Model specification

In order to test the relationships of tax aggressiveness, OLS regressions are used.

The basic regression models employed to investigate differences between family and non-family firms, i.e. hypothesis one, two, four and five, are the following:

Equation 1

TaxAggi,t = 0 + 1Familyi,t +2Familyi,t*CEOgender i,t + 3GEOgenderi,t +

4ROAi,t+ 5LEVi,t+ 6PPEi,t + 7INTANGi,t+ 8EQINCi,t + 9SIZEi,t-1+

10BIG4i,t+ IndFE + YearFE + CompFE + i,t

Equation 2

TaxAggi,t = 0 + 1FamilyOwni,t +2FamilyOwni,t*CEOgender i,t +

3GEOgenderi,t + 4ROAi,t+ 5LEVi,t+ 6PPEi,t + 7INTANGi,t+

8EQINCi,t + 9SIZEi,t-1+ 10BIG4i,t+ IndFE + YearFE + CompFE + i,t

Where

Family i,t = Indicator variable coded as 1 if the firm is a family firm; 0 otherwise. The thresholds are 50- and 10 percent family ownership for private- and listed firms respectively.

FamilyOwn = Continuous family ownership variable

Family i,t *CEOgender i,t = Family indicator variable multiplied by the indicator variable for CEO gender

FamilyOwni,t*CEOgender i,t = Continuous family ownership variable multiplied by the indicator variable for CEO gender

GEOgenderi,t = Indicator variable coded as 1 if the CEO is male; 0 otherwise

ROA i,t = Return on assets for firm i in year t, calculated as profit before tax divided by total lagged assets.

LEV i,t = Leverage for firm i in year t, calculated as long-term debt divided by total lagged assets.

PPE i,t = Property, plant and equipment for firm i in year t, calculated as PPE divided by total lagged assets.

INTANG i,t = Intangible assets for firm i in year t, calculated as intangible assets divided by total lagged assets.

46 EQINC i,t = Proxy for equity income for firm i in year t, calculated as income from subsidiaries, other enterprises in the same group or affiliates

divided by total lagged assets.

SIZE i,t-1 = Size of firm i at the beginning of the year, calculated as the natural logarithm of total assets.

BIG4 i,t = Indicator variable coded as 1 if the auditor of firm i in year t is one of the big 4 companies (KPMG, Deloitte, EY or PwC); 0 otherwise.

IndFE = Industry fixed effects.

YearFE = Year fixed effects.

CompFE = Company specific fixed effects.

Equation three is used to analyse the third hypothesis. The definition of the variables is the same as for those in equation one and two. In addition to the already defined variables, the variable “ListingStatus” is equal to 1 if the firm is publicly listed and zero otherwise, and the interaction term between listing status and family ownership is defined as listing status multiplied by family ownership.

The sample for hypothesis three only consists of family firms, defined by at least 10 percent family ownership, and employs the following regression:

Equation 3

TaxAggi,t = 0 + 1ListingStatusi,t + 2FamilyOwni,t +

3FamilyOwni,t1*ListingStatusi,t +4CEOgender i,t

+5FamilyOwni,t*CEOgender i,t + 6ROAi,t+ 7LEVi,t+ 8PPEi,t +

9INTANGi,t+ 10EQINCi,t + 11SIZEi,t-1+ 10BIG4i,t+ IndFE + YearFE + CompFE + i,t

To ease reading, the equations are also presented in the accompanying tables.

We include CEO gender and the interaction terms between CEO gender and the family firm variables in our models, since we expect these factors to affect firms’

tax behaviour. Return on assets (ROA), which captures firm profitability and efficiency, and leverage (LEV), which capture firm leverage, are included since previous research (Anderson & Reeb, 2003) has found that family firms have better operating performance compared to non-family firms. As highly profitable firms are found to have higher ETR (Steijvers & Niskanen, 2014), family firms

47 could evidently be presented as less tax aggressive if these differences are not considered. Further, firms with higher leverage have been found to reduce corporate taxes more effectively (Gupta & Newberry, 1997) amongst other through interest deductions (Badertscher et al., 2017; Moore et al., 2017;

Richardson, Taylor, et al., 2016; Steijvers & Niskanen, 2014). Higher leveraged firms can be expected to have larger incentives to engage in tax aggressiveness than less leveraged firms due to the need to serve their debt (Langli & Willikens, 2017). To capture the tangible- and intangible presence we include property, plant and equipment (PPE) and intangible assets (INTANG). These are included due to the possibility of depreciations and impairments, which affects the tax rate. PPE is for example found to be negatively related to ETR (Steijvers & Niskanen, 2014;

Gupta and Newberry, 1997) and noted by Richardson, Taylor, et al. (2016) to be positively related to tax aggressiveness. Moreover, intangible assets are by some referred to as being related to tax management (Kiesewetter & Manthey, 2017).

Furthermore, equity income is controlled for due to the reporting of consolidated earnings when employing the equity method, as in (Chen et al., 2010). All of these control variables are common in related research (Chen et al., 2010; Dyreng et al., 2010; Mafrolla & D’Amico, 2016; Steijvers & Niskanen, 2014).

Further, we control for size effects (SIZE). Larger firms seem to have higher ETRs (Steijvers & Niskanen, 2014). Conversely, Richardson et al. (2013), Lanis et al. (2017) and Lin et al. (2014) find that size is associated with tax

aggressiveness. Lanis et al. (2017) further argue that large firms potentially can benefit from economies of scale in their tax planning, but also recognizes that political costs could reduce tax aggressiveness. The existence of economies of scale is further substantiated by Rego (2003). We choose to measure size as the natural logarithm of total assets following Kvaal et al. (2012), Mafrolla and D’Amico (2016) and Steijvers and Niskanen (2014), but other alternatives such as the natural logarithm of equity or market value have been employed (e.g. Chen et al., 2010; Martinez & Ramalho, 2014).

Additionally, the control variables will include a variable indicating whether the company is audited by a big four auditing firm or not. We include this variable based on previous research, which has found relevant effects to our study. These findings include Klassen et al. (2015), who found that big four tax preparers were

48 linked to less tax aggressiveness when they were the auditor and Eshleman and Guo (2014) who found evidence suggesting that big four auditors performed higher quality audits. Further, Kanagaretnam, Lee, Lim and Lobo (2016) found auditor quality to be negatively associated with tax aggressiveness. Contrary, Jones, Temouri and Cobham (2018) found a strong correlation between the use of big four auditors and tax havens for multinational entities.

Since stock prices are not included in our dataset, firms are anonymized and a proxy of the market value of equity among private firms is limited, we will not be able to employ market values in our measures as is common in some of the prior research.