Proceedings of 3rd Global Accounting, Finance and Economics Conference
5 - 7 May, 2013, Rydges Melbourne, Australia, ISBN: 978-1-922069-23-8
Measuring Tax Gap in the Service Industry
Rohaya Md Noor*, Nurul Erzawaty Jamaludin**, Normah Omar*** and
Rozainun Abd Aziz ****
The service industry has contributed a substantial amount of tax revenue
to Malaysia. Nevertheless, tax evasion activities in the industry have also
caused substantial tax lost to the government. For example, in 2011, it
was reported that the service tax deficiency amounted to RM48.90
million. This issue will affect the government spending and national
development for the country. Hence, the objectives of this study are to
examine the tax gap and factors that influence the tax evasion in the
service industry. The study has examined several potential factors such
as tax rates, penalty, level of threshold, type of service providers, size of
business, type of external auditors engaged by service providers and risk
of probability for detection. The sample data comprised of 275 service
providers which have underpaid the service tax for the years 2009 until
2011. The statistical results revealed that there was a significant
difference between the declared value and actual value of service tax
paid by service providers. The current study found that tax evasion in the
service industry can be associated with the level of threshold, the type of
auditor engaged by service providers and non submission of service tax
forms. Thus, the findings have provided strategic information to the tax
authorities by revealing significant indicators which may assist them in
planning tax audit for the service industry.
Keywords: Tax gap, tax evasion, service tax and service industry.
1. Introduction
There is a growing concern on the issue of tax evasion as these activities diminish the
government’s effort to raise the public revenue. Tax evasion is said to occur when taxable
persons fail to comply with their tax obligations. Consequently, the loss of tax revenues may
result in serious damage to the proper functioning of the government, as well as threatening
its capacity to finance its basic expenses (Franzoni, 1998). Undoubtedly, tax evasion may
result in destructive consequences for any tax system. Andreoni, Erard and Feinstein (1998)
reported that evasion may require higher and more distortionary taxes on reported income as
it complicates measures of the distortionary effect of taxation. Economists view tax evasion
as a source of inequality, since the tax system could become regressive instead of
progressive (Gebaeur, Chang and Parsche, 2007).
The tax system is a mechanism to collect government revenue for its public expenditure,
such as to provide education, welfare, national security and other civil services. In Malaysia,
the tax revenue has contributed 71% (RM112,898 million) and 67% (RM106,504 million) of
the country’s total revenue for 2008 and 2009 respectively (Annual Economic Report, 2010
*Dr. Rohaya Md Noor, Accounting Research Institute & Faculty of Accountancy, Universiti Teknologi Mara,
Malaysia. Email: rohay725@salam.uitm.edu.my
**Nurul Erzawaty Jamaludin, Faculty of Accountancy, Universiti Teknologi Mara, Malaysia.
Email: erzam2000@yahoo.com
***Dr. Normah Omar, Accounting Research Institute, Universiti Teknologi Mara, Malaysia.
Email: normah064@salam.uitm.edu.my
**** Rozainun Abd Aziz, Faculty of Accountancy, Universiti Teknologi Mara, Malaysia.
Email: rozainun@salam.uitm.edu.my
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Proceedings of 3rd Global Accounting, Finance and Economics Conference
5 - 7 May, 2013, Rydges Melbourne, Australia, ISBN: 978-1-922069-23-8
and 2011). Apparently, service tax revenue showed a minimal growth by contributing only
around 3% to 4% of the total tax revenues during the years 2005 to 2009 (Annual Economic
Reports, 2007 to 2011). The Malaysian tax authorities have collected RM1,697 million of
additional income taxes and penalties from 1,052,939 tax audit cases in the year 2008 (Tax
Authorities Annual Report, 2008). Furthermore, it was also reported that a total of 2,768
cases have been audited in 2009 resulting in an additional RM49 million of indirect taxes and
penalties collected by the tax authorities (Tax Authorities Annual Report, 2009). Service tax
evasion contributed to 38% of tax underpaid, which is the highest amount of under collection
of indirect taxes for the year 2009.
Most of the previous studies, such as Md. Noor, Mastuki and Bardai (2008) focused on tax
planning and illegal tax avoidance activities on direct taxes. There are very few studies that
examined tax issues related to service taxes. Hence, the current study examined the tax gap
between declared service tax and actual service tax paid, and the factors that influenced the
service tax evasion. This study may possibly provide some inputs to the tax authority in
planning their tax audit programs. In addition, the findings of this study may potentially
support the rationalization for the implementation of Goods and Service Tax (GST).
2. Literature Review
2.1 Service Tax System
In a move to create a more stable tax base and to generate more tax revenues for the
country, the Government is turning to indirect taxes as an essential part of the solution. The
service tax rate has increased from 5% to 6%. Economists foresee that an increment of 1%
in the service tax rate will increase the Government’s revenue from taxable services to RM5
billion in 2011. Currently, the service tax is imposed on a variety of goods and services such
as hotel rooms, food in restaurants and professional services. The recent year saw the
expansion of indirect tax scope when the Government introduced the service tax on credit
cards and charge cards. In addition, the service tax will also be levied on the paid satellite
broadcasting services, which were not subjected to the service tax previously. Furthermore,
Malaysia is in the midst of reforming the indirect tax, whereby the Goods and Service Tax will
replace the sales tax and service tax. The proposal to implement Goods and Service Tax
was made in the Budget 2005 of which the Goods and Service Tax was planned to take of in
2007.
2.2 Related Studies of Tax Evasion and Development of Hypotheses
Tax evasion is defined as the unlawful and intentional non-payment or avoidance of tax owed
(Green, 2009; Manasan, 1988). Sandmo (2005) explained that tax evasion is a violation of
law, in which the taxpayer forbears to report taxable income from labour or capital. This is
consistent with Engel and Hines (1998) who defined tax evasion as the illegal practice where
a person, organization or corporation intentionally avoids paying the true tax liabilities and
generally can be subjected to criminal charges. Whilst, Ali, Cecil and Knoblett (2001) and
Nur-tegin (2008) considered tax evasion as a risky asset, which is determined by the
traditional determinants, that is, the marginal tax rate, the probability of detection, and the
penalty structure.
The tax evasion model developed by Allingham and Sandmo (1972) stated that the decision
to evade income tax was individuals’ rational decision and formulated under the policy tools,
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Proceedings of 3rd Global Accounting, Finance and Economics Conference
5 - 7 May, 2013, Rydges Melbourne, Australia, ISBN: 978-1-922069-23-8
such as audit rates, penalty rates and tax rate schedule. Several other studies have also
been conducted in line with this tax evasion model, especially in describing the relationship
between tax evasion and the punishment oriented policies (Sandmo, 2005 and Clotfelter,
1983). Allingham and Sandmo’s (1972) traditional tax evasion model was further extended
and refined by other studies, such as Borck (2004), Molero and Pujol (2004), Feld and Frey
(2006) and Zaied (2009). In addition, Mohd. Nor, Ahmad and Mohd. Saleh (2010) examined
Malaysian unlisted companies and found that the size of a company and the type of audit firm
have significant effects on tax evasion activities through fraudulent financial reporting.
Previous studies have also examined the effectiveness of deterrence policy on tax evasion
for both direct and indirect taxes. According to Hutton (2009), the concept of deterrence
theory was designed for specific and general manners whereby individuals would reconsider
committing the crimes where the punishment bestowed is severe. The importance of
deterrence for tax compliance has been emphasised in the tax evasion model. Most of the
past studies hypothesized that the higher the fines or penalties and the probability of
detection, the lower the tax evasion. For example, Becker (1968) suggested that “expected
punishment serves as a price each criminal pays for a crime” (cited in Feld, Schmidt and
Schneider, 2007, pg.1). Whilst, Stevens and Payne (1999) stated that criminal punishment
include fines, community services, imprisonment, or even a death penalty. On the other
hand, Feld and Frey (2006) said deterrence is a motivational force in getting people to pay
their taxes. The product of detection probability and the amount of fines imposed, as the two
standard variables of deterrence, are essential for the amount of evaded income (Feld and
Frey, 2006; and Feld et al., 2007). Further, Bergman (1998) suggested that in the deterrence
theory, tax penalty is included as one of the elements that may discourage the intention to
evade taxes.
The Malaysian Income Tax Act 1967 and Service Tax Act 1975 stated that any person is said
to wilfully evade tax when he/ she deliberately omits any income from a return; makes a false
statement or entry in a return; gives false information either verbally or in writing; prepares or
maintains false books of account and other records. In addition, any authorization to anyone
in preparing or falsifying books of accounts or other records is also deemed as evading
taxes. Murphy (2005) suggested that tax can be evaded when a person or company fails to
declare all or part of their income; or makes a claim to deduct an expense from their taxable
income that they did not incur or which they were not entitled to deduct; or submits a tax
return that appears to be legal but only because relevant facts are not disclosed to the tax
authorities.
Weigel, Hessing and Elffers (1986) reported that tax evasion is the difference between the
taxes owed and taxes filed. Clotfelter (1983) measured tax evasion as the difference between
the taxable income calculated by the tax authorities and the taxpayers. According to Green
(2009), the U.S. Internal Revenue Service (IRS) measured the tax gap as the difference
between the amount of tax owed and the amount of tax paid. Consistent with Murphy (2005),
the current study defined tax evasion as illegal activities embarked by service tax providers
that cause reduction or non-payment of service tax, hence creating a tax gap. In other
words, a tax gap is the difference between the expected taxes to be paid, that is declared
service tax and the actual service tax paid. Therefore, the following hypothesis is formulated:
H1: There is a significant difference between declared service tax and actual service tax
value.
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Proceedings of 3rd Global Accounting, Finance and Economics Conference
5 - 7 May, 2013, Rydges Melbourne, Australia, ISBN: 978-1-922069-23-8
Following the deterrence theory, penalties are included as one of the elements that may
discourage the intention to evade taxes (Bergman, 1998). Prior studies have hypothesised
that the tax penalty will negatively affect tax evasion. Therefore, Allingham and Sandmo
(1972) have measured the tax penalty as the amount of income evaded by the taxpayer.
Whilst, Yitzhaki (1974) generated a model which measured penalties based on the level of
taxes evaded. Pommerehne and Weck-Hannemann (1996) examined 25 companies in
Switzerland and found that the penalty tax rates and probability of detection are negatively
related to tax evasion. Whilst, Rickard, Russell and Howroyd (1982) formulated a model on
tax evasion under allowance for retroactive penalties, and suggested that retroactive
penalties will definitely increase deterrence of evasion. Hence, based on the past studies
and the deterrence theory discussed, the following hypothesis is formulated:
H2a: There is a significant negative relationship between penalties and service tax evasion.
Generally, the imposition of service tax is subjected to a specific threshold based on an
annual turnover ranging from no threshold to RM3,000,000. The threshold limits and the type
of taxable services are prescribed under the Second Schedule of the Service Tax
Regulations 1975. For the purpose of licensing, any person or company who run a business
of providing taxable services which has achieved the certain limits of threshold are required
to be licensed. According to Keen and Mintz (2004), the threshold level of turnover, at which
firms are obliged to register for the tax, has been one of the key features of VAT system. The
tax revenue will decrease if the threshold is set too high as the tax burdens of firms’
decreases. Kim (2005) extended Keen and Mintz’s (2004) study by taking into account the
influence of the threshold on the firms’ tax evasion behaviour.
In Malaysia, the government has abolished the service tax threshold for professional,
consultancy and management services effective from 1 January 2008. According to the
Minister of Finance, this move would allow providers of professional, consultancy and
management services to collect service tax as well as to promote healthy competition among
the same service providers. Thus, by abolishing the threshold, the risk of being penalised for
failure to obtain the licence and collection of service tax can be reduced, hence reducing the
compliance cost. Sandford (1995) defined compliance costs as “the costs incurred by the
taxpayers in meeting the requirements imposed on them by the law and the revenue
authorities, over and above the actual payment of tax and over and above any distortion
costs inherent in the nature of the tax” (cited in Sapiei and Abdullah, 2008, pg. 219).
Therefore, based on the compliance cost theory, the following hypothesis is developed:
H2b. There is a significant positive relationship between the level of threshold and service
tax evasion.
The nature of the business is one of the elements for imposing service tax (the Second
Schedule of the Service Tax Regulations 1975). The current study examined the relationship
between the tax evasion and the nature of business of service providers. Mohd. Nor et al.
(2010) reported that the type of industry could influence the misstatements of financial
reporting and is considered as an important indicator for tax evasion. Their results showed
that there was a positive relationship between the construction industry and misstatement of
financial reporting made by companies from this industry. Thus, the current study
hypothesized that:
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Proceedings of 3rd Global Accounting, Finance and Economics Conference
5 - 7 May, 2013, Rydges Melbourne, Australia, ISBN: 978-1-922069-23-8
H2c. There is a significant negative relationship between the type of service provider and
service tax evasion.
There is considerable literature based on U.S. data suggesting that large firms are subject to
greater scrutiny and for that reason they bear higher political costs than smaller firms
(Zimmerman, 1983). Zimmerman (1983) suggested that large firms endure higher political
costs compared to the smaller firms and as a result, large firms are less likely to evade their
tax responsibilities. Previous accounting studies on political cost hypotheses found that large
firms are more likely to apply accounting procedures which are able to reduce reported
income compared to smaller firms (Watts and Zimmerman, 1978). In other words, the larger
the firm, the more likely it is to choose income-reducing accounting procedures. Hanlon,
Mills and Slemrod (2005) explored the relationship between corporate non-compliance and
corporate characteristics. They found that the larger firms with more complex operations
have more tendencies for tax non-compliance. This is further supported by Mohd. Nor et al.
(2010), who investigated 396 Malaysian unlisted companies and found that the size of
companies has a significant negative effect on tax evasion activities. Hence, the next
hypothesis is developed as follows:
H2d. There is a significant negative relationship between the size of a company and service
tax evasion.
Prior studies have investigated the relationship between the size of audit firms and audit
quality. DeAngelo (1981) stated that the size of audit firm is a proxy for audit quality since
larger audit firms have more clients than smaller firms. According to Salehi and Mansoury
(2009), larger audit firms have a reputation to safeguard and therefore will ensure an
independent quality for audit service. The authors concluded that larger client portfolios and
larger audit firms are more able to resist management pressure, as compared to smaller firms
which provide more personalized services due to limited client portfolios. In contrast, Jeong
and Rho (2004) stated that there is no difference in audit-quality between Big 4 auditor and
non-Big 4 auditor. In another study, Boone, Khurana and Raman (2010) found that investors
perceive the Big 4 auditors as providing more credible accounting information for their clients
than the Second-tier auditors. Mohd. Nor et al. (2010) used sample from completed tax audit
cases and provided evidence that the size of audit firms is negatively related to fraudulent
financial reporting. Therefore, the next hypothesis is formulated as follows:
H2e. There is a significant negative relationship between the size of an audit firm and
service tax evasion.
The tax evasion model pioneered by Allingham and Sandmo (1972) predicted a positive
relationship between tax rates and tax evasion, however, subject to risk aversion and the
punishment for evading. Sandmo (2005) stated that the high margins of the tax rates may
encourage tax evasion because of large gains to be made from withholding income from the
tax collector. Similarly, Mc Gee and Ho (2006), Gupta (2008) and Hammar, Jagers and
Nordblom (2009) reported that the tax rate affect positively on the tax evasion. Hence, the
hypothesis is formulated as follows:
H2f. There is a significant positive relationship between the service tax rate and service tax
evasion.
Finally, Sandmo’s A-S model predicted that the probability of detection may increase with the
amount of tax evaded. However, it is dependent on how the taxpayers view such a
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Proceedings of 3rd Global Accounting, Finance and Economics Conference
5 - 7 May, 2013, Rydges Melbourne, Australia, ISBN: 978-1-922069-23-8
probability of detection (Sandmo, 2005). The taxpayers’ probability of detection would likely
increase due to the amount of tax evaded but when the probability of detection decreased,
they may decide to evade more tax. Through the process of tax audit and investigation, non
compliant acts by the taxpayers may be detected. Thus, the final hypothesis is formulated as
follows:
H2g. There is a positive relationship between the risk of probability for detection and service
tax evasion.
3. The Methodology and Model
The sample data was collected from the tax authorities’ office. The final samples comprised
of 275 non compliance service providers which have been subjected to tax audit for the
years from 2009 until 2011. The dependent variable is the service tax evasion which is
measured as the difference between declared tax (DEC_TAX) and actual tax (ACT_TAX).
The measurement of the independent variables, that is the tax rates, tax penalty, the level of
threshold, the type of service provider, the size of a company, the type of the auditor
engaged by service providers and the risk of probability for detection are explained in Table
1.
The service tax evasion model is stated as follows:
TAX EVASIONt = β0 + β1PENALTYt + β2THRESHOLDt + β3PRO_SERVICEt + β4CO_SIZEt +
β5AUDITORt + β6TAX RATESt + β7PROBABILITY OF DETECTIONt +
Table 1 Variable Definitions
TAX EVASIONt
=
Dependent variable; measured as the amount of service tax evasion
i.e. evaded tax divided by actual tax.
β0
=
Intercept or constant
β1PENALTYt
=
Β2THRESHOLDt
=
β3PRO_SERVICEt
=
Penalty; measured as the rate of amount of penalty imposed from a
minimum of 10 percent to a maximum of 50 percent.
Level of threshold; service providers with threshold = 1; no threshold
=0
Type of service provider; Professional = 1; Others = 0
Β4CO_SIZEt
=
Size of service provider’s business; measured as natural log of sales.
β5AUDITORt
=
Type of auditor; Big 4 Auditor = 1; Non Big 4 Auditor = 0
β6TAX RATESt
=
β7PROBABILITY
OF DETECTIONt
€t
=
Service tax rate measured by tariff rates; 5 percent (before 1 January
2011) = 1; 6 percent (after 1 January 2011) = 0
Probability of detection; risk companies with nil return and reminder
for not submitting the service tax form = 1; others = 0
Error term
=
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Proceedings of 3rd Global Accounting, Finance and Economics Conference
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4. Findings and Analyses
Table 2 tabulates the descriptive statistics which report the mean, standard deviation
minimum and maximum values of the dependent and independent variables. The mean for
the TAX EVASION is 0.33 which indicates that tax evasion comprised of 33 percent of the
actual service tax during the period 2009 to 2011 for 275 sample companies which have
been subjected to tax audit. The first independent variable is penalty (PENALTY). The mean
for the penalty imposed on service provider is 48 percent. Further, the descriptive statistic
provides that the mean for the level of threshold (THRESHOLD) is 0.33 which indicates that
33 percent of the sample companies are service providers which are subjected to threshold
limit. These variables are dummy variables which measured the THRESHOLD and are
coded as “1” for no threshold (92 cases) and “0” with threshold (183 cases).
The third independent variable is the size of a company (CO_SIZE) which is measured as a
log of sales. The mean, minimum and maximum values of size are 6.40, 4.10 and 8.98
respectively. The fourth independent variable is the type of service provider
(PRO_SERVICE) which has a mean value of 0.28. The sample comprised of 78 professional
services and 197 non-professional services. The fifth independent variable is the type of
auditor (AUDITOR) which is defined as “1” for the Big 4 Auditor and “0” for non Big 4 Auditor.
The descriptive statistic provides that the mean for AUDITOR is 0.11, meaning 11 percent of
the sample companies engaged Big 4 Auditor. The mean for the sixth independent variable,
service tax rate (TAX_RATES) is 0.05; which indicate that most of the sample companies are
subject to 5 percent service tax. Finally, the seventh independent variable is the risk of
probability for detection (PRO_DETECTION) which is defined as “1” for service providers
who received reminders or nil return, and “0” for others. The mean for PRO_DETECTION is
0.65 which implies that 65 percent of the sample companies are service providers who failed
to submit the service tax return forms.
Table 2: Descriptive Statistics
VARIABLES
TAX EVASION
PENALTY
THRESHOLD
CO_SIZE
PRO_SERVICE
AUDITOR
TAX_RATES
PRO_DETECTION
Mean
0.33
0.48
0.33
6.40
0.28
0.11
0.05
0.65
Standard Deviation Minimu
m
0.37
0.00
0.05
0.12
0.47
0.00
0.80
4.10
0.45
0.00
0.32
0.00
0.01
0.05
0.48
0.00
Maximum
1.18
0.50
1.00
8.98
1.00
1.00
0.06
1.00
Table 3 below depicts the mean comparison between declared and actual service tax value.
The Paired-sample t-test result shows that there is a significance difference between
declared and actual service tax value at 1% significance level, t-statistic -9.492, P-value =
0.000. The statistical result is supported by the Wilcoxon-Signed Rank test as reported in
Table 3. Hence, the statistical results have supported the first hypothesis (H1) that there is a
significant difference between declared service tax and actual service tax value. Thus the
finding confirms the existence of a tax gap in the service industry.
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Proceedings of 3rd Global Accounting, Finance and Economics Conference
5 - 7 May, 2013, Rydges Melbourne, Australia, ISBN: 978-1-922069-23-8
Table 3: Mean Comparison between Declared and Actual Service Tax Value
Paired-sample Test
t-statistic
P-value
-9.492
0.000***
Service Tax
Wilcoxon-Signed Rank
Z-statistic
P-value
-14.374
0.000***
Note: ***Significant at level 0.01, **Significant at level 0.05, and *Significant at level 0.10
The multiple regression analysis is performed to test seven hypotheses and to achieve the
second objective of the study. Table 4 provides the statistical results and the results show
that the tax evasion model is highly significant at 1% level, F-statistic 53.181, p-value 0.000.
The adjusted R-squared value is 0.571 which indicates that 57.1 percent of the tax evasion
model explains the changes of the tax evasion in the service industry. The Durbin-Watson
value is 1.961 which indicates that there is no or ignorable auto-correlation. The statistical
results explain the independence of data between variables to the tax evasion in the service
industry. Thus, it indicates that all explanatory variables are not correlated to one another
which might otherwise influence the outcome of the analysis of the study. The details of the
multiple regression results are tabulated in Table 4.
Table 4: Multiple Regression Results
Model
Constant
PENALTY
THRESHOLD
CO_SIZE
PRO_SERVICE
AUDITOR
TAX_RATES
PRO_DETECTION
R2
Adjusted R2
F-statistics
p-value
Durbin-Watson
N
Coefficient t-statistic
2.038
9.427
4.881E-006
10.438
0.159
2.174
-0.290
-14.488
-0.114
-1.504
-0.105
-2.931
-0.290
-0.091
0.197
5.854
0.582
0.571
53.181
0.000***
1.961
275 companies
P-value
0.000***
0.000***
0.031**
0.000***
0.134
0.004***
0.928
0.000***
The statistical regression results show that there is positive and significant relationship
between PENALTY and tax evasion in the service industry, coefficient 4.881E-006, t-statistic
10.438, P-value 0.000. However, the statistical result do not support hypothesis H2a that
there is a significant negative relationship between penalties and service tax evasion. Hence,
the findings imply that PENALTY is not a deterrent effect of service tax evasion.
The statistical results reveal that there is a significant and positive relationship between
THRESHOLD and service tax evasion at 5% level, coefficient 0.159, t-statistic = 2.174 and
P-value = 0.031. The finding indicates service providers that are subjected to the threshold
limit would evade tax more than service providers which are not subjected to the threshold
limit. Hence, this result supports hypothesis H2b that there is a significant positive
relationship between the level of threshold and service tax evasion. Thus, the result is
consistent with the previous similar studies by Keen and Mintz (2004) and Kim (2005).
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Proceedings of 3rd Global Accounting, Finance and Economics Conference
5 - 7 May, 2013, Rydges Melbourne, Australia, ISBN: 978-1-922069-23-8
Moreover, the statistical results indicate an insignificant relationship between the type of
service provider and service tax evasion, coefficient -0.114, t -statistic -1.504, P-value 0.134.
Hence the result does not support hypothesis H2c that there is a significant negative
relationship between the type of the service provider and service tax evasion.
Table 4 also discloses a significant and negative relationship between CO_SIZE and service
tax evasion at 1% level, coefficient -0.290, t-statistic -14.488, P-value 0.000). The result
indicates that the smaller sized service providers tend to evade service tax more than larger
sized service providers. Hence, the finding supports hypothesis H2d that there is a significant
negative relationship between the size of a company and service tax evasion.
The size of the audit firm is also found to have a significant negative relationship with service
tax evasion, coefficient -0.105, t-statistic -2.931 and P-value 0.004 at 1% level. Hence, the
statistical result supports the hypothesis H2e that there is a significant negative relationship
between the size of audit firm and service tax evasion. Thus the finding implies that
companies which engaged non-Big 4 auditor tend to evade taxes more than companies
which engaged Big 4 auditor. This result is consistent with Mohd. Nor et al. (2010).
Furthermore, the statistical result demonstrates that the TAX RATES does not have
significant relationship with service tax evasion, coefficient -0.290; t-statistic -0.091; P-value
0.928. Therefore, the finding do not support hypothesis H2f that there is a significant positive
relationship between the service tax rate and service tax evasion.
Finally, Table 4 shows a significant positive relationship between PRO_DETECTION and
service tax evasion, coefficient 0.197, t-statistic 5.854 and P-value 0.000 at 1% level. The
result indicates that service providers with high risk tend to evade more tax than others.
Hence, the finding supports hypothesis H2g that there is a significant positive relationship
between the risk of probability of detection and service tax evasion.
5. Summary and Conclusions
This study has examined the tax gap in the service industry and determined the factors for
the service tax evasion. The sample data comprised of 275 taxable service providers which
have been subjected to tax audit during the years 2009 to 2011. The univariate analysis has
provided significant results to confirm the existence of a tax gap in the service industry, thus
supporting the first objective of this study that that there is a significant difference between
declared service tax and actual service tax value. The multiple regression analyses have
also provided evidence of the four factors which can be associated with service tax evasion:
1) Service providers which are subjected to the threshold limit; 2) The smaller size of service
providers; 3) Service providers that engaged smaller sized audit firms; and 4) Service
providers which do not submit the required tax return forms.
The current study has its limitation due to the small numbers of the sample data.
Nevertheless, the findings of the study should provide strategic information to the tax
authorities by revealing significant indicators which may assist them in planning tax audits for
the service industry. It is recommended that future studies should look into the effect of other
potential determinants of indirect taxes, such as import duties, excise duties and sales tax.
Future research on sales tax and import duties may provide a larger scope of tax evasion
from both local and international trades.
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Proceedings of 3rd Global Accounting, Finance and Economics Conference
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Acknowledgments
The authors would like to convey their appreciation to the Ministry of Higher Education for
providing a research grant to undertake this project. Appreciation also goes to the
Accounting Research Institute (ARI), Faculty of Accountancy and Research Management
Institute (RMI) for their support in the research.
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