Does Disclosure of Tax Avoidance Schemes Reduce Corporate Tax Avoidance

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Does Disclosure of Tax Avoidance Schemes Reduce Corporate Tax

Avoidance?

Academic year 2014/2015

N.G.P.M. Koek

ANR: 963023

Supervisor: dr. K.F. Law

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Does Disclosure of Tax Avoidance Schemes Reduce Corporate Tax

Avoidance?

Academic year 2014/2015

N.G.P.M. Koek

ANR: 963023

Msc. Accounting

Supervisor: dr. K.F. Law

Second reader: dr. B.C.G. Dierynck

Date of completion: 8th of December, 2014

Master Thesis

Department of Accountancy

Tilburg School of Economics and Management

Tilburg University

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Does Disclosure of Tax Avoidance Schemes Reduce Corporate

Tax Avoidance?

Abstract: In this thesis, I examine if private disclosure of tax avoidance schemes reduces

corporate tax avoidance. To examine this question, I determine the change in tax avoidance of

firms around the introduction of Disclosure of Tax Avoidance Schemes legislation (DOTAS).

Under DOTAS, firms in the United Kingdom (UK) are required to disclose new tax avoidance

schemes to the UK tax authorities (HMRC) before implementing a new tax avoidance scheme.

I expect firms with more tax planning opportunities to avoid less taxes after the implementation

of DOTAS, compared to firms with less tax planning opportunities. However, I find no

systematic evidence to support my expectations. The results remain consistent after I control

for the differences in industry environment.

Keywords: corporate tax avoidance, private disclosure, DOTAS, tax avoidance schemes,

HMRC, tax aggressiveness.

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1. Introduction

1.1 Importance of tax and tax avoidance discussion

Government budgets are mainly covered by tax revenues. Tax revenues account for around

14.4% of the worldwide GDP in 2012 according to the World Bank (World Bank 2014). Firms

pay a large part of tax revenues collected by the government. In total, taxes on goods and sales

(VAT and sales tax) and corporate income accounted for around 14% of the GDP in OECD

countries in 2012. Tax on corporate profits accumulated to approximately 3% of the GDP in

2012 on an average basis, where in 2007 corporate income tax accounted for roughly 3.7% of

GDP (OECD 2012). Firms are blamed for this reduction in tax revenues, as they use inventive

structures to avoid paying taxes in high-taxed jurisdictions.

As the financial crisis in 2008 staggered economies, governments became even more aware

of firms trying to dodge tax by artificial arrangements. Several governments and intra-

governmental organizations as the OECD and G20 take initiative to tackle these artificial tax

arrangements. The most recent initiative is the Base Erosion and Profit Shifting Plan, an

attempt to solve deficiencies in (inter)national tax systems and to tackle tax avoidance. One of

the main targets of the OECD is to improve transparency of multinationals’ international tax

position, by reporting economic tax related data to tax authorities and improve exchange of tax

information between countries (OECD 2013).

As the media and NGO’s become more aware of firms’ tax planning strategies, the public

debate on tax avoidance in the UK and US grows driven by publicly ‘naming-and-shaming’ of

tax avoiding firms as Google, Starbucks and Amazon. In the UK and US, parliaments organize

public hearings in 2012 to make multinational firms accountable for their tax avoidance

behavior. A nowadays frequently cited quote to demonstrate the key element of the tax

avoidance debate, is Margaret Hodge’s (chairman of the Public Accounts Committee)

accusation during a public hearing in the UK over tax avoidance of Google, Starbucks and

Amazon. She expresses her accusation of tax avoidance behavior to the CFO of Starbucks in

the following sense: “'It seems to us that you are exporting your profits to minimise your tax.'

(…) We are not accusing you of being illegal, we are accusing you of being immoral.”

(Telegraph 2012)

In 2013 the British tax authorities Her Majesty Revenues and Customs (HMRC) reported

a tax gap of around £34 billion (HMRC 2013). The tax gap constitutes the difference between

the amount of tax that should be collected by HMRC in theory, against what is actually

collected. The UK has several anti-tax avoidance measures to combat tax avoidance. One of

those anti-avoidance measures was already introduced in 2004. Since 2004, Disclosure of Tax

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Avoidance Scheme legislation (DOTAS) requires the taxpayer or his tax advisor to disclose

information on the use of tax avoidance schemes to HMRC, the UK tax authorities. DOTAS

can give HMRC a better understanding of British taxpayers’ tax avoidance activities so it can

eventually make adjustments to tax positions and adapt legislation in order to close tax law

loopholes. On the other hand, DOTAS also has some pitfalls, as firms can avoid disclosure by

paying the legal penalty for non-disclosure, and can make use of the attorneys’ legal privilege

to keep tax planning strategies confidential. For a more extensive description of DOTAS, I

refer to paragraph 2.2. The UK claims that it already closed £12 billion in tax avoidance

opportunities by the information of DOTAS. (HMRC 2012) Still, it is not clear if the private

disclosures under DOTAS result in a change of tax planning strategies of firms.

In this study, I will examine if the private disclosure of tax avoidance schemes influences

the corporate tax avoidance behavior of firms. I will test the tax avoidance behavior by

measuring tax avoidance around the implementation of DOTAS. As DOTAS disclosures to the

tax authorities are private, I assume that firms with more tax planning opportunities are more

affected by DOTAS. The expectation is that firms with more tax planning opportunities avoid

less taxes than firms with less tax planning opportunities after the implementation of DOTAS.

Recent literature shows that disclosures can influence corporate tax avoidance behavior.

Hoopes et al. (2012) show that stricter monitoring on tax avoidance by increasing tax audits

deters corporate tax avoidance. Information from DOTAS can give HMRC a better view on

the tax position, and subsequently perform better tax audits. Hope et al. (2013) show that public

disclosure of geographical earnings influences tax avoidance behavior of firms in the US.

Public awareness of geographical earnings in low-tax jurisdictions deters firms from tax

savings from aggressive tax planning. Lisowsky et al. (2013) research if private disclosures of

tax shelter activities to the US tax authorities (IRS) reflect public disclosures of uncertain tax

positions under FIN. 48 and find a strong positive relation. These findings all show effects of

disclosure on tax avoidance behavior.

This study differs from other research, as it focuses on the tax behavior after

implementation of a new private disclosure obligation. Other studies as Hope et al. (2013) and

Dyreng et al. (2014) focus mainly on the public disclosure, as Lisowsky et al. (2013) exploit

tax avoidance behavior and the relation between public and private disclosures. As DOTAS is

only applicable to new tax avoidance schemes, this study tries to measure the effect of private

disclosure on the tax avoidance behavior of firms to involve in new tax avoidance strategies.

Also, DOTAS is a direct way to tackle tax avoidance by disclosures compared to public

disclosure initiatives as FIN. 48. Public disclosure initiatives indirectly affect firms’ tax

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avoidance behavior by the public opinion arising from those disclosures. The private

disclosures of DOTAS give HMRC a better overview of the tax position and reduces the

information asymmetry between the taxpayer and tax authority. Restrictions of DOTAS are

that it does not cover implemented tax avoidance schemes before the introduction of DOTAS

and DOTAS does not require to newly disclose the re-use of tax avoidance schemes. Besides,

I cannot link disclosures of DOTAS directly to firm specific data, as disclosures under DOTAS

are confidential.

To determine the effect of DOTAS on tax avoidance behavior, I use a sample of firm

fundamentals of listed UK companies from the period 2001-2007. As DOTAS disclosures are

private, I use literature to identify tax avoiding firms which are more likely to perform tax

aggressive behavior and are more likely to be affected by DOTAS. After classification I

perform several cross-sectional regression analyses, to measure the effect of DOTAS on the

effective tax rate of all firms.

As a result, I find no systematic evidence to confirm my expectations. I only find some

evidence that capital intense firms avoid less taxes than other firms after implementation of

DOTAS. Contrary to my expectations, firms with higher return on assets and large firms avoid

more taxes after the implementation of DOTAS, than firms with lower return on assets and

smaller firms. These results also hold after I control for differences in the industry environment.

This study gives more insight on the relation between private disclosures and the tax

avoidance behavior of firms. In the tax avoidance debate, it can give government policy makers

better insight in the use of private disclosures to create transparency. This study also shows the

effectiveness in reducing the information asymmetry by private disclosures. Besides, as the

different determinants to indicate tax aggressiveness are mainly based on US research, this

study can contribute to the characterization of firms performing tax avoidance in the UK

specifically.

This study proceeds as follows. Section 2 will proceed with a definition of tax avoidance

and a discussion of the relevant literature about firm characteristics, disclosure and tax

avoidance. Section 3 gives a description of the sample and research method. Section 4

represents the results and sensitivity analysis, where section 5 concludes.

2. Literature review and hypothesis development

2.1 Tax avoidance

Tax avoidance is a topic of growing interest in accounting research (Hanlon and Heitzman,

2010). Research and policy makers use different definitions to define tax avoidance. Dyreng et

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al. (2010) define tax avoidance as anything that reduces the firm’s taxes relative to its pretax

income. The UK tax authority (HMRC) defines tax avoidance as ‘‘(…) bending the rules of

the tax system to gain a tax advantage that Parliament never intended.’’ (HMRC, September

2012). The difference between tax avoidance definitions of HMRC and Dyreng et al. (2010) is

that HMRC does not define taking a tax advantage within the intention of the law as tax

avoidance. Therefore, the tax avoidance definition of Dyreng et al. (2010) is broader as it also

considers tax avoidance within the intention of the law. Both HMRC and Dyreng et al. (2010)

define tax avoidance as a way in which a taxpayer reduces her tax position. There is no

consensus over what unintended tax advantages are, so HMRC’s tax avoidance definition is

hard to measure. It is important to note that tax avoidance is not the same as tax evasion. Tax

evasion encompasses the reduction of the tax position by breaching legal boundaries where tax

avoidance takes place within the boundaries of the (intended or unintended) legislation.

Recent tax avoidance literature addresses the problem of measuring tax avoidance from

financial statements. Hanlon and Heitzman (2010) sum up different tax avoidance measures,

such as the Total Book-to-Tax Difference (BTD), Cash ETR and Current ETR (Desai and

Dharmaphala, 2006; Dyreng et al. 2008; Dyreng and Lindsey 2009). It is generally impossible

for researchers to determine the taxable income of the taxpayer, as most tax returns are

confidential. Only the financial statements provide information on the tax position of a firm,

though due to book-to-tax differences these accounts do not give a reliable view on the firms’

tax position. Book-to-tax differences arise from different principles in accounting regulations

and tax legislation on which net income and taxable income are based. The different treatment

of costs and revenues generates temporary and permanent timing differences in recognition of

costs and revenue which are known as book-to-tax differences.

A number of determinants drive tax avoidance. In general, I distinguish two types of

research on the ‘drivers’ of tax avoidance. First, the managerial aspects of the firm, corporate

governance, and compensation and incentives for managers and directors. Second, I will

elaborate on the firms’ tax avoidance aggressiveness determined by different firm

characteristics. In the next part I will discuss both types of tax avoidance research.

Desai and Dharmapala (2006) find that high-powered incentives can also reduce the level

of tax sheltering. Corporate governance works as a mediator in their research, where high-

powered incentives for managers of firms with poor corporate governance result in a negative

relation with tax avoidance and for firms with a good corporate governance the relationship is

positive. This might not influence this research, as Wahab and Holland (2012) find that for UK

firms there is not such a relation between corporate governance and tax planning as stated in

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Desai and Dharmapala (2006). There are two potential causes for this divergence. First, the

UK corporate governance system could have a different effect on tax planning compared to the

US corporate governance system. Another reason could be that there is insufficient tax related

information for UK firms. Wahab and Holland (2012) argue that researchers should pay

attention to differences between countries in policy and regulations.

Dyreng et al. (2010) show that executives have a significant role in the level of tax

avoidance behavior of a firm. Frank et al. (2012) provide evidence that risk-taking corporate

environments are positively associated with tax avoidance. Armstrong et al. (2012) prove that

incentives compensation for tax directors and the effective tax rate have a negative relationship.

From this point of view, Armstrong et al. (2012) argue that tax directors have incentives to

reduce the tax expense reported in financial statements. These findings show that corporate

culture and executive compensation are drivers of the tax avoidance behavior of a firm.

Besides managerial characteristics of a firm, also financial characteristics of a firm relate

to tax avoidance. Stickney and McGee (1982) find that firms with low effective tax rates are

highly leveraged, heavily capital intensive, and act in the resources industry. Rego (2003) finds

that firms with more foreign operations have lower worldwide effective tax rates than other

firms. Rego and Wilson (2012) investigate the relation between equity risks and the tax

aggressiveness of firms. They find that firms with higher equity risks, involve more in

aggressive tax strategies. Other characteristics of firms which perform tax avoidance are,

among others, return on assets, competition and high intangible assets (Gupta and Newberry,

1997; Rego 2003, Frank et al. 2009). Size is not commonly considered as a characteristic which

is positively or negatively associated with tax avoidance. Zimmerman (1983) and Frank et al.

(2009) find a positive relation between size and tax avoidance, which can be explained by the

reasoning that bigger firms can shift profits more easily to low-tax jurisdictions and can obtain

better advice on their tax planning activities. On the other hand, Mills et al. (1998) find a

negative relationship between size and tax avoidance. DOTAS focuses on public disclosure of

tax avoidance schemes. I will discuss the research in disclosure and tax avoidance in paragraph

2.3, but first I will describe the framework of DOTAS in order to get a better understanding of

the regulation.

2.2 Disclosure of Tax Avoidance Schemes (DOTAS)

Before DOTAS

Before the introduction of DOTAS, companies and individuals had no obligation to

disclose any of their tax avoidance schemes to the HMRC in particular. The moment after fiscal

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years end was the only moment of notice to HMRC of the taxpayers’ tax position. Numerous

times before 2004, HMRC came in the position of capturing a tax avoidance scheme only after

the fiscal year ended. Therefore, there was no time to adapt legislation retroactively, in order

to close tax law deficiencies. On the other hand, as HMRC does not have resources to assess

all tax returns, possibly a lot of new tax avoidance schemes were introduced without HMRC

even knowing it. The first origin of DOTAS was during the analysis of the 2004 UK

governmental budget. During March 2004, Chancellor Brown announced to lawyers,

accountants, and financial advisers that they had to report new tax avoidance schemes to the

Inland Revenue Department (previous wording for HMRC) before implementing new tax

avoidance schemes (The Telegraph, 2004). This new measure resulted in better information to

close so called ‘tax loopholes’ where firms use legislative deficiencies in order to create tax

savings. Eventually, new tax legislation was announced in the Finance Act 2004, of which

DOTAS was part.

Objectives of DOTAS

HMRC explains in one of their tax avoidance reports of 2012 that DOTAS serves three

objectives in order to tackle tax avoidance (HMRC, 2012). The first objective is to provide

information to HMRC to estimate the risk a tax avoidance scheme poses, and subsequently

inform the legislative governmental department to close tax law loopholes. The second

objective is to improve the compliance work of HMRC. DOTAS gives information about

which taxpayers apply certain tax avoidance schemes, and it follows that HMRC can prepare

a better tax audit with that information. HMRC aims to reduce the number of ‘marketed’ tax

avoidance schemes by promoters, by taking away the economic benefit of performing tax

avoidance. The last objective is closely related to the first and second objective. By closing

loopholes, HMRC aims to reduce the economic benefit of promoters and users to use tax

avoidance schemes. The risk in disclosure and subsequent tax audits and adjustments for

taxpayers also impose an economic cost, and have a deterrent effect to perform tax avoidance

activities.

Implementation of DOTAS

By means of section 306 - 319 of the Finance Act 2004, the UK government introduced

DOTAS. Enacted on the 1st of August 2004, the Finance Act 2004 gives an overview of the

duties and requirements when promoters and tax advisors have to disclose tax avoidance

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schemes to the HMRC. In the next section I will elaborate on the different requirements for the

application of DOTAS.

Notifiable arrangements and notifiable proposal

Tax planning activities usually involve implementation of new arrangements (e.g.

contracts) or revision of arrangements. Arrangements and proposals for arrangements have to

meet three criteria in order to be subject to DOTAS. First, the arrangement should meet the

description of notifiable arrangements and proposals set by the Commissioners of Inland

Revenue (Statutory Instruments 2004, no. 1863). Second, the arrangement or proposal must

enable a person to obtain a tax advantage. Third, the tax advantage has to be a main benefit of

the arrangement. I will come back on this last requirement in the subparagraph ‘abuse of

DOTAS legislation’.

The 2004 enacted legislation primarily focuses on arrangements and proposals related to

financial products and employment. Examples of notifiable financial product arrangements are

tax avoidance schemes involving loans, options, shares, and derivatives. For employment, tax

avoidance schemes considering payments to agents and intermediaries are examples of

notifiable arrangements. The HMRC does not give any explanation why these arrangements in

particular are subject to the first DOTAS regulations. A logical explanation could be that tax

avoidance schemes related to financial products and employee arrangements are the most

widely used tax avoidance schemes before the introduction of DOTAS. The Statutory

Instrument explicitly states that the described arrangements include tax avoidance schemes

related to income tax, corporation tax and capital gains tax (Statutory Instruments 2004, no.

1863).

Promoters of tax avoidance schemes

Section 308 of the Finance Act 2004 requires promoters of tax avoidance schemes to notify

the HMRC. (Finance Act 2004, s. 308) The promoter should inform the HMRC at the moment

he becomes aware of any transaction forming part of notifiable arrangements, or if he makes a

proposal available for a client. Also when no promoter is involved in a notifiable arrangement,

the persons involved in the notifiable arrangement should inform HMRC. In return of

informing HMRC, the promoter or involved party receives a reference number from HMRC

within 30 days of notification. This reference number should be included in the tax return of

the taxpayer. In addition, promoters have the legal duty to provide the client the reference

number received from HMRC.

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Legal professional privilege

Section 314 of the Finance Act 2004 states that DOTAS does not require to disclose any

privileged information. This means that for instance attorneys at law (e.g. barristers) which

have discussed private information with a client, are not required to inform HMRC about their

confidential communication. This section gives solicitors the opportunity to make use of the

confidentiality clause in order not to disclose tax avoidance schemes before implementation.

After implementation, the client has to disclose the tax avoidance scheme itself, or waive the

legal privilege in order to let the solicitor disclose the tax avoidance scheme. Solicitors have to

make a disclosure 5 days after the waiver is removed, the client itself has to disclose the

notifiable arrangement within 5 or 30 days after implementation of the notifiable arrangement.

Abuse of DOTAS legislation

In the same manner firms try to find tax loopholes to avoid taxes, firms try to find ways to

avoid DOTAS. The Public Accounts Committee states in one of its reports that firms use

opinions of barristers (attorneys at law) to avoid disclosure of DOTAS. When a legal opinion

constitutes a ‘reasonable excuse’ falling under the legal professional privilege, the taxpayer nor

its promoter has to disclose under DOTAS regulations (Public Accounts Committee 2013).

Another way of how firms avoid DOTAS legislation, is by arguing that the main purpose

of an arrangement is not to obtain a tax advantage. Firms have to argue that other commercial

elements are the main reason to be involved in a new arrangement. In this way, the requirement

of ‘obtaining a tax advantage which is the main objective’ is not fulfilled, and no disclosure is

required.

A last option for firms to avoid the application of DOTAS is to just accept the penalties

HMRC imposes for non-compliance. Non-compliance to the DOTAS regulations, results in a

penalty of £5,000. Every day that the non-compliance continues after the imposed penalty,

results in an additional £600 each day thereafter. (Finance Act 2004, s. 315) Especially very

favorable tax avoidance schemes can be implemented without HMRC knowing it, as the

benefits are higher than the penalties imposed afterwards.

After DOTAS

After the implementation of DOTAS several amendments were made to the legislation. For

this research, I will only discuss one important amendment in the period of 2004 – 2007. In

August 2006, HMRC introduced the hallmark system. In the hallmark system HMRC describes

several hallmarks for tax avoidance schemes and subsequently how to report these schemes.

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Some generally used legal schemes by taxpayers were excluded, to relief the administrative

burden of taxpayers. Furthermore, DOTAS was extended to all schemes related to corporate

income tax, also including standardised tax products, tax loss arrangements and financial lease

arrangements (Statutory Instruments, 2006, no. 1543).

Practicalities for HMRC: benefits and weaknesses of DOTAS

Benefits

DOTAS gives HMRC a better overview of tax avoidance schemes which are in place at a

taxpayer. DOTAS enables HMRC to assess the intended tax avoidance schemes of a taxpayer

in an earlier stage than before 2004. In this perspective, HMRC can perform ad-hoc action to

close any loopholes in the secondary legislation, or inform the legislative body of the

government about any loopholes in primary legislation. HMRC argues that it closed a total of

£12 billion of tax avoidance opportunities in the period from 2004 till 2012 (HMRC, 2012).

HMRC is satisfied with the use of DOTAS, but does not reveal how HMRC calculated the 12

billion of closed tax avoidance opportunities.

The user specific information which DOTAS provides, gives HMRC more options to

challenge tax avoidance schemes at the taxpayers’ level. Without DOTAS, HMRC might miss

newly introduced tax avoidance schemes by taxpayers.

Another advantage of DOTAS is the deterrent effect it could have. Taxpayers with plans

to introduce tax avoidance schemes can adapt their plans in order not to be subject to DOTAS,

as DOTAS can bring additional tax assessment risks to the tax position.

Devereux (2012) describes another advantage of DOTAS. The information that HMRC

gathers with the disclosure, gives a better overview on which parts of tax law contain more risk

for taxpayers to be compliant. Subsequently, HMRC can divide their resources to those ‘high-

risk’ fields, resulting in more accurate tax audits.

Weaknesses

DOTAS only assesses newly implemented tax avoidance schemes. Tax avoidance schemes

already in place at firms before 2004, are not subject to DOTAS. Still there is a risk for firms

that HMRC and legislative bodies of the government adapt legislation on the basis of

information received by DOTAS.

DOTAS legislation contains a ‘legal professional privilege’ clause, which enables attorneys

at law to not disclose information to HMRC based on their professional privilege. Warburton

(2004) argues that taxpayers can still continue to implement tax avoidance schemes when a

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barrister is considered the promoter of the avoidance scheme. Though, the reaction of HMRC

was to make it compulsory to disclose after the waiver of legal professional privilege is

released, or in other cases move the responsibility to disclose to the client. Firms who did not

comply with DOTAS, risked a penalty of £5,000 and a subsequent £600 per day. Some firms

might have taken this risk in receiving a penalty of this amount, making a trade-off between

tax savings benefit and penalty costs when not reporting to HMRC.

When first introduced, DOTAS only applied to financial products and employment

arrangements related to corporate income tax. Whilst these tax avoidance schemes were

probably the most significant tax avoidance schemes used, DOTAS still did not cover all

arrangements related to corporate income tax. On the other hand, the most disclosures were

made in the period August 2004 till April 2005 which points at a better effect right when

DOTAS was implemented (Table 1).

2.3 Disclosure and tax avoidance

Disclosure and tax avoidance is in accounting research a relatively new research field.

Some of the objectives of DOTAS are also subject to this field of research. I will describe the

literature which relates to the objectives of DOTAS. Besides, I will give an overview of the

accounting literature focusing on public disclosure of tax positions to financial markets.

The first objective of DOTAS is to find and effectively close tax law loopholes on the basis

of disclosures made by taxpayers. As accounting literature mainly focuses on taxpayer

behavior and not governmental behavior in tax disclosure, there is little known about the effect

of disclosures on governmental legal action to close loopholes. Devereux (2012) states that on

the explicit basis of DOTAS three adjustments were made to tax law in order to close loopholes.

By the end of 2013, the Public Accounts Committee even states that since its implementation,

93 changes to tax law have been made as a result of information from DOTAS (Public

Accounts Committee 2013).

Another research topic in the relation between disclosure and tax avoidance, is the impact

of tax audit risk on the tax avoidance behavior of the taxpayer. Mills (1998) finds that book-

to-tax differences cannot be maximized by firms without risking increase of adjustments to the

tax position following from a tax audit by the Internal Revenue Service (tax authority in the

U.S.). In relation to this research, DOTAS gives a potential indication of book-to-tax

differences for UK firms, therefore making it easier for HMRC to detect high-tax risk

taxpayers. In addition to Mills (1998), Hoopes et al. (2012) prove that strict monitoring by the

IRS by a general increase of tax audits from 19 percent to 37 percent, results in an increase of

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7 percent of the cash effective tax rate. This implies that more accurate tax audits and an

increase in the number of tax audits restricts tax avoidance behavior.

Another objective is the deterrent effect of DOTAS to refrain taxpayers from tax avoidance

behavior. Similar to DOTAS, FIN 48 in the U.S. requires U.S. taxpayers to analyze and disclose

any uncertain tax positions. (FASB, No. 109) Blouin et al. (2010) show that firms settle more

tax disputes in the period between the enactment and adoption of Fin No. 48. One of the

findings of Blouin et al. (2010) is that policy makers should take into account that in the period

before enactment firms show pre-adoption behavior. A possible explanation is that firms do

not want to show uncertain tax positions to financial markets and the IRS. In contrast with this

research, DOTAS disclosures only apply to firms which implement new tax avoidance schemes

and the disclosure is not public. Similar to the findings of Blouin et al. (2010), firms in the UK

could also pre-adapt before enactment of DOTAS in 2004.

Lisowsky et al. (2013) examine if private disclosures to the IRS reflect tax avoidance,

measured in the form of FIN 48 disclosures. They find strong evidence that the disclosed tax

reserve under FIN 48 is positively associated with the private disclosed tax shelter information

to the IRS. From 2004 onwards, U.S. firms have to disclose geographical earnings in the annual

corporate tax filing by U.S. financial reporting regulation Schedule M-3. In 1997, a comparable

regulation FAS No. 131 was adopted, making it optional to disclose geographical earnings.

(FASB, no. 131) Hope et al. (2013) find that in the period 1998 – 2004 where no disclosures

were required, firms show significantly lower worldwide effective tax rates. This research

points out that financial reporting disclosures influence firms’ tax reporting behavior.

Dyreng et al. (2014) find that public pressure on geographical disclosure of subsidiaries

from NGO’s, in this case Action Aid, can influence corporate tax avoidance. This research

shows that not only disclosure to financial markets and governments have an effect on

corporate tax avoidance behavior, but also societal pressure on the enforcement of rules by the

public can be a key element in corporate tax behavior.

2.4 Hypothesis Development

DOTAS its main purpose is to tackle tax avoidance. Especially firms with more tax

aggressiveness introducing tax avoidance schemes, are subject to the requirements of DOTAS.

As DOTAS disclosures are private, tax avoiding firms cannot be identified on a one-on-one

basis. I will identify firms with more tax aggressiveness by a couple of determinants commonly

used in the previously discussed tax avoidance literature. From the previously discussed

literature and DOTAS regulation, I come to the following hypothesis:

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H1: Firms with high tax aggressiveness before DOTAS avoid less tax than firms with lower

tax aggressiveness after the passage of DOTAS.

As stated before, research states several determinants to define which firms are more tax

aggressive. Therefore, I set 6 additional hypotheses to find support for the main hypothesis:

H1a: Firms with high leverage positions avoid less tax than other firms after the passage

of DOTAS.

H1b: Large firms avoid less tax than other firms after the passage of DOTAS.

H1c: Firms with high ROA ratios avoid less tax than other firms after the passage of

DOTAS.

H1d: Capital intense firms avoid less tax than other firms after the passage of DOTAS.

H1e: Firms with many intangibles avoid less tax than other firms after the passage of

DOTAS.

H1f: Firms with high R&D expenses avoid less tax than other firms after the passage of

DOTAS.

In the next section I will constitute how I compose the sample to conduct my research.

Section 3 will continue with a description of the research method and clarification on which

control variables are part of the regression. In section 4 I will interpret the results from the data

analysis and subsequently in section 5 I will give an overview of my findings and the

limitations of the study.

3. Sample selection

3.1.1 Sample and data collection

To compute the sample, I use the Compustat Global dataset. The Compustat Global dataset

contains firm fundamentals of companies all over the world, annually and quarterly. From

Compustat Global I collect annual firm fundamentals of UK based companies for the fiscal

years 2001-2007.1 Since Compustat Global only contains information of listed companies, the

collected data consists of companies listed at the London Stock Exchange.

1 The dataset was downloaded the 29th of September 2014

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3.1.2 Sample selection procedure and control variable composition

The sample collected from Compustat Global, contains a total of 5827 firm-years. As the

database does not contain information on the tax position of all firms across all fiscal years

(2001-2007), I exclude these firms to come to a sample with non-missing current tax payable

(TXC) fiscal year data. Consistent with the findings of Blouin (2010), I exclude the data of the

fiscal year 2004 to avoid early adopter effects. This results in a sample of 3540 firm-years and

includes 590 firms in the sample. All companies in the sample have non-missing data on

Current Income Tax (TXC).

To construct the control variables, I also use firm fundamentals of Compustat Global. For

missing values, I use different approaches to compose a control variable. For the control

variable R&D (‘R&D’), the missing value is replaced by a zero. For intangibles (‘INTANG’),

I replace missing values by either a 0 when no intangibles are reported in other firm years, in

other cases the lagged value. For PPE (‘PPE’), size (‘SIZE’) and leverage (‘LEV’) I use lagged

data, as these variables are considered ongoing for several years. Paragraph 3.2.2. will give a

more extensive explanation on the construction of control variables.

3.2 Research method

3.2.1 Dependent variable: tax avoidance measure

Tax accounting research uses different measures to measure tax avoidance. Hanlon and

Heitzman (2010) sum up different tax avoidance measures which tax accounting research

commonly uses. The most widely used measure is CASH ETR, though GAAP ETR is also

commonly used for research over US-based firms. As my research will focus on long-run tax

avoidance in the UK over a time horizon of 3 years, I decide to use the CASH ETR long-run

tax avoidance measure from Dyreng et al. (2008). The Compustat Global database does not

contain enough information about cash tax paid (TXPD) for UK based firms, and therefore I

use current income tax (TXC) data from the balance sheet to determine the tax position of each

firm. Due to this data problem, I use the long-run tax avoidance measure CURRENT ETR

instead of the CASH ETR. The current income tax data is not as specific on the tax position as

the cash tax paid, but gives a reliable indication of the level of taxes paid by a firm. The long-

run CURRENT ETR avoidance measure consists of two parts. First, I determine the current

tax payable by a firm. Second, I divide the current tax payable by the pre-tax income minus

special items to avoid incidental changes in pretax income to influence the outcome of

CURRENT ETR.

17

CURRENT ETR =Current Income Tax (TXC)

(Pretax income−Special Items)………………………………………………(1)

Dyreng et al. (2008) acknowledge that the CASH ETR measure is an imperfect measure

for tax avoidance, as it includes settlements of tax disputes with the tax authority originating

from previous years. This is also the case for the adaption of cash tax paid to current income

tax. Dyreng et al. (2008) argue that payable taxes occurring from previous years would result

in an undesirable effect, as they would also be included in the measurement of subsequent

fiscal years. Dyreng et al. (2008) state that taking a long time horizon will minimize this effect

as the previous years are also subject in the tax measurement. Therefore, I choose a time

horizon of 3 years before and after the passage of DOTAS to minimize the effect of tax

settlements arising from previous years. All negative CURRENT ETR are set at 0 and all

CURRENT ETR measures above 1 are set at 1.

3.2.2 Research model design and construction of control variables

DOTAS is designed to detect and act upon tax avoidance schemes. Firms which have more

tax avoidance opportunities, are assumed to be more affected than firms with less tax avoidance

opportunities after passage of DOTAS. Literature describes various determinants to define the

tax aggressiveness of firms, who have more tax avoidance opportunities. For each determinant,

I consider the 10th and 20th percentile top-level firms as high-profile tax avoiders. To indicate

these high profile tax avoiders, I include a dummy variable in the models for the 10th and 20th

percentile levels. (X1) This dummy adapts for each different determinant, to indicate if a firm

falls into the top 10th or 20th percentile. I base the top 10th or 20th percentile on average numbers

over the period 2001-2003. DOTAS is implemented in 2004. To define firm years pre- and

after DOTAS, I include the dummy variable Post-DOTAS dummy into the model. Post-DOTAS

dummy shows if the data in the regression are from the period before or after the introduction

of DOTAS in 2004. The fiscal years 2001-2003 are defined by a zero, the fiscal years 2005-

2007 are defined by 1.

I run two separate regressions for each determinants’ percentile group. I also add an

interaction term between the dummy Post-DOTAS dummy and X1. The interaction term shows

how the firm years CURRENT ETR of the top-10th and 20th percentile after DOTAS are related

to the other firm years in the sample. Next, I will explain how I compose the variables of the

model and which regression models I will use to analyze the relation between the CURRENT

ETR and tax aggressiveness of firms.

18

Some studies suggest that firms with higher debt levels have lower effective tax rates. Mills

et al. (1998) and Dyreng et al. (2008) both find that a high level of debt and tax avoidance are

positively associated. Therefore, LEVi,t is included in the regression as a control variable,

measured by scaling average long-term debt by lagged total assets. The LEVi,t (hereafter: LEV)

control variable is winsorized at the 1st and 99th percentiles.

The size of a firm is also an indicator for tax aggressiveness. A bigger firm can have more

options to e.g. shift profits, bring operations to low-tax jurisdictions or to obtain better advice

on tax planning activities. On the other hand, bigger firms could face more taxes on the transfer

of assets to related entities and withholding taxes. Opinions among literature vary, if size is

positively or negatively related to tax avoidance. Zimmerman (1983) and Frank et al. (2009)

find a positive relationship between size of the firm and the effective tax rate, where Mills et

al. (1998) find a negative relationship. I do include SIZEi,t (hereafter: SIZE) into the equation,

defined by the natural logarithm of the total assets divided by 10.

Literature states that profitability is also a determinant for the tax aggressiveness of tax

avoiders. (Dyreng et al. 2008; Frank et al. 2009; Chen et al. 2010). Fernandez-Rodriguez &

Martinez-Arias (2012) find a positive relationship between profitability and the ETR. To

control for profitability, ROAi,t (hereafter: ROA) is added to the regression. ROA is composed

by taking pre-tax income minus special items, divided by lagged total assets. ROA is also

winsorized at the 1st and 99th percentile.

Stickney and McGee (1982) suggest that capital intensity and the effective tax rate have an

inverse relationship. Also Chen et al. (2010) show that tax avoidance measures and plant,

property and equipment have a negative relationship. Chen et al. (2010) suggest that firms with

a high level of PPEi,t (hereafter: PPE) are affected more by the different treatments of

depreciation expense for tax and financial reporting purposes. To control for the capital

intensity of firms, PPE is added to the regression and measured by the ratio of PPE over lagged

total assets. PPE is also winsorized at the 1st and 99th percentile.

Dyreng et al. (2008) suggest that a higher level of intangibles gives a firm more

opportunities to avoid taxes. As intangibles are more easily transferable than physical assets to

low tax jurisdictions, firms with high level of intangibles should have more opportunities to

avoid taxes. INTANGi,t (hereafter: INTANG) is included in the regression, considered as the

level of intangibles scaled by lagged total assets. INTANG is winsorized at the 5th and 95th

percentile.

19

Besides, most countries (including the U.K.2) provide extra tax incentives for research and

development. Those incentives are captured in the control variable R&Di,t (hereafter: R&D)

being the R&D expense scaled by lagged total assets. R&D is also winsorized at the 1st and

99th percentile.

3.2.3 Regression models

As I state in the previous paragraph, several common determinants of tax avoiders will be

used to identify firms with supposedly tax aggressive behavior. I use one general model in

order to determine the relation between tax avoidance and those determinants. The general

model formula is as follows:

CURRENT ETR = a0 + β1 Post-DOTAS dummy + β2 ROAi,t + β3 LEVi,t + β4 SIZEi,t + β5

PPEi,t + β6 INTANGi,t + β7 R&Di,t + εi,t

…………………………………………………………………………...…………………..(2)

To determine the effect on the CURRENT ETR of firms with high tax aggressiveness, I

add a new variable (X1) and an interaction term (Post-DOTAS dummy × X1). X1 considers if

a firm qualifies as ‘tax aggressive’, based on the top 10th and 20th percentile of each control

variable. The formula now consists of the following variables:

CURRENT ETR = α0 + β1 Post-DOTAS dummy + β2 X1 + β3 (Post-DOTAS dummy × X1) +

β4 ROAi,t + β5 LEVi,t + β6 SIZEi,t + β7 PPEi,t + β8 INTANGi,t + β9 R&Di,t + ε

i,t……………………………………………………………………………………..…….....(3)

The regression model contains 2 dummy variables (post-DOTAS dummy and X1) and 1

interaction term (Post-DOTAS dummy × X1). To test the formulated hypothesis each control

variable indicates if a firm is considered tax aggressive. I make a dummy variable if a firm and

its firm years belong to the top-10th or top-20th percentile of a control variable. Eventually, this

leads to 12 tax aggressiveness dummy variables. Besides, several control variables are

included. Eventually, the beta results of this model formula’s dummy variables and the

interaction variable can be interpreted in order to see if firms with more tax aggressiveness

avoided less taxes than tax non-aggressive firms. All specific formulas are given in appendix

1.

2 In the U.K. known as the R&D Relief for Corporation Tax, giving a 125% enhanced deduction before 2008.

20

4. Results

To draw conclusions from the dataset, I will perform several statistical analyses. First, a

correlation matrix will provide the relation between the dependent variable and independent

control variables. Second, I will use several regression analyses to determine the relationship

between the CURRENT ETR and the dummy, interaction and control variables. To conclude

I will add a sensitivity analysis, by adding industry dummies to the regression models.

4.1 Results

Pearson Correlation matrix analysis

Table 2 shows the correlations between the dependent and independent variables. The

correlation matrix is made by the Pearson model, tested at a two-tailed 1% and 5% significance

level. The correlations between variables are not very large. The post-DOTAS dummy variable

and CURRENT ETR have a correlation of -0,383. This correlation shows that in the years after

DOTAS, firms generally had a lower ETR then before DOTAS was introduced. This has no

direct influence on my hypothesis, as I cannot conclude that tax aggressive firms avoid less

taxes than other firms after the introduction of DOTAS in 2004. The correlation between SIZE

and CURRENT ETR (0,244) and ROA and CURRENT ETR (0,185) show that there is a

positive correlation between the size and profitability of firms and the effective tax rate. This

is consistent with expectations of Zimmerman (1983), who also finds a positive relation

between firm size and effective tax rate. For ROA and ETR, research also finds positive

relationships between tax avoidance and ROA. (Gupta and Newberry 1997; Rego 2003)

Fernandez-Rodriguez & Martinez-Arias (2012) also find a positive relationship, their research

even finds a 0.474 Pearson correlation at a 10% significance level.

Regression analysis

The main regression explains the effect of different firm characteristics on tax avoidance.

Table 3 shows the results of the regression model and the models’ adjusted R Square. The

adjusted R-square gives an indication on how the independent variables collectively predict the

dependent variable, CURRENT ETR. As many factors can influence the current ETR, a value

of 0,236 can be interpreted as sufficient. The VIF-tolerance scores do not indicate a possible

multicollinearity problem. Literature suggests that VIF-tolerance scores higher than 0.10

indicate that the chance on multicollinearity is very low. (Tabachnick and Fidell 2001). As the

VIF-tolerance scores in my model show levels higher than 0.72, I do not find any

multicollinearity problems. Four (SIZE, ROA, PPE and R&D) out of six control variables have

21

a significant effect on the CURRENT ETR. LEV and INTANG have no significant effect on

the CURRENT ETR, and therefore no conclusions can be drawn from these variables. SIZE

(0,17), ROA (0,083) and R&D (0,085) have a positive relationship with the CURRENT ETR.

For SIZE and ROA, this is consistent with prior research and expectations. For R&D the result

is counter intuitive, since the expectation was that companies with R&D expenses receiving

tax relief would pay less taxes and therefore reducing the CURRENT ETR. On the other hand,

PPE shows a negative relationship (-0,023) with the CURRENT ETR. This is also consistent

with prior research, where Stickney & McGee (1982) show that capital intensity and the

CURRENT ETR are negatively related. The dummy variable (-0,147), which accounts for the

period before and after implementation of DOTAS shows a negative relation with ETR.

Therefore, I conclude that before the implementation of DOTAS taxes payable were higher

and decreased over time.

Tax avoidance of firms with high tax aggressiveness after implementation of DOTAS

Table 4a and 4b show the results of the regression analyses of formula 4-9. Table 4a gives

the results for the top 10th percentile (Panel A), where Table 4b gives the results of the top 20th

percentile (Panel B). For each of the 6 determinants (LEV, SIZE, ROA, PPE, INTANG, R&D)

I use two separate regressions for the 10th and 20th percentile dummies.

The first row of results (X1) gives the beta of the dummy which indicates if a firm is

considered as more tax aggressive, based on one of the 6 determinants. This beta shows the

relation between the CURRENT ETR and the firms considered more tax aggressive. A negative

beta, constitutes more tax avoidance by tax aggressive firms over the whole period 2001-2007.

For the top 10th percentile, PPE and INTANG show a negative beta (p-value < 0,01), which

means that capital intense firms and firms with a higher intangible ratio avoid more taxes than

other firms do. There is weak evidence that firms with high R&D expenses avoid more taxes

than other firms (p-value < 0,1). On the other hand, profitable firms avoid less taxes compared

to other firms. The top 20th percentile partly reflects the results for profitable firms (p-value <

0,01) and capital intense firms (p-value < 0,05). Based on the top 20th percentile, large firms

avoid less taxes than other firms (p-value < 0,01).

The interaction term (post-DOTAS dummy × X1) shows the relation between the firms with

high tax aggressiveness performing tax avoidance after DOTAS, and the firms with lower tax

aggressiveness. SIZE (-0,067) and ROA (-0,098) show a significant negative relation (p-value

< 0.01) between firms with more tax aggressiveness and the CURRENT ETR. It follows that

larger firms and more profitable firms avoid more taxes after the implementation of DOTAS.

22

This is contrary to the hypotheses H1b and H1c, where a positive relation would have

confirmed the hypotheses. These hypotheses are therefore rejected. For PPE, only the 10th

percentile group shows a significant result (p-value < 0,01). Firms with a higher level of capital,

considered to be more tax aggressive, are avoiding less taxes after DOTAS. As the 20th

percentile shows insignificant results, only partial evidence is found to confirm the hypothesis

H1d. For the tax aggressiveness indicators leverage, R&D expenses, and intangibles, there is

no evidence to conclude that firms with more tax aggressiveness avoid less taxes after the

implementation of DOTAS (p-value > 0,10). Therefore, the hypotheses H1a, H1e and H1f are

rejected.

4.2 Sensitivity analysis

In this section I examine whether the results of the model remain consistent when the model

is subject to changes in the input. To analyze the sensitivity of the results, I rerun all regressions

for both the main regression (Table 6) as the regressions for tax aggressiveness of firms (Table

7a and 7b). I include in the regressions industry dummies based on the first two digits of GIC

codes. Table 5 gives GIC codes and corresponding industries.

The outcome of the main regression with industry dummies (Table 6), is to a large extend

consistent with the regression results in Table 3. There is still a significant negative relationship

(p-value < 0,01) between the dependent variable CURRENT ETR and independent variables

post-DOTAS dummy (-0,148), SIZE (0,179), ROA (0,074) and R&D (0,107). The control

variable INTANG turns to an insignificant result.

The regressions including industry dummies for tax aggressiveness of firms (Table 7a and

7b), also shows similar results to the regressions without industry dummies (Table 4a and 4b).

Surprisingly, the significance level of the beta of tax aggressive firms in top 10th percentile of

INTANG drops to 5% and the determinant LEV shows insignificant results after controlling

for industry fixed effects. As the levels of significance of other determinants remain to show

similar outcomes, I do not adapt my conclusions drawn from the first results. I still find no

systematic evidence that firms with more tax aggressiveness avoid less taxes after the

implementation of DOTAS.

5. Conclusion

I examine the influence of disclosure of tax avoidance schemes on tax avoidance performed

by firms in the UK, in the period 2001-2007. In order to measure tax avoidance, I use the

CURRENT ETR measure composed of the pretax income divided by the current tax payable

23

from the financial statements’ balance sheet. To identify firms which are subject to the

disclosure of tax avoidance schemes, I define firms with high ‘tax aggressiveness’ by six

determinants including leverage, return on assets, size, capital intensity, intangibles, and R&D

expenses. I determine firms with high tax aggressiveness based on the top 10th and 20th

percentile of each determinant and compare them with firms with lower tax aggressiveness

after the implementation of DOTAS. I expect firms with high tax aggressiveness to avoid less

taxes than firms with lower tax aggressiveness after the passage of DOTAS.

The regression results show no systematic evidence that tax aggressive firms avoid less

taxes after the passage of DOTAS. There is weak evidence that capital intense firms avoid less

taxes after the passage of DOTAS. This supports my expectations. On the other hand, firms

considered more tax aggressive based on profitability and size, avoid more taxes than other

firms after the passage of DOTAS. This is contrary to my expectations. These results also hold

after I control for differences in the industry environment. I therefore conclude that there is no

systematic evidence that firms avoid less taxes due to the implementation of DOTAS.

This study has several limitations. First of all, the disclosures under DOTAS are

confidential and therefore the firms which are affected by DOTAS cannot be identified on an

individual basis. It is also not possible to capture the deterrent effect of DOTAS on firms on

the basis of this data, as the tax avoidance decision process of firms is not included. The

financial statements only contain the outcome of tax avoidance strategies performed by firms.

Besides, firms can avoid DOTAS in several ways, where especially firms with high

opportunities in obtaining tax advantages by implementing new tax planning strategies would

have probably done so and make use of the legal professional privilege, or take the penalty on

non-disclosure for granted. Another limitation is that adaptions in tax legislation in the period

2001-2007 can influence the tax avoidance measure CURRENT ETR. This leads to ‘noise’ in

the tax avoidance measure. Also, the UK introduced several incentives to attract new firms to

the UK, which could also lead to a greater decrease in CURRENT ETR for firms with higher

tax aggressiveness compared to firms with lower tax aggressiveness. To conclude, due to the

lack of data, the CURRENT ETR does not give a truly fair reflection of the taxable income.

CURRENT ETR only reflects the tax payable in a year, where deferred tax assets and deferred

tax liabilities are not offset against the tax payable in a firm year.

24

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28

7. Appendices

Appendix 1 – formulas regression model

Leverage

𝐶𝑈𝑅𝑅𝐸𝑁𝑇 𝐸𝑇𝑅 = 𝛼0 + 𝛽1 𝑝𝑜𝑠𝑡 − 𝐷𝑂𝑇𝐴𝑆 𝑑𝑢𝑚𝑚𝑦 + 𝛽2 𝐿𝐸𝑉 𝑋1 + 𝛽3 (𝑌𝐸𝐴𝑅 × 𝐿𝐸𝑉 𝑋1) +

𝛽4 𝑅𝑂𝐴𝑖, 𝑡 + 𝛽5 𝐿𝐸𝑉𝑖, 𝑡 + 𝛽6 𝑆𝐼𝑍𝐸𝑖, 𝑡 + 𝛽7 𝑃𝑃𝐸𝑖, 𝑡 + 𝛽8 𝐼𝑁𝑇𝐴𝑁𝐺𝑖, 𝑡 + 𝛽9 𝑅&𝐷𝑖, 𝑡 + 𝜀𝑖, 𝑡 ...(4)

Size

𝐶𝑈𝑅𝑅𝐸𝑁𝑇 𝐸𝑇𝑅 = 𝛼0 + 𝛽1 𝑝𝑜𝑠𝑡 − 𝐷𝑂𝑇𝐴𝑆 𝑑𝑢𝑚𝑚𝑦 + 𝛽2 𝑆𝐼𝑍𝐸 𝑋1 + 𝛽3 (𝑌𝐸𝐴𝑅 × 𝑆𝐼𝑍𝐸 𝑋1) +


Return on assets

𝐶𝑈𝑅𝑅𝐸𝑁𝑇 𝐸𝑇𝑅 = 𝛼0 + 𝛽1 𝑝𝑜𝑠𝑡 − 𝐷𝑂𝑇𝐴𝑆 𝑑𝑢𝑚𝑚𝑦 + 𝛽2 𝑅𝑂𝐴 𝑋1 + 𝛽3 (𝑌𝐸𝐴𝑅 × 𝑅𝑂𝐴 𝑋1) +

𝛽4 𝑅𝑂𝐴𝑖, 𝑡 + 𝛽5 𝐿𝐸𝑉𝑖, 𝑡 + 𝛽6 𝑆𝐼𝑍𝐸𝑖, 𝑡 + 𝛽7 𝑃𝑃𝐸𝑖, 𝑡 + 𝛽8 𝐼𝑁𝑇𝐴𝑁𝐺𝑖, 𝑡 + 𝛽9 𝑅&𝐷𝑖, 𝑡 + 𝜀𝑖, 𝑡....(6)

Plant, property and equipment

𝐶𝑈𝑅𝑅𝐸𝑁𝑇 𝐸𝑇𝑅 = 𝛼0 + 𝛽1 𝑝𝑜𝑠𝑡 − 𝐷𝑂𝑇𝐴𝑆 𝑑𝑢𝑚𝑚𝑦 + 𝛽2 𝑃𝑃𝐸 𝑋1 + 𝛽3 (𝑌𝐸𝐴𝑅 × 𝑃𝑃𝐸 𝑋1) +


Intangibles

𝐶𝑈𝑅𝑅𝐸𝑁𝑇 𝐸𝑇𝑅 = 𝛼0 + 𝛽1 𝑝𝑜𝑠𝑡 − 𝐷𝑂𝑇𝐴𝑆 𝑑𝑢𝑚𝑚𝑦 + 𝛽2 𝐼𝑁𝑇𝐴𝑁𝐺 𝑋1 + 𝛽3 (𝑌𝐸𝐴𝑅 × 𝑋1) +


Research and development

𝐶𝑈𝑅𝑅𝐸𝑁𝑇 𝐸𝑇𝑅 = 𝛼0 + 𝛽1 𝑝𝑜𝑠𝑡 − 𝐷𝑂𝑇𝐴𝑆 𝑑𝑢𝑚𝑚𝑦 + 𝛽2 𝑅&𝐷 𝑋1 + 𝛽3 (𝑌𝐸𝐴𝑅 × 𝑅&𝐷 𝑋1) +


29

Appendix 2 – Tables and regression results

Table 1: Sorts of DOTAS disclosures per period and sector. Data from HMRC (HMRC

Disclosure statistics) from: http://www.hmrc.gov.uk/avoidance/stats.pdf

340

94

292

163

287 00 0

125

205

503

122

161

207

0

100

200

300

400

500

600

1st of August 2004 - 31stof March 2005

1st of April 2005 - 31st ofmarch 2006

1st of April 2006 - 31st ofMarch 2007*

1st of April 2007 - 31st ofMarch 2008*

nu

mb

er

of

dis

clo

sure

s

Date of measurement

NUMBER OF DOTAS DISCLOSURES TO HMRC

Financial products EmploymentMain regime (hallmarks after 2006) Total of disclosures per annum

http://www.hmrc.gov.uk/avoidance/stats.pdf

30

Table 2: Pearson Correlation matrix

CURRENT

ETR

Post-

DOTAS

dummy

LEV SIZE ROA PPE INTANG

Post-

DOTAS

dummy

-0,384**

0,000

LEV 0,017 0,046**

0,321 0,006

SIZE 0,243** 0,064** 0,293**

0,000 0,000 0,000

ROA 0,184** 0,088** 0,021 0,414**

0,000 0,000 0,469 0,000

PPE 0,057** -0,073** 0,325** 0,259** 0,107**

0,001 0,000 0,000 0,000 0,000

INTANG -0,045** 0,137** 0,186** 0,055** -0,003 -0,257**

0,008 0,000 0,000 0,001 0,7 0,000

R&D -0,058** 0,034* -0,061** -0,172** -0,338** -0,138** -0,010

0,001 0,045 0,000 0,000 0,000 0,000 0,545

**represents a significant correlation at a 1% level (2-tailed), *. Represents a significant correlation at a

5% level (2-tailed)

31

Table 3: Regression results control variables

Dependent variable = CURRENT ETR

Independent variable: OLS Coefficient t-stat VIF-score

(tolerance)

Constant 0,098*** 13,299

Post-DOTAS dummy -0,147*** -27,610 0.964

LEV -0,017 -1,156 0.770

SIZE 0,170*** 13,568 0.726

ROA 0,083*** 8,253 0.738

PPE -0,023** -2,309 0.749

INTANG -0,005 -0,425 0.837

R&D 0,085** 2,360 0.874

Number of observed firms 590

Adjusted R²

0,236

***, ** and * represent significant results at a 1%, 5% and 10% level.

32

Table 4a: Regression results tax aggressiveness and interaction term. (Panel A)

Panel B (top 10th percentile)


Determinants of tax aggressiveness

independent variable: LEV SIZE ROA PPE INTANG R&D

Tax aggressiveness (X1)

Top 10th percentile -0,011 0,000 0,071*** -0,087*** -0,040*** -0,027*

p-value (0,451) (0,995) 0,000 0,000 0,008 0,072

Post-DOTAS dummy

Top 10th percentile -0,148*** -0,141*** -0,137*** -0,150*** -0,150*** -0,149***

p-value (0,000) (0,000) 0,000 0,000 0,000 0,000

Interaction term

(post-DOTAS dummy × X1)

Top 10th percentile 0,009 -0,067*** -0,098*** 0,054*** 0,016 0,014

p-value (0,603) (0,000) 0,000 0,002 0,359 0,436

Control variables included?

(except determinant) YES YES YES YES YES YES

Number of observed firms 59 59 59 59 59 59

Adjusted R² 0,252 0,257 0,260 0,259 0,253 0,252


33

Table 4b: Regression results tax aggressiveness and interaction term. (Panel B)






Top 20th percentile -0,020* 0,035*** 0,081*** -0,030** -0,002 0,003

p-value 0,066 0,003 0,000 0,011 0,891 0,737

Post-DOTAS dummy


p-value 0,000 0,000 0,000 0,000 0,000 0,000

Interaction term

(post-DOTAS dummy × X1)

Top 20th percentile 0,001 -0,096*** -0,109*** 0,002 -0,018 0,001

p- value 0,927 0,000 0,000 0,908 0,166 0,923

Control variables included?

(except determinant) YES YES YES YES YES YES

Number of observed firms 118 118 118 118 118 118

Adjusted R² 0,252 0,265 0,269 0,253 0,252 0,252


34

Table 5: GIC codes per Industry sector.

GIC code (first two

starting digits)

Sector

10 Energy

15 Materials

20 Industrials

25 Consumer Discretionary

30 Consumer Staples

35 Health Care

40 Financials

45 Information Technology

50 Telecommunication Services

55 Utilities

35

Table 6: Sensitivity analysis, regression results control variables

with additional industry dummies.


Independent variable: OLS coefficient t-stat VIF-score

(tolerance)

Constant 0,099*** 11,455

Post-DOTAS dummy -0,148*** -28,002

0.963

LEV -0,019 -1,319

0.750

SIZE 0,179*** 13,683

0.664

ROA 0,074*** 7,247

0.714

PPE -0,017 -1,575

0.670

INTANG 0,000 -0,025

0.814

R&D 0,107*** 2,735

0.719

Number of observed firms 590

Industry dummies added YES

Adjusted R² 0,248


36

Table 7a: Regression results controlled for industry effects. (Panel A)

Panel A (top 10th percentile)

Dependent variable = CURRENT

ETR




Top 10th percentile -0,011 0,004 0,070*** -0,088*** -0,032** -0,024

p-value (0,430) (0,775) (0,000) (0,000) (0,023) (0,100)

Post-DOTAS dummy


p-value (0,000) (0,000) (0,000) (0,000) (0,000) (0,000)

Interaction term

(post-DOTAS dummy ×

X1)

Top 10th percentile 0,011 -0,074*** -0,097*** 0,056*** 0,016 0,017

p-value (0,529) (0,000) (0,000) (0,001) (0,342) (0,336)

Control variables

included? (except

determinant) YES YES YES YES YES YES

Industry dummies

included? YES YES YES YES YES YES

Number of observed

firms 59 59 59 59 59 59

Adjusted R² 0,252 0,257 0,260 0,259 0,253 0,252


37

Table 7b: Regression results controlled for industry effects. (Panel B)


Dependent variable = CURRENT

ETR




Top 20th percentile -0,018 0,032*** 0,080*** -0,025** 0,001 0,003

p-value (0,102) (0,007) (0,000) (0,033) (0,912) (0,794)

Post-DOTAS dummy


p-value (0,000) (0,000) (0,000) (0,000) (0,000) (0,000)

Interaction term

(post-DOTAS dummy ×

X1)

Top 20th percentile 0,003 -0,100*** -0,108*** 0,003 -0,018 0,002

p-value (0,827) (0,000) (0,000) (0,806) (0,164) (0,899)

Control variables

included? (except

determinant) YES YES YES YES YES YES

Industry dummies

included? YES YES YES YES YES YES

Number of observed

firms 118 118 118 118 118 118

Adjusted R² 0,252 0,265 0,269 0,253 0,252 0,252


Documents

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