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Finance, Accounting and Business Analysis 
Volume 5 Issue 1, 2023 

http://faba.bg/       
ISSN  2603-5324 

 

Tax Knowledge, Tax Complexity and Tax Compliance in South Africa 

 

Baneng Naape  

  
Department of Economics, University of the Witwatersrand, Johannesburg, South Africa 

 

Info Articles   Abstract 

 

History Article: 

Submitted 15 May 2023 

Revised 30 May 2023 

Accepted 1 June 2023 

 

  

Purpose: the key objective of this study is to investigate the influence of 

tax knowledge and tax complexity on tax compliance in South Africa.  

Design: the data collection process involved self-structured 

questionnaires targeted at South African personal income taxpayers. 

The data was analyzed by means of descriptive analysis, inferential 

statistics and binary logistic regression. 

Findings: the findings from the Pearson correlation test revealed that 

knowledge on tax types, tax payment methods and tax penalties is 

positively associated with tax compliance and this association was 

found to be statistically significant. In addition, the results from the 

binary logistic regression revealed that knowledge on tax penalties is 

positively associated with higher probabilities of tax compliance and 

this association was likewise found to be statistically significant. This, 

to some extent, implies that tax penalties are well enforced by the 

government to induce tax compliant behaviour. Meanwhile, 

demographic factors such as the level of educational attainment as well 

as perceptions on the state of democracy were found to play a 

significant role in inducing tax compliance. 

Practical Implications: the study recommends the expansion of 

educational programmes that inform taxpayers about the different tax 

types they are liable for, the procedure for calculating and filing tax 

returns as well as the financial and legal consequences of exhibiting a 

tax non-compliant behaviour. 

Originality: The research topic is relevant for the management of tax 

systems especially during times wherein policymakers are in search of 

approaches to collect additional budget revenues. The study also 

presents a historical overview of problems that are observed in the 

income tax system of the Republic of South Africa, and this analysis is 

linked to the problems of tax compliance. 

 

Keywords:  

tax knowledge, tax 

complexity, tax compliance, 

South Africa 
 

 

JEL: H24, H26, C51, I21  

   

 
 
 
 
* Address Correspondence:   

E-mail : Banengnaape@gmail.com 

 

  

https://orcid.org/0000-0001-8000-8341


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INTRODUCTION 

 

The nexus between tax knowledge, tax complexity and tax compliance has been extensively studied 

by different researchers such as Saad et al. (2004), Palil (2005) and Pau et al. (2007). This is because, tax 

knowledge and tax complexity are critical components of voluntary tax compliance. Without proper 

knowledge and understanding of the different tax types and laws and administrative procedures, taxpayers 

becomes reluctant to file their tax returns and settle their tax liabilities. The year 2000 marked the 

introduction of the eFiling system by the South African Revenue Services (SARS) while the year 2011 saw 

a shift in the power to assess. In terms of the Income Tax Act, No. 58 of 1962, the power to assess was 

vested in the Commissioner. The Tax Administration Bill however, which was passed before the South 

African Parliament, introduced the concept of “self-assessment” (Hofmeyr 2011). Self-assessment in this 

regard, implies that “the taxpayer will have to report the basis of assessment, submit a calculation of the 

tax due and, usually, make payment of the outstanding tax. The onus will be on the taxpayer to calculate 

the correct amount of tax payable” (SARS 2011). Although the eFiling system has gained popularity in 

recent times and accelerated the payment of taxes, the system remains challenged by a number of issues 

including the lack of tax knowledge and technological literacy. Self-assessment also, implies a shift in 

responsibility from tax authorities to the individual taxpayer. Saad (2014) notes that in order for taxpayers 

to execute these responsibilities, they are expected to be well-informed about the exiting tax provisions and 

laws. Thus, one possible way for creating a tax compliant environment is to improve the availability of tax 

information to the public. Similarly, a less sophisticated tax system can go a long way in stimulating a tax 

compliant environment. 

Although SARS has various tax awareness programmes, several studies (e.g., Evans and Joseph 

2015; Bornman and Ramutumbu 2019) have empirically indicated that tax knowledge remains a hindrant 

to tax compliance in South Africa and the rest of the world. The majority of South African citizens fail to 

comprehend different tax laws and provisions set by the government and as a result, have to employ the 

services of tax consultants at additional costs. Also, the self-assessment system requires some knowledge 

on and access to technological devices. This is problematic for a country such as South Africa which has 

failed to harness the digital evolution. Access to technological devices and stable internet connection 

remains limited more especially in the rural areas. While acknowledging that the topic on the influence of 

tax knowledge on tax compliance has been studied before, earlier studies were not able to distinguish the 

different aspects of tax knowledge, a gap which this study aims to fill.  

The study will play a significant role in closing the gap on the relationship between tax knowledge 

and tax compliance in South Africa since few empirical studies have attempted to establish this 

relationship. Also, compared to previous studies, this study does not use the broad definition of tax 

knowledge to estimate its influence on tax compliance but rather breaks tax knowledge into three parts: tax 

calculation knowledge, knowledge on tax reporting and knowledge on tax payments. This is done to 

understand the overall influence of tax knowledge on tax compliance from different angles. As mentioned 

earlier, theory suggests that when citizens are well informed about tax laws and provisions, they are most 

likely to be tax compliant. Thus, this study plays a crucial role by empirically analysing this relationship in 

the context of South Africa. 

The study will be organised as follows: Section 1 will provide an introduction to the study and 

outline the objectives and significance of the study. Section 2 will provide an overview of literature on the 

relationship between tax knowledge and tax compliance, both from a theoretical and empirical 

perspective. Section 3 will unpack the empirical strategy to be executed by the study to estimate the 

influence of tax knowledge on tax compliance in South Africa. Section 4 will detail the findings of the 

study in line with existing studies. Section 5 will provide a brief conclusion of the study as well as 

implications for policymaking. 

 

LITERATURE REVIEW 

 

This section briefly discusses the literature on the influence of tax knowledge on tax compliance. 

The section is twofold: the first part of the section unpacks theories relating to tax knowledge, tax 

complexity and tax compliance while the second part summaries findings from earlier studies on the 

relationship between tax knowledge and tax compliance. 

 

Theoretical literature 
A well-designed tax system is crucial to every nation as it has the potential to enhance tax 

compliance by allowing taxpayers to settle their tax liabilities with ease, thereby reducing administrative 

costs and raising tax yields. Smith (1776) defines a well-designed tax system as one that is founded on 

numerous tax principles including equity, efficiency, certainty and convenience. Of interest, however, are 



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the canons of efficiency and convenience. A tax system is regarded efficient when the cost of collecting and 

settling tax liabilities is minimal as high tax administration costs would reduce the net tax yield and to 

some extent, discourage taxpayers from being tax compliant (Naape and Mahonye 2021). Pigou (1954) 

notes that, because individual taxpayers and businesses plan ahead, tax liabilities due ought to be 

predictable and communicated in time. In addition, the sum, time and manner of payment of taxes should 

be convenient to the contributor. The theoretical literature begins by providing a background on the South 

African Income tax system. This is followed by a discussion on tax knowledge and complexity as well as 

the nexus between tax knowledge, tax complexity and tax compliance.  

 

South African Income Tax System 
The South African Income Tax legislation was first enacted in 1962 through the Income Tax Act 

no.58 of 1962. The legislation was executed to provide guidance on the exercise of powers and 

performance of duties by tax authorities as well as the manner in which tax officials may raise and collect 

taxes from income and donations. During this time, the responsibility to calculate and file tax returns was 

vested in tax authorities. Thus, taxpayers were not required to be fully abreast of the different tax laws and 

procedures followed in the calculation and filing of tax returns as compared to today. Also, several tax 

reforms have since been implemented in the form of tax cuts and hikes as well as changes in tax brackets 

and exemptions. Through these reforms, the government aimed to improve administrative efficiency and 

tax revenue collections (Schoeman and Jordaan 2015). The maximum income tax rate in South Africa 

stood at 50 % in 1980 before a reduction to 45 % in 1987 and a further reduction to 43 % in 1991. During 

the period under study (2008-2021), the maximum income tax rate remained fixed at 40 % between 2008 

and 2015 before an increase to 41 % in 2016 and 45 % in 2017 (Naape and Mahonye 2021). In terms of tax 

revenue collected, taxes collected from income and donations account for a larger share of the total tax 

revenue in South Africa. For example, personal income taxes made up 31 % of the total tax revenue 

collected by the government during the 2008/09 financial year followed by corporate income taxes at 30 % 

of total tax revenue (Statistics South Africa 2019). A decade later, personal income taxes make up 38 % of 

total tax revenue collected by the government followed by Value Added Taxes at 24 % of total tax revenue. 

 

Tax Knowledge 
Saad (2014) notes that basic understanding of the tax payment system is a crucial factor in a 

voluntary tax compliance system, more especially in determining and calculating an accurate tax liability. 

This includes basic understanding of the tax compliance status, filling dates, compliance laws, procedures 

and consequences as well as the manner in which tax liability is calculated and settled. Knowledge of the 

different regulations governing taxes and applicable tax reforms is also crucial to avoid both intended and 

unintended miscalculations. 

 

Tax Complexity 
Tax complexity arises when taxpayers find it difficult to comprehend the different tax laws and 

reforms governing tax administration and compliance (Richardson and Sawyer 2001). Cox and Eger 

(2006) state that tax complexity can take different forms including compliance complexity, computational 

complexity, procedural complexity, rule complexity and forms complexity. The six potential causes of tax 

complexity were first identified by Long and Swingen (1987). This includes record keeping, frequent 

changes to tax laws, calculations, details, ambiguity and forms.  

 

Empirical literature 
Saad (2014) analysed taxpayers views on their level of tax knowledge and whether the income tax 

system in New Zealand is perceived to be complex or not. The data was collected by means of telephonic 

interviews and analysed through thematic analysis. The findings revealed that the participants have a lack 

of technical knowledge on tax and perceived the income tax system to be complex. A study by Bornman 

and Ramutumbu (2019) assessed the tax compliance risk profile of small business owners in the Soweto 

township. The data was collected by means of semi-structured questionnaires and analysed through 

statistical inference and thematic analysis. The findings revealed that a lack of knowledge on tax laws and 

provisions as well as observations on fairness and opportunity for non-compliance, were major 

contributing factors to tax non-compliance in the Soweto township. 

Damajanti and Karim (2017) analysed the effects of tax knowledge on tax compliance by officials in 

the Tax Office of the Java Region. The authors made use of questionnaires as a data collection technique. 

Compared to earlier studies, their study was unique in that they analysed tax knowledge from three 

aspects, namely: tax calculation knowledge, knowledge of tax reporting and knowledge of tax payments. 

Based on findings, the three instruments of tax knowledge were found to have a statistically significant 

influence on the level of tax compliance in the Java Region. Gambo et al. (2014) attempted to establish the 



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relationship between tax complexity and tax compliance in selected African countries. The study made use 

of the Pearson Correlation test and Ordinary Least Squares econometric technique to analyse the sourced 

data. Based on findings, tax complexity has a significant negative impact on tax compliance in the selected 

African countries. In addition, the study found that as a result of the complex tax system, taxpayers spend 

roughly 19 hours more in self-assessment than the regional self-assessment average.  

Matibe et al. (2015) investigated the extent to which tax knowledge and awareness influence 

decisions about tax compliance by firms in Export Processing Zones in Kenya. The authors made use of 

structured questionnaires to gather primary data and analysed the primary data through descriptive and 

inferential statistics. The study revealed that employees who received adequate training on tax knowledge 

and awareness were more tax compliant than those with little or no training on tax knowledge and 

awareness. Thus, it can be inferred that there is a close relationship between tax knowledge and awareness 

and compliance among firms in Export Processing Zones in Kenya. Palil (2010) explored the relationship 

between tax knowledge and tax compliance in Malaysia. The data collection process involved large scale 

postal surveys collected at national level. The individual characteristics of the taxpayer’s knowledge were 

analysed by means of ANOVA and t-test while the relationship between tax knowledge and tax 

compliance was established by means of multiple regression. The study found a significant relationship 

between tax knowledge and tax compliance in Malaysia although the level of tax knowledge differs greatly 

among respondents. Oladipupo and Obazee (2016) estimated the effects of tax knowledge and tax 

penalties on tax compliance across selected Small and Medium Enterprises (SMEs) in Nigeria. The data 

was collected by means of structured questionnaires and analysed through econometric techniques such as 

Ordinary Least Squares. The results revealed that tax knowledge exhibits a positive and statistically 

significant effect on tax compliance while tax penalties exhibit a positive yet statistically insignificant effect 

on tax compliance in Nigeria. The findings suggest that tax knowledge is more effective in inducing a tax 

compliant behaviour among SMEs in Nigeria than tax penalties. 

Cechovsky (2018) established the relationship between tax knowledge and tax compliant attitude in 

Australia. The study employed a mixed methods approach incorporating interviews and self-structured 

questionnaires. The respondents comprised of a group of 700 vocational business students. The study 

revealed that tax knowledge encourages a tax compliant attitude and discourages a tax evasion attitude in 

Australia. Hantono (2021) investigated the influence of selected tax variables including tax awareness, tax 

knowledge and tax morale on tax compliance. The study made use of self-structured questionnaires to 

gather data. This comprised of a pool of 100 qualifying respondents and the incidental sampling technique 

was used to sample the data. By means of the multiple linear regression technique, the study found that tax 

awareness, tax morale and tax knowledge have a significant positive impact on tax compliance in 

Indonesia.  

Wadesango and Mwandambira (2018) examined the effect of tax knowledge on tax compliance 

among Small Medium Enterprises (SMEs) in Zimbabwe using a pool of 35 SMEs and 40 tax officials. The 

findings were that tax knowledge influences a tax compliant behaviour among SMEs and tax officials 

although issues of high tax rates and perceptions on corruption weigh on the willingness of taxpayers to 

settle their tax liabilities. Meanwhile, a similar study by Twum et al. (2020) analysed the influence of tax 

knowledge on tax compliance in panel of 130 managers of SMEs. The data was collected by means of 

surveys and analysed through structural equation modelling. The findings revealed that knowledge of 

employment income, awareness of sanctions and knowledge of tax rights and responsibilities have a 

positive and statistically significant influence on tax compliance in Ghana. 

Musimenta (2020) analysed the effect of tax knowledge, tax complexity and compliance costs on 

tax compliance in Uganda while incorporating the influence of indirect costs of tax compliance. The 

results indicated that tax knowledge is not correlated with compliance costs and that tax knowledge is 

better suited at explaining variations in internal costs than external costs of compliance. In addition, the 

results indicated that tax knowledge has a positive significant influence on tax compliance. Manual and 

Xin (2016) explored the role of selected tax variables including tax knowledge, tax compliance costs and 

tax deterrent measures on tax compliance in West Malaysia. A group of 150 self-employed taxpayers was 

randomly selected. The data collection method included online questionnaire surveys and simple random 

sampling technique and was analysed through the help of the Pearson Correlation technique and multiple 

regression. The findings revealed that tax deterrence measures are significantly correlated with tax 

compliance whereas, on the contrary, tax knowledge and tax compliance costs are not significantly 

correlated with tax compliant behaviour among self-employed taxpayers in West Malaysia.  

Mukhtar et al. (2015) investigated the effect of selected tax compliance factors on government 

revenue mobilisation in Somaliland. The selected tax compliance variables include tax knowledge, 

digitalisation of the tax collection system and tax audit. The target population was business owners in the 

Gobonimo market, and the population was selected using the stratified random sampling technique. In 

addition, the study employed both primary and secondary data for which primary data was collected by 



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means of structured questionnaires while secondary data was collected from reliable secondary data 

sources. The study found that business owners in the Gobonimo market lack the necessary knowledge to 

settle their tax liabilities which limits the government’s ability to mobilise tax revenue. The underlying 

factors included the inability of taxpayers to use the online tax filling system as well as limited information 

on the benefits of paying taxes. 

 

RESEARCH METHODOLOGY 

 

Research Approach 
A research approach provides a detailed overview of the steps and procedures the researcher plans 

to execute in the collection and analysis of data as well as interpretation of research outputs. In most cases, 

the research approach incorporates three components namely, philosophical world view, research methods 

and research design (Grover 2015). Haradhan (2017) notes that decisions on the research approach are 

usually based on the objective of the study, the researcher’s personal experience and audience of the study. 

This study employed a quantitative research approach. This includes collecting primary data through self-

structured questionnaires and analysing the data by means of inferential statistics. 

 

Sampling Strategy 

Identifying and selecting the right sampling technique is crucial in survey research as this ultimately 

predicts the generalizability of the findings obtained in the study. Although several sampling techniques 

exist, ranging from probability sampling to non-probability sampling, the most suitable sampling technique 

for our analysis is the simple probability sampling technique. Under the simple probability sampling 

technique, participants in the population group are sampled by a random process, usually by a random 

number table or random number generator (Taherdoost 2016). The target population consisted of South 

African permanent residents above the age of 18 years and who are registered for income tax with SARS. 

 

Data Collection 

Data collection refers to the steps undertaken by the researcher to collect and measure information 

about the variable(s) of interest (Young 2016). This process enables the researcher to answer specified 

research questions, engage in hypothesis testing and analyse research outcomes. For the purposes of this 

study, the researchers made use of primary data collected by means of electronic surveys in the form of 

self-structured questionnaires. 

 

Data Analysis  

 Data analysis can be described as the process of systematically visualising information collected 

through quantitative or qualitative research instruments either to identify the characteristics of the 

variables in question, to explore the relationship between two or more variables or to measure the impact 

of one variable on the other (Patel 2009). The data collected in this study was assessed statistically by 

means of descriptive analysis and inferential statistics. 

 

Descriptive Analysis 
Descriptive analysis forms the basis of econometric modelling as it provides a summary of the type 

of data the researcher is dealing with. In general terms, Descriptive statistics is the process of describing the 

main characteristics of a dataset which can either be a representation of either the sample of the study or 

population at large (Sharma 2019). There are several factors which distinguish descriptive statistics from 

inferential statistics. For example, descriptive statistics, unlike inferential statistics, aim to summarize 

information about variables while inferential statistics on the other hand, aim to explain the association 

between two or more variables. In addition, while inferential statistics are based on probability theory, 

descriptive statistics are not. In other words, research findings cannot be inferred on the basis of descriptive 

statistics whereas, on the contrary, conclusions can be obtained from inferential statistics. 

 

Pearson Correlation test 
The Pearson correlation test attempts to estimate the presence and strength of a linear relationship 

between two variables (Mukaka 2012). The presence of a linear association between two variables is given 

by the probability value, usually at the 1 %, 5 % or 10 % interval levels. The strength of the linear 

relationship between two variables is given by the coefficient value. Cohen (1988) notes that a coefficient 

value below 10% indicates that there is weak linear association between two concerned variables, while a 

coefficient value between 10 % and 30 % indicates that there is a moderate linear association between two 

concerned variables. Any coefficient value above 50 % is indicative of a strong linear association between 

two concerned variables. The formula for calculating the correlation coefficient for any two continuous 



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variables is given by: 

𝑟 =
∑ (𝑥𝑖 − 𝑥)(𝑦𝑖 − 𝑦)𝑛

𝑖=1

√[∑ (𝑥𝑖 − �̅�)2𝑛
𝑖=1 ][∑ (𝑦𝑖 − �̅�)2𝑛

𝑖=1 ]
 (1) 

Where 𝑥𝑖 and 𝑦𝑖  are values of x and y for the 𝑖𝑡ℎ individual. Mukaka (2012) states that one of the 

conditions of using the Pearson correlation test is that the variables should be normally distributed. 

 

Binary Logistic Regression 

The logistic regression model has been widely used by researchers (e.g., Peng et al. 2002; Mertler 

and Vannatta 2005) to obtain odds ratio in the face of categorical variables. The technique aims to model 

the chance of an outcome based on individual characteristics (Peng et al. 2002). For example, the 

technique can be used to estimate whether males succeed in college, whether female adults are most likely 

to get pregnant or whether male teenagers are most likely to engage in illegal activities or not. The 

technique works in a similar fashion as the linear multiple regression technique except that the outcome 

variable is binary (Sperandei 2013). One advantage of using the logistic regression model over other 

econometric techniques is that it allows the researcher to use continuous independent variables with ease 

and it can handle more than two independent variables simultaneously. A simple logistic regression model 

can be expressed mathematically as follows: 

 

𝑙𝑜𝑔 (
𝜇

1 − 𝜇
) = 𝛼1𝑋1 + 𝛼2𝑋2+. . . 𝛼𝑛𝑋𝑛 (2) 

Where 𝜇 indicates the possibility of consequences for each event, 𝛼𝑖 represents the slope coefficients 

associated with the reference group and the 𝑋𝑖 independent variables. Unlike discriminant analysis, the 

logistic regression model does not assume that the explanatory variables are normally distributed. This 

technique is best situated for our analysis given that the response variable is binary. 

 

Model Specification 

Our estimation model will be guided by earlier studies with a few modifications to put it in line with 

the objectives of the study. The following model will be estimated: 

 

𝑡𝑐 =  𝛽0 +  𝛽𝑖𝛿𝑡 + 𝛼𝑖𝜗𝑡 + 휀𝑡 (3) 

Where 𝑡𝑐 is a binary tax compliance variable taking a value of 1 for compliance and 0 for non-

compliance, 𝛽0 is the constant term, 𝛿𝑡 is a vector for individual level characteristics of the respondent: 

age, sex, education, employment status, wealth and ethnicity. 𝜗𝑡 is a vector for variables that captures 

different aspects of tax knowledge including tax calculation knowledge, knowledge on tax reporting, 

knowledge on methods of payment and knowledge on tax penalties. 휀𝑡  is the idiosyncratic error term. 

 

Table 1. Description of variables 

Gender Categorised as male and female 

Age Ranging between 18 – 65 years 

Education No education, matric, undergraduate, postgraduate 

Employment sector Private sector, public sector, informal sector 

Employment status Employed, unemployed, self-employed 

Reason for evasion Unfair tax system, taxes are too high, government steals money, I know I won’t 

get caught 

Difficulty of evasion Very easy, easy, neither easy nor difficult, difficult, very difficult 

Benefit Taxpayer’s benefit from public services 

Tax morale If the quality of public services inspires the taxpayer  

Trade off The trade-off between higher tax rates and quality public services or lower tax 

rates and poor quality of services 

Social influence If the perceived compliance of others influences the taxpayer 

Corruption Perception on the level of corruption 

Trust in government Perception on the trust in government 

State of democracy Perception on the state of democracy 

Tax liable for This entails knowledge on which type of the taxes the taxpayer is liable for 

Tax return calculation This entails knowledge on how to calculate tax returns 

Payment methods This entails knowledge on different payment methods that can be used to settle 

tax liabilities 

Tax penalty This entails knowledge on the different tax penalties applicable to the taxpayers 

Source: author 



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Ethical considerations 
Fleming and Zegwaard (2018) note that when dealing with fundamental ethical research that 

involves human participation, it is important to consider the ethical dilemmas that might play out. The 

study and data collection instruments were designed in such a way that they take into consideration the 

possibility of harm to participants. In addition, the study did not pose any psychological or physical harm 

to participants. Further to this, the study carried no potential for legal or social harm since no information 

about a participant’s behaviour to illegal activities or substance abuse was collected. The study made use of 

anonymous self-structured questionnaires and surveys wherein participants were not required to give out 

any information pertaining their identify such as their full names, identity numbers, contact numbers, 

email or physical addresses. 

 

FINDINGS AND DISCUSSIONS 

 

This section presents findings from the econometric tests performed including descriptive analysis, 

correlation analysis and binary logit regression. The results are likewise discussed in line with the 

objectives and hypothesis of the study. 

 

Descriptive Analysis 
The descriptive analysis was performed to examine the individual characteristics of the variables 

including the average, standard deviation, range and skewness. The findings are provided in table 2 below.  

 

Table 2. Descriptive statistics 

 

N Minimum Maximum Mean 
Std. 

Deviation 
Skewness 

Statistic Statistic Statistic Statistic Statistic Statistic 
Std. 

Error 

Tax compliance 150 0 1 0.33 0.471 0.747 0.198 

Gender 150 1 3 1.65 0.493 -0.449 0.198 

Education 150 1 4 2.66 0.566 -1.222 0.198 

Age 150 2 7 2.87 0.771 1.824 0.198 

Employ status 150 1 3 2.15 0.488 0.360 0.198 

Employ sector 150 2 4 2.67 0.807 0.664 0.198 

Tax liable for 150 1 2 1.81 0.391 -1.625 0.198 

Tax return 

calculation 
150 1 2 1.58 0.495 -0.327 0.198 

Payment method 150 1 2 1.59 0.494 -0.356 0.198 

Tax penalties 150 1 2 1.63 0.485 -0.529 0.198 

Source: author’s computations 
 

The findings in Table 2 reveal that the variables have a mean value ranging between 0 and 3. Also, 

the findings indicate that the standard deviation across all variables is below unity. The combination of 

lower mean values and standard deviation implies that the data points are closer to the mean. Nonetheless, 

the skewness of the data is more diversified. Several variables such as gender, education, tax return 

calculation, tax penalties and knowledge on payment methods were found to be skewed to the left while 

other including tax compliance, age, employment status and sector were found to be skewed to the right. 

The total number of observations was 150 across all variables.  

 

Correlation Analysis 
The next step involved examining the association between the dependent variable and explanatory 

variables by means of the Pearson correlation test. The statistical significance of the association was also 

examined. The results are provided in table 3 below. 

  



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Table 3. Pearson Correlation 

Variables 
Tax 

compliance 
Gender Educated Age 

Employ 
status 

Employ 
sector 

Tax 
liable 

Tax 
return 

Payment 
method 

Tax 
penalty 

Tax 

compliance 

Corr 1 -0.020 0.193** -0.052 -0.044 -0.124 0.334** 0.074 0.152* 0.214** 

Sig.  0.405 0.009 0.265 0.295 0.066 0.000 0.183 0.032 0.004 

Gender 
Corr -0.020 1 -0.049 0.005 0.143* 0.062 0.004 -0.117 -0.163* -0.078 

Sig. 0.405  0.277 0.476 0.041 0.225 0.482 0.077 0.023 0.171 

Education 
Corr 0.193** -0.049 1 0.131 -0.175* -0.157* -0.016 0.014 0.094 0.023 

Sig. 0.009 0.277  0.054 0.016 0.028 0.424 0.433 0.126 0.388 

Age 
Corr -0.052 0.005 0.131 1 -0.162* -0.056 -0.146* 0.036 0.161* 0.124 

Sig. 0.265 0.476 0.054  0.024 0.247 0.038 0.333 0.024 0.065 

Employ 

status 

Corr -0.044 0.143* -0.175* -0.162* 1 0.742** 0.046 -0.093 -0.236** -0.012 

Sig. 0.295 0.041 0.016 0.024  0.000 0.290 0.129 0.002 0.443 

Employ 

sector 

Corr -0.124 0.062 -0.157* -0.056 0.742** 1 0.103 -0.043 -0.105 -0.005 

Sig. 0.066 0.225 0.028 0.247 0.000  0.104 0.299 0.100 0.476 

Tax liable for 
Corr 0.334** 0.004 -0.016 -0.146* 0.046 0.103 1 0.355** 0.223** 0.232** 

Sig. 0.000 0.482 0.424 0.038 0.290 0.104  0.000 0.003 0.002 

Tax return 

calculation 

Corr 0.074 -0.117 0.014 0.036 -0.093 -0.043 0.355** 1 0.465** 0.321** 

Sig. 0.183 0.077 0.433 0.333 0.129 0.299 0.000  0.000 0.000 

Payment 

method 

Corr 0.152* -0.163* 0.094 0.161* -0.236** -0.105 0.223** 0.465** 1 0.472** 

Sig. 0.032 0.023 0.126 0.024 0.002 0.100 0.003 0.000  0.000 

Tax penalties 
Corr 0.214** -0.078 0.023 0.124 -0.012 -0.005 0.232** 0.321** 0.472** 1 

Sig. 0.004 0.171 0.388 0.065 0.443 0.476 0.002 0.000 0.000  

Source: author’s computations 

Note: ** Correlation is significant at the 0.01 level (1-tailed), * Correlation is significant at the 0.05 level (1-

tailed). 
 

The findings from the Pearson correlation test revealed a positive association between the level of 

educational attainment and tax compliance in South Africa. This implies that educated citizens are most 

likely to be tax compliant given that they are well informed about the different regulations and institutions 

governing taxes. Furthermore, a positive association was revealed between tax compliance and knowledge 

about tax types. Individuals who have knowledge about the different tax types and the taxes they are liable 

for are most likely to be tax compliant, holding other factors constant. Also, the tax payment method and 

tax penalties were found to be positively correlated with tax compliance and the association was found to 

be statistically significant. The next subsection provides findings form the binary logistic regression model. 

 

Binary Logistic Regression 

The binary logistic regression model was estimated to analyse the impact of tax knowledge on tax 

compliance in South Africa. The findings are provided in table 4 below. The variable that captures 

knowledge on tax types was dropped given that it was perfectly correlated with the dependent variable. 

 

Table 4. Model 1 

 B S.E. Wald df Sig. Exp(B) 

 

Tax return calculation -0.092 0.417 0.048 1 0.826 0.913 

Tax payment method 0.337 0.449 0.564 1 0.453 1.401 

Tax penalties 0.894 0.444 4.059 1 0.044** 2.444 

Constant -2.616 0.843 9.632 1 0.002* 0.073 

Omnibus Tests of Model Coefficients (0.051) 

Hosmer and Lemeshow Test Sig (0.972) 

Source: author’s computations, 

Note: *, ** denote significance at the 1% and 5% level, respectively 



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The findings revealed that knowledge on calculation of tax return is negatively associated with 

higher probabilities of tax compliance. This implies that the inability of taxpayers to calculate and file tax 

returns on their own induces a non-compliant behaviour. The finding however was found to be statistically 

insignificant. On the contrary, knowledge on tax payment methods was found to be associated with higher 

probabilities of tax compliance albeit the association was likewise found to be statistically insignificant in 

explaining variations in taxpayers attitudes. Similarly, knowledge on tax penalties was found to be 

associated with higher probabilities of tax compliance and the association was found to be statistically 

significant. This implies that, to some extent, tax penalties in South Africa have been enforced effectively 

and are thus sufficient to induce taxpayers attitudes. 

 

Table 5. Model 2 

 B S.E. Wald df Sig. Exp(B) 

 

Tax return calculation -0.117 0.429 0.075 1 0.785 0.889 

Tax payment method 0.371 0.486 0.582 1 0.445 1.449 

Tax penalties 1.000 0.474 4.455 1 0.035** 2.718 

Gender -0.012 0.386 0.001 1 0.975 0.988 

Education 0.975 0.422 5.330 1 0.021** 2.650 

Age -0.388 0.308 1.579 1 0.209 0.679 

Employment status 0.689 0.673 1.046 1 0.306 1.991 

Employment sector -0.565 0.367 2.369 1 0.124 0.568 

Constant -4.318 2.046 4.454 1 0.035** 0.013 

Omnibus Tests of Model Coefficients (0.017) 

Hosmer and Lemeshow Test Sig (0.953) 

Source: author’s computations 

Note: ** denotes significance at the 5% level 

 

Table 5 presents findings from model 2 which incorporates demographic factors. The findings 

revealed that selected demographic factors including gender, age and employment sector are negatively 

associated with higher probabilities of tax compliance although the association was found to be statistically 

insignificant. In contrast, the level of educational attainment was found to be positively associated with 

higher probabilities of tax compliance. This implies that educated citizens are most likely to behave in a 

tax compliant manner given the amount of knowledge they have on tax policies and regulation.  

 

Table 6. Model 3 

 B S.E. Wald df Sig. Exp(B) 

 

Tax return 

calculation 
-0.057 0.466 0.015 1 0.902 0.944 

Payment method 0.748 0.502 2.220 1 0.136 2.113 

Tax penalties 0.825 0.493 2.807 1 0.094*** 2.282 

Social influence -0.673 0.445 2.284 1 0.131 0.510 

Benefit 0.209 0.455 0.211 1 0.646 1.233 

Tax morale 0.574 0.567 1.024 1 0.312 1.775 

Trade off -0.200 0.402 0.248 1 0.619 0.819 

Constant -2.996 1.500 3.990 1 0.046 0.050 

Omnibus Tests of Model Coefficients (0.063) 

Hosmer and Lemeshow Test Sig (0.353) 

Source: author’s computations 

Note: *** denotes significance at the 10% level 

 

Perceptions on government including public services that taxpayers benefit from as well as 

perceptions on the quality of public services, were incorporated into the initial tax compliance model. The 

findings indicated that all the variables that capture perceptions on government are statistically 

insignificant in explaining variations in taxpayers attitudes. The last point of analysis involved the 

incorporation of political legitimacy into the initial tax compliance model. The findings are provided in 



Baneng Naape / Finance, Accounting and Business Analysis, Volume 5, Issue 1, 2023 

 

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Table 7 below. 

 

Table 7. Model 4 

 B S.E. Wald df Sig. Exp(B) 

 

Tax return calculation -0.376 0.467 0.646 1 0.421 0.687 

Tax payment knowledge 0.409 0.488 0.704 1 0.402 1.505 

Tax penalty knowledge 0.978 0.479 4.167 1 0.041** 2.660 

Percept on Corruption 0.695 1.040 0.446 1 0.504 2.004 

Trust in government  -0.361 1.178 0.094 1 0.760 0.697 

State of democracy 0.799 0.452 3.135 1 0.077*** 2.224 

Constant -3.734 1.472 6.436 1 0.011* 0.024 

Omnibus Tests of Model Coefficients (.102) 

Hosmer and Lemeshow Test Sig (.695) 

Source: author’s computations,  

Note: *,**,*** denote significance at the 1%, 5%  and 10% level 

 

The political legitimacy model revealed that the state of democracy is positively associated with 

higher probabilities of tax compliance in South Africa. This is because, in a democratic state such as South 

Africa, taxpayers believe that they have the authority to deliberate and decide legislation as well as to 

choose governing officials or parties. As such, they are more likely to be tax compliant given that they 

have vested their trust in the ruling party. The state of corruption was found to be positively associated 

with higher probabilities of tax compliance while on the contrary, the state of trust in government was 

found to be negatively associated with higher probabilities of tax compliance. This indicates that citizens 

are dissatisfied with the government’s conduct and are thus less willing to settle their tax liabilities.  

 

CONCLUSION AND RECOMMENDATIONS 

 
This study was aimed at estimating the influence of tax knowledge and tax complexity on tax 

compliance in South Africa. The data collection process involved self-structured questionnaires. The data 

was analysed by means of descriptive analysis and inferential statistics. The study made use of simple 

probability sampling to obtain the sample population and size. The target population consisted of 300 

South African taxpayers although only 151 participants completed the survey. The findings from the 

Pearson correlation test indicated that knowledge on tax types, payment method and tax penalties is 

positively correlated with tax compliance in South Africa. Furthermore, results from the binary logistic 

model revealed that knowledge on tax penalties is positively associated with higher probabilities of tax 

compliance and this associated was found to be statistically significant. This implies that tax penalties are 

well in force and effectively communicated to taxpayers to induce a tax compliant behaviour. In contrast, 

knowledge on tax calculation was found to be negatively associated with probabilities of higher tax 

compliance although the association was found to be statistically insignificant. Demographic factors such 

as the level of educational attainment as well as the state of democracy, were found to play a significant 

role in inducing tax compliance. Given these findings, the study recommends the expansion of 

programmes that educate taxpayers about the different tax types they are liable for, the importance of 

filling tax returns on time as well as payment facilities available to avoid penalties. Various methods of 

communication can be utilised, including through educational television programmes and local radio 

stations, billboards, and telephonic communication. More attention can be channelled towards specific 

career groups such as artists and professional athletes, who in most instances, lack the knowledge on tax 

types they are liable for.  

 

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APPENDIX 

 

   
 

   
 

36%

63%

1%

Male Female Prefer not to

say

Gender

4%

26%

69%

1%

Education

28%

60%

9%

0% 1% 1%

18 - 24 25 - 34 35 - 44 45 - 54 55 - 64 65 or

older

Age

5%

74%

21%

Self employed Employed Unemployed

Employment status



Baneng Naape / Finance, Accounting and Business Analysis, Volume 5, Issue 1, 2023 

 

26 

 

   
 

   

 

   
 

   
 

10%

49%

1%

40%

Unfair tax system

Taxes are too high

I know I won't get

caught

The government steals

tax money

Reasons for evasion

3%

7%

46%

28%

15%

Very easy

Easy

Neither easy nor

difficult

Difficult

Very difficult

Ease of Evasion

19%

81%

No Yes

Knowledge on Tax Types

42%

58%

No Yes

Tax Calculation Knowledge

41%

59%

No Yes

Tax Payment Knowledge

37%

63%

No Yes

Knowledge on Tax Penalties



Baneng Naape / Finance, Accounting and Business Analysis, Volume 5, Issue 1, 2023 

 

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Level of

corruption

Trust in

government

State of

democracy

Yes 5% 4% 27%

No 95% 96% 73%

95% 96%
73%

Do the following encourage you 

to be tax compliant
63%

37%

No Yes

Social Influence


