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Effect of the financial security law on real and accrual-based earnings management: an empirical evidence form Tunisia.

Introduction

Earnings management is a much studied research topic in financial accounting. Empirical studies have documented various approaches in detecting earnings management behavior. For manipulation of earnings, managers have a variety of choices to increase or decrease earnings (Sun and Rath, 2010). Although there are quite a number of earnings management methods recognized in the literature, two of them receive the lion's share of attention. First, the most popular method is accruals management where managers exercise their discretion over the choices of accounting policies and estimations to affect earnings (Healy, 1999; Jones, 1991; Dechow et al., 1995). Second, managers can make sup-optimal real operation decisions to manipulate earnings (Schipper, 1989; Roychowdhury, 2006).

There is substantial evidence that managers engage in accounting earnings management (AEM) and/or real earnings management (REM) to achieve certain earnings targets (Zang, 2007; Cohen and Zarowin, 2008; Chen et al., 2008).

AEM refers to managers' opportunistic use of the flexibility allowed under General Accepted Accounting Principles (GAAP) to change reported earnings without changing the underlying cash flows. REM refers to managers' opportunistic timing and structuring of operating, investment and financing transactions to affect reported earnings in a particular direction; it results in sub-optimal business consequences and imposes a real cost on the firm.

The main goal of the Sarbanes-Oxley Act of 2002 (SOX) was to protect investors by improving the accuracy and reliability of information disclosure. Various U.S. studies compare earnings quality measures before and after SOX, and indeed report evidence suggesting that earnings quality has improved after SOX (see for example Cohen et al. (2008). Similarly, in Tunisia, the implementation of the financial security law of 2005 seeks to achieve greater transparency and improve the credibility of financial information. Consequently, the principal objective of the law no. 2005-96 was to constrain the earnings management in the Tunisian companies.

The purpose of this study, on the one hand, explains how managers manipulate earnings through accruals and/or real earnings management and, on the other, shows the effect of the adoption of the law no. 2005-96 on the reduction of the likelihood of earnings management in the Tunisian context.

The first objective of this study is to test whether managers manipulate earnings through discretionary accruals and/or real earnings management strategically according to the level of the pre-managed earnings relative to the earnings targets. The second objective is to investigate whether managers use accrual-based and real activity earnings management as complementary or substitutive approaches. The third objective is to examine the impact of the adoption of the law of financial security of 2005 on the reduction of the extent of earnings management in the Tunisian context.

This paper contributes to the literature on earnings management in several ways. We extend ongoing research investigating the characteristics and extent of earnings management. In addition, most studies, in the literature of earnings management, examine separately the two approaches AEM and REM except a few recent works (Graham et al., 2005; Cohen et al., 2008; Sam and Tina, 2011). Yet, to our knowledge, none study examines the real earnings management in the Tunisian company and evaluates of implication of the financial security law of 2005 on the reduction of the extent of REM and AEM.

The remainder of this paper is organized as follows. Section 2 reviews past related literature and develops our test hypotheses. Section 3 outlines the research design and describes the empirical data. Section 4 presents and discusses empirical findings.

Literature Review and Hypothesis

The academic literature has studied earnings management through the manipulation of discretionary accruals (e.g., Jones, 1991; Dechow et al., 1995; Tahir et al., 2011; Daniel et al., 2008; Dechow et al., 2010), real transactions (e.g., Bartov, 1993; Gunny, 2009; Graham et al., 2005; Roychowdhury, 2006), or both (e.g., Zhang, 2007; Cohen et al., 2008; Bartov and Cohen, 2009; Cohen and Zarowin, 2010). These findings have led her to conclude that managers treat the two strategies as substitutes.

Approaches of Earnings Management

Athanasakou et al. (2009) have found that, in competition with classificatory shifting and earnings guidance, UK firms are less likely to use accruals and real operation management in attempts to achieve analyst forecasts. In addition, Chapman and Steenburgh (2011) and Cohen et al. (2010) have found that marketing and advertising costs are manipulated for earnings management purposes. On the other hand, Graham et al., 2005 have provided survey evidence that managers have preferred REM to AEM. First, accrual-based earnings management is more likely to draw auditor or regulatory scrutiny than real decisions, such as those related to product pricing, production, and expenditures on research and development or advertising. Second, relying on accrual manipulation alone is risky. In contrary, Dechow et al. (1995) preferred accruals in detecting accounting earnings management. Nevertheless, the major challenge for researchers using accruals to detect earnings management is the ability of the model to correctly separate accruals into discretionary and non-discretionary accruals. Non-discretionary accruals are the portions that resulted from firms normal operations without management intervention. Discretionary accruals are subject to management manipulation.

As far as studied literature shows, the Jones (1991) and the modified Jones model are the most popular models. Based on studies performed by Healy (1985), Jones has tried to develop previous results. The existing literature also presents models and methods to capture earnings management but the modified Jones accruals model (Dechow et al., 1995) has dominated the detection of earnings management activities during the last decade. In addition to their role as a detector of earnings management, increases/decreases in accruals are also presented as evidence of earnings management.

The first model is the modified cross-sectional Jones model (Jones, 1991) as described in Dechow et al., 1995. The modified Jones model is estimated as follows: TA represents total accruals defined as:

[TA.sub.it] = [EBXI.sub.it] - [CFO.sub.it]

where TA is the total assets, EBXI is the earnings before extraordinary items and discontinued operations and CFO is the operating cash flows taken from the statement of cash.

The estimated coefficients are used to estimate the normal accruals ([NA.sub.it]) for our sample firms:

[AN.sub.it] = [[alpha].sub.1] + [[alpha].sub.2] ([[DELTA]CA.sub.it] - [[DELTA]AR.sub.it]) + [[alpha].sub.3] ([PPE.sub.it])

Where, [[DELTA]AR.sub.it] is the change in accounts receivable from the preceding year and [PPE.sub.it] is the gross value of property, plant and equipment. Following the methodology used in the literature, we estimate the regressions using the change in reported revenues implicitly assuming no discretionary choices with respect to revenue recognition. However, while computing the normal accruals, we adjust the reported revenues of the sample firms for the change in accounts receivable to capture any potential accounting discretion arising from credit sales. Our measure of discretionary accruals is the difference between total accruals and the fitted normal accruals defined as:

[DA.sub.it] = ([TA.sub.it]/[Asset.sub.it-i]) - [NA.sub.it].

We used the Dechow et al., 1995 model. The modified Jones model is estimated as follows:

[TA.sub.it] / [A.sub.it-1] = [a.sub.1](1 / [A.sub.it-1]) + [a.sub.2] [([[DELTA]CA.sub.it] - [[DELTA]CCR.sub.it]) / [A.sub.it-1]] + [a.sub.3] ([PPE.sub.it] / [A.sub.it-1]) + [[epsilon].sub.it]

Therefore, we use the cross-sectional modified Jones model and incorporate prior period ROA as suggested by Kothari et al. (2005).

Normal levels of working capital accruals related to sales are controlled through the changes in revenue adjusted for changes in accounts receivable. Normal levels of depreciation expense and related deferred tax accruals are controlled through the property, plant and equipment.

Lagged [ROA.sub.i,t] is added as suggested by Kothari et al. (2005). Finally, the residual ([[epsilon].sub.it]) from the regression is the discretionary accruals.

[[TA.sub.it]/[A.sub.it-1]] = [a.sub.0](1 / [A.sub.it-1]) + [a.sub.1] [([[DELTA]CA.sub.it] - [[DELTA]CCR.sub.it]) / [A.sub.it-1]] + [a.sub.2] ([PPE.sub.it] / [A.sub.it-1]) + [a.sub.3] [ROA.sub.i,t-1] + [[epsilon].sub.it]

where: [ROA.sub.i,t-1] is the return of assets.

Therefore, we use the model of Kothari et al. (2005) and incorporate prior period MTB as suggested by Raman and Shahrur (2008). The model of Raman and Shahrur (2008) is estimated as follows:

[TA.sub.it] / [A.sub.it-1] = [a.sub.1](1 / [A.sub.it-1]) + [a.sub.2] [([[DELTA]CA.sub.it] - [[DELTA]CCR.sub.it]) / [A.sub.it-1]] + [a.sub.3] ([PPE.sub.it] / [A.sub.it-1]) + [a.sub.4] [ROA.sub.i,t-1] + [a.sub.5] [BMK.sub.i,t] + [[epsilon].sub.it]

where: [BMK.sub.it] (ratio market-to-book) is the total assets-total common equity + total value of shares outstanding divided by total assets.

We predict that the quality of the accruals models is associated with the reduction of the extent of accounting earnings management.

Hypothesis 1: The quality of the accruals models is associated with the detection of the earnings management activities.

More recently, REM focuses on detecting earnings management activities with direct cash flow consequences. The literature on REM details the following transactions. Firstly, cutting research and development expenses or selling general and administrative expenditures to increase income. Secondly, overproducing to reduce the cost of goods sold to increase income. Thirdly, cutting prices/offering price discounts to boost sales in the current period. Finally, selling fixed assets with unrealized holding gains or losses.

Roychowdhury (2006) examine earnings management through real productive activities with a focus on operational activities. He finds evidence that managers aggressively reduce discretionary expenses (the sum of R&D spending, advertising, and SG&A expenses) to meet the income objective. In particular, his evidence suggests that managers are providing price discounts to temporarily boost sales, reducing discretionary expenditures in order to improve reported margins, and overproducing to lower the cost of goods sold. Moreover, there is also a growing body of the literature studying AEM and REM together. Furthermore, Zang (2007) has analyzed the tradeoffs between AEM and REM. She suggests that decisions to manage earnings through real actions precede decisions to manage earnings through accruals. Her results show that real manipulation is positively correlated with the costs of accrual manipulation and those accruals and real manipulations are negatively correlated.

REM can reduce firm value because actions taken in the current period to increase earnings can have a negative effect on cash flows in future periods. For example: (1) aggressive price discounts to increase sales volumes and meet some short-term earnings target can lead customers to expect such discounts in future periods as well. This can imply lower margins on future sales, (2) overproduction generates excess inventories that have to be sold in subsequent periods and imposes greater inventory holding costs on the company and (3) managers aggressively reduce discretionary expenses to meet the income objective.

We predict that the Tunisian firms manipulate the real earnings management with sales manipulation, discretionary expenses and abnormal production costs.

Hypothesis 2: Tunisian companies publish abnormal cash-flows of exploitation.

Hypothesis 3: Tunisian companies manipulate the real activities through the abnormal production costs.

Hypothesis 4: Tunisian companies manipulate the real activities through the discretionary expenses.

Earning management and the law of financial security of 2005

Since the enactment of SOX, many studies have examined its effects on the firm's quality of financial reporting. These studies are largely focused on discretionary accruals. For example, Lai (2003) reports a significant reduction in unsigned discretionary accruals from pre-SOX period to the post-SOX period. These empirical findings are consistent with the notion that the SOX has led to less volatile and more caution in accruals reporting. As mentioned earlier, Cohen et al. (2008) find a steady decline in earnings management through discretionary accruals in the post-SOX period. In contrast, they report that real activity management (measured by abnormal production cost and abnormal discretionary expenses) has increased significantly after SOX. Cohen et al. (2008) simply examine the overall distribution of discretionary accruals and abnormal real activities and do not take into account the interaction between these two alternative earnings management mechanisms. In addition, Leuz et al. (2003) finds that in countries with strong legal protection, managers are less aggressive to manage earnings through accrual manipulation. So we argue that in strong legal enforcement economies, managers prefer to manage earnings through real activity manipulation rather than accruals manipulation because AEM is more easily to be detected compared to REM. Likewise, Sari et al. (2010) have found that investor protection determines manager choice between REM and AEM when they have the flexibility to engage both. We expect that earnings management through accrual manipulation decreases in a strong investor's protection. However, for a weak investor's protection, manager have great discretionary to manage earnings with both accrual and real activity manipulations.

The Tunisian financial security law was introduced in 18 October of 2005 to strengthen the governance framework of publicly listed companies. The code sets out the principles and best practices on corporate governance to improve the monitoring function of the board of directors, audit committee, and the external audit. This includes the essential criteria for the structure and operational process of the monitoring units. The principal objective of this law no. 2005-96 is to increase the transparency and reliability of financial information. Indeed, the information might be more certain and investors can respond to it by trading on the stocks of those companies with greater confidence regarding the value relevance of information contained in their set of financial accounts. It was designed to reduce fraud and conflicts of interests, while increasing financial transparency and 1 improving confidence and 1 trust in financial markets. Overall, this law improved earnings quality. Thus, we hypothesize that:

Hypothesis 5: The adoption of the law no. 2005-96 reduced the discretionary accruals and/or real earnings management.

[H.sub.5.1]: Adoption of the law no. 2005-96 reduced the discretionary accruals.

[H.sub.5.2]: Adoption of the law no. 2005-96 reduced real earnings management.

Research design and empirical data

Accrual Earnings Management

In this study, the discretionary accruals are the proxy for measurement of accounting earnings management (Yu, 2008 and Cohen et al., 2008). We admit the demarche of the three models; Dechow et al. (1995), Kothari et al. (2005) and Raman and Shahrur (2008). We use the absolute value of discretionary accruals [absolute value of [DAC.sub.it]] to measure the extent of the accounting earnings management.

The model of Dechow et al. (1995)

We estimate the following equation:

[TA.sub.it] / [A.sub.it-1] = [a.sub.1](1 / [A.sub.it-1]) + [a.sub.2] [([[DELTA]CA.sub.it] - [[DELTA]CCR.sub.it]) / [A.sub.it-1]] + [a.sub.3] ([PPE.sub.it] / [A.sub.it-1]) + [[epsilon].sub.it]

where [ACCR.sub.it] is the total accrual; [[DELTA]REV.sub.it] is the change in revenue measured by change in sales it relative to sales it-1; [[DELTA]REC.sub.it] is the change in net account receivable in year t relative to year t-1 and [PPE.sub.it] is the gross value of property, plant and equipment in year t.

The model of Kothari et al. (2005)

We estimate the following equation:

[TA.sub.it] / [A.sub.it-1] = [a.sub.0](1 / [A.sub.it-1]) + [a.sub.1] [([[DELTA]CA.sub.it] - [[DELTA]CCR.sub.it]) / [A.sub.it-1]] + [a.sub.2] ([PPE.sub.it] / [A.sub.it-1]) + [a.sub.3] [ROA.sub.i,t-1] + [[epsilon].sub.it]

where [ROA.sub.i,t-1] is the return of assets.

The model of Raman et Shahrur (2008)

We estimate the following equation:

[TA.sub.it] / [A.sub.it-1] = [a.sub.0](1 / [A.sub.it-1]) + [a.sub.1] [([[DELTA]CA.sub.it] - [[DELTA]CCR.sub.it]) / [A.sub.it-1]] + [a.sub.2] ([PPE.sub.it] / [A.sub.it-1]) + [a.sub.3] [ROA.sub.i,t-1] + [a.sub.4][BMK.sub.i,t] + [[epsilon].sub.it]

where [BMK.sub.it] (the ratio market-to-book) is the total assets-total common equity + total value of shares outstanding divided by total assets

Real earnings manipulation measure

We rely on prior studies to develop our proxies for real earnings management. As in Roychowdhury (2006), Cohen et al. (2008) and Gunny (2010) where these authors consider the abnormal levels of cash flow from operations, discretionary expenses and abnormal production costs to study the level of real activities manipulations, we examine in this study the real activities manipulation with two proxies (sales manipulation, discretionary expenses and abnormal production costs). We measure the abnormal level of each type of real activities manipulation as the residual from the relevant estimation model.

We use Roychowdhury's (2006) model to estimate the normal level of CFO and express normal CFO as a linear function of sales (S) and change in sales that are scaled by lagged total assets (A). To estimate this model, we run the following regression with pooled data approach:

[CFO.sub.t]/[A.sub.t-1] = [[alpha].sub.0] + [[alpha].sub.1](1/[A.sub.t-1]) + [[beta].sub.1]([S.sub.t]/[A.sub.t-1]) + [[beta].sub.2]([[DELTA]S.sub.t]/[A.sub.t-1]) + [[epsilon].sub.t] (1)

Abnormal CFO is actual CFO minus the normal level of CFO calculated using the estimated coefficient from Equation (1). The normal level of CFO is expressed as a linear function of sales and change in sales.

The second type of real activities manipulation is to produce more goods than necessary to meet expected demand (overproduction). Overproduction reduces cost of goods sold (CGS), which results in higher operating margin. However, additional holding and production costs may be incurred and are very likely to increase marginal costs, which results in higher annual production costs relative to sales.

Production costs are defined as the sum of CGS and change in inventory (INV) during the year.

We model CGS as a linear function of contemporaneous sales:

[CGS.sub.t] / [A.sub.t-1] - [[alpha].sub.0] + [[alpha].sub.1](1 / [A.sub.t-1]) + [[beta].sub.1]([S.sub.t] / [A.sub.t-1]) + [[epsilon].sub.t] (a)

Next, we model inventory growth by the following equation:

[[DELTA]INV.sub.t]/[A.sub.t-1] = [[alpha].sub.0] + [[alpha].sub.1](1/[A.sub.t-1]) + [[beta].sub.1]([[DELTA]S.sub.t]/[A.sub.t-1]) + [[beta].sub.2]([[DELTA]S.sub.t-1]/[A.sub.t-1]) + [[epsilon].sub.t] (b)

Using Equations (a) and (b), we estimate the normal level of production costs (PROD) as follows:

[PROD.sub.it] = [CGS.sub.t] + [DELTA]Inv [PROD.sub.it]/[A.sub.t-i] = [[alpha].sub.0] + [[alpha].sub.1] (l/[A.sub.t-1]) + [[beta].sub.1]([S.sub.t]/[A.sub.t-1]) + [[beta].sub.2]([[DELTA]S.sub.t]/[A.sub.t-1]) + [[beta].sub.3]([[DELTA]S.sub.t-1]/[A.sub.t-1]) + [[epsilon].sub.t] (2)

Finally, discretionary expenses should be also expressed as a linear function of contemporaneous sales, similar to COGS. The relevant regression would then be:

[DISEX.sub.i,t]/[A.sub.i,t-1] = [alpha] (1/[A.sub.i,t-1]) + [[beta].sub.1] ([S.sub.i,t]/[A.sub.i,t-1]) + [[beta].sub.2] ([[DELTA]S.sub.i,t]/[A.sub.i,t-1]) + [[epsilon].sub.i],t (3)

where DISEX is discretionary expenses, defined as research and development (R&D) and selling general and administrative (SG&A) expenses.

Empirical Results

Descriptive Statistics

Table 1 presents the summary statistics; we report the mean of the accounting earnings managements for the three models (Dechow et al., 1995; Kothari et al., 2005 and Raman and Shahrur, 2008) before and after the implementation of the law of financial security in 2005.

Table 1 reveals that the means of the accruals estimated with the models of Dechow et al. (1995), Kothari et al. (2005) and Raman and Shahrur (2008) are respectively 4.5, 2.6 and 3.8 % of total assets in the Pre-law period. Thus, the Tunisian firms used an accruals earnings management to meet their earnings targets; but it seems that they use abnormal accruals in smaller amount. Also, we find that performance (Kothari et al., 2005) and growth opportunity (Raman and Shahrur, 2008) don't explain the accruals-based earnings management. The Jones modified (1995) model is the best one which explains the accruals management in the Tunisian context. These results are consistent with those of Dammek (2002), Othman and Zgal (2006) and Zehri (2008) studying in the Tunisian context.

Table 1 presents the means of the accruals estimated with the model of Dechow et al. (1995) which decrease slightly from 4.5 to 4.2 % in the post-law period while the mean of the accruals estimated with the models of Kothari et al. (2005) and Raman and Shahrur (2008) are increased slightly after the low no. 2005-96. Thus, the law no. 2005-96 is effective to constrain the accruals estimated with the model of Dechow et al. (1995) and is not effective to reduce the accruals estimated with the models of Kothari et al. (2005) and Raman and Shahrur (2008) in the Tunisian context.

In Table 2, we report the means of the real earnings managements.

Table 2 shows the means of the sales manipulation represent approximately 0.8 and 0.6 % of total assets respectively in the pre-law and post-law periods while the abnormal production costs is 1.7 % (1.8) of total assets in the pre-law (post-law) period. Finally, the discretionary expenses represent 1.5 and 1.7 % of total assets respectively in the pre-law and post-law periods. We find that the mean of the abnormal production costs declined slightly from 1.7 to 1.8 % of total assets after the enactment of the law no. 2005-96. We also observe that the mean of the sales manipulation decreases slightly from 0.8 to 0.6 % of total assets in the period preceding the law no. 2005-96. Finally, the discretionary expenses increase 1.5 to 1.7 % of total assets after the implementation of the financial security law of 2005.

We note that the Tunisian manager engages in the tow approaches of earnings management. In addition, the mean for real earnings management is lower than that of the discretionary accruals. The managers prefer to use abnormal accruals instead of real earnings management. These results are similar to those of Sari et al. (2010) and Leuz et al. (2003); these authors have found that in countries with low legal enforcement, managers have great discretionary to manage earnings with both accrual and real activity manipulations.

In summary, the law of financial security (2005) doesn't reduce opportunistic use of accounting and real earnings managements. This result is inconsistent with those of Cohen et al. (2008) and Cohen and Zarowin (2008) hose validate the argument that the Sarbanes-Oxley Act (SOX) has made accruals management more costly and that firms have shifted from accruals to real earnings management after SOX.

Estimation Models

Table 3 reports the regression coefficients for some of the key regressions used to estimate normal levels for all of sample (319 firm -year).

where S is sales revenues, AS is change in sales revenues over time ([S.sub.t]-[S.sub.t-i]), abnormal CFO is actual CFO minus the normal level of CFO calculated using the estimated coefficient from equation (1) for every firm-year, abnormal production cost is the difference between the actual production costs and the expected production costs calculated using the corresponding industry year model (equation 2). I estimate the discretionary accruals with the modified Jones model (Dechow et al., 1995) where [PPE.sub.t] is the gross property, plant and equipment (equation 3). All variables are scaled by total assets at the end of last year's [A.sub.t-1].

We estimate these models using the entire sample of 319 firm-years over the period 2000-2010. In the first column, there is a negative and significant relation between sales and cash from operations ([[beta].sub.1] = -0.04042; equation (1)). In the second column, sales ([[beta].sub.1] = 0.0133; equation (2)) and lagged sales ([[beta].sub.2] = 0.5225; equation (2)) are positively related to abnormal production costs. In the third column, the coefficient of DISEX on sales change is actually positive and marginally significant ([[beta].sub.1] = 0.0421; equation (3)), indicating that conditional on contemporaneous sales, a higher change in sales implies higher discretionary expenses. Finally, there is, on the one hand, a positive and significant relation between the change in sales revenues and discretionary accruals ([[beta].sub.1] = 0.087) and, on the anther hand, a negative and significant relation between the [PPE.sub.t] and discretionary accruals ([[beta].sub.2] = -1.410). The explanatory power of the models is quite high. The adjusted [R.sup.2] is 23 % for CFO, 56 % for abnormal production costs, 18% for discretionary expenses and 62.47 % for discretionary accruals.

As we can see from Tables 4, 5 and 6, values of the adjusted [R.sup.2] for sales manipulation are 5.4 and 33.3 % while those for abnormal production costs are 66.4 and 7.3 % respectively in the prelaw and post-law periods. Finally, the adjusted [R.sup.2] for discretionary expense are 7.2 (22.4) % respectively in the pre-law (post-law) periods. The coefficient of CFO relative to sales change is positive ([[beta].sub.2] = 0.029; equation (1)) and marginally significant after the law of financial security (2005) indicating that a higher change in sales implies a higher CFO. In addition, the coefficient of sales change of year t-1 for abnormal production costs is positive ([[beta].sub.3] = 0.986; equation (2)) and significant in the pre-low period. Similarly, the coefficient of CFO relative to sales change is positive ([[beta].sub.2]= 0.123; equation (3)) and marginally significant after the law of financial security (2005) indicating that a higher change in sales implies a higher discretionary expense.

Univariate Correlations

Table 7 presents correlations between various variables.

Consistent with prior studies (Roychowdhury, 2006), accruals and CFO, as a percentage of total assets, exhibit a strong negative correlation with a correlation coefficient of -8.6 %. The correlation coefficient between abnormal production costs and abnormal discretionary accruals is positive (41.6 %) this is probably because managers engage in activities leading to abnormally high production costs at the same time that they increase discretionary accruals, the common goal being to report higher earnings.

The correlation between abnormal accruals and abnormal CFO is negative (-36.2 %). This is probably because (1) managers engage in accrual manipulation and real activities manipulation at the same time and (2) some manipulation methods, for example abnormal production costs, have a positive effect on abnormal accruals and a negative effect on abnormal CFO, at the same time that they reduce discretionary expenses. In our case, MTB (ratio of market value of equity to book value of equity) and SIZE (logarithm of the market value of equity at the beginning of the year) are variables of control for systematic variation in abnormal CFO, production costs and discretionary accruals. Abnormal accruals and sales manipulation are both significantly and positively correlated with growth opportunities (MTB) and size. Results show also that the abnormal production costs is negative (-0.0125) and significant at the 5 % level with MBK. Finally, results report that the abnormal production costs is positively significant (0.1255) with the size. Overall, the remaining correlations are below 60 % and they do not appear to be problems of multicollinearity.

The effect of the law no. 2005-96 on the reduction of accrual-based and real earnings management

Table 8 presents the effect of the law no. 2005-96 on constraining the extent of earnings management. Before integrating the variable "law" like explanatory variable to test its impact on the REM, we carried out a test of chow which tests the stability of the coefficients of regression for the two periods before and after the law no. 2005-96. We have found that the calculated statistics are lower than the statistics read indicating that the two groups are statistically different. Results of regression of the models of Jones modified (1995) and Roychowdhury (2006) (CFO, discretionary expense and abnormal production costs) show that the variable "law" hasn't any effect on the reduction of the accrual-based manipulation and real earnings management (Table 8).

Table 9 reveals the estimation results of the four equations of earnings management and the interaction terms: law*CFO, law*SURPR, law*DISEX and law*accruals. According to Graham et al. (2005), managers prefer to use accrual-based-versus real earnings. Manipulation strategies must change after the law no. 2005-96. The p-value of the law*CFO and law*DISEX are not significant while those of the law*SURPR and law*accruals are significant.

Conclusion

This study examines how managers manipulate earnings through accruals and/or real earnings management and shows the effect of the implementation of the law no. 2005-96 on the reduction of the extent of earnings management in the Tunisian context.

To capture accruals and real earnings management, we use respectively the modified Jones (1995) and Roychowdhury (2006) models. To estimate abnormal levels of cash flows from operations, discretionary expense and production costs, we use the Roychowdhury (2006) model. Firstly, we have shown that firms use real as well as accrual-based earnings management. Secondly, we have found that Tunisian firms manipulate accruals more than real earnings management after the law no. 2005-96. In addition, this law of financial security of 2005 diminishes, but does not eliminate, the use of discretionary accruals in earnings management while it seems that Tunisian firms use abnormal accruals in smaller amount. Also, we have shown that performance (Kotari et al., 2005) and growth opportunity (Raman and Shahrur, 2008) don't explain the accruals-based earnings management. In addition, we have found that the law no. 2005-96 is effective to reduce the accruals with the model of Dechow et al. (1995). However, this law is not effective to constrain the accruals with the models of Kothari et al. (2005) and Raman and Shahrur (2008). Likewise, we have shown that there is no evidence of the increased use of real earnings management to replace discretionary accruals in the post-law period. This finding is inconsistent with that of Bartov and Cohen (2007), Cohen and Zarowin (2008), Cohen et al. (2008) and Graham et al. (2005). Finally, Tunisia is a country with a weak investor protection; manager have great discretionary to manage earnings with both accrual manipulation and real activity manipulation. This finding is consistent with the results of Sari et al. (2010).

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ZGARNI Inaam (1), HLIOUI Khmoussi (2), and ZEHRI Fatma (3)

(1) Faculty of Economics and Management, University of Sfax, Tunisia

(2) College of Economics, Management and Information System, University of Nizwa, Sultanate Oman

(3) Faculty of Economics and Management, University of Sfax, Tunisia

Corresponding Author

ZGARNI Inaam can be contacted at: inaamez garni@yahoo.fr
Table 1: Estimation of the discretionary accruals for the
three models

 Pre-law no 2005-96

Models Mean Min. Max. Std. Dev.

Dechow et al. (1995) 0.045587 -0.928382 0.634521 0.321767
Kothari et al. (2005) 0.026387 -1.703532 0.199556 0.486250
Raman and Shahrur
(2008) 0.038182 -0.661853 0.288336 0.253714

 Post-law no. 2005-96

Models Mean Min. Max. Std. Dev.

Dechow et al. (1995) 0.042289 -1.323106 1.221421 0.406542
Kothari et al. (2005) 0.052365 -0.168754 0.723653 0.987430
Raman and Shahrur
(2008) 0.043698 -1.638975 0.498754 0.852310

Table 2: Estimation of the real earnings management (sales
manipulation, discretionary expenses and abnormal production costs)

 Pre-law no. 2005-96

 Mean Min. Max. Std. Dev.

[REM.sub.1] 0.008412 -0.124876 0.369824 0.025487
[REM.sub.2] 0.017358 -1.256922 0.452883 1.434717
[REM.sub.3] 0.015487 -0.278961 0.147893 0.214783

 Post-law no. 2005-96

 Mean Min. Max. Std. Dev.

[REM.sub.1] 0.006987 -1.02135 0.987560 0.096813
[REM.sub.2] 0.018987 -3.45314 0.412498 0.309687
[REM.sub.3] 0.017369 -0.12548 0.587469 0.047132

Table 3: Model parameters for all samples

Variables [CFO.sub.t]/ [SURPR.sub.t]/ [DISEX.sub.t]/
 [A.sub.t-1] [A.sub.t-1] [A.sub.t-1]

1/[A.sub.t-1] -1.697724 0.128136 0.849398 **
[S.sub.t]/[A.sub.t-1] -0.040425 ** 0.013362 0.042192 *
[[DELTA]S.sub.t]/ 0.065817 * 0.522597 *
 [A.sub.t-1]
[[DELTA]S.sub.t-1]/ 0.531877 *
 [A.sub.t-1]
[PPE.sub.t]
[R.sup.2] 0.23 0.56 0.18

Variables Accruals

1/[A.sub.t-1] 1.140774
[S.sub.t]/[A.sub.t-1]
[[DELTA]S.sub.t]/ 0.087530 ***
 [A.sub.t-1]
[[DELTA]S.sub.t-1]/
 [A.sub.t-1]
[PPE.sub.t] -1.410753 **
[R.sup.2] 0.62

* Significant at the 10 % level, **Significant at the 5 % level
and *** Significant at the 1 % level.

Table 4: Estimation of the sales manipulation before
and after the law no. 2005-96

Model Pre-law no. 2005-96 Post-law no. 2005-96

 [CFO.sub.t]/[A.sub.t-1] = [[alpha].sub.0] +
 [[alpha].sub.1](1/[A.sub.t-1]) +
 [[beta].sub.1]([S.sub.t]/[A.sub.t-1]) +
 [[beta].sub.2]([[DELTA]S.sub.t]/[A.sub.t-1])
 + [[epsilon].sub.t]

1/[A.sub.t-1] 0.759965 1.604287 **
[S.sub.t]/ 0.016533 -0.037260
 [A.sub.t-1]
[[DELTA]S.sub.t]/ 0.014883 0.029160 *
 [A.sub.t-1]
adjusted [R.sup.2] 0.0548 0.3337

* Significant at the 10 % level, ** Significant at the 5 % level.

Table 5: Estimation of the abnormal production costs and the
application of the law no. 2005-96

 Pre-law no. 2005-96 Post-law no. 2005-96
Model [PROD.sub.t]/[A.sub.t-1] = [[alpha].sub.0] +
 [[alpha].sub.1](1/[A.sub.t-1]) +
 [[beta].sub.1]([S.sub.t]/[A.sub.t-1]) +
 [[beta].sub.3([[DELTA]S.sub.t]/[A.sub.t-1])
 + [epsilon]

1/[A.sub.t-1] 1.474932 ** 1.527539
[S.sub.t]/ -0.228275 -0.008541
 [A.sub.t-1]
[[DELTA]S.sub.t]/ 0.470958 * -0.189214
 [A.sub.t-1]
[[DELTA]S.sub.t-1]/ 0.986594 * -0.211871
 [A.sub.t-1]

adjusted [R.sup.2] 0.6641 0.0730

* Significant at the 10% level. ** Significant at the 5% level.

Table 6: Estimation of the discretionary expense costs and
the application of the law no. 2005-96

 Pre-law no. 2005-96 Post-law no. 2005-96

Model [DISEX.sub.i,t]/[A.sub.i,t-1] =
 [alpha](1/[A.sub.i,t-1]) + [[beta].sub.1]
 ([S.sub.i,t]/[A.sub.i,t-1]) + [[beta].sub.2]
 ([[DELTA]S.sub.i,t]/[A.sub.t-1]) +
 [[epsilon].sub.i],t

1/[A.sub.t-1] 0.236547 * 0.821473 **
[S.sub.t]/ -0.478963 0.123987
 [A.sub.t-1]
[[DELTA]S.sub.t]/ 0.236987 -0.258749 *
 [A.sub.t-1]
adjusted [R.sup.2] 0.07236 0.2245

* Significant at the 10% level. ** Significant at the 5% level.

Table 7: Correlation

 Sales/A CFO/A Accrual/A Prod/A Abnormal
 CFO
Sales/A 1.0000
CFO/A 0.1231 ** 1.0000
Accrual/A 0.0130 * -0.086 * 1.000
Prod/A 0.1345 * 0.012 * 0.1507 * 1.000
Abnormal 0.0598 0.415 * 0.512 *** -0.036 * 1.000
CFO
Abnormal 0.0443 * -0.1059 -0.5023 ** 0.3320 * -0.4309
Prod
DISEX 0.02354 * -0.3654 -0.0164 ** -0.0354 -0.1254
Abnormal 0.0654 ** -0.0094 -0.0790 * -0.069 * -0.3620 *
Accruals
Size 0.0072 * -0.4692 0.5611 * 0.4130 * 0.4792 **
BTM 0.3245 * 0.0245 -0.292 ** 0.0441 0.3712 **

 Abnormal Abnormal
 Prod DISEX Accruals Size BTM

Sales/A
CFO/A
Accrual/A
Prod/A
Abnormal
CFO
Abnormal 1.000
Prod
DISEX -0.4587 * 1.000
Abnormal 0.416 ** 0.763 1.000
Accruals
Size 0.1255 * 0.4369 * 1.00
BTM -0.0125 ** 1.00 0.3112 ** 0.02 1.00

This table reports pooled Pearson correlations for the
entire sample of 319 firm-years over the period 2000-2010.

* Significant at the 10 % level and ** Significant at the 5 % level.

Table 8: Regression of earnings management integrant the
law of financial security law of 2005

Variables [CFO.sub.t]/ [SURPR.sub.t]/ [DISEX.sub.t]/
 [A.sub.t-1] [A.sub.t-1] [A.sub.t-1]

Intercept -0.024587 0.013698 * 0.069874
Sales/A -0.053698 ** 0.023648 0.036987
BTM 0.051203 * 0.521369 * 0.023698

Size -0.002487 ** -0.051479 * 0.004789 *

ROA 0.032654 ** 0.045691 -0.033697 *
Law 0.084157 -0.091617 -0.014789

[R.sup.2] 0.2504 0.5864 0.1457

Variables Accruals

Intercept 0.017063 *
Sales/A 0.096542 **
BTM 0.023698

Size 0.012365

ROA -0.098745 *
Law -0.034225

[R.sup.2] 0.6297

*Significant at the 10 % level and "Significant at the 5 % level.

where MTB = the market value of equity divided by the book value
of equity, Size = natural logarithm of total assets and
ROA = income before extraordinary items divided lagged total assets

Table 9: Regression of earnings management by integrating the
variables: law*CFO, law*SURPR, law*DISEX and law*accruals

Variables [CFO.sub.t]/ [SURPR.sub.t]/ [DISEX.sub.t]/
 [A.sub.t-1] [A.sub.t-1] [A.sub.t-1]

Intercept 0.269745 * 0.036987 ** 0.054712
Sales/A -0.040425 ** 0.013362 0.047896
BTM 0.065817 * 0.525977 * 0.012478 *
Size -0.003254 * -0.004789 -0.098745
ROA 0.084157 -0.916176 * 1.021478 **
law*CFO -0.023659
law *SURPR 0.089745 *
law*DISEX -0.236987
law*accruals
[R.sup.2] 0.2354 0.6712 0.1654

Variables Accruals

Intercept 1.851778 **
Sales/A 0.087530 **
BTM -0.198742
Size 0.047213 *
ROA -0.342254
law*CFO
law *SURPR
law*DISEX
law*accruals -2.006598 **
[R.sup.2] 0.6290

* Significant at the 10 % level and **Significant at the 5 % level.
where MTB = the market value of equity divided by the book value of
equity, Size = natural logarithm of total assets and
ROA = income before extraordinary items divided lagged total assets
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Author:Inaam, Zgarni; Khmoussi, Hlioui; Fatma, Zehri
Publication:Global Business and Management Research: An International Journal
Geographic Code:6TUNI
Date:Apr 1, 2012
Words:6650
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