Women on the corporate board and tax avoidance: The moderating effects of financial distress and government trust

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Women on the corporate board and tax avoidance:

The moderating effects of financial distress and

government trust

Abstract

This paper examines the relationship between the proportion of women on corporate boards and tax avoidance using the GAAP effective tax rate (ETR) as a measure of tax avoidance. Existing literature focusses mainly on country-specific samples. This paper adds to this literature by examining a global sample and testing financial distress and government trust as moderating variables. Using panel data consisting of 12,565 firm-year observations over the period of 2006 to 2017, no relationship is found between the proportion of women on the corporate board and tax avoidance. In addition, firms with at least 30 percent women on the board do not avoid less or more taxes than firms with less women on the board. Furthermore, no evidence is found for a moderating effect of financial distress and government trust on the main relationship. When testing with a different measure of effective tax rate (Cash ETR), the results remain largely the same. However, some evidence is found for a positive relationship between the proportion of women on the board and tax avoidance when including government trust as a moderating variable. This relationship is weakened in countries where government trust is higher.

Keywords: Tax Avoidance, Women, Corporate Board, Financial Distress, Government Trust

Study Program: MSc International Financial Management Name: J.W.J. Nieuwveld

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1. Introduction

Taxes are collected by the government in order to pay for government expenses but can also be used to implement government policies (Surrey, 1970). As of today, this theory still applies and governments and intergovernmental organizations are looking for ways to update tax regulations to the changing and interconnected world. Due to globalization and technological change, it is easier for firms to transfer funds across the world and make use of more favorable tax regulations in other countries (Christensen and Kapoor, 2004). The United States have enacted the Foreign Account Tax Compliance Act (FACTA) in response to offshore tax avoidance (Kaye, 2014). In October 2019, the Organisation for Economic Co-operation and Development (OECD) announced a new proposal in order to reduce tax avoidance by multinationals and tech companies (Dmitracova, 2019). The proposed tax regulations are designed in response to the increasing digitalization of companies such as Amazon and Google. The new regulation is designed to tax firms in countries where they do not have a physical presence. In other words, the proposed regulation allows authorities to tax these firms based on the revenue made in their country. The OECD claims that this contributes to companies paying their ‘fair share’ because digital companies’ income does not necessarily depend on where they have their headquarters. The proposals of new rules show that tax avoidance remains a relevant and current theme. Not only the government is interested in the tax payment behavior of companies, but also the environment in which a company operates. According to Huseynov and Klamm (2012), the general public finds it important to know whether a company is a good corporate citizen and is ‘paying its fair share’. Taxes pay for public goods, meaning that avoiding taxes leads to less funds to pay for these public goods (Christensen and Murphy, 2004).

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tax evasion which is an attempt to illegally cut down taxes (Kirchler, Maciejovsky, and Schneider, 2003). Tax aggressiveness refers to the most intense range of tax avoidance schemes that are more likely to be analyzed by authorities (Hanlon and Heitzman, 2010). When looking at it from an economic point of view, both avoidance and evasion are based on the same motive: to reduce the tax burden (Kirchler et al., 2003). For the remainder of this paper, the approach of Dyreng, Hanlon and Maydew (2008) is followed, meaning that tax avoidance is defined in the broadest sense of the word. Therefore, no distinction is made between tax avoidance, tax aggressiveness and tax evasion. The purpose of this paper is to determine if a firm has been able to avoid its taxes, not how it has been able to do so.

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direct effect of financial distress on tax avoidance has been studied (e.g., Richardson, Lanis, and Taylor, 2015; Richardson, Taylor, and Lanis 2015). This paper uses financial distress as a moderating variable. As a country-specific characteristic, the trust in the government is added as a moderating variable. Similar to financial distress, this variable has been studied before as a direct influence on tax avoidance and compliance rather than as a moderating effect (e.g., Jimenez and Iyer, 2016; Güzel, Özer, and Özcan, 2019). Altogether, the research question that this paper aims to answer is the following:

Does the proportion of women on corporate boards affect a firm’s tax avoidance

and are financial distress and government trust moderating this relationship?

The sample consists of 12,565 firm-year observations from 34 distinct countries over the period of 2006 to 2017. A multiple linear regression in which year, industry and country fixed effects are taken into account is employed to test the effect of women on corporate boards on tax avoidance and the moderating effects of both financial distress and government trust on this relationship. The effective tax rate (ETR) is used as a measure of tax avoidance, where a low ETR suggests tax avoidance. This means that a decrease in ETR, implies an increase in tax avoidance and vice versa.

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The rest of the paper is organized as follows. Section 2 provides an oversight of relevant literature and the hypotheses. Section 3 describes the dataset, the variables and the model. The empirical results are discussed in section 4. Section 5 concludes the paper.

2. Literature review and hypothesis development

2.1 Tax avoidance and stakeholders

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2.2 The role of the board in corporate tax avoidance

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In summary, firms that are family owned and/or have independent board members, are less tax aggressive. Tax avoiding strategies can also be shared between firms through network ties. Moreover, individual executives also influence tax avoiding strategies. These findings provide evidence of how board characteristics and individual characteristics of board members can influence corporate tax avoidance. In this paper, the impact of the proportion of women on the board on tax avoidance is studied. While considering other theories and stakeholders, this paper places tax avoidance in an agency perspective as proposed by Lanis and Richardson (2011). It focusses mainly on the monitoring function of the board and board characteristics.

2.3 Women on corporate boards and tax avoidance

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sample of business students and find that also in non-business related ethical dilemmas, women are more likely to make an ethically responsible decision than men. Bernardi, and Arnold (1997) find that female directors are more risk-averse in terms of financial reporting than their male counterparts. Women also assign more time to qualitative issues like social responsibility (Bear, Rahman, and Post, 2010) and reputation (Bilimoria, 2006). Women bringing different characteristics to the board, can improve monitoring as a board needs a variety of skills and experiences for an effective monitoring function (Hillman and Dalziel, 2003).

The aforementioned literature provides insight in the differences between men and women. Women are more risk-averse than men (Bernardi and Arnold, 1997). According to Armstrong et al. (2016), managerial risk-taking is an important incentive of tax aggressiveness. Thus, more risk-averse board members may engage less in tax aggressive activities to avoid taxes. Not paying the owed amount of taxes is seen as socially irresponsible and unethical (Dowling, 2014). As women are interested in social responsibility (Bear et al., 2010) and tend to behave more ethical both in business and in non-business situations (Wang and Calvano, 2015), it can be expected that female presence in the board increases the probability a company paying its fair share and engaging less in tax avoidance.

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board and tax aggressiveness in the U.S. They used multiple variables (including the corporate effective tax rate) in order to measure tax aggressiveness. Francis, Hasan, Wu, and Yan (2014) study the effect of CFO gender on tax aggressiveness. They find that female CFOs are less likely to be involved in tax aggressiveness due to women being more risk averse. Using a sample of Iranian listed firms, Hoseini and Gerayli (2018) and Hoseini et al. (2019) find a significant and negative relationship between female board representation and tax avoidance. Chen, Gramlich, and Houser (2019) find that female board members are more cautious about possible reputational risks related to tax aggressiveness and therefore engage less in tax avoidance.

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it is important to acknowledge that these findings may differ from findings in western countries due to cultural differences.

Prior research finds mixed effects regarding women on the board and monitoring and tax avoidance. While women bring different characteristics to the board that are not in favor to tax avoidance, they may diminish board effectiveness and not be able to change the view on tax avoidance. Therefore, the effect of women on corporate boards on tax avoidance remains a relevant topic of research. However, because numerous authors find evidence in favor of a negative relation between women on the board and tax avoidance, the first hypothesis is as follows:

Hypothesis 1: There is a negative relation between the proportion of women on the corporate board and tax avoidance

2.4 Financial distress and tax avoidance

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distress and get out of a position of uncertainty. Strategies that were first perceived as too costly, may be more appealing as the benefits of tax avoidance increase (Edwards et al., 2013).

Richardson et al. (2015) conduct an analysis on a sample of US firms and find a significant and positive relation between financial distress and tax aggressiveness, meaning that when a firm is facing financial distress it is more likely to engage in tax avoiding activities. This effect was even stronger in the global financial crisis of 2008. Richardson et al. (2015) conduct the same research on a sample of Australian firms and find the same results. Once again, the relation between financial distress and tax aggressiveness is positive and this effect is magnified by the global crisis.

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hard work in times of financial distress than shareholders. As such, the directors may be less motivated to monitor the management (Chou et al, 2010). This provides opportunities for the firm to engage in more tax avoidance. Lanis et al. (2017) contend that female board members fulfill a similar role as independent board members in monitoring the company. Consequently, one can expect that in times of financial distress, the monitoring function of female board members may be impaired.

In summary, prior research provides evidence that firms that are in financial distress take more risk and engage in more tax avoidance. Firms in financial distress value survival more than reputation. This could mean that the specific traits that women bring to the board such as risk aversity and reputation awareness can be impaired by financial distress. Also, female board members may be less effective monitors in times of financial distress providing an opportunity for the firm for more tax avoidance. Therefore, I expect that financial distress has a weakening effect on the negative relationship between the proportion of women on the corporate board and tax avoidance. This would mean that the negative relation between the proportion of women on the board and tax avoidance, is less negative (weaker) for firms with a high financial distress score than for firms with a lower financial distress score. As such, the second hypothesis will be as follows:

Hypothesis 2: Financial distress moderates the negative relationship between the proportion of women on the corporate board and tax avoidance in such a way, that a higher level of financial distress results in a less negative relationship

2.5 Government trust and tax avoidance

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government, the more capital is available to fund these services. Tax revenue is higher when all taxable persons (including companies) are paying their fair share. A factor that can influence the payment of taxes, is the trust in the government (Richardson, 2008). Richardson (2008) finds that when a country has a low level of trust in the government, there is a higher level of tax evasion. According to Feld and Frey (2002), individuals have a psychological contract with the government which can be maintained by positive actions that are established by trust. They argue that more trust in the government can add to the willingness of entities to commit themselves to conformity and compliance with tax laws. If individuals have the perception that the government acts in their best interest, they are more willing to pay their fair share as they perceive that their tax money is used for the right causes and funds public services (Levi, 1996; Snape, 2007). On the other side, if individuals perceive the government as untrustworthy, they are pushed to tax evasion and are not willing to pay their fair share (Jimenez and Iyer, 2016).

The effect of trust in the government on tax compliance has been researched in recent literature. Using a sample of 392 independent accounting professionals in Turkey, Güzel et al. (2019) find that there is a positive relationship between the trust in the government and tax compliance meaning that individuals are more willing to pay their fair share when they perceive the government as trustworthy. Jimenez and Iyer (2016) find that credibility in the government is related to fairness perception which in turn is related to tax compliance. Batrancea et al. (2019) investigate the impact of trust in authorities and power of authorities on tax compliance. They used a sample of 14,509 students in 44 nations from five continents. They find that a higher trust in authorities leads to a higher intended and voluntary tax compliance. This effect is even stronger for women, meaning that they are more willing to comply with tax legislation when trust in authorities is high.

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women in situations where there is high trust in authorities, are more willing to comply with tax legislation. This indicates that trust in the government has an effect on the main relationship studied in this paper, namely the relation between the proportion of women on the board and tax avoidance. Following the existing literature, I expect that trust in government has a strengthening effect on the expected negative relation between women on the corporate board and tax avoidance. This would mean that the negative relationship between the proportion of women on the board and tax avoidance, is more negative (stronger) for firms that operate in countries with higher trust in the government as opposed to firms in countries with lower trust in the government. Therefore, the third hypothesis is as follows:

Hypothesis 3: Government trust moderates the negative relationship between the proportion of women on the corporate board and tax avoidance in such a way, that a higher level of government trust results in a more negative relationship

3. Methodology

3.1 Operationalization of the independent variables

The proportion of women on the corporate board will be measured as a ratio in two ways. The first way is the proportion of female board members on the board. The second way will be as a dummy variable coded 1 if a board has at least 30% of women on board and 0 otherwise. BoardEx provides the male board ratio (ratio of male directors to the total directors). To prevent misinterpretation issues, the women ratio is preferred. The women ratio is obtained by subtracting the male ratio from 1:

Women ratio = 1 – (Male Ratio)

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3.2 Operationalization of the dependent variable

The dependent variable in this paper is corporate tax avoidance. Tax avoidance is measured by the effective tax rate (ETR), which is calculated as follows:

𝐸𝑇𝑅 = 𝐼𝑛𝑐𝑜𝑚𝑒 𝑡𝑎𝑥 𝑒𝑥𝑝𝑒𝑛𝑠𝑒 𝑃𝑟𝑒𝑡𝑎𝑥 𝑖𝑛𝑐𝑜𝑚𝑒

Rego (2003) argue that the ETR is a useful proxy for tax avoidance because of two reasons. On the one hand, tax-driven transactions reduce a firm’s ETR as tax expenses decrease. On the other hand, corporations often use foreign activities to avoid income taxation, which is captured by the ETR because shifting profits to low-tax countries reduces the firm’s global ETR. The higher the effective tax rate, the more taxes a company is paying and the less it engages in tax avoidance and vice-versa. The measure of ETR employed in this paper, can also be defined as the GAAP ETR (Hanlon and Heitzman, 2010). Dyreng et al. (2010) study the effects of different characteristics of executives on corporate tax avoidance using GAAP ETR as a proxy for tax avoidance. Using this measure, they found that directors affect tax avoidance of a firm. Chen et al. (2010) use the GAAP ETR as a measure to study tax avoidance amongst firms that are owned and run by families. Halioui et al. (2016) study the effect of corporate governance and CEO compensation on tax aggressiveness. They use GAAP ETR as one of the proxies for tax aggressiveness and find evidence for a negative relationship between different corporate governance characteristics and CEO compensation and tax aggressiveness. These mentioned studies all study the effect that boards or board characteristics have on tax avoidance. Since this paper also studies the impact of the board on tax avoidance, I find GAAP ETR (from hereon ETR) a suitable proxy for tax avoidance.

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the ETR is positive although they did not pay any taxes. For firms with a negative pretax income and positive income taxes, the ETR is negative even though they did pay taxes. Therefore, the ETR is set to zero for firms who received a tax return and to 100 percent for firms with positive income taxes and a negative pre-tax income (Gupta and Newberry, 1997). Furthermore, Gupta and Newberry (1997) point out that denominators with small values cause estimation problems as the ETRs can be well over 100 percent. As such, I constrict the ETR of my sample between 0 percent and 100 percent.

3.3 Operationalization of the moderating variables

3.3.1. Financial distress

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financial distress. As prior research on tax matters finds the modified Altman Z score a suitable measure for financial distress, I adopt this approach. The modified Altman Z score is computed as follows: 𝑍 = 1,2 (𝑊𝑜𝑟𝑘𝑖𝑛𝑔 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠 ) + 1,4 ( 𝑅𝑒𝑡𝑎𝑖𝑛𝑒𝑑 𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠 𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠 ) + 3,3 ( 𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠 𝐵𝑒𝑓𝑜𝑟𝑒 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑎𝑛𝑑 𝑇𝑎𝑥𝑒𝑠 𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠 ) + 0,999 ( 𝑆𝑎𝑙𝑒𝑠 𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠)

In line with Richardson et al. (2015), the outcome of the formula is multiplied by -1. This is for measurement issues, as a higher value of the modified Altman Z score represents lower financial distress.

3.3.2. Government trust

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“Reflects perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies.”

The data about the Government Effectiveness per country is retrieved from the Worldwide Governance Indicators (WGI) provided by the Worldbank. Government Effectiveness is an estimate of governance with a score that ranges from -2,5 (weak) to 2,5 (strong). The higher the score, the higher the perception of quality of public services, civil services and the degree of its independence from political pressures, policy formulation and implementation and the credibility of the government’s commitment to such policies. The variable name used for government trust is GovTrust.

3.4 Operationalization of the control variables

In this paper, the following control variables are included: firm size (FIRMSIZE), leverage (LEV), capital intensity (CAPINT), return on assets (ROA), year, industry, and country. As I am particularly interested on the effect of the board on tax avoidance, control variables regarding board characteristics are included as well. These are equity-based remuneration (EQREM) and board size (BOARDSIZE).

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expected (Cheng, Huang, Li, and Stanfield, 2012). CAPINT, measured as capital expenditure divided by total assets, is included to control for more capital-intensive industries as they have a higher need for capital and thus more incentives to avoid taxes (Dyreng et al., 2010). ROA, measured as net income divided by total assets, is included to control for profitability (Richardson et al., 2015). This paper also includes year, industry and country fixed effects. YEAR is included as tax regulations may differ over time. INDUSTRY is included as tax avoidance may be viewed differently by different industries. Henry and Sansing (2018) show that some industries are tax-favored while others are tax-disfavored. COUNTRY is included as different countries have different tax regulations and statutory tax rates.

Two control variables regarding the board are included. The first one, is equity-based remuneration. Rego and Wilson (2012) argue that equity-based remuneration incentives drive managers to initiate risky tax planning. EQREM is measured as the average proportion of equity-based compensation of the board members in terms of their total compensation. The second one is the size of the board. BOARDSIZE is measured as the natural logarithm of the number of directors that serve on the board. When a board is large, it may be harder to communicate and thus harder for the board to carry out its controlling function on management (Jensen, 1993). Hoseini et al. (2019) find a significant and positive relation between board size and tax avoidance.

An overview of all the variables used and their descriptions can be found in Appendix A.

3.5 Data sources

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also provides different databases, namely one for North America and one Global. BoardEx and Compustat are available through Wharton Research Data Services (WRDS). The information regarding the trust in government per country is retrieved from the ‘Worldwide Governance Indicators’ offered by the Worldbank. The information collected from these three databases is merged into one dataset.

BoardEx’s data is on board level while Compustat’s data is on firm level. This means that BoardEx’s data first has to be set to firm level to avoid duplication of observations. After doing so, it is possible to merge the data from both databases. BoardEx data can be categorized based on International Securities Identification Number (ISIN) of firms. This also applies to Compustat – Global, therefore the data of both databases are merged based on the ISIN-codes. Compustat – North America does not provide ISIN-codes. This database does provide Central Index Keys (CIK-codes) which are given to a firm by the United States Securities and Exchange Commission (SEC). It is possible to search for companies in the BoardEx – North America using these CIK-codes. The data from both Compustat - North America and BoardEx – North America can then be merged based on CIK-codes. Last, the data of the Worldwide Governance Indicators are merged with the other dataset based on country and year.

3.6 Sample selection

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Hoi et al., 2013; Lanis et al., 2017). They are excluded based on their Standard Industrial Classification codes (SIC-codes) which are respectively 6000 to 6999 and 4900 to 4999.

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Table 1

Descriptive statistics of sample countries

Panel A: Sample per

country Country N Percentage of the total Australia 84 0.67 Belgium 14 0.11 Bermuda 64 0.51 Canada 23 0.18 Cayman Islands 36 0.29 Cyprus 6 0.05 Falkland Islands 7 0.06 Finland 16 0.13 France 239 1.90 Germany 199 1.58 Gibraltar 23 0.18 Great Britain 6,483 51.60 Guernsey 34 0.27 Hong Kong 1 0.01 India 2 0.02 Ireland 258 2.05 Isle of Man 42 0.33 Israel 10 0.08 Italy 13 0.10 Jersey 154 1.23 Luxembourg 24 0.19 Marshall Islands 2 0.02 Netherlands 128 1.02 New Zealand 2 0.02 Norway 27 0.21

Papua New Guinea 2 0.02

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Table 1 (continued)

Descriptive statistics of sample countries Panel B:

Women ratio per country

Country Mean SD Min Max

Australia 0.051 0.087 0 0.333 Belgium 0.113 0.152 0 0.375 Bermuda 0.009 0.033 0 0.143 Canada 0.013 0.043 0 0.167 Cayman Islands 0.057 0.086 0 0.250 Cyprus 0 0 0 0 Falkland Islands 0 0 0 0 Finland 0.126 0.124 0 0.333 France 0.212 0.139 0 0.455 Germany 0.143 0.094 0 0.400 Gibraltar 0.012 0.041 0 0.167 Great Britain 0.087 0.110 0 0.455 Guernsey 0.006 0.034 0 0.200 Hong Kong 0.167 0 0.167 0.167 India 0.317 0.023 0.3 0.333 Ireland 0.077 0.083 0 0.444 Isle of Man 0.095 0.159 0 0.455 Israel 0.179 0.027 0.167 0.250 Italy 0.005 0.019 0 0.067 Jersey 0.109 0.115 0 0.400 Luxembourg 0.115 0.100 0 0.333 Marshall Islands 0 0 0 0 Netherlands 0.139 0.128 0 0.455 New Zealand 0.072 0.101 0 0.143 Norway 0.326 0.120 0 0.455

Papua New Guinea 0 0 0 0

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Table 1 (continued)

Descriptive statistics of sample countries Panel C:

Effective tax rate per country

Country Mean SD Min Max

Australia 0.212 0.362 0 1 Belgium 0.204 0.109 0 0.429 Bermuda 0.336 0.405 0 1 Canada 0.350 0.425 0 1 Cayman Islands 0.194 0.355 0 1 Cyprus 0.028 0.069 0 0.169 Falkland Islands 0.429 0.535 0 1 Finland 0.495 0.417 0 1 France 0.295 0.190 0 1 Germany 0.332 0.229 0 1 Gibraltar 0.154 0.236 0 1 Great Britain 0.238 0.284 0 1 Guernsey 0.296 0.461 0 1 Hong Kong 0 0 0 0 India 0.280 0 0.280 0.280 Ireland 0.253 0.320 0 1 Isle of Man 0.140 0.334 0 1 Israel 0.465 0.469 0 1 Italy 0.471 0.382 0 1 Jersey 0.251 0.277 0 1 Luxembourg 0.319 0.379 0 1 Marshall Islands 0 0 0 0 Netherlands 0.227 0.209 0 1 New Zealand 0.327 0.130 0.236 0.419 Norway 0.403 0.306 0 1

Papua New Guinea 0.460 0.234 0.295 0.626

Singapore 0.114 0.117 0 0.280 South Africa 0.455 0.210 0.261 1 Spain 0.136 0.113 0 0.335 Sweden 0.331 0.262 0 1 Switzerland 0.232 0.115 0.126 1 United States 0.308 0.231 0 1 Virgin Islands 0.236 0.380 0 1 Zambia 0.265 0 0.265 0.265

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Table 2

Descriptive statistics of sample industries

Panel A: Sample per industry

SIC-code Industry N Percentage

of the total 100-999 Agriculture 23 0.18 1000-1499 Mining 1,576 12.54 1500-1799 Construction 315 2.51 2000-3999 Manufacturing 5,456 43.42 4000-4999 Transportation 891 7.09 5000-5199 Wholesale trade 407 3.24 5200-5999 Retail trade 976 7.77 6000-6799 Finance 0 0 7000-8999 Services 2,838 22.59 9100-9729 Public administration 0 0 9900-9999 Others 83 0.66 Total 12,565 100

Panel B: Women ratio per

industry

SIC-code Industry Mean SD Min Max

100-999 Agriculture 0.038 0.076 0 0.250 1000-1499 Mining 0.060 0.090 0 0.455 1500-1799 Construction 0.120 0.112 0 0.437 2000-3999 Manufacturing 0.127 0.112 0 0.455 4000-4999 Transportation 0.124 0.110 0 0.455 5000-5199 Wholesale trade 0.110 0.105 0 0.429 5200-5999 Retail trade 0.158 0.107 0 0.455 6000-6799 Finance - - - - 7000-8999 Services 0.101 0.111 0 0.4555 9100-9729 Public administration - - - - 9900-9999 Others 0.139 0.116 0 0.455

Panel C: Effective tax rate per

industry

SIC-code Industry Mean SD Min Max

100-999 Agriculture 0.302 0.266 0 1 1000-1499 Mining 0.269 0.364 0 1 1500-1799 Construction 0.245 0.213 0 1 2000-3999 Manufacturing 0.259 0.251 0 1 4000-4999 Transportation 0.268 0.245 0 1 5000-5199 Wholesale trade 0.302 0.199 0 1 5200-5999 Retail trade 0.309 0.225 0 1 6000-6799 Finance - - - - 7000-8999 Services 0.266 0.273 0 1 9100-9729 Public administration - - - - 9900-9999 Others 0.306 0.355 0 1

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3.7 Research method

This paper utilizes a multiple linear regression on panel data. To determine if it is appropriate to include fixed-effects, a Hausman-test is employed (Hausman, 1978). The results are shown in Appendix B. The outcome of this test has a low p-value, meaning that fixed-effects are appropriate to include. More specifically, year, industry and country fixed effects are included in the model. To test for homoscedasticity, the Breusch-Pagan test is employed (Breusch and Pagan, 1979). The results are exhibited in Appendix B. The outcome of this test has a low p-value as well, meaning that the error terms are not constant and heteroscedasticity is present. Therefore, the robust clustered standard errors are used in the model. With a multiple linear regression it is assumed that the variables follow a normal distribution. Where possible, the natural logarithm of variables is used to create a more normally distributed sample. In addition, continuous variables are winsorized at the 1% level to reduce the effect of outliers.

3.8 Model

This paper employs four different estimation models which are discussed below.

3.8.1. Estimation model of Women Ratio and ETR

The first estimation model estimates the relationship between the proportion of female directors on the corporate board and tax avoidance. This model is composed as follows:

𝐸𝑇𝑅𝑖,𝑡 = 𝛽0+ 𝛽1𝑊𝑜𝑚𝑒𝑛 𝑅𝑎𝑡𝑖𝑜𝑖,𝑡+ 𝛽2𝐹𝑖𝑛𝐷𝑖𝑠𝑡𝑟𝑒𝑠𝑠𝑖,𝑡+ 𝛽3𝐺𝑜𝑣𝑇𝑟𝑢𝑠𝑡𝑖,𝑡+

∑ 𝛽𝑘 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑘+ 𝜂𝑖,𝑡+ 𝜇𝑡+ 𝛾𝑖,𝑡+ 𝜀𝑖,𝑡 (1)

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fixed effects, 𝜇 refers to the country fixed effects, 𝛾 refers the industry fixed effects, and 𝜀 is the error term. The coefficient of Women ratio captures the effect that the proportion of female board members have on the ETR and thus on tax avoidance.

The second model estimates the relationship between 30% or more women on the board and tax avoidance. The model is as follows:

𝐸𝑇𝑅𝑖,𝑡 = 𝛽0+ 𝛽1 30% 𝑊𝑜𝑚𝑒𝑛 𝑅𝑎𝑡𝑖𝑜𝑖,𝑡+ 𝛽2𝐹𝑖𝑛𝐷𝑖𝑠𝑡𝑟𝑒𝑠𝑠𝑖,𝑡+ 𝛽3𝐺𝑜𝑣𝑇𝑟𝑢𝑠𝑡𝑖,𝑡+

∑ 𝛽𝑘 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑘+ 𝜂𝑡+ 𝜇𝑡+ 𝛾𝑡+ 𝜀𝑖,𝑡 (2)

3.8.2. Estimation model regarding the moderating effect of financial distress

The third model is concerned with the interaction between the proportion of women on the board and financial distress. The model is formulated as follows:

𝐸𝑇𝑅_𝑖,𝑡 = 𝛽₀+ 𝛽₁𝑊𝑜𝑚𝑒𝑛 𝑅𝑎𝑡𝑖𝑜_𝑖,𝑡+ 𝛽₂𝐹𝑖𝑛𝐷𝑖𝑠𝑡𝑟𝑒𝑠𝑠_𝑖,𝑡+ 𝛽₃ 𝑊𝑜𝑚𝑒𝑛 𝑅𝑎𝑡𝑖𝑜_𝑖,𝑡∗

𝐹𝑖𝑛𝐷𝑖𝑠𝑡𝑟𝑒𝑠𝑠𝑖,𝑡+ 𝛽4𝐺𝑜𝑣𝑇𝑟𝑢𝑠𝑡𝑖,𝑡+ ∑ 𝛽𝑘 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑘+ 𝜂𝑡+ 𝜇𝑡+ 𝛾𝑡 + 𝜀𝑖,𝑡 (3)

3.8.3. Estimation model regarding the moderating effect of government trust

The fourth model is concerned with the interaction between the proportion of women on the board and government trust. The model is formulated as follows:

𝐸𝑇𝑅_𝑖,𝑡 = 𝛽₀+ 𝛽₁𝑊𝑜𝑚𝑒𝑛 𝑅𝑎𝑡𝑖𝑜_𝑖,𝑡+ 𝛽₂𝐹𝑖𝑛𝐷𝑖𝑠𝑡𝑟𝑒𝑠𝑠_𝑖,𝑡+

𝛽₃𝐺𝑜𝑣𝑇𝑟𝑢𝑠𝑡_𝑖,𝑡+ 𝛽₄ 𝑊𝑜𝑚𝑒𝑛 𝑅𝑎𝑡𝑖𝑜_𝑖,𝑡∗ 𝐺𝑜𝑣𝑇𝑟𝑢𝑠𝑡_𝑖,𝑡+ ∑ 𝛽_𝑘𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠_𝑘+ 𝜂_𝑡+ 𝜇_𝑡+ 𝛾_𝑡+ 𝜀_𝑖,𝑡

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4. Results

4.1 Descriptive statistics

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Table 3

Descriptive statistics

Variables N Mean SD Minimum Maximum

ETR 12,565 0.267 0.269 0 1 Women Ratio 12,565 0.114 0.112 0 .4555 30% Women Ratio 12,565 0.064 0.244 0 1 FinDistress 12,565 -0.744 4.204 -4.837 42.900 GovTrust 12,565 1.540 0.240 -0.206 2.206 FIRMSIZE 12,565 7.007 2.739 -2.847 13.483 LEV 12,565 0.163 0.168 0 0.923 CAPINT 12,565 0.045 0.063 0 1 ROA 12,565 0.025 0.111 -0.223 0.316 BOARDSIZE 12,565 2.090 0.381 0.693 3.367 EQREM 12,565 0.551 0.243 0.010 1

This table presents the descriptive statistics. N is the number of firm-year observations. Effective tax rate (ETR) are the tax expenses divided by the pre-tax income. Women ratio is the proportion of women on the board in terms of total board members. 30% Women ratio is a dummy variable which takes value 1 if there are 30% or more women on the board and 0 otherwise. Financial distress (FinDistress) is measured by the modified Altman Z-score. Government trust (GovTrust) is the score of government effectiveness as provided by the WorldBank. Firm size (FIRMSIZE) is measured as is the natural logarithm of the total assets of a firm (in million $). Leverage (LEV) is measured as long-term debt divided by total assets. Capital intensity (CAPINT) is measured as capital expenditure divided by total assets. Return on assets (ROA) is measured as net income divided by total assets. BOARDSIZE is the natural logarithm of the number of directors of a firm. Equity-based remuneration (EQREM) is the average proportion of equity-based compensation of the board members in terms of their total compensation.

All continuous variables are winsorized at the 1% level.

4.2 Correlation matrix

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Table 4

Pearson correlation matrix

Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (1) ETR 1 (2) Women Ratio 0.038*** 1 (3) 30% Women Ratio 0.004 0.575*** 1 (4) FinDistress -0.130*** -0.156*** -0.045*** 1 (5) GovTrust 0.017* 0.040*** -0.007 -0.022** 1 (6) FIRMSIZE 0.154*** 0.435*** 0.122*** -0.422*** 0.023*** 1 (7) LEV 0.080*** 0.173*** 0.034*** -0.031*** 0.038*** 0.394*** 1 (8) CAPINT 0.037*** -0.047*** -0.029*** 0.018** -0.044*** 0.019** 0.044*** 1 (9) ROA -0.020** 0.206*** 0.057*** -0.521*** 0.040** 0.445*** 0.082*** -0.017* 1 (10) BOARDSIZE 0.123*** 0.395*** 0.107*** -0.312*** 0.036*** 0.796*** 0.304*** 0.007 0.331*** 1 (11) EQREM 0.027*** 0.141*** 0.014 -0.054*** -0.015* 0.254*** 0.115*** 0.046*** 0.126*** 0.175*** 1

This table shows the Pearson correlation matrix. Effective tax rate (ETR) are the tax expenses divided by the pre-tax income. Women ratio is the proportion of women on the board in terms of total board members. 30% Women ratio is a dummy variable which takes value 1 if there are 30% or more women on the board and 0 otherwise. Financial distress (FinDistress) is measured by the modified Altman Z-score. Government trust (GovTrust) is the score of government effectiveness as provided by the WorldBank. Firm size (FIRMSIZE) is measured as is the natural logarithm of the total assets of a firm (in million $). Leverage (LEV) is measured as long-term debt divided by total assets. Capital intensity (CAPINT) is measured as capital expenditure divided by total assets. Return on assets (ROA) is measured as net income divided by total assets. BOARDSIZE is the natural logarithm of the number of directors of a firm. Equity-based remuneration (EQREM) is the average proportion of equity-based compensation of the board members in terms of their total compensation.

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4.3 Regression results

4.3.1 Women on corporate boards and tax avoidance

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Table 5

Regression results

ETR ETR ETR ETR ETR ETR ETR

VARIABLES (1) (2) (3) (4) (5) (6) (7) Women ratio -0.042 -0.028 -0.021 -0.237 -0.248 (0.033) (0.033) (0.035) (0.186) (0.191) 30% Women ratio -0.001 (0.011) FinDistress -0.009*** -0.009*** -0.009*** -0.009*** -0.009*** (0.001) (0.001) (0.001) (0.001) (0.001) GovTrust 0.059** 0.059** 0.059** 0.046 0.045 (0.029) (0.029) (0.029) (0.031) (0.031) FIRMSIZE 0.022*** 0.023*** 0.017*** 0.016*** 0.017*** 0.016*** 0.016*** (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) LEV 0.039 0.037 0.066** 0.067** 0.066** 0.066** 0.067** (0.027) (0.026) (0.027) (0.027) (0.027) 0.027) (0.027) CAPINT 0.081 0.082 0.082 0.081 0.081 0.080 0.080 (0.064) (0.064) (0.065) (0.065) (0.065) (0.065) (0.065) ROA -0.314*** -0.314*** -0.435*** -0.435*** -0.433*** -0.436*** -0.434*** (0.040) (0.040) (0.043) (0.043) (0.043) (0.043) (0.043) BOARDSIZE -0.008 -0.006 -0.006 -0.008 -0.006 -0.006 -0.006 (0.015) (0.015) (0.015) (0.014) (0.015) (0.015) (0.015) EQREM -0.036** -0.036** -0.030** -0.031** -0.030** -0.030** -0.030** (0.014) (0..014) (0.014) (0.014) (0.014) (0.014) (0.014) Womenratio*FindDistress 0.009 0.009 (0.008) (0.008) Womenratio*GovTrust 0.135 0.147 (0.119) (0.123) Constant 0.273*** 0.268** 0.195 0.199 0.195 0.215* 0.218* (0.115) (0.117) (0.125) (0.124) (0.125) (0.126) (0.126) Year fixed effects Yes Yes Yes Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Yes Yes Yes Country fixed effects Yes Yes Yes Yes Yes Yes Yes R-squared 0.073 0.074 0.086 0.086 0.086 0.086 0.086 Adjusted R-quared 0.078 0.065 0.078 0.078 0.078 0.078 0.078 Observations 12,565 12,565 12,565 12,565 12,565 12,565 12,565

This table presents the regression results for equation (1) to (4). Effective tax rate (ETR) are the tax expenses divided by the pre-tax income. Women ratio is the proportion of women on the board in terms of total board members. 30% Women ratio is a dummy variable which takes value 1 if there are 30% or more women on the board and 0 otherwise. Financial distress (FinDistress) is measured by the modified Altman Z-score. Government trust (GovTrust) is the score of government

effectiveness as provided by the WorldBank. Firm size (FIRMSIZE) is measured as is the natural logarithm of the total assets of a firm (in million $). Leverage (LEV) is measured as long-term debt divided by total assets. Capital intensity (CAPINT) is measured as capital expenditure divided by total assets. Return on assets (ROA) is measured as net income divided by total assets. BOARDSIZE is the natural logarithm of the number of directors of a firm. Equity-based remuneration (EQREM) is the average proportion of equity-based compensation of the board members in terms of their total compensation.

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and tax avoidance as in this model. The rest of the variables in the regression show similar results as in column 1.

Khaoula and Ali (2012) also find no relationship between the proportion of women on the corporate board and tax avoidance. They argue that this can be explained for the reason that they have a low average of proportion of women on corporate boards in their sample, namely 12 percent. This is similar to this sample, which has an average of 11.4 percent women on corporate boards. To test whether there is a difference in tax avoidance between boards with a lower proportion of women on the board and a higher proportion, another regression is run in which the dummy variable ‘30% Women Ratio’ is included as independent variable. This model is shown in equation (2). The results regarding equation (2) are shown in column 4. The relationship between the control variables, financial distress and government trust and the ETR remain similar as compared to column 3. The relationship between ‘30% Women ratio’ and ETR is insignificant which implies that firms with at least 30% women on the board do not avoid more or less taxes than firms with less women on the board. This finding is in line with the previous found results and confirms that the proportion of women on the board has no effect on the degree of tax avoidance of a company. Thus, no evidence is found for hypothesis 1.

4.3.2. The moderating effects of financial distress and government trust

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distress, is insignificant. This means that for firms that increase the proportion of women on the corporate board and have higher financial distress scores, the relationship between the proportion on women on the board and tax avoidance is not less negative than for firms that have a lower financial distress score. The results provide no evidence that confirms hypothesis 2. Financial distress does not moderate the relationship between the proportion of women on the corporate board and tax avoidance in any way. This is not in line with the expectation that female traits such as risk-aversity, ethical and reputation awareness are impaired by financial distress, nor does it support the assumption that the monitoring function of women is weakened in times of financial distress. The significance and the sign of the rest of the variables remains the same when compared to column 3 of table 5.

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own becomes insignificant. This implies that, on average, the variable government trust has a statistically significant and positive effect on the ETR. However, when government trust as moderating variable is included, neither of the two groups has a significant effect. This indicates that the two variables do not have combined effects but are independent. The significance and the sign of the rest of the variables do not differ from those of column 3 in table 5.

In column 7 of table 5, equation (3) and (4) are combined and both moderating effects of financial distress and government trust are included. Once again, no evidence is found for a moderating effect of financial distress and government trust on the main relationship between the proportion of women on the board and tax avoidance. Government trust on itself is again insignificant, as is the case in column 6. The sign and significance of the rest of the variables remain the same as in the other models. Again, no relationship is found between the proportion of women on the corporate board and tax avoidance.

4.3.4 Robustness tests

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𝐶𝑎𝑠ℎ 𝐸𝑇𝑅 = 𝐶𝑎𝑠ℎ 𝑡𝑎𝑥𝑒𝑠 𝑝𝑎𝑖𝑑

(𝑃𝑟𝑒𝑡𝑎𝑥 𝑖𝑛𝑐𝑜𝑚𝑒 − 𝑆𝑝𝑒𝑐𝑖𝑎𝑙 𝑖𝑡𝑒𝑚𝑠)

The results of the regression are shown in table 6. The equations (1) to (4) are altered so that Cash ETR is the dependent variable. The number of observations has decreased from 12,565 to 12,538 as firms that do not report cash taxes paid or special items are left out. Column 1 shows the results without including ‘Women ratio’ as independent variable and both financial distress and government trust. Similar to the results in column 1 of table 5, a positive and significant relationship between tax avoidance and firm size is found. The

relationship is significant at the 1% level. The relationship between leverage and ETR is again positive but now significant at the 10% level. No evidence is found for a relationship between capital intensity and board size. Similar to the regression with GAAP ETR as dependent variable, a negative relationship is found between tax avoidance and ROA. The relation is significant at the 1% level. Similar to the prior model, the relationship between equity-based remuneration and ETR is negative but is now significant at the 1% level. The results where the independent variable ‘Women ratio’ is added in the model are shown in column 2. Once again, no significant relationship between the proportion of women on the board and tax avoidance is found. The sign and significance of the control variables are similar to the results shown in column 1. In column 3, the results where the variables financial distress and

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Table 6

Robustness tests using Cash ETR as dependent variable

Cash ETR Cash ETR Cash ETR Cash ETR Cash ETR Cash ETR Cash ETR

VARIABLES (1) (2) (3) (4) (5) (6) (7) Women ratio 0.018 0.018 0.022 -0.341* -0.348* (0.037) (0.037) (0.038) (0.193) (0.197) 30% Women ratio 0.013 (0.012) FinDistress -0.012*** -0.012*** -0.012*** -0.012*** -0.012*** (0.001) (0.001) (0.001) (0.001) (0.001) GovTrust -0.016 -0.015 -0.016 -0.038 -0.039 (0.022) (0.022) (0.022) (0.025) (0.025) FIRMSIZE 0.030*** 0.030*** 0.022*** 0.022*** 0.022*** 0.022*** 0.021*** (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003) LEV 0.051* 0.051* 0.089*** 0.089*** 0.089*** 0.089*** 0.090*** (0.030) (0.030) (0.030) (0.030) (0.030) (0.030) (0.030) CAPINT -0.062 -0.062 -0.064 -0.064 -0.065 -0.067 -0.067 (0.058) (0.058) (0.057) (0.057) (0.057) (0.057) (0.057) ROA -0.700*** -0.700*** -0.862*** -0.862*** -0.861*** -0.863*** -0.863*** (0.045) (0.045) (0.049) (0.049) (0.049) (0.049) (0.049) BOARDSIZE 0.003 0.003 0.003 0.003 0.003 0.004 0.004 (0.017) (0.017) (0.017) (0.016) (0.017) (0.016) (0.016) EQREM -0.063*** -0.063*** -0.056*** -0.055*** -0.055*** -0.055*** -0.055*** (0.015) (0.015) (0.015) (0.015) (0.015) (0.015) (0.015) Womenratio*FindDistress 0.005 0.006 (0.006) (0.006) Womenratio*GovTrust 0.232* 0.240* (0.123) (0.126) Constant 0.110 0.111** 0.179** 0.176** 0.179** 0.213** 0.215** (0.082) (0.083) (0.090) (0.090) (0.090) (0.092) (0.092) Year fixed effects Yes Yes Yes Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Yes Yes Yes Yes Country fixed effects Yes Yes Yes Yes Yes Yes Yes R-squared 0.112 0.112 0.129 0.129 0.129 0.129 0.129 Adjusted R-quared 0.121 0.104 0.121 0.121 0.121 0.122 0.122 Observations 12,538 12,538 12,538 12,538 12,538 12,538 12,538

This table presents the regression results of the robustness test where Cash ETR (measured as cash taxes paid divided by Pretax income – Special items) is the dependent variable. Women ratio is the proportion of women on the board in terms of total board members. 30% Women ratio is a dummy variable which takes value 1 if there are 30% or more women on the board and 0 otherwise. Financial distress (FinDistress) is measured by the modified Altman Z-score. Government trust (GovTrust) is the score of government effectiveness as provided by the WorldBank. Firm size (FIRMSIZE) is measured as is the natural logarithm of the total assets of a firm (in million $). Leverage (LEV) is measured as long-term debt divided by total assets. Capital intensity (CAPINT) is measured as capital expenditure divided by total assets. Return on assets (ROA) is measured as net income divided by total assets. BOARDSIZE is the natural logarithm of the number of directors of a firm. Equity-based remuneration (EQREM) is the average proportion of equity-based compensation of the board members in terms of their total compensation.

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As opposed to the results that are shown in column 3 of table 5, no significant relationship can be observed between government trust and ETR, meaning that there is no evidence for a relationship between government trust and tax avoidance when using Cash ETR as a proxy for tax avoidance. The relationship between leverage and ETR is now significant at the 1% level, where in column 1 and 2 it is significant at the 10% level. When compared to the results in table 5, the significance of this relationship has increased. In column 4, the results of the regression where the dummy variable ‘30% Women ratio’ is included as independent variable are exhibited. Again, no significant relationship is found. The significance and sign of the other variables remain unchanged when compared to column 3.

In column 5 to 7, the moderating effects of financial distress and government

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government trust moderates the negative relationship between the proportion of women on the board and ETR in such a way, that it becomes less negative. In other words, an increase of the proportion of women on the board results in an increase of tax avoidance. However, this relationship is weaker in countries where there is high government trust compared to countries where there is lower government trust. In column 7, the results where both

moderating variables are included are shown. Again, there is a negative relationship between the proportion of women on the board and the ETR. The absolute coefficient has increased to 0.348, implying that a one increase in the women ratio leads to a decrease of 34.8 percentage point in ETR. The results provide no evidence of a moderating effect of financial distress on the main relation. The moderating effect of government trust on the main relationship is positive and significant at the 10% level, as is the case in column 6.

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5. Conclusion

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pretax income. For Cash ETR, the taxes paid are divided by the pretax income minus special items. The difference of the effect of government trust on tax avoidance, as well as the effect of the moderator government trust on the relationship between the proportion of women on the board and tax avoidance, may possibly be explained by the differences between tax expenses (GAAP ETR) and taxes paid and special items (Cash ETR) for firms in the sample.

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measure of tax avoidance is used. An in depth analysis of the two measures and how government trust impacts both these measures may provide insights. Last, this study finds a negative relationship between leverage and tax avoidance. There is no literature that finds a similar relationship, although Hanlon and Heitzman (2010) raise some questions regarding the reliability of leverage if measured using information from the financial statements. According to Auerbach (2002), the assumption that higher leverage leads to lower tax costs and thus more tax avoidance, is not as straightforward when there is malleability in how financial claims are classified for non-tax and tax purposes. For example, firms regularly report higher interest expense on tax returns than in financial statements (Mills and Newberry, 2005). This is partially explained by the adoption of different financing methods such as R&D partnerships and leases. If there are firms in this sample relying heavily on alternative finance structures, then the assumption of a negative relationship between leverage and tax avoidance for these firms may not apply. For this reason, more extensive research on the effect of different ways of financing on tax avoidance can provide more insights on this relationship.

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