Evidence from a High Book-Tax Conformity Country

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Finance Research Letters

journal homepage:www.elsevier.com/locate/frl

Do firms using real earnings management care about taxes?

Evidence from a high book-tax conformity country

Michał Kałdoński

^⁎

, Tomasz Jewartowski

Department of Corporate Finance, Poznań University of Economics and Business, Al.Niepodległości 10, 61-875 Poznań, Poland

A R T I C L E I N F O Keywords:

Earnings management Corporate tax aggressiveness Earnings targets

JEL Classification:

G30H26 M40

A B S T R A C T

Using a sample of 1,149 firm-year observations we show that benchmark-beating firms entering into real earnings manipulation are less willing to engage in aggressive tax planning. Controlling for a “normal level” of tax aggressiveness within the industry we provide evidence that average GAAP effective tax rate for benchmark-beaters is higher than for their industry peers. One of the possible reasons may be unwanted scrutiny by tax authorities and external monitors that hinders the real activities manipulation. All in all, our results suggest that real earnings management is even more costly than is widely considered.

1. Introduction

Public companies inflate earnings mainly to avoid reporting losses, to beat earnings reported in previous years or to meet or beat the targets based on analysts’ forecasts (Burgstahler and Dichev, 1997). Managers manipulate earnings via “accounting choices” or real “operational” decisions, both positively related to their compensation, as documented in previous studies (Li, 2019). While accrual-based earnings management (AEM) changes only reported earnings, real earnings management (REM) affects both earnings and cash flows. They also differ in terms of tax consequences. The former basically leaves the income tax unchanged while the latter increases reported taxes, making this form of earnings management more costly (Zang, 2012).

Firms engaging in REM can utilize tax planning strategies to reduce the increased tax burden. However, corporate tax aggres- siveness typically results in a higher book-tax difference that increases scrutiny by regulators and external monitors (Badertscher et al., 2009). Companies involved in REM may be reluctant to “stand out” too much from their industry peers in terms of the tax burden, as they want to avoid raising the suspicion of tax authorities, regulators, or savvy investors (Armstrong et al., 2019).

Such scrutiny makes it harder to hide the real motives of managers’ actions (Hanlon et al., 2014).

We thus expect thatfirms that use real activities manipulation to meet or beat earnings targets are less tax aggressive than their industry peers not engaged in earnings management.

Most studies on the effect of tax authority monitoring on managerial misbehaviour are limited to the US setting where the level of book-tax conformity is relatively low. Tax authority provides an effective monitoring mechanism under a high level of book-tax alignment and the monitoring role increases with tax enforcement. We study companies listed on the Warsaw Stock Exchange for two reasons. Firstly, previous research indicates that the so-called book-tax gap is relatively low in Poland (Watrin et al., 2014). Secondly,

https://doi.org/10.1016/j.frl.2019.101351

Received 19 July 2019; Received in revised form 17 September 2019; Accepted 6 November 2019

⁎Corresponding author.

E-mail addresses:michal.kaldonski@ue.poznan.pl(M. Kałdoński),tomasz.jewartowski@ue.poznan.pl(T. Jewartowski).

Available online 07 November 2019

1544-6123/ © 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/).

T

during recent years, Poland has experienced a radical change in tax enforcement.¹We believe that this provides a unique opportunity to assess the relation between REM and corporate tax aggressiveness. Companies listed on the WSE serve as an excellent laboratory for investigating the relationship between real earnings management and tax aggressiveness, due to the relatively high level of book- tax conformity and considerable intensification of tax enforcement increasing monitoring from the tax authorities.

Our findings confirm our hypothesis. We find a negative relation between REM and the industry- and size-adjusted tax expenses reported by benchmark-beating firms with strong incentives to manage earnings. The relation is more significant (both statistically and economically) for overvalued benchmark-beaters, especially those with high analyst coverage.

Our paper contributes to the literature on earnings management. We answer the call fromRoychowdhury (2006)for research on the implications of managerial activities altering normal firm operations and add to the literature on the consequences of REM. We show that managers sacrifice potential benefits from tax planning for the private benefits of earnings management.

We also add to the literature on tax aggressiveness. Our results indirectly confirm the arguments used byArmstrong et al. (2019) to explain the corporate tax planning adjustments as a response to peers’ tax planning decisions, which they call “strategic tax planning behaviour”. More broadly, respondingHanlon and Heitzman's (2010)call for research on the determinants of corporate tax avoidance, our study sheds some light on the so-called “under-sheltering puzzle”, partly explaining why some firms engage in tax avoidance less than others.

2. Research design

2.1. Measurement of corporate tax aggressiveness and real earnings management

Although several measures of tax aggressiveness have been proposed in the literature, we use industry- and size-adjusted GAAP effective tax rate (TA_GAAP) introduced byBalakrishnan et al. (2019).

First, we measurei'thcompany GAAP effective tax rate (GAAP ETR) in yeartas the total income tax expense (IT) scaled by pre-tax income (EBT)²:

= GAAP ETR IT

i t EBTi t

i t

, ,

, (1)

Then we calculate the average GAAP ETR for the portfolio of industry peers in the same tercile of total assets (GAAP ETR ISP t,).

TA_GAAP for thei'thfirm is the difference between the average industry-size matched GAAP ETR and the firm's GAAP ETR:

=

TA GAAP_ _{i t,} GAAP ETR _{ISP t,} GAAP ETR _{i t,} (2)

Firms with negative TA_GAAP are less tax aggressive than the industry-size matched portfolio average. The higher the TA_GAAP, the higher the firm's tax aggressiveness.

Measuring tax aggressiveness relative to “industry normal” level is based on the notion that some industries have greater ability to take advantage of various tax planning strategies than others. Controlling for the firm size captures economies of scale-based dif- ferences in the ability to reduce the tax burden. Finally, our measure is also positively associated with the likelihood of media coverage (Chen et al., 2018), which can induce unwanted scrutiny by external monitors.

Following Roychowdhury (2006), we calculate abnormal operating cash flows (ABOCF), abnormal discretionary expenses³ (ABSGE), and abnormal production costs (ABPROD) as proxies for deviations in real operations from industry-year “norms” in- dicating REM (Eqs. 3–5, respectively). Abnormal levels are calculated as residuals from models proposed byRoychowdhury (2006):

= + × + × + × +

OCF

A A

S A

S A

i t 1

i t i t

i t i t

i t

i t i t

,

, 1 0 1

, 1 1 ,

, 1 2 ,

, 1 ,

(3)

= + × + × +

SGE

A A

S A

i t 1

i t i t

i t

i t i t

,

, 1 0 1

, 1

, 1

, 1 ,

(4)

= + × + × + × + × +

PROD

A A

S A

S A

S A 1

i t

i t i t

i t i t

i t i t

i t

i t i t

,

, 1 0 1

, 1 1 ,

, 1 2 ,

, 1 3 , 1

, 1 ,

(5) We multiply ABOCF and ABSGE by –1, so that higher proxies indicate higher REM. Finally, we sum the proxies to get the overall measure of REM.

2.2. Empirical model

Benchmark-beating firms engage more in REM than others (Bushee, 1998;Gunny, 2010;Roychowdhury, 2006). We identify firm- years suspected of managing earnings in a given year and create a dummy variable BENCH. It's equal to 1 if either the return on assets

1The changes include the implementation of the general anti-abuse rule, stricter regulations on transfer pricing and thin capitalization, new tax rules for controlled foreign corporations, improvements in IT solutions for tax audit purposes, and organizational changes in the Ministry of Finance.

2Following previous research, GAAP ETR is set as missing when the pre-tax income is zero or negative. We truncate GAAP ETR to the range [0,1].

3Due to the lack of data, we exclude R&D expenses from discretionary expenses.

for a given year ranges between 0 and 1%, or the change in the ratio between that year and previous year is between 0 and 1 percentage point. Otherwise, it is coded as 0.

We test the relation between REM and corporate tax aggressiveness by estimating the following model:

= + + + × + +

TA GAAP_ i t, 0 1BENCHi t, 2REMi t, 3BENCHi t, REMi t, jCONTROLSi t, 1 i t, (6) We include several control variables used in prior research on tax aggressiveness.⁴FollowingArmstrong et al. (2015), we include variables representing managerial incentives. We also control for general firm characteristics followingChen et al. (2010)and for financial reporting quality followingFrank et al. (2009).

Highly overvalued firms engage more in earnings management (Badertscher, 2011; Chi and Gupta, 2009). Previous studies confirm this relation for firms listed on the Warsaw Stock Exchange (Kałdoński and Jewartowski, 2017). To test whether over- valuation moderates the effect of REM by benchmark-beaters on their tax aggressiveness, we estimate the second model:

= + + + × + + ×

+ × + × × + +

TA GAAP BENCH REM BENCH REM OVER BENCH OVER

REM OVER BENCH REM OVER CONTROLS

_ i t i t i t i t i t i t i t i t

i t i t i t i t i t j i t i t

, 0 1 , 2 , 3 , , 4 , 1 5 , , 1

6 , , 1 7 , , , 1 , 1 , (7)

To identify overvaluation we use the approach proposed byRhodes–Kropf et al. (2005), based on market-to-book ratio (MB) decomposition. We decompose MB ratio for each firm-year observation into firm-specific error, industry-level error, and long-run valuation error (LR_VB) that captures growth opportunities. Then we sum the first two components to achieve total valuation error (TOT_ERR) that captures mis-valuation, and we classify firms in a given year as overvalued (OVER) if lagged TOT_ERR is positive.

3. Data

Our study is based on a sample of non-financial companies listed on the Warsaw Stock Exchange in the years 2005–2017. The final sample is limited to 1149 firm-year observations (211 unique companies).⁵All data were derived from the Capital IQ database.

Table 1presents the distribution of our sample by industry. We classify approximately 20% of firm-years as benchmark-beaters and approximately 9% as overvalued benchmark-beaters.

The summary statistics is presented inTable 2. The benchmark-beating firms are less tax aggressive than non-beaters. They report relatively lower income tax expenses than their industry-size peers. They also seem to engage more in REM.

4. Empirical results

To test the relation between REM and corporate tax aggressiveness, we use pooled OLS regression with fixed industry and year effects.Table 3presents our primary results.

The first model includes only the main explanatory variables: REM, BENCH and the interaction term. We find significant negative relation between both variables and TA_GAAP. It means that REM is negatively related to tax aggressiveness for non-beaters and that benchmark-beaters that do not engage in REM are less tax aggressive than non-beaters avoiding REM practices as well. Our inter- active variable is negative and statistically significant, meaning that benchmark-beaters that engage in REM are the least tax ag- gressive group of companies.

In the second model, we add the control variables finding the relations qualitatively unchanged. However, the coefficient on REM becomes insignificant. This finding suggests that when firms’ deviation in real operations from industry norms represents the effects of adopting a unique business strategy, firms engage in tax planning activities at “industry normal” level.

The last two models include a dummy variable OVER indicating overvalued companies. We find significant negative relation between the interactive variable BENCH×REM×OVER and TA_GAAP. The results show that overvalued companies that engage in REM to meet or beat earnings targets are less tax aggressive than their peers.

The results suggest that firms with strong incentives to manage earnings prefer benefits from these manipulations over benefits from tax planning. One of the possible reasons for such behaviour is the unwanted scrutiny by tax authorities, auditors, and in- formation providers that might be induced by “standing-out” from industry peers in terms of tax aggressiveness. To examine this avenue, we refer to the monitoring role of financial analysts. Higher analyst coverage makes firms’ tax planning policy more visible to investors, regulators and tax authorities, which reduces corporate tax aggressiveness (Allen et al., 2016). Therefore, one can expect that firms using REM do not engage in aggressive tax planning to deflect the attention of suspicious analysts.

We test this hypothesis by dividing the sample into two subsamples of firms with high (above sample median) and low analyst coverage. As the demand for information by institutional investors affects analysts’ decision to follow the firm (Boone and White, 2015), we additionally control for institutional ownership level (INSTOWN) and institutional shareholders horizon (INSTMSP) to strengthen the power of our test.

As shown inTable 4the coefficients on BENCH×REM×OVER are negative and significant in all specifications. However, they are more significant both statistically and economically for the firms with high analyst coverage. It is consistent with our expectation that benchmark-beaters which are more intensively tracked by financial analysts engage less in tax planning.

4Definitions of all control variables used in our study are presented in the Appendix.

5Following previous research, we require at least 15 observations for each industry-year to estimate REM as well as to compute TA_GAAP.

5. Robustness tests 5.1. Alternative explanations

Prior research suggests that reputational costs limit tax aggressiveness (Graham et al., 2014). Reputational concerns are especially important for firms in consumer-oriented industries (Austin and Wilson, 2017). To test whether our results are driven by reputational concerns, we exclude retail industries from the sample and replicate our analyses (model(1)inTable 5).

Firms with dual class shares engage less in both earnings management (van Nguyen and Xu, 2010) and tax planning (McGuire et al., 2014). Therefore, it is possible that our findings, at least partly, manifest “managerial entrenchment effect” driven by dual-class structure rather than “a strategic reaction in tax planning decisions”. To explore this scenario, we exclude the companies Table 1

Sample distribution by industry.

Industry 4 GICS CODE Firm - Years

All firms Bench Bench (%) Over Bench&Over Bench&Over (%)

Materials 1510 282 46 16% 157 23 15%

Capital Goods 2010 480 112 23% 225 51 23%

Consumer Durables & Apparel 2520 166 21 13% 85 8 9%

Food, Beverage & Tobacco 3020 70 18 26% 30 8 27%

Software & Services 4510 144 25 17% 58 8 14%

Real Estate 6010 7 3 43% 2 1 50%

Total 1149 225 20% 557 99 18%

The table reports the sample distribution by industry classified on the basis of 4-digit Global Industry Classification System (GICS). The distribution is presented separately for the whole sample (ALL FIRMS) and the subsamples of firms classified as benchmark-beaters (BENCH), overvalued firms (OVER) and overvalued benchmark-beaters (BENCH & OVER).

Table 2

Summary statistics .

All firms Non-benchmark firms Benchmark firms

Mean Median Mean Median Mean Median

Corporate Tax Aggressiveness

TA_GAAP −0.005 0.020 0.010 0.024 −0.067^⁎⁎⁎ −0.002^⁎⁎⁎

Earnings management characteristics

REM −0.161 −0.150 −0.169 −0.166 −0.128* −0.135

ABOCF −0.028 −0.031 −0.029 −0.032 −0.022 −0.027

ABPROD −0.035 −0.030 −0.037 −0.031 −0.026 −0.028

ABSGE −0.099 −0.073 −0.104 −0.076 −0.078^⁎⁎ −0.064

Managerial incentives

CEOOWN 0.062 0.000 0.061 0.000 0.063 0.000

STOCK_COMP 0.062 0.000 0.066 0.000 0.044 0.000

DUAL_STOCK 0.279 0.000 0.285 0.000 0.253 0.000

General control variables

ROA 0.086 0.069 0.094 0.075 0.057^⁎⁎⁎ 0.044^⁎⁎⁎

LEV 0.074 0.040 0.072 0.040 0.081 0.039

NOL_DUMMY 0.031 0.000 0.032 0.000 0.027 0.000

NOL_CHANGE −0.041 0.000 −0.047 0.000 −0.017 0.000

FOREIGN 0.522 1.000 0.528 1.000 0.498 0.000

PPE 0.341 0.330 0.350 0.338 0.307^⁎⁎ 0.291^⁎⁎

INTANGIBLE 0.087 0.022 0.087 0.022 0.091 0.019

EQUITYINC 0.001 0.000 0.001 0.000 0.000 0.000

SIZE 4.221 4.024 4.333 4.172 3.764^⁎⁎⁎ 3.613^⁎⁎⁎

MB 1.201 1.168 1.294 1.252 1.132^⁎⁎⁎ 1.137^⁎⁎⁎

ACCR_ABS 0.075 0.053 0.077 0.055 0.065^⁎⁎ 0.045^⁎⁎

Misvaluation variables

TOT_ERR −0.018 −0.029 −0.007 −0.015 −0.063 −0.099*

LR_VB 0.199 0.210 0.261 0.257 −0.056^⁎⁎⁎ 0.027^⁎⁎⁎

External monitoring variables

ANALYST_COVER 0.334 0.000 0.357 0.000 0.241^⁎⁎ 0.000

INSTOWN 0.269 0.250 0.271 0.250 0.264 0.245

INSTMSP 4.309 4.318 4.233 4.261 4.622^⁎⁎⁎ 4.696^⁎⁎⁎

The table reports summary statistics of the research sample and the subsamples of benchmark firms and non-benchmark firms. We winsorize all continuous variables at the bottom and at the top percentile. The correlation matrix (untabulated) shows no significant correlations between the variables. *,^⁎⁎,^⁎⁎⁎denote the significance of the observed difference in mean (median) of a given variable between both subsamples at the 10%, 5%, and 1% levels, respectively. We uset-test for differences in means and Mann–Whitney U test for differences in medians.

with dual-class shares (321 firm-years) from the basic sample and rerun our analyses (model(2)inTable 5).

5.2. Alternative measures

Our primary measure of tax aggressiveness is based on total income tax expense. To test whether our findings are robust to the way in which we compute the effective tax rate, we leave only current income tax expense calculating ETR (model(3)inTable 5).

The negative relation between REM and tax aggressiveness might be mechanically driven. When managers pursue cuts in dis- cretionary expenses, they also decrease the budget for tax planning activities. To deal with this issue, we exclude ABSGE from our primary REM measure and rerun our analyses (model(4)inTable 5).

We identify benchmark-beaters as firms beating prior year earnings by a small margin, or those reporting a very small profit. In this section we classify as benchmark-beaters also firms beating the consensus of analysts’ EPS forecasts by not more than 1 cent. This Table 3

Real earnings management by benchmark-beating firms and corporate tax aggressiveness.

(1) (2) (3) (4)

Intercept 0.078 0.089 0.075 0.126

(0.891) (0.978) (0.853) (1.362)

BENCH β1 −0.102^⁎⁎⁎ −0.088^⁎⁎⁎ −0.038 −0.021

(−4.315) (−3.814) (−1.257) (−0.775)

REM β2 −0.048^⁎⁎ −0.020 −0.037 −0.028

(−2.535) (−0.981) (−1.288) (−1.074)

BENCH * REM β3 −0.175^⁎⁎ −0.169^⁎⁎ 0.022 0.029

(−2.468) (−2.431) (0.239) (0.330)

OVER β4 X X −0.005 −0.036^⁎⁎

X X (−0.384) (−2.267)

BENCH * OVER β5 X X −0.147^⁎⁎⁎ −0.139^⁎⁎⁎

X X (−2.928) (−2.688)

REM * OVER β6 X X −0.020 0.011

X X (−0.626) (0.363)

BENCH * REM * OVER β7 X X −0.381^⁎⁎⁎ −0.367^⁎⁎

X X (−2.772) (−2.578)

Managerial Incentives

CEOOWN X 0.054 X 0.073^⁎⁎

X (1.287) X (1.966)

STOCK_COMP X 0.022 X 0.016

X (1.289) X (0.937)

General Control Variables

ROA X 0.314^⁎⁎⁎ X 0.199^⁎⁎

X (3.030) X (2.037)

LEV X 0.030 X −5.872

X (0.451) X (−0.000)

NOL_DUMMY X 0.005 X 0.007

X (0.209) X (0.333)

NOL_CHANGE X −0.039^⁎⁎⁎ X −0.038^⁎⁎⁎

X (−7.354) X (−7.587)

FOREIGN X 0.002 X 0.006

X (0.185) X (0.493)

PPE X −0.004 X 0.001

X (−0.155) X (0.031)

INTANGIBLE X −0.006 X 0.015

X (−0.136) X (0.338)

EQUITYINC X 2.161* X 1.323

X (1.875) X (1.280)

SIZE X −0.005 X −0.004

X (−1.059) X (−0.933)

MB X 0.019 X 0.046^⁎⁎⁎

X (1.632) X (3.561)

ACCR_ABS X −0.088 X −0.102

X (−0.975) X (−1.132)

Industry Fixed Effects YES YES YES YES

Year Fixed Effects YES YES YES YES

Obs. 1149 1149 1149 1149

Adjusted R² 0.045 0.064 0.071 0.096

The table presents the results of pooled OLS regressions of tax aggressiveness (TA_GAAP) on real earnings management (REM) in benchmark-beating firms (BENCH) with fixed industry and year effects. Models(3)and(4)show corresponding results for a narrower group covering overvalued firms (OVER). We control for other possible variables that might explain corporate tax aggressiveness, grouped in managerial incentives variables and general control variables. We estimate t-statistics (in parentheses) using robust standard errors clustered at the firm level.^⁎⁎⁎,^⁎⁎and * indicate significance at the 1%, 5% and 10% level, respectively.

reclassifies an additional 20 firms as benchmark-beaters (model(5)inTable 5).

5.3. Endogeneity

The observed relation between REM and tax aggressiveness might be driven by some unobservable firm characteristics that are correlated with both REM and tax aggressiveness. To address the concern that omitted time invariant firm characteristics may be driving our results, we adopt a firm-fixed effect regression (model(6)inTable 5).

Our tests presuppose that REM influences corporate tax aggressiveness. However, the relation may indicate that higher tax aggressiveness restricts REM as a substitute for tax planning used to manage earnings. Hence, we augmentEq. (6)with a lagged tax aggressiveness measure (model(7)inTable 5).

We also employ an instrumental variable approach as another way to address the endogeneity problem. Referring to the costs of REM identified byZang (2012), we employ institutional ownership, market share, and financial distress as instruments for REM.

Model (8) inTable 5provides the estimates from the second-stage regression.

All the robustness tests performed confirm our preliminary findings of the negative relation between REM and corporate tax aggressiveness.

6. Conclusions

Benchmark-beating firms enter into real earnings manipulation to inflate earnings. However, this form of earnings management is relatively costly. The costs include extra taxes as REM is a basically tax conforming form of inflating earnings. One could expect that this additional tax burden can be lowered by firms by entering into tax planning activities. The results of our study show the opposite – benchmark-beaters that engage in REM are less tax aggressive than their industry-size peers.

Our results suggest that the observed relation may be explained by strategic behaviour aimed at avoiding unwanted scrutiny by tax authorities and external monitors. Our findings are robust to alternative variable measures, model specifications, and estimation techniques. We also control for other explanations of firms’ reluctance to engage in aggressive tax planning, such as reputation costs and managerial entrenchment. The relations that we observe for the Polish capital market may exist or appear in other capital Table 4

Real earnings management by overvalued benchmark-beating firms and corporate tax aggressiveness conditional on analyst coverage .

High analyst coverage Low analyst coverage

(1) (2) (3) (4)

Intercept 0.031 0.040 0.118 0.121

(0.439) (0.510) (1.255) (1.213)

BENCH 0.014 0.015 −0.024 −0.023

(0.510) (0.586) (−0.718) (−0.718)

REM −0.030 −0.033 −0.025 −0.025

(−0.548) (−0.615) (−0.796) (−0.798)

BENCH * REM 0.041 0.047 0.034 0.033

(0.342) (0.390) (0.318) (0.310)

OVER −0.043 −0.041 −0.038* −0.038*

(−1.287) (−1.235) (−1.944) (−1.924)

BENCH * OVER −0.175^⁎⁎⁎ −0.180^⁎⁎⁎ −0.129^⁎⁎ −0.128^⁎⁎

(−3.488) (−3.623) (−2.068) (−2.053)

REM * OVER −0.013 −0.012 −0.008 −0.009

(−0.198) (−0.182) (−0.234) (−0.238)

BENCH * REM * OVER −0.397^⁎⁎ −0.405^⁎⁎ −0.346* −0.345*

(−2.434) (−2.478) (−1.833) (−1.822)

INSTOWN X −0.025 X 0.000

X (−0.677) X (0.009)

INSTMSP X 0.001 X −0.000

X (0.159) X (−0.102)

Managerial incentives/General Control Variables YES YES YES YES

Industry Fixed Effects YES YES YES YES

Year Fixed Effects YES YES YES YES

Obs. 327 327 822 822

Adjusted R² 0.087 0.082 0.099 0.097

The table presents the results of pooled OLS regressions of tax aggressiveness (TA_GAAP) on real earnings management (REM) in overvalued (OVER) benchmark-beating firms (BENCH) with fixed industry effects and fixed year effects. Models(1)and(2)show results for subsample of firms with high (above sample median) analyst coverage and models(3)and(4)for subsample of firms with low analyst coverage, respectively. We control for other possible variables that might explain corporate tax aggressiveness, grouped in managerial incentives variables and general control variables.

In models(2)and(4)we additionally control for institutional investors ownership (INSTOWN) and institutional shareholders horizon (INSTMSP).

We estimate t-statistics (in parentheses) using robust standard errors clustered at the firm level.^⁎⁎⁎,^⁎⁎and * indicate significance at the 1%, 5% and 10% level, respectively.

markets, especially in the case of regulatory changes increasing the book-tax conformity.

Our results have several implications for policymakers and regulators. They suggest that narrowing book-tax gap (increasing book-tax conformity) can have positive side effects in terms of reducing the agency costs stemming from private benefits of earnings manipulations. They also indicate that the applied tax aggressiveness measure can be red flag for the earnings quality, which is also important for investors.

Acknowledgments

We would like to thank the participants at the INFINITI 2019 conference as well as at the FinSem WNEiZ seminar for their comments and suggestions on previous versions of the paper. We gratefully acknowledge the financial support of the Polish National Science Centre(Research project no. 2014/13/B/HS4/01556).

Appendix – Control variable definitions Variable Variable definition

Managerial Incentives Variables

CEOOWN percentage of common equity owned by chief executive officer in year t-1.

STOCK_COMP = 1 if the firm used stock based compensation (options, restricted stock etc.) in year t-1, 0 otherwise.

DUAL_STOCK = 1 if the firm used stock with different voting rights in year t-1, 0 otherwise.

General control variables

ROA return on assets for year t computed as operating income in year t scaled by total assets in year t-1.

LEV leverage ratio (long-term debt in year t, scaled by total assets in year t-1).

NOL_DUMMY indicator variable coded as one if loss carry forward is positive as of the beginning of the year t.

NOL_CHANGE change in loss carry forward for year t scaled by total assets in year t-1.

FOREIGN indicator variable coded as one if a firm has nonzero foreign sales in year t.

PPE property, plant, and equipment for year t, scaled by total assets in year t-1.

INTANGIBLE intangible assets for year t, scaled by total assets in year t-1.

Table 5

Real earnings management by benchmark-beating firms and corporate tax aggressiveness – robustness tests .

Alternative explanations Alternative measures Endogeneity

Consumer oriented industries excluded

Dual-class shares companies excluded

TACU_GAAP as dependent variable

REMexcluding SGA

BENCH including analyst consensus

Fixed Effects

Model OLS with

lagged dep.

var.

2SLS

(1) (2) (3) (4) (5) (6) (7) (8)

Intercept 0.093 0.046 −0.015 0.082 0.085 −0.000 −0.028 0.087

(1.033) (0.418) (−0.338) (0.924) (0.945) (−0.001) (−0.728) (0.922)

BENCH −0.086^⁎⁎⁎ −0.079^⁎⁎⁎ −0.095^⁎⁎⁎ −0.077^⁎⁎⁎ −0.081^⁎⁎⁎ −0.091^⁎⁎⁎ −0.062^⁎⁎⁎ −0.125^⁎⁎⁎

(−3.412) (−3.008) (−3.079) (−3.992) (−3.587) (−4.289) (−2.890) (−3.386)

REM −0.029 −0.041 0.032 −0.037 −0.018 −0.035 −0.006 0.088

(−1.170) (−1.552) (1.140) (−1.366) (−0.910) (−1.107) (−0.248) (0.540)

BENCH * REM −0.169^⁎⁎ −0.135* −0.227^⁎⁎ −0.222^⁎⁎ −0.169^⁎⁎ −0.180^⁎⁎⁎ −0.120* −0.464^⁎⁎

(−2.199) (−1.676) (−2.153) (−2.371) (−2.489) (−2.824) (−1.890) (−2.380)

LAGG_TA_GAAP X X X X X X 0.110^⁎⁎ X

X X X X X X (2.082) X

Managerial incentives/

General Control Variables

Yes Yes Yes Yes Yes Yes Yes Yes

Industry Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes

Year Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes

Obs. 913 828 916 1 149 1 149 1 149 822 1 149

Adjusted R² 0.057 0.056 0.050 0.066 0.060 0.099 0.054 0.034

The table presents the results of regressions of tax aggressiveness (TA_GAAP) on real earnings management (REM) in benchmark-beating firms (BENCH) using alternative model specifications and estimation techniques. Models(1)and(2)present the results of pooled OLS regressions for subsample of firms from non-consumer oriented industries and subsample of firms non- using dual class shares. Model(3)presents the results of pooled OLS regression using the measure of tax aggressiveness based on current tax expense (TACU_GAAP) as a dependant variable. Lack of data availability on current tax expense reduced our sample to 916 firm-year observations. Models(4)and(5)show the results of pooled OLS regression with REM measure excluding abnormal selling, general and administration expenses (REM_SGAEXCL) and beating benchmark status including firms beating analysts’ forecasts (BENCH_AFCINCL) as independent variables, respectively. Model(6)reports the results of the firm -fixed effect re- gression. In model(7), one year lagged tax aggressiveness measure (LAGG_TA_GAAP) is included as an independent variable. Model (8) reports the results of regression estimated using 2SLS technique.

We estimate t-statistics (in parentheses) using robust standard errors clustered at the firm level. ***, ** and * denote significance at the 1%, 5% and 10% level, respectively.

EQUITYINC equity income in earnings for year t, scaled by total assets in year t-1.

SIZE natural logarithm of the market value of equity at the beginning of year t.

ROA return on assets for year t computed as net income before extraordinary items for year t scaled by total assets in year t-1.

MB market-to-book ratio in year t-1.

ACCR_ABS absolute value of discretionary accruals derived from the performance-adjusted modified Jones model for year t. The modified Jones model is estimated for each 4-digit GICS industry and year group. SeeKothari et al. (2005)for complete details.

External monitoring variables

ANALYST natural log of 1 plus the number of analysts following the firm in year t-1.

INSTOWN aggregate institutional ownership in year t-1.

INSTMSP average number of quarters in which institutional investors maintain the stake (keep the same proportion or increase the holding) over the three-year period in year t-1.

Robustness tests - additional variables

TACU_GAAP the difference between the firm's size and industry peers (i.e. those in the same tercile of total assets in the same 4-digit GICS industry) GAAP ETR (ETR, computed as the firm's current tax expense scaled by pre-tax income) and the firm's GAAP ETR in year t. SeeArmstrong et al. (2015) for complete details.

REM_SGAEXCL level of real earnings management, which is the sum of ABOCF and ABPROD for year t (excluding ABSGE). SeeRoychowdhury et al. (2006)for complete details.

BENCH_AFCINCL indicator variable coded as one if the firm just meets or beats zero earnings forecasts or last –year earnings or analyst EPS forecast consensus in year t.

DISTRESS =1 if a firm's Altman Z-score is less than 2.675 in year t-1, and 0 otherwise.

MRK_SHR percentage of a company's sales to total four-digit GICS industry in year t-1.

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