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THE INFLUENCE OF CEO CHARACTER ON TAX AVOIDANCE IN

PROPERTY AND REAL ESTATE COMPANIES LISTED ON THE

INDONESIA STOCK EXCHANGE 2020-2021

Nanda Resqy Annisa

, Hasnawati

Accounting Major of International Class Program, Faculty of Economics and Business, Trisakti

University,Jakarta, Indonesia

1,2

nanda023001[email protected]

, [email protected]

ABSTRACT

This study analyzes and obtains empirical evidence on CEO character, including CEO age, CEO tenure,

CEO education level, and CEO gender in tax avoidance. This study uses a sample of the property and

real estate sectors listed on the Indonesia Stock Exchange (IDX) during the 2020-2021 period. The

number of companies that are sampled are 64 companies with observations for two years using the

purposive sampling method-the hypothesis testing of this study uses multiple regression models. The

results showed that the CEO's education level and CEO's age had a positive effect on tax avoidance.

While CEO tenure, gender does not affect tax avoidance.

Keywords: CEO age; CEO tenure; CEO education level; CEO gender; tax avoidance.

INTRODUCTION

Taxation is an important component of state revenue because it accounts for the majority

of state revenue (Menteri Keuangan, 2021). Consequently, it is natural for the government to

attempt to collect taxes until it reaches the optimal level of revenue so that state treasury revenues

are stable and state development can proceed efficiently. Although most people see taxes as an

unavoidable burden, it is obvious that taxes are a source of income for the government (Sartika,

2021).

The Law of the Republic of Indonesia, Number 6 of 1983, as Amended, governs general

provisions and tax procedures. Numerous times, including most recently in Law of the Republic

of Indonesia Number 16 of 2009, the term "tax" has been used to describe a required payment

made to an individual or business entity. The law states that the compensation received is used

for the public good rather than being directly received (Simanjuntak & Mukhlis, 2012). The tax

sector is beneficial as a source of state revenue for development; therefore, the government will

increase revenue from the tax sector, but there are different views from the personal and corporate

taxpayers. Corporate taxpayers who pay taxes minimize the tax burden because the company's

income is getting bigger, so the tax payable is also large (Gartika & Wijaya, 2018).

A key factor in accelerating Indonesia's economic growth is the slowdown in national

economic growth in recent years, which has reduced domestic liquidity availability and tax

revenues. Large amounts of liquid and illiquid assets held by Indonesians abroad should be used

to boost domestic liquidity and promote national economic growth (H. Insan & Maghijn, 2018).

At this time, Indonesia has implemented a tax amnesty (tax amnesty) volume II. In-Law

No. 11 of 2016 concerning Tax Amnesty. Tax Amnesty is the abolition of tax payable but is not

subject to administrative sanctions and sanctions in the field of taxation. When the tax directorate

has a voluntary or second-volume tax amnesty (PPS) program, it is an opportunity for taxpayers

to report taxes or taxes that have not been fulfilled voluntarily. Through this program, which is

similar to the first volume of the tax amnesty, the government provides an opportunity for

taxpayers to report assets that have not been included in the SPT without imposing a fine

(Pangestu Pratama, 2022). With tax amnesty, there is a potential for revenue to increase in our

APBN, either this year or next year. the previous year which will make our state budget more

sustainable (Kementerian Keuangan Republik Indonesia, 2016).

Implementing this tax amnesty also aims to increase state tax revenues, which can then be

used to improve the economy and welfare of the Indonesian people. In addition, this tax amnesty

Is intended to prevent taxpayers/institutions from evading taxes because it facilitates handling tax

problems.

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Globalization has allowed businesses and taxpayers to organize their operations under one

command with the common goal of generating profits and lowering all costs, including tax costs.

To maximize their finances, they will avoid paying taxes. As a result, unexpectedly, every

jurisdiction is competing to attract investigations. A company must pay more taxes to the state

because of its high income. Companies often use various strategies to reduce the tax burden to

maintain high corporate profits because this tax is a burden for those who can lower profits. This

practice is commonly known as tax evasion. Therefore, we can infer that tax evasion is an effort

to avoid paying taxes by attempting to lessen or eliminate the tax debt that they must pay without

breaking the law (Doho & Santoso, 2020).

In Indonesia's real estate and property sectors, there is a problem with tax evasion. The

government should be receiving taxes from the sale and purchase of land or buildings by

developers, which include a 5% income tax and a 10% value-added tax for non-residential

properties. However, some sellers are not paying these taxes. A 5% Land and Building Rights

Acquisition Fee (BPHTB) is the local government's tax on real estate transactions. The

Directorate General of Taxes discovered a potential loss of tax revenue as a result of the failure

to report the actual transactions of buying and selling land/buildings, including property, real

estate, and apartments. It happens as a result of taxes being levied based on transactions based on

the Sales Value of Tax Objects (NJOP) as opposed to actual or legal trades (Tannuka, 2019).

An example of the tax avoidance phenomenon in property and real estate companies is the

property company PT. Agung Podomoro Land Tbk., which was involved in a tax evasion case as

a result of the leak of 11.5 million documents known as the Panama Papers. Attachments for 2.1

million PDF documents, 1.1 million photos, 32,000 text documents, and roughly 2000 additional

files are contained in the 4.8 million emails that make up the document (Redaksi Solopos.com,

2016).

There are both internal and external governance factors that can lead to tax avoidance. The

Chief Executive Officer's (CEO's) ability to exercise discretion over operational matters is an

example of an internal factor (Harymawan et al., 2019).

Chief Executive Officers (CEOs) hold top positions in the company's management. They

are responsible for the operations and performance of the company. Age, tenure, gender, and

educational background determine executive characteristics. The executive who focuses on this

research is the CEO of an Indonesian public company. The CEO is responsible for the company's

performance so that the CEO can influence the financial statements (Feng et al., 2011).

Several factors of a CEO's personality can encourage the CEO to do tax avoidance, one of

which is narcissism or overconfidence. Research shows by (Hsieh et al., 2018) stated that

overconfident CEOs are more likely to engage in tax avoidance activities. Another factor that

affects a CEO is the length of tenure of the CEO in a company. The results of the study Goldman,

Powers, & Williams (2017) show that CEO tenure affects tax avoidance practices. Various

characteristics of CEOs, such as age, years of service, gender, and educational background

(Goldman et al., 2017).

The issue of tax avoidance is complex and unique because, on the one hand, it does not

constitute illegal activity (legal). On the other hand, the government does not want tax evasion

because it decreases state revenue (Putri & Putra, 2017). Based on the above problems related to

the results of previous studies with existing theories and the inconsistency of previous studies'

effects, the authors are encouraged to re-test to find out the factors that affect tax avoidance. The

results of the test are expected to provide a better picture of the effect of CEO characteristics on

the company.

Based on the limitations of the inconsistency of the research conducted (Karina & Jeksen,

2021). The research shows that the factors that influence Tax Avoidance are the age of the director

and CEO tenure, which has no significant effect on tax avoidance. The difference between this

study and the research conducted by (Karina & Jeksen, 2021) is that there are additional independent

variables, namely the CEO's gender, and education level, which are included in the CEO

Characteristics section.

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This is where the researcher gets additional variables from the research conducted by For

research, it is necessary to review whether each added variable, namely education level and CEO

gender, affects Tax Avoidance in the Property and Real Estate sector. The reason for this research

is that tax avoidance is still a big problem, especially among companies in Indonesia.

For company management, this research is expected to be an evaluation and reference

material related to tax policies and strategies. The tax avoidance strategy is beneficial for the

company because it can save significant tax payments (Chen et al., 2007). Still, on the other hand,

it can harm the company, such as the risk of being penalized by the tax authorities (Chen et al.,

2007; Desai & Dharmapala, 2006). and hefty fees for tax expert payments as well as reputational

fines (Midiastuty et al., 2017). The government's regulatory body, the Directorate General of

Taxes (DGT), is expected to use the study's findings to help formulate taxation regulations.

Improved tax law analysis is necessary, especially for corporate taxpayers with access to tax

evasion techniques like transfer pricing, which is covered in Law No. 36 of 2008, Article 18

paragraph 3. (a) (Farouq, 2018).

RESEARCH METHOD

Research design

This research was carried out in stages; at the beginning, the researcher did the planning

and design of the previous examination. This study can determine the relationship between the

variables examined. Researchers use hypothesis testing; researchers use methods in collecting

data from time series and cross-sections, namely examining several objects at once in a certain

period. This study uses secondary data sources (indirect data) by collecting annual reports and

financial statements from non-financial sector companies listed on the IDX.

Table 1

Whole Company Sample Quantity List

Criteria

amount

Companies listed on the IDX (2020-2021)

Total of Companies that do not meet the criteria:

(17)

Companies that do not publish an annual report

(7)

Companies that do not publish financial statements

(10)

Company use as a sample

Company observation data in 2 years

128

Data outlier

All data tested

Variables and Measurements

This study measures tax avoidance as the outcome variable, while the CEO's characteristics

such as age, tenure, education level, and gender are considered as the independent variables.This

study measures tax avoidance as the outcome variable, while the CEO's characteristics such as

age, tenure, education level, and gender are considered as the independent variables.

Dependent Variable

Tax avoidance refers to a company's attempt to minimize its profit to minimize the amount

of tax that must be paid. The effective tax rate, which represents the percentage of tax the company

has produced, is used to assess tax avoidance. ETR can describe the tax the company will pay

because it calculates the sum of current expenses with deferred expenses. If the company carries

out tax strategies such as accelerating depreciation, this will not affect the calculation of ETR

(Hanlon & Heitzman, 2010). The greater the ETR value, the lower the tax avoidance. On the

contrary, the smaller the ETR value, the higher the tax avoidance (Astuti & Aryani, 2017).

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𝐸 =

𝑇𝑎𝑥 𝐸𝑥𝑝𝑒𝑛𝑠𝑒

𝑃𝑟𝑒𝑡𝑎𝑥 𝐼𝑛𝑐𝑜𝑚𝑒

TAX AVOIDANCE = ETR x-1

The variables being studied independently are listed below.

1. CEO age is the lifetime attached to a CEO. The older you get, the wiser a person will be.

The CEO age proxy was calculated using a dummy variable. A CEO who has a young age

under 50 is worth 1, but if the CEO is over 50 years old, it is worth 0.

2. Educational background is a description of the education that has been taken until someone

finally occupies a position or serves as CEO. CEO Education Level This measurement

refers to the combination of Erlim and Juliana (2017) and Putri (2020) indicators. In this

study, the dummy calculation was carried out by combining two hands (Education and

Certification Level) from the two studies as follows (EDU):

1. CEO with S1 education level without certification:1

2. CEO with S1 education level with certification:2

3. CEO with S2 education level without certification:2

4. CEO with S2 education level with certification :3

5. CEO with S3 education level without certification :3

6. CEO with S3 education level with certification:4

7. CEO has not reached the minimum education level of S1 :0

3. Gender is an essential thing that distinguishes a person from others. Gender is determined

between men and women. Gender proxy using Dummy. This study develops the logic that

the agentic qualities that are identical to men make them tend to be risk-takers for every

decision, including tax aggressiveness, so that the CEO gender variable is represented

through Dummy as a measurement proxy, with male CEOs being assigned a value of 1 and

female CEOs being assigned a value of 0, (Duan et al., 2018).

4. Tenure or term of office is the time a person has served as CEO. Tenure in this study is

proxied by the length of time a person has served as company CEO. Therefore, the tenure

calculation uses a period of years (Ali & Zhang, 2015).

Sampling technique

In 2020-2021, the study included all 81 real estate and property companies listed on the

Indonesia Stock Exchange (IDX). The sampling process used the purposive approach, taking into

account specific criteria when selecting the sample. Some of the criteria used for selection were:

1. Property and real estate sector companies listed on the Indonesia Stock Exchange (IDX) in

a row 2020-2021.

2. Companies that publish financial reports in a row for the years 2020-2021.

3. Companies that publish annual reports in a row for 2020-2021.

RESULT AND DISCUSSION

Descriptive Test

When conducting research, descriptive statistical testing is used to provide an overview of

the data's characteristics. This includes analyzing the frequency distribution of variables in the

study, such as the mean, median, minimum, maximum, and standard deviation. The table below

presents the descriptive analysis's results.

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Table 2

Descriptive Statistics

Minimum

Maximum

Mean

Std. Deviation

Tax Avoidance

CEO Age

Education Level of CEO

CEO Gender

CEO Tenure

Valid N (listwise)

128

-113,238

7,782

-2,43594

55,19

1,45

,95

6,55

15,002872

11,004

,674

,277

8,128

Using the descriptive statistical table provided, we can conclude that:

a. The ETR variable before being an outlier had a minimum value of -113238 for MDLN

companies in 2020, a maximum value of 7.782 for FMII companies, and a mean value of -

2.43596, and a standard deviation of 15.003.

b. The Age of Director variable ranges from 35 to 78, with an average value of 55.118 and a

standard deviation of 11.004.

c. The variable level of education has a minimum value of 0 and a maximum value of 3. The

average value for the variable level of education is 1.453, with a standard deviation of 0.674.

d. For the Gender variable with a minimum value of 0 and a maximum value of 1. The average

value for the Gender variable is 0.94 with a standard deviation of 0.243

e. The Tenure variable ranges from 1 to 45. On average, the tenure variable is 6.55 with a

standard deviation of 8.128.

Classic assumption test

Normality

Statistical analysis is one part of the normality test. The research used the Komologorov-

Smirnov (K-S) parametric statistical test. If the value of the Kolmogorov-Smirnov test (K-S) >

0.05, the data is normally distributed.

In the results of data processing, it was found that there were abnormalities in the data;

therefore, the authors removed the samples that were before outlier with the following data:

Table 3

One-Sampel Kalmogorov-Smirnov Test

Unstandardiz

ed Residual

Normal Parameters

a,b

Most Extreme Differences

Test Statistic

Asymp. Sig. (2-tailed)

Mean

Std. Deviation

Absolute

Positive

Negarive

128

,0000000

14,94669872

,455

,343

-,455

,000°

a. Test distribution is Normal.

b. Calculated from data.

c. Lilliefors Significance Correction.

After conducting a normality test called one-sample Kolmogorov, any data that is considered

abnormal can be identified by the Asymp.Sig value being less than 0.05. In order to address this

issue, researchers may need to remove outliers or reduce data with extreme values.

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Figure 1

normal P-P Plot Regression Standardized Residual

Based on the output chart above, we can see the plot graph. The points in the p-plot image can be

seen to move away from and not follow the diagonal line, which indicates that the regression

model does not adhere to the traditional assumptions.

After outlier

To ensure that the data is usually distributed, there is no other way, namely using the Kolmogorov

Smirnov formula normality method as follows:

Table 4

One-Sampel Kalmogorov-Smirnov Test

Unstandardiz

ed Residual

Normal Parameters

a,b

Most Extreme Differences

Test Statistic

Asymp. Sig. (2-tailed)

Mean

Std. Deviation

Absolute

Positive

Negarive

,0000000

,12970375

,091

,080

-,091

,091

,061°

a. Test distribution is Normal.

b. Calculated from data.

c. Lilliefors Significance Correction.

From the table presented, it can be concluded that the research data has passed the normality test

after removing any outliers or reducing the data. This is indicated by the Asymp sig (2-tailed)

value of 0.061, which is higher than the significance level of 0.05.

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Figure 2

Normal P-P Plot of Regression Standarized Residual

Based on the output chart displayed above, we can get a plot graph; the p-plot image shows the

points following and approaching the diagonal line so that it can be concluded that the regression

model meets the classical assumptions.

Multicollinear Test

The multicollinearity test aims to examine whether the regression model identified a

relationship between the independent variables (independent). The independent variables in a

good regression model shouldn't be correlated. The tolerance value commonly used to indicate

the presence of multicollinearity is the Cut-off value. To ensure no multicollinearity, the tolerance

value must be greater than or equal to 0.10 or the VIF value 10, respectively (Ho is accepted).

The results of the multicollinearity test are displayed in the following table:

Table 5

coefficients

Model

Collinearity Statistics

Tolerance

VIF

CEO age

Education Level of CEO

CEO Gender

CEO Tenure

,883

,961

,830

,749

1,133

1,041

1,205

1,334

a. Dependent Variabel: Tax Avoidance

Based on the table above, the tolerance value of each research variable exceeds 0.10, and the

calculated VIF value shows a number less than 10, so it can be concluded that the research data

has passed the multicollinearity test.

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Heteroscedasticity Test

The heteroscedasticity test using ScatterPlots is part of the classical assumption test in the

regression model. Meanwhile, the occurrence of heteroscedasticity symptoms or problems will

result in a doubt (inaccuracy) in the results of the regression analysis carried out.

Scatterplot after outlier

It is clear from the scatter plot's output above that the points are dispersed and do not

follow a particular, distinct pattern. There must be no signs of heteroscedasticity, which is one

requirement that must be fulfilled in a good regression model. Therefore, it can be said that the

issue of heteroscedasticity does not exist.

Autocorrelation Test

The purpose of the autocorrelation test is to determine if there is a correlation between the

nuisance error from the previous period (t-1) in the linear model. In this study, the Durbin-Watson

(DW) test is utilized for autocorrelation analysis.

If the Durbin-Watson (DW) value is greater than 0 and less than dl, then there is a positive

autocorrelation. On the other hand, if the Durbin-Watson (DW) value is greater than 4-dl and less

than 4, then there is a negative correlation. If the Durbin-Watson (DW) value is greater than dl

and less is equal to du, then it cannot be concluded that it is in the inconclusive region. If the

Durbin-Watson (DW) value is greater than 4-du and more minor is equal to 4-dl, it cannot be

concluded because it is in an inconclusive area.

The autocorrelation test performed with the Durbin Watson test can be expressed as

follows:

Figure 3

Scatterplot

Dependent Variable: Tax Avoidance

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Figure 4

The autocorrelation test performed with the Durbin Watson test can be expressed as follows:

Table 6

Model Summary

Model

R Square

Adjusted R

Square

Std. Error of

the Estimate

Durbin-

Watson

,352

,124

,082

,132721

2,096

a. Predictors: (Constant), CEO Tenure, Education Level of CEO, CEO age, CEO gender

b. Dependent Variable: Tax Avoidance

Table 7

Image of Autocorrelation Reception Area

0 1,5656 1,7508 2,096 2,2492 2,4344 4

Based on this study with four independent variables with a value of N = 90, the dL value is 1.5656

with a dU value of 1.7508. Based on the results of the autocorrelation test above, the dW value is

2.096, which indicates no autocorrelation in this study.

Hypothesis Test

Determination Test

The adjusted R2 test is used to determine the extent to which the independent variable

affects the dependent variable. As the R2 value approaches 1, it indicates that the independent

variable has a greater impact on the dependent variable. The table below shows the R square

values.

Table 8

Model Summary

Model

R Square

Adjusted R

Square

Std. Error of

the Estimate

,352

,124

,082

,132721

a. Predictors: (Constant), CEO Tenure, Education Level of CEO,

CEO age, CEO gender

Based on the table above, the variables of Education Level, Director's Age, Gender, and Tenure

influence 8.2% on ETR, while the other 91.8% are influenced by other variables not explained in

this study.

There is a

positive auto

Inconclusive

No autocorrelation

Inconclusive

There is an auto

negative

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f . test

The F statistical test shows whether all the independent variables included in the model

have a common effect on the dependent variable.If less than 0.05 is entered for the significance

value(<0.05, = 5%), then Ha is accepted. Hypothesis tested:

Table 9

ANOVA

Model

Sum of

Squares

Mean Square

Sig.

Regression

Residual

Total

,211

1,497

1,708

,053

,061

2,997

,023

a. Dependent Variable : Tax Avoidance

b. Predictors: (Constant), CEO Tenure, Education Level of CEO, CEO age, CEO gender

Based on the table above, with N = 90 with the number of independent variables 4 having a Fcount

value of 2,997 > Ftable 2.47 and a Sig value of 0.023 <0.05. it can be concluded that the variables

of Education Level, Director's Age, Gender, and Tenure have a simultaneous effect on ETR.

T test

Table 10

T test

variable

prediction

Unstandardized B

Standardized

BETA

Sig.

Conclusion

(Constant)

-0,342

-3,353

0,001

CEO Age

0,003

0,0241

2,225

0,029

H1 Accepted

Education

Level of CEO

0,057

0,282

2,719

0,008

H2 Accepted

CEO gender

-0,04

-0,068

-0,614

0,541

H3 Rejected

CEO Tenure

-0,001

-0,079

-0,674

0,502

H4 Rejected

Adj.R2

0,082

F Test

2,997

F sig

0,023

Based on the table above, which has a sample size of 90 (N=90), it can be concluded that

Education Level, Director Age, Gender, and Tenure all have a combined effect on ETR. This is

supported by a Ttable value of 1.987.

a. For the variable Age of the director has a value of Tcount -2.225 < Ttable 1.987 with a Sig

value of 0.029 > 0.05 and a B value of 0.003 in a positive direction, it can be concluded that

the Age of the director affects ETR. Based on H1, Director's Age positively affects ETR,

then H1 is accepted.

b. The education level variable has a value of Tcount 2.719 < Ttable 1.987 with a Sig value of

0.008 > 0.05 and a B value of 0.057 in a positive direction, it can be concluded that the level

of education affects ETR. Based on H2, Education Level positively affects ETR, then H2 is

accepted.

c. For the Gender variable has a value of Tcount -0.614 < Ttable 1.987 with a Sig value of

0.541 > 0.05 and a B value of -0.040 in the negative direction, it can be concluded that

Gender does not affect ETR. Based on H3, Gender has no positive effect on ETR, then H3

is rejected.

d. For the Tenure variable has a value of Tcount -0.674 < Ttable 1.987 with a Sig value of 0.572

> 0.05 and a B value of -0.001 in a negative direction, it can be concluded that director tenure

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does not affect ETR. Based on H4, if Tenure does not positively affect ETR, then H4 is

rejected.

Discussion

The Effect of Director Age on Tax Avoidance

Based on the results of the first hypothesis, the director's age positively affects tax

avoidance. This shows that if the director is older, it can be indicated that the company avoids its

obligation to pay taxes because of the significant effect of the director's age. According to the

research test results, Halioui (Halioui et al., 2016) said there is a significant positive effect

between the director's age and tax avoidance. In addition, if the age of the president director has

a negative effect on tax avoidance, it can be interpreted that this is because older age will behave

more ethically and conservatively in decision making and avoid opportunities in tax avoidance

according to the results of the study of Minnick and Noga (Minnick & Noga, 2010).

The Effect of CEO Gender on Tax Avoidance

Based on the results of the second hypothesis, the gender of the CEO on the practice of Tax

Avoidance has no effect. These results refer to the results of the t-test. In theory, companies with

female CEOs are more able to influence company performance and impact the quality of

information produced. The upper echelon theory (Hambrick & Mason, 1984) states that top

management's decisions must be different according to the abilities and characteristics of top

management. One of these characteristics is gender. Gender differences are closely related to the

characteristics of each sex, where women are more gentle and need a sense of security. This is

not related to the results of the study. Even though there is discrimination based on gender, which

favors men over women, it is undeniable that women are now more courageous in making

corporate decisions. In addition, the small sample of female CEOs in this study led to the lack of

significance of this CEO gender variable.

The Effect of CEO Tenure on Tax Avoidance

The study's four hypotheses suggest that CEO tenure does not have a significant impact on

tax avoidance. These findings align with previous research studies conducted by Putra and

Fitriasari (2014), Huang and Zhang (2019), and Yuwono and Fuad (2019) that also found no

significant effect of CEO tenure on tax avoidance. However, this differs from Halioui's (Halioui

et al., 2016) opinion, which suggests a positive and significant effect, meaning that the longer the

CEO has been in their position, the more likely they are to engage in tax evasion. Despite this

difference, the current study's results indicate that the effect of CEO tenure on tax avoidance is

not significant.

The Effect of CEO Education Level on Tax Avoidance

Based on the results of the fourth hypothesis, the level of education in this study positively

affects tax avoidance. According to Jackson & Milliron (1986), the level of education is one of

the critical factors in tax avoidance (Tax Avoidance). A CEO's educational background affects

tax aggressiveness. The hypothesis was confirmed by the findings of the regression analysis. That

is, the educational background of the CEO is proven to affect the level of tax aggressiveness in

family companies. This is supported by (Aliani, 2014), who also states that CEOs with financial,

accounting, and tax backgrounds have an effect on the level of CEO aggressiveness in tax

planning. Education is included in the demographic characteristics of top-level management as

described in the Upper Echelons Theory (Hambrick & Mason, 1984). These demographic

characteristics will affect an organisation's decisions and policies (outcomes).

Meanwhile, a high level of education and competence is needed for someone to occupy the

position of CEO, who is also a center in top-level management, to have a greater chance of

recruiting (Bhagat et al., 2010). So that in deciding how much tax minimization is carried out by

the company he leads, the decision will be influenced by the CEO's knowledge of related tax

regulations, his competence in managing the company's financial accounting in such a way to

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achieve a smaller tax burden for more efficiency in spending. The company, so it has cost

leadership.

CONCLUSION

This research examined how the director's age, education level, gender, and tenure

influence tax avoidance. The study focused on companies in the real estate and property industries

that were listed on the Indonesia Stock Exchange (IDX) between 2020 and 2021.According to the

criteria of 64 companies separated by two years, 128 samples can be collected for a total of 128

samples. In accordance with the findings and discussion in this study, the conclusions can be

summarized as follows: The age of the director has a positive effect on tax avoidance; Education

level has a positive effect on tax avoidance; Gender does not affect tax avoidance; Tenure does

not affect tax avoidance.

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