lies in its novelty, as it is clear that the effects of monetary policies in Nigeria have not been adequately analyzed to evaluate their short- and long-term consequences on corporate tax revenues. The specific objectives of this study reveal, through the data presented in Table 6 and Table 7, that the broad money supply (LNM2_M3) positively impacts corporate tax in the short term (as shown in Table 7), yet this effect is negligible in the long term (refer to Table 6). In contrast, the cost of borrowing (LNINT) has a detrimental effect on corporate income tax over an extended period and strong negative relationships with money supply, credit facility to private sectors, and corporate tax. Furthermore, the fluctuations in the exchange rate and the credit provided to the private sector do not significantly influence corporate tax variations in Nigeria.

The sustained intangible ramifications of broad money suggest that enterprises tend to investigate tax legislation for loopholes. As time elapses, they devise strategies to sidestep corporate tax duties. Hence, it is crucial for the government to strengthen its regulatory capabilities and uphold social responsibilities to reduce the prevalence of tax avoidance. The policy implication is that if borrowing costs are not reduced, corporate bodies will use all of their earnings to service bank loans, leaving the government impoverished because there will be no profits to charge corporate tax on, as taxes are only paid on companies’ profits after the deduction of allowable expenses and interest payments. The deduction of huge interest charges will leave the organization with little or no assessable profit to be taxed. In the process, corporate bodies will devise means of avoiding and evading tax liabilities. This is why monetary policies become a major determining factor of corporate income tax, and this study has invariably given support to the Keynes’s monetary policy hypothesis, which supports a reduction in interest rates in favor of private sector operations and government corporate tax collections.

This analysis excludes other nations in sub-Saharan Africa. As a result, this study suggests that future research in this field include other sub-Saharan African nations. The focus of this study is on the implications of financial policies, utilizing financial instruments, for corporate tax income from enterprises, and control variables such as GDP, inflation rate, and information sector operation were employed as control variables.