Kusbandiyahet al. (2021) examined determinants of tax avoidance using some corporate governance variables that can as well proxy for company characteristics, for example ownership structure. They sampled a total of 28 companies for 6yrs (2012-2017) and used panel regression method. Their result showed that institutional foreign ownership, family ownership and independent directors did not significantly influence tax avoidance, while the CG-score Index has a significant effect on tax avoidance among companies listed in Bursa Malaysia. They did not provide any policy recommendation, save for suggestion for further studies in which they recommend the use of permanent BTD and temporary BTD as tax avoidance measures,

Akintoye et al.(2020) examined the impact of tax planning strategies on the profit performance of listed manufacturing companies in Nigeria. The used the Taro Yamani formula in arriving at a sample of 46 manufacturing firms from 2008 to 2017. The made of use of descriptive and inferential statistics in analysing the secondary data. Their result showed that there is no significant effect of tax planning on the profitability (proxied using ROA) of manufacturing firms in Nigeria. The study recommended that tax managers and finance officers should reduce thin capitalization and capital intensity to balance the source of income of manufacturing firms. However, their study focused on the manufacturing firms which is distinctive to the direction of this study – with focus on the financial sector which has stricter regulatory monitoring and large stakeholder’s followership due to the financial intermediary roles they play in the economy. 

Yahaya and Yusuf (2020) examined the impact of firm characteristics on tax aggressiveness in Nigerian listed Insurance firms. The focused on firm size, firm age, profitability and leverage as independent variables and measures of firm characteristics. Their sample consists of twenty (20) insurance firms quoted on the Nigerian Stock Exchange from 2010 to 2018. They analysed using the two-step system GMM panel regression model and found that firm size and leverage affect tax aggressiveness positively while firm age and profitability assert negative significant impacts on tax aggressiveness. The study recommended that firm size should be formulated in line with the regulatory provisions. However, their study focused on only the Insurance companies which are just a sub-set of the entire financial sector which is the focus of this study. Hence, it is expected that the outcome of this study will be more generalizable than theirs.

Onatuyeh and Odu (2019) examined the impact of corporate characteristics on tax aggressiveness in Nigeria using secondary data of 49 listed manufacturing companies from 2011-2016. They used panel data regression technique and found that board size and board independence exert negative and significant impacts on tax aggressiveness, while board gender exerts no significant effects on the level of tax aggressiveness. They recommend that listed manufacturing firms in Nigeria should ensure more women are included in their boards of directors. One key con of their study is that their recommendation is at variance with their result. For example, they found that gender diversity has no significant effect on tax aggressiveness and went ahead and recommended for greater women inclusion in the board.

Ifurueze et al. (2018) investigated the effect of corporate tax aggressiveness on firm growth in Nigeria using the ex post facto research design. They made use of secondary data comprising of seven (7) quoted manufacturing companies (2007-2016). They analysed using the pooled OLS method and found that the influence of ETR on firm growth is not statistically significant, and so, should be ignored as a key determinant of firm growth. They also found that leverage (LEV) impact positively on firm growth, but this impact is not statistically significant. They however recommended that on the efficient use of tax rate to generate firm growth should be encouraged. The key observable anomaly of their study, just like as pointed out in Onatuyeh and Odu (2019), is the deviation of their recommendation from the major finding. As observed, aggressive tax practice was found as a non-significant driver of firm growth, and same was recommended for firm growth.

Atu et al.(2018) examined the effect of firm attributes on tax aggressiveness in Nigeria using secondary data comprising of fifteen (15) DMBs from 2013-2017. They deployed the use of the OLS regression technique. Their result showed that firm size, leverage, and liquidity have significant impacts on tax aggressiveness in Nigeria while profitability has a non-significant impact on tax aggressiveness. They recommended that the initial focus of tax authorities should be on creating a tax culture amongst the people, and less on maximizing revenue or enforcing stringent tax compliance measures. The observable defect of this study is the wrong quotation of population as there were only thirteen (13) commercial banks listed on the Nigerian Stock Exchange during the period of their study.

Ugbogbo et al.(2018) evaluated the corporate determinants of aggressive tax avoidance in Nigeria from the dimension of the firm-specific attributes. The used secondary data extracted from the annual reports of 40 Nigerian listed companies from year 2013 to 2017. With the aid of the OLS multiple regression technique, they found empirical evidence that firm size has positive relationship with corporate tax aggressive avoidance while profitability and leverage have negative significant relationships with corporate tax aggressive avoidance. They recommend that profitability, firm size and leverage should be given more attention in the course of considering the determinants that affects tax aggressive by various stakeholders, especially in Nigeria. A major defect of their study is that they were not explicit on the number of sectors considered as ‘manufacturing companies’ leading to their reliance on a sample of only 40 companies excluding other sectors that are still into manufacturing such as the oil and gas sector.

Inua (2018) studied the determinants of corporate effective tax rates in Nigeria using a sample of 30 manufacturing companies. Secondary data was used as obtained from the annual accounts of the sampled companies from 2011-2016. Using the panel data regression technique, the result showed that firm leverage, board independence and board size are negatively and significantly related to ETR, while firm size is negatively and non-insignificantly related to ETR. The study recommends that external board members with experience in accounting, finance and management issues should be highly encouraged as this will reduce the tax rate and bring about efficient tax practices. Despite studying the same sector, Inua’ result on firm size contradicts that of Ugbogbo et al. (2018), showing negative and positive relationships respectively. This may be as a result of the miniature sample employed by the former – which applied unscientific method in sampling only 30 companies out of a population of 170. 

Salaudeen and Eze (2018) studied the effect of firm-specific attributes on corporate effective tax rate in Nigeria. They sampled a total of 59 non-financial firms from 10 different sectors quoted in the NSE from 2010-2014. They employed the panel data regression technique and found that the level of ETR differs based on sector/industry type. The result also showed that larger and more profitable firms have high tax burden while firms with high leverage, capital intensity, and tax expert (auditor type) are faced with lower ETR. Their result however showed no significant relationship between ETR and labour intensity. They concluded that tax incentives provided by the Nigerian tax authorities are substantially significant even though they vary from sector to sector. They queried the dispersion in the ETR found amongst the different sectors which challenges the equity of the corporate tax system in Nigeria. However, Salaudeen and Eze (2018) did not proffer any recommendation which can be considered a defect to the study.

Salaudeen (2017) also examined the corporate effective tax rates in the Nigerian financial sector and their determinants using a sample of 24 financial companies from listed on the NSE from 2010-2014. Salaudeen used two different proxies of tax aggressive measures, the GAAP ETR and Cash ETR. Using the panel regression analyses, Salaudeen found that profitability, firm leverage and capital intensiveness as the determinants of the both GAAP ETR and CASH ETR, meaning they negatively affect tax aggressiveness. The study recommends the need to increase tax incentives in activities auxiliary to banking services and insurance activities sub sector of the financial services sector where the mortgage firms belong. It is worthy of note that his recommendation was not based on the findings.

Ogbeide (2017) examined the impact of firm characteristics on tax aggressiveness in Nigeria. He sampled a total of 85 non-financial companies listed on the NSE for the period 2012 to 2016. He used the panel data method of regression and found that firm size exerts positive and significant effects on tax aggressiveness, while leverage is significant and exerts negative effect on tax aggressiveness. Also, external audit quality (audit fees) and tax aggressiveness are significantly positively related. The study recommends that listed firms in Nigeria should make it a practice to adequately compensate managers and board of directors strategically as this will assist to reduce their tendency to engage in rent seeking/managerial opportunism and lead to lower effective tax rate. Just like the observation in Salaudeen (2017), Ogbeide (2017) failed to tailor his recommendation to flow from his findings.

Balakrishnan et al. (2017) examined the impact of tax aggressiveness on corporate transparency in United Kingdom. They used secondary data comprising of 40,193 firm-year observations that run from 1990 to 2013 extracted from the Compustat database. They used GAAP-ETR tax aggressive measure in a multiple regression analysis technique and found that aggressive tax planning is significantly associated with lower corporate transparency. The concluded that managers at tax aggressive firms attempt to mitigate these transparency problems by increasing various tax-related disclosures. As a result, firms face a trade-off between tax benefits and financial transparency when choosing the aggressiveness of their tax planning. They, however, did not give any policy recommendation based on their result.

Nwaobia and Jayeoba (2017) examined the effect of tax planning strategies on firms’ liquidity. Various tax planning strategies were discussed but the strategies of Capital Intensity (CAPINT), Thin Capitalization (TINCAP), Lease Option (LOPT) and Industry sector incentives (IND) were selected as the independent variable. The Criterion variable used was firms’ liquidity measured in this study by the Current Ratio (CR) while firm size (SIZE) was adopted as the control variable. Data obtained from 154 firm-year observations were described and regression analysis was used to test the hypothesis developed. The results reveal that tax planning strategies exert negative effects on firms’ liquidity. Their study recommend appropriate measures and skill should be applied in determining appropriate mix of strategies toadopt for tax planning purpose as some strategies if not properly designed and applied mayreduce tax liability at the expense of firm’s liquidity. This researcher considers the sampling of only 11 companies is rather too small for the sample size which makes the outcome of the study non-generalizable.

Irianto et al.(2017) examined the factors that affect the company’s tax avoidance. There are several factors used include size, leverage, profitability, and capital intensity. The purpose of this study is to determine the influence of firm size, leverage, profitability and capital intensity ratio on tax avoidance in manufacture companies listed on the Indonesian Stock Exchange 2013-2015. Population taken as the object of observation amounted to 156 manufacturing companies listed in Indonesia Stock Exchange in the period 2013-2015. Determination of the sample was made by applying purposive sampling method and obtains a sample of 36 manufacturing companies based on certain criteria. Their results showed that the size positive influence on the effective tax rate, while leverage, profitability and capital intensity ratio does not significantly influence tax avoidance practices. Their study did not provide a single recommendation, save for conclusion and summary of findings.

Rania et al.(2017) analysed the effects of the corporate’s characteristics on tax avoidance and to analyse the effects of moderation of earnings management on the relationship between the corporate’s characteristics and tax avoidance. The corporate’s characteristics in their study were proxied using profitability, leverage, and company size. They selected 49 manufacturing companies listed on the Indonesia Stock Exchange of the period of 2012-2016 as samples using the cluster random sampling technique. The result of their panel data regression with random effect model shows that the characteristics of a company, namely the profitability and the size have a significant negative effect on tax avoidance, whereas the leverage has a significant positive effect on tax avoidance. Their study did not give any recommendation which is considered a major limitation.

Kim and Im (2017) conducted a study on the effect and determinants of small-and medium-sized entities conducting tax avoidance using companies listed on the Korean Stock Exchange. Their sample consists 18,954 audited firms including those external audited from 2011 to 2013. They argue that the financial determinants of tax aggressiveness vary between small (SMES) and big (non-SMEs) companies. They used the BTD measure and found that firm size negatively affected tax avoidance, while profitability, the leverage, the operating cash flow, the capital intensity, the R&D intensity, and the growth rate had a positive effect on tax avoidance. They conclude that there is significant difference between the drivers of tax avoidance of small and large firms. They recommend that large firms should be properly monitored an observed during in order to curtail aggressive tax practices. Their study differs from this current study owing to their focus on unlisted firms.

Yangyang et al. (2017) investigated the effect of liquidity on corporate tax avoidance. They document that firms with higher liquidity engage less in extreme (i.e., either overly aggressive or overly conservative) tax avoidance. The effect of liquidity on tax avoidance is economically meaningful, is robust across alternative measures of tax avoidance and stock liquidity, and holds after controlling for potential endogenous effects. They further document that the effect of liquidity on tax avoidance is amplified for firms with a high proportion of activist shareholders, and is attenuated for firms with high levels of stock price in formativeness. The entirety of the findings was consistent with the view that stock liquidity mitigates extreme tax avoidance by enhancing shareholders’ control over firm management. However, no recommendation, in line the outcome of the study, was found in their work which tends to make the work incomplete in form.

Anouar and Houria (2017) examined the significant relationship that exists between tax avoidance and firm size. The study made use of 45 listed Moroccan corporate groups, over 2011–2015 periods. The study made use of the multiple regression models. The study indicates that highly indebted firms are likely to take advantage of the main characteristics of debt-capital in order to avoid a significant corporate tax burden. They added that tax considerations have made debt financing, the preferential form of financing in areas with high taxation. Their study did not make any policy recommendation, save for suggestions that future studies should look at the determinants of tax avoidance from the areas of the relationship of the directors’ board with the shareholders, the role of the tax authority, the personnel’s skills and the tax havens.

Yetty et al.(2016) studied the role of leverage on corporate tax avoidance in Indonesia using manufacturing firms listed on Indonesian Stock Exchange for the period 2010-2014. The study used the purposive sampling technique to select 108 firms. This study used secondary data such as Annual Reports and Accounts that was published during the observation year. The multiple linear regression equation was used and the study results revealed that leverage does not have a significant effect on tax avoidance. The study did not make recommendations for policy in line with their results, they only suggested that future researchers should expand the study by including other variables that are absent in their study.

Dharma and Ardiana (2016) examined the effects of the leverage, the fixed asset intensity, the size, and the political connections in the manufacturing companies listed on the Indonesia Stock Exchange. The results showed that the leverage and the fixed asset intensity had a positive effect on tax avoidance. The size negatively affected tax avoidance, whereas the political connections negatively affected tax avoidance but were not significant. They did not explicitly provide any recommendations and that constitutes a major defect of the study.

Ana et al.(2015) investigated the determinants of effective tax rates: firms’ characteristics and corporate governance using 45 publicly-listed Porto corporate groups, over 2010–2013 periods. The study employed the Ordinary Least Squares (OLS) regression and found a positive relationship between profitability and effective tax rates. The study states that firms with high profitability are most likely to engage in tax avoidance practices in order to reduce their tax liabilities. They conclude that larger and more profitable firms have higher ETRs, while capital intensity, leverage and R&D expenses have a negative impact on ETRs. They did not provide any recommendation based on the findings of each variable in their study and that is one aspect of research study that this current study will not overlook. 

Ezugwu and Akubo(2014) studied the effect of high corporate tax rate on the profitability of corporate organizations in Nigeria. The study used the down-stream oil sector of the economy as the population which comprises forty-five (45) corporate organizations that pay their corporate taxes, as obtained from Federal Inland Revenue Service, Lagos office. Data collected was tested using regression analysis. Their findings show that high corporate tax rate impact negatively on realized profit and depict a direct positive relationship between corporate tax rate and realized profit. A major issue with their study is the use of questionnaire which is highly subject to manipulation as well as their focus on only Lagos State for which the findings may not be same in other states of the country.

Akanksha et al. (2013) examined the impact of corporate tax aggressiveness and the role of debt in the U.S.A. The study sample consisted of 9,648 unique firms, over the period 1986-2012. The impact of leverage on tax aggressiveness was tested using the U.S model’s predictions. Findings showed that leverage deters tax aggressiveness. It was also evident that although leverage reduces tax aggressiveness in absolute value, it exacerbates it when the latter is measured as a proportion of the firm’s pre-tax book income. This is consistent with the hypothesis that leverage may actually cause the manager to avoid more taxes in the non-bankrupt states of the world, when the perceived benefits therefrom are positive. 

Stanfield (2011) used data from Compustat for the years 1992 to 2009 for U.S to analyse tax avoidance and illiquidity of firms. The study found greater tax avoidance or lower ETR (cash taxes paid divided by pre-tax income) for firm with insufficient cash, that is, an inverse relationship with liquidity and tax avoidance. Cash or liquidity is measured by the quick ratio, free cash flows, and insufficient cash holdings. Stanfield also found an increase in tax avoidance for firms that meet or just beat the consensus cash flow forecast. The study recommends that firms should only employ debt as capital only when necessary as the study reveals a positive relationship between leverage and effective tax rate as an increase in the use of leverage will increase the effective tax rate of firms. And the recommendation of the study did not spread to the other variables and thus incomplete.

Heshmati et al.(2010) analysed the effects of ETRs on the size distribution of Swedish firms from 1973 – 2002. Time and industry effects were considered. They found that ETRs differ by firm size, industry and over time. Smaller firms had a higher ETR than larger firms, and there was inequality in mean and variance of ETRs between industrial sectors. They conclude that ETRs affect the size distribution of firms as well as the composition of industries, and that the Swedish tax system favours capital-intensive sectors and firms. Despite the fact that their study did not make any recommendation, their study of a 27-year period amounting to 6,357 firm-year observations) is highly commendable and shows evidence of a robust result in the Swedish context.

Rohaya et al.(2010) showed evidence that larger companies endure higher effective tax rates (ETR) in the examination of Malaysian public companies listed on Bursa Malaysia. This conclusion was established during official assessment system and self-assessment system tax regimes. The study also concluded that lower ETRs are significantly related to highly leverage companies, greater investment in fixed assets and lower investment in inventory. They recommend that tax authorities to undertake tax auditing and investigation to trace illegal tax planning activities, but did not provide recommendations based on the variables used in the study. The results of the investigation by Abdul-Wahab and Holland (2012) which sought to investigate the relationship between tax planning savings of firms and their value utilised the regression model and a negative relationship. They conclude that the relationship between firm value and tax planning activities from the perception is negative, and that as tax planning activities increase, the tax costs and risks outweighs the benefits. However, their study is based on perceptions and lacks strong empirical underpinning.

Rego (2003) examines whether multinational corporations avoid more taxes than U.S. domestic-only companies, resulting in lower effective tax rates. He finds that the scale of international operations results in more tax avoidance opportunities which leads to lower domestic and foreign ETRs. The study points out that after controlling for pre-tax income, foreign operations, industry membership, year, and geographic location, larger firms exhibit higher ETRs which is an indication that larger firms are subject to political costs which increase their ETRs. Also, Rego (2003) documents that after controlling for firm size, companies with a higher level of pre-tax income exhibit lower ETRs indicating a negative relation between pre-tax income and ETRs. He concludes by affirming the evidence of economies of scale and economies of scope to tax planning, and recommends that both researchers and regulators should consider ‘size’ as a strong driver of tax avoidance practices. While this researcher agrees with the postulations of Rego concerning size, that may not be relevant in the Nigerian context considering the minimal presence of multinationals in the Nigerian financial sector.

2.4 Gap in Literature

The review of the prior studies by both foreign and local threw up some gaps in literature.  Firstly, majority of the related researches which have been conducted in Nigeria were largely limited to manufacturing companies and non-financial sector (See for example: Onatuyeh & Odu, 2019, Ifurueze et al, 2018, Inua, 2018, Ugbogbo et al, 2018, Salaudeen & Eze, 2018, Salaudeen & Akano, 2018, Salaudeen & Ejeh, 2018; among others). The only study among the log that sampled the Nigerian banking sector is that of Atu et al (2018) but the variables used were limited to firm size, profitability, liquidity and leverage; excluding firm complexity and firm age which this present study incorporates. Secondly, almost the entire reviewed studies by Nigerian authors used the GAAP effective tax rate (ETR) as proxy/measure for tax aggressiveness and did not consider other measures of tax aggressiveness. This constitutes a gap in literature which the introduction of the Total BTD measures based on the recommendation by the recent study of Kusbandiyah et al (2021). It is expected that the outcome will contribute to the existing knowledge and enabling the understanding as to whether the inconsistences in prior studies can be attributed to methodological issues or sector-based heterogeneities.

CHAPTER THREE

METHODOLOGY

3.1 Research Design

This study adopts ex-post facto research design and this is because the data for the study are historical in nature as they already exist in the secondary form. This study is also considered longitudinal because the sample objects of the study covers different firms for various years and with use of verifiable archival information that cannot be manipulated by the researcher.

3.2 Population of the Study

The target population of the study consist of all quoted financial services companies in Nigeria. As at year ended December 2021, there are a total of forty-eight (48) companies listed under the financial sector on the Nigerian Exchange Group (NGX) among which includes thirteen (13) Commercial Banks and thirty-seven (35) other financial institutions (including Insurance Firms, Mortgage Banks, Micro-Finance Banks and one Islamic Bank). 

3.3 Sample Size of the Study

The sample size of the study is the same as the population. Considering that the researcher tends to achieve a robust result that will be useful for generalisation purpose,

3.4 Sampling technique

The study adopted the Census sampling technique and therefore captured the entire population.

3.5 Sources of Data

The study made use of secondary data which were sourced from various annual reports of the sampled financial companies deposited at the libraries and website of the NSE (https://ngxgroup.com). The research covers a period of ten (10) financial years (2012-2021). The nine-year period is being proposed because there is need to generate data for the estimations from the same accounting reporting regime (that is, IFRS) – especially since Nigeria adopted IFRS in 2012.

3.6 Data Analysis Method

The study conducted normality test on the data to be used for analysis using descriptive statistics and panel unit root test where necessary. Other conventional diagnostic tests such as multicollinearity, heteroskedasticity, and Ramsey RESET Test will also be conducted to address the basic regression analysis assumptions. The relationships between and amongst the variables will be examined using the correlation analysis and to also help check for possible multicollinearity. For the purpose of the hypotheses tests, the effect of firm attributes on tax aggressiveness was examined using panel multiple regression. In deciding which of the regression model (fixed effect and random effect) to interpret for the analysis, the study employed the Hausman test for endogeneity.

3.7 Model Specification

The econometric models of the study as adapted from the studies of Ilaboya et al. (2016), Ogbeide (2017) and Atu et al. (2018). The models of Ilaboya et al. (2016) and Ogbeide (2017) specified that tax aggressiveness is a function of firm size, audit quality, leverage, and interest charges, while the model used by Atu et al. (2018) specified tax aggressiveness to be a function of firm size, leverage, profitability and liquidity. The above models were thus modified with the introduction of firm complexity, as earlier justified in the summary of review. 

Thus, in order to ascertain the effect of firm attributes on tax aggressiveness of quoted financial services companies in Nigeria, this study proposes the following panel regression models in a bid to providing answers to the formulated null hypotheses of this study.

The functional linear equation for the model is shown below.

BTD = f(SIZ + AGE + ROA + SUBS)

The general econometric model for the study is specified thus:

BTD = β0 + 1SIZit + β2AGEit + β3ROAit + β4SUBSit + µit   ……….. Model 

Where:

0 = represents the constant

β1 – β4= represents the parameters to be estimated

= Book tax difference (proxy for tax aggressiveness)

=Firm size

= Firm age

= Firm profitability

SUBS = Subsidiaries representing Firm complexity

i = Firms

t = Time/Period

µ = the error term.

3.8 Operationalization of the variables

The measurements and sources of the variables used in this study are defined in Table 3.1.

Table 3.1: Operationalization of Variables

Variables

Acronym

Type

Measurement

apriori sign

Source/Used by

Book Tax Difference

BTD

Dependent

Pre-tax income-taxable income/total assets (Taxable income = dividing income tax expense by statutory tax rate)

-nil-

Kusbandiyah et al. (2021); Kim & Jang (2018)

Firm size

SIZ

Independent

Natural log of total assets

+

Ilaboya et al. (2016)

Firm age

AGE

Independent

Current year less year of incorporation

–

Ilaboya et al. (2016)

Firm profitability

ROA

Independent

Ratio of profit after tax to total asset

_

Salaudeen (2017)

Firm complexity

SUBS

Independent

Number of a firm’s operating segments or subsidiaries.

+

Balakrishnan et al. (2017)

Source: Researcher’s compilation (2022).

Also, considering that the study projects to employ two different measures of tax aggressiveness as dependent variables in a dual model approach, some model comparison tests such as root mean squared error (RMSE) and the mean absolute error (MAE) was  used to compare the forecasting performance of the two models. The model that passed as the most fitted with better forecast ability will be relied upon for the tests of hypothesis and making inferences. However, the outcome of both models formed part of the discussions and for making inferences.

CHAPTER FOUR

DATA PRESENTATION AND ANALYSES OF RESULTS

4.1 Data Presentation

The set of data used for this study which comprises figures for Nigerian quoted financial services companies: Subsidiaries of the companies (SUBS), Firm size (FSZ), Natural logarithm of firm size (L_FSZ), firm profitability (ROA), Firm Age (AGE) and book tax difference (BTD) the dependent variable for a ten year period between 2012 and 2021 is attached as Appendix A.

4.2 Data Analysis

4.2 1 Univariate Analyses: Descriptive Statistics

This sub-section presents the preliminary analysis of the data using descriptive statistics as presented in Table 4.1 below.

    Variable        Obs        Mean         Std. Dev.     Min Max

        BTD        460             0.013         0.049 -0.463      0.509

      L_FSZ      459             7.718         1.092 5.58      10.07

        AGE        459           18.203       12.21 51   52

        ROA        459           0.018         0.128 -0.692      1.13

      SUBS        461           3.464          4.851   0        27

  Source: STATA 15 software output.

As observed from Table 4.1, the variable of BTD showed a mean value if 0.013 which represents the average book-tax difference (BTD) level of the sampled financial companies. The positive book-tax difference is suggestive that the sampled firms, on the average, engaged in upward earnings management (tax avoidance) activities. According to Prawira (2017), unlike the ETR tax aggressive measures, the bigger the BTD, the bigger the company is tax aggressive. The variable of FSZ and AGE have mean values of 7.718 and 18.203 respectively that are less than their standard deviations (1.092 and 0.128 in that order) indicating that these variables do not disperse vey widely during the period under study. However, ROA and SUBS have standard deviation (0.128 and 4.851 respectively) which are higher than their mean values (0.018 and 3.464 in the same order) indicating that these three variable were more widely dispersed from their mean values. Also, the mean value of AGE (measured by the number of years since listing on the NGX) show that the average listing age of the sampled firms is approximately 18 years. The oldest company among the sample (Union Bank) has being listed for 52 years as at 2021, while the newest listed company in the sample (Jaiz bank) was just a year old as at 2012 and Omoluabi (Living Trust) mortgage bank of 2013

On the performance of the companies in terms of firm profitability (ROA), it could be deduced from the result that ROA has a mean value of 0.018 (about 1.8%). The positive ROA indicates an upward profit trend among the sample taken together, while the negative minimum value of -0.692 is an indication that some of the sampled companies did not manage their assets effectively towards income generation within the 10 year period covered by the study. 

On the complexity of the firms, in terms of the numbers of operating business segments, the mean value of SUBS showed 3.46 which means that the sampled firms have an average of 3 subsidiaries.. Also, while the minimum value of zero suggests that some of the sampled companies (such as Jaiz bank and Fidelity Bank) have no active subsidiary within the 9-year period studied, some (like UBA) have up to 27 active subsidiaries within the studied periods

4.2.2 Bivariate Analysis

As part of the preliminary analysis of the data, this sub-section (as reported in Table 4.2) presents the results of the correlation matrix for observing the associations among the variables and determining if there is presence of multicollinearity in the series.

Table 4.2 Correlation Matrix for Multicollinearity

Table 4.2 below shows the correlation coefficients of the variables obtained with the aid of Pearson correlation matrix. The decision rule is the accept the presence of multicollinearity is a pair or more of the independent variables correlate above 0.85 or reject multicollinearity if none of the independent variables correlate with another above the stated threshold.

Table 4.2 Pearson Correlation Matrix

                        BTD     SUBS    L_FSZ      ROA    AGE

         BTD     1.000

        SUBS    0.570     1.000

       L_FSZ    0.609    0.621     1.000

         ROA     0.029    0.039      0.047      1.000

         AGE    0.280     0.480      0.383      0.059      1.000

         Source: STATA 15 (2022).

From the outcome of the correlation matrix presented in Table 4.2, it can be observed that the measures of firm size (FSZ), firm age (AGE), firm profitability (ROA) and  firm subsidiaries (SUBS) are all positively correlated with the tax aggressive measure of book-tax-difference (BTD) with correlation coefficients of 0.609, 0.280, 0.029 and 0.570, implying that the variables of FSZ, AGE, ROA and SUBS all move in the same direction with the independent variable (BTD). 

The highest correlation among the independent variables is between SUBS and AGE at 0.480 which means that, based on the decision rule, there is no multicollinearity as the highest correlation is below 0.85 (Hair et al.,2005) 

Regression Diagnostic Tests

Some diagnostic tests were conducted to ensure that the basic assumptions underlying regression modelling are not violated. This sub-section presents the outcomes of Variance Inflation Factor (VIF) for multicollinearity, the heteroskedasticity testsfor variance stability and the Ramsey RESET test for omitted variables.

4.2.3 Variance Inflation Factor Test

Table 4.3 below presents the results of the variance inflation factor to confirm or disprove the correlation coefficient as provided in Table 4.2 above. The decision rule is to accept that effect of multicollinearity in the model is severe if the mean VIF is greater than 10 or that there is severe effect of multicollinearity in the model if the mean VIF is less than 10.

Table 4.3 Variable Inflation Factor

    Variable         VIF       1/VIF  

       L_FSZ        2.19      0.456

         AGE         1.38      0.723

         ROA         1.01      0.993

        SUBS        2.04      0.489

    Mean VIF           1.66

Source: STATA 15 output (2022).

Results presented in Table 4.3 above show that the highest VIF was 2.19 for firm size (FSZ) while the mean VIF was 1.66. Based on the decision rule, multicollinearity does not exist and/or if does, it is of inconsequential effect in the model.

4.2.4 Heteroskedasticity Test

Table 4.4 presents the results of the heteroskedasticity test to establish the stability of the residuals variance of the model. The decision rule is reject the null hypothesis which states that the model has constant variance if the p-value is lower than or equals to 0.05 or accept the hypothesis is the p-value is higher than 0.05

Table 4.4 Heteroskedasticity Test: Breusch-Pagan-Godfrey:

F-statistics                                627.24

Prob                                          0.353

Source: STATA 15 output.

Table 4.4 above reveals that the model gas a p-value of 0.353 which is greater than 0.05 and based on the decision rule, the model has constant variance and hence homoskedal as heterokedasticity is not present.

4.2.5 Ramsey RESET

Table 4.5 below shows the result of the Ramsey regression equation specification test conducted to determine whether or not any indispensable variables were omitted in the model. The decision rule is that there are omitted variables if the model has a p-value lower than or equals to 0.05 or does not have omitted variables if the p-value is higher than 0.05.

Table 4.5 Ramsey RESET

Ramsey RESET test using powers of the fitted values of btd

       Ho:  model has no omitted variables

                 F(3, 440) =    1123.41

     Prob > F =          0.1946

Source: STATA 15 software output.

Table 4.5 the outcome of the Ramsey reset test for model mis-specification was reported to test the accuracy of the regression model. The result reveals a t-statistic of 1123.41 and a probability value of 0.1946 (19%). The p-value of 0.19 is higher than 0.05 and based on the decision rule, there is no evidence of mis-specification in the equation as there is no omitted variables.

4.2.6 Multivariate Analysis

This sub-section presents the analyses of the panel regression model as specified in the third chapter of the study. The Pooled OLS, Fixed and Random Effect techniques were all estimated (as deposited in the appendix) in order to provide a comprehensive overview of the results. However, since one of the cons of the pooled OLS technique is that it does not recognize the heterogeneity among samples and our sample cuts across different sub-sectors (insurance, deposit money banks, mortgage bank, Islamic bank, microfinance banks and other financial institutions and securities) making up the financial sector, the study relied on the fixed and random effect techniques. However, in order to choose the best among the two, the Hausman test was thus employed to help determine the most appropriate model between the fixed and random effects. The abridged outcome of the Hausman tests is presented in Table 4.5 below, while the full result can be found in the appendix section.

Table 4.6 Result of the Hausman Test

Test Summary

Chi-Sq. Statistic

Chi-Sq. 

d.f.

Prob. 

Cross-section random

111.0024

6

0.030

Source: STATA 15 Output (2022).

From the above result, the null hypothesis (Ho) is that the random effect model is consistent while the alternative (H1) is that, fixed effect model is consistent. The decision rule is to reject the null hypothesis if p-value is less than 0.05, otherwise if p-value is higher than 0.05, accept the alternative hypothesis. From the result of the Hausman test as presented in Table 4.6, the probability value is 0.030 and since it is lower than the critical p-value of 5%, it then means that the fixed effect model is more appropriate for the study than the random effect in capturing the relationships in the panel estimation. The extracted summary of the fixed effect results is presented in Table 4.7 below.

Table 4.2.7 Panel Regression Result

The summary of the regression analysis of the model using fixed effect method is presented in the table below using fixed effect estimation. The system output is attached as appendix B.

Table 4.7 Regression Analysis

         BTD |      Coef.      Std. Err.       t          P>|t|     

       L_FSZ     0.276      0.117       2.36      0.022** 

       AGE     -1.880       1.033      -1.82       0.061

         ROA     -0.650     0.216      -3.01      0.000*** 

       SUBS      1.142       0.342       3.34     0.000***          

       _cons     -0.092      0.011      -0.82      0.272

R-sqd. overall               0.4117

      F-statisics                     111.62

         Prob>F                        0.0000

Note: *** = 1%, ** = 5% significance levels

Source: STATA 15 software output (2022).

As observed from Table 4.7, the F-statistic value of 111.62and the corresponding probability value of 0.0000 (at 1% significance) show that a significant linear relationship exists between the dependent variable BTD and the explanatory (independent) variables taken together. The R-squared overall of (0.4117) approximately 41% which represents the coefficient of determination indicates that about 41% of the systematic cross-sectional variations of the dependent variable(BTD) has been explained by the independent variables of SIZ, AGE, ROA and SUBS jointly. This shows that the model has a fairly low explanatory power as about 59% of variances in BTD was unaccounted for by the variables not included in the regression model and has been captured by the error term.

Table 4.7 further show that SUBS which measures the complexity of the firm and firm size (FSZ) have significant positive relationship with book tax differences (BTD) with p-values of 0.000 and 0.022 respectively. The results also show that ROA has a significant (0.000) but (-3.01) negative relationship, while, AGE has an insignificant (0.061) negative (1.82) relationship with BTD.

4.3 Test of Hypotheses

The four null hypotheses formulated in the course of the study were tested in this sub-section. The decision rule is to accept the HO (null hypothesis) when the probability value exceeds the significance test value of 0.05 (5%), but if the probability value is less than or equals to 0.5 (5%), the null hypothesis should be rejected.

Ho1: There is no significant effect of firm size on tax aggressiveness in Nigerian quoted financial companies.

Table 4.7 shows that firm size (FSZ) has a beta coefficient of 0.276, a t-statistics of 2.36 and a p-value of 0.022, which is less than0.05 (5%) significance value. Based on the decision rule, the null hypothesis which states that firm size has no significant effect on tax aggressiveness is rejected.

Ho2: There is no significant effect of firm age on tax aggressiveness in Nigerian quoted financial companies.

The result in Table 4.7 shows that firm age (AGE) with a negative coefficient of -1.880, a t-stat. of -1.82 and p-value of 0.061 (significant only at 10% level) is statistically not significant as the p-value of 0.061 is greater than 0.05. Based on the decision rule, the null hypothesis that firm age has no significant relationship with tax aggressiveness is accepted.

Ho3: There is no significant effect of firm profitability on tax aggressiveness in Nigerian quoted financial companies.

Table 4.7 shows that ROA has a coefficient of -0.650, a t-stat. of -3.01 and a p-value of 0.000 which is significant at the 1% level. This finding indicates that ROA has a significant negative relationship with tax aggressiveness of the examined companies. In line with the decision rule, the null hypothesis that firm profitability has no significant relationship with tax aggressiveness is hereby rejected. 

Ho4: There is no significant effect of firm complexity on tax aggressiveness in Nigerian quoted financial companies.

Table 4.7 shows that firm complexity measured by the number of the firms’ subsidiaries (SUBS) has a positive coefficient of 1.243, a t-stat. of 3.21 and a p-value of 0.001 (at 1% significance), which is lower than the 0.05 critical value. The findings shows that the null hypothesis four (Ho4) which states that firm complexity has no significant effect of tax aggressiveness in Nigerian listed financial companies is rejected. 

4.4 Discussion of Findings

The study finds that firm size (FSZ) has a significant effect on tax aggressiveness measured by book tax difference (BTD) with a positive coefficient of 0.276, a t-statistics of 2.36 and a p-value of 0.022 indicating that a unit increase the firms’ total asset will lead to increase in their tax aggressiveness. This finding agrees with those of Atuet al. (2018), Rani et al. (2018), Iriantoet al. (2017), Ogbeide (2017), Pratama (2017), Ugbogboet al. (2018), Salaudeen and Akano (2018) who reported that firm size has a significant effect on tax aggressiveness, but, it disagrees with those of Inua (2018) and Ogundajo (2016) who observed that firm size has an insignificant effect on tax aggressiveness.

The study finds that firm age(AGE) has an insignificant negative effect on tax aggressiveness measured by book tax difference (BTD) with a coefficient of -1.880, a t-statistics of -1.82 and a p-value of 0.061 indicating that a unit increase the firms age of existence will lead to a decrease in their tax aggressiveness. This finding corroborates those of Nwaobia et al. (2016) and Ogundajo and Onakoya (2016), Fernández-Rodríguez et al. (2019), Ogundajo and Onakoya (2016) and Pratama (2017) who reported that firm age has an insignificant effect on tax aggressiveness.

The study finds that firm profitability measured by return on asset (ROA) has a significant negative effect on tax aggressiveness measured by book tax difference (BTD) with a coefficient of -0.650, a t-statistics of -3.01 and a p-value of 0.000 indicating that a unit increase the firms’ profitability will lead to a significant decrease in their tax aggressiveness. This finding is line with those of Zhu et al. (2019), Rani et al. (2018) and Chytiset al. (2018) who reported that profitability has a significant effect on tax aggressiveness, but, it contradict those of Atuet al. (2018), Salawu and Adedeji (2018) and Onyali and Okafor (2018) who observed that profitability has an insignificant effect on tax aggressiveness.

The study finds that firm complexity represented by the number of subsidiaries (SUBS) has a significant positive effect on tax aggressiveness measured by book tax difference (BTD) with a coefficient of 1.142, a t-statistics of 3.34 and a p-value of 0.000 indicating that a unit increase the firms’ subsidiaries lead to significant increase in their tax aggressiveness. This finding agrees with those of Chen et al. (2010) and Pratama (2017) who reported that firm complexity has a significant effect on tax aggressiveness. 

Table 4.5 Summary of Findings

Hypothesis                               Statement                                                              Decision

Ho1:                    Firm size has no significant effect of tax aggressiveness of           Rejected

                           financial institutions in Nigeria.

Ho2:                    Firm age has no significant effect of tax aggressiveness of            Accepted

                           financial institutions in Nigeria.

Ho3:                    Profitability has no significant effect of tax aggressiveness of        Rejected

                           financial institutions in Nigeria.

Ho4:                     Firm complexity has no significant effect of tax aggressiveness     Rejected

                            of financial institutions in Nigeria.

Source: Table 4.7 above.

CHAPTER FIVE

SUMMARY, CONCLUSION AND RECOMMENDATIONS

5.1 Summary

The following were the major findings of the study:

1. The study found evidence that the impact of firm size on tax aggressiveness (using the BTD measure) is negative and statistically non-significant. This means that the level or degree of tax avoidance practices by Nigerian financial companies does not depend on their size – in terms of total assets.
2. Using the BTD measure of tax aggressiveness, the study found that the variable of firm age has an inverse and non-significant impact on tax aggressiveness. This means that in the context of the Nigerian financial sector, and holding other variables constant, the listing age of a company does not have any meaningful impact on the tax planning activities. 
3. The study also found that the variable of firm profitability (using ROA) affects tax aggressiveness negatively and significantly. This means that in the Nigerian financial sector of both countries, and in the context of this study, highly profitable financial companies are associated with lesser tax aggressive behaviour.
4. The study also found evidence of a significant positive relationship between firm complexity and tax aggressiveness in Nigerian listed financial companies. This implies that in the context of this study and holding other variables constant, the highly diversified financial companies are associated with higher degree of tax aggressiveness.

5.2 Conclusion

Recent occurrences, such as the dwindling prices of crude oil in the global market, have grossly threatened Nigeria’s major source of government revenue. Resultantly, government’s attention has since shifted to taxation and other sources of government revenue as a means of boosting revenue generation for the financing of government’s expenditures. Company income tax is considered very vital in augmenting the size of tax revenue generation as it captures the taxes of all incorporated entities in the country. While government’s efforts to foster greater compliance through tax reforms may not have yielded the desired optimal results due to tax avoidance strategies of listed corporations, researchers have increased the enquiry into the determinants of firms’ level of tax aggressiveness.

Against the backdrop of the knowledge that different characteristics the company influences several organisational outcomes, and in a bid to contribute to the existing literature, this study was designed to empirically ascertain the company-specific characteristics that affect the level of tax aggressiveness in Nigeria. Specifically, the study examined how firm size, age, profitability, and firm complexity relate with tax aggressiveness in Nigeria. The study employed the total book-tax-difference (BTD) measure of tax aggressiveness which has been scarcely used in related studies in this context. The census method of sampling was adopted in selecting the entire fifty (50) companies listed under the financial sector of the NGX beginning from 2012 to 2020. The four (4) aforementioned firm-specific attributes constitute the independent variables, run against BTD as proxy for tax aggressiveness. In all, the four independent variables culminated into four null hypotheses which was tested in the previous chapter based on the outcome of the fixed effect panel regression model in line with the suggestion of the Hausman test.

The result showed that among the four firm-attributes that made up the independent variables of the study two: FSZ, SUBS and ROAwere found statistically significant at the 5%, 1%, 10%, and 5% levels of significance while the remaining AGE had an statistically insignificant owing to high probability values. Based on the above outcomes, as already summarised in sub-section 5.2 above, it can thus be concluded that in terms of the impact of firm attributes on tax aggressiveness of listed financial companies in Nigeria (proxied using the BTD measure of tax aggressiveness), the major variables of interests are firm size, firm profitability and firm complexity; while firm age we statistically non-significant and implicationally, can be considered as not of crucial importance within the 9-year period captured by the study.

5.3 Recommendations

In view of the findings and conclusions drawn from the results of the study, the following recommendations were proffered for possible policy implementation.

1. Based on the result that large financial companies are likely more tax aggressive, the regulatory bodies and tax authorities should beam their searchlight on the tax saving strategies of larger-sized companies, with a view of discouraging aggressive tax avoidance schemes. Also, due to the significance of the variable, tax auditors should regard the size of the companies when accessing red flags of potential tax offenders.
2. The notion that older firms have higher reputational risks and would resort to less-risky tax management practices did not hold in the context of the Nigerian listed financial companies as the variable was non-significant. However, due to the obtained negative sign, regulators should increase their monitoring on newly listed firms while encouraging appropriate tax savings strategies and greater compliance.
3. Considering that highly profitable financial firms are found to be less tax aggressive, the key stakeholders should that financial companies adhere to strong corporate governance mechanisms in order to ensure that the intended gains from tax avoidance activities are not opportunistically misused by the managers.
4. On firm complexity, the notion that highly diversified firms engage in less tax aggression did not hold in the context of this study, rather on the contrary. This could be because most diversified Nigerian financial companies have subsidiaries and cross-border affiliations with foreign countries where that have to contend with their local and complex tax laws. Thus, there is need for government to establish trade agreements with many more countries (to guard against multiple taxation) and increase focus on creating tax culture in order to foster voluntary compliance amongst multinationals.
5. Contribution to Knowledge

This research has contributed to existing knowledge by responding to the recent call by Alkurdi and Mardini (2020) for the inclusion of the book-tax-difference (BTD) measure of tax aggressiveness in developing countries. This is considered a valuable contribution to literature in the Nigerian context which can ignite the needed paradigm shift from the dominant conventional use of the ETR measures in prior Nigerian studies.

5.5 Suggestion for Further studies

The following suggestions are made for further studies:

1. The study covers only the firm-specific attributes based on its defined scope. Future researchers can consider other variables such as those related to the corporate governance of listed firms in line with the requirements of the current Nigerian Code of Corporate Governance which became operational in January 2020. There are indications from literature that strong governance mechanisms can influence firms’ tax avoidance policies.
2. The study was delimited to only companies listed in the financial sector of the Nigerian Exchange Group (NGX). Considering that sector-based heterogeneities can influence the behaviours of certain company variables, these current findings may likely not hold for companies in the other sectors (i.e. the non-financial companies). Thus, future studies should expand the scope to cover the non-financial companies in order to enhance the generalisation potentials of the findings. Alternatively, future researchers can conduct a sector-based comparative analysis on the determinants of tax aggressiveness in order to provide more robust evidence as to whether industry heterogeneities moderate the impact of firm attributes on tax aggressiveness.
3. This study was limited to a single measure of tax aggressiveness. Future studies should compare the BTD measures (for example, Total, Permanent and Temporary BTD) with the ETR measures (for example, Cash and Long-Run Cash ETRs) in order to understand which has better forecast accuracy as well as to understand the behaviours of the different explanatory variables towards the variations of different proxies of tax aggressiveness.

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