Family ownership and discretionary accruals quality

Transcription

1 Bond University Bond Business School Publications Bond Business School Family ownership and discretionary accruals quality Tim Hasso Bond University, Keith Duncan Bond University, Follow this and additional works at: Part of the Corporate Finance Commons Recommended Citation Hasso, T., & Duncan, K. (2012). Family ownership and discretionary accruals quality. Retrieved from This Conference Paper is brought to you by the Bond Business School at It has been accepted for inclusion in Bond Business School Publications by an authorized administrator of For more information, please contact Bond University's Repository Coordinator.

2 Family ownership and discretionary accruals quality Abstract This paper addresses the conflicting evidence on the role of family ownership in accruals quality. We argue that prior evidence has failed to control for innate characteristics that reflect the firm s business and operating environment (Francis et al., 2005). To address this issue, we decompose accruals quality into two components, innate and discretionary accruals. This allows us to investigate the relationship between family ownership and discretionary accruals in specific. We show that family ownership is associated with higher quality of discretionary accruals. Furthermore, we find that the association is marginally stronger when a family member servers as the chief executive officer. Our results consider other governance mechanisms that have been shown to impact discretionary accruals in prior studies. 2

3 1. Introduction This paper investigates the association between family ownership and accruals quality. It has been suggested that the traditional owner-manager agency conflict, Type I agency problem, is mitigated in publically listed family firms (Demsetz and Lehn, 1985, Anderson et al., 2003, Villalonga and Amit, 2006). Nevertheless, the concentration of ownership and management in these firms leads to Type II agency problems, as the family is able to act opportunistically and expropriate wealth from the firm at the expense of minority shareholders. Given this interesting agency setting, there has been considerable research conducted in this area. Prior studies have found conflicting evidence regarding this relationship, with studies from the US showing a positive relationship between family ownership and accruals quality (Wang, 2006, Ali et al., 2007), while some non-us studies show a negative relationship (Kim and Yi, 2006, Prencipe et al., 2008, Yang, 2010). However, a common theme in this prior work is that it uses accruals quality measures that do not discriminate between accruals arising from the economic fundamentals of the firm and accruals arising from earnings management. These economic fundamentals have been found to drive accruals in prior research, both in a US setting (Dechow and Dichev, 2002, Francis et al., 2005), and more recently within the Australian setting (Aldamen and Duncan, 2011, Kent et al., 2010). These studies have found that when taking into account the economic fundamentals of the firm, the conclusions regarding what drives accruals quality may change (Francis et al., 2005). Consequently, the existing evidence regarding the association between family ownership and accruals quality is not convincing. We thus extend the research in this area by decomposing the accruals quality measure into innate and discretionary accruals, and focus on the relationship between family ownership and discretionary accruals in specific. This study is important as both innate and discretionary accruals impact the quality, and thus the faithful representation of financial information. The Conceptual Framework for 3

4 Financial Reporting (the IFRS framework) specifies that one of the fundamental qualitative characteristics of decision-useful information is faithful representation (IASB, 2010), thus it becomes critical from a regulatory perspective to investigate factors and governance environments in which this characteristic may be impacted. Without knowledge of best practice in governance, the regulators are unable to effectively protect investors. However, this issue is also of relevance to actual users of the information. It has been shown that the usefulness of accounting information, as measured by value-relevance, is associated with accruals quality (Marquardt and Wiedman, 2004). Lower quality information decreases the association between accounting information and market values and thus decreases the usefulness of the accounting information for equity valuation purposes. Furthermore, recent work by Aldamen and Duncan (2011) shows that discretionary accruals quality is negatively related to cost of debt. Thus this study is also relevant for debt investors. This paper contributes to the existing research on family ownership and its association to accruals quality in two ways. Firstly, we provide an important methodological contribution by adopting the accruals measure devised by Dechow and Dichev (2002) (hereafter DD) and later extended by McNichols (2002). Prior literature in this area has primarily used the Jones (1991) accruals model and its various derivatives. While extensively used in research, the model measures accruals quality indirectly (Francis et al., 2005). In contrast, the DD model provides a more direct measure of accruals quality by investigating the relation between working capital accruals and past, present, and future operating cash flows (Aboody et al., 2005). In light of the work by McNichols (2002) we also consider the revenues and property, plant and equipment (PPE) as explanatory variables for changes in working capital. Furthermore, we adopt the technique of Francis et al. (2005) to decompose the accruals quality measure into innate and discretionary accruals. Thus we are able to investigate the impact of family ownership on the accruals that are discretionary instead of using a more 4

5 noisy measure capturing all accruals. Our second major contribution is the study of family ownership and its relation to accruals quality in an Australian setting. This environment is distinguished by its stability and high investor protection (La Porta et al., 1999). Thus our results provide a useful comparison to prior studies that have primarily focused on countries with low investor protection. In our study we show that family ownership impacts accruals quality, even when we control for the firm s governance practices that have been found to be associated with accruals quality (Kent et al., 2010). We also confirm our initial conjecture, that a portion of accruals quality can be explained by the innate characteristics of the firm. More importantly, we find that family ownership has a positive impact upon the discretionary accruals quality of the firm. This relationship is somewhat stronger when a family member of the controlling family also serves as the chief executive officer of the firm. This paper proceeds as follows. Section two reviews prior literature and distils testable hypotheses about the relationship between family ownership and accruals quality. Section three describes the research design and section four tests the hypotheses and discusses the results of the data analyses. Finally, section five concludes the paper and discusses the implications of the results. 2. Literature Review and Hypotheses The majority of accruals quality research focuses on publicly listed firms. However, recently researchers have started to accept the notion that these publicly listed firms are not homogenous in nature, and that a significant portion of these firms are in fact controlled by a single family (Bhaumik and Gregoriou, 2009). Indeed, family firms are the most predominant business structure in the world (La Porta et al., 1999), roughly 65% to 80% of the world businesses can be deemed as family firms. The prevalence of family firms is higher in 5

6 emerging economies, nevertheless even in developed countries like Australia at least 50% of all businesses are family firms (Moores and Mula, 2000). Additionally, there is a common misconception that these firms are small and thus insignificant to the economy. However, family firms contribute 45% to 70% to a country s GDP (Astrachan and Shanker, 2003). Furthermore, roughly a third of all publically listed firms in the world are family firms (Anderson and Reeb, 2003, Maury, 2006, Sraer and Thesmar, 2007). These factors together establish the prevalence and the significance of family firms in the economy. This is of particular interest to accruals quality research as this field is often predicated on the agency relationship between owners and managers. Given that a large portion of firms are family owned or controlled, and that these firms experience distinct agency conflicts, the generalizeability of the accruals quality research findings to these firms is questionable. Prior research has suggested that family firms differ significantly from non-family businesses, both in terms of managerial practices and financial performance (Villalonga and Amit, 2006, Anderson and Reeb, 2003, Miller and Le Breton-Miller, 2005). The most notable difference is the long-term orientation of the family firm. As the firm is managed with future generations in mind there is a decrease in myopic behaviour (James, 1999). However, the focus on the family and its future benefits can also lead to detrimental effects for minority shareholders if the family uses their ability to control for personal gain (Wang, 2006). In the section below we discuss how the unique characteristics of family firms have the potential to impact the accruals quality, and specifically discretionary accruals quality. 2.1 Family Firms and Accruals Quality Drawing upon agency theory we can identify two competing effects of family ownership on accruals quality: the alignment effect (typical Type I agency conflict) and the entrenchment effect (typical Type II agency conflict). 6

7 According to the alignment effect, family ownership has the potential of diminishing Type I agency conflicts by exerting greater managerial monitoring. In these firms the family members often hold senior positions, virtually eliminating Type I agency conflicts (Bhaumik and Gregoriou, 2009) as there exists natural alignment between owners and managers. However, even in cases where the family uses a professional manager the undiversified nature of the family s investment portfolio incentivises them to carefully monitor the manager and decrease the incentive for opportunistic management behaviour (Prencipe and Bar-Yosef, 2011). Together this should lead to an improvement in corporate governance. Anderson et al. (2003) found that family firms are able to derive a lower cost of debt, this indicating that bond-holders perceive the governance provided by the families as superior to non-family governance. As the interests of owners and managers are aligned there exists less possibility of earnings management and the potential to extract benefits from the firms at the detriments of shareholders. Furthermore, the bridging of the family reputation and the firm s reputation provides an incentive for the family to use their position in the firm in an appropriate manner (Wang, 2006, Miller and Le Breton-Miller, 2005). This leads to an increase in accruals quality, and research has provided empirical support for this argument in a US setting (Wang, 2006, Ali et al., 2007). There is however a large amount of literature that argues that concentrated ownership may lead to an increase in Type II agency conflicts through the entrenchment effect (Schleifer and Vishny, 1997, Morck et al., 1988). In these situations the controlling shareholders have an incentive to opportunistically expropriate wealth from minority shareholders. Moreover, one of the primary characteristics of family firms is the desire for continuity across generations. This has the potential to detrimentally impact the firm as the family may be using their power to prioritize firm survival rather than maximization of shareholder wealth (Prencipe et al., 2008). The controlling families may thus be perceived to be entrenched and 7

8 self interested. As agency theory provides competing views on the effects of family ownership upon discretionary accruals quality we choose to use a non-directional hypothesis for this study. Thus, the direction of the relationship between family ownership and discretionary accruals quality becomes an empirical question. Hypothesis 1: Family ownership is related to discretionary accruals quality. It is also important to differentiate between family firms where the firm uses a professional manager and family firms where the CEO position is held by a family member. These two CEO types can be distinguished in their incentives to manage accruals. Similar to non-family firms, family firms with professional CEOs may indeed suffer from Type I agency problems. While the concentrated ownership of the family incentivises greater monitoring; the manager still may act opportunistically and manage accruals for his or her own benefit (Yang, 2010). On the other hand, family members in the CEO position can ensure that the family is able to control the firm effectively and according to the wishes of the family (Wang, 2006). As such we believe that there is a relationship between family members in the CEO position and discretionary accruals quality. Hypothesis2: Family members in the CEO position are related to discretionary accruals quality Hypothesis 2 is also non-directional, and its direction may be somewhat influenced by the behaviour of the family firm and findings for hypothesis 1. If the family firm is opportunistic and extracts benefits from minority shareholders then we can expect a family member in the CEO position to increase this effect as the family then has greater control and more opportunities to act self-interested. However, if family firms have greater discretionary accruals quality as a whole then discretionary accruals quality may be even higher in family firms where the family member is the CEO, as they will be able to ensure that the family s strategic direction is implemented. 8

9 3. Research Design 3.1 Family Firm Definition For this research, we use Villalonga and Amit s (2006) family firm definition, in which a firm is said to be a family firm when the family is the largest shareholder and has at least one officer or one director currently in the firm. This definition is widely used in research (Livingston, 2007, Villalonga and Amit, 2010), and is based on the assumption that the family is able to use its position as the largest shareholder to impact the business operations. 3.2 Sample Selection The sample consists of public firms listed on the Australian Stock Exchange between 2002 and We chose this period to avoid any issues arising from the Global Financial Crisis (GFC), which may cause a structural break in the relationship investigated. The initial sample frame consisted of 2034 listed firms, however as the accruals measurement used requires seven years (one year prior and one year after the sample period) of specific continuous data the sample was constrained to 627 firms. Of these firms, 127 were identified as family firms. We acknowledge that the data restrictions of the sample introduce survival bias to our results. However, as the surviving firms tend to be larger firms they would be expected to have higher quality and less noisy accruals. As such, the survival bias may manifest itself by the lack of significant relationships, as the variation in accruals quality is decreased (Francis et al., 2005). To improve research design we use a matched firm method. Each identified family firm is matched with a firm in the same sector and within twenty percent of its size (based on market value). If no suitable match is found the firm is dropped from the sample. For firms 9

10 with more than one match, the matching firm is chosen on closeness in age. We chose to use market value as our measure of size as it is not biased by accounting estimations and judgements. The use of age allows us to improve the match by attempting to control for the firm s lifecycle. Using this procedure we establish a sample of 91 family firms and 91 nonfamily firms. One of these firms is determined to be an extreme outlier through the use of cook s distance method and we thus remove this firm and its match from the sample. The final sample thus consists of 90 family firms and 90 non-family firms that were listed on the Australian Stock Exchange (ASX) during the financial years of Data was collected from Worldscope fundamentals, AspectHuntley sdatanalysis and FinAnalysis and Bureau van Dijk s Osiris. The firms age of incorporation and other governance characteristics were collated from AspectHuntley s DatAnalysis and FinAnalysis. Financial performance and financial structure were collected from Worldscope fundamentals through Datastream. Supplemental variables were obtained from the Osiris database. Table 1 shows the breakdown of the sample across GICS sectors. The sample is diverse but is dominated by the consumer discretionary sector that makes up 33.3 % of the sample, followed by the industrials sector (17.8%) and the information technology sector (14.4%). (Table 1 about here) 3.3 Accruals Quality For our study we use the DD (2002) model as modified by McNichols (2002) to measure accruals quality. In this model, the total accruals of the firm is regressed on past, present, and future operating cash flows; and on the change in revenue and the level of PPE. WWWW jj,tt = ββ 0 + ββ 1 CCCCCC jj,tt 1 + ββ 2 CCCCCC jj,tt + ββ 3 CCCCCC jj,tt+1 + ββ 4 RRRRRR jj,tt + ββ 5 PPPPPP jj,tt + εε jj,tt (1) 10

11 where, for firm j, WWWW jj,tt is a comprehensive measure of change in working capital accruals, including change in accounts receivable, accounts payable, current inventory, current investments, current provisions and other current assets and liabilities in year t, CCCCCC jj,tt is cash flow from operations in year t, RRRRRR jj,tt is the change in operating revenue between year t-1 and year t, and PPPPPP jj,tt is property plant and equipment in year t. All variables in equation (1) are scaled by average total assets from year t-1 to t. For each year, equation (1) is estimated pooled across all firms, consistent with the Australian accruals quality study by Kent et al. (2010). Accruals quality, AQ, is derived by taking the standard deviation of the firm-year specific residual (εε jj,tt ) from equitation (1) for the years t-5 to t. A high variation in the error indicates that accruals map poorly into cash flows, revenues and PPE, which implies lower quality accruals. A low standard deviation, or AQ measure, signals high quality of accruals quality. Furthermore, we decompose the accruals quality measure, AQ, into innate and discretionary subcomponents in accordance with prior studies (Aldamen and Duncan, 2011, Kent et al., 2010). We regress AQ on five innate factors identified by Dechow and Dichev (2002) and Francis et al. (2005), namely company size, standard deviation of cash flow from operations, standard deviation of sales revenue, length of operating cycle, and earnings losses as follows: AAAA jj = φφ 0 + φφ 1 SSSSSSSS jj + φφ 2 σσ(cccccc) jj + φφ 3 σσ(ssssssssss) jj + φφ 4 OOOOOOOOOOOOOO jj + φφ 5 NNNNNNNNNNNNNN jj + εε jj (2) where for firm j, AQ is the accruals quality measure, SSSSSSSS jj is the log of average total assets for 2002 to 2006, σσ(cccccc) jj is the standard deviation of cash flow from operation (scaled by average total assets) over the past five years, and σσ(ssssssssss) jj is the standard 11

12 deviation of sales (operating revenue, scaled by average total assets) over the past five years. OpCycle is the average age of inventory plus the average age of receivables (in days) between 2002 and 2006 (after winsorizing at 365 days), and NNNNNNNNNNNNNN jj is number of years, out of the past five, where reporting income before extraordinary item is negative. The predicted values from equation (2) are the estimated innate components of the jth firm s accruals quality, IAQ. The residual values from equation (2) are the estimated discretionary components of the jth firm s accruals quality, DAQ. 3.4 Family Ownership and Discretionary Accruals To test hypothesis one we measure the association between DAQ and Family 1 variables through an OLS regression. However, given as prior research has established specific governance mechanisms that are associated with discretionary accruals in Australia, we choose to start our analysis by testing these associations and thus comparing the results to validate our sample. Our governance variables are based on research by Kent et al. (2010) that found that discretionary accruals are associated with audit quality characteristics. Specifically, the number of individuals on the audit committee and the usage of a Big 4 firm for the firm audit: DDDDDD jj = φφ 0 + φφ 1 NNNNNNNNNNNN jj + φφ 2 BBBBBB4 jj + εε jj (3) where for firm j, DAQ is the discretionary accruals quality measure derived from the residuals in equation 2, NDIRAC is the number of directors on the audit committee in 2006 and BIG4 is a dummy variable coded as 1 if the firm uses a Big 4 firm for their firm audit. As 1 Family is a dummy variable coded as 1 if the firm is identified as a family firm and 0 if it is not. 12

13 DAQ is an inverse measure of discretionary accruals quality we expect both φφ 1 and φφ 2 to be negative. We extend this equation by introducing the Family variable to test for hypothesis 1, that there is a relationship between family ownership and discretionary accruals quality. DDDDDD jj = φφ 0 + φφ 1 NNNNNNNNNNNN jj + φφ 2 BBBBBB4 jj + φφ 3 FFFFFFFFFFFF jj + εε jj (4) where for firm j, DAQ is the discretionary accruals quality measure derived from the residuals in equation 2, NDIRAC is the number of directors on the audit committee in 2006, BIG4 is a dummy variable coded as 1 if the firm uses a Big 4 firm for their firm audit, and Family is a dummy variable coded as 1 if the firm is identified as a family firm and 0 if it is not. As hypothesis 1 is non-directional, the sign of φφ 3 is unknown. Next, we address hypothesis 2, the issue of having a family member in the CEO position. To test H2 we alter equation 4 by substituting the Family variable with the FamilyManager variable. While using the two variables in the same equation would have been ideal, the multicollinearity between these two variables prohibits us from doing so. DDDDDD jj = φφ 0 + φφ 1 NNNNNNNNNNNN jj + φφ 2 BBBBBB4 jj + φφ 3 FFFFFFFFFFFFFFFFFFFFFFFFFF jj + εε jj (5) where for firm j, DAQ is the discretionary accruals quality measure derived from the residuals in equation 2, NDIRAC is the number of directors on the audit committee in 2006, BIG4 is a dummy variable coded as 1 if the firm uses a Big 4 firm for their firm audit, and FamilyManager is a dummy variable coded as 1 if the firm is identified as a family firm where the CEO is a family member and 0 in all other cases. Similar to hypothesis 1, hypothesis 2 is non-directional, so the sign of φφ 3 is unknown. We do however expect to find a stronger association between DAQ and FamilyManager than between DAQ and Family. 13

14 4. Results 4.1 Descriptive Statistics We begin the analysis by calculating the descriptive statistics for the sample. Table 2 presents the descriptive statistics for the two groups in the sample, and the results for the Wil- Coxon Z-test of difference between the two groups. The mean EBIT for the family firms (non-family firms) is $25.39 ($25.15) million, while the mean market value is $ ($274.14) million for family firms (non-family firms). This indicates that the family-firms in the sample slightly smaller in size, however this difference is found to be not significant. Furthermore, the average age of the sample is approximately 15 years for both groups (based on 2006 as the relative year). This number is partially driven by our extensive sample restrictions. The remainder of the variables reported in the descriptive statistics are the variables used in the analysis. The size variable is the natural logarithm of total assets, calculated as an average for The only significant difference between the two groups is for δ(sales) (p < 0.10), indicating that operating revenues or family firms are less volatile. 53 % of family firms use a Big 4 auditor, while 59 % of non-family firms do so, firms both groups have a median of 3 board members in their audit committees. As such there appears very little difference in their governance variables. We can also observe that the audit quality measure AQ (which is inverse), is on average for family firms and for non-family firms, indicating that family firms may have higher quality accruals overall. If we specificely look at the discretionary accruals, DAQ, we do indeed observe a difference as family firms appear to have lower discretionary accruals on average and based on percentiles it seems it can be traced to the fact that non-family firms have a fat right tail in their distribution. The 75 th percentile of DAQ for family firms is compared to Table 3 14

15 shows the correlation matrices for the independent variables in equation 2 and 3. As in the study of Kent et al. (2001) we observe strong correlations between all variables in equation 1 (except for operational cycle, which is not correlated with the other variables in the equation). For equation 3, the only significant correlation is between the Family and FamilyManager variables. This is expected; as FamilyManager is effectively a subgroup of Family (this of course introduces multicollinearity and prohibits us from using the two variables simultaneously to explain discretionary accruals quality). (Table 2 and 3 about here) 4.2 Accruals Estimation The conventional two-stage process is followed to estimate the discretionary accruals quality measure. In the first stage, we estimate equation (1), the cross-sectional total current accruals, as a pooled model. The estimated annual pooled model is consistent with other Australian accruals quality studies (Kent et al., 2010, Aldamen and Duncan, 2011). The analysis is reported in Table 4. (Table 4 about here) The second stage is the estimation of the relationship between accruals quality, the standard deviation of the residuals from equation (1), and innate characteristics (economic fundamentals) of the firm. Table 5 reports the regression estimation of equation (2). The size measure was found to be not significant in explaining AQ. However, all other variables were found to be significant in explaining AQ; σ(cfo) (p < 0.01), σ(sales) (p < 0.05), OpCycle (p < 0.01), and NegEarn (p < 0.01). Our accruals model results are largely consistent with the prior Australian evidence. An exception is the fact that the variable σσ(ssssssssss) is found to be negative, in contrast to our expectations. The adjusted R 2 for the model was 36.5% (vs. 47.6% for Aldamen and Duncan (2011) and 18.3% for Kent et al. (2010)). Following the 15

16 conventional methodology, the predicted values from equation (2) are our IAQ accruals quality measures and the residual from equation (2), is the estimated discretionary accruals quality, DAQ. (Table 5 about here) 4.3 Discretionary Accruals and Family Ownership A relationship (non-directional) was hypothesised between family ownership and discretionary accruals quality (hypothesis 1), while family management was hypothesized to have a stronger relationship (hypothesis 2). Before testing these two hypotheses we consider the impact of the audit committee size and the usage of a Big 4 auditor, as these two variables were shown to be the two significant drivers of discretionary accruals in Australia by Kent et al. (2010). Table 6 shows the results for equation 3, they confirm the previous results of Kent et al. (2010), and both NDIRAC (p < 0.01) and BIG4 (p < 0.01) are shown to be significant and negative. This means that there exists a positive relationship between audit committee size and discretionary accruals quality, as well as a positive relationship between using a Big 4 auditor and discretionary accruals quality. We proceed to estimate equation 4 to test hypothesis 1, if there is a relationship between family ownership and discretionary accruals quality. Table 6 shows the results for equation 4, the variable Family (p < 0.05) is shown to be significant and has a negative coefficient. In other words, family ownership has a negative association with discretionary accruals and as the measure is inverse; family ownership has a positive association with discretionary accruals quality. To test hypothesis 2 we run equation 5, where the Family variable is substituted by the FamilyManager variable, this provides us with a comparison of the adjusted R 2. The results are shown in table 6 and indicate that family firms with a family member in the CEO position are associated with higher quality discretionary accruals (p < 16

17 0.05). However, it should be noted that while both the coefficients and adjusted R 2 are higher for the FamilyManager variable, the differences are quite small in comparison to Family (eq. 4 adjusted R 2 of 7.7 % vs. eq. 5 adjusted R 2 of 7.8%). In conclusion, both hypothesis one and two are not rejected, and furthermore the directionality is established as positive. 4.4 Sensitivity Analysis The primary contribution of our paper is the usage of a more refined accruals quality measure that allows us to discriminate between innate and discretionary accruals. In our primary analysis we choose to use the DD (2002) model as modified by McNichols (2002) to measure accruals quality. To establish that our results are not sensitive to the choice of a specific model we run a sensitivity analysis by using the original DD (2002) model to measure accruals quality. WWWW jj,tt = ββ 0 + ββ 1 CCCCCC jj,tt 1 + ββ 2 CCCCCC jj,tt + ββ 3 CCCCCC jj,tt+1 + εε jj,tt (6) where, for firm j, WWWW jj,tt is a comprehensive measure of change in working capital accruals, including change in accounts receivable, accounts payable, current inventory, current investments, current provisions and other current assets and liabilities in year t, CCCCCC jj,tt is cash flow from operations in year t, RRRRRR jj,tt is the change in operating revenue between year t-1 and year t, and PPPPPP jj,tt is property plant and equipment in year t. All variables in equation (6) are scaled by average total assets from year t-1 to t. For each year, equation (1) is estimated pooled across all firms, consistent with the Australian accruals quality study by Kent et al. (2010). The remainder of the sensitivity testing replicates the analysis for equation 2-5, and instead using the AQ measure as derived from the original DD (2002) model. Similar to the McNichols (2002) modification, AQ is measured by taking the standard deviation of 17

18 the firm-year specific residual (εε jj,tt ) from equitation (6) for the years t-5 to t. A high variation in the error indicates that accruals map poorly into cash flows, which implies lower quality accruals. A low standard deviation, or AQ measure, signals high quality of accruals quality. Table 7 to 9 show the results for our primary analysis when using the original DD (2002) model. We show that our results do not change through the use of the DD (2002) model, the direction and significance of the variables in equations 2 to 5 do not change. 5. Conclusion The objective of this research was to investigate the relationship between family ownership and discretionary accruals quality. The prior US evidence suggested that there was a significant positive relationship between family ownership and accruals quality (Wang, 2006, Ali et al., 2007), while prior non-us evidence is mixed (Kim and Yi, 2006, Prencipe et al., 2008, Yang, 2010). We contribute to the literature by demonstrating that the usage of an overall accruals quality measure was crude, as a portion of firms accruals are driven by economic fundamentals. As such, by decomposing the accruals quality measure into innate and discretionary accruals we were able to investigate the aforementioned relationship using a less noisy measure. Our evidence, which includes controls for governance, is consistent with Wang s (2006) US evidence, indicating that family ownership has a positive relationship with accruals quality. However, we were able to provide evidence that this impact is specific for discretionary accruals. The implication of our results is that family ownership should be considered to be a sound governance mechanism, specifically in Australia, where the high investor protection is able to mitigate the Type II agency problems that have been found to be present in other countries (La Porta et al., 1999). 18

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21 Table 1: GICS sector classifications Sector n Percent Consumer Discretionary Consumer Staples Energy Financials Health Care Industrials Information Technology Materials Telecommunication Services Total

22 Table 2: Descriptive statistics Family Firms Non-Family Firms Test of difference Percentiles Percentiles Wil-Coxon Z-test Sig. 50th 50th Mean Std. Dev. 25th (Median) 75th Mean Std. Dev. 25th (Median) 75th Z (2-tailed) EBIT (million) MV (million) Age (years) Size δ(cfo) δ(sales) OpCycle NegEarn NDIRAC BIG AQ IAQ DAQ EBIT is earnings before interest and tax for the financial year of 2006, it is stated in millions, MV is market value in 2006, it is stated in millions, Age is the number of years since incorporation, SSSSSSSS is the log of average total assets for 2002 to 2006, σσ(cccccc) is the standard deviation of cash flow from operation (scaled by average total assets) over the past five years, and σσ(ssssssssss) is the standard deviation of sales (operating revenue, scaled by average total assets) over the past five years. OpCycle is the average age of inventory plus the average age of receivables (in days) between 2002 and 2006 (after winsorizing at 365 days), and NNNNNNNNNNNNNN is number of years, out of the past five, where reporting income before extraordinary item is negative. NDIRAC is the number of directors on the audit committee in 2006 and BIG4 is a dummy variable coded as 1 if the firm uses a Big 4 firm for their firm audit. Accruals quality, AQ, is derived by taking the standard deviation of the firm-year specific residual (εε jj,tt ) from equitation (1) for the years t-5 to t. IAQ are the predicted values from equation (2) and estimate the innate components of the jth firm s accruals quality, DAQ is the discretionary accruals quality as estimated by the residual values from equation (2).

23 Table 3: Correlation matrices - innate accruals and discretionary accruals Size δ(cfo) δ(sales) OpCycle NegEarn Size 1 δ(cfo) ** 1 δ(sales) ** 0.530** 1 OpCycle NegEarn ** 0.343** 0.201** NDIRAC 1 BIG NDIRAC BIG4 Family FamilyManager Family FamilyManager ** 1 Notes: ***, **, and * denote significant at the 1%, 5% and 10% levels (one-tailed) SSSSSSSS is the log of average total assets for 2002 to 2006, σσ(cccccc) is the standard deviation of cash flow from operation (scaled by average total assets) over the past five years, and σσ(ssssssssss) is the standard deviation of sales (operating revenue, scaled by average total assets) over the past five years. OpCycle is the average age of inventory plus the average age of receivables (in days) between 2002 and 2006 (after winsorizing at 365 days), and NNNNNNNNNNNNNN is number of years, out of the past five, where reporting income before extraordinary item is negative. NDIRAC is the number of directors on the audit committee in 2006 and BIG4 is a dummy variable coded as 1 if the firm uses a Big 4 firm for their firm audit. Family is a dummy variable coded as 1 if the firm is identified as a family firm and 0 if it is not. FamilyManager is a dummy variable coded as 1 if the firm is identified as a family firm where the CEO is a family member and 0 in all other cases.

24 Table 4: Regression results - accruals quality Int. CFO t-1 CFO t CFO t+1 ΔREV t PPE t Adj. r Coefficient t-statistic p-value Coefficient t-statistic p-value Coefficient t-statistic p-value Coefficient t-statistic p-value Coefficient t-statistic p-value WWWW jj,tt = ββ 0 + ββ 1 CCCCCC jj,tt 1 + ββ 2 CCCCCC jj,tt + ββ 3 CCCCCC jj,tt+1 + ββ 4 RRRRRR jj,tt + ββ 5 PPPPPP jj,tt + εε jj,tt WWWW jj,tt is a comprehensive measure of change in working capital accruals, including change in accounts receivable, accounts payable, current inventory, current investments, current provisions and other current assets and liabilities in year t; CFO t-1 is the cash flow from operations in t-1 ; CFO t is the cash flow from operations in t ; CFO t+1 is the cash flow from operations in t + 1 ; ΔREV t is the change in operating revenue from t-1 to t ; PPE t is the property plant and equipment reported at t. All variables are scaled by average total assets. Cashflows are winsorized at 1 and 99 percentiles. 24

25 Table 5: Regression results accruals quality and innate characteristics Expected Sign Coefficient t-stat p-value Int Size δ(cfo) δ(sales) OpCycle NegEarn Adj. R F-stat P-value AAAA jj = φφ 0 + φφ 1 SSSSSSSS jj + φφ 2 σσ(cccccc) jj + φφ 3 σσ(ssssssssss) jj + φφ 4 OOOOOOOOOOOOOO jj + φφ 5 NNNNNNNNNNNNNN jj + vv jj,tt AQ is the standard deviation of firms' residuals from cross-sectional estimation of the modified Dechow-Dichev (2002). Size is the natural log of average total assets between 2002 and 2006, σ(cfo) is standard deviation of CFO scaled by total assets over the past five years, σ(sales) is the standard deviation of sales scaled by total assets between 2002 and 2006, OpCycle is the average age of inventory plus the average age of receivables (in days) between 2002 and 2006 (after winsorizing at 365 days), and NegEarn is the of number of years in which a loss was reported for between 2002 and

26 Table 6: Regression results family ownership and discretionary accruals Variables Expected Sign Eq (3) Eq (4) Eq (5) Int *** *** 3.884*** NDIRAC *** *** *** BIG ** ** ** Family ** FamilyManager ** Adj. R F-stat p-value Notes: ***, **, and * denote significant at the 1%, 5% and 10% levels (one-tailed) DDDDDD jj = φφ 0 + φφ 1 NNNNNNNNNNNN jj + φφ 2 BBBBBB4 jj + εε jj DDDDDD jj = φφ 0 + φφ 1 NNNNNNNNNNNN jj + φφ 2 BBBBBB4 jj + φφ 3 FFFFFFFFFFFF jj + εε jj DDDDDD jj = φφ 0 + φφ 1 NNNNNNNNNNNN jj + φφ 2 BBBBBB4 jj + φφ 3 FFFFFFFFFFFFFFFFFFFFFFFFFF jj + εε jj NDIRAC is the number of directors on the audit committee in 2006 and BIG4 is a dummy variable coded as 1 if the firm uses a Big 4 firm for their firm audit. Family is a dummy variable coded as 1 if the firm is identified as a family firm and 0 if it is not. FamilyManager is a dummy variable coded as 1 if the firm is identified as a family firm where the CEO is a family member and 0 in all other cases. 26

27 Table 7: Regression results - accruals quality using original DD (2002) model Int. CFO t-1 CFO t CFO t+1 Adj. r Coefficient t-statistic p-value Coefficient t-statistic p-value Coefficient t-statistic p-value Coefficient t-statistic p-value Coefficient t-statistic p-value WWWW jj,tt = ββ 0 + ββ 1 CCCCCC jj,tt 1 + ββ 2 CCCCCC jj,tt + ββ 3 CCCCCC jj,tt+1 + εε jj,tt WWWW jj,tt is a comprehensive measure of change in working capital accruals, including change in accounts receivable, accounts payable, current inventory, current investments, current provisions and other current assets and liabilities in year t; CFO t-1 is the cash flow from operations in t-1 ; CFO t is the cash flow from operations in t ; CFO t+1 is the cash flow from operations in t + 1 ; All variables are scaled by average total assets. Cashflows are winsorized at 1 and 99 percentiles. 27

28 Table 8: Regression results accruals quality and innate characteristics using original DD (2002) model Expected Sign Coefficient t-stat p-value Int Size δ(cfo) δ(sales) OpCycle NegEarn Adj. R F-stat P-value AAAA jj = φφ 0 + φφ 1 SSSSSSSS jj + φφ 2 σσ(cccccc) jj + φφ 3 σσ(ssssssssss) jj + φφ 4 OOOOOOOOOOOOOO jj + φφ 5 NNNNNNNNNNNNNN jj + vv jj,tt AQ is the standard deviation of firms' residuals from cross-sectional estimation of the modified Dechow-Dichev (2002). Sizeis the natural log of average total assets between 2002 and 2006, σ(cfo) is standard deviation of CFO scaled by total assets over the past five years, σ(sales) is the standard deviation of sales scaled by total assets between 2002 and 2006, OpCycleis average age of inventory plus the average age of receivables (in days) between 2002 and 2006 (after winsorizing at 365 days), and NegEarn is the of number of years in which a loss was reported for between 2002 and

29 Table 9: Regression results family ownership and discretionary accruals using original DD (2002) model Variables Expected Sign Eq (3) Eq (4) Eq (5) Int *** 3.872*** 3.927*** NDIRAC *** *** *** BIG ** ** ** Family ** FamilyManager ** Adj. R F-stat p-value Notes: ***, **, and * denote significant at the 1%, 5% and 10% levels (one-tailed) DDDDDD jj = φφ 0 + φφ 1 NNNNNNNNNNNN jj + φφ 2 BBBBBB4 jj + εε jj DDDDDD jj = φφ 0 + φφ 1 NNNNNNNNNNNN jj + φφ 2 BBBBBB4 jj + φφ 3 FFFFFFFFFFFF jj + εε jj DDDDDD jj = φφ 0 + φφ 1 NNNNNNNNNNNN jj + φφ 2 BBBBBB4 jj + φφ 3 FFFFFFFFFFFFFFFFFFFFFFFFFF jj + εε jj NDIRAC is the number of directors on the audit committee in 2006 and BIG4 is a dummy variable coded as 1 if the firm uses a Big 4 firm for their firm audit. Family is a dummy variable coded as 1 if the firm is identified as a family firm and 0 if it is not. FamilyManager is a dummy variable coded as 1 if the firm is identified as a family firm where the CEO is a family member and 0 in all other cases. 29