The Relevance of Complex Group Structures for

Income Shifting and Investors’ Valuation of Tax Avoidance

Tim Wagener

University of Münster, Germany

tim.wagener@wiwi.uni-muenster.de

and

Christoph Watrin

University of Münster, Germany

christoph.watrin@wiwi.uni-muenster.de

Abstract

This study contributes to the recent debate on multinational firms’ extensive tax avoidance by

investigating whether firms make use of complex legal group structures to facilitate income shifting and

repatriation, and by examining whether structural complexity moderates the association between tax

avoidance and firm value. Based on the Amadeus ownership database, we construct a composite measure

of corporate group complexity covering the dimensions number of subsidiaries, maximum ownership

chain length, number of cross-country links and percentage of holdings. Using a sample of European

multinational firms, we find a positive association between tax incentives to shift income within the group

and the complexity index. This association is stronger for income mobile firms. We also find that

structural complexity weakens the positive association between tax avoidance and firm value. This finding

speaks to the agency view of tax avoidance and shows that investors may put a price discount on a firm’s

shares if they are not able to understand the firm’s tax strategy, which may be used by managers to mask

the extraction of rents.

Keywords: complexity; tax avoidance; income shifting; market reaction

JEL classification: F23; G14; G23; H2

We thank Dhammika Dharmapala (our discussant at the 2013 NTA Annual Conference on Taxation), Eva

Eberhartinger (our discussant at the 2014 EAA Annual Congress), Michael Stimmelmayr (our discussant at the 2013

Taxing Multinational Firms Conference at the University of Mannheim), Johannes Becker, Adrian Kubata, Gerrit

Lietz, Robert Ullmann, participants at the 2013 NTA Annual Conference on Taxation, at the 2013 Taxing

Multinational Firms Conference at the University of Mannheim, at the 2014 EAA Annual Congress, and colloquium

participants at the Institute of Public Economics I at the University of Münster for many helpful comments and

suggestions.

1

The Relevance of Complex Group Structures for

Income Shifting and Investors’ Valuation of Tax Avoidance

I. INTRODUCTION

This study examines whether European multinational firms use complex group structures to

facilitate income shifting and repatriation, and how investors perceive the use of structural complexity1 to

avoid taxes. A prominent example of the use of complex group structures to shift income to low-tax

jurisdictions is Google whose income shifting strategy is known as the “Double Irish and Dutch

Sandwich”. This strategy helped Google lower its effective tax rate to 2.4% in 2009 (Drucker 2010).2 The

use of complex structures to facilitate income shifting, however, is not limited to this specific example.3

The organizational structure, including the tax and legal structure, is seen as an important determinant of a

multinational entity’s “ability to achieve its global tax planning goals and objectives” (Merks, Petriccione,

1 Throughout this study, our construct of complexity refers to the complexity of a group’s legal structure, involving

legally independent entities and their relation to other group affiliates. We use the terms “complexity”, “group

complexity”, and “structural complexity” synonymously. 2 This tax planning scheme requires the transfer of substantial intellectual property to a subsidiary incorporated in

Ireland. Irish law provides for the opportunity that this subsidiary is treated as a Bermudan company for tax

purposes. This first Irish company generates income, taxed at the Bermudan corporate tax rate of 0%, from

licensing its intellectual property to a second Irish company (a subsidiary of the first Irish company) which

generates income from Google’s European operations. The royalty payments to the Bermudan company can be

deducted and the remaining profits are taxed at the low Irish corporate tax rate of 12.5%. From a U.S.

perspective, the first subsidiary is still considered an Irish company although taxed at the Bermudan tax rate. The

second subsidiary files a U.S. “check-the-box” election to be regarded as a branch of the first subsidiary. As a

result, inter-company transactions (i.e. royalty payments) will be hid from the IRS, and the income of both

entities will be combined to determine whether the sales are viewed as “foreign base company sales income”

leading to the current inclusion of “subpart F income” in the U.S. tax return. See Darby III and Lemaster (2007)

for a detailed description of this strategy. By inserting a company based in the Netherlands into the structure,

which receives the royalty payments from the second subsidiary and passes them on to the Bermuda-based

company, Google can also avoid the Irish withholding tax on royalty payments (Lowder 2011). Ireland is not

allowed to levy withholding tax payments to a company based in the Netherlands because of the EU interest and

royalties directive (Directive 2003/49/EC of 3 June 2003). The Netherlands only take a small fee for transferring

the payments to Bermuda. 3 In fact, Google’s “Double Irish and Dutch Sandwich” structure is not considered in this study because our sample

only includes European multinational corporations.

2

and Russo 2007, p. 82). Complex group structures may result from a firm’s efforts to arbitrage

institutional restrictions such as tax codes and financial restrictions (Bodnar, Tang, and Weintrop 1999).

The first part of this study investigates the role of complex group structures in the income shifting

process, before the valuation implications of “complex tax avoidance” are examined in the second part.

Prior research has provided evidence that multinational firms react to tax incentives to shift income

geographically. For example, Huizinga and Laeven (2008) find that a single entity’s profitability responds

to tax incentives within the corporate group. Their results show that a group affiliate facing a high

incentive to shift income away (because the applicable tax rate in the domicile country is higher than a

weighted average of other available tax rates in the group4) on average reports lower profits and vice

versa. They interpret this finding as consistent with geographical income shifting in response to tax

incentives. We argue that structural complexity helps firms achieve its income shifting goals. First, a firm

may wish to lower the costs of repatriating previously shifted income. Employing specific well-defined

structures, such as the use of holdings in other countries, results in a reduction of withholding taxes and in

an increase of structural complexity. Second, a firm may engage in complex structures to facilitate the

process of income shifting itself by hiding favorable transactions from tax authorities.5 We predict that

firms make use of complex group structures in the income shifting process (H1a) and test this hypothesis

by investigating whether incentives to shift income within the corporate group are associated with more

complex structures.

We measure tax incentives within a corporate group in two ways. First, we calculate the difference

between the maximum statutory tax rate and the minimum statutory tax rate within the corporate group.

The intuition of this measure is that the higher this tax rate differential the more beneficial it is for the

4 Their measure of tax incentives also takes into account the resources that are available for shifting.

5 Desai and Dharmapala (2006) state that firms shelter income from tax authorities by taking obfuscatory actions.

If complex transfer pricing schemes complicate shareholders’ efforts to understand the firm’s operations

(Bushman, Chen, Engel, and Smith 2004), complex tax structures will also increase the difficulty for tax

authorities to understand the transactions.

3

group to shift income within the group, from its high-tax affiliates to its low-tax affiliates. Second, we

measure tax incentives by a group’s tax haven involvement. We argue that if a firm has at least one

affiliate in a tax haven location this firm will have higher incentives to shift income than a firm without

subsidiaries in a tax haven country. We develop a new composite measure of structural complexity which

includes the number of subsidiaries, the maximum length of an ownership chain, the number of cross-

country ownership links and the percentage of holding companies. By sorting observations into quintiles

we obtain a complexity score ranging from zero to sixteen. We show that all index components load on

the single construct “complexity” and that the four complexity dimensions are positively correlated with

each other without one dimension being a perfect substitute for another. In the robustness tests section, we

show that the dimensions “number of cross-country ownership links” and “percentage of holding

companies” contribute most to the results. We also demonstrate that the results are largely unaffected by

changes to the index.

Using a European sample of 3,023 unique parent companies owning 180,234 subsidiaries obtained

from the Compustat Global and Bureau van Dijk’s Amadeus database, we regress the newly developed

complexity index on either of the two tax incentive measures and control variables in the cross-section of

the year 2010 and find a significantly positive correlation between tax incentives and group complexity.

This result is robust to the inclusion of several firm- and country-specific control variables such as the

number of countries in which the group has affiliates in, firm size, age, EU membership of the firm’s

home country, legal tradition, investor rights, and ownership concentration. The findings are consistent

with firms exploiting international tax rate differences through complex group structures.

We then consider whether the extent of the association between tax incentives and complexity is

moderated by a firm’s income mobility. Following de Simone and Stomberg (2012), the construct income

mobility represents a firm’s ability to tax-efficiently structure global operations. Income mobility is

measured using a composite measure which includes a firm’s industry membership (firms in

4

pharmaceutical, high-tech and service industries are considered income mobile), intangible assets and

gross profit percentage. We hypothesize that income mobility has an incremental effect on the association

between tax incentives and complexity because income mobile firms have more resources and lower costs

to shift income geographically. We find evidence supporting this hypothesis, although the effect is only

significant when the firm’s tax haven involvement is used to proxy for tax incentives to shift income

within the group.

The second research question of this study examines how investors value tax avoidance in firms

with complex group structures. The increased opacity resulting from the use of complex corporate tax

shelters may lead to agency costs because opacity facilitates managerial rent extraction (Desai and

Dharmapala 2006). In addition, tax avoidance achieved through the use of complex structures may

increase the risk of penalties and back-payments. Although prior research (e.g., Desai and Dharmapala

2009; Wilson 2009) has generally found corporate tax avoidance to be value-enhancing in well-governed

firms, as it increases net income and/or cash flows, we hypothesize that investors will view aggressive tax

planning less positive if the observed level of tax avoidance is accompanied by complex group structures.

To test this hypothesis, we regress Tobin’s q, a standard measure of firm value used in finance

studies, on a proxy for corporate tax avoidance, the composite measure of a group’s complexity, and on

the interaction term between these two variables. We employ three different measures of tax avoidance

that have been used by prior literature: the book effective tax rate (GAAP ETR), the cash effective tax rate

(CASH ETR), and total book-tax differences. We use the same sample of European multinational firms

employed in the first set of hypotheses, with the exception that we extend the dataset to a panel including

six years.6 We find evidence consistent with our predictions. While investors generally value corporate tax

avoidance positively, the relationship between tax avoidance and firm value is significantly weaker the

6 The use of a panel dataset requires the assumption that the group structure remains unchanged in the years under

investigation.

5

more complex a group is structured. The findings also show that the general association between tax

avoidance and firm value may turn negative if a certain complexity threshold is achieved. These results

are robust to the inclusion of several control variables that are potentially correlated with Tobin’s q, the

degree of tax avoidance, and group complexity.7 To investigate whether the effect of equity-based

compensation on tax avoidance (Phillips 2003; Hanlon, Mills, and Slemrod 2007; Armstrong, Blouin, and

Larcker 2012; Rego and Wilson 2012) influences our results, we perform a robustness test on a small

sample of 556 firm-year observations for which we obtain executive compensation data (the ratio of bonus

payments across board members to their total compensation) from Amadeus. Despite the low power of

this test, we find evidence supporting our results. Using a two-stage least squares approach, we also

account for the potential endogeneity of tax avoidance and firm value und show that the results hold.

As an additional robustness check, we investigate whether small changes to the index affect the

results from the main tests. Specifically, we subsequently exclude each complexity dimension from the

index. We find that the results remain unchanged; in one specification, however, they indicate that the

number of cross-country links and the percentage of holdings contribute most to the positive association

between tax incentives (as measured by the tax rate differential) and the complexity score.

Our study contributes to the literature in several ways. First, we contribute to the literature

examining geographical income shifting. Prior studies (e.g., Collins, Kemsley, and Lang 1998; Mills and

Newberry 2004; Huizinga and Laeven 2008; Klassen and Laplante 2012a) provide evidence consistent

with firms shifting income from high- to low-tax jurisdictions in response to tax incentives. By analyzing

the role of complex group structures in the income shifting process, we shed light on the question how

firms shift income.

7 Specifically, we control for firm size, growth, stock return volatility, and corporate governance quality measured

by institutional ownership in our main regressions.

6

Second, we extend the line of research examining the cross-sectional variation of investors’

valuation of corporate tax avoidance. Prior literature has focused on corporate governance (Desai and

Dharmapala 2009; Wilson 2009), income mobility (de Simone and Stomberg 2012) or corporate

transparency8 (Wang 2011). We exploit the detailed data on subsidiaries available for a large sample of

European multinational firms to investigate whether differences in the complexity of a firm’s group

structure affect investor valuation of corporate tax avoidance. This setting allows us to examine the

relevance of agency costs of corporate tax avoidance (Desai and Dharmapala 2006) to the market in a

more direct way. While prior work investigates factors that potentially mitigate agency costs, such as

corporate governance (Desai and Dharmapala 2009), legal enforcement (Desai, Dyck, and Zingales 2007)

or transparency (Wang 2011), we focus on the structures that give rise to agency costs in the first place.

The investigation thus corresponds directly to the theory of Desai and Dharmapala (2006) who state that

agency costs are driven by complex tax schemes.

Third, the study speaks to the literature on corporate transparency. Prior studies suggest that

aggressive tax planning decreases financial transparency (Balakrishnan, Blouin, and Guay 2012). Because

group complexity can be seen as an important determinant of financial reporting transparency, our study

sheds light on the question whether firms use complex structures to exploit tax incentives despite the

reduced transparency resulting from these structures.

8 Although being a related concept, transparency, defined as the “widespread availability of firm-specific

information concerning publicly listed firms in the economy to those outside the firm” (Bushman et al. 2004),

refers to the informational environment of the firm (and not to its real actions), which is determined by the firm’s

financial reporting behavior and by the importance that analysts place on the firm. In contrast, our construct of

group complexity directly captures different aspects of the firm’s legal structure. A complex structure may induce

firms to extend the financial reporting and may increase analysts’ efforts to evaluate the firm so that, as a result,

high complexity may be associated with increased transparency, as measured by common proxies. To the extent

that transparency is intended to capture how understandable a company’s tax-planning transactions are, it may be

a noisy measure. Our construct of inherent complexity is much closer related to tax-planning transactions and

thus provides a more direct test of the agency view of corporate tax avoidance.

7

Fourth, our study relates to organizational, management and finance research investigating the

determinants and consequences of complex organizational structures9, decision-making processes, or

diversification. For example, a controversial question discussed in the finance literature is whether a

conglomerate discount exists for diversified firms (e.g., Lang and Stulz 1994; Campa and Kedia 2002).

We contribute to this literature by investigating the implications of complex structures for investors’

valuation of corporate tax avoidance.

Fifth, we also contribute to the literature by developing a composite measure of corporate

complexity which focuses on the legal aspects of organizational complexity and which we hope will be

useful for future research.

The study proceeds as follows. In Section II, we present prior literature and develop our

hypotheses. Section III describes the sample, develops a composite complexity index, and explains the

regression models used in our analysis. In Section IV, we present and discuss the results. Section V

provides results for alternative complexity index specifications. Section VI concludes.

II. PRIOR RESEARCH AND DEVELOPMENT OF HYPOTHESES

1. Multinational firms’ incentives to shift income geographically

Multinational firms have incentives to shift income geographically until the marginal tax savings

equal the incremental costs (Mills and Newberry 2004). Income shifting is beneficial because parts of the

global income can be taxed at lower tax rates.10

Managers of multinational firms also have personal

incentives to engage in income shifting if their compensation is linked to after-tax outcomes. Prior studies

9 Prior literature suggests that organizational complexity is mainly driven by non-tax factors. Specifically, prior

literature has shown that organizational structure is associated with a firm’s environment (e.g., Duncan 1972;

Keats and Hitt 1988; Dess and Beard 1984), technology (e.g., Perrow 1967; Woodward 1994; Miller, Glick,

Wang, and Huber 1991), and strategy (e.g., Whittington 2002). 10

In a territorial tax system, the reporting of income in low-tax jurisdictions thus directly decreases cash tax

payments and the tax expense in the financial statements. In worldwide tax system, such as in the U.S., firms at

least benefit from a tax deferral because foreign income is only taxed at the (higher) local tax rate upon

repatriation.

8

provide evidence consistent with compensation being an important determinant of corporate tax avoidance

(Phillips 2003; Armstrong et al. 2012; Rego and Wilson 2012). Besides these potential benefits, income

shifting can be costly because real trade and investment might be disturbed, because of transaction costs,

documentation requirements, anti-abuse regulations, and potential penalties if a firm has engaged in illegal

activities (Klassen, Lang, and Wolfson 1993; Mills and Newberry 2004; Huizinga and Laeven 2008).11

Prior research has provided evidence that firms adjust the allocation of income across borders in

response to tax incentives. Harris (1993) finds that a reduction in the U.S. corporate tax rate from 45% to

34% and the reduction of tax subsidies for capital investment due to Tax Reform Act 1986 (TRA 1986)

are associated with more income shifting of U.S. based multinational corporations into the U.S.12

Klassen

et al. (1993) provide evidence consistent with geographic income shifting in response to tax rate changes

of several countries. Grubert (2003) shows that opportunities for income shifting, i.e., international tax

rate differences, influence the real behavior of R&D intensive firms, such as their choice of location.

Using a matched sample of financial data on foreign multinationals and confidential U.S. income tax

return data on foreign controlled corporations, Mills and Newberry (2004) investigate whether tax

incentives in non-U.S. based multinational corporations, measured by the difference between the U.S.

statutory corporate tax rate and the average foreign tax rate of the foreign multinational parent,13

affect the

magnitude of foreign multinationals’ income reported in the U.S. They find evidence consistent with their

expectations that the allocation of income in foreign multinational corporations responds to these tax

incentives. Huizinga and Laeven (2008) develop a measure (the composite tax variable C) which reflects

both the incentives and the opportunities of a group affiliate to shift profits by taking into consideration

11

Following Hines and Rice (1994), Huizinga and Laeven (2008) assume in their model that the marginal cost of

shifting profits rises in proportion to the ratio of shifted profits to true profits. 12

Jacob (1996) extends the analysis of Harris (1993) by providing evidence that the extent of geographic income

shifting depends on the volume of intra-company transactions. 13

They use the difference between the U.S. statutory corporate tax rate and the statutory corporate tax rate of the

foreign multinational corporation’s home country as an alternative measure for worldwide tax incentives. Collins

et al. (1998) use a similar measure of tax incentives to investigate the extent of income shifting in U.S.

multinational corporations and investors’ valuation of income shifting.

9

the relation between the statutory tax rate of the affiliate’s home country and the statutory tax rates of the

other members of the corporate group and the scale of activities in each country. Using a sample of

European multinational corporations, they find evidence consistent with tax incentives leading to a

substantial redistribution of national corporate tax revenues.

2. Benefits and costs of using complex group structures in the income shifting process

Benefits and costs of geographical income shifting may be affected by complex group structures.

Specifically, we consider two different roles of complexity in the income shifting process. First,

complexity reduces the costs of repatriating income from low-tax countries to the parent company’s

domicile country. Practice literature (e.g., Merks et al. 2007; Saunders 2011) documents the use of

specific tax schemes to decrease the repatriation costs associated with income shifting. For example,

repatriation of income which has been shifted to low-tax countries is generally subject to withholding

taxes. Firms may use intermediate companies in a different country with a favorable tax treaty network

with the sole purpose to collect dividends and pass them on to the parent company (“treaty shopping”),

thereby reducing withholding taxes and increasing structural complexity. Other tax schemes that can be

used to facilitate the repatriation of income from low-tax countries to the parent company’s domicile

country include, e.g., directive shopping, participation exemption shopping, avoidance of the application

of the credit method, and income conversion. All of these tax schemes require additional (holding)

companies in different countries, extend ownership chains, and increase the number of cross-country

ownership links und thus increase group complexity.14

Second, complexity can be beneficial to the income shifting process itself. Some income shifting

strategies are only feasible if they are structured in a certain, complex way. For example, a firm may wish

to transfer intellectual property to a low-tax country. The subsidiary in the low-tax country can then earn

14

We thus include these aspects of complexity in our composite measure, which is developed in Section III.

10

royalties from licensing the intellectual property to group affiliates in high-tax countries where the

licensing expense is deductible from the tax base. If this structure is too obvious, however, anti-abuse

rules of the parent company’s domicile country may be applicable so that the passive income earned by

the subsidiary is recognized on the tax return of the parent company. Thus, the firm may only engage in

this income shifting transaction if it can structure it in a way that avoids the application of CFC rules, for

example by using intermediate companies. The more complex a transaction is structured the more

difficulties has a tax authority to understand the whole structure and the more jurisdictions have to work

together to detect illegal practices.

Thus, complexity may increase the benefits of income shifting through lowering repatriation costs

or by facilitating shifting in the first place. However, complex structures may also increase the costs of

income shifting because they involve administrational costs as more legally independent entities are

involved. Further, complex structures that avoid CFC rules may require the relocation of functions, such

as the place of management, which can cause operational inefficiencies. Another possible cost factor is the

potential loss of reputation, which can impede the use of complex structures. Loss of reputation appears to

be an important consideration given the recent negative press coverage concerning Google’s, Apple’s or

Starbucks’ use of complex structures to shelter income from tax authorities.15

Empirical evidence on the

association between reputational costs and tax avoidance, however, is mixed. Graham, Hanlon, Shevlin,

and Shroff (2012) provide survey evidence that ex ante reputation concerns are important for firms when

considering the engagement in tax planning strategies.16

15

Starbucks even decided to voluntarily pay additional corporation tax in the UK to limit the damage to its brand

reputation (Baker 2012). 16

Hanlon and Slemrod (2009) find (limited) ex post evidence that the market reaction to news of using tax shelters

is more negative for firms in the retail industry relative firms in other industries. Gallemore, Maydew, and

Thornock (2012), however, do not find a relation between potential reputational costs and the probability of

engaging in tax shelters. Austin and Wilson (2013) do not find evidence that firms owning valuable brands

engage in less tax avoidance, but their results suggest that these firms might have used discretion over financial

reporting rules to report the benefits of tax planning more conservatively.

11

We argue that the benefits of using complex structures to facilitate income shifting or the

repatriation of shifted income increase in the exploitable tax rate differentials within the group,17

while the

costs of complex structures are independent of the incentives to shift income. We thus hypothesize that

firms with high tax incentives to shift income have more complex structures.

H1a: Firms with high incentives to shift income have a more complex group structure.

Prior research has provided evidence that a firm’s level of tax avoidance is associated with various

firm characteristics. From an array of previously documented firm characteristics positively associated

with tax avoidance, de Simone and Stomberg (2012) construct a single factor labeled “income mobility”,

which represents “a firm’s ability to structure key components of its global business operations in a tax-

efficient manner” (de Simone and Stomberg 2012, p. 6). This construct includes intellectual property (e.g.,

Gupta and Newberry 1997; Dyreng, Hanlon, and Maydew 2008) foreign operations (e.g., Mills, Erickson,

and Maydew 1998; Rego 2003) and industry membership (e.g., Mills et al. 1998; Dyreng et al. 2008). We

view tax incentives (tax rate differences within the corporate group) as a necessary condition for a tax-

motivated increase in structural complexity, and consider income mobility a sufficient condition. We posit

that a firm will not engage in complex structures that facilitate income shifting or repatriation if the tax

benefits (shifting income from high-tax to low-tax jurisdictions) arising through these structures are zero.

Given the satisfaction of this necessary condition, the firm will only use complex structures if it has

sufficient means to shift income to low-tax jurisdictions. We thus expect to find more complex structures

in income mobile firms (sufficient condition).

17

Firms with main operations in high-tax countries will have more incentives to set up complex structures to shift

income to low-tax countries than firms whose principal operations are located in low- or average-tax countries.

Generally, firms with little variation in the statutory tax rates of their affiliates will have fewer incentives to set

up complex structures to shift income between their affiliates than firms having set up subsidiaries in both high-

tax and low-tax jurisdictions.

12

H1b: Income mobility positively affects the association between tax incentives and complex group

structures.

3. The role of complexity for investors’ valuation of tax avoidance

Prior research has generally documented a positive association between tax avoidance and market

valuation in well-governed firms. Desai and Dharmapala (2009) find that the effect of tax avoidance,

measured as total book-tax differences, on firm value is a function of corporate governance. The results

indicate that investors value tax avoidance in well-governed firms positively. Hanlon and Slemrod (2009)

focus on tax sheltering, an activity on the more aggressive end of the tax avoidance continuum (Hanlon

and Heitzman 2010), and find that tax shelter involvement is viewed as bad news by the capital market.

Wilson (2009) documents positive abnormal market returns in well-governed firms before, during, and

after tax shelter participation compared to a control sample of matched firms that do not engage in tax

shelters in the years under consideration.18

We investigate how investors value tax avoidance that is accompanied by complex structures. We

argue that the benefits for investors of an observed level of tax avoidance do not depend on the degree of

complexity that is used to achieve the desired level of tax avoidance. However, complex structures may

increase the costs of corporate tax avoidance in two ways. First, the use of complex structures to achieve

desired tax outcomes may increase the agency costs of corporate tax avoidance because shareholders are

not able to observe the managers’ actions due to the increased level of obscurity. As suggested by Desai

18

Apart from these studies investigating the relationship between investor valuation and tax avoidance in general, a

number of studies examine the valuation implications of corporate inversions (Desai and Hines Jr 2002; Cloyd,

Mills, and Weaver 2003; Seida and Wempe 2002), provisions for uncertain tax benefits under FASB

Interpretation No. 48 Accounting for Uncertainty in Income Taxes, an Interpretation of FASB Statement No. 109

Accounting for Income Taxes (FIN 48) (Koester 2011), the promulgation of Schedule M-3, which requires large

firms to provide a detailed reconciliation of book income to taxable income to the IRS (Donohoe and McGill

2011).

13

and Dharmapala (2006), managers may use the additional obscurity, which was initially intended to hide

tax strategies from tax authorities, to divert rents. If firms have not implemented mechanisms to restrict

managerial opportunism investors may put a price discount on the firm’s stock.19

Second, the use of complex structures may give investors reason to believe that the firm’s tax

positions bear more downside risk than tax strategies that do not involve complex structures. Although

initially approved by tax authorities, complex structures bear the risk that additional facts are eventually

revealed that induce tax authorities to disallow the tax planning strategy. This revelation of new facts can

be due to increased cooperation between tax authorities, media coverage or insider information. Even

without the release of new facts a government may decide to disallow structures that were allowed in the

past. As a result, the tax savings generated by certain strategies may not be sustainable and may even

provoke penalties or back-payments.

We thus formulate the following hypothesis:

H2: Group complexity weakens the positive association between tax avoidance and firm value.

III. RESEARCH DESIGN

1. Sample selection

We start with an initial sample of 6,149 European firms available on Compustat Global and match

them with Bureau van Dijk’s Amadeus database using the International Securities Identification Number

(ISIN). The matching procedure results in a sample of 4,883 firms available in both databases.20

The

19

Balakrishnan et al. (2012) provide evidence consistent with firms foregoing the benefits of tax avoidance if they

anticipate negative effects on transparency, which in turn could provoke a negative market reaction. 20

We use the Compustat Global population as a starting point for our sample selection because we want to identify

European parent companies that publish consolidated financial statements. Amadeus also allows for the

possibility to identify “Global Ultimate Owners” but the definition is not always accurate because missing data

14

ownership module of Amadeus provides static data on worldwide subsidiaries of European parent

companies.21

We use financial data from Compustat22

in which the most recent year with available

financial data is 2010.23

We select all worldwide subsidiaries of European parent companies from the

Amadeus database that are owned at least by 50%, which returns a total number of 219,063 subsidiaries.

Despite the requirement that subsidiaries have to be owned by at least 50%, some subsidiaries appear in

more than one group. Because we cannot assess which group can exercise control over the respective

subsidiary, we exclude these duplicates from our dataset (22,695 subsidiaries excluded). In addition, we

delete subsidiaries that are missing a country code (2,008 subsidiaries excluded). We also restrict our

sample to corporate groups that have affiliates in at least two countries (1,784 groups and 12,929

subsidiaries excluded) and arrive at a sample of 3,099 European groups owning 181,431 worldwide

subsidiaries. Building on this common baseline sample, we differentiate the sample for the investigation

of H1a/b and H2.

The sample used for the first set of hypotheses is based on the cross-section of firms in the year

2010. The reason for restricting the sample to one year is that Amadeus does not include historical

ownership data in a machine-readable format. However, the main structure of our analysis suggests that

both the dependent (group complexity) and independent (tax incentives) variables are relatively stable

through time. A panel approach would thus not be the appropriate method. Excluding group-level

observations with missing total assets results in a final sample used in the analysis of H1a and H1b of

3,023 groups owning 180,234 subsidiaries in 205 countries. Table 1 gives an overview of the countries

can lead to a wrong classification. Using Compustat firms as our initial sample ensures that the selected firms are

the parent companies of their respective group. 21

We retrieved the data in January 2013. We are not able, however, to identify the date when the data items have

been last updated. 22

We use financial data exclusively from Compustat Global because of the better data availability. In addition, the

Compustat items are more familiar to the reader. 23

We retrieve Compustat data from Standard & Poor’s Research Insight. Data availability may thus vary from other

Compustat providers.

15

contributing to our sample, both on the group- and subsidiary level; it also includes statutory tax rates

collected from various sources for each country included in the sample.

(insert Table 1 about here)

The data required to investigate H2 (firm value, tax avoidance) exhibits substantially more

temporal variation. We thus extend the sample to a panel dataset by including observations from the years

2005–2010. Because the ownership data is static, we have to make the assumption that the ownership

structure remains constant during the sample period.24

We believe, however, that the benefits of a panel

dataset outweigh the problems associated with this restrictive assumption. The sample starts in 2005 to

ensure that most sample companies apply the same accounting standards25

so that investors interpret

financial statement information, such as effective tax rates, in a comparable way. We delete 2,164

observations because they are missing data needed to calculate variables that are used in all regression

models. The final sample of 16,430 firm-year observations is further reduced due to missing data required

to calculate the tax avoidance variables.26

Table 2 gives an overview of the sample selection process and

illustrates how the samples used in the investigation of H1a/b and H2 correspond to each other.

(insert Table 2 about here)

24

Other studies using Bureau van Dijk’s ownership data within a panel approach, such as Markle and Shackelford

(2012), have to make the same assumption that ownership structures remain constant over several periods. 25

All EU member states require the preparation of consolidated financial statement in accordance with International

Financial Reporting Standards (IFRS) from 2005. Non-EU countries included in our sample are Croatia (12

firms), Iceland (2 firms), Monaco (1 firm), Norway (114 firms), Russian Federation (34 firms), Switzerland (147

firms), and Turkey (20 firms). Bulgaria (1 firm) joined the EU in 2007. 26

Because data items needed to calculate CASH ETRs have particularly low availability compared to data items

needed for the calculation of the other tax avoidance proxies, we run the analysis on three different samples

instead of using the same (small) sample for all model specifications.

16

2. Development of a composite measure of structural complexity

Prior literature has developed measures for organizational complexity, a construct that can be

broadly defined as the “amount of differentiation that exists within different elements constituting the

organization” (Dooley 2001). Organizational complexity is thus related to our construct of group

complexity which focuses on the legal aspects of the group structure. Measures of organizational

complexity include geographic or product line diversification measured by revenue- or asset-based

Hirfindahl-Hirschman indices (e.g., Rose and Shepard 1997; Denis, Denis, and Sarin 1997; Denis, Denis,

and Yost 2002; Bushman et al. 2004), the number of reported segments (e.g., Denis et al. 1997; Denis et

al. 2002), the fraction of foreign sales to total sales27

, the number of 4-digit SIC codes assigned to the firm

by Compustat (Denis et al. 1997), entropy measures (Bushman, Indjejikian, and Smith 1995) or by a

composite measure of previously used proxies (Duru and Reeb 2002).

These measures mainly capture the diversification of a firm’s operations regardless of the group’s

legal structure. Because we are interested in the question whether firms use complex legal structures to

exploit tax rate differences, we develop a new composite measure of complexity which is based on

different aspects of a group’s legal and organizational structure: (1) number of subsidiaries, (2) maximum

ownership chain length, (3) number of cross-country ownership links, and (4) percentage of holdings. To

construct the index measure, we consider the effect of the number of countries in which a firm has

affiliates on the complexity dimensions and first rank all group-level observations by the number of

countries to form number-of-countries deciles. To calculate the complexity score in each dimension, we

then classify the group-level observations into quintiles according to the respective dimension and

separately for each number-of-countries decile. Observations in the highest quintile are assigned a score of

27

See Sullivan (1994) for an overview of studies using this measure.

17

four; observations in the lowest quintile obtain a score of zero. We then sum over the four dimensions to

obtain a composite measure of a group’s structural complexity, which ranges from 0 to 16.28

The first element of the composite measure is the number of subsidiaries within a corporate group.

The idea behind this dimension of complexity is that a firm with high incentives to shift income to low-tax

countries needs more legally independent subsidiaries than a firm lacking these incentives. Additional

affiliates promote income shifting because they increase the general obscurity of a firm and facilitate the

repatriation of shifted income. To account for the effect of the number of countries on the number of

subsidiaries, we rank the observations according to their number of affiliates29

within each number-of-

countries decile and then assign quintile values to calculate the partial score.

Maximum ownership chain length is the second element of the composite complexity measure.

The use of intermediate companies, e.g., to redirect income to reduce withholdings taxes, is reflected in

the observable length of an ownership chain. Firms will also extend ownership chains if they intend to

hide passive income by interposing companies. As with the first element, we calculate the partial score

relative to number-of-country-deciles to control for the effect of the number of countries on the length of

ownership chains. Amadeus provides the data item level30

, which indicates the number of steps in the

ownership chain from the subsidiary to its ultimate owner. Within a corporate group, we use the maximum

value of the item level to calculate the partial score.

The third dimension of complexity incorporated in the composite measure is the number of cross-

country ownership links within a group measured as the number of legal cross-border links starting from

the second level of ownership, i.e., without taking into consideration the direct links from a group’s parent

28

Within the context of this study, it is important to consider the effect that the number of countries has on both the

complexity score and the tax incentive variables. By first classifying observations into number-of-countries

deciles, we are able to linearize the relationship between the number of countries and the complexity score so that

the OLS assumptions are not violated. 29

The number of subsidiaries recorded in the Amadeus Ownership Database is limited to 1,000 per parent

company. Instead of using the data item “No. of recorded subsidiaries” (that also includes subsidiaries that are

owned by less than 50%), we count the number of subsidiaries that a firm has in the final sample. 30

The number of levels recorded in the Amadeus Ownership Database is limited to 10.

18

company to its subsidiaries located in different countries. The reason for the omission of these direct links

is that they would exist even in a flat structure with no intentions to shift income using complex structures.

The number of cross-country links increases when a firm redirects income over different countries.

Lewellen and Robinson (2013) find that the withholding tax rate on dividends flowing from the subsidiary

to its parent is significantly and negatively associated with the probability that the country link exists. This

finding suggests that one reason for the use of cross-country links is the reduction of withholding taxes.

Like with increasing the length of ownership chains, increasing the number of cross-country links adds to

the general obscurity of a firm, yet captures a different aspect of obscurity than ownership chains. For the

cross-country links to contribute to complexity, the chains do not necessarily have to be long because

cross-country links can exist on any step of the ownership chain. To calculate the number of cross-country

ownership links within a group, we first reimport the list of our sample subsidiaries to Amadeus and

search for foreign subsidiaries that are at least owned by 50%.31

We then count the number of cross-

country links for each subsidiary and merge the total number back to our initial dataset, which allows us to

allocate the subsidiaries to the respective parent company. We sum over all subsidiaries within a corporate

group to obtain the total number of cross-country links for each group, and assign quintile values

separately for each number-of-countries decile to obtain the partial score.

Finally, the percentage of holdings, calculated as the number of holdings relative to the total

number of affiliates within a corporate group represents a further aspect of group complexity. The

theoretical analysis in Section II has shown that firms use companies without significant own operations to

achieve the desired group structure. The percentage of holdings adds information to the complexity

dimensions explained above because the number of affiliates, the length of ownership chains, and the

number of cross-country links can also be due to vertical or horizontal integration, i.e. the acquisition of

31

Of the 180,234 unique sample subsidiaries, only 66,607 have available data in Amadeus. The remainders are

displayed as group affiliates in our initial search but the available information includes only name and location.

19

suppliers or competitors. By including the percentage of holdings in the composite measure we ensure that

the index captures complex structures that are especially set up to facilitate income shifting, such as firms

without significant own operations. We identify a subsidiary as a holding if the corresponding NAICS

2007 code starts with 55 (“Management of Companies and Enterprises”).32

For each number-of-countries

decile we calculate the percentage of holding companies to form quintiles and assign the quintile score

accordingly.33

(insert Figure 1 about here)

Figure 1 shows the distribution of the composite complexity measure. We perform a principal

component analysis to analyze whether the four index elements actually represent the construct

“complexity”. Table 3, Panel A contains the results of this analysis. The scree plot (untabulated) suggests

that there exists one main component which explains 58.4% of the variance in the composite index. All

four index elements load positively and significantly on that main component. We thus conclude that our

composite measure reliably captures the construct “complexity”. Panel B of Table 3 provides Pearson

correlation coefficients describing the association between the complexity dimensions. All correlations are

significantly positive indicating that on average, firms that are complex in one dimension are also complex

in other dimensions. The strongest association can be found between the number of subsidiaries score and

the maximum level score (0.650), whereas the relationship is weakest between the cross-country link

score and the holdings score (0.271). The correlation table also shows that all correlations are substantially

lower than 1 so that all dimensions capture different aspects of the construct “complexity”.

32

NAICS 2007 codes starting with 55 include the categories “Offices of Bank Holding Companies”, “Offices of

Other Holding Companies”, and “Corporate, Subsidiary, and Regional Managing Offices”. 33

We assign the quintile scores separately for each number-of-countries decile in addition to using the percentage

of holdings to rank the observations because firms with operations in many countries possibly need a higher

percentage of holdings to structure their organization in an efficient way.

20

(insert Table 3 about here)

3. Regression models

To test H1a, we estimate the following regression in the cross-section:

IndustryFEononcentratiOwnershipC

ghtsInvestorRiCommonLawEUMemberAge

SizeesNumCountriCFCveTaxIncentiScoreComplexity

9

8765

4321

(1)

where ComplexityScore is the composite measure of a firm’s structural complexity constructed as

explained above. TaxIncentive represents two different measures of incentives to shift income within the

corporate group.34

First, we use the difference between the maximum statutory tax rate and the minimum

statutory tax rate within the group (StrDiff).35

The higher this difference, the more benefits a firm has to

shift income from affiliates in high-tax countries to affiliates in low-tax countries.36

Second, we employ an

indicator variable (TaxHaven) set to one if the firm has at least one subsidiary in a tax haven location. The

idea of this measure is that if a firm has subsidiaries in a tax haven country it will have high incentives to

shift income to this low-tax jurisdiction.37

TaxHaven may capture different aspects than StrDiff because

the statutory tax rates used to calculate StrDiff are the standard tax rates that apply to national

corporations. If, for example, the standard statutory tax rate in a tax haven country is 30% but foreign

34

Prior literature has employed several proxies to measure a multinational firm’s incentives to shift income,

including the difference between a firm’s average foreign tax rate and the U.S. statutory corporate tax rate over

one (Collins et al. 1998; Mills and Newberry 2004; Klassen and Laplante 2012b) or multiple periods (Dyreng et

al. 2008; Klassen and Laplante 2012a). Studies based on single financial statements in an international context

(Markle 2011; de Simone 2013) use a weighted tax rate differential (the tax variable C developed by Huizinga

and Laeven 2008), which reflects the incentive to shift income to or away from one group affiliate, to measure

tax incentives. 35

We hand-collect statutory tax rates data from various sources listed in Table 2. Because of the large number of

countries in our sample (206), we were not able to identify a single source providing all necessary data. 36

An advantage of this measure is that it does not rely on the statutory tax rate of the parent company. Incentives to

shift income within the group may occur regardless of the statutory tax rate that the parent company faces. 37

Dyreng and Markle (2013) also use tax haven involvement to measure tax incentives to shift income out of the

U.S.

21

investors benefit from a lower tax rate, then this incentive will not be reflected by StrDiff.38

If firms use

complex group structures to facilitate income shifting and repatriation we expect to find a significantly

positive coefficient on TaxIncentive.

We include an indicator variable (CFC) set to one if the group’s parent company faces CFC rules

in its country of incorporation.39

As noted above, it is also important to control for the number of countries

(NumCountries) because it has an effect on both the complexity index and the tax incentive variables.

Neglecting this influence could result in a spuriously positive relationship between tax incentives and the

complexity score. We further control for firm size (Size) measured as total assets (Compustat item AT)

because large companies have more complex structures even in the absence of tax planning opportunities.

Firm size can also affect our tax incentive measures because larger firms are more likely to have

subsidiaries in many countries, a fact that increases the available range of national tax rates and thus likely

affects the tax rate differential. If the number of countries is increased, one could also expect an increase

in the probability that the firm establishes a subsidiary in a tax haven so that our second tax incentive

measure (TaxHaven) may also be affected by firm size. We also control for a firm’s age (Age40

) because

corporate structures may evolve randomly over time (Lewellen and Robinson 2013, p. 7) so that older

companies will naturally exhibit a higher degree of complexity. A firm’s age may also be associated with

our tax incentive measures because older firms are likely to have established operations in all countries of

38

Like with the tax incentive measure of Huizinga and Laeven (2008), our measures reflect incentives to shift

income within a corporate group that are a result of prior location decisions. We thus do not, and do not intend to,

measure incentives to set up new companies in low-tax countries because we are interested in how the firms’

current structural complexity is associated with incentives to shift income. 39

The indicator variable CFC is based on Deloitte’s Controlled Foreign Company Regimes Report 2012 (Deloitte

2012). This report lists four countries (Austria, Greece, Latvia, the Netherlands) as having an alternative method

to capture income in low tax jurisdiction. We assign the value one to the variable CFC for these countries. 40

For some firms, Amadeus only reports two-digit years of incorporation. We set the year to 19XX if the two-digit

number exceeds 12. For the remaining firms that have a two-digit number between 0 and 12 (N=475), we

perform an Internet-based search using company websites and financial reports to determine the correct year of

incorporation. We note that the date of incorporation of the parent company is a noisy measure of true firm age

because the date is affected by restructurings, so that a firm with a long history may appear as a young firm if its

parent company changed its legal form in recent years. However, these restructurings may have had the purpose

of simplifying a long grown corporate structure so that the measured firm age better explains group complexity.

Despite its caveats, we believe that the date of incorporation sufficiently captures firm age.

22

interest compared to young and growing firms which have not had the resources to enter all of their

targeted markets. We include several country variables to account for fundamental differences between

the sample firms’ home countries that might affect their complexity and tax incentives. We first include a

dummy variable (EUMember) indicating whether a firm is based in an EU member state because the

freedom of movement of capital generates opportunities to shift income within the EU and likely also

affects a firm’s use of complex structures to save taxes. We further include three country variables taken

from la Porta, Lopez-de-Silanes, Shleifer, and Vishny (1998): CommonLaw is an indicator variable set to

one if the firm’s home country legal tradition is based on common law, InvestorRights is an index of anti-

director rights representing minority shareholder rights and ranging from zero to five, and

OwnershipConcentration measures the median percentage of common shares owned by the largest (by

market capitalization) three shareholders in the ten largest domestic and publicly traded nonfinancial

firms. We also include industry fixed effects based on a firm’s one-digit SIC code to account for

differences between industries that affect tax planning opportunities and firm complexity.

To investigate whether income mobility affects a firm’s reaction toward tax incentives (H1b), we

include the interaction between the income mobility indicator variable (IncomeMoblity) based on de

Simone and Stomberg (2012) and the respective tax incentive measure:

IndustryFEononcentratiOwnershipC

ghtsInvestorRiCommonLawEUMemberAge

SizeesNumCountriCFClityIncomeMobiveTaxIncenti

lityIncomeMobiveTaxIncentiScoreComplexity

11

10987

6543

21

* (2)

where IncomeMobility is based on de Simone and Stomberg (2012). We make some adjustment to

their index to be able to use it in the context of this study. De Simone and Stomberg (2012) use income

mobile industry membership, R&D expense and advertising expense, scaled by total assets, the ratio of

foreign sales to total sales, and gross profit percentage as their four dimensions of income mobility. While

23

we also employ industry membership (SIC codes 283, 357, 367, 737, or 738)41

and gross profit

percentage, we use intangible assets scaled by total assets instead of R&D and advertising expense

because these items are only available for a small fraction of firms in the Compustat Global database42

.

We do not use the ratio of foreign sales to total sales because the extent of a firm’s international operations

is already reflected in our tax incentive measure. Intangible assets are intended to capture a firm’s

potential to shift income using licensing fees because arm’s length prices for these assets are hard to

determine. While industry membership captures a similar aspect, it is also intended to identify firms

whose products generate a global demand so that profits are available for shifting in many different

jurisdictions. Finally, gross profit percentage is included to identify firms which generate high profits from

new technology or brand value. Following de Simone and Stomberg (2012), we rank all observations

separately for the intangible assets and gross profit percentage component and assign the respective

quintile value. Observations in the highest quintile are assigned the value four and observations in the

lowest quintile obtain the value zero. We then add four to the sum of both components if the firm is a

member of an income mobile industry. The index measure thus ranges from zero to twelve. Following de

Simone and Stomberg (2012), we use an indicator variable (IncomeMobility) set to one for observations in

the top mobility score quintile and set to zero otherwise. All other variables are defined as above.

To test H2, we estimate the following regression using pooled OLS43

:

YearFE

iablesCountryVarhipInstOwnersVolatilityhSalesGrowtLogSales

ScoreComplexityceTaxAvoidanScoreComplexityceTaxAvoidanTobin's q

7654

321 *

(3)

We use Tobin’s q to measure firm value. Tobin’s q is considered a standard measure of firm value

in the corporate finance literature44

and has also been used in the tax avoidance literature (see, e.g., Desai

41

As de Simone and Stomberg (2012) note, these SIC codes have been associated with high-tech (357, 367, 737),

pharmaceutical (283), and service (738) firms by Barth, Braver, Hand, and Landsman (1999). 42

De Simone and Stomberg (2012) use a sample of firms available on the Compustat North America database. 43

We do not use firm fixed effects because the complexity index is assumed to be constant over time for each firm.

24

and Dharmapala 2009). It is calculated as the sum of the book value of a firm’s liabilities and the market

value of its equity, divided by the book value of its total assets and can be interpreted as a measure of the

contribution of the firm’s intangible assets (including, e.g., organizational capital, reputational capital,

investment opportunities etc.) to its market value (Lang and Stulz 1994). Within the context of our study,

Tobin’s q captures the market’s expectations associated with corporate tax avoidance, dependent on the

firm’s structural complexity.

TaxAvoidance is one of the three measures of tax avoidance that are commonly used in the

literature: the GAAP ETR, CASH ETR, or total book-tax differences. We subtract GAAP ETR and CASH

ETR from 1 to obtain three proxies of tax avoidance for which higher values indicate more tax avoidance.

Because theory and prior empirical evidence documents a positive effect of tax avoidance on firm value45

,

we expect the coefficient on tax avoidance to be positive. We do not make a prediction concerning the

direction of the main effect of group complexity (ComplexityScore) on firm value because although

complexity is likely to be costly it might be necessary for the firm to perform well.46

As stated in H2, the interaction between TaxAvoidance and ComplexityScore is our variable of

interest, and we expect to find a significantly negative coefficient (β3) on this interaction term. This

finding would be consistent with group complexity reducing the (positive) effect of corporate tax

avoidance on firm value. We include several control variables that are potentially correlated with firm

value as well as TaxAvoidance or ComplexityScore. Specifically, we include the natural logarithm of sales

to account for firm size. We use sales instead of total assets to control for size because total assets are used

to calculate Tobin’s q, which would result in a mechanical correlation with q (Desai and Dharmapala

2009). We expect a negative sign on LogSales because prior literature has documented a negative

44

See, e.g., Lindenberg and Ross (1981), Morck, Shleifer, and Vishny (1988), Kaplan and Zingales (1997), and

Gompers, Ishii, and Metrick (2003). 45

As discussed in Section II, the effect is generally positive but can also be negative in cases of tax sheltering and

when corporate governance is weak. 46

Prior literature suggests that organizational structure is associated with a firm’s environment (e.g., Duncan 1972),

technology (e.g., Perrow 1967), and strategy (e.g., Whittington 2002).

25

association between firm value and size (e.g., Morck et al. 1988). Prior literature has also demonstrated

that size is an important determinant of corporate tax avoidance because of economies of scale in tax

planning, which suggest a positive relationship, or political costs, which could explain a negative

association (Gupta and Newberry 1997; Manzon and Plesko 2002; Rego 2003).47

In addition, we include

LogSales because size likely affects the complexity index. We control for firm growth using the three year

sales growth rate (SalesGrowth) because growth is expected to increase firm value if it generates

opportunities to earn abnormally high future returns (Miller and Modigliani 1961). We thus expect a

positive coefficient on SalesGrowth. Growth may also be correlated with corporate tax avoidance because

the opening of new markets in foreign countries can generate opportunities for income shifting. It may

also affect ComplexityScore because high growth promotes an uncontrolled development of corporate

structures.

Following prior work (e.g., Desai and Dharmapala 2009), we include stock return volatility as a

control variable to capture the association between risk and firm value but do not make a prediction on the

sign of the coefficient.48

Volatility is calculated as the annualized standard deviation of monthly dividend

adjusted stock returns over 60 months.49

Stock return volatility may also be correlated with tax avoidance

because aggressive tax planning is a risky activity (Guenther, Matsunaga, and Williams 2012; Rego and

Wilson 2012).50

Further, stock return volatility may be correlated with the complexity index because

47

The political cost hypothesis states that large firms are subject to greater regulatory scrutiny than small firms (see,

e.g., Zimmerman 1983). Large firms are therefore possibly limited in their ability to exploit tax-planning

opportunities (Manzon and Plesko 2002). 48

Prior literature on the relationship between the return of an asset and its volatility is controversial. Most asset

pricing models (e.g., Sharpe 1964; Lintner 1965) postulate a positive relationship between expected portfolio

returns and volatility. In different studies (e.g., Bekaert and Wu 2000), however, stock return volatility is

modeled as negatively associated with stock returns. We refer to Li, Yang, Hsiao, and Chang (2005) and Baillie

and DeGennaro (1990) for an overview on this controversy. 49

We obtain pricing data from Standard and Poor’s Research Insight, which provides stock price information

matched to Compustat Global data. 50

Rego and Wilson (2012) find that a CEO’s risk taking incentives are negatively related to a firm’s CASH ETR.

26

young firms with flat structures may have more volatile returns because their business model is not yet

established.

We also include a measure of institutional ownership to proxy for corporate governance as a

control variable because prior literature has shown that corporate governance is an important determinant

of investors’ valuation of corporate tax avoidance (Desai and Dharmapala 2006; Desai and Dharmapala

2009; Hanlon and Slemrod 2009).51

We obtain shareholder data from Amadeus. For each firm, we delete

non-institutional investors and then sum the direct shareholdings over all remaining shareholders.52

Prior

U.S. studies investigating investor valuation of corporate tax avoidance (Desai and Dharmapala 2009;

Wang 2011; de Simone and Stomberg 2012) also include a proxy for equity-based compensation because

stock-based compensation is likely to be associated with firm value (Morck et al. 1988; Mehran 1995).

Compensation has also been found to be a determinant of corporate tax avoidance (Phillips 2003; Hanlon

et al. 2007; Armstrong et al. 2012).53

Many U.S. studies obtain compensation data from Execucomp. In a

European setting, however, data on executive compensation is not available for a large sample of firms.

Because Amadeus only provides static data on manager compensation for 556 firm-year observations, we

51

Prior literature also measures corporate governance using composite indices like the governance index developed

by Gompers et al. (2003) and the entrenchment index developed by Bebchuk, Cohen, and Ferrell (2009). To

construct these indices, data on certain properties of the corporate governance system (such as anti-takeover

provisions) is gathered and aggregated in a composite measure. While studies based in a U.S. setting have these

data available in a machine-readable format, no similar database exists for European firms. We thus use

institutional ownership to proxy for good corporate governance. The idea behind this measure is that institutional

investors that generally own a higher stake in the company’s stock than private investors can exercise their power

to monitor the firm’s management, which may result in an improved quality of the firm’s corporate governance. 52

We delete investors other than banks, financial companies, insurance companies, mutual funds, pension funds,

and private equity firms. While historical subsidiaries data are not available on Amadeus, the database does

provide historical shareholder data. However, the number of shareholders that are listed for each firm is limited to

50 so that our measure of institutional ownership may understate the true value. In rare cases the sum of

institutional shareholdings exceeds 100%, which is likely due to data errors in the database. We set the variable to

100% in these cases. We set institutional ownership to 0 if no shareholder is recorded. 53

While many studies document a positive relationship between compensation and tax avoidance, Desai and

Dharmapala (2009) hypothesize and find that equity-based compensation is negatively associated with tax

avoidance in poorly governed firms because of positive feedback effects between tax sheltering and managerial

diversion.

27

do not include a compensation variable in our primary tests but provide a small sample robustness test in

Section V. All other variables are defined as explained above.

IV. RESULTS

1. Descriptive statistics

Table 4, Panel A presents descriptive statistics for the sample employed in the analysis of H1a and

H1b, which includes 3,023 firms and covers one year. The sample firms have an average complexity score

of 6.28 and all complexity dimensions contribute in a balanced way to the composite index. The first tax

incentive measure (StrDiff) ranges from 0 to 0.55, with a mean of 0.20 and a standard deviation of 0.12

and thus exhibits a plausible variation. With regards to the second tax incentive measure, the descriptive

statistics show that 57% of the sample firms have at least one affiliate in a tax haven country.

(insert Table 4 about here)

Panel B of Table 4 reports descriptive sample for the sample used in the investigation of H2,

which includes the same companies but spans over a 6-year period (2005–2010). Because the sample size

changes substantially for the three different tax avoidance measures, we provide descriptive statistics

separate for the regression models that use either of the three tax avoidance measures. The mean Tobin’s q

is 1.676 for the GAAP ETR sample and similar in all three samples. The mean GAAP ETR is 24.2%,

based on a sample of 13,344 firm-year observations. Compared to the mean statutory tax rate in this

sample of 29.09% (untabulated), multinational firms thus exhibit a lower tax burden on average. The

mean CASH ETR in a sample of 2,607 firm-year observations is 23.8%, which is slightly lower than the

mean GAAP ETR. The sample used for regression models based on total book-tax differences consists of

11,547 firm-year observations with positive pretax income. The mean scaled total book-tax differences are

28

positive (0.06) which suggests that on average book income exceeds taxable income. The complexity

score is lowest in the GAAP ETR sample (mean of 6.594) and slightly higher in the total book-tax

differences sample (mean of 6.880) and in the CASH ETR sample (mean of 7.517). In the GAAP ETR

sample, the complexity dimension number of subsidiaries contributes 30.05% to the complexity score

mean, while the other dimensions contribute around 22-24% each (untabulated). This composition is

similar in the other samples and demonstrates that the dimensions contribute in a well-balanced way to the

composite index. We conclude that, despite the variation in sample sizes, the sample observations exhibit

similar characteristics.

Table 5 provides Pearson correlation coefficients of all variables that are included in the

regression models. Panel A presents the relevant correlation from the regression model used in the

examination of H1a and H1b. The table shows the importance of controlling for the number of countries,

size, and age because these variables are both significantly correlated with the dependent variable

(ComplexityScore) and the independent variable (TaxIncentive). In Panel B, showing the respective

correlation coefficients for the variables employed in the model that we use to investigate H2, all tax

avoidance proxies are positively correlated with each other. The remaining variables exhibit a significant

correlation with Tobin’s q, ComplexityScore (with the exception of SalesGrowth), and at least one of the

tax avoidance proxies, which shows the importance of controlling for their influences.

2. Test of H1a

Table 6, Panel A and B, present results from tests of H1a. Panel A reports results for those

specifications in which we use StrDiff as our measure of tax incentives; the respective results from using

TaxHaven as incentive measure are presented in Panel B. The first column provides results from

estimating equation (1). In Panel A, the main effect variable TaxIncentive (StrDiff) is significantly and

positively associated with ComplexityScore. The coefficient on TaxIncentive is 1.977 (t-value of 2.10).

29

Thus, an increase in StrDiff by one standard deviation (0.12) is associated with an increase in

ComplexityScore by 0.237, which represents 3.8% of the sample mean ComplexityScore. In Panel B, we

also find a significantly positive association between TaxIncentive (TaxHaven) and ComplexityScore. The

respective coefficient amounts to 0.944 (t-value of 5.01), thus indicating that having at least one

subsidiary in a tax haven location increases the complexity score on average by 0.944, which represents

15.0% of the sample mean ComplexityScore. This result is consistent with higher incentives to shift

income within the group being associated with higher group complexity and the magnitude of the effect is

economically significant. As predicted, NumCountries, Size, and Age are also significantly and positively

related to ComplexityScore in both panels. The existence of CFC rules in the parent company’s country of

incorporation, however, has no significant effect on group complexity.

(insert Table 6 about here)

3. Test of H1b

The second column in Table 6, Panel A and B, presents results from the tests of H1b where our

variable of interest is the interaction between TaxIncentive and IncomeMobility. In both panels, we find

that firms classified as income mobile (IncomeMobility = 1) respond more to tax incentives to shift

income by exhibiting a higher degree of group complexity, which is consistent with H1b. The coefficient

on the interaction term is significant at the 5%-level in Panel B (t-value of 2.02) and of borderline

significance in Panel A (t-value of 1.24, p-value of 0.107 in a one-tailed test). Firms classified as income

mobile react approximately twice as strongly toward tax incentives as firms that are not income mobile. In

Panel B, having at least one subsidiary in a tax haven location increases Complexity Score by 0.81 when

the firm is not income mobile. For firms classified as income mobile, having a tax haven subsidiary is

associated with an increase in ComplexityScore by 1.58 (0.81+0.77), which represents 25.16% of the

30

sample mean ComplexityScore. This result indicates that income mobility can be seen as a sufficient

condition for firms to exploit tax rate differentials by using complex structures.54

4. Test of H2

Table 6, Panel C, presents the results from tests of H2. The three columns show the results for

alternative tax avoidance measures. The first column uses 1–GAAP ETR as a proxy for tax avoidance; the

second column uses 1–CASH ETR; the third column uses total book-tax differences. We use year fixed

effects in all model specifications to account for the fact that residuals of a given year are correlated across

firms, e.g., because macroeconomic effects may influence the value of all sample firms at the same time;

these effects may also be correlated with the independent variables. We also cluster the standard errors by

firm because Tobin’s q is likely to be correlated over time for a given firm.55

The results in all panels and

models show that the various measures of corporate tax avoidance are positively associated with Tobin’s

q. This positive relation is mitigated when firms employ complex group structures to achieve their desired

level of tax avoidance. In the first model, the coefficient on the GAAP ETR is 1.248 and significant (t-stat

of 6.42). Because we subtract the GAAP ETR from 1, a positive estimated coefficient indicates that tax

avoidance is positively associated with Tobin’s q. In the second model, the coefficient on 1–CASH ETR is

54

Although we did not make any prediction on the sign of the coefficient of IncomeMobility, we find it to be

significantly negative in both panels. This coefficient measures the relationship between IncomeMobility and

ComplexityScore conditional on a firm’s tax incentives. If a firm has a tax rate differential of 0 (in Panel A),

being classified as income mobile is associated with a decrease in ComplexityScore by 1.17. At the sample mean

tax rate differential of 0.2, the association between IncomeMobility and ComplexityScore is reduced to –0.78 (–

1.165+0.2*1.915). This negative relationship can possibly be explained by the fact that an important

characteristic of income mobile firms are large profit margins (and the construction of the income mobility index

involves the factor “gross profit margin”). Firms that, ceteris paribus, pay more attention to their costs to achieve

high margins are likely to structure their operations in a more efficient (and thus less complex) way, which may

result in a negative association between income mobility and complexity. Another possible explanation is that

income mobile firms have simpler structures because innovative firms such as Google can offer their products in

many countries without the need to establish production facilities in each market. While this negative relationship

is an interesting finding, our conclusions with regards to the positive effect that income mobility has on the

relationship between TaxIncentive and ComplexityScore remain unaffected. 55

Because our proxies for tax avoidance and especially ComplexityScore, which we assume to be constant over

time, are also serially correlated, OLS standard errors will understate the true standard error (Petersen 2009;

Gow, Ormazabal, and Taylor 2010).

31

0.800 and significant at the 1%- level; the coefficient on total book-tax differences in the third model

amounts 6.722 and is also significant at the 1%- level.

The coefficient on the interaction term between the tax avoidance measure and ComplexityScore is

significantly negative in all three panels. In the first model, the estimated coefficient on the interaction

term is –0.110 and significant at the 1%-level (t-stat of –4.76). Thus, an increase in the complexity score

by 1 decreases the slope of Tobin’s q on tax avoidance by 0.110, which represents a 8.81% decrease in

investors’ positive valuation of corporate tax avoidance. The effect is thus economically significant. The

direction and significance of the effect of complexity on the relation between tax avoidance and firm value

has the same direction and is of similar significance across all three models. This result is consistent with

firm complexity decreasing the positive effect of tax avoidance on firm value. Interestingly, for firms that

have a complexity score of 12 or more in the first model (11 or more in the second model), the main effect

turns negative indicating that investors value tax avoidance negatively if a firm reaches a certain

complexity level. Because this threshold is well under the maximum value of ComplexityScore (16), this

potentially negative overall relationship is relevant to the sample. Only in the third, which uses total book-

tax differences as the tax avoidance measure, the threshold is at a complexity score of around 28 and thus

outside the relevant range. The potential change of sign highlights the importance of this study’s findings.

While some companies might accept that the stock market reacts less positive in response to their use of

complex structures, they might rethink their tax policy if they were aware of the fact that tax avoidance

may actually provoke a negative market reaction in a sufficiently complex firm.

Table 6, Panel C, also shows that the direction of the main effect of group complexity on Tobin’s

q is not clear. In the first model, the coefficient on ComplexityScore is positive (0.052) and significant (t-

stat of 3.15). Thus, complexity positively influences firm value if 1–GAAP ETR is 0 (i.e., the GAAP ETR

equals 1). Because this represents an unrealistic value, we rather look at the main effect of complexity on

32

Tobin’s q at the sample mean of GAAP ETR.56

The estimated coefficients indicate that at the mean GAAP

ETR value, the effect of complexity on firm value is slightly negative.57

In the second model, in which we

use the CASH ETR as the proxy for tax avoidance, the effect is insignificant. In the third model, in which

total book-tax differences are used to proxy for tax avoidance, the association between complexity and

firm value is significantly, even for total book-tax differences of zero.

As stated above, equity-based compensation might be an important covariate in equation (3)

because prior studies have found that equity incentives are determinants of both firm value and corporate

tax avoidance. Low data availability on executive compensation in a European sample, however,

substantially decreases the sample size so that we do not include equity incentives in our main regressions.

Amadeus provides compensation data for 108 sample firms. For each company, we retrieve data on

current members of the board of directors or the executive board. Amadeus allows for a differentiation

between salary and total compensation. We subtract the salary from a manager’s total compensation to

obtain an estimate of her bonus payments as a proxy for equity-based incentives. Because for some firms,

Amadeus provides records for several board members, we sum the bonus payments over all executives of

a given company and divide the sum by the sum of total compensation for all executives to obtain firm-

level observations of the percentage of variable remuneration. Amadeus only provides static compensation

data as of the latest available date. The mean percentage of bonus payments (variable bonus) in the sample

of 108 observations is 48.21% with a standard deviation of 24.68% (untabulated). We assume this ratio to

be constant during the sample period and include the variable in the regression model. Table 7 presents the

results of estimating (3) in the GAAP ETR specification with the additional control variable. The

coefficient on Bonus is positive but not significant (t-stat of 1.14). Despite the small sample, the effects of

56

The mean GAAP ETR is 0.242. Thus the mean value of 1–GAAP ETR is 0.758. 57

If TaxAvoidance is set to the sample mean of 0.758 the overall effect of complexity on firm value amounts to

(0.052–0.110*0.758=–0.031).

33

interest remain significant. We thus conclude that our results are robust to controlling for equity-based

incentives.58

(insert Table 7 about here)

A potential problem of estimating equation (3) with OLS is the endogeneity of corporate tax

avoidance. A firm may choose to avoid taxes if it performs well or poorly. As a result, the tax avoidance

measure would be correlated with the residual, which in turn would lead to inconsistent coefficient

estimators. We address this issue by using a set of instruments that is correlated with the tax avoidance

measures but that we expect to be uncorrelated with the error term, i.e., the set of instruments should not

be determined by a firm’s choices.59

A general issue of instrumental variables approaches is that it is hard

to identify good instruments. We use 8 indicator variables indicating the firm’s one-digit SIC

classification as well as their interactions with ComplexityScore as instruments for tax avoidance. The

underlying idea of these instruments is that industry membership is an important determinant of corporate

tax avoidance. We argue that only in rare cases firms will change the industry in which they are operating

in response to a certain firm value.60

We employ two-stage least squares to estimate the model.61

In the first stage regressions, we

obtain F-stats on the joint significance of the instruments of 8.92 (6.20) in the regression of 1–GAAP ETR

(1–GAAP ETR * ComplexityScore) on the instruments and control variables. The first stage regressions

(untabulated) thus indicate that industry dummies reliably predict tax avoidance. Table 8 presents the

58

The results are similar if we use total book-tax differences as the measure of tax avoidance. We do not include

Bonus in the CASH ETR regressions because the inclusion reduces the sample to 280 observations. 59

In the following, we only look at models that use the GAAP ETR to proxy for tax avoidance. The results are

qualitatively similar if we use the CASH ETR or total book-tax differences as proxies for tax avoidance. We note,

however, that the instruments are weaker in these specifications. 60

Because TaxAvoidance is interacted with ComplexityScore, there are two potentially endogenous variables in the

model that have to be instrumented for using at least two instruments. 61

We use heteroskedasticity-consistent standard errors in all specifications.

34

second stage regression in which the two endogenous variables are instrumented for using the set of

instruments described above. Consistent with the OLS estimates, tax avoidance is positively associated

with Tobin’s q. The coefficient on the interaction term between TaxAvoidance and ComplexityScore

remains significantly negative, consistent with group complexity decreasing the positive effect that tax

avoidance has on firm value. We thus view the findings from the instrumental variables approach as

support for the results from our main tests.

(insert Table 8 about here)

V. ALTERNATIVE INDEX SPECIFICATIONS

To examine the robustness of our results to small changes to the index, we subsequently exclude

each component from the complexity score and examine whether this exclusion affects the results from

the main tests. In the different columns of Table 9, Panels A and B, we subsequently exclude each

complexity dimension from the index. The column labeled “Score1” thus excludes complexity component

1 (subsidiary score), the column “Score 2” excludes the second component (maximum level score), the

column “Score 3” excludes the third component (cross-country links score), and the column “Score 4”

excludes the fourth component (holdings score). In Panel A, we reproduce the results from the analysis of

H1a using StrDiff as the tax incentive variable. We find that omitting the cross-country links score leads to

a weaker significance of the main effect. If we build our index without the partial score that covers the

percentage of holdings, the main effect is insignificant. Omitting the first two components, however, does

not change the results. This finding suggests that these two complexity dimensions (cross-country links

score and holdings score) contribute most to the results. If we use TaxHaven as the tax incentive variable

(untabulated), however, the coefficient on TaxIncentive remains significant at the 1%- level for all

alternative index specifications.

35

Panel B demonstrates that the results concerning H2 remain highly significant and do not depend

on the exclusion of certain complexity dimensions. The tabulated results use 1–GAAP ETR to proxy for

tax avoidance but they are very similar if we use 1–CASH ETR or total book-tax differences instead.

(insert Table 9 about here)

VI. CONCLUDING REMARKS

This study investigates whether tax incentives to shift income geographically within multinational

firms are associated with complex group structures and whether investors differentially value tax

avoidance of firms with complex group structures. To measure group complexity, we use Bureau van

Dijk’s Amadeus ownership database to develop a composite measure of complexity, including the

dimensions number of subsidiaries, maximum length of an ownership chain, number of cross-country

ownership links, and percentage of holdings.

In the first part of the study, we provide robust evidence consistent with European firms using

complex group structures to exploit international tax rate differences, either to facilitate income shifting or

repatriation. In addition, the study provides evidence that income mobile firms respond more to tax

incentives than non-income mobile firms. In the second part, we generally document a positive relation

between tax avoidance and Tobin’s q, our measure of firm value. The results indicate that this positive

relation is negatively influenced by the existence of complex group structures. Our findings are thus

consistent with investors placing a discount on the firms’ shares if a certain degree of tax avoidance is

achieved through the use of complex structures. If firm complexity is sufficiently high, we observe that

the overall association between tax avoidance and firm value turns negative, a finding that may alert firms

that consider the use of complex tax schemes. Using an additional test, we also show that the results are

unlikely to be driven by different levels of equity-based compensation.

36

Our research is timely given the current discussions about multinational firms’ extensive use of

complex networks of subsidiaries as part of their tax strategy. While the media focus on particular cases of

tax avoidance through the use of complex structures, such as Google or Amazon, we provide large-sample

evidence on the use of complex structures in response to tax incentives. The findings are relevant to tax

authorities that have an interest in understanding whether and how firms systematically intend to bypass

existing CFC regulations. In addition, the results are relevant for investors who are interested in

understanding the structures of firms they invest in. An improved understanding of the tax reasons for

complex group structures will facilitate their decision making. The findings are also of interest for

managers in multinational firms because they reveal possible negative consequences of using complex

group structures as these structures may raise investors’ doubts on the benefits of corporate tax avoidance,

either because the tax positions are perceived to be more risky or because a general obscurity provokes

agency costs.

37

Appendix A: Variable Definitions

Variable Definition

Complexity score

Number of subsidiaries Number of subsidiaries per group that are at least owned by 50% directly or

indirectly. We count the actual number of subsidiaries in the dataset instead of

using the Amadeus data item Number of recorded subsidiaries because the item

includes "subsidiaries" that are owned by less than 50%.

Maximum ownership chain length Length of the longest ownership chain within a corporate group, measured by the

maximum value of the Amadeus data item Level.

Number of cross-country

ownership links

Total number of cross-border ownership links with at least 50% of ownership

within a corporate group, excluding ownership links on the first level (i.e.

between parent company and direct subsidiaries).

Percentage of holdings Number of holdings relative to number of subsidiaries within a corporate group.

We consider a company a holding if the corresponding NAICS 2007 code starts

with 55 ("Management of Companies and Enterprises").

ComplexityScore Composite index of the four above listed dimensions. For each dimension, we

sort the group-level observations and assign quintile values ranging from zero to

four. We assign the partial scores separately for each number-of-countries-decile

and obtain ComplexityScore by adding the partial scores. The index ranges from

zero to 16.

Tax incentive

StrDiff Difference between the highest and the lowest statutory tax rate of the group

affiliates

TaxHaven Indicator variable indicating whether a firm has at least one subsidiary in a tax

haven location. The tax haven classification is obtained from Dyreng and

Lindsey (2009).

Income mobility

Intangible assets Intangible assets (Compustat item INTAN) scaled by total assets (AT). We set

the variable to zero if the observation is missing on Compustat.

Gross profit percentage Gross profit, scales by sales (Compustat item GPM). We set the variable to zero

if the observation is missing on Compustat.

Industry membership Observations are classified as income mobile if observations belong to the SIC

codes (Compustat item SIC) 283, 357, 367, 737, or 738.

IncomeMobility Observations are assigned a quintile value to obtain the partial score of the

respective income mobility component. The partial scores are added, and a

dummy variable is created taking the value one if an observation falls into top

mobility score quintile, and zero otherwise.

Firm Value

Tobin's q The sum of total assets (AT) and market value of equity (PRCC*CSHO) less

book value of equity (CEQ), scaled by total assets (AT)

Tax Avoidance

GAAP ETR Total tax expense (TXT) divided by pre-tax income (PI); set to missing if

observation is outside [0;1].

CASH ETR Cash taxes paid (TXPD) divided by pre-tax income (PI); set to missing if

observation is outside [0;1].

38

TotalBTD The difference between pre-tax income (PI) and taxable income, where taxable

income is calculated as current tax expense (TXC) grossed up by the respective

statutory tax rate, scaled by total assets (AT).

Control Variables

Size Total assets (Compustat item AT)

Age 2013 - date of incorporation of the parent company (Amadeus item Date of

Incorporation)

CFC Indicator variable equal to one if the parent company is located in a country

which has implemented a CFC legislation (based on Deloitte's CFC Regimes

Report 2012).

EU Member Indicator variable equal to one if the parent company is located in a EU member

country.

Common Law Indicator variable equal to one if the parent company is located in a common law

country (taken from la Porta et al. 1998).

InvestorRights Index of anti-director rights ranging from zero to five (taken from la Porta et al.

1998).

OwnershipConcentration Median percentage of common shares owned by the largest three shareholders in

the ten largest domestic and publicly traded nonfinancial firms (taken from la

Porta et al. 1998).

LogSales Natural Logarithm of sales (SALE).

SalesGrowth Sales in year t less sales in year t-2, divided by sales in year t-2, representing the

3-year sales growth.

Volatility The annualized standard deviation of monthly stock returns over 60 months,

where the returns are calculated as price in month t (PRCCM) less price in month

t-1 plus dividends per share (DIV), divided by price in month t-1.

InstOwnership Percentage of shares held by institutional investors (banks, financial companies,

insurance companies, mutual funds, pension funds, and private equity funds.

Bonus The sum of total compensation for all available board members less the sum of

salaries for all available board members, divided by the sum of total

compensation for all available board members.

39

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Table 1: Statutory tax rates and sample composition

Country

Statutory

Corporate Tax Rate

Tax

Haven

No. of

subsidiaries

No. of

ultimate owners

Country

Statutory

Corporate Tax Rate

Tax

Haven

No. of

subsidiaries

No. of

ultimate owners

Afghanistan 20.00% a no 3 -

Cambodia 20.00% a no 22 -

Albania 10.00% a no 54 -

Cameroon 38.50% c no 53 -

Algeria 19.00% b no 161 -

Canada 26.00% a no 2,069 -

Andorra 10.00% b yes 13 -

Cape Verde 25.00% c yes 19 -

Angola 35.00% a no 104 -

Cayman Islands 0.00% a yes 169 -

Anguilla 0.00% b yes 3 -

Central African Republic 30.00% f no 9 -

Antigua and Barbuda 25.00% c yes 24 -

Chad 40.00% c no 7 -

Argentina 35.00% a no 649 -

Chile 18.50% a no 739 -

Armenia 20.00% a no 18 -

China 25.00% a no 3,450 -

Aruba 28.00% a no 9 -

Colombia 33.00% a no 346 -

Australia 30.00% a no 3,021 -

Comoros N/A

no 2 -

Austria 25.00% a no 2,328 56

Congo 34.00% c no 53 -

Azerbaijan 20.00% c no 24 -

Congo, D. R. 40.00% c no 31 -

Bahamas 0.00% a yes 53 -

Costa Rica 30.00% a yes 123 -

Bahrain 0.00% a yes 50 -

Côte d'Ivoire 25.00% c no 85 -

Bangladesh 27.50% a no 47 -

Croatia 20.00% a no 453 12

Barbados 25.00% a yes 33 -

Cuba N/A

no 21 -

Belarus 18.00% a no 57 -

Curacao 27.50% a no 73 -

Belgium 33.99% a no 3,007 87

Cyprus 10.00% a yes 818 14

Belize 25.00% d yes 2 -

Czech Republic 19.00% a no 1,666 2

Benin 30.00% b no 28 -

Denmark 25.00% a no 1,848 95

Bermuda 0.00% a yes 197 -

Djibouti N/A

no 8 -

Bhutan 30.00% b no 1 -

Dominica 30.00% c yes 7 -

Bolivia 25.00% a no 60 -

Dominican Republic 29.00% a no 81 -

Bosnia and Herzegovina 10.00% a no 133 -

Ecuador 23.00% a no 116 -

Botswana 22.00% a yes 39 -

Egypt 25.00% a no 344 -

Brazil 34.00% a no 2,049 -

El Salvador 30.00% a no 48 -

Brunei Darussalam 20.00% b yes 12 -

Equatorial Guinea 35.00% c no 15 -

Bulgaria 10.00% a no 477 1

Eritrea N/A

no 3 -

Burkina Faso 27.50% e no 46 -

Estonia 21.00% a no 514 11

Burundi 35.00% f no 1 -

Ethiopia 35.00% g no 35 -

Country

Statutory

Corporate Tax

Rate

Tax

Haven No. of

subsidiaries

No. of

ultimate

owners

Country

Statutory

Corporate Tax

Rate

Tax

Haven No. of

subsidiaries

No. of

ultimate

owners

Fiji 28.00% a no 20 -

Jordan 14.00% a no 50 -

Finland 24.50% a no 2,010 102

Kazakhstan 20.00% a no 204 -

France 33.33% a no 17,008 410

Kenya 30.00% a no 101 -

French Polynesia N/A

no 4 -

Korea, D.P.R. N/A

no 28 -

Gabon 35.00% c no 60 -

Korea, Republic of 24.20% a no 459 -

Gambia 33.00% d no 5 -

Kuwait 15.00% a no 14 -

Georgia 30.00% g no 42 -

Kyrgyzstan 10.00% c no 7 -

Germany 29.37% a no 18,189 387

Lao P.D.R. 28.00% c no 9 -

Ghana 50.00% g no 62 -

Latvia 15.00% a yes 340 4

Gibraltar 10.00% a yes 61 -

Lebanon 15.00% c yes 70 -

Greece 20.00% a no 1,378 56

Lesotho 25.00% g no 11 -

Grenada 30.00% d yes 1 -

Liberia N/A

yes 60 -

Guadeloupe N/A

no 1 -

Libya 20.00% a no 18 -

Guatemala 31.00% a no 96 -

Liechtenstein 12.50% a yes 19 -

Guinea 35.00% g no 31 -

Lithuania 15.00% a no 552 11

Guinea-Bissau N/A

no 7 -

Luxembourg 28.80% a yes 1,294 21

Guyana 40.00% c no 7 -

Macao 12.00% a yes 40 -

Haiti N/A

no 3 -

Macedonia 10.00% a no 84 -

Honduras 35.00% a no 39 -

Madagascar 21.00% c no 42 -

Hong Kong 16.50% a no 1,150 -

Malawi 30.00% a no 22 -

Hungary 19.00% a no 1,071 5

Malaysia 25.00% a no 661 -

Iceland 20.00% a no 69 2

Maldives 0.00% g yes 6 -

India 32.44% a no 1,269 -

Mali 35.00% b no 29 -

Indonesia 25.00% a no 382 -

Malta 35.00% a yes 249 3

Iran, Islamic Republic of N/A

no 57 -

Maritius 15.00% a yes 201 -

Iraq 15.00% c no 14 -

Marshall Islands 0.00% b yes 17 -

Ireland 12.50% a yes 2,176 28

Martinique N/A

no 1 -

Israel 25.00% a no 383 -

Mauritania 25.00% f no 21 -

Italy 31.40% a no 4,733 153

Mexico 30.00% a no 1,730 -

Jamaica 33.33% a no 33 -

Micronesia, Fed. States of 0.00% f no 2 -

Japan 38.01% a no 938 -

Moldova, Republic of 12.00% c no 36 -

Country

Statutory

Corporate Tax

Rate

Tax

Haven No. of

subsidiaries

No. of

ultimate

owners

Country

Statutory

Corporate Tax

Rate

Tax

Haven No. of

subsidiaries

No. of

ultimate

owners

Monaco 33.00% b yes 48 1

Saint Kitts and Nevis 35.00% c yes 6 -

Mongolia 25.00% c no 17 -

Saint Lucia 33.33% c yes 13 -

Montenegro 9.00% a no 44 -

Saint Vincent and the Gren. N/A

yes 3 -

Morocco 30.00% c no 439 -

Samoa 27.00% a yes 6 -

Mozambique 32.00% a no 80 -

San Marino N/A

yes 9 -

Myanmar 30.00% b no 10 -

Sao Tome and Principe 25.00% e no 5 -

Namibia 34.00% a no 43 -

Saudi Arabia 20.00% a no 175 -

Nepal 25.00% b no 6 -

Senegal 25.00% c no 78 -

Netherlands 25.00% a no 7,466 90

Serbia 10.00% a no 331 -

New Caledonia 30.00% b no 18 -

Seychelles 33.00% g yes 7 -

New Zealand 28.00% a no 390 -

Sierra Leone 35.00% d no 14 -

Nicaragua 30.00% c no 40 -

Singapore 17.00% a yes 1,253 -

Niger 30.00% b no 22 -

Sint Maarten 34.50% a no 1 -

Nigeria 30.00% a no 185 -

Slovak Republic 19.00% a no 657 1

Norway 28.00% a no 3,102 114

Slovenia 18.00% a no 373 12

Oman 12.00% a no 47 -

Solomon Islands 30.00% b no 5 -

Pakistan 35.00% a no 85 -

South Africa 34.55% a no 1,015 -

Palestinian, State of 15.00% g no 2 -

South Sudan 15.00% b no 1 -

Panama 25.00% a yes 211 -

Spain 30.00% a no 9,390 93

Papua New Guinea 30.00% a no 39 -

Sri Lanka 28.00% a no 53 -

Paraguay 10.00% a no 26 -

Sudan 35.00% a no 6 -

Peru 30.00% a no 344 -

Suriname 36.00% f no 4 -

Philippines 30.00% a no 232 -

Swaziland 30.00% c no 11 -

Poland 19.00% a no 4,042 94

Sweden 26.30% a no 7,558 241

Portugal 25.00% a no 1,976 30

Switzerland 21.17% a yes 3,021 147

Puerto Rico 30.00% c no 34 -

Syrian Arab Republic 28.00% a no 10 -

Qatar 10.00% a no 38 -

Taiwan 17.00% a no 288 -

Reunion N/A

no 4 -

Tajikistan 15.00% c no 5 -

Romania 16.00% a no 1,147 -

Tanzania 30.00% a no 57 -

Russian Federation 20.00% a no 5,042 34

Thailand 23.00% a no 427 -

Rwanda 30.00% c no 3 -

Togo N/A

no 22 -

Country

Statutory

Corporate Tax

Rate

Tax

Haven No. of

subsidiaries

No. of

ultimate

owners

Country

Statutory

Corporate Tax

Rate

Tax

Haven No. of

subsidiaries

No. of

ultimate

owners

Tonga 25.00% e no 1 -

Uruguay 25.00% a yes 212 -

Trinidad and Tobago 25.00% a no 43 -

Usbekistan 9.00% g no 16 -

Tunisia 30.00% a no 232 -

Vanuatu 0.00% a yes 3 -

Turkey 20.00% a no 990 20

Venezuela 34.00% a no 283 -

Turkmenistan 20.00% c no 4 -

Vietnam 25.00% a no 170 -

Uganda 30.00% a no 33 -

Virgin Islands, British 0.00% f yes 276 -

Ukraine 21.00% a no 771 -

Yemen 20.00% a no 4 -

United Arab Emirates 55.00% a no 397 -

Zambia 35.00% a no 44 -

United Kingdom 24.00% a no 29,913 686

Zimbabwe 25.75% a no 64 -

United States 40.00% a no 13,186 -

180,234 3,023

This table shows the standard corporate tax rate of each country within the sample. We report tax rates that include any surtax levied on all corporations. For countries in which

the statutory tax rate is size-related we select the highest rate. The tax haven classification is taken from Dyreng and Lindsey (2009). Global ultimate owners are only based in

Europe because Amadeus only provides data on European firms. However, the ownership database of Amadeus includes data on the location of European firm’s worldwide

subsidiaries. Subsidiaries have to be owned by the ultimate owner by more than 50% directly or indirectly to be included in the sample. The tax rate data are taken from various

sources (where the publication does not contain the 2012 statutory tax rates, we assume that they have remained unchanged): a: KPMG Corporate Tax Rate Table 2012, available at: http://www.kpmg.com/global/en/services/tax/tax-tools-and-resources/pages/corporate-tax-rates-table.aspx. b: Deloitte Corporate Tax Rates Matrix 2012, available at: http://www.deloitte.com/assets/Dcom-

Global/Local%20Assets/Documents/Tax/Taxation%20and%20Investment%20Guides/

matrices/dttl_corporate_tax_rates_2012.pdf. c: PWC Worldwide Tax Summaries 2012/2013, available at: http://www.pwc.com/gx/en/tax/corporate-tax/pdf/pwc-worldwide-tax-summaries-corporate-2012-13.pdf. d: PKF Worldwide Tax Guide 2011, available at: http://www.pkf.com/media/387417/pkf%20worldwide%20tax%20guide%202011%20v2.pdf. e: Ernst & Young 2011 Worldwide Corporate Tax Guide, available at:

http://www.ey.com/Publication/vwLUAssets/Worldwide_Corporate_Tax_Guide_PDF_Publication/$FILE/

Worldwide_Corporate_Tax_Guide_2011.pdf. f: Deloitte Corporate Tax Rates Matrix 2010, available at http://www.deloitte.com/assets/Dcom-

Denmark/Local%20Assets/Documents/Presserum/Satser_for_selskabsskat_2010.pdf. g: PWC Paying Taxes 2011, available at http://www.pwc.com/gx/en/paying-taxes/pdf/paying-taxes-2011.pdf.

49

Table 2: Sample Selection Process

subsidiaries

(cross-section

of 2010)

group-level

observations

(cross-section

of 2010)

group-level

observations

(2005-2010)

H1a, H1b H2

European firms available on Compustat

6,149

- parent companies not available on Amadeus -1,316

Initial sample 219,063 4,883 29,298

- subsidiaries included in more than one group -22,695

- subsidiaries with missing country code -2,008

- groups that only have affiliates in one country -12,929 -1,784 -10,704

181,431 3,099 18,594

H1a,

H1b

- missing data for H1a, H1b -1,197 -76

Sample used for H1a, H1b 180,234 3,023

H2

- missing data for H2 -2,164

Sample before calculation of tax avoidance variables 16,430

- observations with missing data to calculate GAAP ETR -3,086

GAAP ETR sample 13,344

- observations with missing data to calculate CASH ETR -13,823

CASH ETR sample 2,607

- observations with missing data to calculate total book-tax

differences -4,883

Total book-tax differences sample 11,547

This table describes the sample selection process. The initial dataset is taken from Compustat Global, and then

matched to Amadeus ownership data using the ISIN number.

50

Figure 1: Histogram of the complexity index

This histogram shows the distribution of the composite measure of group complexity, which ranges from zero to

sixteen and represents the dimensions number of subsidiaries, maximum length of an ownership chain, number of

cross-country ownership links, and percentage of holdings.

0

100

200

300

400

500

Fre

que

ncy

0 5 10 15complexscore

51

Table 3: Complexity Score Properties

Panel A

Principal components

Component Eigenvalue Difference Proportion

#1 2.328 1.604 0.584

#2 0.729 0.187 0.183

#3 0.601 0.150 0.136

#4 0.342 . 0.098

Rotated component loading

Variable #1 Subsidiaries score 0.5394 Maximum level score 0.5640 Cross-country link score 0.4428 Holdings score 0.4414

This panel presents results of a principal component analysis of the four elements that are included in the composite

measure of group complexity. Variable definitions are provided in Appendix A.

Panel B

Pearson Correlation Coefficients

Subsidiaries

Score

Maximum Level

Score

Cross-country

Link Score

Holdings Score

Subsidiaries score 1

Maximum level score 0.650

1

Cross-country link score 0.396

0.451

1

Holdings score 0.395 0.449 0.271 1

This panel presents Pearson correlation coefficients of the complexity score dimensions. Variable definitions are

provided in Appendix A. Coefficients that are significant at the 1% level are bolded (two-tailed).

Table 4: Descriptive statistics

Panel A

N Mean Std Dev Median Min Max

Complexity score

Number of subsidiaries 3,023 61.24 118.07 21.00 2.00 1,231.00

Subsidiaries score 3,023 1.93 1.45 2.00 0.00 4.00

Maximum level 3,023 2.91 1.78 2.00 1.00 10.00

Maximum level score 3,023 1.41 1.45 1.00 0.00 4.00

Number of cross-country links 3,023 9.23 27.63 1.00 0.00 363.00

Cross-country link score 3,023 1.43 1.60 1.00 0.00 4.00

Percentage holdings 3,023 0.05 0.08 0.02 0.00 0.67

Holdings score 3,023 1.51 1.66 0.00 0.00 4.00

ComplexityScore 3,023 6.28 4.66 6.00 0.00 16.00

Tax incentive

StrDiff 3,023 0.20 0.12 0.17 0.00 0.55

TaxHaven 3,023 0.57 0.50 1.00 0.00 1.00

Income mobility

Intangible assets 3,023 0.18 0.20 0.11 0.00 0.98

Gross profit margin 3,023 6.97 644.80 28.06 -21,800.00 16,242.85

Number of observations in mobile industries 601

IncomeMobility 3,023 0.19 0.39 0.00 0.00 1.00

Firm characteristics and control variables

Number of countries 3,023 11.15 14.63 14.63 2.00 146.00

Total assets 3,023 10,842 189,366 321 0 9,235,993

Age 3,023 36.62 35.04 23.00 2.00 330.00

CFC 3,023 0.85 0.36 1.00 0.00 1.00

Panel A presents descriptive statistics for the sample used in the examination of H1a and H1b. Variable definitions are provided in Appendix A. All

continuous variables are winsorized at the 1% and 99% percentile.

Panel B

TAXAVOID=1–GAAP ETR TAXAVOID=1–CASH ETR TAXAVOID=TOTALBTD

N Mean Median Std Dev N Mean Median Std Dev N Mean Median Std Dev

Dependent Variable

Tobin's q 13,344 1.676 1.303 1.215 2,607 1.639 1.351 1.010 11,547 1.615 1.321 1.027

Variables of Interest

TAXAVOID 13,344 0.758 0.747 0.160 2,607 0.762 0.781 0.165 11,547 0.060 0.046 0.069

Complexity Score 13,344 6.594 6.000 4.638 2,607 7.517 8.000 4.581 11,547 6.880 7.000 4.562

Control Variables

LogSales 12,089 5.812 5.856 2.444 2,602 6.659 6.637 2.296 11,152 6.128 6.051 2.167

3YearSalesGrowth 11,774 0.425 0.139 0.481 2,553 0.098 0.126 0.391 10,917 0.135 0.148 0.356

Volatility 12,702 0.261 0.366 0.222 2,524 0.380 0.343 0.171 11,054 0.393 0.349 0.187

InstOwnership 13,000 21.082 12.630 23.481 2,550 21.215 13.695 22.995 11,246 20.895 12.260 23.416

CommonLaw 12,536 0.261 0.000 0.440 2,447 0.253 0.000 0.435 10,819 0.232 0.000 0.422

AntidirectorRights 12,536 2.937 3.000 1.511 2,447 2.751 3.000 1.554 10,819 2.850 3.000 1.484

OwnershipConcentration 12,536 0.344 0.310 0.162 2,447 0.366 0.360 0.163 10,819 0.353 0.310 0.163

EU 13,344 0.901 1.000 0.298 2,607 0.888 1.000 0.316 11,547 0.900 1.000 0.300

Panel B presents descriptive statistics for the sample used in the examination of H2. The three different subsamples result from differential data

requirements to calculate one of the three tax avoidance measures (GAAP ETR, CASH ETR, total book-tax differences) used in our regressions.

Variable definitions are provided in Appendix A. All continuous variables are winsorized at the 1% and 99% percentile.

Table 5: Pearson Correlation Coefficients

Panel A

Pearson Correlation Coefficients

Complexity

Score StrDiff

TaxHaven

Income

Mobility CFC

Num

Countries Size

Age

Complexity Score 1

StrDiff

0.173

1

TaxHaven

0.193

0.524

1

Income Mobility –0.143

0.020

–0.005

1

CFC

0.023

–0.015

–0.153

0.066

1

NumCountries 0.201

0.712

0.407

0.005

–0.016

1

Size

0.048

0.050

0.043

–0.013

–0.043

0.068

1

Age

0.158

0.155

0.097

–0.157

–0.014

0.234

0.003

1

This table presents Pearson correlation coefficients of the main variables used in the analysis of H1a and H1b. Variable definitions are provided in

Appendix A. Coefficients that are significant at the 1% level are bolded (two-tailed).

Panel B

Pearson Correlation Coefficients

Tobin's q

GAAP

ETR

CASH

ETR

Total

BTD

Com-

plexity

Score

LogSales

Sales

Growth Volatility

Inst

Owner-

ship

Tobin's q

1

GAAP ETR

0.128

1

CASH ETR

0.076

0.474

1

Total BTD

0.351

0.196

0.230

1

Complexity Score

–0.171

–0.099

–0.077

–0.132

1

LogSales

–0.197

–0.207

–0.126

–0.059

0.453

1

SalesGrowth

0.097

–0.070

–0.022

0.081

0.022

0.103

1

Volatility

0.166

0.221

0.170

0.042

–0.225

–0.418

–0.058

1

InstOwnership

–0.031

–0.007

0.010

–0.021

0.055

0.046

–0.002

-0.012

1

This panel presents Pearson correlation coefficients of the main variables used in the analysis of H2. Variable definitions are provided in Appendix A.

Coefficients that are significant at the 1% level are bolded (two-tailed).

56

Table 6: Regression Results

Panel A

Dependent Variable: ComplexityScore

Pred.

Sign

(H1a)

(H1b)

Coeff

t-stat

Coeff

t-stat

TaxIncentive = StrDiff

+

1.977

2.10 **

1.770

1.79 **

IncomeMobility*TaxIncentive +

1.915

1.24

IncomeMobility

?

–1.165

–2.87 ***

CFC

?

–0.080

–0.23

–0.067

–0.19

NumCountries

+

0.045

5.72 *** 0.045

5.72 ***

Size

+

0.000

3.96 *** 0.000

3.99 ***

Age

+

0.019

7.32 *** 0.018

6.95 ***

EUMember

?

0.851

2.52 **

0.838

2.49 **

CommonLaw

?

0.911

2.63 *** 0.938

2.72 ***

InvestorRights

?

0.335

2.45 **

0.314

2.30 **

OwnershipConcentration

?

1.410

1.42

1.225

1.23

Intercept

?

1.126

0.94

1.318

1.10

IndustryFE

yes

yes

Observations

2,795

2,795

Adjusted R² 0.183 0.186

This panel presents results from regression models to test H1a and H1b. In this panel, we use StrDiff to measure tax incentives.

Variable definitions are provided in Appendix A. We use robust standard errors in each model. *,**,*** denote statistical significance

at the 10%, 5%, and 1% levels, respectively. The indications of statistical significance are based on one-tailed test if we have a

prediction concerning the direction of the influence and two-tailed otherwise. The first column presents results from testing H1a; the

second column reports results from the test of H1b. All continuous variables are winsorized at the 1% and 99% percentile.

Panel B

Dependent Variable: ComplexityScore

Pred.

Sign

(H1a)

(H1b)

Coeff

t-stat

Coeff

t-stat

TaxIncentive = TaxHaven

+

0.944

5.01 *** 0.805

3.84 ***

IncomeMobility*TaxIncentive +

0.769

2.02 **

IncomeMobility

?

–1.216

–3.95 ***

CFC

?

0.190

0.53

0.186

0.53

NumCountries

+

0.044

7.21 *** 0.044

7.26 ***

Size

+

0.000

3.97 *** 0.000

3.98 ***

Age

+

0.018

7.23 *** 0.018

6.93 ***

EUMember

?

0.823

2.46 **

0.803

2.41 **

CommonLaw

?

0.947

2.77 *** 0.988

2.89 ***

InvestorRights

?

0.329

2.42 **

0.311

2.29 **

OwnershipConcentration

?

1.326

1.34

1.141

1.15

Intercept

?

0.756

0.64

0.994

0.83

IndustryFE

yes

yes

Observations

2,795

2,795

Adjusted R² 0.190 0.193

This panel presents results from regression models to test H1a and H1b. In this panel, we use TaxHaven to measure tax incentives.

Variable definitions are provided in Appendix A. We use robust standard errors in each model. *,**,*** denote statistical significance

at the 10%, 5%, and 1% levels, respectively. The indications of statistical significance are based on one-tailed test if we have a

prediction concerning the direction of the influence and two-tailed otherwise. The first column presents results from testing H1a; the

second column reports results from the test of H1b. All continuous variables are winsorized at the 1% and 99% percentile.

57

Panel C

Dependent Variable: Tobin's q (H2)

Pred.

Sign

TaxAvoidance =

1–GAAP ETR

TaxAvoidance =

1–CASH ETR

TaxAvoidance =

Total BTD

Coeff

t-stat

Coeff

t-stat

Coeff

t-stat

TaxAvoidance

+ 1.248 6.42 *** 0.800 2.66 *** 6.722 10.45 ***

ComplexityScore

? 0.052

3.15 *** 0.009

0.42

–0.010

–2.04 **

TaxAvoidance*ComplexityScore

– –0.110

–4.76 *** –0.073

–2.42 *** –0.242

–3.17 ***

LogSales

– –0.028

–2.53 *** 0.017

0.99

0.023

2.65 ***

SalesGrowth

+ 0.194 4.39 *** 0.123 0.91

0.224 4.79 ***

Volatility

? 0.289 2.76 *** –0.022 –0.14

0.137 1.37

InstOwnership

? –0.001

–1.55

0.000

0.29

–0.001

–1.31

EUMember

? –0.177

–2.40 **

–0.004

–0.05

–0.105

–1.66 *

CommonLaw

? 0.099

1.19

0.163

1.59

0.288

4.21 ***

InvestorRights

? 0.035

1.09

–0.013

–0.37

0.032

1.18

OwnershipConcentration

? –0.213

–1.01

–0.507

–1.78 *

0.125

0.68

Intercept

? 1.278

5.49 *** 1.601

4.75 *** 1.175

7.08 ***

YearFE

yes

yes

yes

CountryFE

no

no

no

Observations

10,505

2,310

9,733

Adjusted R² 0.120 0.111 0.233

This panel presents results from regression models used to test H2, in which we measure tax avoidance by three alternative tax

avoidance proxies. We use 1–GAAP ETR in column (1), 1–CASH ETR in column (2), and total book-tax differences in column (3) to

proxy for tax avoidance. Variable definitions are provided in Appendix A. We use standard errors clustered by firm in each model.

*,**,*** denote statistical significance at the 10%, 5%, and 1% levels, respectively. The indications of statistical significance are

based on a one-tailed test if we have a prediction concerning the direction of the influence and on a two-tailed test otherwise. All

continuous variables are winsorized at the 1% and 99% percentile.

58

Table 7: Results concerning H2 Including a Control for Equity-based Compensation

Dependent Variable: Tobin's q

Tax avoidance variable: 1–GAAP ETR

Pred.

Sign Coeff

t-stat

TaxAvoidance

+

1.813

1.69 **

ComplexityScore

?

0.125

1.77 *

TaxAvoidance*ComplexityScore –

–0.213

-2.21 **

Bonus

+

0.316

1.14

LogSales

–

–0.112

-3.41 ***

SalesGrowth

+

0.140

0.77 **

Volatility

?

–0.313

-0.87

InstOwnership

?

–0.002

-0.6

Intercept

?

1.745

2.0 *

YearFE

yes

CountryFE

no

Observations

556

Adjusted R² 0.194

This table presents results from regressions of Tobin’s q on 1–GAAP ETR, ComplexityScore, the interaction between 1–

GAAP ETR and ComplexityScore, and control variables, where the list of control variables additionally includes a

measure of equity-based compensation (Bonus). We cluster standard errors by firm. *,**,*** denote statistical

significance at the 10%, 5%, and 1% levels, respectively. The indications of statistical significance are based on a one-

tailed test if we have a prediction concerning the direction of the influence and on a two-tailed test otherwise. All

continuous variables are winsorized at the 1% and 99% percentile.

59

Table 8: Second Stage Regression of the Instrumental Variables Approach

Second stage regression

Dependent variable: Tobin's q

Pred.

Sign

Coeff

t-stat

1–GAAP ETR

+

1.607

1.720 **

ComplexityScore

?

0.331

3.770 ***

(1–GAAP ETR)*ComplexityScore

–

–0.487

–4.090 ***

LogSales

–

–0.055

–6.200 ***

SalesGrowth

+

0.185

4.600 ***

Volatility

?

0.269

2.750 ***

InstOwnership

?

–0.001

–3.110 ***

Intercept

?

0.852

1.220

YearFE

yes

CountryFE

yes

Observations 11,117

This table presents the results from the second stage regression within the two-stage least squares approach. We use

indicator variables for the firm’s one-digit SIC classification as well as their interactions with ComplexityScore as

instruments. We use robust standard errors. *,**,*** denote statistical significance at the 10%, 5%, and 1% levels,

respectively. The indications of statistical significance are based on a one-tailed test if we have a prediction concerning

the direction of the influence and on a two-tailed otherwise. All continuous variables are winsorized at the 1% and 99%

percentile.

Table 9: Regression Results for Alternative Index Specifications

Panel A

Dependent Variable: ComplexityScore

Pred.

Sign

Score1

Score2

Score3

Score4

Coeff

t-stat

Coeff

t-stat

Coeff

t-stat

Coeff

t-stat

TaxIncentive = StrDiff

+

2.440

3.26 *** 2.249

3.16 ***

1.01

1.35 *

0.23

0.31

CFC

?

–0.336

–1.19

–0.127

–0.47

0.24

0.86

–0.02

–0.07

NumCountries

+

0.033

5.21 *** 0.032

5.45 ***

0.03

4.87 ***

0.04

6.39 ***

Size

+

0.000

3.30 *** 0.000

4.26 ***

0.00

3.74 ***

0.00

4.01 ***

Age

+

0.011

5.81 *** 0.015

7.73 ***

0.02

8.33 ***

0.01

6.18 ***

EUMember

?

0.659

2.46 **

0.730

2.83 ***

0.47

1.84 *

0.69

2.45 **

CommonLaw

?

0.636

2.32 **

0.276

1.05

1.90

6.98 ***

–0.08

–0.27

InvestorRights

?

0.113

1.03

0.318

3.08 ***

–0.05

–0.42

0.62

5.61 ***

OwnershipConcentration

?

–0.191

–0.24

1.416

1.88 *

0.39

0.50

2.62

3.28 ***

Intercept

?

0.918

1.03

0.966

1.03

2.02

1.77 *

–0.52

–0.63

IndustryFE

yes

yes

yes

yes

Observations

2,795

2,795

2,795

2,795

Adjusted R² 0.156 0.175 0.196 0.175

This panel presents results from regression models that test H1a. We use StrDiff to measure tax incentives. Variable definitions are provided in

Appendix A. Robust standard errors are used in each model. *,**,*** denote statistical significance at the 10%, 5%, and 1% levels, respectively. The

indications of statistical significance are based on one-tailed test if we have a prediction concerning the direction of the influence and two-tailed

otherwise. The first column excludes the first complexity dimension; the second column excludes the second complexity dimension etc.

Panel B

Dependent Variable: Tobin's q

Pred.

Sign

Score1

Score2

Score3

Score4

Coeff

t-stat

Coeff

t-stat

Coeff

t-stat

Coeff

t-stat

TaxAvoidance

+ 1.082 5.94 *** 1.272 6.62 ***

1.275 6.83 ***

1.162 6.13 ***

ComplexityScore

? 0.061 2.83 *** 0.072 3.39 ***

0.070 3.39 ***

0.059 2.81 ***

TaxAvoidance*ComplexityScore – –0.120 –3.93 *** –0.148 –5.07 ***

–0.150 –5.28 ***

–0.127 –4.23 ***

LogSales

–

–0.037

–3.37 *** –0.029

–2.63 ***

–0.027

–2.49 ***

–0.032

–2.86 ***

SalesGrowth

+

0.196

4.40 *** 0.194

4.39 ***

0.195

4.40 ***

0.196

4.42 ***

Volatility

?

0.306

2.91 *** 0.283

2.71 ***

0.290

2.77 ***

0.302

2.88 ***

InstOwnership

?

–0.001

–1.59

–0.001

–1.51

–0.001

–1.56

–0.001

–1.57

EUMember

?

–0.193

–2.60 *** –0.175

–2.37 **

–0.170

–2.32 **

–0.179

–2.43 **

CommonLaw

?

0.074

0.90

0.078

0.95

0.155

1.78 *

0.058

0.71

InvestorRights

?

0.029

0.90

0.038

1.18

0.023

0.69

0.049

1.50

OwnershipConcentration

?

-0.254

–1.20

-0.198

–0.94

-0.247

–1.16

-0.171

–0.81

Intercept

?

1.430

6.34 *** 1.253

5.42 ***

1.288

5.62 ***

1.297

5.52 ***

YearFE

yes

yes

yes

yes

Observations

10,505

10,505

10,505

10,505

Adjusted R² 0.114 0.120 0.123 0.118

This panel presents results from regression models that test H2. We use 1–GAAP ETR to measure tax avoidance. Variable definitions are provided in

Appendix A. Standard errors clustered by firm are used in each model. *,**,*** denote statistical significance at the 10%, 5%, and 1% levels,

respectively. The indications of statistical significance are based on one-tailed test if we have a prediction concerning the direction of the influence and

two-tailed otherwise. The first column excludes the first complexity dimension; the second column excludes the second complexity dimension etc.