TAX HAVEN FDI THEN FAMILY OWNED MNEs?
by
Lars Frankema
University of Groningen
Faculty of Economics and Business
ABSTRACT
This study examines if non family owned MNE’s are more likely to engage in tax haven FDI then family owned MNE’s. Next to that this study examines the effect of the factors firm performance, firm size, firm age, long term debt and intangible assets in relation to tax haven FDI. The effect and relation of all these factors is measured by the statistical program SPSS. The study is conducted on a sample of 1168 MNE’s and the data used in this research is obtained from database ORBIS. The results of this study are obtained through a binary logistic regression analysis. According to the results obtained from this analysis the data shows that family ownership has a positive significant relation to tax haven FDI. This study therefore concludes that non family owned MNE’s are less likely to engage in tax haven FDI in comparison with family owned MNE’s.
Keywords: Tax haven FDI, tax haven, MNE, family owned, non-family owned. Research theme: International business and tax haven FDI
Supervisor: Drs. A. Visscher
TABLE OF CONTENTS
1. Introduction and Central Research Question ... 4
2. Theory ... 7
2.1 Family owned firm ... 7
2.2 Tax haven FDI ... 9
2.3 Tax haven countries ... 13
3. Research Methodology ... 17 3.1 Sample ... 17 3.2 Methodology ... 18 3.3 Dependent variable ... 19 3.4 Independent variables ... 19 3.5 Control variables ... 20
4. Data Analysis and Results ... 21
4.1 Descriptive statistics ... 21
4.2 Correlation ... 22
4.3 Binary logistic regression ... 23
5. Conclusion and Discussion ... 26
5.1 Conclusion ... 26
5.2 Discussion ... 29
APPENDIXES ... 34
Appendix A - SPSS output tables. ... 34
1. Introduction and Central Research Question
Since decades tax avoidance has had a high priority on the political agendas of worldwide policy makers around the world (Gravelle, 2009; Johannesen & Zucman, 2014). Recently published articles in several newspapers brought to light that a lot more firms engaged in tax avoidance than previously thought before (FinancialTimes.com, 2016). With the recent financial crisis in the back of the mind of a lot of people, tax avoidance by big multinational firms gives somewhat of a friction with the common tax payer. People think it is unfair to engage in such practices, despite the fact that it is most of the time legal what they are doing. In addition to that, recent studies like that of Johannesen & Zucman, (2014) even found that tax evaders just shifted deposits to havens that were not covered by new bank secrecy rules, set up after the crisis. The crisis just caused a relocation of tax avoidance activities, they stated.
This research tries to study what kind of firms engage in tax haven activities. In recent news came out that the usage of tax haven countries by MNE’s is far bigger than previously expected. Especially the usage of tax havens by smaller firms came out as a surprise to many. Because of the recent exposure by the so called ‘Panama Papers’ a lot of those secrets came to light. Questions that rise are: what kind of firms make use of these possibilities? And what drives these firms to do this?
The contribution of this study is important because it elaborates on current research into factors that determine the usage of tax havens by MNE’s. Moreover, this research partly builds on the research of Chen, Chen, Cheng & Shevlin (2010). They state that tax is a major form of costs in business, and that shareholders in general prefer tax aggressiveness in order to cut costs and gain higher profits. In their research they studied differences of tax aggressiveness between family and non-family owned firms. They found that non-family owned firms, which are owned by shareholders, are more tax aggressive than family owned firms. The results from their study also conclude that family owners are less tax aggressive and more afraid of the possible consequences and reputation harm than non-family owned firms. The study of Chen et al. (2010) is mainly focussed of the characteristics of a firm and its owners. In addition to that, this study tries to expand this research by examining if a firm that is assumed to be more tax aggressive will actually also invest in tax havens.
Therefore the research question of this research is formulated as follows:
Are non-family owned MNEs more likely to engage in tax haven FDI then family owned MNEs?
In the end this research is an extension to the tax haven literature which will try to give new insights into what the possible determinants and importance is of ownership structure in relation to tax haven FDI.
2. Theory
The next chapter will give a broad description of previously written theory, specifically on what is already written about family firms and tax havens, tax haven FDI and tax haven countries. Furthermore this section describes several definitions of family firms provided by different studies. Furthermore it will provide the hypotheses of this study which are based upon the relevant literature.
2.1 Family owned firm
Following The Economist (2015), more than 90 percent of the world’s companies are family-managed or -controlled, including several of the largest, like News Corp and Volkswagen, which even has two key family owners. The economic magazine also stated that The Boston Consultancy Group examined that families own or control 33 percent of American businesses and 40 percent of German and French companies. They add that the dominance of family control in emerging economies is even bigger.
affiliates) or power throughout management (fraction of family associates in the top of the managing team). Furtermore, Klein (2000) states that for a firm to be a family firm, certain shares have to be kept inside the family.
Following the Center for Family Business at the University of St.Gallen, Switzerland, this research focusses on their definition of a family business. They state that for a privately owned business, an organization is categorised as a family business whenever a family owns above 50 percent of the available voting rights. Describing a publicly listed firm, a business is categorized as a family business if the family is keeping at minimum 32 percent of the available voting rights. The 32 percent part of the total is clarified by the research results that in OECD countries averagely 30 percent of the votes are enough to dominate the general assembly of a publicly listed organization. This stands because on averagely just approximately 60 percent of the votes are present in the general assembly. To be more conventional in their categorization they decided to utilize a 32 percent part, which happens to be also a lot more conventional than previous academic papers which most of the time use a 25 or 20 percent limit. The list of firms in their research was constructed out of ownership data according to the year 2013, respectively the last presented year. When describing the market segments, according to Chen et al. (2010) there happen to be more family organizations than non-family organizations in leisure, publication, clothing, pharmaceuticals, building, electronics, transport, vending and eating places. Contrary to that, non-family business overshadow family business in steel machinery, equipment, oil and gas, services, hoteling, finance, and trade.
foreign firm ownership leads to a reduction rather than an increase in corporate tax rates. Question remains what kind of firms engage in these kind of tax avoidance practices? Are those firms family owned or non-family owned? Chen et al. (2010) found interesting results regarding these questions. They state that tax is a major form of costs in business, and that shareholders in general prefer tax aggressiveness. In their research they studied differences of tax aggressiveness between family and non-family owned firms. They found that that non-family owned firms are more tax aggressive than family owned firms. The results from this study also concludes that family owners are more afraid of the possible consequences and harm of reputation than non-family owned firms. In the end it can be concluded that no study so far has focussed on the ownership structure of a firm as a predicting effect on the utilisation of tax havens.
Hypothesis 1:
An MNE is more likely to engage in tax haven FDI when it is non-family owned.
2.2 Tax haven FDI
tax that they are in fact lawfully obliged to pay. Furthermore, persons that are investing in overseas capital may not declare the revenues they earn from their investment. Next to individual tax reductions, estimates have also been made about corporate tax minimization which can also arise from profit movement. Approximations of the income losses that arise from corporate profit movement fluctuate substantially but seem to be even bigger than individual tax reduction. Overall estimations are alternating from 10 billion US dollar to 90 billion US dollar per year in the US only (Gravelle, 2009).
Just like the estimates of tax evasion and tax avoidance, the overall estimations of the economic influence of tax havens also differs. According to Palan (2002), several analysts state that more than 50 percent of the global reserves of money and cash are passing through a tax haven. Furthermore he states that in previous literature estimations have been made that 20 percent of the sum of all international private fortune and 22 percent of all the banks’ external properties and resources are invested in tax havens. He also states that several other studies make estimations of the total resources situated at tax havens which lay around 5.1 trillion US dollar. Previous research, like that of Hines & Rice (1994) already made estimations of US firms that by 1994 the total quantity of US investments in foreign tax havens was already around $359 billion US dollar. Which could be compared with over one-quarter of corporate activity ($1.39 trillion US dollar in total) conducted around the world by offshore affiliates of US businesses. With no exceptions, hence, the tax haven occurrence is of vast and increasing significance to the economy of this day.
Following Jones & Temouri, (2016), the usage of tax havens by firms can be described as tax haven FDI. Previous literature on tax haven FDI, like that of Jones & Temouri (2016), focused their research on the determinants of tax haven FDI. Their main focus was on the effects of firm specific advantages and country specific advantages on tax haven FDI. They concluded that MNE’s size in the form of the companies’ turnover can be a cause of tax haven FDI. Namely, the larger the firm the higher its revenues are presumably. With higher revenues it is assumed to be more attractive to invest in tax haven FDI because the higher the revenue the more tax you have to pay in your home (non-tax haven) country. They add that the results of their study also imply that MNEs with bigger cash flows and lower long term debts the chances of being active in tax haven FDI are way higher.
Hypothesis 2:
An MNE is more likely to engage in tax haven FDI when it has high firm performance in terms of overall revenue.
Hypothesis 3:
An MNE is more likely to engage in tax haven FDI when it has low long term debts.
Hypothesis 4:
An MNE is more likely to engage in tax haven FDI when it has a high amount of intangible assets.
Several previous studies focussed on the usage of tax havens by firms before (Orlov, 2004; Allen & Morse, 2013). For example Dyreng, Lindsey & Thornock (2013), examined whether some US states can be used as possible tax havens by firms. In addition, Van Dijk et al. (2006), concluded in their study that the Netherlands can be considered a tax haven. Other literature like that of Hanlon & Slemrod (2009) focused on stock price reactions to news about firms engaging in tax haven FDI. They found that the stock price of firms declines when associated with tax havens. Research of Sutherland & Matthews (2009) examined Chinese outward FDI into Caribbean tax havens and concluded that tax haven FDI can be linked to institutional quality and weak regulatory of government policies. Companies are facing huge environmental changes because of the globalization of world markets and production. In order to increase their competitive advantage, firms are intensifying their businesses into multiple regions. Several previous studies focussed on the effects of the degree of internationalization (DOI) on firm performance (Gerpott, & Jakopin, 2005; Glaum & Oesterle, 2007; Yang & Zhang, 2009). Following Sullivan (1994), the DOI is defined by foreign sales as a percentage of total sales. He states that the DOI of a firm has three features: ‘performance (what goes on overseas), structural (what resources are overseas) and attitudinal (what is top management's international orientation’ (p. 331). In addition, Desai et al. (2006), found that firms that grow in firm performance are more likely to engage in tax haven operations. They add that firms with significant large amount of foreign operations gain the greatest from the usage of tax haven possibilities. By analysing associate-level data from American firms, they came to the conclusion that bigger, larger internationally focussed firms, especially the ones with widespread intra firm business, have a higher tendency to make use of foreign tax havens. Therefore it can be assumed that firms with a higher degree of internationalization are more likely to engage in tax haven FDI.
Hypothesis 5:
In addition to that, Chen et al. (2010) ad that by making use of 3,865 business-year measurements from 1500 different organizations in the period of the years 1996 till 2000, they documented that family businesses show less tax aggressiveness than the non-family equivalents, as confirmed by the more prominent effective tax rates and their subordinate book-tax dissimilarities. Following Chen et al. (2010), these measurements hold both before and after they controlled for firm appearances which are cross-sectionally linked with their tax aggressiveness observations: ‘firm performance, intangible assets and firm size’ (p.4.). Taking into account these control variables ensure that the observed dissimilarities of tax aggressiveness among family and non-family business is not determined through fundamentals. Following previous research of Chen et al. (2010) the researcher adds the following control variables. First firm age, because family businesses are not as much tax educated as non-family businesses, for the reason that they are generally younger and organizations become skilled by experience in running a business. Secondly firm size, as larger firms could possibly invest more in tax preferred assets that create timing modifications in the identification of expenditures.
Hypothesis 6:
An MNE is more likely to engage in tax haven FDI when it is older.
Hypothesis 7:
An MNE is more likely to engage in tax haven FDI when it has a bigger firm size.
2.3 Tax haven countries
The government misses both individual as well as corporate income tax resulting from transferring of taxable income to low-tax nations, mostly described as tax havens (Gravelle, 2009). According to Palan, Murphy & Chavagneux (2013), there is no consensus to what a ‘tax haven’ means and therefore it lacks a well-defined definition.
use of tax havens are also rarely mentioning their tax haven activities. In fact, they compensate for the opportunity of “hiring” a mailbox over there. Which can also be seen as taking the benefit of the juridical services presented to the companies and which Palan (2002) states, can indirectly be called “effective international tax strategy,” which in other words is a fancy way of mentioning the avoidance or evading of tax, he adds. However with the growing amount of available tax havens and companies that are willing to make use of them, the theories of supply and demand seem to normalize the costs of licensing payments and the characteristics of the lawful shelter that tax havens have to offer. As another way of saying, tax havens are facilitating convenience into matters of placement of businesses that want to invest in tax havens.
In addition, Palan (2002) states that most of the time, tax havens can be seen as a completely lawful approach for progress particularly suitable to so called microstates. Previous studies acknowledge, in spite of this motive that the reason for these kind of nations is to gain income rents from the companies which would otherwise only be credited to bigger nations. Several studies view these developments as a commercialization of sovereignty, as it is carried out by these nations to be an exploitation of the regulations and policies of sovereignty. Palan (2002) mentions that previous literature states that it is a completely lawful approach, however that it could possibly lead to exploitations in a way that it would support tax evasion and white washing of cash and capital. Furthermore, he adds that previous literature state that such exploitations can be improved if worldwide morals, values and regulation are approved on and actually be followed as stated. The overall unanimity appears to be that tax havens are flourishing for the reason that regulations and tax policies, in more developed industrial economies and nations, are increasing.
According to Jones & Temouri (2016), tax havens let MNEs transfer profits from high tax areas into low tax territories. These countries are typified by a high level of confidentiality and remarkably low (frequently nothing) amounts of corporate income taxes. This way firms avoid detection of tax avoidance by the government (Chen et al. 2010). Moreover, some studies also found positive outcomes of tax haven usage. Dharmapala, (2008), found that the existence of tax havens can increase efficiency and even moderate tax competition and facilitate investment. In his research he found that it is possible that tax havens allow high-tax countries to impose lower corporate tax rates on highly mobile companies and taxing immobile companies higher. This way highly mobile firms have less reason to move to tax haven countries.
In 2000 the OECD (2000, p.17) reported a list of 35 countries and territories that meet the criteria of a tax haven. They excluded six countries because they promised to improve their tax regulations in order to prevent companies from engaging in tax avoidance. Previous studies, like that of Dharmapala & Hines (2009), add these six countries and use the total of 41 selected countries listed as tax havens according to the OECD lists.
According to Palan (2002), inside this wide ranging lists of tax havens, it is normal to differentiate between four types of tax haven countries:
1. Nations without income tax and where transcontinental firms are only compensated for authorization costs (examples of these countries are: Anguilla, the Bahamas, Bahrain, Bermuda, the Cayman Islands, Cook Islands, Djibouti, Turks and Caicos, and Vanuatu). 2. Nations with little to no taxes (examples are Liechtenstein, Oman, Switzerland, Jersey,
Guernsey, and the British Virgin Islands).
3. Nations that only charge tax on domestic chargeable activities. Moreover, proceeds from overseas resources are both not taxed or tax is charged at minimal percentages (illustrations could be: Liberia, Panama, Philippines, Venezuela, and Hong Kong). 4. Nations that allow particular tax rights to specific kinds of businesses or companies.
(cases of those nations are: the Channel Islands, Liechtenstein, Luxembourg, the Isle of Man, Monaco, the Netherlands, the Netherlands Antilles, Austria, and Singapore).
3. Research Methodology
The Following chapter will provide the research methodology. Moreover, it will show what the research sample is consisting of and it displays the meaning and definitions of the dependent, independent and control variables.
3.1 Sample
This research is focussed on multinational enterprises with subsidiaries in dot tax havens. The sample in this research contains MNE’s who engage in tax haven FDI and MNE’s who do not engage in tax haven FDI. In both cases MNE’s can be family firms or non-family firms. Dot tax havens where chosen because this increases the chance that firms engage in tax haven FDI for tax reasons only. The sample of firms is chosen upon the research criteria selected in the literature part. This research therefore focusses on the selection criteria revenue, intangible assets, long term debt, family ownership, firm age and firm size. Literature shows that firms which possess these criteria are most likely to be active in tax haven FDI, which makes this sample not entirely randomly selected. To make a selection of firms this research makes use of ORBIS which is a firm-level database offered by Bureau van Dijk, a prominent electronic issuer of yearly account information balances of organizations from every country of the world.
Most of the previous research found that firms with high firm performance were more likely to engage in tax haven FDI (Jones & Temouri, 2016; Chen et al., 2010; Desai, 2006). Following these statements the sample of this research will contain out of the 2500 largest MNE’s, measured in the amount of revenue, which can be found in the database ORBIS. Because of missing data in the database the sample excludes 1332 MNE’s from this list. In the end the ultimate sample will contain out of 1168 MNE’s. A list of these firms can be found in Appendix B.
engage FDI in dot tax havens. For example, it could be possible that a firm owns a subsidiary in a dot tax haven and also in a ‘big-7’ tax haven. In this research that firm would end up in category 0 (no tax haven FDI), regardless of the fact that it does invest in a dot tax haven. In the end this could lead to a smaller sample size and less firms falling into category 1. Taking into account that a firm could both invest in a dot tax haven as well as in a ‘big-7’ tax haven could therefore influence the outcomes of the research.
3.2 Methodology
3.3 Dependent variable
Tax haven FDI
Because of the secrecy provided by tax haven countries it is hard to determine if a firm engages in tax avoidance. Therefore Jones & Temouri (2016), define the usage of tax havens by firms as ‘tax haven FDI’. They use tax haven FDI as a binary variable which corresponds 1 if an MNE owns a subsidiary in a tax haven country, and equals zero if it does not own a subsidiary in a tax haven country. Measurement of this dependent variable is done via the database ORBIS. ORBIS is a firm-level database offered by Bureau van Dijk, a prominent electronic issuer of annual reports and financial data from firms throughout the world. Because this variable is a dummy variable, it means it has limitations. It would be preferable to have more detailed information about the exact amount that firms invest in tax haven FDI, but due to the bank secrecy policies in tax haven countries and because firms do not present their financial information from tax havens in their annual reports, that kind of data will not be available for all firms.
3.4 Independent variables
Firm ownership
Following the database and definition of a family firm according to the Center for Family Business at the University of St.Gallen, Switzerland this research can describe if a firm is family or non-family owned. This variable will be a binary variable which correspondents 1 if a firm is family owned and zero if a firm is non-family owned.
Intangible assets
Long term debt
A low amount of long term debts are an indication that firms are more likely to invest in tax haven FDI. This data is also obtained from the database of ORBIS. Long term debt can be described as a financial statement in the balance sheet account which is specifically described as the advances and financial contracts of a firm which are owed for a period that exceeds one year. This may consist of normal bank loans, hypothecation bonds, debentures and other obligations which are not due for one year.
Firm performance
Lastly firms are selected by their amount of firm revenue as a measurement of firm performance. As stated by Jones & Temouri, 2016; Chen et al., 2010 and Desai, 2006, the larger the firm’s revenue, the larger is the likelihood of a firm investing in a tax haven country. Data for this measurement are also to be found in the database of ORBIS. The company’s revenue is stated at the balance sheet account and can be described as the total operating revenue (net sales + other operating revenue + stock variations). These numbers do not contain VAT or eliminate tax or comparable compulsory outflows.
Degree of internationalization
Following Sullivan (1994) a common measure of degree of internationalization is Foreign Sales as a Percentage of Total Sales. This study will also follow this measurement and tries to find this data regarding the DOI in the database of ORBIS.
3.5 Control variables
In order to investigate the relation of other variables related to the firm, a number of control variables are added. Following previous research of Chen et al. (2010) the researcher adds the following control variables.
Firm age
Firm age is measured as the number of years from the year that the firm was founded, which data is available in the database of ORBIS.
Firm size
4. Data Analysis and Results
This part of the research will provide the overall results and analysis of the data, gathered through the database ORBIS and measured by the statistics program SPSS.
4.1 Descriptive statistics
From the total sample of 2500 MNE’s an ultimate list is conducted. Because of missing data on several variables some of the firms have been excluded from the total sample. This means that the ultimate sample size contains out of 1168 MNE’s, as can be seen in table 2 on page 22. This table shows that 1097 MNE’s were non-family firms (93.9%) and 71 MNE’s were family firms (6,1%). Table 1 on page 21 shows the descriptive statistics that were required in this study. In this research the dependent variable is tax haven FDI, which is a dummy variable that displays if an MNE is engaging in tax haven FDI (value = 1) or if it does not engage in tax haven FDI (value = 0). The independent variables are Family ownership, containing the value 1 if the MNE is a family owned firm and containing the value 0 if it is not a family owned firm. The dummy variables can be seen in table 2. Firm performance, measured in the amount of revenue, intangible assets and long term debt are the remaining independent variables. Lastly, firm size, measured in the number of employees and firm age are the control variables in this research. Moreover, because of missing data, the independent variable degree of internationalization cannot be measured correctly, therefore this variable is excluded from the actual research.
Table 1 – Descriptive statistics
Mean St. Deviation Minimum Maximum Dependent variable
Table 2 - Family ownership * Tax haven FDI Cross tabulation
Tax haven FDI Total
NO YES Family ownership NO Count 717 380 1097 % 65.4% 34.6% 100.0% YES Count 27 44 71 % 38.0% 62.0% 100.0% Total Count 744 424 1168 % 63.7% 36.3% 100.0%
Looking at the numbers and percentages shown in table 2, the researcher can already see that a lot of family owned firms are engaged in tax haven FDI. To be precise, 44 of 71 MNE’s (62%), are active in tax haven FDI. This already gives the idea that family firms are relatively more engaged in tax haven FDI then non family firms. More tests in this research will tell if these findings are found to of a significant value to the researcher.
4.2 Correlation
Table 3 – Correlation matrix
1 2 3 4 5 6 7 1. Tax haven FDI 1
2. Family Ownership 0.136** 1 3. Revenue 0.128** 0.031 1 4. Intangibles 0.194** 0.046 0.188** 1 5. Debt 0.006 -0.002 0.118** 0.132** 1 6. Employees 0.174** 0.145** 0.523** 0.148** 0.038 1 7. Age 0.159** 0.081** 0.018 0.022 -0.024 0.083** 1
**. Correlation is significant at the 0.01 level (2-tailed).
4.3 Binary logistic regression
For testing the hypotheses a binary logistic regression analysis has been carried out to calculate and predict the usage of tax haven FDI for 1168 MNE’s using revenue, intangible assets, long term debt, number of employees, firms’ age, and family ownership as predictors. As can be seen in appendix A: SPSS output tables, two models where tested. One model with the variables revenue, intangibles, debt, employees and age and a second model including the variable family ownership. This way the different outcomes between adding and leaving the predictor variable family ownership can be studied.
As can be seen in table 4 on page 24, a test of the model including all predictors was partly statistically significant, implying that a part of the predictors as a set consistently differentiated between making use of tax haven FDI and not making use of it (chi square = 117.207, p < .001 with df = 6, appendix A: table 2).
As can be seen in appendix A: table 3, the first model without predictor family ownership presented a Nagelkerke’s R2 score of 0.117. As can be seen in appendix A: table 4, in
moderate relationship between prediction and grouping. Meaning that the new model is more likely to predict tax haven FDI then the model without predictor family ownership.
Looking at appendix A: table 5, the prediction success score of the first model (without predictor variable family ownership) was 68%. Looking at appendix A: table 6, the prediction success rate overall after the predictor variable family ownership was added, increased to 69.1%. This means that by adding the independent variable family ownership to the model, results in a better prediction of MNE’s that are likely to engage in tax haven FDI. More specifically, as can be seen in table 4 on page 24, the Wald criterion showed that solely intangible assets, employees, age of the firm and family ownership had a significant influence to the prediction of the dependent variable tax haven FDI, all with a p value of 0.000 < 0.01. Looking at table 4 it can be seen that the independent variables revenue and long term debt were no significant predictors of tax haven FDI with a p value of 0.175>0.01 and 0.547>0.01. Therefore there is already enough evidence to reject hypothesis 2, which stated that an MNE is more likely to engage in tax haven FDI when it has high firm performance in terms of overall revenue. And enough evidence to reject hypothesis 3, which stated that an MNE is more likely to engage in tax haven FDI when it has low long term debts.
Table 4 – Summary Results Binary Logistic Regression
Dependent variable: Taxhaven FDI
Sig. B Exp(B) Constant 0.000 -1.415 0.243 Control variables Age 0.000 0.007 1.007 Employees 0.000 0.000 1.000 Independent variables Revenue 0.175 0.000 1.000 Intangibles 0.000 0.000 1.000 Debt 0.547 0.000 1.000 Family ownership 0.000 0.929 2.532
goes up with one unit this means that the target group (coded 1 in tax haven FDI) goes up with the B value of that predictor. Exp(B) indicates the exponent of B, which shows the so called ‘odds ratio’. This means that if the predictor variable is raised by one unit, the odds ratio of the target group (coded 1 in tax haven FDI) is Exp(B) times more likely to engage in tax haven FDI.
For example, the predictor family ownership has a B value of 0.929, indicating a positive relation. The Exp(B) value 2.532 (= e0,929) indicates that when family ownership is raised by one unit (meaning it is indicated as a family firm), the odds ratio is 2.532 times as large and therefore family owned MNE’s are 2.532 more times likely to engage in tax haven FDI than non-family MNE’s. This means that there is enough evidence to say that hypothesis 1 can be rejected. Resulting in the fact that non family owned MNE’s are less likely to engage in tax haven FDI in comparison with family owned MNE’s.
Looking at table 4, the B value of age is indicated with a positive coefficient of 0.007 and with an Exp(B) value of 1.007. This means that an older MNE is 1.007 times more likely to engage in tax haven FDI. Therefore there is enough reason to accept hypothesis 6: an MNE is more likely to engage in tax haven FDI when it is older.
Looking at the other predictors in table 4, intangible assets and employees, seem to have a significant relation to tax haven FDI, both with a p value of 0.000<0.01. This means that they can be stated as correct and significant predictors of tax haven FDI. However, the B value of both predictors is indicated at 0.000 and the Exp(B) value at 1.000. This means that the odds ratio provides the same probability of engaging in tax haven FDI at two events. Therefore, the outcome is the same at two situations. Concluding in the fact that there is no difference in MNE’s that are bigger in size of employees or MNE’s with higher amount of intangible assets to be more likely to engage in tax haven FDI. This gives the researcher enough reason to reject hypotheses 4: an MNE is more likely to engage in tax haven FDI when it has a high amount of intangible assets. It also provides enough evidence to reject hypothesis 7: an MNE is more likely to engage in tax haven FDI when it has a bigger firm size.
5. Conclusion and Discussion
The following part of this research displays the overall conclusions of this study and tries to give an answer to the research question stated at the beginning of this study. Moreover, this chapter will provide a discussion which contains the limitations and strengths of the research. Lastly, recommendations for future research are given to provide other researchers with new ideas and insights into expanding or broadening this research.
5.1 Conclusion
The research question of this research was:
Are non-family owned MNEs more likely to engage in tax haven FDI then family owned MNEs?
Furthermore, this study tried to examine if there are more factors influencing tax haven activity. Following previous research this study looked at the effects of firm performance, firm size, firm age, the amount of intangible assets in a firm and a firm’s long term debt in relation to the likelihood of firms undertaking tax haven FDI.
Looking at the binary logistic regression analyses performed in chapter 3, there is enough evidence to reject hypothesis 1. The measurements display that there is a significant relation between family ownership and tax haven FDI. However, this relation is positive, meaning that non family firms are less likely to engage in tax haven FDI in comparison to family firms. Surprisingly, this positive relation is not in relation with the assumptions that are taken from previous research of Chen et al. (2010). They state that family firms are considered less tax aggressive, more conservative and tend to take less risk in comparison with non-family owned firms. This research contributes to the assumptions of Chen et al. (2010), by putting these assumptions to the test and by using actual tax haven data from 1168 MNE’s. Overall this study contributes in concluding that, while family firms were previously assumed to be less tax aggressive than non-family firms, according to new findings in this research, family firms are actually more active in tax havens then non-family firms. Resulting in exactly the opposite of what was predicted according to assumptions of previous literature.
firm can be an important stakeholder group. In his study he found that in family firms, non-family executives play an important role in strategic decision making. This feeds the assumption of the agency theory of Adam Smith (1796), which is based on the assumptions that the separation of ownership and management leads to a principal-agent relationship where the agents (managers) might not make decisions that result in the best interest of the principles (owners). Despite the conservative definition of a family firm in this study, in the end this could explain the result of family firms engaging in tax aggressive activities, like investing in tax haven FDI.
Next to that, Sharma (2004), explained that the national fiscal laws on heritage also have an influence on different types of family firms that are present in a nation. This could mean that, next to corporate tax evasion there can be more reasons for family firms to invest in tax havens. An example could be the presence of the next generation of family members that might inherit the family business. Assuming that in family firms the chance of presence of next generation family members is higher than at non-family firms, might possibly explain the higher presence of family firms in tax havens. In addition to that, explanations for the outcome of this research could be that in this study the researcher was only able to take into account a few of the possible predictors of tax haven usage, while in practise more strategic or personal norms and values could also play a role.
Furthermore, the results shows that there is enough reason to reject hypothesis 4. The measurement of the data show that the likelihood of engaging in tax haven FDI is the same for firms with a low amount of intangible assets as for firms with high amount of intangible assets. This result is in contradiction with previous literature. Reason for this outcome could be that the dependent variable is measured as a dummy variable. Therefore it can be that the variable has a significant relation to the dependent variable, but not enough to result in a value of 1 (tax haven FDI) or the value of a 0 (no tax haven FDI).
Moreover, the data shows that hypothesis 3 can also be rejected. Long term debt is not a good predictor of tax haven FDI. According to Sharma (2004) a possible explanation for this outcome can be found in previous research that focussed on examining the funds of financial capital, used by family firms and has discovered a “hierarchy”. Here the highest preference was given to internal financing, followed by debt and equity financing.
The only hypothesis that can be accepted in this study is hypothesis 6. The older the MNE, the more likely it is that it engages in tax haven FDI. Reason for this could be that the older a firm is, the more experience it gets. With more experience, a firm can better interpret its risks and its outcomes, therefore it could be easier to manage and engage in tax haven FDI.
In addition, there is enough evidence to reject hypothesis 7. Firm size does have significant influence on tax haven FDI, but the likelihood to engage in tax haven FDI is the same for smaller firms and for firms with a higher amount of employees. This is in contrast with previous research of Chen et al. (2010). Reason for this result could be likewise to that of hypothesis 4, which argues the limitations of a dichotomous dependent variable. Other reasons for this outcome could be that next to large firms, small firms may even profit as much from tax haven FDI as large firms. This is also in accordance with the easy possibilities for small firms to engage in tax haven FDI. Recent news from the so called panama papers brought out that even SME’s engage in tax haven FDI (Trouw.nl, 2016).
5.2 Discussion
To critically analyse the results of this study, the researcher is aware of the limitations that can be found in this study. According to the results only family ownership has a huge impact on tax haven FDI, and after that also age. The researcher could conclude from this that only family ownership is a good predictor of tax haven FDI, but in reality this result could be rather arbitrary. Next to family ownership there could be more variables at play, which are not mentioned in the analysis of this study. This study focussed on predictors of tax haven FDI at a firm level, but predictors could also be at a country level. According to Richardson (2016), examples of these kind of variables could be complexity of the tax system, income source, education level, tax morale and fairness. Their overall findings display that a lower level of complexity and a higher level of services income source, education, tax morale and fairness result in a lower level of tax evasion. Moreover, the definition of a family firm in this research is quite conservative, although being more conventional, it also excludes a lot of firms from the sample which could otherwise also be described as family firms. Next to that, the definition of a tax haven is rather limited. This research chose to select only ‘dot tax havens’ in its sample. On one side this increases the likelihood that firms invest in these kind of countries because of tax purposes, on the other hand it is a limitation because it excludes firms that invest in the other ‘big 7’ tax haven countries for the same reasons, namely tax evasion.
Even though ORBIS data provides some degree of financial figures on the quantity of assets situated in overseas tax havens the overall covering is insufficient. Future research could work on this limitation by gathering more specified data among tax haven countries. Next to the limitations of this study there are also factors that describe the strengths of this research. One of the strengths is that the research data used in this study is quantifiable. Because the data in this research comes from database ORBIS it is of high quality and outcomes can be generalizable for a larger population. In addition the sample size of this study was high, which makes the research more generalizable. Furthermore, by making use of quantitative data instead of qualitative data a bias of the researchers’ opinion is excluded.
5.3 Recommendations for future research
This research provides a lot of information and research methods to implement in future research. In this study only a few factors are used to predict tax haven FDI. Future research could add more variables to give a more specific and broader answer to the research question. For example, this study excluded the factor degree of internationalization in predicting tax haven usage. Future research could examine this further by adding this variable to the research model that is provided in this study.
Furthermore the results of this study were quite surprising to the researcher. As the results of this research show, assumptions of previous research cannot always be taken for granted. The theoretical part assumed that non-family firms would be more likely to engage in tax haven FDI and not the other way around. Future research could build on these findings by examining why family owned firms are more likely to engage in tax haven FDI. This way a more specific view of firms’ determinants to engage in tax haven activities can be given.
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APPENDIXES
Appendix A - SPSS output tables.
Table 1: Omnibus Tests of Model Coefficientsa
Chi-square df Sig.
Step 1
Step 104,550 5 ,000
Block 104,550 5 ,000
Model 104,550 5 ,000
a. Variable(s) entered on step 1: Revenue, Intangibles, Debt, Employees, and Age.
Table 2: Omnibus Tests of Model Coefficientsa
Chi-square df Sig.
Step 1
Step 117.207 6 .000
Block 117.207 6 .000
Model 117.207 6 .000
a. Variable(s) entered on step 1: Revenue, Intangibles, Debt, Employees, Age and Family ownership.
Table 3: Model summarya,b
Step -2 Log
likelihood
Cox & Snell R Square
Nagelkerke R Square
1 1425,839a ,086 ,117
a. Variable(s) entered on step 1: Revenue, Intangibles, Debt, Employees, and Age. The cut value is .500.
b. Estimation terminated at iteration number 4 because parameter estimates changed by less than .001.
Table 4: Model Summarya,b
Step -2 Log
likelihood
Cox & Snell R Square
Nagelkerke R Square
1 1413.182a .095 .131
a. Variable(s) entered on step 1: Revenue, Intangibles, Debt, Employees, Age, and Family Ownership. The cut value is .500.
Table 5: Classification Tablea Observed Predicted TakshavenFDI Percentage Correct NO YES Step 1 TakshavenFDI NO 690 54 92,7 YES 320 104 24,5 Overall Percentage 68,0
a. Variable(s) entered on step 1: Revenue, Intangibles, Debt, Employees, and Age. The cut value is .500.
Table 6: Classification Tablaa
Observed Predicted TakshavenFDI Percentage Correct NO YES Step 1 TakshavenFDI NO 684 60 91.9 YES 301 123 29.0 Overall Percentage 69.1
a. Variable(s) entered on step 1: Family Ownership, Revenue, Intangibles, Debt, Employees, Age, and Family Ownership. The cut value is .500.
Table 7: Variables in the Equationa
B S.E. Wald df Sig. Exp(B)
Step 1a Revenue ,000 ,000 1,256 1 ,262 1,000 Intangibles ,000 ,000 22,905 1 ,000 1,000 Debt ,000 ,000 ,378 1 ,539 1,000 Employees ,000 ,000 16,110 1 ,000 1,000 Age ,008 ,002 22,537 1 ,000 1,008 Constant -1,370 ,121 128,739 1 ,000 ,254
Table 8: Variables in the Equationa
B S.E. Wald df Sig. Exp(B)
Step 1a Revenue ,000 ,000 1,840 1 ,175 1,000 Intangibles ,000 ,000 22,107 1 ,000 1,000 Debt ,000 ,000 ,363 1 ,547 1,000 Employees ,000 ,000 14,184 1 ,000 1,000 Age ,007 ,002 20,539 1 ,000 1,007 Family_Ownership ,929 ,264 12,391 1 ,000 2,532 Constant -1,415 ,122 133,678 1 ,000 ,243
Appendix B - Sample list
Appendix A – Sample list
1. 3663 FIRST FOR FOOD SERVICE (PTY) LTD
2. 3M SOUTH AFRICA (PTY) LTD
3. 7 ELEVEN INC
4. A.P. MOLLER - MAERSK A/S
5. A.P. MOLLER - MAERSK A/S
6. AB ELECTROLUX
7. AB VOLVO
8. ABB LTD
9. ABN AMRO BANK N.V.
10. ACCENTURE PLC
11. ACS ACTIVIDADES DE CONSTRUCCION Y SERVICIOS, S.A.
12. ADAM OPEL AG 13. ADECCO GROUP AG 14. ADIDAS AG
15. ADOLF WUERTH GMBH & CO KG 16. ADVANCEPCS 17. AECOM 18. AEON CO LTD 19. AEROSPATIALE MATRA SA 20. AIR CHINA LTD 21. AIR FRANCE - KLM 22. AIRBUS GROUP SE 23. AIRBUS OPERATIONS 24. AIRBUS OPERATIONS GMBH 25. AIRBUS SAS 26. AISIN SEIKI CO LTD 27. AKZO NOBEL NV
28. ALBERTSONS COMPANIES, INC. 29. ALBERTSON'S LLC
30. ALCAN INC
31. ALCATEL CANADA INC 32. ALCATEL-LUCENT S.A.
33. ALCATEL-LUCENT SOUTH AFRICA (PTY) LTD
34. ALCOA CORPORATION 35. ALFA, S.A.B. DE C.V.
36. ALFRED C. TOEPFER INTERNATIONAL B.V.
37. ALFRESA HOLDINGS CORPORATION 38. ALIBABA GROUP HOLDING LIMITED 39. ALIMENTATION COUCHE-TARD INC.
40. ALL NIPPON AIRWAYS CO., LTD 41. ALLERGAN PLC
42. ALMACENES EXITO S.A.
43. ALSTOM AFRICA HOLDINGS (PTY) LTD 44. ALSTOM S AND E AFRICA (PTY) LTD 45. ALTICE N.V.
46. ALUMINUM CORPORATION OF CHINA LIMITED
47. AMAZON EU SARL, UK BRANCH 48. AMBEV SA
49. AMERICA TELECOM S.A. DE C.V. 50. AMERICAN AIRLINES INC
51. AMERICAN ELECTRIC POWER COMPANY INC
52. AMERISOURCEBERGEN CORP 53. AMPRION GMBH
54. ANA HOLDINGS INC. 55. ANGLO AMERICAN PLC
56. ANGLO AMERICAN SOUTH AFRICA LTD 57. ANGLO SOUTH AFRICA (PTY) LTD 58. ANHEUSER-BUSCH COMPANIES, INC. 59. ANHEUSER-BUSCH INBEV SA/NV 60. ANTARCHILE S.A.
61. AON PUBLIC LIMITED COMPANY 62. APL APOLLO TUBES LTD. 63. APPLE INC.
64. ARAMARK CORP 65. ARCANDOR AG 66. ARCELORMITTAL S.A. 67. ARGOS GROUP B.V.
68. ARGOS GROUP HOLDING B.V. 69. ARLA FOODS AMBA
70. ASAHI GLASS COMPANY LIMITED 71. ASAHI GROUP HOLDINGS LTD. 72. ASAHI KASEI CORPORATION 73. ASDA GROUP LIMITED 74. ASDA STORES LIMITED
75. ASKO DEUTSCHE KAUFHAUS AG
76. ASSICURAZIONI GENERALI-SOCIETA PER AZIONI
79. ASTRAZENECA UK LIMITED
80. ASUSTEK COMPUTER INCORPORATION 81. AT&T CORP. 82. ATLAS COPCO AB 83. ATOS SE 84. AU OPTRONICS CORPORATION 85. AUCHAN FRANCE 86. AUCHAN HOLDING 87. AUDI AG 88. AURUBIS AG
89. AUSTRALIA AND NEW ZEALAND BANKING GROUP LIMITED
90. AUTOMATIC DATA PROCESSING INC 91. AUTOMOBILES PEUGEOT
92. AUTOZONE INC 93. AVENTIS SA 94. AVNET INC 95. BAE SYSTEMS PLC
96. BAIC MOTOR CORPORATION LIMITED 97. BAIC MOTOR CORPORATION LIMITED 98. BANK OF AMERICA, NATIONAL
ASSOCIATION 99. BANK OF MONTREAL 100. BANK OF NOVA SCOTIA 101. BARRICK ENERGY INC. 102. BASF SE
103. BAYER AG
104. BAYERISCHE MOTOREN WERKE AG 105. BAYWA AG
106. BED BATH & BEYOND INC
107. BEIJING AUTOMOTIVE GROUP CO., LTD. 108. BELL CANADA
109. BELLSOUTH CORP 110. BERGEMANN GMBH 111. BERGEN BRUNSWIG CORP 112. BERTELSMANN SE & CO. KGAA 113. BEST BUY CO, INC
114. BG ENERGY HOLDINGS LIMITED 115. BG GROUP LIMITED
116. BHARAT PETROLEUM CORPORATION LIMITED
117. BHARTI AIRTEL LIMITED 118. BHP BILLITON (AUS) LIMITED 119. BHP BILLITON LIMITED
120. BHP BILLITON MINERALS PTY LTD
121. BHP BILLITON PLC 122. BIDOFFICE FURNITURE
MANUFACTURING (PTY) LTD
123. BIDVEST BAKERY SOLUTIONS (PTY) LTD 124. BIDVEST FOOD INGREDIENTS (PTY) LTD 125. BIDVEST FOOD SOUTHERN AFRICA (PTY)
LTD
126. BIDVEST LEASING (PTY) LTD 127. BIDVEST PROPERTIES (PTY) LTD 128. BMW BRILLIANCE AUTOMOTIVE LTD. 129. BMW CHINA AUTOMOTIVE TRADING
LTD.
130. BNSF RAILWAY COMPANY 131. BOLLORE
132. BOLLORE PARTICIPATIONS 133. BOOTS UK LIMITED
134. BOPHELONG CONSTRUCTION (PTY) LTD 135. BOTAS BORU HATLARI ILE
PETROLTASIMA ANONIM SIRKETI 136. BOUYGUES CONSTRUCTION 137. BOUYGUES SA
138. BP AUSTRALIA GROUP PTY LTD 139. BP AUSTRALIA INVESTMENTS PTY LTD 140. BP EUROPA SE 141. BP INTERNATIONAL LIMITED 142. BP PLC 143. BRASKEM S.A. 144. BRENNTAG AG 145. BRIDGESTONE CORPORATION
146. BRILLIANCE AUTO GROUP HOLDING CO., LTD.
147. BRITISH AIRWAYS PLC 148. BRITISH AIRWAYS PLC 149. BRITISH AIRWAYS PLC 150. BRITISH AIRWAYS PLC
151. BRITISH AMERICAN TOBACCO P.L.C. 152. BRITISH AMERICAN TOBACCO SOUTH
AFRICA (PTY) LTD
153. BRITISH GAS TRADING LIMITED 154. BRITISH TELECOMMUNICATIONS PLC 155. BRITISH TELECOMMUNICATIONS
PUBLIC LIMITED COMPANY
156. BROADSTREET GREAT WILSON EUROPE LIMITED
157. BROOKFIELD MANAGEMENT EQUITY PLANS LIMITED
160. BURLINGTON NORTHERN SANTA FE CORPORATION
161. BYD COMPANY LIMITED
162. C.H. BOEHRINGER SOHN AG & CO. KG 163. CALTEX AUSTRALIA LIMITED 164. CANON EUROPA NV
165. CANON INC 166. CAP GEMINI
167. CARDINAL HEALTH INC 168. CARNIVAL PLC
169. CARREFOUR SA
170. CARSO GLOBAL TELECOM, S.A.B. DE C.V. 171. CASINO GUICHARD-PERRACHON SA 172. CATAMARAN CORPORATION 173. CATHAY PACIFIC AIRWAYS LIMITED 174. CATHAY PACIFIC AIRWAYS LIMITED 175. CBS CORPORATION
176. CECIL NURSE (PTY) LTD 177. CELESIO AG
178. CEMEX CONCRETOS 179. CEMEX, S.A.B. DE C.V. 180. CENCOSUD SA
181. CENTRAL JAPAN RAILWAY COMPANY 182. CENTRAL NIPPON EXPRESSWAY
COMPANY LIMITED 183. CENTRICA PLC
184. CHAROEN POKPHAND FOODS PCL 185. CHEMOIL ENERGY LIMITED 186. CHINA AGRI-INDUSTRIES HOLDINGS
LIMITED
187. CHINA CNR CORPORATION LIMITED 188. CHINA CNR CORPORATION LIMITED 189. CHINA COMMUNICATIONS
CONSTRUCTION COMPANY LIMITED 190. CHINA COMMUNICATIONS SERVICES
CORPORATION LIMITED 191. CHINA DATANG CORPORATION 192. CHINA EASTERN AIRLINES
CORPORATION LIMITED 193. CHINA ENERGY ENGINEERING
CORPORATION LIMITED 194. CHINA ENERGY ENGINEERING
CORPORATION LIMITED 195. CHINA EVERGRANDE GROUP 196. CHINA EVERGRANDE GROUP
197. CHINA FAW GROUP CORPORATION (THE FIRST AUTOMOBILE FACTORY)
198. CHINA GEZHOUBA GROUP CO., LTD. 199. CHINA GRAND AUTOMOTIVE SERVICES
CO., LTD.
200. CHINA GREATWALL COMPUTER SHENZHEN CO., LTD.
201. CHINA HUADIAN CORPORATION 202. CHINA HUANENG GROUP 203. CHINA MEDICINE GROUP TOTAL
COMPANY
204. CHINA MOBILE LIMITED
205. CHINA NATIONAL BUILDING MATERIAL COMPANY LIMITED
206. CHINA NATIONAL BUILDING MATERIAL COMPANY LIMITED
207. CHINA NATIONAL CHEMICAL CORPORATION
208. CHINA OVERSEAS LAND & INVESTMENT LIMITED
209. CHINA PACIFIC INSURANCE (GROUP) COMPANY LIMITED
210. CHINA PETROLEUM & CHEMICAL CORPORATION
211. CHINA PETROLEUM & CHEMICAL CORPORATION
212. CHINA POST GROUP CORPORATION 213. CHINA RAILWAY CONSTRUCTION
CORPORATION LIMITED 214. CHINA RAILWAY GROUP LTD.
215. CHINA RAILWAY MATERIALS CO., LTD. 216. CHINA RESOURCES BEER (HOLDINGS)
COMPANY LIMITED
217. CHINA RESOURCES LAND LIMITED 218. CHINA RESOURCES LAND LIMITED 219. CHINA RESOURCES PHARMACEUTICAL
GROUP LIMITED
220. CHINA SHENHUA ENERGY COMPANY LIMITED
221. CHINA SOUTHERN AIRLINES COMPANY LIMITED
222. CHINA SOUTHERN POWER GRID CO., LTD.
223. CHINA STATE CONSTRUCTION ENGINEERING CORPORATION LTD 224. CHINA TELECOM CORPORATION
LIMITED
225. CHINA UNICOM (HONG KONG) LIMITED 226. CHINA UNITED NETWORK
COMMUNICATIONS CO., LTD. 227. CHINA UNITED NETWORK
230. CHRISTIAN DIOR SE 231. CHRYSLER CORPORATION 232. CHS INC.
233. CISCO SYSTEMS INC 234. CITIBANK N.A 235. CITIC LIMITED
236. CJ CHEILJEDANG CORPORATION 237. CJ CORP.
238. CK HUTCHISON HOLDINGS LIMITED 239. CK HUTCHISON HOLDINGS LIMITED 240. CMA CGM
241. CMA CGM AGENCIES WORLDWIDE 242. CNH GLOBAL N.V.
243. CNOOC LIMITED 244. COAL INDIA LIMITED
245. COCA-COLA FORTUNE (PTY) LTD 246. COLAS SA
247. COMMERZBANK AKTIENGESELLSCHAFT 248. COMMONWEALTH BANK OF AUSTRALIA 249. COMPAGNIE DE SAINT GOBAIN SA 250. COMPAGNIE FINANCIERE RICHEMONT
SA
251. COMPAGNIE GENERALE DES
ETABLISSEMENTS MICHELIN (C.G.E.M.) SCA
252. COMPAL ELECTRONICS INC 253. COMPANHIA BRASILEIRA DE
DISTRIBUICAO
254. COMPANIA DE DISTRIBUCION INTEGRAL LOGISTA HOLDINGS, S.A.
255. COMPANIA ESPANOLA DE PETROLEOS SAU
256. COMPASS GROUP PLC 257. CONAGRA FOODS, INC.
258. CONAGRA INTERNATIONAL FERTILIZER COMPANY
259. CONOCO INC.
260. CONSTELLATION ENERGY GROUP INC 261. CONTINENTAL AG
262. CON-WAY INC. 263. COOP GROUPE
264. COOPERATIEVE RABOBANK U.A. 265. COOP-GRUPPE GENOSSENSCHAFT 266. CORPORACION NACIONAL DEL COBRE -
CODELCO
267. COSMO ENERGY HOLDINGS CO., LTD.
268. COSMO OIL CO.,LTD.
269. COUNTRY GARDEN HOLDINGS COMPANY LIMITED
270. COUNTRY GARDEN HOLDINGS COMPANY LIMITED
271. COVESTRO AG
272. CP ALL PUBLIC COMPANY LIMITED 273. CP&P, INC.
274. CPC CORPORATION, TAIWAN 275. CREDIT AGRICOLE S.A. 276. CREDIT SUISSE GROUP AG 277. CRH PUBLIC LIMITED COMPANY 278. CRRC CORPORATION LIMITED 279. CSF
280. CSR CORPORATION LIMITED 281. DAEWOO SHIPBUILDING & MARINE
ENGINEERING CO., LTD
282. DAEWOO SHIPBUILDING & MARINE ENGINEERING CO., LTD.
283. DAI NIPPON PRINTING CO LTD 284. DAIHATSU MOTOR CO LTD 285. DAIKIN INDUSTRIES LIMITED 286. DAIMLER AG
287. DAIRY FARM INTERNATIONAL HOLDINGS LIMITED
288. DAIRY FARM INTERNATIONAL HOLDINGS LIMITED
289. DAITO TRUST CONSTRUCTION CO LTD 290. DAIWA HOUSE INDUSTRY COMPANY
LIMITED
291. DALIAN WANDA COMMERCIAL PROPERTIES CO., LTD.
292. DALIAN WANDA COMMERCIAL PROPERTIES COMPANY LIMITED 293. DALIAN WANDA GROUP CO., LTD. 294. DANONE
295. DAQING OILFIELD LIMITED COMPANY 296. DATONG COAL MINING GROUP CO., LTD. 297. DAYE NONFERROUS METALS GROUP
HOLDINGS CO., LTD.
298. DCC PUBLIC LIMITED COMPANY 299. DELHAIZE GROUP SA
300. DELL CORPORATION LIMITED 301. DELL GLOBAL B.V.
302. DELL TECHNOLOGIES INC. 303. DELL, INC.
308. DENEL VEHICLE SYSTEMS (PTY) LTD 309. DENSO CORPORATION
310. DENTSPLY INTERNATIONAL INC. 311. DEUTSCHE BAHN AG 312. DEUTSCHE BP AKTIENGESELLSCHAFT 313. DEUTSCHE LUFTHANSA AG 314. DEUTSCHE LUFTHANSA AG 315. DEUTSCHE LUFTHANSA AKTIENGESELLSCHAFT 316. DEUTSCHE POST AG 317. DEUTSCHE TELEKOM AG 318. DIAGEO PLC 319. DIAGEO PLC 320. DIGNITY HEALTH
321. DIRECTV GROUP, INC. (THE) 322. DIRECTV LLC
323. DIXONS CARPHONE PLC
324. DOCUMENT SCIENCES CORPORATION 325. DOLLAR GENERAL CORP
326. DOLLAR TREE, INC. 327. DONG ENERGY A/S
328. DONGFENG MOTOR GROUP COMPANY LIMITED
329. DOOSAN CORP.
330. DOOSAN HEAVY INDUSTRIES & CONSTRUCTION CO.,LTD. 331. DR. ING. H.C. F. PORSCHE
AKTIENGESELLSCHAFT
332. DUPLEIX LIQUID METERS (PTY) LTD 333. DZ BANK AG, DEUTSCHE ZENTRAL -
GENOSSENSCHAFTSBANK, FRANKFURT-AM-MAIN
334. E. MERCK BETEILIGUNGEN KG 335. E.ON RUHRGAS AG
336. E.ON SE
337. E.ON VERTRIEB DEUTSCHLAND GMBH 338. EAST JAPAN RAILWAY COMPANY 339. EATON CORPORATION PLC 340. EDEKA ZENTRALE AG & CO. KG 341. EDF ENERGY HOLDINGS LIMITED 342. EDF ENERGY PLC
343. EDISON S.P.A. 344. EDIZIONE S.R.L.
345. EDP - ENERGIAS DE PORTUGAL, S.A. 346. EIFFAGE
347. EIM (FX) LIMITED
348. EL CORTE INGLES SA
349. EL PASO PIPELINE PARTNERS LP 350. ELECTRABEL
351. ELECTRICITE DE FRANCE SA
352. ELECTRICITY GENERATION AUTHORITY OF THAILAND PCL
353. ELECTRONIC DATA SYSTEMS, LLC 354. E-MART INC. 355. EMC CORP 356. EMERSON ELECTRIC CO 357. EMIRATES AIRLINES 358. EMIRATES TELECOMMUNICATION GROUP COMPANY PJSC 359. EMPIRE COMPANY LTD
360. EMPRESA COLOMBIANA DE PETROLEOS - ECOPETROL S.A.
361. EMPRESAS COPEC S.A.
362. ENBW ENERGIE BADEN-WURTTEMBERG AG
363. ENBW TRADING GMBH 364. ENDESA ENERGIA SAU 365. ENDESA, S.A.
366. ENEL SPA
367. ENEL TRADE S.P.A. 368. ENGIE
369. ENI S.P.A.
370. ENTERPRISE GP HOLDINGS L.P. 371. ESKOM ENTERPRISES (PTY) LTD 372. ESKOM HOLDINGS SOC LIMITED 373. ESSAR ENERGY LIMITED 374. ESSAR OIL LIMITED 375. ESSO NEDERLAND B.V.
376. ESSO PETROLEUM COMPANY LIMITED 377. ESSO SA
378. ESTEE LAUDER COMPANIES INC. (THE) 379. EURIS
380. EVONIK INDUSTRIES AG 381. EXELON GENERATION CO LLC 382. EXOR S.P.A.
383. EXXONMOBIL PETROLEUM & CHEMICAL 384. FAST RETAILING CO., LTD
385. FAURECIA SA
