Audit Fees Samples

Table 2 details the sampling method for the audit fees samples. These samples consist of matched US firm years between Compustat and Audit Analytics from 2001 - 2013. Consistent with three measures for corporate citizenship, this study uses three samples for the audit fees test: (1) Tax Sample, (2) Wage Sample and (3) Philanthropy Sample. Although different, all three samples require a similar set of control variables for the audit

fees model (as in Equation Model [4]); therefore, all three samples are drawn from the main sample. The samples differ only in relation to how their measurements bias their sample sizes.

To develop the main sample for the audit fees test, this study first collects the financial variables from Compustat on all available US firms from 1997 to 2014. The financial variables are collected three years prior to develop the variables, which requires computation of average, for example, to compute for the first corporate citizenship, two- and three-year cumulative average tax fairness (e.g., CASH_TPR2YR, t and CASH_TPR3YR, t), and normal rate of ROE, ROE, for the use of the second test (the Ohlson test). It then matches the US firm-year cases from Compustat to Audit Analytics using the Central Index Key, CIK.21 _{Audit Analytics provides data on audit fees and other auditor-related} characteristics. This process restricts the initial sample to 91,263 firm-year observations and the period 2000–2014, because Audit Analytics only provides data from 2000. This study then merges the matched US firm years of Compustat and Audit Analytics to three other databases—Execucomp, BoardEX and MSCI–KLD—to obtain data on CEOs’ pay, philanthropy and other measures of social corporate performance (CSR). It further excludes the matched firm years to non-zero total assets and non-missing Global Industry Classification Standard (GISC) industry code, non-missing GISC industry code and those with positive return on assets, ROA. This study excludes the loss-making firms from the main sample for two reasons: (1) it is a common practice in prior tax literature to exclude loss-making firms for a meaningful interpretation of the results;22 _{and (2) positive net} income can serve as a control for managerial expertise in regression analyses. According to source credibility theory, two components are important in influencing an information source’s credibility—namely, expertise and trustworthiness (Pornpitakpan 2004). As discussed in Chapter 3, this study measures source trustworthiness using three corporate citizenship components in relation to tax fairness, wage unfairness and philanthropy. Prior literature uses several methods to measure managerial expertise or abilities, including by measuring management forecast accuracy (Bartov, Givoly and Hayn 2002;

21 _{The Central Index Key (CIK) is a unique number assigned to firms by the US Securities and Exchange} Commission (SEC) to identify their specific disclosure.

22 _{The loss-making firms are not required to pay tax. Further, under the US corporate tax system, losses can} be carried back two years to reduce their current tax liability (Appendix 5.4.1 OECD-10: Treatment of losses).

Graham, Harvey and Rajgopal 2005; Goodman et al. 2013), managerial ability score (Demerjian et al. 2012), managers’ reputation (Milbourn 2003; Francis et al. 2008) and superior historical earnings (Farrell and Whidbee 2003; Fee and Hadlock 2003).

Given that there are a number of ways to measure managers’ abilities and expertise, this study selects positive income performance as the simplest approach. This might serve as a broad measure for capturing managerial abilities; however, it should be adequate and effective because this study is more concerned with the trustworthiness component of source credibility. Using positive income as a control is also consistent with prior tax literature practice, which usually excludes loss-making firms. Further, prior literature shows that financially distressed firms are likely to increase concern for opportunistic reporting (Koch 2002). Therefore, controlling for loss-making firms allows for less variation caused by upward bias reporting related to bad earnings performance.

To develop the Tax Sample, the sample is restricted to firms that have non-missing tax fairness measures and non-missing control variables required by the audit fees model, as stated in Equation Model (4). The control variables include firms’ social performance in environment and employee welfare protection (CSR controls). The CSR controls limit the Tax Sample to 12,851 firm-year observation and a sample period of 2001–2013. To develop the Wage Sample, employees’ and CEOs’ pay data are required. An early assessment of Compustat shows a lack of employees’ pay data. This affects the second corporate citizenship measure, wage unfairness, CEO_PAY_RATIO. In addition, the firm- year search on Execucomp from 2000 to 2014 provides only 36,942 observations after restricting to the non-missing matching variable, GVKEY and non-missing annual CEOs’ pay. The annual CEO describes the executive who serves the longest during the financial period. The annual CEO’s pay is selected because their pay is more representative of the firm’s reputation in that period. The concern with a large number of missing employees’ data and small number of firm-year observations in Execucomp raises a concern that wage unfairness is likely to produce a small sample size.

To address the lack of employee’s pay data, CEOs’ pay data are collected from BoardEx to develop an alternative measure of, and therefore an alternative sample for, wage unfairness. The alternative measure for wage unfairness, CEO Compensation Excess (CEO_EXCESS), has zero reliance on employees’ pay data; therefore, it can serve as an alternative sample to the main wage unfairness measure (CEO_PAY_RATIO). However,

CEOs’ pay data from BoardEx are shown to be less helpful. After imposing similar sorting procedures on CEOs’ pay used in Execucomp, BoardEx provides up to 67,260 firm-year observations for CEOs’ pay data. However, of these observations, 83 per cent have missing CEOs’ pay data. The rest (17 per cent) are mostly already available from Execucomp. Therefore, collecting from BoardEx does not seem to add value to CEOs’ pay data from Execucomp. In addition, there is no variable in BoardEx to allow for similar sorting for the annual CEO, as in Execucomp. For this reason, CEOs’ pay in BoardEx is restricted to the highest-paid CEO. The sorting procedure carried out in Execucomp is inconsistent and is therefore a limitation of the Wage Sample.

To develop the Philanthropy Sample, data are collected on corporate giving and donation in the community dimension, which is one of the six main social dimensions available in the MSCI–KLD database.23 _{A detailed examination of the community dimension and} other social dimensions in the MSCI–KLD database raises considerable concerns related to the reliability of the philanthropy data and, consequently, its effects on the results. First, it is not certain how each individual component’s score builds up to the total score in each social category. The lack of explanation regarding the ranking procedure leads to ambiguity when determining the basis for the MSCI–KLD social score. There is also a noticeable number of reclassifications and discontinuations in subcategories across the six main social categories. This suggests that there is significant inconsistency in the data, as well as comparability issues with social performance data in MSCI–KLD. This probably explains why some researchers prefer to use the net score to measure firms’ social performance in the MSCI–KLD database, although Mattingly and Berman (2006) suggest that using net score may lead to result bias. Therefore, this study uses an individual score from two types of philanthropy: domestic donation and foreign donation. However, domestic donation data were discontinued after 2009, and foreign donation were discontinued after 2011. This restricts the Philanthropy Sample from 2001 to 2009, with a sample size of 8,505 firm-year observations.

23 _{MSCI–KLD provides more than six social dimensions, but prior research usually limits corporate social} activities to six main dimensions: (1) community, (2) diversity, (3) employee, (4) corporate governance, (5) environment and (6) product.

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Table 2: Sample Selection Method for Audit Fees Test

N

Initial sample—matched US firm years between Compustat and Audit Analytics (2000–2014) 91,263

Less:

Number of firm years with zero total assets 369

Number of firm years with missing GISC 292

Number of unmatched firm years with Execucomp and BoardEx 38,239

Number of unmatched firm years from MSCI–KLD 25,765

Number of firm years from the financial sector (GISC 40) 4,978

Number of firm years with negative ROA* 3,803

Main sample 17,817 Tax Sample Wage Sample Philanthropy Sample N N N

From the main sample 17,817 17,817 17,817

Less:

Number of firm years with missing current cash taxes paid 2,031

Number of firm years with missing CEOs’ pay data 3,845

Number of firm years with missing employees’ pay data 11,203

Number of firm years with missing philanthropy 8,108

Number of firm years with missing required variables 2,935 1,946 1,204

12,851 823 8,505

This table describes the sampling procedures for audit fees samples using one-year tax fairness performance, CASH_TPR1YR, t, one-year wage unfairness, CEO_PAY_RATIO1YR, t and one-year domestic donation, US_DON1YR, t.